Abiotic stressors in poultry production: A comprehensive review.

In modern animal husbandry, stress can be viewed as an automatic response triggered by exposure to adverse environmental conditions. This response can range from mild discomfort to severe consequences, including mortality. The poultry industry, which significantly contributes to human nutrition, is not exempt from this issue. Although genetic selection has been employed for several decades to enhance production output, it has also resulted in poor stress resilience. Stress is manifested through a series of physiological reactions, such as the identification of the stressful stimulus, activation of the sympathetic nervous system and the adrenal medulla, and subsequent hormonal cascades. While brief periods of stress can be tolerated, prolonged exposure can have more severe consequences. For instance, extreme fluctuations in environmental temperature can lead to the accumulation of reactive oxygen species, impairment of reproductive performance, and reduced immunity. In addition, excessive noise in poultry slaughterhouses has been linked to altered bird behaviour and decreased production efficiency. Mechanical vibrations have also been shown to negatively impact the meat quality of broilers during transport as well as the egg quality and hatchability in hatcheries. Lastly, egg production is heavily influenced by light intensity and regimens, and inadequate light management can result in deficiencies, including visual anomalies, skeletal deformities, and circulatory problems. Although there is a growing body of evidence demonstrating the impact of environmental stressors on poultry physiology, there is a disproportionate representation of stressors in research. Recent studies have been focused on chronic heat stress, reflecting the current interest of the scientific community in climate change. Therefore, this review aims to highlight the major abiotic stressors in poultry production and elucidate their underlying mechanisms, addressing the need for a more comprehensive understanding of stress in diverse environmental contexts.

Structurally Regulated Design Strategy of Bi0.5Na0.5TiO3-Based Ceramics for High Energy-Storage Performance at a Low Electric Field.

Dielectric ceramic capacitors are prospective energy-storage devices for pulsed-power systems owing to their ultrafast charge-discharge speed. However, low energy-storage density makes them difficult to commercialize for high-pulse-power technology applications. Herein, we presented a structurally regulated design strategy to disrupt a long-range ferroelectric order, refined grains, and eventually achieve excellent comprehensive energy-storage performance in (1 - x) (0.7Bi0.5Na0.5TiO3-0.3SrTiO3)-x Sm(Zn2/3Nb1/3)O3 eco-friendly ceramics. A large Wrec of ∼7.43 ± 0.05 J/cm3 and a high η of ∼85 ± 0.5% of 0.96 (0.7Bi0.5Na0.5TiO3-0.3SrTiO3)-0.04 Sm(Zn2/3Nb1/3)O3 were obtained at a low electric field of 290 kV cm-1 with good energy-storage temperature (25-120 °C), frequency (1-100 Hz) stability, and charge-discharge properties (PD ∼ 74 ± 1 MW/cm3 and τ0.9 ∼ 159 ± 2 ns). This strategy inspires rational structurally regulated designs and aims to promote the development of eco-friendly 0.7Bi0.5Na0.5TiO3-based ceramics with excellent energy-storage characteristics.

Lipase and Protease Production Ability of Multi-drug Resistant Bacteria Worsens the Outcomes of Wound Infections.

