Precision healthcare: A deep dive into machine learning algorithms and feature selection strategies for accurate heart disease prediction.

This paper presents a comprehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. By focusing on diverse datasets encompassing various challenges, the research sheds light on optimal strategies for early detection. MLAs such as Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and others were studied, with precision and recall metrics emphasized for robust predictions. Our study addresses challenges in real-world data through data cleaning and one-hot encoding, enhancing the integrity of our predictive models. Feature extraction techniques-Recursive Feature Extraction (RFE), Principal Component Analysis (PCA), and univariate feature selection-play a crucial role in identifying relevant features and reducing data dimensionality. Our findings showcase the impact of these techniques on improving prediction accuracy. Optimized models for each dataset have been achieved through grid search hyperparameter tuning, with configurations meticulously outlined. Notably, a remarkable 99.12 % accuracy was achieved on the first Kaggle dataset, showcasing the potential for accurate HDP. Model robustness across diverse datasets was highlighted, with caution against overfitting. The study emphasizes the need for validation of unseen data and encourages ongoing research for generalizability. Serving as a practical guide, this research aids researchers and practitioners in HDP model development, influencing clinical decisions and healthcare resource allocation. By providing insights into effective algorithms and techniques, the paper contributes to reducing heart disease-related morbidity and mortality, supporting the healthcare community's ongoing efforts.

Amyloid-ß and Phosphorylated Tau are the Key Biomarkers and Predictors of Alzheimer's Disease.

Alzheimer's disease (AD) is a age-related neurodegenerative disease and is a major public health concern both in Texas, US and Worldwide. This neurodegenerative disease is mainly characterized by amyloid-beta (Aß) and phosphorylated Tau (p-Tau) accumulation in the brains of patients with AD and increasing evidence suggests that these are key biomarkers in AD. Both Aß and p-tau can be detected through various imaging techniques (such as positron emission tomography, PET) and cerebrospinal fluid (CSF) analysis. The presence of these biomarkers in individuals, who are asymptomatic or have mild cognitive impairment can indicate an increased risk of developing AD in the future. Furthermore, the combination of Aß and p-tau biomarkers is often used for more accurate diagnosis and prediction of AD progression. Along with AD being a neurodegenerative disease, it is associated with other chronic conditions such as cardiovascular disease, obesity, depression, and diabetes because studies have shown that these comorbid conditions make people more vulnerable to AD. In the first part of this review, we discuss that biofluid-based biomarkers such as Aß, p-Tau in cerebrospinal fluid (CSF) and Aß & p-Tau in plasma could be used as an alternative sensitive technique to diagnose AD. In the second part, we discuss the underlying molecular mechanisms of chronic conditions linked with AD and how they affect the patients in clinical care.

SuperAgers and Centenarians, Dynamics of Healthy Ageing with Cognitive Resilience.

Graceful healthy ageing and extended longevity is the most desired goal for human race. The process of ageing is inevitable and has a profound impact on the gradual deterioration of our physiology and health since it triggers the onset of many chronic conditions like dementia, osteoporosis, diabetes, arthritis, cancer, and cardiovascular disease. However, some people who lived/live more than 100 years called 'Centenarians" and how do they achieve their extended lifespans are not completely understood. Studying these unknown factors of longevity is important not only to establish a longer human lifespan but also to manage and treat people with shortened lifespans suffering from age-related morbidities. Furthermore, older adults who maintain strong cognitive function are referred to as "SuperAgers" and may be resistant to risk factors linked to cognitive decline. Investigating the mechanisms underlying their cognitive resilience may contribute to the development of therapeutic strategies that support the preservation of cognitive function as people age. The key to a long, physically, and cognitively healthy life has been a mystery to scientists for ages. Developments in the medical sciences helps us to a better understanding of human physiological function and greater access to medical care has led us to an increase in life expectancy. Moreover, inheriting favorable genetic traits and adopting a healthy lifestyle play pivotal roles in promoting longer and healthier lives. Engaging in regular physical activity, maintaining a balanced diet, and avoiding harmful habits such as smoking contribute to overall well-being. The synergy between positive lifestyle choices, access to education, socio-economic factors, environmental determinants and genetic supremacy enhances the potential for a longer and healthier life. Our article aims to examine the factors associated with healthy ageing, particularly focusing on cognitive health in centenarians. We will also be discussing different aspects of ageing including genomic instability, metabolic burden, oxidative stress and inflammation, mitochondrial dysfunction, cellular senescence, immunosenescence, and sarcopenia.

