Biochemical and In Silico Studies on Triazole Derivatives as Tyrosinase Inhibitors: Potential Treatment of Hyperpigmentation Related Skin Disorders.

INTRODUCTION: Tyrosinase is a versatile, glycosylated copper-containing oxidase enzyme that mainly catalyzes the biosynthesis of melanin in mammals. Its overexpression leads to the formation of excess melanin, resulting in hyperpigmentary skin disorders, such as dark spots, melasma, freckles, etc. Therefore, inhibition of tyrosinase is a therapeutic approach for the treatment of hyperpigmentation. METHOD: The current study focused on evaluating tyrosinase inhibitory activities of triazole derivatives 1-20, bearing different substituents on the phenyl ring. 17 derivatives have shown a potent tyrosinase inhibition with IC50 values between 1.6 to 13 µM, as compared to the standard drug, i.e., kojic acid (IC50 = 24.1 ± 0.5 µM). Particularly, compounds 11 and 15 displayed 12 times more potent inhibitory effects than the kojic acid. RESULT: The structure-activity relationship revealed that substituting halogens at the C-4 position of the benzene ring renders remarkable anti-tyrosinase activities. Compounds 1-3 and 8 showed a competitive type of inhibition, while compounds 5, 11, and 15 showed a non-competitive mode of inhibition. Next, we performed molecular docking analyses to study the binding modes and interactions between the ligands (inhibitors) and the active site of the tyrosinase enzyme (receptor). Besides this, we have assessed the toxicity profile of inhibitors on the BJ human fibroblast cell line. CONCLUSION: The majority of the newly identified tyrosinase inhibitors were found to be noncytotoxic. The results presented herein form the basis of further studies on triazole derivatives as potential drug leads against tyrosinase-related diseases.

Enhancing heart disease prediction using a self-attention-based transformer model.

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction.

Geo-statistical analysis of climatic variability and trend detection in the Hindu Kush region, North Pakistan.

This paper is an effort of geo-statistical analysis of rainfall variability and trend detection in the eastern Hindu Kush region located in the north-west of Pakistan. The eastern section of the HK region lies in the western part of Pakistan. Exploring rainfall variability and quantifying its trend and magnitude is one of the key indicators among all climatic parameters. In the study area, Pakistan Meteorology Department (PMD) has established seven meteorological stations: Drosh, Chitral, Dir, Timergara, Saidu Sharif, Malam Jabba, and Kalam. Daily, mean monthly, and mean annual rainfall time series data for all the met stations were geo-statistically analyzed in the GIS environment for detecting monthly and annual variability in rainfall, variability, and trend detection. Mann-Kendall (MK) and Theil-Sen's slope (TSS) statistical tests were applied to rainfall data. Initially, the MK test was applied for detection of trends and TSS test was used to quantify the change in magnitude. The results indicate that the rainfall variability in intensity and trend pattern detection. The analysis confirms that an extremely significant rainfall trend in the case of mean annual rainfall was predicted at Dir and Malam Jabba meteorological stations. Opposite to this, at Kalam and Chitral stations, a less significant rainfall trend was noted. In a similar context, no prominent rainfall trend has been found at Drosh, Timergara, and Saidu Sharif meteorological stations. Likewise, using TSS, an extremely negative variation in the magnitude of rainfall was verified at Kalam and Malam Jabba. However, a noteworthy positive change in rainfall magnitude has been noted at Dir and Saidu Sharif meteorological stations. The findings of this research have the potential to assist the decision and policy makers and academicians to think truly and conduct more scientific research studies to mitigate climate change.

Spatiotemporal variation in the vegetation cover of Peshawar Basin in response to climate change.

