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Abstract Viruses are submicroscopic agents that can infect other lifeforms and use their hosts’ cells to replicate themselves. Despite having simplistic genetic structures among all living beings, viruses are highly adaptable, resilient, and capable of causing severe complications in their hosts’ bodies. Due to their multiple transmission pathways, high contagion rate, and lethality, viruses pose the biggest biological threat both animal and plant species face. It is often challenging to promptly detect a virus in a host and accurately determine its type using manual examination techniques. However, computer-based automatic diagnosis methods, especially the ones using Transmission Electron Microscopy (TEM) images, have proven effective in instant virus identification. Using TEM images collected from a recent dataset, this article proposes a deep learning-based classification model to identify the virus type within those images. The methodology of this study includes two coherent image processing techniques to reduce the noise present in raw microscopy images and a functional Convolutional Neural Network (CNN) model for classification. Experimental results show that it can differentiate among 14 types of viruses with a maximum of 97.44% classification accuracy and F1-score, which asserts the effectiveness and reliability of the proposed method. Implementing this scheme will impart a fast and dependable virus identification scheme subsidiary to the thorough diagnostic procedures.

Background: Didactic teaching being teacher centric usually does not generate sense of responsibility for learning among the students. The Jigsaw method is a form of cooperative learning that improves not only the cognitive but also the affective and psychomotor skills, which are required by a student to become an Indian Medical Graduate. Thus, this study was aimed at assessing the effectiveness of Jigsaw strategy in comparison to didactic teaching and to understand the perception of the students. Methodology: This was a quasi-experimental study conducted among second year MBBS students. They were randomly assigned to lecture and Jigsaw groups. A topic on general toxicology was chosen. Handouts were prepared which were given to all the students. The lecture group was taught using audio visual aids. The topic was sub divided into five subtopics for Jigsaw group. A post test consisting of MCQs was used to assess the effectiveness of the method. A validated questionnaire was used to gather feedback from the students belonging to Jigsaw Group. Descriptive statistics were used to analyze responses to the Likert scale questions. Results: The present study included a total of 65 II year MBBS students, out of which 30 students were in Jigsaw group. A total of 94% students agreed to participate actively in the Jigsaw session. Most of the students (93%) students listened to and respected the ideas of their peers. A total of 72% students found this method of learning useful in Forensic Medicine and Toxicology. Only 53% of the students felt that the time for preparation and presentation of the topic was sufficient. Conclusions: The present study advocates this cooperating learning method to be an effective tool in better understanding of the subject, to improve communication skills and to inculcate lifelong learning attitude among the medical graduates. The jigsaw method, however, requires more time for planning and execution, to yield positive outcomes.

Introduction: There is a notable association between hypothyroidism and reduced muscle function as well as compromised neuromuscular coordination. In this study, we aimed to investigate the potential link between Vitamin D and handgrip strength (HGS) in patients with hypothyroidism. Methods: The study involved 40 patients diagnosed with hypothyroidism and subclinical hypothyroidism while excluding individuals with hypothyroid medications, cardiovascular, pulmonary, renal, neurological, reproductive disorders, hand anomalies, Parkinson’s disease, and Vitamin D supplementation. They were evaluated for T3, T4, and thyroid-stimulating hormone (TSH) levels, HGS, and serum Vitamin D levels; triceps fold was measured using a Lange caliper, and a correlation between these variables was assessed. Results: The mean age of the study group was 39.95 ± 6.41 years, with the majority falling in the age group of 41–50 years. A female preponderance (70%) was observed among the study group. The mean value for Vitamin D and HGS was 14.69 ± 6.12 ng/ml and 19.65 ± 5.38 kg, respectively. The mean values for T3, T4, and TSH were 47.23 ± 14.56 ng/dl, 5.86 ± 2.12 μg/dl, and 18.28 ± 7.64 mIU/ml, respectively. A robust, significant positive correlation was found between HGS and Vitamin D (r = 0.42, P < 0.01). The correlation between body mass index (BMI) and Vitamin D was moderately positive but was not statistically significant (r = 0.26, P > 0.01). No significant correlation was observed between Vitamin D and T4, TSH, and triceps fold. A moderate negative correlation was observed for TSH and BMI but was insignificant (r = −0.27, P > 0.05). Conclusion: A strong, significant positive correlation was found between HGS and Vitamin D, while BMI showed moderate positive correlations. No significant correlation was found between Vitamin D, T4, TSH, or triceps fold.

Introduction: Regular monitoring of Vitamin B12 (VB12) levels and intake of supplements is crucial since their deficiency may cause serious health conditions including neurological and cardiovascular complications. Medical professionals recommend supplements in the form of oral tablets and parenteral replacement for patients who cannot obtain these nutrients through diet or who are deficient due to other health conditions. This study aimed to compare the efficacy of oral medications and parenteral administrations, especially intramuscular (IM) injections for managing VB12 deficiency. Methods: The patients diagnosed with confirmed VB12 deficiency (

Abstract Wireless communication systems are inherently challenged by factors such as fading, path loss, and shadowing, leading to potential errors in data transmission. Traditional methods to mitigate these issues include power control, diversification, variable beamforming, and modulation techniques. However, the unpredictable nature of the wireless medium often limits their effectiveness. A new approach to address these challenges is the implementation of cascaded intelligent reflecting surfaces (IRS). IRS systems consist of multiple passive elements that intelligently reflect electromagnetic waves, thereby enhancing signal quality. The Advanced Discrete Fourier Transform (ADFT) matrix scheme is explored in channel estimation, a novel method particularly suitable for wireless networks utilising cascaded IRS. The ADFT matrix scheme is significant for its efficiency in managing the common‐link configuration of cascading channel coefficients, which effectively reduces pilot overhead. When compared to traditional channel estimation methods like the Least Square|least squares, Maximal a posteriori probability, and Linear Minimum Mean Square Error, the ADFT matrix scheme exhibits superior performance. It achieves a remarkable reduction in normalised mean squared error (NMSE) – 66% and 80% at 20 dB and 15 dB Signal to‐Noise Ratios (SNR), respectively. Furthermore, increasing the pilot length correlates with enhanced NMSE performance, with a noted 33% improvement as the base station distance increases. Simulations demonstrate that with an escalation in the number of IRS elements and SNR, the ADFT matrix scheme consistently surpasses conventional methods. This advancement represents a significant leap in the field of wireless communication technology.

Summary: Background: We aimed to study the clinical characteristics, myocardial injury, and longitudinal outcomes of COVID-19 vaccine-associated myocarditis (C-VAM). Methods: In this longitudinal retrospective observational cohort multicenter study across 38 hospitals in the United States, 333 patients with C-VAM were compared with 100 patients with multisystem inflammatory syndrome in children (MIS-C). We included patients ≤30 years of age with a clinical diagnosis of acute myocarditis after COVID-19 vaccination based on clinical presentation, abnormal biomarkers and/or cardiovascular imaging findings. Demographics, past medical history, hospital course, biochemistry results, cardiovascular imaging, and follow-up information from April 2021 to November 2022 were collected. The primary outcome was presence of myocardial injury as evidenced by late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) imaging. Findings: Patients with C-VAM were predominantly white (67%) adolescent males (91%, 15.7 ± 2.8 years). Their initial clinical course was more likely to be mild (80% vs. 23%, p 15 years, OR 2.74 [95% CI: 1.28, 5.83, p = 0.009]) and when C-VAM occurred after the first or second dose as compared to the third dose of mRNA vaccine. Mid-term clinical outcomes of C-VAM at a median follow-up of 178 days (IQR 114–285 days) were reassuring. No cardiac deaths or heart transplantations were reported until the time of submission of this report. LGE persisted in 60% of the patients at follow up. Interpretation: Myocardial injury at initial presentation and its persistence at follow up, despite a mild initial course and favorable mid-term clinical outcome, warrants continued clinical surveillance and long-term studies in affected patients with C-VAM. Funding: The U.S. Food and Drug Administration.