Discovery of Bacterial Key Genes from 16S rRNA-Seq Profiles That Are Associated with the Complications of SARS-CoV-2 Infections and Provide Therapeutic Indications.

SARS-CoV-2 infections, commonly referred to as COVID-19, remain a critical risk to both human life and global economies. Particularly, COVID-19 patients with weak immunity may suffer from different complications due to the bacterial co-infections/super-infections/secondary infections. Therefore, different variants of alternative antibacterial therapeutic agents are required to inhibit those infection-causing drug-resistant pathogenic bacteria. This study attempted to explore these bacterial pathogens and their inhibitors by using integrated statistical and bioinformatics approaches. By analyzing bacterial 16S rRNA sequence profiles, at first, we detected five bacterial genera and taxa (Bacteroides, Parabacteroides, Prevotella Clostridium, Atopobium, and Peptostreptococcus) based on differentially abundant bacteria between SARS-CoV-2 infection and control samples that are significantly enriched in 23 metabolic pathways. A total of 183 bacterial genes were found in the enriched pathways. Then, the top-ranked 10 bacterial genes (accB, ftsB, glyQ, hldD, lpxC, lptD, mlaA, ppsA, ppc, and tamB) were selected as the pathogenic bacterial key genes (bKGs) by their protein-protein interaction (PPI) network analysis. Then, we detected bKG-guided top-ranked eight drug molecules (Bemcentinib, Ledipasvir, Velpatasvir, Tirilazad, Acetyldigitoxin, Entreatinib, Digitoxin, and Elbasvir) by molecular docking. Finally, the binding stability of the top-ranked three drug molecules (Bemcentinib, Ledipasvir, and Velpatasvir) against three receptors (hldD, mlaA, and lptD) was investigated by computing their binding free energies with molecular dynamic (MD) simulation-based MM-PBSA techniques, respectively, and was found to be stable. Therefore, the findings of this study could be useful resources for developing a proper treatment plan against bacterial co-/super-/secondary-infection in SARS-CoV-2 infections.

Nanomedicine: A New Frontier in Alzheimer's Disease Drug Targeting.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting elderly individuals, characterized by progressive cognitive decline leading to dementia. This review examines the challenges posed by anatomical and biochemical barriers such as the blood-brain barrier (BBB), blood-cerebrospinal fluid barrier (BCSFB), and p-glycoproteins in delivering effective therapeutic agents to the central nervous system (CNS) for AD treatment. This article outlines the fundamental role of acetylcholinesterase inhibitors (AChEIs) and NMDA(N-Methyl-D-Aspartate) receptor antagonists in conventional AD therapy and highlights their limitations in terms of brain-specific delivery. It delves into the intricacies of BBB and pglycoprotein-mediated efflux mechanisms that impede drug transport to the CNS. The review further discusses cutting-edge nanomedicine-based strategies, detailing their composition and mechanisms that enable effective bypassing of BBB and enhancing drug accumulation in brain tissues. Conventional therapies, namely AChEIs and NMDA receptor antagonists, have shown limited efficacy and are hindered by suboptimal brain penetration. The advent of nanotechnology-driven therapeutic delivery systems offers promising strategies to enhance CNS targeting and bioavailability, thereby addressing the shortcomings of conventional treatments. Various nanomedicines, encompassing polymeric and metallic nanoparticles (MNPs), solid lipid nanoparticles (SLNs), liposomes, micelles, dendrimers, nanoemulsions, and carbon nanotubes, have been investigated for their potential in delivering anti-AD agents like AChEIs, polyphenols, curcumin, and resveratrol. These nanocarriers exhibit the ability to traverse the BBB and deliver therapeutic payloads to the brain, thereby holding immense potential for effective AD treatment and early diagnostic approaches. Notably, nanocarriers loaded with AChEIs have shown promising results in preclinical studies, exhibiting improved therapeutic efficacy and sustained release profiles. This review underscores the urgency of innovative drug delivery approaches to overcome barriers in AD therapy. Nanomedicine-based solutions offer a promising avenue for achieving effective CNS targeting, enabling enhanced bioavailability and sustained therapeutic effects. As ongoing research continues to elucidate the complexities of CNS drug delivery, these advancements hold great potential for revolutionizing AD treatment and diagnosis.

