A proposed device based on MoSe2-ZnO heterojunctions on rGO for enhanced ethanol gas sensing performances at room temperature.

In this research, we report an enhanced sensing response ethanol gas sensing device based on a ternary nanocomposite of molybdenum diselenide-zinc oxide heterojunctions decorated rGO (MoSe2/ZnO/rGO) at room temperature. The sensing performance of the ternary nanocomposite sensing device has been analysed for various concentrations of ethanol gas (1-500 ppm). The gas-sensing results have revealed that for 500 ppm ethanol gas concentration, the sensing device has exhibited an enhanced response value(Rg/Ra)of 50.2. Significantly, the sensing device has displayed a quick response and recovery time of 6.2 and 12.9 s respectively. In addition to this, the sensing device has shown a great prospect for long-term detection of ethanol gas (45 days). The sensing device has demonstrated the ability to detect ethanol at remarkably low concentrations of 1 ppm. The enhanced sensing performance of the ternary nanocomposite sensing device has highlighted the effective synergistic effect between MoSe2nanosheets, ZnO nanorods, and rGO nanosheets. This has been attributed to the formation of two heterojunctions in the ternary nanocomposite sensor: a p-n heterojunction between MoSe2and ZnO and a p-p heterojunction between MoSe2and rGO. The analysis of the results has suggested that the proposed MoSe2/ZnO/rGO nanocomposite sensing device could be considered a promising candidate for the real-time detection of ethanol gas.

Machine learning for predicting diabetic metabolism in the Indian population using polar metabolomic and lipidomic features.

AIMS: To identify metabolite and lipid biomarkers of diabetes in the Indian subpopulation in newly diagnosed diabetic and long-term diabetic individuals. To utilize the global polar metabolomic and lipidomic profiles to predict the susceptibility of an individual to diabetes using machine learning algorithms. MATERIALS AND METHODS: 87 individuals, including healthy, newly diabetic, and long-term diabetics on medication, were included in the study. Post consent, their serum was used to isolate polar metabolome and lipidome. NMR and LCMS were used to identify the polar metabolites and lipids, respectively. Statistical analysis was done to determine significantly altered molecules. NMR and LCMS comprehensive data were utilized to generate diabetic models using machine learning algorithms. 10 more individuals (pre-diabetic) were recruited, and their polar metabolomic and lipidomic profiles were generated. Pre-diabetic metabolic profiles were then utilized to predict the diabetic status of the metabolome and lipidome beyond glucose levels. RESULTS: Mannose, Betaine, Xanthine, Triglyceride (38:1), Sphingomyelin (d63:7), and Phosphatidic acid (37:2) are some of the top key biomarkers of diabetes. The predictive model generated showed the receiver operating characteristic area under the curve (ROC-AUC) as 1 on both test and validation data indicating excellent accuracy. This model then predicted the diabetic closeness of the metabolism of pre-diabetic individuals based on probability scores. CONCLUSION: Polar metabolic and lipid profile of diabetic individuals is very different from that of healthy individuals. Lipid profile alters before the polar metabolic profile in diabetes-susceptible individuals. Without regard to glucose, the diabetic closeness of the metabolism of any individual can be determined.

An Emotion Care Model using Multimodal Textual Analysis on COVID-19.

At the dawn of the year 2020, the world was hit by a significant pandemic COVID-19, that traumatized the entire planet. The infectious spread grew in leaps and bounds and forced the policymakers and governments to move towards lockdown. The lockdown further compelled people to stay under house arrest, which further resulted in an outbreak of emotions on social media platforms. Perceiving people's emotional state during these times becomes critically and strategically important for the government and the policymakers. In this regard, a novel emotion care scheme has been proposed in this paper to analyze multimodal textual data contained in real-time tweets related to COVID-19. Moreover, this paper studies 8-scale emotions (Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust) over multiple categories such as nature, lockdown, health, education, market, and politics. This is the first of its kind linguistic analysis on multiple modes pertaining to the pandemic to the best of our understanding. Taking India as a case study, we inferred from this textual analysis that 'joy' has been lesser towards everything (~9-15%) but nature (~17%) due to the apparent fact of lessened pollution. The education system entailed more trust (~29%) due to teachers' fraternity's consistent efforts. The health sector witnessed sadness (~16%) and fear (~18%) as the dominant emotions among the masses as human lives were at stake. Additionally, the state-wise and emotion-wise depiction is also provided. An interactive internet application has also been developed for the same.