This study presented a new approach to seizure classification utilizing electroencephalogram (EEG) data. We introduced the NeuroWave-Net, an innovative hybrid model that seamlessly integrates convolutional neural networks (CNN) and long short-term memory (LSTM) architectures. Unlike conventional methods, our model capitalized on CNN's proficiency in feature extraction and LSTM's prowess in classifying seizure. The key strength of the NeuroWave-Net lies in its ability to combine these distinct architectures, synergizing their capabilities for enhanced accuracy in identifying seizure conditions within EEG data. Our proposed model exhibited outstanding performance, achieving a classification accuracy of 99.48%. This study contributed to the advancement of seizure classification models, providing a robust and streamlined approach for accurate categorization within EEG datasets. NeuroWave-Net stands as a testament to the potential of hybrid neural network architectures in neurological diagnostics.

People exhibit all sorts of irrational behaviour during pandemics; occurrences during the Covid-19 pandemic also agree with this statement. Millions of people died during Covid-19 pandemic and people observed these deaths very closely. Therefore, it is highly probable that these deaths have a mental toll on people. One of the specific irrational incident types is - attacks on healthcare personnel (AoHP). These attacks include both verbal and physical abuse. Naturally, it is the duty of governing agencies to provide protection for these pandemic front-liners. In this work, we have explored the data related to AoHP incidents to fully understand the gravity of the situation. The current scenario is analysed through the utilization of data visualization techniques, thereby offering an insightful perspective. We have noticed that maximum cases are reported in India. In addition, civilians are mainly responsible for most occurrences. All things considered, we put forward an AoHP-type news detection mechanism, which is proposed through real experiments by applying several generic machine learning (ML) algorithms (Naive Bayes, Logistic Regression, Support Vector Machine, Extreme Gradient Boosting, Random Forest) and deep learning (DL) models (Artificial Neural Network, Long Short Term Memory, and Gated Recurrent Unit) over AoHP type news & reports for quick identification of occurrence, which would be helpful to alert the authorities. We have deployed four feature engineering techniques along with generic ML algorithms and pinpointed the most suitable one for our case. We declare Random Forest as the best fit for our purpose of classification, which achieved an F1-score of 97% in our experiments.

Background and aimBone marrow stem cells (BM-SCs) and their progeny play a central role in tissue repair and regeneration. In patients with chronic liver failure, bone marrow (BM) reserve is severally compromised and they showed marked defects in the resolution of injury and infection, leading to liver failure and the onset of decompensation. Whether BM failure is the cause or consequence of liver failure during cirrhosis is not known. In this study, we aimed to determine the underlying relationship between BM failure and regeneration failure in cirrhosis.MethodologyC57Bl/6(J) mice were used to develop chronic liver injury through intra-peritoneal administration of carbon tetrachloride (CCl4) for 15 weeks (0.1-0.5 ml/kg). Animals were sacrificed to study the transition of cirrhosis and BM defects. To restore the BM-SC reserve; healthy BM cells were infused via intra-BM infusion and assessed for changes in liver injury, regeneration, and BM-SC reserve.ResultsUsing a CCl4-induced animal - model of cirrhosis, we showed the loss of BM-SCs reserve occurred before regeneration failure and the onset of non-acute decompensation. Intra-BM infusion of healthy BM cells induced the repopulation of native hematopoietic stem cells (HSCs) in cirrhotic BM. Restoring BM-HSCs reserve augments liver macrophage-mediated clearance of infection and inflammation dampens neutrophil-mediated inflammation, accelerates fibrosis regression, enhances hepatocyte proliferation, and delays the onset of non-acute decompensation.ConclusionThese findings suggest that loss of BM-HSCs reserve underlies the compromised innate immune function of the liver, drives regeneration failure, and the onset of non-acute decompensation. We further provide the proof-of-concept that rejuvenating BM-HSC reserve can serve as a potential therapeutic approach for preventing regeneration failure and transition to decompensated cirrhosis.

Abstract Ribonucleic acid (RNA) structure is vital to its ability to function within the cell. The ability to predict RNA structure is essential to implementing new medications and understanding genetic illnesses. It is also important in synthetic and computational biology. All these functions are directly related to its secondary structure. Also prediction of RNA secondary structure process is the most significant step to determining the tertiary structure of RNA. On account of this, prediction of secondary structure of RNA is the crying topic in bioinformatics. In this research, we present the swarm-based metaheuristic Butterfly Optimization Algorithm (BOA) method for predicting the secondary structure of RNA. The main feather of the BOA is that it can conduct both local and global search simultaneously. According to the problem perspective, we have redesigned the operators of BOA to perform global and local search operations in different ways. We have followed a thermodynamic model for the selection of the stable secondary structure with minimum Gibbs free energy. Predicting the minimum free energy value we also developed an “Optimize” function to search the new optimize structure. This function increases the prediction efficiency, creating new stable structure and also decreases the time complexity of global searching procedure. We have used a public dataset to perform the prediction operation. To accuse our prediction efficiency, we have compared our outcomes to existing popular algorithms. The result shows that the proposed approach can predict secondary RNA structure better than other state-of-the-art algorithms.

Abstract To meet the growing demand for vegetable production and promote sustainable agriculture, it is imperative to implement effective input management and adopt eco-friendly farming practices. This study aims to compare the environmental impacts of conventional and organic tomato cultivation in the northern plains of India. This study utilizes SimaPro 9.1.1 software for a comprehensive cradle-to-farm gate Life Cycle Assessment (LCA), assessing production stages, identifying key environmental factors, and incorporating ReCiPe Midpoint and Endpoint methods with one-hectare as a functional unit. Findings reveal that conventional cultivation is more affected by fertilizer application and transplanting, while organic cultivation emphasizes transplanting and irrigation. Organic cultivation contributes 904.708 kg CO2, while conventional cultivation contributes 1307.917 kg CO2 to Global Warming potential. Switching to organic cultivation leads to a significant 35.04% decrease in all impact categories. Using the endpoint method, organic cultivation achieves a notable 27.16% reduction, scoring 58.30 compared to conventional cultivation's 80.04. The LCA analysis of tomato cultivation highlights Fertilizer application as the predominant environmental concern, emphasizing the need for sustainable techniques to minimize waste and mitigate environmental impacts. This study recommends imposing restrictions on fertilizer and pesticide use and formulating effective policies to promote the adoption of sustainable practices.