Red ginseng ameliorates lipotoxicity-induced renal fibrosis in hyperuricemia mice.

ETHNOPHARMACOLOGICAL RELEVANCE: Chronic kidney disease can be caused by numerous diseases including obesity and hyperuricemia (HUA). Obesity may exacerbate the renal injury caused by HUA. Red ginseng, a steamed products of Panax ginseng Meyer root, is known for its remarkable efficacy in improving metabolic syndrome, such as maintaining lipid metabolic balance. However, the role of red ginseng on hyperuricemia-induced renal injury in obese cases remains unclear. AIM OF THE STUDY: This study aimed to investigate the action of red ginseng extract (RGE) on lipotoxicity-induced renal injury in HUA mice. MATERIALS AND METHODS: A high-fat diet (HFD)-induced obesity model was employed to initially investigate the effects of RGE on body weight, TC, OGTT, renal lipid droplets, and renal function indices such as uric acid, creatinine, and urea nitrogen. Renal structural improvement was demonstrated by H&E staining. Subsequently, an animal model combining obesity and HUA was established to further study the impact of RGE on OAT1 and ACC1 expression levels. The mechanisms underlying renal injury regulation by RGE were postulated on the basis of RNA sequencing, which was verified by immunohistochemical (including F4/80, Ki67, TGF-ß1, α-SMA, and E-cadherin), Masson, and Sirius red staining. RESULTS: RGE modulated HFD-induced weight gain, glucose metabolism, and abnormalities of uric acid, urea nitrogen, and creatinine. RGE alleviated the more severe renal histopathological changes induced by obesity combined with HUA, with down-regulated the protein levels of ACC1, F4/80, Ki67, TGF-ß1, and α-SMA, and up-regulated OAT1 and E-cadherin. CONCLUSIONS: RGE has ameliorative effects on chronic kidney disease caused by obesity combined with HUA by maintaining lipid balance and reducing renal inflammation and fibrosis.

Structural, functional, molecular docking analysis of a hypothetical protein from Talaromyces marneffei and its molecular dynamic simulation: an in-silico approach.

Telaromyces marneffei (formerly Penicillium marneffei) is an endemic pathogenic fungus in Southern China and Southeast Asia. It can cause disease in patients with travel-related exposure to this organism and high morbidity and mortality in acquired immune deficiency syndrome (AIDS). In this study, we analyzed the structure and function of a hypothetical protein from T. marneffei using several bioinformatics tools and servers to unveil novel pharmacological targets and design a peptide vaccine against specific epitopes. A total of seven functional epitopes were screened on the protein, and 'STGVDMWSV' was the most antigenic, non-allergenic and non-toxic. Molecular docking showed stronger affinity between the CTL epitope 'STGVDMWSV' and the MHC I allele HLA-A*02:01, a higher docking score -234.98 kcal/mol, revealed stable interactions during a 100 ns molecular dynamic simulation. Overall, the results of this study revealed that this hypothetical protein is crucial for comprehending biochemical, physiological pathways and identifying novel therapeutic targets for human health. Communicated by Ramaswamy H. Sarma.

A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets.

