Stability and performance analysis of storage root yield in a dataset of sweet potato varieties (Ipomoea batatas L.).

The dataset focuses on evaluating the performance of 17 sweet potato varieties (G) released by the Bangladesh Agricultural Research Institute (BARI) in terms of storage root yield and stability across five locations (E) in Bangladesh-Gazipur, Bogura, Jamalpur, Jashore, and Chattogram. The result revealed that BARI Mistialu-12 exhibited the highest average storage root yield at 45.35 t/ha, closely followed by BARI Mistialu-16 at 44.64 t/ha. Conversely, BARI Mistialu-1 had the lowest mean yield of 25.99 t/ha. Among the locations, Bogura recorded the highest mean root yield at 37.05 t/ha, while Chattogram exhibited the lowest at 31.27 t/ha. A combined analysis of variance revealed the presence of variability in storage root yield attributed to the genotype-location (environment) interaction (GEI). To delve deeper into this interaction, additive and multiplicative interaction effect models (AMMI) along with a linear mixed model (LMM) were employed for further investigations to confirm the significant contribution of GEI variance to root yield. The LMM results showed genetic variance (%), heritability (%), selection accuracy (%), and GEI correlation coefficients of 52.27, 54, 94, and 30, respectively. The AMMI analysis indicated that the first two principal components accounted for 74.60 % of GEI, with 20.16 % attributed to it. Assessing significant Interaction Principal Component Analyses (IPCAs) through the Weighted Average of Absolute Scores (WAAS) indicated that BARI Mistialu-12 is the most stable genotype, followed by BARI Mistialu-16 and BARI Mistialu-8, all displaying above-average root yield. The mega-environment analysis associated the highest root production of BARI Mistialu-11 and BARI Mistialu-2 with the Jamalpur location, while Gazipur, Bogura, and Jashore were linked with the superior performance of BARI Mistialu-12 and BARI Mistialu-16 genotypes. These findings are crucial for future breeding programs and the rapidly growing sweet potato industry, given the stable high-yield potential across diverse agro-ecological conditions. However, it is imperative to repeat the study to ensure reliable outcomes.

The reactive cholera vaccination campaign in urban Dhaka in 2022: experience, lessons learned and future directions.

Introduction: An upsurge of diarrheal cases occurred in Dhaka, Bangladesh, with approximately 30% of the cases being identified as cholera in 2022. To combat this situation, a reactive Oral Cholera Vaccination campaign was organized in five highly cholera-affected areas of Dhaka city. The paper is a descriptive tale of experience gathering, organization and implementation of reactive oral cholera vaccination campaign. Study design: This is a descriptive report of a reactive oral cholera vaccination campaign. Methods: Population density maps were generated using GIS technology before launching the campaign. The target population comprised individuals aged over one year, excluding pregnant women, totaling 2,374,976 people residing in above mentioned areas. The campaign utilized Euvichol-Plus, an OCV with adherence to the necessary cold chain requirements. Total 700 teams, each consisting of six members, were deployed across the five zones. The campaign was conducted in two rounds, where first round took place in June-July 2022, followed by second round in August 2022. During the campaign, data on adverse events following immunization (AEFI) was collected. Expert teams from various government and non-government organizations monitored regularly and ensured the campaign's success. Results: The first round achieved a coverage rate of 99%, whereas in the second round, 86.3% of individuals among the first dose recipients. During the campaigns, a total of 57 AEFIs were reported. Conclusions: This campaign serves as a model for a multispectral approach in combating cholera epidemics, highlighting the collaborative efforts of policymakers, health authorities, local communities, and health partners.

Polysaccharides polymers for glaucoma treatment-a review.