BACKGROUND: Surgical site infections are one of the major clinical problems in surgical departments that cost hundreds of millions of dollars to healthcare systems around the world. AIM: The study aimed to address the pressing issue of surgical site infections, which pose significant clinical and financial burdens on healthcare systems globally. Recognizing the substantial costs incurred due to these infections, the research has focused on understanding the role of lipase and protease production by multi-drug resistant bacteria isolated from surgical wounds in the development of post-surgical wound infections. METHODS: For these purposes, 153 pus specimens were collected from patients with severe post-surgical wound infections having prolonged hospital stays. The specimens were inoculated on appropriate culture media. Gram staining and biochemical tests were used for the identification of bacterial growth on suitable culture media after 24 hours of incubation. The isolated pathogens were then applied for lipase and protease, key enzymes that could contribute to wound development, on tributyrin and skimmed milk agar, respectively. Following the CSLI guidelines, the Kirby-Bauer disc diffusion method was used to assess antibiotic susceptibility patterns. The results revealed that a significant proportion of the samples (127 out of 153) showed bacterial growth of Gram-negative (n = 66) and Gram-positive (n = 61) bacteria. In total, isolated 37 subjects were declared MDR due to their resistance to three or more than three antimicrobial agents. The most prevalent bacteria were Staphylococcus aureus (29.13%), followed by S. epidermidis (18.89%), Klebsiella pneumoniae (18.89%), Escherichia coli (14.96%), Pseudomonas aeruginosa (10.23%), and Proteus mirabilis (7.87%). Moreover, a considerable number of these bacteria exhibited lipase and protease activity with 70 bacterial strains as lipase positive on tributyrin agar, whereas 74 bacteria showed protease activity on skimmed milk agar with P. aeruginosa as the highest lipase (69.23%) and protease (76.92%) producer, followed by S. aureus (lipase 62.16% and protease 70.27%). RESULTS: The antimicrobial resistance was evaluated among enzyme producers and non-producers and it was found that the lipase and protease-producing bacteria revealed higher resistance to selected antibiotics than non-producers. Notably, fosfomycin and carbapenem were identified as effective antibiotics against the isolated bacterial strains. However, gram-positive bacteria displayed high resistance to lincomycin and clindamycin, while gram-negative bacteria were more resistant to cefuroxime and gentamicin. CONCLUSION: In conclusion, the findings suggest that lipases and proteases produced by bacteria could contribute to drug resistance and act as virulence factors in the development of surgical site infections. Understanding the role of these enzymes may inform strategies for preventing and managing post-surgical wound infections more effectively.

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.

A holistic approach toward development of plant-based meat alternatives through incorporation of novel microalgae-based ingredients.

This study explored the changes in the physiochemical, textural, sensory, and functional characteristics of plant-based meat (PBM) after incorporating novel plant-based ingredients including spirulina (SPI), duck Weed (DW), and yellow Chlorella (YC). In the chromaticity evaluation, the YC group (YCI YC2, and YC3%) displayed significant differences (p < 0.05) in lightness (L*) indices as compared to the control. Whereas, based on concertation gradient of SPI microalgae (SP0.5, SP0.7, and SP1%) incorporated into PBM patties demonstrated that SPI 1 had the lowest values (p < 0.05) in redness (a*) and yellowness (b*) followed by SPI 0.7 and SPI 0.5% concentration, respectively. The concentration gradient of the YC group indicated that YC3 was intended to be the highest crude fat value followed by YC2 and YCI. The ash content in PBM patties increased considerably (p < 0.05) as the concentration level of microalgae advanced in all treated groups. Based on the concentration level of YC incorporated microalgae into PBM patties indicated that YC 3 had the highest (p < 0.05) gumminess and chewiness while YC 1 had the lowest reported values in terms of gumminess and chewiness. Moreover, springiness and cohesiveness showed considerable differences between SPI and YC groups. In the sensory evaluation, SPI 1 showed the lowest value only in color and appearance (p < 0.05), conversely, the other sensory parameters were non-significant among all treatment groups (p > 0.05). The micronutrient in PBM presented an irregular pattern after incorporating various ingredients. However, levels were higher (p < 0.05) in the DW group (DW 0.5 DW 0.7, and DW% 1) than those in the other groups. Moreover, the SPI and YC groups showed detectable levels of diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity with, SP 1 showing the highest level of antioxidant activity. Acknowledging the limited research on PBM production, extraction technologies, and selecting various novel suitable ingredients in meat substitutes. Hence, to fill this knowledge gap an attempt has been made to incorporate various concentrations of microalgae including SPI, YC, and DW to enhance the quality and functionality of meat alternatives. To the best of our knowledge, this is the first report that describes the physiochemical, textural, sensory, and nutritional attributes of PBM incorporated with novel microalgae. Collectively these results indicate that the incorporation of SPI, DW, and YC may improve the quality of PBM without showing deleterious outcomes on the quality and functionality of the ultimate PBM products.