Parkinson's Disease (PD) is a prevalent neurodegenerative disorder with significant clinical implications. Early and accurate diagnosis of PD is crucial for timely intervention and personalized treatment. In recent years, Machine Learning (ML) and Deep Learning (DL) techniques have emerged as promis-ing tools for improving PD diagnosis. This review paper presents a detailed analysis of the current state of ML and DL-based PD diagnosis, focusing on voice, handwriting, and wave spiral datasets. The study also evaluates the effectiveness of various ML and DL algorithms, including classifiers, on these datasets and highlights their potential in enhancing diagnostic accuracy and aiding clinical decision-making. Additionally, the paper explores the identifi-cation of biomarkers using these techniques, offering insights into improving the diagnostic process. The discussion encompasses different data formats and commonly employed ML and DL methods in PD diagnosis, providing a comprehensive overview of the field. This review serves as a roadmap for future research, guiding the development of ML and DL-based tools for PD detection. It is expected to benefit both the scientific community and medical practitioners by advancing our understanding of PD diagnosis and ultimately improving patient outcomes.

Respiratory Syncytial Virus-Associated Deaths among Children under Five before and during the COVID-19 Pandemic in Bangladesh.

Respiratory syncytial virus (RSV) is a leading cause of acute lower respiratory infections in young children worldwide. RSV-associated deaths in children are underreported in Bangladesh. We analyzed hospital-based surveillance data on severe acute respiratory infections (SARIs) in under-five children before (August 2009-February 2020) and during the COVID-19 pandemic (March 2020-March 2022). Using the World Health Organization definition, we identified SARI cases in 14 tertiary-level hospitals. Nasopharyngeal and oropharyngeal swabs were collected for real-time reverse-transcriptase-polymerase chain reaction (rRT-PCR) testing of six respiratory viruses, including RSV. SARI deaths during the pandemic (2.6%, 66) were higher than pre-pandemic (1.8%, 159; p < 0.001). Nearly half of pandemic deaths (47%) had underlying respiratory viruses, similar to the pre-pandemic rate (45%). RSV detection in deaths was consistent pre-pandemic (13%, 20/159) and during the pandemic (12%, 8/66). Children aged < 6 months constituted 57% (16) of RSV-related deaths. Evaluating interventions like maternal vaccination and infant monoclonal antibody prophylaxis is crucial to address RSV, a major contributor to under-five SARI deaths.

Acceptability, cost-effectiveness, and capacity of a facility-based seasonal influenza vaccination among high-risk groups: a study protocol in selected tertiary care hospitals of Bangladesh.

BACKGROUND: In Bangladesh, seasonal influenza imposes considerable disease and economic burden, especially for those at high-risk of severe disease. The most successful approach for influenza prevention is the administration of a vaccine. Many poor and middle-income nations, including Bangladesh, do not have a national strategy or program in place for seasonal influenza vaccines, despite the World Health Organization's (WHO) advice to prioritize high-risk populations. Additionally, there is a scarcity of substantial data on the cost-effectiveness of seasonal influenza vaccination in these countries. The aim of our study is to determine acceptability, health beliefs, barriers, and intention of receiving influenza vaccine among high-risk populations, assess the cost-effectiveness of implementing a facility-based seasonal influenza vaccination programme, and investigate the required capacity for a potential seasonal influenza vaccination programme. METHODS: We will undertake this study following STROBE guidelines. We will conduct the study in inpatient and outpatient departments of three selected tertiary-level hospitals leveraging the ongoing hospital-based influenza surveillance (HBIS) platform. The study population will include the WHO-defined four high-risk groups excluding healthcare workers: children six months to eight years, pregnant women, elderly ≥ 60 years, and adults with chronic diseases. We will collect quantitative data on participants' acceptability, health beliefs, barriers, and vaccination intentions using the health belief model (HBM) from patients meeting the criteria for high-risk populations attending two public tertiary-level hospitals. In one of the two public tertiary-level hospitals, we will arrange an influenza vaccination campaign before the influenza season, where the vaccine will be offered free of cost to high-risk patients, and in the second hospital, vaccination will not be offered. Both the vaccinated and unvaccinated participants will then be followed-up once a month for one year to record any influenza-like illness, hospitalization, and death. Additional data for objective two will be collected from patients with symptoms of influenza-like illness (ILI) and severe acute respiratory infection (SARI) at one public and one private hospital to determine both direct and indirect costs associated with influenza illness. We will estimate the required number of influenza vaccines, safe injections, and total storage volume utilizing secondary data. We will use a deterministic Markov decision-analytic model to estimate the cost-effectiveness of facility-based influenza vaccination in Bangladesh. DISCUSSION: The results of this study will enable the National Immunization Technical Advisory Group and the Ministry of Health & Family Welfare of Bangladesh to decide what steps to take to develop and implement an influenza vaccination strategy targeting high-risk populations. TRIAL REGISTRATION: The Clinicaltrials.gov registration number is NCT05996549. The registration for the protocol version 2.0 took place in August 2023, with the initial participant being enrolled in March 2022.