Electronic Medical Images (EMI) have grown with the increase in population. EMI are used for medical diagnosis for medical emergencies hence, they should be correct and clear for accurate diagnosis. In general, EMI are of varying sizes and dimensions. The aim is to enhance the security and privacy of EMI with reduced computational time while dealing with a larger and varying-sized data set. Reducing computational time will make the method suitable for real-time applications (RTA). Hence, a data set of 5856 secret X-ray images all varying in dimensions, total sized up to 1.16 GB, are applied with a hybrid of steganography and cryptography. Here, one X-Ray image is taken at a time then hidden into a single cover image using Edge-based steganography and then encrypted using five layers of cryptography. Various performance evaluation tests such as Structural Similarity Index Metrics (SSIM) achieved a value close to 1 which is the preferred value, Peak Signal-to-Noise Ratio (PSNR) is 82.51967 dB which is good, Mean Square Error (MSE) is 5.6E-09 which is close to zero indicating no addition of noise in the retrieved X-ray images, the Correlation (R) is 1. Therefore, the extracted image is the same as the original X-ray images, while remaining tests such as RMSE, Entropy, KLD, BER, SNR, CV, MAPE, PRD, etc. achieved good results. The computational time is measured by the Encryption Time (ET) is 0.37 seconds while the Decryption Time (DT) is 3.9275 sec. Thus, it can be concluded that a hybrid method (HM) could be implemented for RTA.

Students’ emotional state and attention significantly impact how they handle stress and interact with their studies. These factors are crucial in defining their learning objectives and general personal growth, influencing their academic achievement. Because Bangladesh has distinct educational and mental health difficulties, it is important to comprehend these dynamics. Time series analysis is a useful technique for tracking lecture activities in class and how they affect student participation since it provides insightful information about behavior patterns across time. This investigation aims to address these problems by using MotionWatch 8 and a comprehensive questionnaire to analyze class lecture activities. This study employs ensemble methodologies, deep Learning algorithms, and a diverse range of machine learning models to assess and predict student behavior. A hybrid model is one of the techniques that produced the most stunning results, proving how well it could capture complex patterns in time series data. To assess the robustness of the algorithms, the study also looked at how different datasets performed. Ultimately, the models’ interpretability was improved, and their decision-making processes were given a more profound understanding by utilizing explainable AI methods, including SHAP, LIME, and permutation importance. The effort establishes new standards for improving student engagement and well-being through data-driven insights, sophisticated models, and explainable AI.

Closed-loop deep brain stimulation (cl-DSB) is recognized as one of the most prominent schemes of neurostimulation therapy which aims to reduce the level of hand tremor in Parkinson’s disease (PD). Recently, modern neurostimulation has integrated wireless sensors and programming devices with communication infrastructures to monitor and regulate hand speed remotely. However, deploying wireless communication technologies introduces degradation procedures (i.e., latency, time delay, and so on) which adversely affect the neurostimulation treatment. Thus, in this paper, an adaptive fractional-order model predictive control (FOMPC) is designed to address the challenges of wireless neurostimulation in the cl-DSB system. For this purpose, a deep on-policy learning (DOL) algorithm is adopted to design parameters of the FOMPC in such a way that minimizes the fluctuations of hand speed and eliminates side effects of stimulation. Compared with the integer version of the model predictive controller, further flexibility is achieved by the non-integer orders. Deep neural networks (DNNs) of on-policy learning are trained to regulate tremor speed by dynamically adjusting the parameters of FOMPC in an episodic manner. The main novelty of this work is to consider the challenges of unreliable wireless communications, as it allows us to investigate the effects of latency and time delays in cl-DSB systems. This makes the suggested controller a prominent option for real-time applications where the presence of time delay can threaten the system’s stability. Extensive simulation scenarios under various levels of random communication delays are considered to assess the feasibility of suggested stimulators for wireless cl-DSB systems. Comparative analysis of cl-DBS’s responses reveals the superiority of the suggested FOMPC controller (designed by deep-on-policy learning) to regulate hand tremors over other state-of-the-art strategies.

Diagnosing brain tumors using magnetic resonance imaging (MRI) presents significant challenges due to the complexities of segmentation and the variability in tumor characteristics. To address the limitations inherent in traditional methods, this research employs an advanced deep learning approach, integrating ResNet50 for feature extraction and Generative Adversarial Networks (GANs) for data augmentation. A comprehensive evaluation of ten transfer learning algorithms, including GoogLeNet and VGG-16, was conducted for the classification of brain tumors. Model performance was assessed using precision, recall, and F1-score metrics, complemented by additional metrics such as Hamming loss and the Matthews correlation coefficient to provide a more comprehensive insight. To ensure transparency in image predictions, Explainable AI techniques, specifically Local Interpretable Model-Agnostic Explanations (LIME), were utilized. The study involved the analysis of 7023 MRI images, with TumorGANet being trained on a dataset encompassing gliomas, meningiomas, non-tumorous cases, and pituitary tumors. The results demonstrate the exceptional performance of proposed model named TumorGANet, achieving an accuracy of 99.53%, precision and recall rates of 100%, F1 scores of 99%, and a Hamming loss of 0.2%.

In the wake of COVID-19, rising monkeypox cases pose a potential pandemic threat. While less severe than COVID-19, its increasing spread underscores the urgency of early detection and isolation to control the disease. The main difficulty in diagnosing monkeypox arises from its prolonged diagnostic process and symptoms that are similar to those of other skin diseases, making early detection and isolation challenging. To address this, the deployment of deep learning models on edge devices presents a viable solution for the rapid and accurate detection of monkeypox. However, the resource constraints of edge devices require the use of lightweight deep learning models. The limitation of these models often involves a trade-off with accuracy, which is unacceptable in the context of medical diagnostics. Therefore, the development of optimized deep learning models that are both resource-efficient for edge computing and highly accurate becomes imperative. To this end, an attention-based MobileNetV2 model for monkeypox detection, capitalizing on the inherent lightweight design of MobileNetV2 for effective deployment on edge devices, is proposed. This model, enhanced with both spatial and channel attention mechanisms, is tailored for rapid and early-stage diagnosis of monkeypox with better accuracy. We significantly improved the Monkeypox Skin Images Dataset (MSID) by incorporating a broader range of classes for similar skin diseases, thereby substantially enriching and diversifying the training dataset. This helps better distinguish monkeypox from other similar skin diseases, particularly in its early stages or when a detailed medical examination is unavailable. To ensure transparency and interpretability, we incorporated Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-Agnostic Explanations (LIME) to provide clear insights into the model’s diagnostic reasoning. Finally, to comprehensively assess the performance of our model, we employed a range of evaluation metrics, including Cohen’s Kappa, Matthews Correlation Coefficient, and Youden’s J Index, alongside traditional measures like accuracy, F1-score, precision, recall, sensitivity, and specificity. The attention-based MobileNetV2 model demonstrated impressive results, outperforming the baseline models by achieving 92.28% accuracy in the extended MSID dataset, 98.19% in the original MSID dataset, and 93.33% in the Monkeypox Skin Lesion Dataset (MSLD) dataset.

Abstract Brain tumors (BTs) are one of the deadliest diseases that can significantly shorten a person’s life. In recent years, deep learning has become increasingly popular for detecting and classifying BTs. In this paper, we propose a deep neural network architecture called NeuroNet19. It utilizes VGG19 as its backbone and incorporates a novel module named the Inverted Pyramid Pooling Module (iPPM). The iPPM captures multi-scale feature maps, ensuring the extraction of both local and global image contexts. This enhances the feature maps produced by the backbone, regardless of the spatial positioning or size of the tumors. To ensure the model’s transparency and accountability, we employ Explainable AI. Specifically, we use Local Interpretable Model-Agnostic Explanations (LIME), which highlights the features or areas focused on while predicting individual images. NeuroNet19 is trained on four classes of BTs: glioma, meningioma, no tumor, and pituitary tumors. It is tested on a public dataset containing 7023 images. Our research demonstrates that NeuroNet19 achieves the highest accuracy at 99.3%, with precision, recall, and F1 scores at 99.2% and a Cohen Kappa coefficient (CKC) of 99%.