One of the major challenges in preventing glaucoma progression is patient compliance with medication regimens. Since conventional ophthalmic dosage forms have numerous limitations, researchers have been intensively working on developing polymers-based delivery systems for glaucoma drugs. Specifically, research and development efforts have increased using polysaccharide polymers such as sodium alginate, cellulose, ß-cyclodextrin, hyaluronic acid, chitosan, pectin, gellan gum, galactomannans for sustained release to the eye to overcome treatment challenges, showing promise in improving drug release and delivery, patient experience, and treatment compliance. In the recent past, multiple research groups have successfully designed sustained drug delivery systems, promoting the efficacy as well as the feasibility of glaucoma drugs with single/combinations of polysaccharides to eliminate the drawbacks associated with the glaucoma treatment. Naturally available polysaccharides, when used as drug vehicles can increase the retention time of eye drops on the ocular surface, leading to improved drug absorption and bioavailability. Additionally, some polysaccharides can form gels or matrices that can release drugs slowly over time, providing sustained drug delivery and reducing the need for frequent dosing. Thus, this review aims to provide an overview of the pre-clinical and clinical studies of polysaccharide polymers applied for glaucoma treatment along with their therapeutic outcomes.

Adherence to Anti-Malarial Treatment in Malaria Endemic Areas of Bangladesh.

Ensuring adherence to antimalarial treatment is crucial for achieving a radical cure and elimination of malaria, especially in hard-to-reach areas. We conducted this study to assess the current scenario of drug adherence in four endemic sub-districts of Bangladesh. Among 110 enrolled participants, 70% were mono-infected with Plasmodium falciparum and the remaining 30% with P. vivax. The overall treatment adherence frequency was 92.7% (95% CI: 83.0-96.3%). A total of eight participants were found to be nonadherent to treatment and all of them were from Bandarban. Level of nonadherence was equally observed in two age groups: 11-17 and 18+ years. However, male participants (n = 6) were found to be more nonadherent than females (n = 2). Among 7.3% with nonadherence to treatment, a single participant with P. falciparum mono-infection refused to take medication and became nonadherent. Remaining participants stated that they were feeling well and going to work, thus leaving treatment course uncompleted. Although overall compliance with malaria medication seems good, a gradual increase in noncompliance to P. vivax malaria treatment suggests that the National Malaria Elimination Program must be enhanced and monitored to fulfil the projected malaria elimination goal before 2030 from Bangladesh.

Distribution and treatment needs of soil-transmitted helminthiasis in Bangladesh: A Bayesian geostatistical analysis of 2017-2020 national survey data.

BACKGROUND: In Bangladesh, preventive chemotherapy targeting soil-transmitted helminth (STH) infections in school-age children has been implemented since 2008. To evaluate the success of this strategy, surveys were conducted between 2017 and 2020 in 10 out of 64 districts. We estimate the geographic distribution of STH infections by species at high spatial resolution, identify risk factors, and estimate treatment needs at different population subgroups. METHODOLOGY: Bayesian geostatistical models were fitted to prevalence data of each STH species. Climatic, environmental, and socioeconomic predictors were extracted from satellite images, open-access, model-based databases, and demographic household surveys, and used to predict the prevalence of infection over a gridded surface at 1 x 1 km spatial resolution across the country, via Bayesian kriging. These estimates were combined with gridded population data to estimate the number of required treatments for different risk groups. PRINCIPAL FINDINGS: The population-adjusted prevalence of Ascaris lumbricoides, Trichuris trichiura, and hookworm across all ages is estimated at 9.9% (95% Bayesian credible interval: 8.0-13.0%), 4.3% (3.0-7.3%), and 0.6% (0.4-0.9%), respectively. There were 24 out of 64 districts with an estimated population-adjusted STH infection prevalence above 20%. The proportion of households with improved sanitation showed a statistically important, protective association for both, A. lumbricoides and T. trichiura prevalence. Precipitation in the driest month of the year was negatively associated with A. lumbricoides prevalence. High organic carbon concentration in the soil's fine earth fraction was related to a high hookworm prevalence. Furthermore, we estimated that 30.5 (27.2; 36.0) million dosages of anthelmintic treatments for school-age children were required per year in Bangladesh. CONCLUSIONS/SIGNIFICANCE: For each of the STH species, the prevalence was reduced by at least 80% since treatment was scaled up more than a decade ago. The current number of deworming dosages could be reduced by up to 61% if the treatment strategy was adapted to the local prevalence.