Climate factors like temperature, precipitation, humidity, and sunshine time exert a profound influence on vegetation. The intricate interplay between the two is crucial to understand in the face of changing climate to develop mitigation strategies. In the current exploration, we delve how climate variability (CV) has impacted the vegetation in the Peshawar Basin (PB) using remote sensing data tools. The trend of climatic variability was investigated using the modified Mann-Kendall test and Sen's slope statistics. The changing climatic parameters were regressed on the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The NDVI was further analyzed for spatiotemporal variability under land surface temperature (LST) influence. Results revealed that among the climate factors, average annual temperature and solar radiation have a significant (p < 0.05) negative impact on vegetation while precipitation and relative humidity significantly (p < 0.05) influence NDVI positively. The overall positive trend shows that vegetation improved between 2001 and 2020 with time, however some years (2010, 2012, 2014, 2016, and 2017) with low NDVI. NDVI varied in space considerably due to climatic extremes brought on by CV and the urbanization of agricultural land. NDVI regressed on LST showed that there was no or very little vegetation in the grids with high LST. The study concluded that the region is significantly impacted by both CV-related extreme weather events and anthropogenic activities. The vegetation is improving, but it is in danger of being destroyed by deforestation due to CV and human activities that exacerbate the risk of future calamities. To protect vegetation and avoid disasters, there is an immense need for adaptation and mitigation measures to deal with the region's fast-changing environment. The study urges local authorities to create climate-resilient governmental policies and supports regional sustainable development and vegetation restoration.

Comparative study of quantum Otto and Carnot engines powered by a spin working substance.

Quantum Otto and Carnot engines have recently been receiving attention due to their ability to achieve high efficiencies and powers based on the laws of quantum mechanics. This paper discusses the theory, progress, and possible applications of quantum Otto and Carnot engines, such as energy production, cooling, and nanoscale technologies. In particular, we investigate a two-spin Heisenberg system that works as a substance in quantum Otto and Carnot cycles while exposed to an external magnetic field with both Dzyaloshinsky-Moriya and dipole-dipole interactions. The four stages of engine cycles are subject to analysis with respect to the heat exchanges that occur between the hot and cold reservoirs, alongside the work done during each stage. The operating conditions of the heat engine, refrigerator, thermal accelerator, and heater are all achieved. Moreover, our results demonstrate that the laws of thermodynamics are strictly upheld and the Carnot cycle produces more useful work than that of the Otto cycle.

Extremal quantum correlation generation using a hybrid channel.

The preservation of quantum correlations requires optimal procedures and the proper design of the transmitting channels. In this regard, we address designing a hybrid channel comprising a single-mode cavity accompanied by a super-Gaussian beam and local dephasing parts based on the dynamics of quantum characteristics. We choose two-level atoms and various functions such as traced-distance discord, concurrence, and local-quantum uncertainty to analyze the effectiveness of the hybrid channel to preserve quantum correlations along with entropy suppression discussed using linear entropy. The joint configuration of the considered fields is found to not only preserve but also generate quantum correlations even in the presence of local dephasing. Most importantly, within certain limits, the proposed channel can be readily regulated to generate maximal quantum correlations and complete suppression of the disorder. Besides, compared to the individual parts, mixing the Fock state cavity, super-Gaussian beam, and local dephasing remains a resourceful choice for the prolonged quantum correlations' preservation. Finally, we present an interrelationship between the considered two-qubit correlations' functions, showing the deviation between each two correlations and of the considered state from maximal entanglement under the influence of the assumed hybrid channel.

Antibiotic Resistance Profiling and Phylogenicity of Uropathogenic Bacteria Isolated from Patients with Urinary Tract Infections.