Our knowledge of fluorine's unique and complex properties has significantly increased over the past 20 years. Consequently, more sophisticated and innovative techniques have emerged to incorporate this feature into the design of potential drug candidates. In recent years, researchers have become interested in synthesizing fluoro-sulphonamide compounds to discover new chemical entities with distinct and unexpected physical, chemical, and biological characteristics. The fluorinated sulphonamide molecules have shown significant biomedical importance. Their potential is not limited to biomedical applications but also includes crop protection. The discovery of novel fluorine and Sulfur compounds has highlighted their importance in the chemical sector, particularly in the agrochemical and medicinal fields. Recently, several fluorinated sulphonamide derivatives have been developed and frequently used by agriculturalists to produce food for the growing global population. These molecules have also exhibited their potential in health by inhibiting various human diseases. In today's world, it is crucial to have a steady supply of innovative pharmaceutical and agrochemical molecules that are highly effective, less harmful to the environment, and affordable. This review summarizes the available information on the activity of Fluorine and Sulphonamide compounds, which have proven active in pharmaceuticals and agrochemicals with excellent environmental and human health approaches. Moreover, it focuses on the current literature on the chemical structures, the application of fluorinated sulphonamide compounds against various pathological conditions, and their effectiveness in crop protection.

Cardiovascular disease is the leading cause of morbidity and mortality in patients with autoimmune rheumatic diseases. Much of this may be attributed to systemic inflammation resulting in coronary atherosclerosis and myocarditis. Cardiac magnetic resonance imaging is the gold standard for the evaluation of cardiac structure and function, including tissue characterization, which allows for detection of myocardial edema, inflammation, and fibrosis. Advances in parametric mapping and coronary flow reserve measurement techniques have the potential to change the diagnosis, risk stratification, and management of patients with autoimmune rheumatic diseases. We provide an overview of the current evidence and suggest potential future roles for the use of comprehensive cardiac magnetic resonance in patients with autoimmune rheumatic diseases in the field of cardio-rheumatology.

Airway complications following lung transplantation remain an important cause of morbidity and mortality. We aimed to identify the incidence, risk factors and outcomes associated with clinically significant airway ischemia (CSAI) in our center. We reviewed 217 lung transplants (386 airway anastomoses) performed at our institution between February 2016 and December 2020. Airway images were graded using the 2018 ISHLT grading guidelines modified slightly for retrospective analysis. Airways were considered to have CSAI if they developed ischemia severity >B2, stenosis >50%, and/or any degree of dehiscence within 6-months of transplant. Regression analyses were used to evaluate outcomes and risk factors for CSAI. Eighty-two patients (37.8%) met criteria for CSAI. Of these, twenty-six (32%) developed stenosis and/or dehiscence, and 17 (21%) required interventions. Patients with CSAI had lower one-year (80.5% vs. 91.9%, p = 0.05) and three-year (67.1% vs. 77.8%, p = 0.08) survival than patients without CSAI. Factors associated with CSAI included younger recipient age, recipient diabetes, single running suture technique, performance of the left anastomosis first, lower venous oxygen saturation within 48-h, and takeback for major bleeding. Our single-center analysis suggests that airway ischemia remains a major obstacle in contemporary lung transplantation. Improving the local healing milieu of the airway anastomosis could potentially mitigate this risk.

Abstract The COVID-19 pandemic has unleashed an unprecedented global crisis, releasing a wave of illness, mortality, and economic disarray of unparalleled proportions. Numerous societal and behavioral aspects have conspired to fuel the rampant spread of COVID-19 across the globe. These factors encompass densely populated areas, adherence to mask-wearing protocols, inadequate awareness levels, and various behavioral and social practices. Despite the extensive research surrounding COVID-19 detection, an unfortunate dearth of studies has emerged to meticulously evaluate the intricate interplay between socio-demographic and behavioral factors and the likelihood of COVID-19 infection. Thus, a comprehensive online-based cross-sectional survey was methodically orchestrated, amassing data from a substantial sample size of 500 respondents. The precisely designed survey questionnaire encompassed various variables encompassing socio-demographics, behaviors, and social factors. The Bivariate Pearson’s Chi-square association test was deftly employed to unravel the complex associations between the explanatory variables and COVID-19 infection. The feature importance approach was also introduced to discern the utmost critical features underpinning this infectious predicament. Four distinct Machine Learning (ML) algorithms, specifically Decision Tree, Random Forest, CatBoost, and XGBoost, were employed to accurately predict COVID-19 infection based on a comprehensive analysis of socio-demographic and behavioral factors. The performance of these models was rigorously assessed using a range of evaluation metrics, including accuracy, recall, precision, ROC-AUC score, and F1 score. Pearson’s Chi-square test revealed a statistically significant association between vaccination status and COVID-19 infection. The use of sanitizer and masks, the timing of infection, and the interval between the first and second vaccine doses were significantly correlated with the likelihood of contracting the COVID-19 virus. Among the ML models tested, the XGBoost classifier demonstrated the highest classification accuracy, achieving an impressive 97.6%. These findings provide valuable insights for individuals, communities, and policymakers to implement targeted strategies aimed at mitigating the impact of the COVID-19 pandemic.

Background: The size and shape of the adult corpus callosum (CC) may vary with gender. There is, however, less literature available on data involving the morphometry of CC among the Indian population. Aims and Objectives: To measure the size of CC using magnetic resonance imaging (MRI) scans of normal Indian adult females and males and identify gender-related differences, if any. Materials and Methods: The dimensions of CC were measured on MRI scans on a midsagittal section view belonging to 150 (59 females, 91 males) normal Indian adults (North India) using e-measurement tools. The measurements included the maximum length and height of CC, the thickness of various parts of CC, the CC index (CCI), and the distance of CC from the frontal and occipital poles of the cerebral hemisphere. The study was carried out in the Department of Anatomy in collaboration with the Department of Radiodiagnosis, King George’s Medical University, U.P., Lucknow, India. The data was analyzed using the statistical package for social sciences, 23rd version. Means were compared for significant differences using the independent unpaired t-test. Results: The mean length of the CC was found to be 6.94±0.63 cm; mean height was 2.57±0.43 cm; the mean thickness of the genu was 9.16±2.26 mm; the mean thickness of the body was 5.09±0.99 mm; the mean thickness of splenium was 9.10±2.22 mm; the mean distance from the frontal pole was 3.66±0.35 cm; the mean distance from the occipital pole was 5.70±0.74 cm; and the mean CCI calculations showed to be 3.37±0.56. All measurements were found to be greater in males as compared to females except mean height (males 2.56±0.47 cm; females 2.59±0.37 cm) and mean thickness of the body (males 5.04±0.94 mm; females 5.17±1.06 mm). A statistically significant difference was observed in the distance of CC from the frontal pole in our population with respect to gender (P=0.03). Conclusion: Based on observations made in the study, normative data of CC measurements was generated, and it was found that there was no significant gender-related difference in the morphology of CC; the only significant difference was in the distance of the genu from the frontal pole, which was greater in females as compared to males.