A combination of isoliquiritigenin with Artemisia argyi and Ohwia caudata water extracts attenuates oxidative stress, inflammation, and apoptosis by modulating Nrf2/Ho-1 signaling pathways in SD rats with doxorubicin-induced acute cardiotoxicity.

Ohwia caudata (Thunb.) H. Ohashi (Leguminosae) also called as "Evergreen shrub" and Artemisia argyi H.Lév. and Vaniot (Compositae) also named as "Chinese mugwort" those two-leaf extracts frequently used as herbal medicine, especially in south east Asia and eastern Asia. Anthracyclines such as doxorubicin (DOX) are commonly used as effective chemotherapeutic drugs in anticancer therapy around the world. However, chemotherapy-induced cardiotoxicity, dilated cardiomyopathy, and congestive heart failure are seen in patients who receive DOX therapy, with the mechanisms underlying DOX-induced cardiac toxicity remaining unclear. Mitochondrial dysfunction, oxidative stress, inflammatory response, and cardiomyocytes have been shown to play crucial roles in DOX-induced cardiotoxicity. Isoliquiritigenin (ISL, 10 mg/kg) is a bioactive flavonoid compound with protective effects against inflammation, neurodegeneration, cancer, and diabetes. Here, in this study, our aim is to find out the Artemisia argyi (AA) and Ohwia caudata (OC) leaf extract combination with Isoliquiritigenin in potentiating and complementing effect against chemo drug side effect to ameliorate cardiac damage and improve the cardiac function. In this study, we showed that a combination of low (AA 300 mg/kg; OC 100 mg/kg) and high-dose(AA 600 mg/kg; OC 300 mg/kg) AA and OC water extract with ISL activated the cell survival-related AKT/PI3K signaling pathway in DOX-treated cardiac tissue leading to the upregulation of the antioxidant markers SOD, HO-1, and Keap-1 and regulated mitochondrial dysfunction through the Nrf2 signaling pathway. Moreover, the water extract of AA and OC with ISL inhibited the inflammatory response genes IL-6 and IL-1ß, possibly through the NFκB/AKT/PI3K/p38α/NRLP3 signaling pathways. The water extract of AA and OC with ISL could be a potential herbal drug treatment for cardiac hypertrophy, inflammatory disease, and apoptosis, which can lead to sudden heart failure.

Assessment of treatment outcomes of visceral leishmaniasis (VL) treated cases and impact of COVID-19 on VL management and control services in Bangladesh.

BACKGROUND: COVID-19 has largely impacted the management of Visceral leishmaniasis (VL), like several other Neglected Tropical Diseases. The impact was particularly evident in Lower and Middle-Income countries where the already inadequate healthcare resources were diverted to managing the COVID-19 pandemic. Bangladesh achieved the elimination target for VL in 2016. To sustain this success, early diagnosis and treatment, effective vector control, and periodic surveillance are paramount. However, the specific control measures for VL in Bangladesh that were hampered during COVID-19 and their extent are unknown. METHODS: This study aimed at identifying the gaps and challenges in the follow-up of treated VL patients by interviewing both the treated VL cases and their health service providers. We followed VL cases treated between 2019 and 2020 in five VL endemic subdistricts (upazilas) both retrospectively and prospectively to monitor clinical improvement, relapse, or other consequences. Moreover, interviews were conducted with the health service providers to assess the impact of COVID-19 on VL case detection, treatment, reporting, vector control operations, and logistic supply chain management. RESULTS: There was no added delay for VL diagnosis; however, VL treatment initiation and reporting time increased almost two-fold due to COVID-19. Indoor Residual Spraying activity was significantly hampered due to a shortage of insecticides. Out of 44 enrolled and treated VL patients, two relapsed (4.5 %), two developed Para Kala-Azar Dermal Leishmaniasis (4.5 %), and three (6.8 %) Post Kala-Azar Dermal Leishmaniasis (PKDL). The health service providers highlighted patients` unwillingness to visit the hospital, financial constraints, and distance from the hospitals as the main reasons for missed follow-up visits (20.5 %). Building good communication in the community, awareness schemes, and incentive-based approaches were suggested as possible solutions to mitigate these problems. CONCLUSION: Long-term follow-up is required for the early detection and management of VL relapse and PKDL cases. Effective vector control measures, capacity development, and identification of new VL hotspots are pivotal in the VL endemic regions to sustain the elimination goal.