Urinary tract infections (UTIs) are healthcare problems that commonly involve bacterial and, in some rare instances, fungal or viral infections. The irrational prescription and use of antibiotics in UTI treatment have led to an increase in antibiotic resistance. Urine samples (145) were collected from male and female patients from Lower Dir, Khyber Pakhtunkhwa (KP), Pakistan. Biochemical analyses were carried out to identify uropathogens. Molecular analysis for the identification of 16S ribosomal RNA in samples was performed via Sanger sequencing. Evolutionary linkage was determined using Molecular Evolutionary Genetics Analysis-7 (MEGA-7). The study observed significant growth in 52% of the samples (83/145). Gram-negative bacteria were identified in 85.5% of samples, while Gram-positive bacteria were reported in 14.5%. The UTI prevalence was 67.5% in females and 32.5% in males. The most prevalent uropathogenic bacteria were Klebsiella pneumoniae (39.7%, 33/83), followed by Escherichia coli (27.7%, 23/83), Pseudomonas aeruginosa (10.8%, 9/83), Staphylococcus aureus (9.6%, 8/83), Proteus mirabilis (7.2%, 6/83) and Staphylococcus saprophyticus (4.8%, 4/83). Phylogenetic analysis was performed using the neighbor-joining method, further confirming the relation of the isolates in our study with previously reported uropathogenic isolates. Antibiotic susceptibility tests identified K. pneumonia as being sensitive to imipenem (100%) and fosfomycin (78.7%) and resistant to cefuroxime (100%) and ciprofloxacin (94%). Similarly, E. coli showed high susceptibility to imipenem (100%), fosfomycin (78.2%) and nitrofurantoin (78.2%), and resistance to ciprofloxacin (100%) and cefuroxime (100%). Imipenem was identified as the most effective antibiotic, while cefuroxime and ciprofloxacin were the least. The phylogenetic tree analysis indicated that K. pneumoniae, E. coli, P. aeruginosa, S. aureus and P. mirabilis clustered with each other and the reference sequences, indicating high similarity (based on 16S rRNA sequencing). It can be concluded that genetically varied uropathogenic organisms are commonly present within the KP population. Our findings demonstrate the need to optimize antibiotic use in treating UTIs and the prevention of antibiotic resistance in the KP population.

Insights on the Nutritional Profiling of Cantaloupe (Cucumis melo L.) via 1-Naphthalene Acetic Acid.

The nutritional components of cantaloupe, including vitamins, minerals, antioxidants, and dietary fiber, contribute to overall health, improved immunity, hydration, and protection against chronic diseases. This study was conducted to investigate the influence of different concentrations (0 (control), 100, 150, and 200 ppm) of 1-naphthalene acetic acid (1-NAA) on the nutritional components of the cantaloupe (Cucumis melo L. Var. Super White Honey). All the studied treatments were applied twice at the 2nd and 4th leaf stages. The applied concentrations of 1-NAA significantly improved the sex expression and fruit yield attributes. Different nutritional components like proximate contents, minerals, vitamins, selected fatty acids, and amino acids were analyzed. The results showed that the maximum moisture content, proteins, carbohydrates, ash, and energy were recorded with 100 ppm. The higher lipids were recorded during the supplementation of 150 ppm. Significantly greater fibers were recorded using 200 ppm. Regarding minerals, 100 ppm was found to be the best as it increased calcium (Ca), magnesium (Mg), potassium (K), sodium (Na), phosphorous (P), manganese (Mn), copper (Cu), iron (Fe), and zinc (Zn). Vitamins were also found to be the maximum with 100 ppm, including vitamin A, vitamin B, vitamin C, vitamin D, vitamin E, and vitamin K. Total selected fatty acids and amino acids were also found significantly greater in the fruits administered 100 ppm.

New Records of Ditylenchus Species from Southern Alberta, Canada.

The presence of plant-parasitic nematodes (PPNs) in cultivated areas is a limiting factor in achieving marketable crop yield. To control and alleviate the effects of these nematodes and determine appropriate management strategies, species-level identification is crucial. Therefore, we conducted a nematode diversity survey, which resulted in the detection of four Ditylenchus species in cultivated areas of southern Alberta, Canada. The recovered species had six lines in the lateral field, delicate stylets (>10 µm long), distinct postvulval uterine sacs, and pointed to rounded tail tips. The morphological and molecular characterization of these nematodes revealed their identity as D. anchilisposomus, D. clarus, D. tenuidens and D. valveus, all of which are members of the D. triformis group. All of the identified species were found to be new records in Canada except for D. valveus. Accurate Ditylenchus species identification is crucial because false-positive identification can result in the implementation of quarantine measures over the detected area. Our current study not only documented the presence of Ditylenchus species from southern Alberta, but also described their morpho-molecular characteristics and subsequent phylogenetic relationships with related species. The results of our study will aid in the decision on whether these species should become a part of nematode management programs since nontarget species can become pests due to changes in cropping patterns or climate.

Three new constituents from the Tujia ethnomedicine Swertia punicea Hemsl.