Abstract The discipline of forecasting and prediction is witnessing a surge in the application of these techniques as a direct result of the strong empirical performance that approaches based on machine learning (ML) have shown over the past few years. Especially to predict wind direction, air and water quality, and flooding. In the context of doing this research, an MLP-LSTM Hybrid Model was developed to be able to generate predictions of this nature. An investigation into the Beijing Multi-Site Air-Quality Data Set was carried out in the context of an experiment. In this particular scenario, the model generated MSE values that came in at 0.00016, MAE values that came in at 0.00746, RMSE values that came in at 13.45, MAPE values that came in at 0.42, and R 2 values that came in at 0.95. This is an indication that the model is functioning effectively. The conventional modeling techniques for forecasting, do not give the level of performance that is required. On the other hand, the results of this study will be useful for any type of time-specific forecasting prediction that requires a high level of accuracy.

Abstract The gut microbiome influences the pathogenesis and course of metabolic disorders such as diabetes. While it is likely that duodenal mucosa associated microbiota contributes to the genesis and progression of increased blood sugar, including the pre-diabetic stage, it is much less studied than stool. We investigated paired stool and duodenal microbiota in subjects with hyperglycemia (HbA1c ≥ 5.7% and fasting plasma glucose > 100 mg/dl) compared to normoglycemic. We found patients with hyperglycemia (n = 33) had higher duodenal bacterial count (p = 0.008), increased pathobionts and reduction in beneficial flora compared to normoglycemic (n = 21). The microenvironment of duodenum was assessed by measuring oxygen saturation using T-Stat, serum inflammatory markers and zonulin for gut permeability. We observed that bacterial overload was correlated with increased serum zonulin (p = 0.061) and higher TNF-α (p = 0.054). Moreover, reduced oxygen saturation (p = 0.021) and a systemic proinflammatory state [increased total leukocyte count (p = 0.031) and reduced IL-10 (p = 0.015)] characterized the duodenum of hyperglycemic. Unlike stool flora, the variability in duodenal bacterial profile was associated with glycemic status and was predicted by bioinformatic analysis to adversely affect nutrient metabolism. Our findings offer new understanding of the compositional changes in the small intestine bacteria by identifying duodenal dysbiosis and altered local metabolism as potentially early events in hyperglycemia.

The study investigates the antecedents that influence social distancing behavior during the COVID-19 outbreak and develops a hypothetical model integrating additional pertinent variables into the classic theory of planned behavior (TPB) to grasp the social distancing behavior accurately. The extended model was investigated employing structural equation modeling using the data collected through Snowball Technology from 331 respondents. The results unearth significant influences of all the predictor variables on their outcome variables. Surprisingly, the supportive government strategy has no crucial impact on perceived behavioral control toward social distancing. Furthermore, the study reports no trivial difference between young and old and high-low income groups on the intention-actual social distancing. The study outlines policy interventions to prevent mass transmission of the COVID-19 catastrophe. Lastly, limitations are specified to open up novel insights for future researchers.

Panchatikta is an Ayurvedic multiherbal combination containing five bitter-tasting drugs—Azadirachta indica, Tinospora cordifolia, Justicia adhatoda, Solanum virginianum, and Trichosanthes dioica. The objective of this study is to develop standards for Panchatikta ingredients using pharmacognostic parameters and to analyze the chemical composition. The plant materials were collected from different natural habitats, authenticated, dried, ground, and the combination was prepared by mixing the ingredients in equal amounts followed by macroscopic, organoleptic, microscopic, histochemical, and physicochemical characterization according to the standard protocol of the Ayurvedic Pharmacopoeia of India. Special morphological characteristics of the A. indica (stem bark), T. cordifolia (stem), J. adhatoda (roots), S. virginainum (roots), and T. dioica (aerial parts) were identified. The anatomical structure of each drug was observed, and microscopic markers were developed for identification. Histochemical deposits such as starch, oil, lignin, and tannin were located in each plant material. Powder characters such as fibers from phloem with pointed ends and narrow lumen, parenchyma cells containing starch grains, crystal fibers, fragments of bordered pitted vessels, fragments of pitted vessels, cells containing black powdery masses, and epidermal cells from leaves in surface view with an anomocytic stomata were identified as the key features for the Panchatikta combination. The organoleptic characterization of component drugs and combinations was performed. The physicochemical evaluation of parameters such as ash content (5.53 ± 0.29), water-insoluble ash (4.57 ± 0.32), acid-insoluble ash (1.55 ± 0.31), alcohol-soluble extract (5.9 ± 1.21), water-soluble extract (9.19 ± 0.21), fiber content (33.27 ± 1.37), total sugar (1.83 ± 0.65), and reducing sugar (1.73 ± 0.40) were evaluated for the Panchatikta combination. Preliminary phytochemical tests were carried out to verify the presence of alkaloids, flavonoids, saponins, tannins, phenols, glycosides, and steroids. The HPTLC profile comparison of ethanolic, hydroalcoholic, and aqueous extracts of Panchatikta and single drugs showed some type of synergy and antagonistic activities among the chemical principles present. Marker-based screening of azadirachtin, betulinic acid, and lupeol was done in Panchatikta ethanol using HPTLC. As Panchatikta is prescribed for various life-threatening diseases including cancer, the standards developed here ensure the authenticity of the ingredients, which in turn ensures the quality of the prepared medicines.

Abstract The thyroid gland is the crucial organ in the human body, secreting two hormones that help to regulate the human body’s metabolism. Thyroid disease is a severe medical complaint that could be developed by high Thyroid Stimulating Hormone (TSH) levels or an infection in the thyroid tissues. Hypothyroidism and hyperthyroidism are two critical conditions caused by insufficient thyroid hormone production and excessive thyroid hormone production, respectively. Machine learning models can be used to precisely process the data generated from different medical sectors and to build a model to predict several diseases. In this paper, we use different machine-learning algorithms to predict hypothyroidism and hyperthyroidism. Moreover, we identified the most significant features, which can be used to detect thyroid diseases more precisely. After completing the pre-processing and feature selection steps, we applied our modified and original data to several classification models to predict thyroidism. We found Random Forest (RF) is giving the maximum evaluation score in all sectors in our dataset, and Naive Bayes is performing very poorly. Moreover selecting the feature by using the feature importance method RF provides the best accuracy of 91.42%, precision of 92%, recall of 92% and F1-score of 92%. Further, by analyzing the characteristics and behavior of the dataset, we identified the most important features (TSH, T3, TT4, and FTI) of the dataset. In terms of accuracy and other performance evaluation criteria, this study could advocate the use of effective classifiers and features backed by machine learning algorithms to detect and diagnose thyroid disease. Finally, we did some explainability analysis of our best classifier to understand the internal black-box of our machine learning model and datasets. This study could further pave the way for the researcher as well as healthcare professionals to analyze thyroid disease in real time applications.