Epidemiological, serological, and entomological aspects of visceral leishmaniasis (VL) in suspected new VL foci in Bangladesh.

The study aimed to explore epidemiological, serological, and entomological aspects of visceral leishmaniasis (VL) in suspected new VL foci and assess the knowledge, attitude, and practices of the community living in the alleged new VL foci. The study investigated new visceral leishmaniasis (VL) cases reported between 2019 and 2020 in four sub-districts (Dharmapasha, Hakimpur, Islampur and Savar) where we tested 560 members using the rK39 rapid test and conducted vector collections in six neighbouring houses of the index cases to assess sandfly density and distribution, examined sandflies' infection, and determined the spatial relationship with VL infection. Furthermore, we highlighted the importance of early detection, and community awareness in controlling the spread of the disease. The study screened 1078 people from 231 households in the four sub-districts for fever, history of visceral leishmaniasis (VL), and PKDL-like skin lesions. Among sub-districts, positivity rate for rK39 rapid test was highest (3.5 %) in Savar. Sandflies were present across all areas except in Dharmapasha, but all 21 collected female P. argentipes sandflies were negative for Leishmania parasite DNA. We found one person from Islampur with a history of VL, and one from Islampur and another one from Savar had PKDL. After the awareness intervention, more people became familiar with VL infection (91.2 %), and their knowledge concerning sandflies being the vector of the disease and the risk of having VL increased significantly (30.1 %). The study found no active case in the suspected new foci, but some asymptomatic individuals were present. As sandfly vectors exist in these areas, the National Kala-azar Elimination Programme (NKEP) should consider these areas as kala-azar endemic and initiate control activities as per national guidelines.

A snap-shot of a diarrheal epidemic in Dhaka due to enterotoxigenic Escherichia coli and Vibrio cholerae O1 in 2022.

Background: Enterotoxigenic Escherichia coli (ETEC) and Vibrio cholerae O1 are most common bacterial causes of diarrheal diseases in Bangladesh. This analysis projected distribution of ETEC and V. cholerae O1 among diarrheal patients of icddr,b, Dhaka hospital in two diarrheal peaks of 2022. Methodology: Under the 2% systematic surveillance system, stool samples collected from diarrheal patients of icddr,b hospital were cultured and diagnostic testing was done for ETEC and V. cholerae O1. Comparison of positive cases was done between first peak (March-April) and second peak (October-November) in 2022. Results: A total of 2,937 stool specimens were tested of which 12% were ETEC and 20% were V. cholerae O1. About 40% of the severe dehydration cases were infected with V. cholerae O1. Predominant ETEC enterotoxin type was 'LT/ST' (41%). The LT enterotoxin significantly increased from 13% to 28% in the second peak (p = 0.015). The predominant colonization factors (CFs) on ETEC were CS5 + CS6 (23%), followed by CS6 (15%). CF-positive isolates was significantly higher in the second peak (36%) than in the first peak (22%) (p = 0.043). Total 14% cases were co-infected with ETEC and V. cholerae O1. Significant differences in the distribution of enterotoxin types were observed (p = 0.029) among the co-infection cases. Conclusion: Changing patterns of enterotoxin and CFs observed in ETEC pathogens should be taken into consideration for ETEC vaccine development. Considering cholera and ETEC biannual trends in causing diarrheal epidemics and outbreaks, emphasizes the need for thoughts on combination vaccine strategies for preventing acute watery diarrhea due to the two major bacterial pathogens.

