Hygienic Features of Production of Organic Plant Food Products and Control of them by Supervisor Authorities in Russia

Introduction

Currently, three segments have clearly taken shape and are functioning in world agriculture and the world food market: the industrial sectoral system, called conventional in the West, dominant and based on intensive technologies; the production of genetically modified crops is a new direction that has already been introduced into the agricultural sector of many countries, and organic agriculture, which at the present stage acts as an alternative to the first two segments that dominate the global food market. Organic agriculture is a production system that improves the ecosystem, preserves soil fertility, protects human health, and, taking into account local conditions and relying on ecological cycles, preserves biological diversity, does not use components that can harm the environment. Organic products are products grown, harvested, processed and packaged in accordance with the standards of organic farming and agricultural production adopted in Europe, the USA, Japan, Russia and other countries.

The requirements for organic food differ from those for conventional agricultural products. The process of their production is an integral part of their identification, labeling and filing a claim [1]. The production of organic food products that are safer for human health is ensured by compliance with the principles of organic production and a complex and expensive procedure for certification of production and products. At the same time, in the process of production, preparation and circulation of organic products, both socio-economic, organizational, legislative problems and hygienic problems arise, which ultimately have a negative impact on the quality and safety of products.

Materials and Methods

The purpose of the research was to study the problems arising in the production of organic vegetable products, supervision of their safety in the Russian Federation by Rospotrebnadzor, as well as to determine some safety indicators and nutritional value in organic vegetables grown in 2 peasant farms in the Novgorod and Leningrad regions. The work uses the method of literary research and analysis of domestic and foreign legislation in the segment of organic food production, as well as standard laboratory methods for determining a number of pesticides, toxic metal compounds, nitrates, potassium, vitamin C and в-carotene in food products.

Results and Discussion

An analysis of the legislation of the EU and the Russian Federation [2-4] allowed us to state that it does not provide for specific distances from sources of pollution to cultivated fields and the regulation of permitted plant protection products (PPPs) in organic food products. A serious problem for organic vegetable growing is dependence on imports of a number of components used in the production of organic products. Organic seeds are practically not produced in Russia. Plant protection preparations and components for the production of products are also mainly imported and are not available to most manufacturers of organic food products [5]. When using biopesticides, the question of their correct use and dosage remains open, mainly due to their selective action. Plant protection products allowed in organic production – preparations based on pyrethrins and rotenone, spinosad, azadirachtin, etc., can be found in food products in concentrations from minimal to maximum values, and methods for their determination and identification have been developed abroad and in the Russian Federation [6,7], are not used in our country. In organic farming, where, in comparison with traditional crop rotations, the use of synthetic mineral fertilizers, chemical plant protection products and other techniques characteristic of conventional farming is prohibited, agricultural technologies for obtaining high yields of various crops have not yet been developed [5,8].

It should also be noted that, due to general environmental pollution, organic production methods alone are not always able to ensure the complete absence of pesticide residues and other xenobiotics in food products. Analysis of the reporting forms of Rospotrebnadzor allowed us to state that organic food products are not distinguished when examining food products based on sanitary-chemical, parasitological, microbiological indicators and specific activity of radioactive substances. Due to the small volumes of organic products on the Russian market, it is not economically efficient for Rospotrebnadzor to develop and implement methods for determining plant protection products in them, since the development of such methods is a complex and expensive process. At the same time, in St. Petersburg the Office of Rospotrebnadzor for 2019-2023. 130 samples of organic products were selected for the content of pesticides, antibiotics, GMOs and compliance with labeling. All organic food products complied with TR CU 021/2011 for the studied indicators, with the exception of 1 sample, which did not comply with TR CU 022/2011 “Food products in terms of their labeling.”

After studying the problems arising during the production of organic vegetable products and the supervision of their safety by Rospotrebnadzor, we, together with specialists from the sanitary- chemical laboratory of the Federal State Budgetary Institution “Center for Hygiene and Epidemiology in the city of St. Petersburg and the Leningrad Region”, conducted studies on the content of 34 pesticides, nitrates, lead, arsenic, cadmium, mercury, as well as a number of nutrients in organic vegetables grown in 2 peasant farms in the Novgorod and Leningrad regions. Laboratory studies used standard methods for determining the above substances. 0.1, which is not typical for similar products grown according to As a result of the research, it can be stated that the quantitative content of all studied pesticides, toxic metal compounds and nitrates in potatoes, carrots and beets, harvested in 2023, is many times lower than the permissible level of traditional intensive technology (Table 1). Studies of the nutritional value of organic vegetables grown in 2 peasant farms in the Novgorod and Leningrad regions also indicate that the quantitative content of в-carotene in red carrots is 1.8 times more, and vitamin C in potatoes is 1.1 times more than in appropriate vegetables grown using traditional technology. However, the content of vitamin potassium in the vegetables studied turned out to be less than in their traditional counterparts [9-11].

Table 1: Content of xenobiotics in organic vegetables harvested in 2023, grown on peasant farms in the Novgorod and Leningrad regions.

Conclusion

Thus, the conducted research revealed a number of hygienic problems in the production and supervision of the quality and safety of organic plant food products associated with the lack of regulation, proper use and dosage of plant protection products approved for use in organic farming. In the practical activities of Rospotrebnadzor, organic food products are not highlighted in statistical reporting, and methods for determining and identifying biopesticides are not used due to the small volume of organic products on the Russian market. At the same time, as a result of a study of organic vegetables from the 2023 harvest, grown in peasant farms in the Novgorod and Leningrad regions, data were obtained confirming that the studied organic vegetables were safer in terms of the content of a number of pesticides, toxic metal compounds, nitrates, and also had higher nutritional value in terms of some nutrients than similar products grown using traditional intensive technology.

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Role of Lipid Management in Cardiovascular Risk Assessment and Improving Prognosis

Introduction

The dawn of the 21st century has seen a troubling rise in cardiovascular diseases (CVDs) across India, making them the leading cause of mortality in the country [1]. It is particularly alarming when compared to their European counterparts that Indians are experiencing CVDs a decade earlier in life. This premature onset of CVDs can be attributed to a mix of genetic predisposition and environmental factors like unhealthy diet, smoking, lack of physical activity, diabetes, and elevated cholesterol levels. Among Indians, non-traditional risk factors such as hyperinsulinemia, insulin resistance, and lipoprotein (a) (Lp(a)) further compound the issue, contributing to the earlier onset of CVDs [2]. Acute myocardial infarction (MI), a significant form of CVD, affects a considerable number of individuals annually, with a substantial mortality rate. Various lipid parameters, including total cholesterol, triglycerides, LDL cholesterol, HDL cholesterol, and Lp(a), play pivotal roles in the development of CVDs, particularly coronary artery disease (CAD). Recent advancements in lipid profile assessment have introduced novel indices such as the Lipid Tetrad Index (LTI) and Lipid Pentad Index (LPI), offering more comprehensive evaluations of lipid profiles and emerging risk factors [3].

These indices provide a more holistic approach to global risk assessment by incorporating parameters like Lp(a), apolipoprotein AI (apoA-I), and apoB, thus enabling a more accurate estimation of the total burden of dyslipidemia and aiding in the early identification of individuals at high risk for CVDs [4]. Given the escalating incidence of CVDs, especially among young populations, routine screening for dyslipidemia becomes imperative for early detection and prevention of adverse cardiovascular events. Screening protocols should encompass various risk factors, including genetic predisposition, obesity, unhealthy dietary habits, and sedentary lifestyles. Selective, universal, or clinical-based screening approaches can be adopted based on cost-effectiveness and resource availability, with a focus on identifying high-risk individuals and initiating timely interventions [5]. Additionally, cascade screening strategies targeting families with confirmed cases of familial hyperlipidemia can significantly contribute to early diagnosis and preventive measures [6]. Genetic screenings, coupled with advanced imaging techniques like coronary artery calcium (CAC) scoring, offer personalized risk assessments and guide therapeutic decisions, especially in low- to intermediate-risk patients [7].

Conclusion

In conclusion, the integration of novel lipid indices, along with traditional lipid parameters, holds promise in improving risk stratification and early intervention strategies for CVDs, particularly in young populations. By leveraging advancements in screening methodologies and embracing a multidisciplinary approach involving healthcare providers and patients, we can effectively combat the rising burden of CVDs and mitigate their impact on public health.