Cross-sectional area reference values of the median nerve at the palm using ultrasound.

BACKGROUND: The most common neuromuscular ultrasound abnormality in carpal tunnel syndrome is enlargement of the median nerve near the distal wrist crease, but sometimes the median nerve enlarges in the palm, just distal to the transverse carpal ligament. This study was conducted to devise a protocol for measuring the median nerve in the palm and to generate reference values for nerve cross-sectional area at this site. METHODS: A systematic protocol was developed, 30 healthy individuals underwent ultrasound of the median nerve in the palm bilaterally, and cross-sectional area was recorded. RESULTS: The mean cross-sectional area of the median nerve at the palm was between 11.4 mm2 (95% confidence interval [CI] [10.4,12.4]) and 11.6 mm2 (95%CI [10.6,12.6]). CONCLUSIONS: The median nerve can be measured just distal to the hook of the hamate in the palm, and the mean cross-sectional area at this site is slightly larger than it is at the distal wrist crease.

Asymmetric Oxidative Cycloetherification of Naphtholic Alcohols.

The catalytic, enantioselective oxidative cyclization of naphthol-derived carboxylic acids mediated by chiral hypervalent iodine reagents has been studied extensively in the recent past, but analogous reactions of non-carboxylic substrates are yet unknown. This paper describes a catalytic, enantioselective, hypervalent iodine-promoted oxidative cycloetherification reaction of naphtholic alcohols. The new process relies on a variant of the Uyanik-Ishihara catalyst, in which the stereogenic centers have been relocated closer to the iodine atom. The new catalyst design affords optical yields comparable to those available with Uyanik-Ishihara iodides, but chemical yields are sensibly higher, at least with the tests substrates. An even more problematic reaction is the catalytic, enantioselective oxidative cyclization of naphtholic sulfonamides. In this case, the new catalyst affords significantly higher optical inductions than Uyanik-Ishihara iodides. The kinetic resolution of particular naphtholic alcohols is demonstrated. The absolute configuration of a numver of enantioenriched compounds obtained in this study was ascertained by X-ray diffractometry.

Reactive oxygen species in endothelial signaling in COVID-19: Protective role of the novel peptide PIP-2.

INTRODUCTION: Recent research suggests that endothelial activation plays a role in coronavirus disease 2019 (COVID-19) pathogenesis by promoting a pro-inflammatory state. However, the mechanism by which the endothelium is activated in COVID-19 remains unclear. OBJECTIVE: To investigate the mechanism by which COVID-19 activates the pulmonary endothelium and drives pro-inflammatory phenotypes. HYPOTHESIS: The "inflammatory load or burden" (cytokine storm) of the systemic circulation activates endothelial NADPH oxidase 2 (NOX2) which leads to the production of reactive oxygen species (ROS) by the pulmonary endothelium. Endothelial ROS subsequently activates pro-inflammatory pathways. METHODS: The inflammatory burden of COVID-19 on the endothelial network, was recreated in vitro, by exposing human pulmonary microvascular endothelial cells (HPMVEC) to media supplemented with serum from COVID-19 affected individuals (sera were acquired from patients with COVID-19 infection that eventually died. Sera was isolated from blood collected at admission to the Intensive Care Unit of the Hospital of the University of Pennsylvania). Endothelial activation, inflammation and cell death were assessed in HPMVEC treated with serum either from patients with COVID-19 or from healthy individuals. Activation was monitored by measuring NOX2 activation (Rac1 translocation) and ROS production; inflammation (or appearance of a pro-inflammatory phenotype) was monitored by measuring the induction of moieties such as intercellular adhesion molecule (ICAM-1), P-selectin and the NLRP3 inflammasome; cell death was measured via SYTOX™ Green assays. RESULTS: Endothelial activation (i.e., NOX2 activation and subsequent ROS production) and cell death were significantly higher in the COVID-19 model than in healthy samples. When HPMVEC were pre-treated with the novel peptide PIP-2, which blocks NOX2 activation (via inhibition of Ca2+-independent phospholipase A2, aiPLA2), significant abrogation of ROS was observed. Endothelial inflammation and cell death were also significantly blunted. CONCLUSIONS: The endothelium is activated during COVID-19 via cytokine storm-driven NOX2-ROS activation, which causes a pro-inflammatory phenotype. The concept of endothelial NOX2-ROS production as a unifying pathophysiological axis in COVID-19 raises the possibility of using PIP-2 to maintain vascular health.