Abstract Chronic Kidney Disease (CKD) has become a major problem in modern times, and it is dubbed the silent assassin due to its delayed signs. To overcome these critical issues, early identification may minimize the prevalence of chronic diseases, though it is quite difficult because of different kinds of limitations in the dataset. The novelty of our study is that we extracted the best features from the dataset in order to provide the best classification models for diagnosing patients with chronic kidney disease. In our study, we used CKD patients’ clinical datasets to predict CKD using some popular machine learning algorithms. After handling missing values, K-means clustering has been performed. Then feature selection was done by applying the XGBoost feature selection algorithm. After selecting features from our dataset, we have used a variety of machine learning models to determine the best classification models, including Neural Network (NN), Random Forest (RF), Support Vector Machine (SVM), Random Tree (RT), and Bagging Tree Model (BTM). Accuracy, Sensitivity, Specificity, and Kappa values were used to evaluate model performance.

Introduction: Osteoporosis is a condition which affects both men and women but occurs a decade later in men as compared to women. Males constitute a very small percentage of patients who report for dual-energy X-ray absorptiometry (DEXA) scan. This difference may be largely because of lack of information about risk factors for secondary osteoporosis in men. In the present study, we have tried to explore the attributes of males undergoing DEXA scan at a tertiary care center. Materials and Methods: It was a cross-sectional study conducted in males visiting the general physician outpatient department in a tertiary care institute. All study participants who were males, based on inclusion and exclusion were included in the study. DEXA scan for hip and vertebra was performed for all the participants. Results: A total of 486 males fitting the inclusion criteria were included in the study. The mean age of the study participants was 66.98 (standard deviation: 8.55) years. Fifty-six (11.52%) of these participants had hypertension and 41 (8.43%) subjects had diabetes. Patient attributes and DEXA scan parameters of hip and vertebra showed a significant negative correlation with age and positive correlation with body mass index (BMI). Stratified analysis revealed that only 39% of individuals with a BMI of more than 25 kg/m2 had a high risk of osteoporosis (T ≤ −2) as compared to 58.4% of individuals with a BMI of

Scheduling of robots is one of the imperative assignment in a multi robot system. Scheduling is prerequisite when there is a multiple task need to be assigned to multi robot in an arranged manner. There is a growing need for robots to perform complex tasks autonomously. Multi-robot environment becomes complex as there are multiple factors need to be addressed simultaneously which require fast computation and more space. Using cloud computing platform could be one of the optimal solution for this problem. This paper presents the use of cloud computing platform for implementing the proposed Periodic Min-Max Algorithm (PMW) for multi robot task scheduling. Amazon web service (AWS) platform is utilized for deploying the algorithm for multi robot task scheduling. The task performed by the robots is considered as a single service in context with cloud platform and it withdraw an advantage when the number of services increases with time. Time requirement to complete the task and the load balancing parameter are analysed using the proposed approach and is compared with other relevant work. The results presented in the paper clearly shows the performance improvement in both the parameters. There is an improvement of about 3-7% in both the parameters and are reported in the paper. The paper also emphasize on the deployment of cloud computing platform for the service robots. Time completion factor is analysed and reported in the paper to proof the advantage of using cloud platform for the service robots. The novel way of using the algorithm with cloud server seeks many advantage are also observed, analysed and presented in the paper.

Rainfall prediction plays a crucial role in raising awareness about the potential dangers associated with rain and enabling individuals to take proactive measures for their safety. This study aims to utilize machine learning algorithms to accurately predict rainfall, considering the significant impact of scarcity or extreme rainfall on both rural and urban life. The complex nature of rainfall, influenced by various atmospheric, oceanic, and geographical factors, makes it a challenging phenomenon to forecast. This research employs data preprocessing techniques, outlier analysis, correlation analysis, feature selection, and several machine learning algorithms such as Naive Bayes (NB), Decision Tree, Support Vector Machine (SVM), Random Forest, and Logistic Regression. The study focuses on developing the most accurate rainfall prediction model by utilizing machine learning and feature selection techniques. The Artificial Neural Network (ANN) achieves a maximum accuracy of 90% and 91% before and after feature selection, respectively. Furthermore, k-means clustering and Principal Component Analysis (PCA) are applied to examine regional rainfall patterns in Australia. Lastly, to make our proposed machine learning simpler and more usable for general people, we have formulated a web-based application system using Flask in our research paper. Overall, this research demonstrates the effectiveness of different machine-learning techniques in predicting rainfall using Australian weather data.

Cloud computing has revolutionized the management and analysis of data for organizations, offering scalability, flexibility, and cost-effectiveness. Effective task scheduling in cloud systems is crucial to optimize resource utilization and ensure timely job completion. This research presents a novel method for job scheduling in cloud computing, employing the Johnson Sequencing algorithm across three servers. Originally developed for scheduling tasks in a manufacturing context, the Johnson Sequencing method has proven successful in resolving task scheduling challenges. Here, we adapt this method to address job scheduling among three servers within a cloud computing environment. The primary objective of the algorithm is to minimize the makespan, representing the total time required to complete all tasks. This study considers a scenario where a diverse set of jobs, each with varying processing durations, needs to be distributed across three servers using the Johnson Sequencing method. The algorithm strategically determines the optimal order for task execution on each server while accounting for job interdependencies and processing times on the individual servers. To put the Johnson Sequencing algorithm into practice for cloud computing job scheduling, we propose a three-step approach. First, we construct a precedence graph by analyzing the relationships among jobs. Subsequently, the precedence graph is transformed into a two-machine Johnson Sequencing problem by allocating jobs to servers. Finally, we employ the Dynamic Heuristic Johnson Sequencing method to determine the best order of jobs on each server, effectively minimizing the makespan. Through comprehensive simulations and testing, we compare the performance of our suggested Dynamic Heuristic Johnson Sequencing technique with existing scheduling algorithms. The results demonstrate significant improvements in terms of makespan reduction and resource utilization when employing our proposed method with three servers. Furthermore, our approach exhibits remarkable scalability and effectiveness in resolving complex job scheduling challenges within cloud computing settings. The outcomes of this research contribute to the optimization of resource allocation and task management in cloud systems, offering potential benefits to a wide range of industries and applications.

The early detection of digestive problems is essential for lowering the chance of acquiring any form of gastrointestinal cancer, including esophageal cancer. Endoscopy is the method that is used the majority of the time for the purpose of examining and taking photos of this sort of illness. The application of artificial intelligence is now proving to be very efficient in enhancing the identification of gastrointestinal polyps and other abnormal features located inside the gastrointestinal system. As a direct consequence of this development, the use of AI within this sector has seen substantial growth. In the framework of artificial intelligence, this research investigates how well various types of algorithms perform in terms of polyp and abnormal feature recognition accuracy, efficiency, and detection. And introduced a model in this work that is GastroNet. It is developed by doing hyperparameter fine-tuning on YOLOv5 in order to find specific polyps and abnormal characteristics, particularly esophagitis. In this method, a single neural network is used to do an analysis on the whole picture before it is disassembled into its component parts and the bounding boxes and probabilities for each one are calculated independently. The goal of the hyperparameter fine-tuning is to further enhance the overall optimization of the model. Two different methods of annotation were used on a collection of data that consisted of one thousand separate images that needed to be labelled. In addition to implementing the fine-tuned SSD model, this study used three distinct backbone networks: MobileNet v2, MobileNet v2 FPN Lite, and Resnet50 v1 FPN. Additionally, this study has used CSPdarknet53 to create the improved YOLOv4 model. The results of the studies demonstrate that the proposed model, GastroNet was effective in correctly recognizing polyps and aberrant characteristics, reaching a high mAP (mean Average Precision), F1 score, and precision with a value of 0.99 and recall with a value of 1.00. The findings of this research will be a great help to physicians in the proper identification and diagnosis of abnormal features and gastrointestinal polyps.