Hydrogen-rich water: a key player in boosting wheat (Triticum aestivum L.) seedling growth and drought resilience.

In the modern world, wheat, a vital global cereal and the second most consumed, is vulnerable to climate change impacts. These include erratic rainfall and extreme temperatures, endangering global food security. Research on hydrogen-rich water (HRW) has gained momentum in plant and agricultural sciences due to its diverse functions. This study examined the effects of different HRW treatment durations on wheat, revealing that the 4-h treatment had the highest germination rate, enhancing potential, vigor, and germination indexes. This treatment also boosted relative water content, root and shoot weight, and average lengths. Moreover, the 4-h HRW treatment resulted in the highest chlorophyll and soluble protein concentrations in seeds while reducing cell death. The 4-h and 5-h HRW treatments significantly increased H2O2 levels, with the highest NO detected in both root and shoot after 4-h HRW exposure. Additionally, HRW-treated seeds exhibited increased Zn and Fe concentrations, along with antioxidant enzyme activities (CAT, SOD, APX) in roots and shoots. These findings suggest that HRW treatment could enhance wheat seed germination, growth, and nutrient absorption, thereby increasing agricultural productivity. Molecular analysis indicated significant upregulation of the Dreb1 gene with a 4-h HRW treatment. Thus, it shows promise in addressing climate change effects on wheat production. Therefore, HRW treatment could be a hopeful strategy for enhancing wheat plant drought tolerance, requiring further investigation (field experiments) to validate its impact on plant growth and drought stress mitigation.

Characterizing deaths among adult patients with severe acute respiratory infection: during the pre- and COVID-19 pandemic periods in Bangladesh, 2018-2022.

BACKGROUND: Severe acute respiratory infection (SARI) is a leading cause of mortality globally, peaking during the COVID-19 pandemic. We analyzed SARI-associated deaths during the pre-and-pandemic periods in Bangladesh to identify the contributing factors. METHODS: We analyzed data from hospital-based influenza surveillance at nine tertiary-level hospitals in Bangladesh. We considered March 2018-February 2020 as the pre-pandemic period and March 2020-February 2022 as the pandemic period and included adult (≥ 18 years) participants in our study. Surveillance physicians identified WHO-SARI case definition meeting inpatients and collected demographics, clinical characteristics, and outcomes at hospital discharge and 30 days post-discharge. We performed rRT-PCR for influenza and SARS-CoV-2 viruses on collected nasopharyngeal and oropharyngeal swabs. We used multivariable Cox's regression models to calculate the hazard ratio (HR) for factors associated with SARI deaths in these adult patients. RESULTS: We enrolled 4392 SARI patients during the pre-pandemic and 3824 SARI patients during the pandemic period. Case fatality ratio was higher during the pandemic: 13.62% (521) [in-hospital: 6.45% (247); post-discharge: 7.17% (274)] compared to pre-pandemic, 6.01% (264) [in-hospital: 2.01% (89), post-discharge: 4% (175)] (p < 0.001). Pre-pandemic, influenza was detected in 14% (37/264) of SARI deaths. Influenza was detected during the pandemic in 2.3% (12/521), SARS-CoV-2 in 41.8% (218/521), and both viruses in only one SARI death. History of smoking and the presence of 1 or more co-morbid conditions independently attributed to SARI deaths in adults in the pre-pandemic period. SARI deaths in such patients were also associated with respiratory difficulties on admission in both pre-pandemic (aHR 2.36; 95% CI:1.65-3.36) and pandemic period (aHR 2.30; 95% CI: 1.57-3.35) after accounting for age, sex, smoking status, presence of 1 or more co-morbid conditions, and detection of influenza and SARS-CoV-2 viruses. CONCLUSIONS: During the pandemic, SARI mortality increased; influenza-associated mortality declined, and SARS-CoV-2 caused over a third of SARI deaths. Post-discharge mortality was higher than in-hospital mortality during both periods. Limiting premature discharge and strengthening post-discharge monitoring and nursing services could reduce unexpected deaths. Formative research to better understand post-discharge mortality is essential to reduce SARI deaths.

