Seasonal and climatic factors have a significant influence on fertility associated sperm phenomic attributes in crossbred breeding bulls (Bos taurus × Bos indicus).

Although seasonal variations in semen quality and fertility have been studied to a considerable extent in breeding bulls, the effect of climatic variables on sperm functional competency has not been understood in detail. The present study analyzed sperm functional parameters in breeding bulls, over a period of 1 year, and assessed the effect of climatic variables on fertility associated sperm parameters. Seasons were categorized into summer, rainy, autumn, and winter based on the meteorological data. Semen was collected from crossbred bulls (n = 7) across the seasons and evaluated for functional membrane integrity, acrosome reaction status, protamine deficiency, capacitation, and lipid peroxidation status using specific fluorescent probes. The results of the present study revealed that bulls produced higher (p < 0.05) viable and acrosome intact spermatozoa during the autumn. The proportion of uncapacitated spermatozoa was also higher (p < 0.05) during autumn. Further, correlation of sperm functional attributes with environmental variables revealed that sperm viability was significantly (p < 0.05) and negatively correlated with daylength and temperature; acrosomal integrity was significantly (p < 0.05) and negatively correlated with day length; and protamine deficiency had significant (p < 0.05) positive correlation with day length and average temperature, and negative correlation with relative humidity. It was concluded that semen produced during autumn was superior to the semen produced during other seasons in terms of sperm functional competencies required for fertility.

Antibacterial Potential of Bacopa monnieri (L.) Wettst. and Its Bioactive Molecules against Uropathogens-An In Silico Study to Identify Potential Lead Molecule(s) for the Development of New Drugs to Treat Urinary Tract Infections.

Urinary tract infections (UTIs) are becoming more common, requiring extensive protection from antimicrobials. The global expansion of multi-drug resistance uropathogens in the past decade emphasizes the necessity of newer antibiotic treatments and prevention strategies for UTIs. Medicinal plants have wide therapeutic applications in both the prevention and management of many ailments. Bacopa monnieri is a medicinal plant that is found in the warmer and wetlands regions of the world. It has been used in Ayurvedic systems for centuries. The present study aimed to investigate the antibacterial potential of the extract of B. monnieri leaves and its bioactive molecules against UTIs that are caused by Klebsiella pneumoniae and Proteus mirabilis. This in vitro experimental study was conducted by an agar well diffusion method to evaluate the antimicrobial effect of 80% methanol, 96% ethanol, and aqueous extracts of B. monnieri leaves on uropathogens. Then, further screening of their phytochemicals was carried out using standard methods. To validate the bioactive molecules and the microbe interactions, AutoDock Vina software was used for molecular docking with the Klebsiella pneumoniae fosfomycin resistance protein (5WEW) and the Zn-dependent receptor-binding domain of Proteus mirabilis MR/P fimbrial adhesin MrpH (6Y4F). Toxicity prediction and drug likeness were predicted using ProTox-II and Molinspiration, respectively. A molecular dynamics (MD) simulation was carried out to study the protein ligand complexes. The methanolic leaves extract of B. monnieri revealed a 22.3 mm ± 0.6 mm to 25.0 mm ± 0.5 mm inhibition zone, while ethanolic extract seemed to produce 19.3 mm ± 0.8 mm to 23.0 mm ± 0.4 mm inhibition zones against K. pneumoniae with the use of increasing concentrations. In the case of P. mirabilis activity, the methanolic extracts showed a 21.0 mm ± 0.8 mm to 24.0 mm ± 0.6 mm zone of inhibition and the ethanol extract produced a 17.0 mm ± 0.9 mm to 23.0 mm ± 0.7 mm inhibition zone with increasing concentrations. Carbohydrates, flavonoids, saponin, phenolic, and terpenoid were common phytoconstituents identified in B. monnieri extracts. Oroxindin showed the best interactions with the binding energies with 5WEW and 6Y4F, -7.5 kcal/mol and -7.4 kcal/mol, respectively. Oroxindin, a bioactive molecule, followed Lipinski's rule of five and exhibited stability in the MD simulation. The overall results suggest that Oroxindin from B. monnieri can be a potent inhibitor for the effective killing of K. pneumoniae and P. mirabilis. Additionally, its safety has been established, indicating its potential for future drug discovery and development in the treatment for UTIs.

Genome-wide analysis of G-quadruplexes in herpesvirus genomes.

BACKGROUND: G-quadruplexes are increasingly recognized as regulatory elements in human, animal, bacterial and plant genomes. The presence and function of G-quadruplexes are not well studied among herpesviruses; in particular, there are no systematic genome-wide analysis of these important secondary structures in herpesvirus genomes. RESULTS: We performed genome-wide analysis of putative quadruplex sequences (PQS) in human herpesviruses. We found unusually high PQS densities among human herpesviruses. PQS are enriched in the repeat regions and regulatory regions of human herpesviruses. Interestingly, PQS densities are higher in regulatory regions of immediate early genes compared to early and late genes in most herpesviruses. In addition, the majority of genes functionally conserved across human herpesviruses contain one or more PQS within the regulatory regions. We also describe the existence of unique intramolecular PQS repeats or repetitive G-quadruplex motifs in herpesviruses. Functional studies confirm a role for G-quadruplexes in regulating the gene expression of human herpesviruses. CONCLUSION: The pervasiveness of PQS, their enrichment and conservation at specific genomic locations suggest that these structural entities may represent a novel class of functional elements in herpesviruses. Our findings provide the necessary framework for studies on the biological role of G-quadruplexes in herpesviruses.