Three new constituents: 1,5R-dihydroxy-3,8S-dimethoxy-5,6,7,8-tetrahydroxanthone (1), (3S,4R,16S,17R)-3,16,23-trihydroxyoleana-11,13(18)-dien-28-aldehyde-3-O-ß-D-glucopyranoside (2), and new natural product (S)-gentiandiol (3), along with 41 known compounds were isolated from Tujia ethnomedicine Shuihuanglian, namely, the whole plant of Swertia punicea. Structures of all these compounds were established through extensive spectroscopic techniques, namely 1D, 2D-NMR spectroscopy, HRESIMS analysis, and the absolute configuration of the new compounds was discerned by circular dichroism (CD) spectroscopy. Antioxidative effects of these compounds were evaluated by using the DPPH radical scavenging method, compounds 7, 9 and 14 showed antioxidant activities with IC50 values of 68.9, 50.8 and 48.2 µM, respectively.

Kidney Cancer Prediction Empowered with Blockchain Security Using Transfer Learning.

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient's data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

Long-time protection of thermal correlations in a hybrid-spin system under random telegraph noise.

The engineering features of transmitting mediums and their impact on different characteristics of a quantum system play a significant role in the efficient performance of nonlocal protocols. For this purpose, the dynamics of open quantum systems and coupling mediums remain a pathway. In this work, we investigate the dynamics of quantum correlations using negativity, uncertainty-induced nonlocality, and local quantum Fisher information in a hybrid qubit-qutrit thermal state when coupled with a magnetic field and influenced by random telegraph noise. Different features of the system parameters are taken into account while designing longer preservation of qubit-qutrit correlations. We show that the temperature has an inverse impact on the initial values of negativity, uncertainty-induced nonlocality, and local quantum Fisher information. When the magnetic field is characterized by different features, the entanglement, nonlocality, and Fisher information show a variety of dynamical maps, assuring their distinct nature. In addition, the qubit-qutrit correlations undergo repeated revivals when the configuration is restricted to the non-Markovian regime. On the other hand, an exponential drop with a single minimum is observed in the Markovian regime of the coupled field. Most importantly, our findings reveal that the present coupled fields have several advantages that can be leveraged to generate the optimal degree of entanglement, nonlocality, and local quantum Fisher information preservation in quantum dynamical maps.

IoMT-Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning.

A genetic disorder is a serious disease that affects a large number of individuals around the world. There are various types of genetic illnesses, however, we focus on mitochondrial and multifactorial genetic disorders for prediction. Genetic illness is caused by a number of factors, including a defective maternal or paternal gene, excessive abortions, a lack of blood cells, and low white blood cell count. For premature or teenage life development, early detection of genetic diseases is crucial. Although it is difficult to forecast genetic disorders ahead of time, this prediction is very critical since a person's life progress depends on it. Machine learning algorithms are used to diagnose genetic disorders with high accuracy utilizing datasets collected and constructed from a large number of patient medical reports. A lot of studies have been conducted recently employing genome sequencing for illness detection, but fewer studies have been presented using patient medical history. The accuracy of existing studies that use a patient's history is restricted. The internet of medical things (IoMT) based proposed model for genetic disease prediction in this article uses two separate machine learning algorithms: support vector machine (SVM) and K-Nearest Neighbor (KNN). Experimental results show that SVM has outperformed the KNN and existing prediction methods in terms of accuracy. SVM achieved an accuracy of 94.99% and 86.6% for training and testing, respectively.

ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches.

According to the World Health Organization (WHO) report, heart disease is spreading throughout the world very rapidly and the situation is becoming alarming in people aged 40 or above (Xu, 2020). Different methods and procedures are adopted to detect and diagnose heart abnormalities. Data scientists are working on finding the different methods with the required accuracy (Strodthoff et al., 2021). Electrocardiogram (ECG) is the procedure to find the heart condition in the waveform. For ages, the machine learning techniques, which are feature based, played a vital role in the medical sciences and centralized the data in cloud computing and having access throughout the world. Furthermore, deep learning or transfer learning widens the vision and introduces different transfer learning methods to ensure accuracy and time management to detect the ECG in a better way in comparison to the previous and machine learning methods. Hence, it is said that transfer learning has turned world research into more appropriate and innovative research. Here, the proposed comparison and accuracy analysis of different transfer learning methods by using ECG classification for detecting ECG Arrhythmia (CAA-TL). The CAA-TL model has the multiclassification of the ECG dataset, which has been taken from Kaggle. Some of the healthy and unhealthy datasets have been taken in real-time, augmented, and fused with the Kaggle dataset, i.e., Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH dataset). The CAA-TL worked on the accuracy of heart problem detection by using different methods like ResNet50, AlexNet, and SqueezeNet. All three deep learning methods showed remarkable accuracy, which is improved from the previous research. The comparison of different deep learning approaches with respect to layers widens the research and gives the more clarity and accuracy and at the same time finds it time-consuming while working with multiclassification with massive dataset of ECG. The implementation of the proposed method showed an accuracy of 98.8%, 90.08%, and 91% for AlexNet, SqueezeNet, and ResNet50, respectively.