Large animal models of cardiac ischemia-reperfusion injury: Where are we now?

Large animal models of cardiac ischemia-reperfusion are critical for evaluation of the efficacy of cardioprotective interventions prior to clinical translation. Nonetheless, current cardioprotective strategies/interventions formulated in preclinical cardiovascular research are often limited to small animal models, which are not transferable or reproducible in large animal models due to different factors such as: (i) complex and varied features of human ischemic cardiac disease (ICD), which are challenging to mimic in animal models, (ii) significant differences in surgical techniques applied, and (iii) differences in cardiovascular anatomy and physiology between small versus large animals. This article highlights the advantages and disadvantages of different large animal models of preclinical cardiac ischemic reperfusion injury (IRI), as well as the different methods used to induce and assess IRI, and the obstacles faced in using large animals for translational research in the settings of cardiac IR.

Production and Analytical Aspects of Natural Pigments to Enhance Alternative Meat Product Color.

Color is a major feature that strongly influences the consumer's perception, selection, and acceptance of various foods. An improved understanding regarding bio-safety protocols, health welfare, and the nutritional importance of food colorants has shifted the attention of the scientific community toward natural pigments to replace their toxic synthetic counterparts. However, owing to safety and toxicity concerns, incorporating natural colorants directly from viable sources into plant-based meat (PBM) has many limitations. Nonetheless, over time, safe and cheap extraction techniques have been developed to extract the purified form of coloring agents from raw materials to be incorporated into PBM products. Subsequently, extracted anthocyanin has displayed compounds like Delphinidin-3-mono glucoside (D3G) at 3.1 min and Petunidin-3-mono glucoside (P3G) at 5.1 277, 515, and 546 nm at chromatographic lambda. Fe-pheophytin was successfully generated from chlorophyll through the ion exchange method. Likewise, the optical density (OD) of synthesized leghemoglobin (LegH) indicated that pBHA bacteria grow more rigorously containing ampicillin with a dilution factor of 10 after 1 h of inoculation. The potential LegH sequence was identified at 2500 bp through gel electrophoresis. The color coordinates and absorbance level of natural pigments showed significant differences (p < 0.05) with the control. The development of coloring agents originating from natural sources for PBM can be considered advantageous compared to animal myoglobin in terms of health and functionality. Therefore, the purpose of this study was to produce natural coloring agents for PBM by extracting and developing chlorophyll from spinach, extracting anthocyanins from black beans, and inserting recombinant plasmids into microorganisms to produce LegH.

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.

Long COVID-19 and the Heart: Is Cardiac Mitochondria the Missing Link?

Significance: Although corona virus disease 2019 (COVID-19) has now gradually been categorized as an endemic, the long-term effect of COVID-19 in causing multiorgan disorders, including a perturbed cardiovascular system, is beginning to gain attention. Nonetheless, the underlying mechanism triggering post-COVID-19 cardiovascular dysfunction remains enigmatic. Are cardiac mitochondria the key to mediating cardiac dysfunction post-severe acute respiratory syndrome coronavirus 2 (post-SARS-CoV-2) infection? Recent Advances: Cardiovascular complications post-SARS-CoV-2 infection include myocarditis, myocardial injury, microvascular injury, pericarditis, acute coronary syndrome, and arrhythmias (fast or slow). Different types of myocardial damage or reduced heart function can occur after a lung infection or lung injury. Myocardial/coronary injury or decreased cardiac function is directly associated with increased mortality after hospital discharge in patients with COVID-19. The incidence of adverse cardiovascular events increases even in recovered COVID-19 patients. Disrupted cardiac mitochondria postinfection have been postulated to lead to cardiovascular dysfunction in the COVID-19 patients. Further studies are crucial to unravel the association between SARS-CoV-2 infection, mitochondrial dysfunction, and ensuing cardiovascular disorders (CVD). Critical Issues: The relationship between COVID-19 and myocardial injury or cardiovascular dysfunction has not been elucidated. In particular, the role of the cardiac mitochondria in this association remains to be determined. Future Directions: Elucidating the cause of cardiac mitochondrial dysfunction post-SARS-CoV-2 infection may allow a deeper understanding of long COVID-19 and resulting CVD, thus providing a potential therapeutic target. Antioxid. Redox Signal. 38, 599-618.