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Convolutional Neural Networks for Enhancing Clinical Decision-Making

Introduction

Convolutional Neural Networks, or CNNs, are a revolutionary technology [1] that has gained significant attention in recent years and has the potential to revolutionize several industries, including healthcare. CNNs have demonstrated a great deal of potential in the field [2] of healthcare to improve clinical decision-making, especially in medical imaging analysis. The purpose of this introduction is to give a general overview of CNNs and their uses in healthcare, with an emphasis on how they might enhance patient outcomes, treatment planning, and diagnostic accuracy [3,4]. A class of deep learning algorithms called Convolutional Neural Networks (CNNs) is motivated by the structure and operation of the human visual cortex. CNNs are made expressly to handle and analyses both structured and unstructured data having a grid-like architecture, like sequences and images, in contrast to typical machine learning techniques. Convolutional, pooling, and fully linked layers are among the layers that make up a CNN [5].

Convolutional layers extract information from input images by using convolution procedures, thereby capturing spatial hierarchies of features at various scales. Feature maps are down sampled by pooling layers, which lowers computational complexity without sacrificing crucial information [6]. Lastly, using the features that were retrieved, fully linked layers carry out tasks like regression or classification. CNNs have proven to be remarkably useful in a number of clinical domains, such as but not exclusive to: a. Diagnosing Diseases: CNNs are capable of precisely recognizing and categorizing illnesses based on pictures from medical exams, such as tumors in MRI scans or pneumonia in chest X-rays [7]. CNNs supplement human expertise and offer quick diagnostic insights by examining tiny patterns and abnormalities, especially in time-sensitive scenarios. Treatment Planning: By evaluating medical images and forecasting treatment outcomes, CNNs help physicians create individualized treatment plans. For instance, CNN-based models can suggest the best radiation therapy regimens for cancer patients based on the features of the tumor and the surrounding anatomy, enhancing the effectiveness of the treatment and reducing adverse effects’ [8]. Prognosis and Risk Prediction: By examining longitudinal medical imaging data, CNNs make it possible to forecast how a disease will proceed and how a patient will turn out.

Literature Survey

In medical image processing, neural networks in particular, deep learning models—have demonstrated impressive efficacy [9]. For tasks like image classification, segmentation, and detection in modalities like X-ray, MRI, CT [10], and histopathological pictures, Convolutional Neural Networks (CNNs) have been widely used [11]. In radiology, for example, CNNs may precisely identify tumours in MRI scans, detect abnormalities in chest X-rays [12], and segment organs or lesions for treatment planning. Neural networks have the ability to accurately identify diseases like cancer and classify different types of tissue in pathology by analysing images from histopathological studies [13]. Neural networks have been used for a variety of clinical data types outside of medical imaging, such as genomics, wearable sensor data, and electronic health records (EHRs). Particularly well-suited for sequential tasks are Long Short-Term Memory (LSTM) networks and Recurrent Neural Networks (RNNs) [14]. The capacity of neural networks to extract intricate patterns and relationships from massive amounts of data is one of its main advantages in clinical decision- making. In addition to eliminating the need for manual feature engineering, deep learning models can automatically extract pertinent features from raw data, perhaps revealing hidden insights that are not immediately evident to human specialists. Better patient outcomes are possible because to this data-driven approach [15], which also makes diagnosis and prognosis more accurate.

Neural networks also make it easier to create decision support systems (DSS), which help doctors make evidence-based decisions. Based on the examination of patient data, these systems can offer recommendations, risk evaluations, and treatment ideas in real time. Neural network integration into clinical procedures allows DSS to improve treatment plans, lower diagnostic errors, and increase diagnostic accuracy [16]. In summary, the use of convolutional neural networks in medical imaging analysis has shown them to have enormous promise for improving clinical decision-making [17]. CNNs are incredibly helpful to physicians in identifying diseases, planning therapies, and enhancing patient outcomes. They may be used for anything from image classification and segmentation to object recognition and multimodal fusion. For CNN-based clinical decision support systems to be widely used in healthcare settings, issues with data availability, interpretability, and dependability must be resolved as this field of study develops [18].

Deign Convolutional Neural Networks for Enhancing Clinical Decision-Making

To guarantee convolutional neural networks’ (CNNs) efficacy and dependability in medical applications, a number of critical procedures must be taken during the design process. A general framework for creating CNNs specifically for this use is as follows: Clearly define the clinical task (i.e., disease diagnosis, tumour detection, treatment planning) that the CNN will be used for when making clinical decisions. Gathering and Preparing Data: assemble a large dataset of photographs related to medicine that are pertinent to the clinical endeavour. To guarantee consistency, adjust for artefacts, and normalise intensity levels, preprocess the data. Expand the dataset to add more diversity and size, particularly if there is a shortage of data. Architecture Selection: Based on the task’s complexity and the features of the medical images, select a CNN architecture that makes sense. Conventional designs like AlexNet, VGG, and ResNet are popular options, as are specialised architectures made for tasks involving medical imaging, like segmentation with U-Net. Personalisation of the Model: Adjust the selected architecture to meet the unique needs of the given clinical task. To improve performance, this can entail changing the network’s width or depth or including task-specific layers like spatial transformers or attention mechanisms. Choosing a Loss Function: Establish a suitable loss function that corresponds with the task’s therapeutic goals. For classification tasks, dice coefficient is a common choice; for segmentation tasks, it’s the dice coefficient; and for regression tasks, mean squared error is a common choice.

Results and Comparison

Convolutional neural networks (CNNs) have been used to improve clinical decision-making. The analysis of these results requires analysing the model’s performance metrics, determining how the model affects clinical processes, and understanding the significance of the results. Here’s how to carry out a thorough study of the outcomes. Evaluation of Performance Metrics: Accuracy: Evaluate how well the CNN classified or segmented medical images overall. Sensitivity and Specificity: To assess the diagnostic performance of the model, consider how well it can detect positive cases (sensitivity) and negative ones (specificity). Region Under the ROC Curve (AUC): Analyze the AUC value to determine how well the model can discriminate between various thresholds. Dice Coefficient: To measure the accuracy of segmentation, measure the overlap between the segmentations that are predicted and the segmentations that are based on ground truth. Clinical Impact Assessment: Clinical Utility: Determine the extent to which clinical judgement and patient outcomes are impacted by the predictions made by CNN. Evaluate whether the model enhances treatment planning, helps with diagnosis, or offers insightful information. Time Efficiency: Examine whether, in comparison to conventional methods, CNN shortens the time needed for image interpretation or other clinical procedures. Resource Allocation: Examine whether CNN helps physicians focus on instances that need quick attention or intervention, hence optimizing the use of resources as Figures 1 & 2.

Figure 1

Figure 2

Conclusion

Convolutional Neural Networks (CNNs) have great potential to improve clinical decision-making in a variety of medical fields, to sum up. CNNs provide insightful analysis that can help physicians with diagnosis, prognosis, and treatment planning because of their capacity to evaluate complicated medical data, including imaging investigations and patient records. CNNs lead to better patient outcomes and more efficient healthcare operations by delivering high accuracy and specificity in tasks like illness identification and picture segmentation. Notwithstanding, it is imperative to tackle obstacles like interpretability, data privacy, and model generalisation to guarantee the extensive integration and moral application of CNN-driven decision support systems in medical practice. To fully utilise CNNs’ potential to improve healthcare delivery and advance precision, more research and collaboration between data scientists, physicians, and policymakers are required.

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Impact of Land Use on Manganese Distribution in Selected Soils Under Different Land Uses

Introduction

Any toxic metal may be called heavy metals, irrespective of their atomic mass or density (Neetu, et al. 2011). Such heavy metals as mercury, arsenic, copper, cyanide, iron, lead, cadmium, chromium, nickel, and manganese are found in highly urbanized areas (Louella et al. 2006). It has been proven that Manganese is a heavy metal and it is very dangerous to the health and to the environment. It may also be known as a hazardous metal as high concentrations may harm an organism and accumulate in animal bodies to produce a variety of diseases.

High levels of heavy metal toxicity are being found in urban soils (Adeniran, Ilugbami & Oyeniran, [1]). Research has indicated that soils impacted by the oxidation of pyritic minerals exhibit elevated levels of metal contamination. Wildlife can become poisonous due to heavy metals. Urban soils have been significantly damaged due to intense human activity and high population density. As a result, a multitude of environmental concerns have surfaced, with heavy metal contamination continuing to be a primary concern [1]. Numerous sources, including automobile emissions, the chemical industry, the burning of coal, the deposition of dust and other suspended materials in the environment, and municipal solid waste, can discharge pollutants into the atmosphere. Heavy metals have been steadily introduced to urban soils by these pollutants, and even after the sources of pollution have been eliminated, these metals will still be there for many years. A hazardous disease called manganese poisoning is brought on by prolonged exposure to manganese [2].