IoMT-Based Osteosarcoma Cancer Detection in Histopathology Images Using Transfer Learning Empowered with Blockchain, Fog Computing, and Edge Computing.

Bone tumors, such as osteosarcomas, can occur anywhere in the bones, though they usually occur in the extremities of long bones near metaphyseal growth plates. Osteosarcoma is a malignant lesion caused by a malignant osteoid growing from primitive mesenchymal cells. In most cases, osteosarcoma develops as a solitary lesion within the most rapidly growing areas of the long bones in children. The distal femur, proximal tibia, and proximal humerus are the most frequently affected bones, but virtually any bone can be affected. Early detection can reduce mortality rates. Osteosarcoma's manual detection requires expertise, and it can be tedious. With the assistance of modern technology, medical images can now be analyzed and classified automatically, which enables faster and more efficient data processing. A deep learning-based automatic detection system based on whole slide images (WSIs) is presented in this paper to detect osteosarcoma automatically. Experiments conducted on a large dataset of WSIs yielded up to 99.3% accuracy. This model ensures the privacy and integrity of patient information with the implementation of blockchain technology. Utilizing edge computing and fog computing technologies, the model reduces the load on centralized servers and improves efficiency.

Isolation, derivatization, in-vitro, and in-silico studies of potent butyrylcholinesterase inhibitors from Berberis parkeriana Schneid.

Seven known isoquinoline alkaloids 1-7 were isolated from the root extracts of Berberis parkeriana Schneid. Nine new derivatives 8-16 of one of the isolated compounds, jatrorrhizine (7), were synthesized. All the isolated as well as derivatized compounds were evaluated for their in-vitro acetylcholinesterase (AChE), and butyrylcholinesterase (BChE) inhibitory activity. Functionalized compounds selectively exhibited a potent-to-moderate activity with IC50 = 5.5 ± 0.3-124.5 ± 0.4 µM against butyrylcholinesterase enzyme. Among them, compound 15 was a potent BChE inhibitor (IC50 = 5.5 ± 0.3 µM), as compared to the standard drug galantamine hydrobromide (IC50 = 40.83 ± 0.37 µM). Active compounds were further subjected to kinetic, and molecular docking studies to predict their modes of inhibition, and interactions with the receptor (BChE), respectively. Enzyme kinetics studies showed that compounds 9 (IC50 = 25.3 ± 0.5 µM), and 14 (IC50 = 23.9 ± 0.5 µM) were non-competitive inhibitors, while compound 15 exhibited a competitive inhibition. In addition, these compounds were found to be non-cytotoxic against human fibroblast (BJ) cell line, except 9 (IC50 = 17.1 ± 1.0 µM), and 10 (IC50 = 18.4 ± 0.3 µM). Inhibition of cholinesterases is an important approach for development of drugs against Alzheimer's disease, and thus discoveries presented here deserve further investigation.

Intelligent breast cancer diagnostic system empowered by deep extreme gradient descent optimization.