Sjögren's syndrome (SS) is a chronic autoimmune disorder characterized by lymphocytic infiltration of exocrine glands, leading to dryness of the mouth and eyes. Although it can affect all age groups, it is relatively less common in older adults. Renal involvement is a rare, and as initial presentation even rarer reported, a serious complication of SS, which can have a significant impact on patients' quality of life, functional and physical status, and their overall survival. This is a case report of an elderly male patient who presented to us with rapidly progressive renal dysfunction and uremic symptoms, who was diagnosed as a case of SS with a high possibility of renal involvement. However, the patient succumbed to sepsis before the initiation of disease-modifying therapy. This case report emphasizes an extensive workup of unexplained renal dysfunction, even in geriatric patients, so that potentially manageable conditions like SS can be caught early, and allow early initiation of disease-modifying therapies that can slow down progression, and prevent life-threatening exacerbations of such diseases, which overall will reduce morbidity burden and improve quality of life of elderly patients.

Introduction: Ayurvedic formulations have a long history of use and have increased amazingly to follow the global interest in natural products. However, for many classical ayurvedic formulations, no quality parameters of standardization are available. Vyoshadi Churna, a classical Ayurvedic polyherbal formulation that cures indigestion and diarrhea, is being used since ancient times. However, there is no standard method available for its formulation and evaluation. Hence, the current study aims to develop the quality standards for Vyoshadi churna. Methods: The standard procedures for preparation and standardization of Churna described in Ayurvedic Pharmacopoeia of India (API) were used in the present study. Standardization includes pharmacognostical, chromatographic, and physico-chemical studies. The other essential studies were also done in accordance to API and World Health Organization guidelines to find the limits of microbial load, aflatoxin, heavy metals, and pesticide residue using modern analytical techniques such as gas chromatography-mass spectroscopy (MS) and inductively coupled plasma-MS/MS. Results: Vyoshadi Churna is of brown color and bitter in taste. Pharmacognostical studies have shown the characteristic features of almost each contributory crude drug. A unique chemical profile was also developed using toluene: ethyl acetate: methanol: formic acid (4:4:1:0.5) as the mobile phase to resolve maximum components. Furthermore, the standardized limits for the physicochemical studies, microbial load, heavy metals, aflatoxins, and pesticide residues were established and found in limits. Conclusion: This is for the first time when the standard operating procedure of Vyoshadi Churna was developed. Hence, the current technical work would be practically helpful for the future preparation and standardization study of Vyoshadi Churna.

Abstract The spectrum of gastrointestinal (GI) issues in the older population varies from common physiological age-related changes to devastating, less common sinister pathological illness. GI system has direct exposure to external environment. Thus, it is modeled to embrace the pathophysiological changes that occur due to interaction with external factors. Gastrointestinal tract (GIT) per se is more resilient to aging as compared to other organ systems. On the other hand, elderly may present with a large plethora of GI symptoms. This presents a challenge to all echelons of medical consultation for accurate attribution for the aging process or pathophysiological causation of GI symptoms. This dichotomy leads to hindrance in adequate and appropriate treatment of GI ailments. In GI system, non-neoplastic disorders are far more common than neoplastic disorders. Hence, it becomes imperative to understand the aging evolution of the GI system and management of GI disorders in the older population.

Presents corrections to the paper, Attention to Monkeypox: An Interpretable Monkeypox Detection Technique Using Attention Mechanism

Abstract Patients with acute‐on‐chronic liver failure (ACLF) have a high probability of developing systemic inflammation and sepsis due to immune dysregulation. Fifty‐nine patients with ACLF (12 without and 19 with systemic inflammation, and 28 with sepsis) were serially monitored for clinical and immunological changes at baseline, 6 hours, 24 hours, day 3, and day 7 following hospitalization. Ten healthy controls were also included. At all time points, soluble plasma factors and monocyte functions were studied. Patients with ACLF and systemic inflammation showed higher interleukin (IL)–6, vascular endothelial growth factor‐a, monocyte chemoattractant protein 1, and macrophage inflammatory protein 1β than patients with no systemic inflammation. Patients with ACLF with sepsis had raised (p

Steel structures are commonly utilized in vast areas in industries, and also now a days they are used in residential settings as well. Structures made of steel is a better alternative as their constructions have high strength, light weight and quick compared to other construction materials. Steel structure degradation is frequently related to an engineering system's underperformance and leads to collapse. Therefore, it is essential to identify the problem and take remedial steps to make sure that structures function as intended throughout their design lives. Among the best non-destructive assessment methods for finding problems is acoustic emission (AE). The current study evaluates the available literature on this method in a few major areas and discusses historical advances in each category. The pros and cons of each approach are discussed, and future study directions are suggested. This review examines the fundamental Acoustic Emission techniques and contemporary research to identify damage in different types of steel structures using various localization approaches. This research aims to find the ideal placement for a real-time sensor to detect deterioration in a steel-framed construction. Finally, the artificial intelligence techniques used to identify deterioration in the steel frame construction are discussed.

The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation’s economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2’s pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset.

Transabdominal ultrasonography, contrast-enhanced computed tomography, and magnetic resonance imaging (MRI) are the common diagnostic tests for the detection of hepatic lesions. Use of enhanced and advanced MRI technique, that is, diffusion weighted MRI and hepatocyte-specific contrast agents, has further improved the accuracy of detection of metastatic liver lesions ≤ 10 mm in diameter. However, even with these advanced imaging modalities sensitivity is low for lesions smaller than 10 mm when compared with standard intraoperative ultrasound. Endoscopic ultrasound (EUS) is an emerging imaging modality with resolution sufficient to detect and sample lesions as small as 5 mm in diameter. In this news and views, we have discussed the role of standard and enhanced EUS for the detection of metastatic liver lesions.

Background: In India, inquest is carried out by police or magistrate depending upon the type of death and medico-legal autopsy is carried out by doctor. In some cases, inquest is sufficient to make out necessary information related to death and medico-legal autopsy can be avoided in such death, but medico-legal autopsy has its own value. Materials and Methods: A retrospective record-based study was carried out at RVRS Medical College, Bhilwara, Rajasthan, regarding the correlation of inquest and medico-legal autopsy findings in community deaths which were brought dead to hospital in the year 2020. Results: A total of 467 cases of community deaths were brought dead for medico-legal autopsy in the year 2020. As per inquest, 7.28% died due to hanging, 3.85% died due to burns, 3.43% died due to electrocution, 16.27% died due to poisoning, and the same was established via medico-legal autopsy without any difference from inquest report. After inquest, in 3% of cases, the cause of death remained undetermined, while after the medico-legal autopsy, it was only 0.86%. The manner of death was undetermined in 3% of cases after inquest, while after the medico-legal autopsy, only in 0.86% of cases. Conclusion: We recommend proper training of the investigating officers who conduct inquest to overcome the discrepancies observed between the investigating officer and the doctor in ascertaining the cause of death and manner of death in all unnatural deaths in India. If well-trained person will conduct in depth inquest, then medico-legal autopsies can be avoided in certain types of death.