CAR-T cell therapy: a game-changer in cancer treatment and beyond.

In recent years, cancer has become one of the primary causes of mortality, approximately 10 million deaths worldwide each year. The most advanced, chimeric antigen receptor (CAR) T cell immunotherapy has turned out as a promising treatment for cancer. CAR-T cell therapy involves the genetic modification of T cells obtained from the patient's blood, and infusion back to the patients. CAR-T cell immunotherapy has led to a significant improvement in the remission rates of hematological cancers. CAR-T cell therapy presently limited to hematological cancers, there are ongoing efforts to develop additional CAR constructs such as bispecific CAR, tandem CAR, inhibitory CAR, combined antigens, CRISPR gene-editing, and nanoparticle delivery. With these advancements, CAR-T cell therapy holds promise concerning potential to improve upon traditional cancer treatments such as chemotherapy and radiation while reducing associated toxicities. This review covers recent advances and advantages of CAR-T cell immunotherapy.

Circulating and non-circulating proteins and nucleic acids as biomarkers and therapeutic molecules in ovarian cancer.

Ovarian cancer is the second most fatal gynecological cancer. For the last decade or so significant use of non-circulating and circulating biomarkers has been highlighted. However, the study of such biomarkers at nanovesicle technology such as exosomes, proteomic and genomics studies could further contribute to better identification of anomalous protein and networks which could act as potential targets for biomarker and immunotherapy development. This review provides an overview of the circulating and non-circulating biomarkers with the aim of addressing the current challenges and potential biomarkers that could lead to early ovarian cancer diagnosis and better management. By means of this review we also lay a hypothesis that characterization of exosomal protein, nucleic acid content from body fluids (serum, plasma, urine, etc.) can decode the secret of disease and potentially improve diagnostic sensitivity which could further lead to more effective screening and early detection of the disease.

Predicting Pain in People With Sickle Cell Disease in the Day Hospital Using the Commercial Wearable Apple Watch: Feasibility Study.

BACKGROUND: Sickle cell disease (SCD) is a genetic red blood cell disorder associated with severe complications including chronic anemia, stroke, and vaso-occlusive crises (VOCs). VOCs are unpredictable, difficult to treat, and the leading cause of hospitalization. Recent efforts have focused on the use of mobile health technology to develop algorithms to predict pain in people with sickle cell disease. Combining the data collection abilities of a consumer wearable, such as the Apple Watch, and machine learning techniques may help us better understand the pain experience and find trends to predict pain from VOCs. OBJECTIVE: The aim of this study is to (1) determine the feasibility of using the Apple Watch to predict the pain scores in people with sickle cell disease admitted to the Duke University SCD Day Hospital, referred to as the Day Hospital, and (2) build and evaluate machine learning algorithms to predict the pain scores of VOCs with the Apple Watch. METHODS: Following approval of the institutional review board, patients with sickle cell disease, older than 18 years, and admitted to Day Hospital for a VOC between July 2021 and September 2021 were approached to participate in the study. Participants were provided with an Apple Watch Series 3, which is to be worn for the duration of their visit. Data collected from the Apple Watch included heart rate, heart rate variability (calculated), and calories. Pain scores and vital signs were collected from the electronic medical record. Data were analyzed using 3 different machine learning models: multinomial logistic regression, gradient boosting, and random forest, and 2 null models, to assess the accuracy of pain scores. The evaluation metrics considered were accuracy (F1-score), area under the receiving operating characteristic curve, and root-mean-square error (RMSE). RESULTS: We enrolled 20 patients with sickle cell disease, all of whom identified as Black or African American and consisted of 12 (60%) females and 8 (40%) males. There were 14 individuals diagnosed with hemoglobin type SS (70%). The median age of the population was 35.5 (IQR 30-41) years. The median time each individual spent wearing the Apple Watch was 2 hours and 17 minutes and a total of 15,683 data points were collected across the population. All models outperformed the null models, and the best-performing model was the random forest model, which was able to predict the pain scores with an accuracy of 84.5%, and a RMSE of 0.84. CONCLUSIONS: The strong performance of the model in all metrics validates feasibility and the ability to use data collected from a noninvasive device, the Apple Watch, to predict the pain scores during VOCs. It is a novel and feasible approach and presents a low-cost method that could benefit clinicians and individuals with sickle cell disease in the treatment of VOCs.

Use of consumer wearables to monitor and predict pain in patients with sickle cell disease.