Manganese-induced symptoms are often caused by exposure to ambient manganese air concentrations more than 5 mg Mn/m3. It may cause low energy, harm to the blood’s essential organs the liver, kidneys, lungs, and central nervous system—as well as impaired or diminished brain and central nervous system function. Long-term exposure can induce a degenerative process of the muscles and nervous system that resembles multiple sclerosis, Parkinson’s disease, Alzheimer’s disease, and dystrophy [3,4]. Allergies are rare, and persistent long-term contact with certain metals (or their compounds) can potentially lead to cancer. Manganese, though widely distributed in soils, also occurs naturally in food and water and unlike some heavy metals with no known biological function, manganese is an essential nutrient and in trace amounts is very essential for growth and development. Land use affects and modifies soils positively or negatively as the case may be. Soil contamination is growing alarmingly and it is grossly determined by land use (Mercy et. al, 2013). The use of land cannot be over-emphasised in every sphere of human activities. The land encompasses all the features of natural resources including soil [5]. The positive results from the use of land are appreciated since they improve the standard of living of human beings either directly or indirectly. However, the adverse effects which sometimes result from the different land use can be detrimental to human beings directly and indirectly. There hasn’t been much study done on how heavy metals are distributed across various land uses, though. It is desirable to do research on the amounts of heavy metals in various urban soil types because the contaminants in these soil types may have distinct effects on public health [6,7].

This study has been carried out in countries like Iran by Sayadii et al. (2013), also in Spain, by Eduardo et al. (2008) and Nigeria by Elias et al. (2008) and in actual sense, the concentration of manganese in the ancient cities of Nigeria (Abeokuta and Ibadan) has yet been unknown based on the different forms of land use [8]. This study is therefore aimed at studying the impact/effect of various land uses, including business area (BA), classical gardens (CG), culture and education area (CEA), public green space (PGS), residential area (RA) and roadside area (RSA), industrial area (IA) on the distribution of Manganese in Abeokuta and Ibadan soils as it will provide us with scientific data for evaluation of contamination level in the soils. The objectives of this study are to evaluate the effect of various land uses on the distribution of manganese in some soils of Abeokuta and Ibadan and to evaluate the downward mobility of manganese in some soils of Abeokuta and Ibadan [9].

Literature Review

Although heavy metals are naturally occurring components of the earth’s crust, human activity has significantly changed the geochemical cycles and biological balance of these elements. Because of this, metals accumulate in plant portions that include secondary metabolites, which give rise to a specific pharmacological action. Human health risks can arise from prolonged exposure to heavy metals as manganese, cadmium, copper, lead, nickel, and zinc [10]. As well as being an ill-defined subset of elements with metallic characteristics, heavy metals are also hazardous metals. These consist of actinides, lanthanides, transition metals, and some metalloids [11]. According to one source, heavy metals include common transition metals like copper, lead, and zinc. These metals are responsible for contamination of the environment from sources including leaded gasoline, industrial effluent, and acid rain’s leaching of metal ions from the soil into lakes and rivers. Any species of metal (or metalloid) that appears in an unwelcome location or in a concentration or form that has negative effects on people or the environment might be classified as a “contaminant.” Lead (Pb), cadmium (Cd), copper (Cu), nickel (Ni), and zinc (Zn) are examples of metals and metalloids. Other less frequent metallic pollutants are molybdenum (Mo), manganese (Mn), cobalt (Co), and aluminium (Al).

Heavy Metal and Living Organism

Heavy metal requirements differ among living things. Humans need iron, cobalt, copper, and manganese. At greater amounts, all metals are hazardous. The organism may be harmed by excessive concentrations. Some heavy metals, like lead and mercury, are poisonous and have no known helpful or essential effects on living things. Over time, they may accumulate in the bodies of animals and cause catastrophic sickness [12].

Types of Heavy Metals and their Effect on Human Health

There are two ways that heavy metals impair metabolic processes: 1) They build up and cause malfunctions in important glands and organs including the heart, brain, etc.

2) They interfere with the biological function of the essential dietary minerals by moving them from their natural location. However, living in a heavy metal-free environment is not feasible [13].

Heavy Metals and Environmental Pollution

The content of metals in soil can vary from less than one to up to 100,000 mg/kg. The primary class of inorganic pollutants are heavy metals, which are found in a sizable portion of the land due to their usage in municipal compost or sewage, pesticides, fertilizers, and leftovers from metalliferous mining and smelting enterprises. Regardless of the source of the metals in the soil, high concentrations of various metals can lead to reduced crop yields, poor quality agricultural output, and deteriorating soil quality, all of which pose serious risks to the health of people, animals, and ecosystems [14,15].

Micronutrients (Fe, Mn, Zn and Cu)

Certain elements that plants need in extremely tiny amounts are referred to as micronutrients. This phrase is typically used to describe elements found in plant tissues at concentrations of less than 100 mg/ kg (Foth and Ellis, 1997). The same authors state that, in contrast to boron and molybdenum, the four vital micronutrients that are present in soil as cat ions are zinc (Zn), copper (Cu), iron (Fe), and manganese (Mn). An important process for eliminating micronutrients from the soil solution is adsorption of micronutrients, either by soil OM or by clay-size inorganic soil components (Foth & Ellis, 1997). As a result, each can be provided as a soluble salt, by mineral weathering, or by mineralizing organic matter. This increases the soil’s supply of soluble micronutrients (Foth & Ellis, 1997). Parent material, soil response, soil texture, and soil organic matter are factors that impact the availability of micronutrients (Brady and Weil, 2002). According to Tisdale et al. (1995), micronutrients show negative relationships with coarser sand particles and favourable relationships with fine mineral fractions like silt and clay. This is because these elements diffuse due to their high moisture retention (Tisdale et al., 1995). Micronutrient availability is also highly influenced by the amount of organic matter in the soil. As per Hodgson’s (1963) findings, the existence of organic matter (OM) might enhance the accessibility of certain elements by providing soluble complex agents that disrupt their fixation. The primary chemical factors influencing sedimentary manganese cycling are the oxygen content of the surrounding water, the oxygen’s capacity to penetrate the sediments, and the presence of benthic organic carbon [16,17].

Manganese in soil has the ability to leach soluble substances from it as well as disperse into the air and water as particulate particles. The two primary variables that impact the solubility of manganese in soils are the oxidation-reduction potential and pH. Aquatic biota has the ability to significantly bio-concentrate manganese in water at lower tropical levels. Bio-concentration factors (BCFs) have been estimated for the following: fish (35-930), phytoplankton (2500-6300), marine microalgae (300-5500), intertidal mussels (830-830), and freshwater and marine plants (2000-20,000). While dissolved oxygen has little effect, temperature and pH have a considerable impact on the absorption of manganese by fish and other aquatic invertebrates [18- 20]. It has been shown that manganese absorption rises when salinity falls. The concentrations of manganese in the air are typically highest in urban areas (65-166ng/m3), greater in rural regions (40ng/m3), and lowest in distant places (0.5-14ng/m3). Quantities of manganese in the air can exceed 8000ng/m3, with source-dominated locations often having the greatest quantities. Air near foundries may have annual averages of 200-300ng/m3 of manganese, whereas air near ferro and silico-manganese industries may have concentrations of exceeding 500 ng/m3. In naturally occurring waters with minimal human interference, the levels of dissolved manganese can vary between 10 and more than 10,000μg/litre. Nonetheless, naturally occurring surface waters typically have dissolved manganese concentrations of less than 200μg/litre and very seldom surpass 1000μg/litre. Manganese is an important mineral for bacteria, plants, and animals. Manganese concentrations in river sediments ranged from 410 to 6700 mg/ kg dry weight; silt from an urban lake getting inputs from industrial sources. Terrestrial plants need between 10 and 50 milligrams of manganese per kilogram of tissue. The critical nutritional values of different species and cultivars of the same species differ significantly [21-23]. Manganese-deficient plants grow in calcareous soils, particularly those with high levels of organic matter and poor drainage.