Lewis acid catalyzed S(N)2-type ring opening of N-activated aziridines with electron-rich arenes/heteroarenes.

An efficient Lewis acid catalyzed S(N)2-type ring opening of substituted aziridines with electron-rich arenes/heteroarenes to provide substituted 2,2-diaryl/heteroarylethylamines in excellent yields and stereoselectivity (er, dr >99:1) is described.

Covert Conditioning for Persistent Aggressive Behaviors: A Case Illustration.

In psychotherapy practice and training, single case study design plays an indispensable role by effectively articulating the application of textbook knowledge, thereby bridging the gap between theory and practice. This article, on similar lines, illustrates one such successful example of the application of the classical behavioral technique of covert conditioning modified with a component of verbal challenging. A woman in her late-thirties reported with long-standing seemingly-resistant-to-treat symptoms of aggressive behavior of beating children. The client had a total of 10 daily sessions of 60-90 minutes each. By the end of one week, she reported not beating children in this period. She felt extremely relieved because it had happened for the first time in 10 years. The intensity of anger had decreased drastically, and she was not shouting any longer. She had to discontinue sessions abruptly due to unavoidable circumstances. Although she was suggested to follow up the intensive sessions again, she was not able to do it due to feasibility issues. The improvement was maintained on follow-up visits after two weeks, four weeks, and three months.

A study of the association between Vitamin D deficiency and Dry Eye Syndrome (DES) in the Indian population.

PURPOSE: A study of the association between vitamin D deficiency and dry eye syndrome (DES) in the Indian population. METHODS: This was a cross-sectional, hospital-based observational study. Sixty patients diagnosed with vitamin D deficiency (<20 ng/dl) who met the inclusion criteria were sent to the Eye OPD from the Endocrinology OPD (case) were compared to 60 subjects with normal vitamin D levels (≥20 ng/dl) who attended the Eye OPD (controls). The examination of the tear film was done using Whatman filter paper in Schirmer test I and Schirmer test I (with anesthesia). The tear film break-up time (TFBUT) was determined by slit-lamp examination using the fluorescein stain, and scoring using the ocular surface disease index (OSDI) was done. RESULTS: A significant difference in the mean values of Schirmer I and Schirmer I test (with anesthesia) (P < 0.001) was seen between the case and control groups. A significant difference in the mean values of TFBUT (P < 0.001) and OSDI scores (P < 0.01) was also seen between the two groups. CONCLUSION: A positive association was found between vitamin D deficiency and dry eye on comparing the above parameters.

Understanding cartoon emotion using integrated deep neural network on large dataset.

Emotion is an instinctive or intuitive feeling as distinguished from reasoning or knowledge. It varies over time, since it is a natural instinctive state of mind deriving from one's circumstances, mood, or relationships with others. Since emotions vary over time, it is important to understand and analyze them appropriately. Existing works have mostly focused well on recognizing basic emotions from human faces. However, the emotion recognition from cartoon images has not been extensively covered. Therefore, in this paper, we present an integrated Deep Neural Network (DNN) approach that deals with recognizing emotions from cartoon images. Since state-of-works do not have large amount of data, we collected a dataset of size 8 K from two cartoon characters: 'Tom' & 'Jerry' with four different emotions, namely happy, sad, angry, and surprise. The proposed integrated DNN approach, trained on a large dataset consisting of animations for both the characters (Tom and Jerry), correctly identifies the character, segments their face masks, and recognizes the consequent emotions with an accuracy score of 0.96. The approach utilizes Mask R-CNN for character detection and state-of-the-art deep learning models, namely ResNet-50, MobileNetV2, InceptionV3, and VGG 16 for emotion classification. In our study, to classify emotions, VGG 16 outperforms others with an accuracy of 96% and F1 score of 0.85. The proposed integrated DNN outperforms the state-of-the-art approaches.

Improved COVID-19 detection with chest x-ray images using deep learning.