Cancer is a manifestation of disorders caused by the changes in the body's cells that go far beyond healthy development as well as stabilization. Breast cancer is a common disease. According to the stats given by the World Health Organization (WHO), 7.8 million women are diagnosed with breast cancer. Breast cancer is the name of the malignant tumor which is normally developed by the cells in the breast. Machine learning (ML) approaches, on the other hand, provide a variety of probabilistic and statistical ways for intelligent systems to learn from prior experiences to recognize patterns in a dataset that can be used, in the future, for decision making. This endeavor aims to build a deep learning-based model for the prediction of breast cancer with a better accuracy. A novel deep extreme gradient descent optimization (DEGDO) has been developed for the breast cancer detection. The proposed model consists of two stages of training and validation. The training phase, in turn, consists of three major layers data acquisition layer, preprocessing layer, and application layer. The data acquisition layer takes the data and passes it to preprocessing layer. In the preprocessing layer, noise and missing values are converted to the normalized which is then fed to the application layer. In application layer, the model is trained with a deep extreme gradient descent optimization technique. The trained model is stored on the server. In the validation phase, it is imported to process the actual data to diagnose. This study has used Wisconsin Breast Cancer Diagnostic dataset to train and test the model. The results obtained by the proposed model outperform many other approaches by attaining 98.73 % accuracy, 99.60% specificity, 99.43% sensitivity, and 99.48% precision.

Assessment of heavy metals accumulation in agricultural soil, vegetables and associated health risks.

Industrialization plays a vital role in the development of a country's economy. However, it also adversely affects the environment by discharging various unwanted and harmful substances such as heavy metals into the surface and subsurface aquifers. The current research work investigates the identification, characterization, and evaluation of specific heavy metals in industrial wastewater (IWW) and different composite samples of soil and vegetables (onion, pumpkin, lady finger, and green pepper) collected from selected agricultural fields irrigated with canals fed IWW in Mingora city of Swat (Pakistan). Obtained results were compared with the tube well water irrigated soil and vegetables grown in it. Heavy metals accumulation was tested through wet digestion method and atomic absorption spectrophotometry (AAS). The metal transfer factor (MTF) of heavy metals from soil to vegetables was also determined along with the health index (HI) to assess the potential health risk of the metals towards consumers using Monte Carlo simulation technique. Analysis of water samples showed that the concentration in mg l-1 of heavy metals in IWW follows the trend Fe (6.72) > Cr (0.537) > Pb (0.393) > Co (0.204) > Mn (0.125) > Ni (0.121). Analysis of the soil samples irrigated with IWW followed the order of Fe (47.27) > Pb (2.92) > Cr (2.90) >Ni (1.02) > Mn (0.90) > Co (0.68) and Fe (17.12) > Pb (2.12) > Cr (2.03) >Ni (0.76) > Co (0.49) > Mn (0.23) irrigated with TWW. Heavy metals concentration values found in soil irrigated with IWW were higher than the soil irrigated with TWW. Similar trends were found for agricultural produces grown on soil irrigated with IWW and found higher than the normal allowable WHO limits, indicating higher possibilities of health risks if continuously consumed. MTF values were found higher than 1 for ladyfinger and green pepper for Pb intake and pumpkin for Mn intake. The current study suggests the continuous monitoring of soil, irrigation water and agricultural products to prevent heavy metals concentration beyond allowable limits, in the food chain. Thus, concrete preventive measures must be taken to reduce heavy metal accumulation through wastewater irrigation to protect both human and animal health in the study area of Mingora Swat Pakistan.

Breast Cancer Prediction Empowered with Fine-Tuning.

In the world, in the past recent five years, breast cancer is diagnosed about 7.8 million women's and making it the most widespread cancer, and it is the second major reason for women's death. So, early prevention and diagnosis systems of breast cancer could be more helpful and significant. Neural networks can extract multiple features automatically and perform predictions on breast cancer. There is a need for several labeled images to train neural networks which is a nonconventional method for some types of data images such as breast magnetic resonance imaging (MRI) images. So, there is only one significant solution for this query is to apply fine-tuning in the neural network. In this paper, we proposed a fine-tuning model using AlexNet in the neural network to extract features from breast cancer images for training purposes. So, in the proposed model, we updated the first and last three layers of AlexNet to detect the normal and abnormal regions of breast cancer. The proposed model is more efficient and significant because, during the training and testing process, the proposed model achieves higher accuracy 98.44% and 98.1% of training and testing, respectively. So, this study shows that the use of fine-tuning in the neural network can detect breast cancer using MRI images and train a neural network classifier by feature extraction using the proposed model is faster and more efficient.