Environmental Transport, Distribution, Transformation, and Accumulation Transport and Distribution Between Media

Although elemental manganese and inorganic manganese compounds have very low vapour pressures, they can nevertheless be found in the atmosphere as suspended particulate matter from soil erosion or industrial emissions (US EPA, 1984). Manganese is likely to be found in the troposphere as mineral complexes linked to its natural occurrence in soil or rock, or in oxide, sulphate, or nitrate forms (Stokes et al., 1988). Particles containing manganese are composed of manganese and its compounds, which are mostly extracted from the atmosphere by rain or gravity settling (US EPA, 1984). Manganese-containing soil particle matter can travel through the atmosphere. The size, density, wind direction, and speed all have a major role in determining the destiny and movement of manganese in the atmosphere. It is estimated that particles with a mass median equivalent diameter (MMED) of less than 5 μm account for 80% of the manganese in suspended particulate matter, while particles with an MMED of less than 2 μm are responsible for 50% of this manganese. It is unclear if these results apply to particles in urban or rural locations. It is known, however, that the size of manganese particles in the air tends to differ depending on the source; big particles tend to predominate around mining operations, whereas small particles tend to predominate around ferromanganese and dry-cell battery industries (WHO, 1999). A mean manganese content of 1338 mg/kg was found in airborne particles (>2 μm) collected over the seas (Lee & Duffield, 1979).

Widespread airborne dispersal would be anticipated based on these results (IPCS, 1981). While manganese deposition showed little spatial variation between urban and rural areas, Fergusson & Stewart (1992) reported that manganese concentrations in New Orleans, Louisiana, USA, increased four times between rural and urban areas. This is in contrast to other metals that are deposited in different ways, such as copper, lead, cadmium, and zinc. On manganese’s atmospheric reactions, very little information is known (USEPA, 1984). Manganese can react with nitrogen dioxide and sulphur dioxide; however, it has not been shown that these reactions happen in the atmosphere. Manganese’s arithmetically averaged annual wet flow was 1900 μg/m2 for a sea loch in Scotland and 1190 μg/m2 for the USA’s Chesapeake Bay (Scudlark et al., 1994; Hall et al., 1996). Manganese deposition rates in the western Mediterranean Sea (northwest Corsica) during 1985-1987 varied from 0.0023 to 0.0072μg/cm2 daily. The greatest manganese atmospheric deposition was caused by sporadic but powerful Saharan sandstorms (Remoudaki et al., 1991). The manganese deposition rate for Burnaby Lake, British Columbia, Canada, was determined to be 350 μg/m2 per day. For the whole watershed, the anticipated yearly position rate was 7.7 tons (Brewer & Belzer, 2001). There are two primary types of manganese found in aquatic environments: Mn(II) and Mn(IV). Oxidation and reduction processes, which can be abiotic or microbially driven, mediate the change between these two forms (Nealson, 1983; Thamdrup et al., 2000; Heal, 2001). pH and redox circumstances have a major role in the environmental chemistry of manganese; Mn(II) predominates at lower pH and redox potential, and in non-dystrophic waters, the fraction of colloidal manganese oxy-hydroxides increases above pH 5.5 (LaZerte & Burling, 1990). Dissolved manganese concentrations in waterways receiving acid mine drainage varied from 250 to 4400μg/litre below pH 3, but were less than 40 μg/litre above pH 5.5 (Filipek et al., 1987). Cherry et al. (2001) found that manganese concentrations in sediment dropped from 400 mg/kg at pH 5.6-5.9 to 8 mg/kg below pH 3. This decline was attributed to manganese dissolution affected by acid mine drainage. When Mn(II) is present in anaerobic conditions, a complicated series of oxidation/ precipitation and adsorption events take place, ultimately rendering the manganese as insoluble manganese dioxide physiologically inaccessible. However, in waters with a pH lower than 8.5, the kinetics of Mn(II) oxidation are sluggish (Zaw & Chiswell, 1999). Days can pass in natural water before manganese oxidizes and precipitates, but years can pass in artificial water (Stokes et al., 1988). However, as pH rises or when catalytic surfaces like manganese dioxide are present, manganese oxidation rates rise as well (Huntsman & Sunda, 1980). Rapid manganese oxide oxidation and precipitation occurred in a stream receiving manganese-rich inflows due to accident drainage (Scott et al., 2002). The series of events that precipitate as manganese dioxide after Mn(II) is oxidized involves the simultaneous occurrence of multiple manganese forms (e.g., dissolved Mn(II), hydrous oxides of Mn(III), Mn(II) adsorbed to particulates, and Mn (II) ligand complexes), each of whose concentration is influenced by a variety of variables such as pH, organic and inorganic carbon, sulphate, chloride, temperature, and time (Stokes et al., 1988). Both chemically and bacterially reducing Mn(IV) to the Mn(II) oxidation state is possible in low oxygen groundwater (Jaudon et al., 1989). Manganese is relatively weakly linked to dissolve organic carbon, and there is minimal evidence of organic connections between manganese and natural fluids (L’Her Roux et al., 1998). Therefore, manganese speciation in natural waters is not significantly influenced by organic complexation. Even with significant amounts of naturally occurring dissolved organic carbon, field research has shown that organically bound manganese is negligible (Laxen et al., 1984). Manganese will often become more accessible when pH and redox potential decrease because the Mn(II) ion is more soluble than the Mn(IV) ion (Heal, 2001). Manganese solubility can be increased by the presence of chlorides, nitrates, and sulphates, which will boost aqueous mobility and plant absorption (Reimer, 1999). Manganese speciation in tropical north Australia’s Magela Creek was investigated by Hart et al. (1992). They postulated that equilibrium between Mn(II) and oxidized species may be achieved during the typical residence period of water in the stream at higher temperatures (30 °C) and greater rates of bacterially induced oxidation. Colloidal manganese has the potential to dominate speciation through this method. There is proof that surface waters now contain higher quantities of manganese due to upland regions being reforested. Mean manganese concentrations and the amount of conifer cover in the watershed are significantly positively correlated, according to an analysis of sites in the United Kingdom conducted between 1988 and 1996 (Heal, 2001).

Increased manganese concentrations result from the minerals being taken up by the trees from the environment through foliar leaching and wash-off of manganese in fine mist and dry particles (Shanley, 1986; Heal, 2001). Conifer plantation litter may potentially accelerate the leaching of manganese from the soil into runoff. There is ample evidence linking increased manganese concentrations in surface waters to soil and water acidification in catchments covered with conifers (Heal, 2001). Soil type and catchment hydrology affect how much land use affects manganese concentrations in upland catchments (Heal, 2001; Heal et al., 2002). According to Heal et al. (2002), the summer-autumn hydrological transition and summer base flow are crucial times for higher manganese concentrations in run-off. Manganese will not become dominant in chemical processes and the system will reach equilibrium speciation until it enters lakes, estuaries, and the ocean, where residence durations are significantly longer (Laxen et al., 1984). In rivers, manganese is frequently adsorbed to suspended particles.

The majority of the manganese in the Paraiba do Sul-Guandu River, Rio de Janeiro, Brazil, that came from industrial sources (metallurgical and chemical industries), was attached to suspended particles Malm et al., 1988. For a wide range of UK rivers, a positive association has been documented between manganese concentrations and suspended sediment levels Laxen et al., 1984; Neal et al., 1998, 2000. Soluble manganese compounds’ propensity to adsorb to soils and sediments can vary greatly, primarily based on the soil’s organic content and cat ion exchange capacity Hemstock & Low, 1953; Schnitzer, 1969; McBride, 1979; Curtin et al., 1980; Baes & Sharp, 1983; Kabata Pendias & Pendias, 1984. The “particulate” and “dissolved” phases of rivers and streams may become dissociated from weathering processes, according to Laxen et al. (1984). This might result in suspended sediment and influxes of Mn(II) species leaching from anoxic soil and ground waters.

Materials and Methods

Study Area

Abeokuta: Abeokuta, the capital city of Ogun state is the largest city in the southwestern region of Nigeria. It is located on the eastern bank of Ogun River, with its centre located at 7oN & 3oE, 48 mols (71 km) North of Lagos by railway, 81 miles (130km) by water. Olumo Rock, a granite outcropping is located at the city centre and is of course the site of traditional celebrations with a population of over 500, 000 people.

Ibadan: Ibadan is the capital city of Oyo state. It is also the third largest metropolitan area; Ibadan is the largest and most populous city with a population of 1,338,659. It is also located in the southwestern part of Nigeria, 128km inland northeast of Lagos and 530km southwest of Abuja.

Sampling Sites: The soil samples were collected from eight different land uses in different areas in the two locations, Ibadan and Abeokuta respectively. The eight land uses include crop farms, animal farms, educational areas, residential areas, roadside, industrial areas, markets, and forests in Abeokuta and Ibadan. The selected areas of both locations are described in the tables below.