The novel coronavirus disease, which originated in Wuhan, developed into a severe public health problem worldwide. Immense stress in the society and health department was advanced due to the multiplying numbers of COVID carriers and deaths. This stress can be lowered by performing a high-speed diagnosis for the disease, which can be a crucial stride for opposing the deadly virus. A good large amount of time is consumed in the diagnosis. Some applications that use medical images like X-Rays or CT-Scans can pace up the time used in diagnosis. Hence, this paper aims to create a computer-aided-design system that will use the chest X-Ray as input and further classify it into one of the three classes, namely COVID-19, viral Pneumonia, and healthy. Since the COVID-19 positive chest X-Rays dataset was low, we have exploited four pre-trained deep neural networks (DNNs) to find the best for this system. The dataset consisted of 2905 images with 219 COVID-19 cases, 1341 healthy cases, and 1345 viral pneumonia cases. Out of these images, the models were evaluated on 30 images of each class for the testing, while the rest of them were used for training. It is observed that AlexNet attained an accuracy of 97.6% with an average precision, recall, and F1 score of 0.98, 0.97, and 0.98, respectively.

Periapical dental X-ray image classification using deep neural networks.

This paper studies the problem of detection of dental diseases. Dental problems affect the vast majority of the world's population. Caries, RCT (Root Canal Treatment), Abscess, Bone Loss, and missing teeth are some of the most common dental conditions that affect people of all ages all over the world. Delayed or incorrect diagnosis may result in mistreatment, affecting not only an individual's oral health but also his or her overall health, thereby making it an important research area in medicine and engineering. We propose a pipelined Deep Neural Network (DNN) approach to detect healthy and non-healthy periapical dental X-ray images. Even a minor enhancement or improvement in existing techniques can go a long way in providing significant health benefits in the medical field. This paper has made a successful attempt to contribute a different type of pipelined approach using AlexNet in this regard. The approach is trained on a large dataset of 16,000 dental X-ray images, correctly identifying healthy and non-healthy X-ray images. We use an optimized Convolutional Neural Networks and three state-of-the-art DNN models, namely Res-Net-18, ResNet-34, and AlexNet for disease classification. In our study, the AlexNet model outperforms the other models with an accuracy of 0.852. The precision, recall and F1 scores of AlexNet also surpass the other models with a score of 0.850 across all metrics. The area under ROC curve also signifies that both the false-positive rate and false-negative rate are low. We conclude that even with a big data set and raw X-ray pictures, the AlexNet model generalizes effectively to previously unseen data and can aid in the diagnosis of a variety of dental diseases.

Computational structural assessment of BReast CAncer type 1 susceptibility protein (BRCA1) and BRCA1-Associated Ring Domain protein 1 (BARD1) mutations on the protein-protein interface.

Breast cancer type 1 susceptibility protein (BRCA1) is closely related to the BRCA2 (breast cancer type 2 susceptibility protein) and BARD1 (BRCA1-associated RING domain-1) proteins. The homodimers were formed through their RING fingers; however they form more compact heterodimers preferentially, influencing BRCA1 residues 1-109 and BARD1 residues 26-119. We implemented an integrative computational pipeline to screen all the mutations in BRCA1 and identify the most significant mutations influencing the Protein-Protein Interactions (PPI) in the BRCA1-BARD1 protein complex. The amino acids involved in the PPI regions were identified from the PDBsum database with the PDB ID: 1JM7. We screened 2118 missense mutations in BRCA1 and none in BARD1 for pathogenicity and stability and analyzed the amino acid sequences for conserved residues. We identified the most significant mutations from these screenings as V11G, M18K, L22S, and T97R positioned in the PPI regions of the BRCA1-BARD1 protein complex. We further performed protein-protein docking using the ZDOCK server. The native protein-protein complex showed the highest binding score of 2118.613, and the V11G mutant protein complex showed the least binding score of 1992.949. The other three mutation protein complexes had binding scores between the native and V11G protein complexes. Finally, a molecular dynamics simulation study using GROMACS was performed to comprehend changes in the BRCA1-BARD1 complex's binding pattern due to the mutation. From the analysis, we observed the highest deviation with lowest compactness and a decrease in the intramolecular h-bonds in the BRCA1-BARD1 protein complex with the V11G mutation compared to the native complex or the complexes with other mutations.