Effects of gamma-radiation on microbial, nutritional, and functional properties of Katimon mango peels: A combined biochemical and in silico studies.

Gamma radiation has notable impacts on the flesh of mangoes. In this research, Katimon mangoes were subjected to different levels of irradiation (0.5, 1.0, 1.5, and 2.0 kGy) using a60Co irradiator. The results showed that irradiation significantly reduced the microbial population in the mango peels, with the 1.5 kGy dose showing the most significant reduction. Irradiation also delayed ripening and extended the shelf life of the mango peels. The total fat, protein, ash, moisture, and sugar content of the mango peels were all affected by irradiation. The total protein content, ash content and moisture content increased after irradiation, while the fat content remained relatively unchanged. The sugar content increased in all samples after storage, but the non-irradiated samples had higher sugar levels than the irradiated ones. The dietary fiber content of the mango peels was not significantly affected by irradiation. The vitamin C content decreased in all samples after storage. The titratable acidity and total soluble solids content of the mango peels increased after storage, but there were no significant differences between the irradiated and non-irradiated samples. Antioxidant activity and cytotoxicity assessment highlighted the antioxidant potential and reduced toxicity of irradiated samples. Additionally, the antimicrobial effectiveness of irradiated mango peels was evaluated. The most substantial inhibitory zones (measuring 16.90 ± 0.35) against Pseudomonas sp. were observed at a radiation dose of 1.5 kGy with 150 µg/disc. To identify potential antimicrobial agents, the volatile components of mangoes irradiated with 1.5 kGy were analyzed through GC-MS. Subsequently, these compounds were subjected to in silico studies against a viable protein, TgpA, of Pseudomonas sp. (PDB ID: 6G49). Based on molecular dynamic simulations and ADMET properties, (-)-Carvone (-6.2), p-Cymene (-6.1), and Acetic acid phenylmethyl ester (-6.1) were identified as promising compounds for controlling Pseudomonas sp.

A review on self-healing featured soft robotics.

Soft robots are becoming more popular because they can solve issues stiff robots cannot. Soft component and system design have seen several innovations recently. Next-generation robot-human interactions will depend on soft robotics. Soft material technologies integrate safety at the material level, speeding its integration with biological systems. Soft robotic systems must be as resilient as biological systems in unexpected, uncontrolled situations. Self-healing materials, especially polymeric and elastomeric ones, are widely studied. Since most currently under-development soft robotic systems are composed of polymeric or elastomeric materials, this finding may provide immediate assistance to the community developing soft robots. Self-healing and damage-resilient systems are making their way into actuators, structures, and sensors, even if soft robotics remains in its infancy. In the future, self-repairing soft robotic systems composed of polymers might save both money and the environment. Over the last decade, academics and businesses have grown interested in soft robotics. Despite several literature evaluations of the soft robotics subject, there seems to be a lack of systematic research on its intellectual structure and development despite the rising number of articles. This article gives an in-depth overview of the existing knowledge base on damage resistance and self-healing materials' fundamental structure and classifications. Current uses, problems with future implementation, and solutions to those problems are all included in this overview. Also discussed are potential applications and future directions for self-repairing soft robots.

Conservation agriculture's impact on total and labile organic carbon pools in calcareous and non-calcareous floodplain soils under a sub-tropical rice-based system.