Sample collection: A total number of 96 soil samples were collected from the selected areas. The soil samples were collected based on two depths (0-20cm and 20-40cm deep, respectively), three replicates, from different spots in each area of the two locations, Abeokuta and Ibadan respectively. The samples were collected using soil auger. The coordinates of each position of areas in which the samples were taken with the Global Positioning System (GPS) tool (Table 1).

Table 1: Description of the selected areas in Abeokuta under the land uses.

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Note: FUNAAB- Federal University of Agriculture Abeokuta
MAPOLY-Mashood Abiola Polytechnic

Sample Preparation: The soil samples were air-dried; the impurities such as stones and tree leaves were removed from them and then passed through a 2mm sieve.

Sampling Analysis: The digestions of the samples were done, and the concentration of the toxic element (Nickel) was measured using Atomic Absorption Spectrophotometry, AAS (Burt, 2004). Soil analysis of other soil parameters including soil pH, phosphorus content, electrical conductivity, nitrogen content and particle size was done (Table 2).

Table 2: Description of the selected areas in Ibadan under the land uses.

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Procedure for Determination of Electrical Conductivity

1) Gather a fresh soil sample in a plastic zip-loc bag. Try to get a profile from the top 6” of soil that the plants will grow in and take care not to contaminate the sample by touching anything.
2) Open the bag and let it air-dry for a few hours until it is mostly dried.
3) Mix the soil in the bag to ensure a homogenous sample and then use a sieve with approximate 2mm spacing to remove any large soil clumps.
4) Measure out ½ of a cup of the dried soil and put it into a glass beaker.
5) Measure out ½ of a cup of distilled water and put this into the glass beaker with the soil.
6) Stir the mixture gently for 30 seconds. Do not mix too harshly as you may destroy the humus structure and the soil may give up elements that it otherwise would not do in nature. Let the soil-water suspension stand for 30 minutes.
7) Stir water gently again before taking the EC measurement. Insert the EC meter into the beaker and swirl it gently around in the soil-water extract. After approximately 30-60 seconds or after the EC reading has stabilized, read the digital display on your meter. Nitrogen Determination Procedure

Digestion:

1) Fill a digestion flask with about 1 g of the ground material, and note the weight to the closest 0.1 mg. As a way to verify that the digestion settings are accurate, add reagent blank and high-purity lysine HCl. For the purpose of determining dry matter in a lab, weigh a second subsample. 2) Include 15 grams of potassium sulphate, 0.04 grams of anhydrous copper sulphate, 0.5 to 1.0 grams of alundum granules, or 16.7 grams of K2SO4, 0.01 grams of anhydrous copper sulphate, 0.6 grams of TiO2, and 0.3 grams of pumice. Add 20 mL of sulfuric acid after that. In case the sample weight exceeds 1 g, add 1.0 mL sulfuric acid for every 0.1 g of fat or 0.2 g of other organic stuff. 3) Set the flask on the burner that has been prepared; adjust so that it will boil 250 mL of water at 25 °C for five minutes. 4) Swirl slowly and heat for a further 90 minutes for copper catalyst or 40 minutes for CuSO4/ TiO2 mixed catalyst, or until white fumes clear the flask’s bulb. 5) Allow it cool, then slowly stir in 250 mL of distilled water to bring it down to room temperature (less than 25 ℃). Note: You can add more water to the distillation, up to around 275 mL, if bumping happens.

Distillation:

1) To prepare the titration flask, fill it with water to the right level so that the condenser tip is submerged. If you are not sure how much acid to use, try 15 mL of it and 70 mL of water. Pour around 85 mL of water into a pipette containing 1 mL of acid to create the reagent blank. Add three to four drops of the indicator solution methyl red.
2) To lessen foaming, add two to three drops of tributyl citrate or another antifoam agent to the digesting flask.
3) Include an additional 0.5–1.0 g of alundum granules.
4) Add enough 45% sodium hydroxide solution (about 80 mL) slowly down the side of the flask to make the mixture highly alkali. (To avoid losing ammonia, do not combine until the flask is attached to the distillation device.)
5) Attach the flask to the distillation apparatus right away, and distil until at least 150mL of distillate is collected in a titrating flask at a rate of roughly 7.5 boil rate (temperature adjusted to bring 250mL of water at 25oC to boil in 7.5 min).
6) Take out the titrating and digestion flasks from the unit. As you remove the flasks, rinse the condenser tube with distilled water.

Titration: Record the volume to the closest 0.01mL (VNaOH) and titrate surplus acid with standard sodium hydroxide solution to the orange endpoint (colour shift from red to orange to yellow). Analogously, titrate the reagent blank (B).

Determination of Organic Matter

The following methods are used to identify organic matter:

1) Transfer 1 gram of dirt into a 500 millilitre Erlenmeyer flask.
2) Add a 1N potassium dichromate solution (10 millilitres).
3) Add 20 ml of sulfuric acid, and stir slowly for a minute, being careful not to propel soil up onto the flask’s edges. Give it a halfhour to stand.
4) Use deionized water to dilute to 200 millilitres.
5) Include 10 drops of diphenylamine indicator, 0.2 grams of ammonium fluoride, and 10 millilitres of phosphoric acid.
6) Titrate the solution with 0.5N ferrous ammonium sulphate until the colour turns turbid blue from dull green. Drop by drop, add the titrating solution until the colour changes to a vivid green, which indicates the endpoint.
7) Use the same procedures to prepare and titrate a blank.
8) After each set of samples is examined, prepare one duplicate sample and one quality control sample.

The Recommended Procedure for the Determination of Soil pH

Wash the electrodes and the cup well after calibrating the equipment. To get a rough estimate of the pH, fill the cup partially with the solution to be tested. This value is generally considered to be an estimate and will wander. The pH values obtained from taking numerous readings on the same solution will become increasingly stable with time. For well-buffered solutions, three parts could be enough to provide pH readings that exhibit drifts of less than ± 0.04 unit in one or two minutes and are repeatable to ±0.04 unit. Up to six parts of the test solution could be needed for extremely diluted or unbuffered solutions, and the pH results might drift and only be repeatable to ± 0.05 unit. To achieve a precision of more than 0.1 pH unit, the temperature of the test solutions, glass and calomel electrodes, standard solutions, and wash water must all be within 2 °C of one another. Additionally, the electrodes, standard solutions, test solutions, and wash water must all be maintained at the measurement temperature for a minimum of two hours prior to the measurement in order to minimize the effects of thermal or electrical hysteresis on the electrodes (Table 3).

Table 3: Sample collection locations in Abeokuta and their coordinates.

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Note: FUNAAB- Federal University of Agriculture Abeokuta
MAPOLY- Mashood Abiola Polytechnic

Determination of Available Phosphorus in Soil by using the Bray-1 Method

5 g of air-dried soil should be weighed, and in an extraction cup, 25 ml of 2NH4F + 25 ml of 2N HCl reagent should be measured. You may either shake for five minutes on a mechanical shaker or stir for one minute. Pipette the sample (8 ml) into a pair of cups. Mix well after adding 5 drops of phosphorus ammonium molybdate solution. Next, thoroughly mix with 5 drops of the phosphorus C reagent. After letting the solution remain for half an hour, measure the wavelength at 660 millimetres on the colorimeter. After taking measurements, use the standard curve to calculate P values (Table 4).

Table 4: Sample collection locations in Abeokuta and their coordinates.

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Note: FRIN- Forestry Research Institute of Nigeria UI- University of Ibadan NIHORT- National Horticultural Research Institute

Data Analysis

Manganese and other soil parameters were correlated after statistical analysis using Analysis of Variance (ANOVA) and Pearson’s rank correlation (data with normal distribution) was completed.

Results

In the table below, it was observed that there was no significant difference (P ˃ 0.05) between soil depths (0- 20 and 20-40 cm) in electrical conductivity in Abeokuta and manganese in both Abeokuta and Ibadan soils. However, soil depth of 20- 40cm had significantly higher manganese content than 0- 20cm in Ibadan soil. This suggests that depth did not affect the distribution of manganese in Abeokuta and Ibadan soils (Table 5) . The values of manganese in Ibadan are relatively greater than that of Abeokuta. There is also evidence of manganese movement down the profile in Ibadan. In Table 6, it was observed that there was no significant difference (P ˃ 0.05) between land uses (crop farm, animal farm, education, forest, industrial, residential, market, roadside) in electrical conductivity in Ibadan except for Abeokuta where there was significantly different between land uses in electrical conductivity. There was no significant difference (P ˃ 0.05) between land uses in manganese content in both Abeokuta and Ibadan soils. However, the values of manganese concentration in Ibadan are relatively higher than that of Abeokuta with industrial, forest and residential areas with the highest values.