OX40 agonist stimulation increases and sustains humoral and cell-mediated responses to SARS-CoV-2 protein and saRNA vaccines.

To prevent SARS-CoV-2 infections and generate long-lasting immunity, vaccines need to generate strong viral-specific B and T cell responses. Previous results from our lab and others have shown that immunizations in the presence of an OX40 agonist antibody lead to higher antibody titers and increased numbers of long-lived antigen-specific CD4 and CD8 T cells. Using a similar strategy, we explored the effect of OX40 co-stimulation in a prime and boost vaccination scheme using an adjuvanted SARS-CoV-2 spike protein vaccine in C57BL/6 mice. Our results show that OX40 engagement during vaccination significantly increases long-lived antibody responses to the spike protein. In addition, after immunization spike protein-specific proliferation was greatly increased for both CD4 and CD8 T cells, with enhanced, spike-specific secretion of IFN-γ and IL-2. Booster (3rd injection) immunizations combined with an OX40 agonist (7 months post-prime) further increased vaccine-specific antibody and T cell responses. Initial experiments assessing a self-amplifying mRNA (saRNA) vaccine encoding the spike protein antigen show a robust antigen-specific CD8 T cell response. The saRNA spike-specific CD8 T cells express high levels of GrzmB, IFN-γ and TNF-α which was not observed with protein immunization and this response was further increased by the OX40 agonist. Similar to protein immunizations the OX40 agonist also increased vaccine-specific CD4 T cell responses. In summary, this study compares and contrasts the effects and benefits of both protein and saRNA vaccination and the extent to which an OX40 agonist enhances and sustains the immune response against the SARS-CoV-2 spike protein.

Acute Pancreatitis Induced by Linagliptin: A Rare but Dangerous Side Effect.

Diabetes mellitus type 2 (DMT2) is a highly prevalent disease both in the United States and worldwide. Multiple treatment options are currently available, and several new groups of medications have been introduced over the last couple of decades. One of these groups is dipeptidyl peptidase-4 (DPP-4) inhibitors. These medications have side effects, some of which are severe and potentially life-threatening; however, some of their side effects have been underreported since they are relatively new. When prescribing these medications, it is essential to be cautious, especially with patients at an increased risk of developing an adverse effect. We present the case of a 57-year-old male who developed DPP-4 inhibitor-induced acute pancreatitis after the initiation of linagliptin. The patient did not have any apparent risk factors for pancreatitis as he did not drink alcohol or smoke cigarettes, his lipid panel was within normal limits, and he had a cholecystectomy five years prior. His linagliptin was held in the hospital and discontinued post-discharge, which led to the resolution of his symptoms.

Leptomeningeal Carcinomatosis in Epithelial Ovarian Cancer: A Diagnostic Challenge.

Leptomeningeal carcinomatosis (LCM), also known as neoplastic meningitis, is a rare entity. It is generally seen in solid tumors. Ovarian cancers can infrequently cause LCM. The clinical presentation is variable. Diagnosis is made by a lumbar puncture that shows malignant cells in the cerebrospinal fluid (CSF) and usually correlates with imaging findings. Given the low individual sensitivities of lumbar puncture (55%) and magnetic resonance imaging (70%), it is recommended to combine both modalities for optimal diagnostic results. Treatment options vary depending on the type of primary carcinoma, however, the prognosis is guarded. We report a case of LCM in a patient with stage IV epithelial ovarian cancer in remission, which became a diagnostic challenge due to a lack of imaging findings.

Lung Adenocarcinoma Presenting as Malignant Pericardial Effusion/Tamponade.

Lung cancers are the most common primary tumors that involve the pericardium with a prevalence of up to 50%. Usually, pericardial involvement goes undetected with almost 10%-12% found among all cancer related autopsies. Rarely pericardial effusions can be the initial site of metastasis and initial manifestation of a primary tumor. In our case, we report a 57-year-old female presenting with cardiac tamponade and subsequent testing was done which revealed lung adenocarcinoma. Malignant pericardial effusions are often silent, but certain times can present with symptoms of shortness of breath, chest pain, cough, arrhythmias, and rarely as pericardial tamponade. A high index of suspicion is required when a patient presents with tamponade to diagnose malignancy. Emergent pericardiocentesis may be warranted depending on the clinical presentation but quite often, patients tend to have a poor prognosis despite therapy given the extent of disease.