IntroductionThe effect of leader pro-social rule breaking on employees is a critical albeit underexplored topic within the domain of study on the consequences of pro-social rule breaking in organizations. This study attempts to make up for the gap by exploring the relationship between leader pro-social rule breaking and employee voice. Drawing on the theory of self-verification, we theorize that leaders who perform pro-social rule breaking will seek feedback from their subordinates, while employees being sought will be triggered to voice upwardly, the extent to which intensity of voice is moderated by the moral courage of employees.MethodsA total of 283 dyads data of supervisor–subordinate from Shanghai, China, in a three-wave time-lagged survey provided support for our hypotheses.ResultsThe results show that leader pro-social rule breaking is positively related to leader feedback-seeking, which is positively related to employee upward voice and mediates the relationship between the two. Moreover, the positive relationship between leader pro-social rule breaking and leader feedback-seeking as well as the indirect effect of leader pro-social rule breaking on employee upward voice via leader feedback-seeking was weakened when moral courage is high.DiscussionThe present study promotes the theoretical research on the positive results of leader pro-social rule breaking and also suggests that feedback-seeking would be an effective way for leaders to motivate employees’ upward voice.

Background: From ancient times, India and many other countries have utilised natural products to manage various illnesses. Because they are abundantly available and have fewer adverse effects than synthetic medications, flavonoids are secondary metabolites with diverse biological activities. But till now, no formulations are developed by utilizing Diosmetin and no technique was formulated by utilizing self-biosynthesized Diosmetin (pure). It is a flavone (subclass of flavonoids) isolated from multiple medicinal plants but, in large amounts, is found in citrus plants. They have a variety of biological and pharmacological properties. They also improve lymphatic drainage by raising the duration and severity of lymphatic compression and the exact number of fully operational lymphatic capillaries. Methodology: We have procured Diosmetin from Otto Kemi Pvt. Ltd. and other chemicals from SIGMA chemicals. Furthermore, the characterisation of selected flavone has been performed through solubility studies and method development for development and validation by the UV method for nano-formulation. Result: Diosmetin is pure and soluble in various solvents and has a melting point between 259.14 and 261.84°Celsius, and is more soluble in Acetone, Chloroform, and DMSO, but we have utilised Diosmetin for Cancer studies, so we have taken it in 5% DMSO for further studies. Diosmetin is stable in DMSO till 20 mg/ml at room temperature.

Many species of bats rely on echoes to forage and navigate in densely vegetated environments. Foliage echoes in some cases can help bats gather information about the environment, whereas in others may generate clutter that can mask prey echoes during foraging. It is therefore important to study foliage echoes and their role in bat's sensory ecology. In our prior work, a foliage echo simulator has been developed; simulated echoes has been compared with field recordings using a biomimetic sonar head. In this work, we improve the existing simulator by allowing more flexible experimental setups and enabling a closer match with the experiments. Specifically, we add additional features into the simulator including separate directivity patterns for emitter and receiver, the ability to place emitter and receiver at distinct locations, and multiple options to orient the foliage to mimic natural conditions like strong wind. To study how accurately the simulator can replicate the real echo-generating process, we compare simulated echoes with experimental echoes measured by ensonifying a single leaf across four different species of trees. We further extend the prior work on estimating foliage parameters to estimating a map of the environment.

Abstract Evidence showing a strengthening of intense cold surge event (CSE) in East Asia, e.g. CSE of Jan 2016 and Jan–Feb 2008, is focusing attention towards the science of CSE onset prediction. Predicting the onset of such strong CSEs remains elusive as the extent of these surges varies over spatial and temporal scales. Changes in radiative cooling over Siberia in winter as potentially affected by changes in the Arctic are further expected to influence CSE occurrences in East Asia. Moreover, unprecedented and long lasting CSEs in East Asia have a very distinct Jet Stream pattern via their shifts from the climatological mean, influencing the lower troposphere. Here, using modelling framework we propose a new relationship between Jet Stream and Aleutian Low for identifying and characterizing atmospheric process that leads to CSEs in East Asia. Our results reveal new insight into the mechanisms of CSEs occurrences, the absence of which may lead to major constraints on reducing CSE onset prediction error.

Abstract Vacancy dynamics of high‐density 2D colloidal crystals with a polydispersity in particle size are studied experimentally. Heterogeneity in vacancy dynamics is observed. Inert vacancies that hardly hop to other lattice sites and active vacancies that hop frequently between different lattice sites are found within the same samples. The vacancies show high probabilities of first hopping from one lattice site to another neighboring lattice site, then staying at the new site for some time, and later hopping back to the original site in the next hop. This back‐returning hop probability increases monotonically with the increase in packing fraction, up to 83%. This memory effect makes the active vacancies diffuse sluggishly or even get trapped in local regions. Strain‐induced vacancy motion on a distorted lattice is also observed. New glassy properties in the disordered crystals are discovered, including the dynamical heterogeneity, the presence of cooperative rearranging regions, memory effect, etc. Similarities between the colloidal disordered crystals and the high‐entropy alloys (HEAs) are also discussed. Molecular dynamics simulations further support the experimental observations. These results help to understand the microscopic origin of the sluggish dynamics in materials with ordered structures but in random energy landscapes, such as high‐entropy alloys.

Sunflower is a crop that has many economic values and ornamental usages. However, its production can be hampered due to various diseases such as downy mildew, gray mold, and leaf scars, and it is challenging for farmers to identify disease-prone conditions with traditional approaches. Thus, a computerized model composed of vision, artificial intelligence, and machine learning is the demand of the age to detect diseases in plants efficiently. In this paper, we develop a hybrid model with transfer learning (TL) and a simple CNN using a small dataset for detecting sunflower diseases. Out of the eight models tested on the dataset of four different classes (downy mildew, gray mold, leaf scars, and fresh leaf), the VGG19 + CNN hybrid model achieves the best results in terms of precision, recall, F1-score, accuracy, Hamming loss, Matthews coefficient, Jaccard score, and Cohen’s kappa metrics. The experimental outcomes show that the proposed model provides better precision, recall, and accuracy than other approaches on the benchmark dataset.

The whole world faces a pandemic situation due to the deadly virus, namely COVID-19. It takes considerable time to get the virus well-matured to be traced, and during this time, it may be transmitted among other people. To get rid of this unexpected situation, quick identification of COVID-19 patients is required. We have designed and optimized a machine learning-based framework using inpatient's facility data that will give a user-friendly, cost-effective, and time-efficient solution to this pandemic. The proposed framework uses Bayesian optimization to optimize the hyperparameters of the classifier and ADAptive SYNthetic (ADASYN) algorithm to balance the COVID and non-COVID classes of the dataset. Although the proposed technique has been applied to nine state-of-the-art classifiers to show the efficacy, it can be used to many classifiers and classification problems. It is evident from this study that eXtreme Gradient Boosting (XGB) provides the highest Kappa index of 97.00%. Compared to without ADASYN, our proposed approach yields an improvement in the kappa index of 96.94%. Besides, Bayesian optimization has been compared to grid search, random search to show efficiency. Furthermore, the most dominating features have been identified using SHapely Adaptive exPlanations (SHAP) analysis. A comparison has also been made among other related works. The proposed method is capable enough of tracing COVID patients spending less time than that of the conventional techniques. Finally, two potential applications, namely, clinically operable decision tree and decision support system, have been demonstrated to support clinical staff and build a recommender system.