The Calcineurin-Drp1-Mediated Mitochondrial Fragmentation Is Aligned with the Differentiation of c-Kit Cardiac Progenitor Cells.

Objective: The heart contains a pool of c-kit+ progenitor cells which is believed to be able to regenerate. The differentiation of these progenitor cells is reliant on different physiological cues. Unraveling the underlying signals to direct differentiation of progenitor cells will be beneficial in controlling progenitor cell fate. In this regard, the role of the mitochondria in mediating cardiac progenitor cell fate remains unclear. Specifically, the association between changes in mitochondrial morphology with the differentiation status of c-kit+ CPCs remains elusive. In this study, we investigated the relationship between mitochondrial morphology and the differentiation status of c-kit+ progenitor cells. Methods and Results: c-kit+ CPCs were isolated from 2-month-old male wild-type FVB mice. To activate differentiation, CPCs were incubated in α-minimal essential medium containing 10 nM dexamethasone for up to 7 days. To inhibit Drp1-mediated mitochondrial fragmentation, either 10 µM or 50 µM mdivi-1 was administered once at Day 0 and again at Day 2 of differentiation. To inhibit calcineurin, either 1 µM or 5 µM ciclosporin-A (CsA) was administered once at Day 0 and again at Day 2 of differentiation. Dexamethasone-induced differentiation of c-kit+ progenitor cells is aligned with fragmentation of the mitochondria via a calcineurin-Drp1 pathway. Pharmacologically inhibiting mitochondrial fragmentation retains the undifferentiated state of the c-kit+ progenitor cells. Conclusions: The findings from this study provide an alternative view of the role of mitochondrial fusion-fission in the differentiation of cardiac progenitor cells and the potential of pharmacologically manipulating the mitochondria to direct progenitor cell fate.

Ischemic Preconditioning and Postconditioning Protect the Heart by Preserving the Mitochondrial Network.

Background: Mitochondria fuse to form elongated networks which are more tolerable to stress and injury. Ischemic pre- and postconditioning (IPC and IPost, respectively) are established cardioprotective strategies in the preclinical setting. Whether IPC and IPost modulates mitochondrial morphology is unknown. We hypothesize that the protective effects of IPC and IPost may be conferred via preservation of mitochondrial network. Methods: IPC and IPost were applied to the H9c2 rat myoblast cells, isolated adult primary murine cardiomyocytes, and the Langendorff-isolated perfused rat hearts. The effects of IPC and IPost on cardiac cell death following ischemia-reperfusion injury (IRI), mitochondrial morphology, and gene expression of mitochondrial-shaping proteins were investigated. Results: IPC and IPost successfully reduced cardiac cell death and myocardial infarct size. IPC and IPost maintained the mitochondrial network in both H9c2 and isolated adult primary murine cardiomyocytes. 2D-length measurement of the 3 mitochondrial subpopulations showed that IPC and IPost significantly increased the length of interfibrillar mitochondria (IFM). Gene expression of the pro-fusion protein, Mfn1, was significantly increased by IPC, while the pro-fission protein, Drp1, was significantly reduced by IPost in the H9c2 cells. In the primary cardiomyocytes, gene expression of both Mfn1 and Mfn2 were significantly upregulated by IPC and IPost, while Drp1 was significantly downregulated by IPost. In the Langendorff-isolated perfused heart, gene expression of Drp1 was significantly downregulated by both IPC and IPost. Conclusion: IPC and IPost-mediated upregulation of pro-fusion proteins (Mfn1 and Mfn2) and downregulation of pro-fission (Drp1) promote maintenance of the interconnected mitochondrial network, ultimately conferring cardioprotection against IRI.

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.

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.

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.

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.

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.

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.