Table 5: Effect of soil depth on manganese concentration and the electrical conductivity of Abeokuta and Ibadan soils.

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Table 6: Effect of land use on manganese concentration and the electrical conductivity of Abeokuta and Ibadan soils.

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These denote that land use affected the distribution of manganese content in some soils of both Abeokuta and Ibadan. The correlation coefficients shown in Table 7 indicated that soil manganese is positively correlated with most of the other heavy metals at both locations except pH, silt and clay in Abeokuta (-0.15, -0.31, -0.17, P ≤ 0.01). In Table 8, the table shows how the value content of electrical conductivity and manganese concentration were affected by the different factors including the soil depths, the locations (Abeokuta and Ibadan), and the land use. The soil depths did not affect both the electrical conductivity and manganese content as there are no significant differences between the values of electrical conductivity and manganese content respectively. There are significant differences between the values of manganese content under the factor of locations in some soils of both Abeokuta and Ibadan but there was no significant difference between the values of electrical conductivity in some soils of both Abeokuta and Ibadan soils.

Table 7: Correlation between manganese and other soil parameters in Ibadan and Abeokuta soils.

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Note: *, ** significant at P<0.05 and <0.01 respectively

Table 8: The effect of soil depth, location and land use on the distribution of electrical conductivity and manganese in both of the locations (Abeokuta and Ibadan).

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This denotes that the locations have an effect on the distribution of manganese concentration but do not have an effect on its electrical conductivity. In comparing both locations, that is Ibadan and Abeokuta respectively, Ibadan has the highest values of manganese. The correlation coefficients shown in the table above indicated that the soil manganese is positively correlated with other heavy metals at both locations; and soil manganese positively correlated with most of the other soil parameters (Sand, N, C, P at P < 0.05 and EC at P < 0.01). There are also significant negative correlations between soil nickel and pH, silt and clay (P < 0.01) in both locations (Table 9).

Table 9: Correlation between nickel and other soil parameters in both the Abeokuta and Ibadan soils.

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Note: *, ** significant at P<0.05 and <0.01 respectively

Discussion

After the analysis, and from the result land use effect on Manganese concentration should that there is a significant difference in the values of land use although crop farm, forest, and residential land use are not significantly different from each other so also, animal farm, roadside, education and market area are also not significantly different from each other, however, the values of manganese concentration in Ibadan is significantly different from each other, however, the values of manganese concentration in Ibadan is higher than that of Abeokuta most especially in industrial areas. Forest and residential areas also have high values of manganese concentration in Ibadan. This must be because Ibadan has more industries than Abeokuta most especially production industries e.g. 7 up companies. The electrical conductivity of soils in Ibadan are not significantly different from each other while it differs in Abeokuta as industrial, forest, education and residential areas are not significantly different from each other while crop farm and roadside are also not significantly different from each other, however, animal farms have the highest values of electrical conductivity.

The downward mobility of manganese in Ibadan is high however in some soils of Abeokuta; it appears that there is no downward mobility of electrical conductivity. The electrical conductivity of some soils in Abeokuta and Ibadan is not significantly different from each other. The average manganese soil concentration is 40-900 mg/kg. The soils analysed fell in the range of average manganese soil concentration in natural soil and this shows that the soils are not contaminated although this is no proof that other heavy metals are not contaminating the soils. Correlation with other soil parameters most especially soil pH, soil organic matter, particle size and phosphorus of the analysed soils shows the relationship with each other.

Conclusion

The analysis revealed that land use affected the distribution of the manganese in some soils of both Abeokuta and Ibadan. Manganese concentration in Ibadan is very high in the industrial areas almost reaching the maximum range of tolerable levels in natural soils. Agriculture might be difficult to practice in these areas as it can be toxic since manganese is needed in trace quantity for plant growth. Its toxicity alters physiological, biochemical and molecular processes at the cell level. The downward mobility of electrical conductivity in both soils is high and it is an indication of the amount of nutrients available for crop absorption. It aids less use of fertilizer though weed control will be more. Electrical conductivity measurements are consistently correlated to soil properties that affect soil productivity including soil texture, cat ion exchange capacity, and organic matter. It is a great prediction of soil health.

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Networks of Violence in Literature from 2019 to 2024

Introduction

Until April 2024, the pandemic indirectly and directly generated the death of 20 million people (García, et al. [1]). This figure includes the cases of violence due to the confinement and distancing of people, as well as due to the interrelation of them in the home (Sánchez, et al. [2]). Therefore, violence during the pandemic is distinguished from violence prior to the health crisis as a network of grievances between confined and distanced people (García, et al. [3]). In this process, the discipline of Social Work managed to document, observe, analyze and discuss a series of categories related to violence in the pandemic (Carreón, et al. [4]). The unveiling of this network of dimensions of violence contributes to the debate around the intervention of social work in the family, residence and domestic sphere, an environment in which there is little production (Carreón, et al. [5]). The objective of the study was to establish the structure of networks of relationships between edges and nodes related to violence and its dimensions: social, health, media, citizen, family, individual and Internet user. Are there significant differences between the theoretical structure of violence and its dimensions with respect to the observations, analysis and discussion of violence in the literature from 2019 to 2024 of the present study? The premises that guide this work suggest that the pandemic modified the relationships between political and social actors, as well as public and private sectors (García, et al. [5]). In the case of social violence, it was oriented towards the impact of the pandemic on contagion, disease and death of vulnerable groups (García, et al. [6]).

Regarding health violence, the pandemic led to a stigma towards health professionals as carriers of the new coronavirus (Coronado, et al. [7]). Regarding media violence, it was distinguished by the generation and spread of false news that affected the decisions of exposure to the coronavirus (Carreón, et al. [8]). The citizen violence that was distinguished by holding the State responsible for the health and economic crisis, was reoriented towards the denunciation of civilians who did not comply with the recommendations of distancing, confinement and carrying anti-COVID-19 devices (García, et al. [8]). The family violence that revealed the asymmetries between men and women when exercising a socially determined economic power, now legitimized the physical and verbal aggression against women for being the ones most affected by unemployment (García, [9]). Violence against oneself implied a minimal level of self-control and non-existent self-esteem, leading to the suicide of those who were diagnosed with PCR tests (García, [4]). Electronic networks such as Facebook, Twitter, WhatsApp, Instagram, YouTube or TikTok were already instruments of political violence and with the pandemic they specialized in influencing elections (Hernández, et al. [10]). In this way, the dimensions of violence were modified by the pandemic and observed in the literature from 2019 to 2024 (Lirios, et al. [11]).

Method

A documentary, cross-sectional and exploratory study was carried out with a sample of findings in the literature from 2019 to 2024 and searched using keywords: social, economic, political, media, family, individual, Internet violence (see Table 1). The Delphi inventory was used to record the ratings of the statements related to the degree of disagreement or according to the impact of COVID-19 on the dimensions sought. In three rounds, the first qualifier, the second comparative and the third reconsideration or reiteration, the respondents evaluated the statements considering: 0 = “not at all in agreement” to 5 = “quite in agreement”. Respondents were contacted through their institutional mail. They were informed about the objective of the project and those responsible for carrying it out (García, [12]). Confidentiality and anonymity were guaranteed in writing. It was made clear to them that their participation would not imply remuneration. The Helsinki and APA protocols for studies with experts were followed. The data was captured in Excel and processed in JASP version 14. The structuring, centrality and grouping coefficients were estimated in order to test the hypothesis regarding the significant differences between the violence published in the literature from 2019 to 2022 with respect to the analysis of the present study. Values close to zero were assumed as evidence of the network of violence and values close to unity as evidence of a dispersed, inconsistent, or volatile structure.

Table 1: Sample Descriptives.

Note: Source Prepared with study data.

Results

Figure 1 shows the structuring values. The prevalence of negative relationships (red color) between the nodes of the dimensions suggest that violence is asymmetric. In other words, exposure to violence is not systematic in the literature from 2019 to 2022. This means that the risks of violence are diverse and tend to be heterogeneous. It also highlights the input and output of findings related to family violence. It then means that the pandemic oriented the dimensions of violence to the family environment in a mode of negative interaction.