Background: In sickle cell disease (SCD), unpredictable episodes of acute severe pain, known as vaso-occlusive crises (VOC), disrupt school, work activities and family life and ultimately lead to multiple hospitalizations. The ability to predict VOCs would allow a timely and adequate intervention. The first step towards this ultimate goal is to use patient-friendly and accessible technology to collect relevant data that helps infer a patient's pain experience during VOC. This study aims to: (1) determine the feasibility of remotely monitoring with a consumer wearable during hospitalization for VOC and up to 30 days after discharge, and (2) evaluate the accuracy of pain prediction using machine learning models based on physiological parameters measured by a consumer wearable. Methods: Patients with SCD (≥18 years) who were admitted for a vaso-occlusive crisis were enrolled at a single academic center. Participants were instructed to report daily pain scores (0-10) in a mobile app (Nanbar) and to continuously wear an Apple Watch up to 30 days after discharge. Data included heart rate (in rest, average and variability) and step count. Demographics, SCD genotype, and details of hospitalization including pain scores reported to nurses, were extracted from electronic medical records. Physiological data from the wearable were associated with pain scores to fit 3 different machine learning classification models. The performance of the machine learning models was evaluated using: accuracy, F1, root-mean-square error and area under the receiver-operating curve. Results: Between April and June 2022, 19 patients (74% HbSS genotype) were included in this study and followed for a median time of 28 days [IQR 22-34], yielding a dataset of 2,395 pain data points. Ten participants were enrolled while hospitalized for VOC. The metrics of the best performing model, the random forest model, were micro-averaged accuracy of 92%, micro-averaged F1-score of 0.63, root-mean-square error of 1.1, and area under the receiving operating characteristic curve of 0.9. Conclusion: Our random forest model accurately predicts high pain scores during admission for VOC and after discharge. The Apple Watch was a feasible method to collect physiologic data and provided accuracy in prediction of pain scores.

Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization.

OBJECTIVE: Cardiovascular disease (CVD) is a major healthcare challenge and therefore early risk assessment is vital. Previous assessment techniques use either "conventional CVD risk calculators (CCVRC)" or machine learning (ML) paradigms. These techniques are ad-hoc, unreliable, not fully automated, and have variabilities. We, therefore, introduce AtheroEdge-MCDLAI (AE3.0DL) windows-based platform using multiclass Deep Learning (DL) system. METHODS: Data was collected on 500 patients having both carotid ultrasound and corresponding coronary angiography scores (CAS), measured as stenosis in coronary arteries and considered as the gold standard. A total of 39 covariates were used, clubbed into three clusters, namely (i) Office-based: age, gender, body mass index, smoker, hypertension, systolic blood pressure, and diastolic blood pressure; (ii) Laboratory-based: Hyperlipidemia, hemoglobin A1c, and estimated glomerular filtration rate; and (iii) Carotid ultrasound image phenotypes: maximum plaque height, total plaque area, and intra-plaque neovascularization. Baseline characteristics for four classes (target labels) having significant (p < 0.0001) values were calculated using Chi-square and ANOVA. For handling the cohort's imbalance in the risk classes, AE3.0DL used the synthetic minority over-sampling technique (SMOTE). AE3.0DL used Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) DL models and the performance (accuracy and area-under-the-curve) was computed using 10-fold cross-validation (90% training, 10% testing) frameworks. AE3.0DL was validated and benchmarked. RESULTS: The AE3.0DL using RNN and LSTM showed an accuracy and AUC (p < 0.0001) pairs as (95.00% and 0.98), and (95.34% and 0.99), respectively, and showed an improvement of 32.93% and 9.94% against CCVRC and ML, respectively. AE3.0DL runs in <1 s. CONCLUSION: DL algorithms are a powerful paradigm for coronary artery disease (CAD) risk prediction and CVD risk stratification.

PNME - A gene-gene parallel network module extraction method.

In the domain of gene-gene network analysis, construction of co-expression networks and extraction of network modules have opened up enormous possibilities for exploring the role of genes in biological processes. Through such analysis, one can extract interesting behaviour of genes and would help in the discovery of genes participating in a common biological process. However, such network analysis methods in sequential processing mode often have been found time-consuming even for a moderately sized dataset. It is observed that most existing network construction techniques are capable of handling only positive correlations in gene-expression data whereas biologically-significant genes exhibit both positive and negative correlations. To address these problems, we propose a faster method for construction and analysis of gene-gene network and extraction of modules using a similarity measure which can identify both negatively and positively correlated co-expressed patterns. Our method utilizes General-purpose computing on graphics processing units (GPGPU) to provide fast, efficient and parallel extraction of biologically relevant network modules to support biomarker identification for breast cancer. The modules extracted are validated using p-value and q-value for both metastasis and non-metastasis stages of breast cancer. PNME has been found capable of identifying interesting biomarkers for this critical disease. We identified six genes with the interesting behaviours which have been found to cause breast cancer in homo-sapiens.