To evaluate the effects of conservation agriculture (CA) on SOC pools and their lability, field experiments (2015-2020) were conducted on contrasting soils under subtropical climates. The experiment on non-calcareous soils, was comprised of tillage (minimum [MT] vs. conventional [CT]) in main plots, cropping systems (Wheat [Triticum aestivum]-Aus and Aman rice [Oryza sativa L.], WRR; Lentil [Lens culinaris]-Aus and Aman rice, LRR; and Mustard [Brassica nigra]- Boro and Aman rice, MRR) in the sub-plots, and crop residue (with or without 20% residue) in the sub-sub plots. The experiment on calcareous soils, was comprised of tillage (strip-till, ST; no-till, NT; and CT) and crop residue (high residue, HR at 50% by height vs. low residue, LR at 15%). Results showed that the MT had higher SOC contents by 18.8% than the CT in non-calcareous soils. Likewise, SOC was 12.5% and 6.7% higher in the NT and ST, respectively, than in the CT in calcareous soils. Significantly higher particulate organic (POC), permanganate oxidizable (POXC), and microbial biomass carbon (MBC) were observed in the MT, NT, and ST than in the CT at both locations. Reduced tillage with residue retention under LRR had a higher SOC, including labile C pools compared to WRR and MRR systems. Similarly, carbon management index (1.2-1.5 and 1.0-1.2) in both soils had significant positive correlations with SOC lability via POXC, POC, and MBC pools, indicating a SOC sequestration potential. In conclusion, our results showed positive effects of CA on SOC and its lability across soils.

Antioxidant, Cytotoxicity, Antimicrobial Activity, and In Silico Analysis of the Methanolic Leaf and Flower Extracts of Clitoria ternatea.

Infectious diseases pose a significant threat to human health worldwide. To address this challenge, we conducted a comprehensive study on the leaf and flower extracts of Clitoria ternatea plants. Our research encompassed in vitro assessments of their antibacterial, antibiofilm, antioxidant, and cytotoxic properties. Additionally, we employed in silico screening to identify promising compounds with potential applications in developing novel anti-Escherichia coli medications. Notably, our investigation revealed a remarkable inhibition zone of 13.00 ± 1 mm when applying the leaf extract (200 µg/ml) against E. coli, showcasing its potent antibacterial properties. Furthermore, both the leaf and flower extracts exhibited substantial biofilm inhibition efficacy against S. aureus, with inhibition percentages of 54% and 58%, respectively. In the realm of antioxidant activity, the leaf and flower extracts of C. ternatea displayed noteworthy DPPH free radical scavenging capabilities. Specifically, the leaf extract exhibited a substantial activity of 62.39% at a concentration of 150 µg/ml, while the flower extract achieved 44.08% at the same concentration. Our study also evaluated the impact on brine shrimp, where the floral extract displayed a significantly higher mortality rate of 93.33% at a dosage of 200 µg/ml compared to the leaf extract. To elucidate potential therapeutic targets, we utilized molecular docking techniques, focusing on the acbR protein (5ENR) associated with antibiotic resistance in E. coli. In this analysis, compounds isolated from the C. ternatea leaf extract, namely D1 (CID-14478556), D2 (CID-6423376), and D3 (CID-20393), exhibited binding energies of -8.2 kcal/mol, -6.5 kcal/mol, and -6.3 kcal/mol, respectively. Additionally, compounds from the flower extract, E1 (CID-5282761), E2 (CID-538757), and E3 (CID-536762), displayed binding energies of -5.4 kcal/mol, -5.3 kcal/mol, and -5.1 kcal/mol, respectively. In conclusion, the leaf and flower extracts derived from C. ternatea represent a promising natural resource with potential therapeutic applications in combating antibiotic-resistant pathogens.

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer.

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

Lessons learned from identifying clusters of severe acute respiratory infections with influenza sentinel surveillance, Bangladesh, 2009-2020.