Figure 1

Figure 2 shows the centrality values that refer to the distance between edges and nodes between the seven dimensions of analysis. In each of the four coefficients, nodes 13 and 14 related to family violence are dispersed to a greater extent than the other indicators. It is possible to appreciate that family violence in the literature from 2019 to 2024 is distant from itself. In other words, the sources do not establish a consistent relationship that allows us to observe the configuration of a network of violence between the family dimension and the other dimensions. Figure 3 shows the clustering values between nodes and edges of the seven dimensions of violence in the literature from 2019 to 2024. The Barrat and WS parameters indicate clustering consistent with structure and centrality in that edges and nodes are oriented around second-order violence. In other words, the impact of the pandemic on asymmetric relationships in seven dimensions is self-configured in only one. Onnelay ‘s coefficients Zang suggest that nodes and edges are configured around a second order dimension.

It means that the network of nodes and edges is ambivalent, it can be qualified as a result of the interrelation of the seven dimensions, but also as an effect of the prevalence of the family dimension. In short, the measures of structuring, centrality, and clustering suggest that the network of violence in the literature from 2019 to 2024 is ambiguous. The search, analysis and discussion of findings related to seven dimensions of violence do not show a consistent and systematic structure. In other words, violence was impacted asymmetrically by the pandemic and mitigation and containment policies, as well as by social distancing and confinement. The hypothesis of significant differences between the literature from 2019 to 2022 on violence with respect to the observations of the present study cannot be rejected.

Figure 2

Figure 3

Discussion

The contribution of the present work lies in the contrast of the network and findings on violence in the literature from 2019 to 2024 with respect to the neural network analyzes of the present work. The results show that it is an ambiguous network and therefore not comparable with any other network of findings related to violence and the seven dimensions reported in the literature. Such results are consistent with studies on security where an asymmetric structure is observed that indicates the communication of state risks unrelated to risk perceptions (García, et al. [8]). This phenomenon of differences between state policies and social needs has been observed in situations of risk, contingencies and disasters (García, et al. [13]). In the case of violence, the stigma towards those who are attributed with carrying the coronavirus or the risk of contagion determines the differences between users of public services and health professionals or workers (García, [14]). Thus, the results are consistent with previous findings in the pre-pandemic literature. Lines of research related to the determinants of violence will allow us to appreciate the asymmetries between its dimensions [15-17].

Conclusion

The objective of this work was to compare the theoretical structure of violence in the literature from 2019 to 2024, the period of the pandemic, with respect to the analysis of neural networks that are based on the establishment of an agenda of findings oriented towards a central node. and that reflect meaningful learning. The coefficients that measure the structure, centrality and grouping are not conclusive with respect to violence as a phenomenon impacted by the pandemic, although they contribute to the discussion of the differences between those who govern and those who are governed in risk scenarios. It is recommended to extend the study to other phenomena such as security or stigma for a more comprehensive analysis of the social consequences of the pandemic.

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Comparison of Shaping Ability of ProTaper Next and WaveOneGold File Systems in Curved Resin Blocks – An In-Vitro Experimental Study

Introduction

Chemo-mechanical debridement is widely recognized as a pivotal stage in endodontic treatment, holding immense significance. Its purpose is the elimination of infected and necrotic tissue from the root canals and the establishment of smooth canal walls that enhance the ease of irrigation and obturation [1,2]. Root canal instrumentation creates a consistently tapered funnel-shaped canal, enlarging it to the greatest extent possible while preserving the original canal anatomy [2,3]. This goal becomes particularly challenging in narrow and curved canals [2,4,5]. Difficulties encountered in these cases include apical foramen transportation, formation of ledges, zips, perforations, and instrument separation [6]. These issues can impede effective disinfection of root canal space and its obturation, consequently diminishing endodontic treatment success [4,6].

The introduction of nickel-titanium (NiTi) instruments has lead a revolutionary change to root canal preparations by achieving well-tapered canals, reducing operator fatigue, and significantly decreasing shaping time [7,8]. Additionally, these instruments minimize the likelihood transportation of root canal wall while maintaining the canal original anatomical shape [7,9,10]. ProTaper Next (PTN) and Wave- One Gold (WOG) file systems have been recently developed root canal shaping systems that offer innovative approaches to canal preparation [6,11,12]. PTN (Dentsply Maillefer, Ballaigues, Switzerland) introduces an innovative system that uses multiple files and incorporates a design with a varying taper that gradually decreases and an off-centre rectangular cross-sectional shape [11,13]. These features are specifically engineered to minimize contact area with the walls of the canal, reducing instrument fatigue during use [11,14]. The PTN system consists of five instruments that has different tip sizes and tapers, namely X1 (#17/0.04), X2 (#25/0.06), X3 (#30/0.07), X4 (#40/0.06), and X5 (#50/0.06) [14,15]. Additionally, these instruments are constructed with M-Wire NiTi alloy [6,11,14].

WOG (Dentsply Mallefer, Ballaigues, Switzerland) in contrast, is comprised of a single file which operates through a reciprocating motion [16,17]. It incorporates an innovative NiTi alloy known as M-Wire with tapered variations along its working part [16,18]. Additionally, WOG utilizes an asymmetric reciprocating motion, which enhances the instrument resistance to fatigue [19,20]. Despite numerous studies conducted on PTN and WOG systems shaping ability, the findings have yielded varying results [14,21]. For example, Saeid, et al. [2] reported superior shaping ability of PTN as compared to WOG file system; while another study by Laura et al. [6] reported comparable clinical outcomes of WOG and PTN in terms of maintaining original canal anatomy. Owing to the contrasting evidence in literature, this study compares the canal shaping ability (centric ability & canal transportation) of PTN versus WOG systems in simulated curved resin blocks. We speculated that there is a significant difference in the shape of the simulated curved resin blocks prepared with PTN and WOG file systems.

Methodology

This study was an in-vitro experimental investigation carried out at dental clinics of Aga Khan University Hospital (AKUH) in Karachi, Pakistan, following the guidelines set forth in the Declaration of Helsinki. Ethical exemption was obtained from the institutional Ethical Review Committee (ERC exemption #: 2023-9242-26474). Sample size calculation was done using the OpenEpi version 3.01 (opensource statistics for public health, http://www.openepi.com). Based on previous studies, the mean centric ability of ProTaper and WaveOne Gold systems were 0.095±0.012 and 0.119±0.048 respectively[6]. The sample size calculated was 34 resin blocks per group (total 68 resin blocks) with a confidence interval of 95% and a power of 80%. The samples included in the study were prefabricated standardized resin blocks comprising a single canal (length=17mm, with 10mm long straight part and 7mm long curved part; canal curvature=30°-35°). Any samples that were previously instrumented or damaged were excluded. This study included sixty-eight curved resin blocks (Endo Training Block-L, Dentsply Maillefer, Ballaigues, Switzerland). Patency of the canal was confirmed using a size 10 K-file, after which the resin blocks were allocated to one of two groups (n = 34 canals/ group). Preoperative photographs of each resin block were captured using a digital camera (cannon EOS 4000D). Before taking the pictures, the blocks were filled with black ink (S. DOLLAR) to enhance their outlines and ensure standardized images.

A tripod stand was employed to maintain a consistent distance for standardized comparison purposes. For group A, PTN rotary files were utilized with a torque control motor (Dentsply Maillefer) following manufacturer’s guidelines (300 rpm speed; 2.0 Ncm torque). The following instrumentation order was followed: X1 (size 17, 0.04 taper); X2 (size 25, 0.06 taper) up to the full working length. The files were used in a pecking motion with an amplitude under 3 mm and three pecks per application, as per the manufacturer’s guidelines. After three in-and-out movements, the flutes of the instrument were cleared of debris. For lubrication, EDTA cream (RC prep-META BIOMED CO>LTD) was applied with each instrument. After using each rotary file, the simulated curved canal in the resin block were flushed with distilled water. Irrigation was performed using a 27-gauge tip plastic syringe, containing 5ml of distilled water. Additionally, after the removal of each rotary instrument, canal patency was verified with 10 size K-file. For group B, WOG primary file (apical taper = 0.07, tip size = 25) was employed with WaveOne motor (Dentsply Maillefer) in the “WaveOne All” mode, using a reciprocating motion. The canals were instrumented and irrigated as for PTN group, following the manufacturer’s guidelines. A single skilled operator performed the preparation of all the canals and each file was exclusively dedicated to the preparation of a single canal. Following instrumentation, red ink (S. DOLLAR) was used to fill all the canals and were subsequently photographed once more under the same identical conditions as mentioned earlier.