Identification of potential inhibitors against pathogenic missense mutations of PMM2 using a structure-based virtual screening approach.

The autosomal recessive phosphomannomutase 2-congenital disorder of glycosylation (PMM2-CDG) is characterized by defective functioning of the PMM2 enzyme, which is necessary for the conversion of mannose-6-phosphate into mannose-1-phosphate. Here, a computational pipeline was drawn to identify the most significant mutations, and further, we used a virtual screening approach to identify a new lead compound to treat the identified significant mutations. We searched for missense mutation data related to PMM2-CDG in HGMD®, UniProt, and ClinVar. Our search yielded a total of 103 mutations, of which 91 are missense mutations. The D65Y, I132N, I132T, and F183S mutations were classified as deleterious, destabilizing, and altering the biophysical properties using the PredictSNP, iStable, and Align GVGD in silico prediction tools. Additionally, we applied a multistep protocol to screen for an alternative lead compound to the existing CID2876053 (1-(3-chlorophenyl)-3,3-bis(pyridine-2-yl)urea) with affinity to these identified significant mutants. Two compounds, CHEMBL1491007 (6-chloro-4-phenyl-3-(4-pyridin-2-ylpiperazin-1-yl)-1H-quinolin-2-one) and CHEMBL3653029 (5-chloro-4-[6-[(3-fluorophenyl)methylamino]pyridin-2-yl]-N-(piperidin-4-ylmethyl)pyridin-2-amine), exhibited the highest binding affinity with the selected mutants and were chosen for further analysis. Through molecular docking, molecular dynamics simulation, and MMPBSA analysis, we found that the known compound, i.e. CID2876053, has stronger interaction with the D65Y mutant. The newly identified lead compound CHEMBL1491007 showed stronger interaction with the I132N and I132T mutants, whereas the most deleterious mutant, F183S, showed stronger interaction with CHEMBL3653029. This study is expected to aid in the field of precision medicine, and further to in vivo and in vitro analysis of these lead compounds might shed light on the treatment of PMM2-CDG. Communicated by Ramaswamy H. Sarma.

Syncope as the Initial Manifestation of a Late-Stage Renal Cell Carcinoma With Metastasis to the Brain.

Renal cell carcinoma (RCC) is the most common neoplasm that arises from renal parenchyma. About one-third of patients with RCC develop metastatic spread, with common sites including the lung, liver, bone, adrenal gland, and brain. Distant metastases can be difficult to detect unless symptoms appear. We report a case of a 56-year-old female who presented to the emergency department with the unresponsiveness of unknown duration. She underwent a thorough laboratory workup, and the computed tomography (CT) scan revealed a retroperitoneal mass originating from the right kidney and a large hemorrhagic brain mass in the left frontal lobe. The patient underwent emergent full craniotomy for tumor removal, and histology confirmed metastatic RCC. Since several patients with RCC are asymptomatic, the slow growth of tumors leading to distant metastasis can be overlooked. Thus, this case demonstrates the importance of early detection of RCC to help prevent or delay further disease progression.

Prediction modelling of COVID using machine learning methods from B-cell dataset.

Coronavirus is a pandemic that has become a concern for the whole world. This disease has stepped out to its greatest extent and is expanding day by day. Coronavirus, termed as a worldwide disease, has caused more than 8 lakh deaths worldwide. The foremost cause of the spread of coronavirus is SARS-CoV and SARS-CoV-2, which are part of the coronavirus family. Thus, predicting the patients suffering from such pandemic diseases would help to formulate the difference in inaccurate and infeasible time duration. This paper mainly focuses on the prediction of SARS-CoV and SARS-CoV-2 using the B-cells dataset. The paper also proposes different ensemble learning strategies that came out to be beneficial while making predictions. The predictions are made using various machine learning models. The numerous machine learning models, such as SVM, Naïve Bayes, K-nearest neighbors, AdaBoost, Gradient boosting, XGBoost, Random forest, ensembles, and neural networks are used in predicting and analyzing the dataset. The most accurate result was obtained using the proposed algorithm with 0.919 AUC score and 87.248% validation accuracy for predicting SARS-CoV and 0.923 AUC and 87.7934% validation accuracy for predicting SARS-CoV-2 virus.