Background: We explored whether hospital-based surveillance is useful in detecting severe acute respiratory infection (SARI) clusters and how often these events result in outbreak investigation and community mitigation. Methods: During May 2009-December 2020, physicians at 14 sentinel hospitals prospectively identified SARI clusters (i.e., ≥2 SARI cases who developed symptoms ≤10 days of each other and lived <30 min walk or <3 km from each other). Oropharyngeal and nasopharyngeal swabs were tested for influenza and other respiratory viruses by real-time reverse transcriptase-polymerase chain reaction (rRT-PCR). We describe the demographic of persons within clusters, laboratory results, and outbreak investigations. Results: Field staff identified 464 clusters comprising 1427 SARI cases (range 0-13 clusters per month). Sixty percent of clusters had three, 23% had two, and 17% had ≥4 cases. Their median age was 2 years (inter-quartile range [IQR] 0.4-25) and 63% were male. Laboratory results were available for the 464 clusters with a median of 9 days (IQR = 6-13 days) after cluster identification. Less than one in five clusters had cases that tested positive for the same virus: respiratory syncytial virus (RSV) in 58 (13%), influenza viruses in 24 (5%), human metapneumovirus (HMPV) in five (1%), human parainfluenza virus (HPIV) in three (0.6%), adenovirus in two (0.4%). While 102/464 (22%) had poultry exposure, none tested positive for influenza A (H5N1) or A (H7N9). None of the 464 clusters led to field deployments for outbreak response. Conclusions: For 11 years, none of the hundreds of identified clusters led to an emergency response. The value of this event-based surveillance might be improved by seeking larger clusters, with stronger epidemiologic ties or decedents.

Exploring media consumption and mental health among young adults during the second wave of COVID-19 in Bangladesh.

Background: This study examined the association between media exposure and mental health during the second wave of lockdown among the general population of Bangladesh. Methods: A total of 449 adult participants were enrolled in the study to evaluate their levels of exposure to total media, electronic media, and social media. Mental health was assessed using a 7-item generalized anxiety disorder (GAD-7) scale. A multivariate logistic regression model was constructed to explore the relationships between media exposure levels and anxiety. The models included covariates such as sex, daily working hours, health problems, media distraction, and income from social media. Results: The results showed that 44.5%, 39.2%, and 16.3% of participants reported low, medium, and high levels of exposure to different media during the COVID-19 period, respectively. Additionally, 96.4%, 2.7%, and 0.9% of participants had low, medium, and high levels of exposure to electronic media, respectively, and 89.1%, 10.5%, and 0.4% of participants had low, medium, and high levels of exposure to social media, respectively. The overall prevalence of anxiety was 25.38% among the respondents. Participants with high levels of total media exposure were significantly more likely to experience anxiety, with an odds ratio of 2.75 (95% CI = 1.40-5.14, p < 0.01). Females were 2.26 times more likely to experience anxiety than males (95% CI = 1.37-3.74, p < 0.01), and participants with health problems were also more likely to develop anxiety compared to those who did not. Conclusion: Our results show a positive relationship between increased media exposure and anxiety levels, providing useful insights for both academics and public health practitioners.

Phytochemical-assisted green synthesis of CuFeOx nano-rose electrocatalysts for oxygen evolution reaction in alkaline media.

This study represents a green synthesis method for fabricating an oxygen evolution reaction (OER) electrode by depositing two-dimensional CuFeOx on nickel foam (NF). Two-dimensional CuFeOx was deposited on NF using in situ hydrothermal synthesis in the presence of Aloe vera extract. This phytochemical-assisted synthesis of CuFeOx resulted in a unique nano-rose-like morphology (petal diameter 30-70 nm), which significantly improved the electrochemical surface area of the electrode. The synthesized electrode was analyzed for its OER electrocatalytic activity and it was observed that using 75% Aloe vera extract in the phytochemical-assisted synthesis of CuFeOx resulted in improved OER electrocatalytic performance by attaining an overpotential of 310 mV for 50 mA cm-2 and 410 mV for 100 mA cm-2. The electrode also sustained robust stability throughout the 50 h of chronopotentiometry studies under alkaline electrolyte conditions, demonstrating its potential as an efficient OER electrode material. This study highlights the promising use of Aloe vera extract as a green and cost-effective way to synthesize efficient OER electrode materials.