An Adobe software application (Adobe Photoshop Elements 7.0; Adobe Systems, San Jose, CA, USA) was utilized to superimpose all the images taken before and after instrumentation, resulting in a composite image. The composite images were overlaid with a measuring template. Using Image J software, the amount of removed resin due to instrumentation was analyzed at 13 levels/points of the root canal (apical third: 0-4 mm, middle third: 5-8 mm, coronal third: 9-12 mm) in a perpendicular manner to the canal surface and there was 1 mm spacing between each point (Figure 1). Measurements were conducted to determine the distance between the initial root canal outline and the outline after instrumentation, both on inner side (Xi) and outer side (Xo) of the root canal at 13 levels (apical third: 0-4 mm, middle third: 5-8 mm, coronal third: 9-12 mm). Furthermore, the total width of the shaped root canal (Y) was also documented. Centric ability and canal transportation were calculated using the following equations [6]:

• Centric Ability (CA) = (Xi-Xo)/Y
• Canal Transportation (CT) = Xi-Xo

Figure 1

Two independent examiners, blinded of the group allocation, assessed both the images taken before and after instrumentation. All the measurements were then documented on a customized proforma. Data was analysed using SPSS version 23.0. Intra-class correlation coefficient was used for assessment of intra-examiner reliability. Cohen’s kappa statistics was used for inter-examiner reliability. The data distribution was analysed using the Kolmogorov-Smirnov test. Descriptive statistics, such as the mean and standard deviation, were calculated. ANOVA and factorial design ANOVA were utilized to compare the outcomes between the study groups. The significance level was set at <0.05.

Results

No instrument separation occurred while preparation of the resin blocks. Therefore, all the specimens were used for statistical analysis. In middle third of the canal (points 5, 7 and 8) WOG produced less transportation of the canal (p <0.05) and maintained the canal more centric (points 5 and 8) [p <0.05] than PTN (Table 2). At coronal and apical thirds, both PTN and WOG file systems demonstrated no statistically significant difference in CA and CT (p > 0.05) (Tables 1 & 3).

Table 1: Xi, Xo, CA and CT Mean Value, Standard Deviation, and ANOVA for Apical Third.

Table 2: Xi, Xo, CA and CT Mean Value, Standard Deviation, and ANOVA for Middle Third.

Note: *Sign indicates statistical significance.

Table 3: Xi, Xo, CA and CT Mean Value, Standard Deviation, and ANOVA for Coronal Third.

Discussion

Preserving the root canal’s original anatomy and avoiding irregularities in the canal wall lead to enhanced antimicrobial action and sealing ability, [22] as well as a decreased risk of compromising the structural integrity of the tooth [20]. The PTN and WOG file systems are both widely used in endodontics, but studies suggest differences in their performance. This study involved comparing the mean values of centric ability and canal transportation for PTN and WOG at three levels: apical (0-4 mm), middle (5-8 mm), and coronal (9-12mm) thirds from the anatomical apex of simulated resin block. This study reported statistically significant difference in the middle third for CA and CT between PTN and WOG, with the latter resulting in better CA and less CT than PTN at levels 5,8 and 5,7,8 respectively. However, both the file systems had comparable results for CA and CT at coronal and apical thirds. Hence, the null hypothesis was partially rejected. The possible explanation for WOG file system in maintaining the integrity of the original canal anatomy, arises from the association of three primary factors: the reciprocating motion, the file’s cross-sectional design, and the type of alloy used [23,24]. The use of a single file in a reciprocating motion for achieving sufficient shaping of the root canal has been investigated in previous studies [24]. When the instrument encounters the wall of the canal, its counter clockwise rotation disengages it, encouraging a safer application of single-file instruments in curved canals [24].

Additionally, continuous rotational movement tends to move the center of the preparation in a clockwise direction. In contrast, a more symmetrical movement, such as reciprocating motion where the instruments cut in both directions, is expected to mitigate this tendency [25]. PTN features an eccentric rectangular cross-sectional design, facilitating debris removal in a coronal direction. This design creates additional space around the instrument’s flutes, enhancing cutting efficiency by ensuring continuous contact of the instrument blades with the adjacent dentin walls. In contrast, WOG instruments possess an alternating eccentric parallelogram-shaped cross-sectional design with two 85° cutting edges, restricting to 1 or 2 contact points between the file and dentin at any given cross-section. This modified cross-sectional design of WOG, derived from its predecessor, is purported to enhance its flexibility. Both PTN and WOG are made of an M-wire alloy, but an additional heat treatment converts M-wire to Gold-wire in WOG which further increases its flexibility and thereby reduces the risk of canal aberrations [26]. Our results partly agree with those by Peet J, et al. [21] in which PTN and WOG had similar centric ability, but PTN demonstrated significantly more canal transportation than WOG. Similar results were reported by Eliasz, et al. [27]. whereby WOG resulted in less canal transportation than PTN in extracted human teeth. In contrast, Troiano, et al. [28] reported PTN to have better centered the canals than WOG which they reported to be due to different methodologies and models being used in their study.

The present study utilized simulated resin blocks which enables the standardization of the degree, location, radius of curvature, and width of root canals. Preoperative and postoperative root canal outlines can be superimposed through digital microphotographs, digital measurements can be analyzed and any deviations at various points in the root canals can be assessed. This model enables high level of standardization and reproducibility in the experimental design. However, resin blocks allow only a two-dimensional analysis of the shaping ability as opposed to radiographic three-dimensional analysis of extracted human teeth [29]. Also, one of the major disadvantage of resin blocks is that it differ from natural teeth in terms of hardness. Dentin, found in natural teeth, is harder than the resin used in simulated canals, leading to variations in the formation of debris during instrumentation and heat generation which might cause instrument binding in the canal wall. As a result, the process of instrumenting natural teeth may not mirror that of resin blocks. Furthermore, natural teeth typically exhibit more intricate anatomy compared to the patent single canal structure in resin blocks [30]. In clinical settings, clinicians often encounter root canal systems that are more complex than the straightforward single canal portrayed in resin blocks [29].

Conclusion

Within the limitations of the present study WOG resulted in superior preservation of the original canal anatomy, exhibiting fewer alterations to the canal curvature when compared to the PTN. Additional research is necessary to determine whether the improved performance of the instrument can be attributed to the reciprocating motion, variable section design, alloy type and treatment or a combination of these factors.

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Grossly Abnormal Trephine (Core) Biopsy Specimen Obtained from a Patient with Multiple Myeloma

Clinical image

The gross or macroscopic examination which includes the colour, size, and other features of a trephine (core) biopsy specimen may be useful in identifying a diseased process even before sectioned and stained slides are examined. A case is presented where a gross macroscopic examination of the trephine (core) biopsy specimen revealed features indicative of bone marrow involvement by a malignant process. The patient is a 65-year-old white female who presented with a history of persistent productive cough for several months and increasing malaise and weakness [1]. She also complained of low back pain, weight loss, and blurred vision. Her laboratory investigations revealed raised plasma viscosity. A serum protein electrophoresis revealed a discrete band in the Gamma region which was typed as IgG kappa. Urinary Bence Jones protein was positive for kappa light chains. A pelvic x-ray showed a destructive lesion in the right ilium. A CT scan confirmed a 9 cm bony mass in the right ilium and a metastatic bone survey showed lytic lesions in the skull, humeri, and femurs. A bone marrow aspirate was a dry tap. However, smears made from the small number of materials left within the aspiration needle following the dry tap showed a few plasma cells (Figure 1).

The trephine (core) biopsy specimen (Figure 2) showed markedly hypercellular marrow (Figure 3) with massive infiltration by plasma cells (Figures 4 & 5) confirming the diagnosis. Unlike conventional bone marrow trephine (core) biopsy specimens which are usually deep red or reddish as in hypercellular marrow (myeloproliferative disorders/leukemia/lymphoma) or pale (pancytopenia/aplastic anemia) this particular biopsy specimen (Figure 2) appeared grossly abnormal, and it showed patches of yellowish (diseased marrow) and admixed with reddish residual hematopoietic marrow with some degree of plasma cell involvement. One important preanalytical observation, [although incorporated in the 2008 International Council for Standardization in Hematology (ICSH) guidelines but rarely documented in the standard BM requisition form], is the on-site evaluation related to the nature of aspirated material, trephine core length, and its gross appearance. As a result, there has been a paucity of literature surrounding the gross appearance of bone marrow trephine (core) biopsy and its diagnostic utility in routine hematopathology practice. We believe careful documentation of the gross appearance of a trephine (core) biopsy specimen (in conjunction with findings of other investigative procedures) may provide valuable clues for the morphological diagnosis.

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