Native Trichoderma Induced the Defense-Related Enzymes and Genes in Rice against Xanthomonas oryzae pv. oryzae (Xoo).

The application of Trichoderma is a form of biological control that has been effective in combating Xanthomonas oryzae pv. oryzae, the causative agent of the devastating disease known as bacterial blight of rice. In this present study, four strains of Trichoderma, viz., T. paraviridescens (BDISOF67), T. erinaceum (BDISOF91), T. asperellum (BDISOF08), and T. asperellum (BDISOF09), were collected from the rice rhizosphere and used to test their potentiality in reducing bacterial blight. The expression patterns of several core defense-related enzymes and genes related to SA and JA pathways were studied to explore the mechanism of induced resistance by those Trichoderma strains. The results primarily indicated that all Trichoderma were significantly efficient in reducing the lesion length of the leaf over rice check variety (IR24) through enhancing the expression of core defense-related enzymes, such as PAL, PPO, CAT, and POD activities by 4.27, 1.77, 3.53, and 1.57-fold, respectively, over control. Moreover, the results of qRT-PCR exhibited an upregulation of genes OsPR1, OsPR10, OsWRKY45, OsWRKY62, OsWRKY71, OsHI-LOX, and OsACS2 after 24 h of inoculation with all tested Trichoderma strains. However, in the case of RT-PCR, no major changes in OsPR1 and OsPR10 expression were observed in plants treated with different Trichoderma strains during different courses of time. Collectively, Trichoderma induced resistance in rice against X. oryzae pv. oryzae by triggering these core defense-related enzymes and genes associated with SA and JA pathways.

Implementation and Delivery of Oral Cholera Vaccination Campaigns in Humanitarian Crisis Settings among Rohingya Myanmar nationals in Cox's Bazar, Bangladesh.

BACKGROUND: Over 700,000 Myanmar nationals known as the 'Rohingyas' fled into Cox's Bazar, Bangladesh, in late 2017. Due to this huge displacement into unhygienic areas, these people became vulnerable to communicable diseases including cholera. Assessing the risk, the Government of Bangladesh (GoB), with the help of the International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B) and other international partners, decided to take preventive measures, one of which is the execution of oral cholera vaccination (OCV) campaigns. This paper describes the implementation and delivery of OCV campaigns during humanitarian crises in Bangladesh. METHODS: Seven rounds of OCV campaigns were conducted between October 2017 and December 2021. The OCV campaigns were conducted by applying different strategies. RESULTS: Approximately 900,000 Rohingya Myanmar nationals (RMNs) and the host population (amounting to 528,297) received OCV across seven campaigns. In total, 4,661,187 doses of OCVs were administered, which included 765,499 doses for RMNs, and 895,688 doses for the host community. The vaccine was well accepted, and as a result, a high level of coverage was achieved, ranging from 87% to 108% in different campaigns. CONCLUSIONS: After successful pre-emptive campaigns in Cox's Bazar humanitarian camps, no cholera outbreaks were detected either in the RMN or host communities.

Interpretable Differential Diagnosis of Non-COVID Viral Pneumonia, Lung Opacity and COVID-19 Using Tuned Transfer Learning and Explainable AI.

The coronavirus epidemic has spread to virtually every country on the globe, inflicting enormous health, financial, and emotional devastation, as well as the collapse of healthcare systems in some countries. Any automated COVID detection system that allows for fast detection of the COVID-19 infection might be highly beneficial to the healthcare service and people around the world. Molecular or antigen testing along with radiology X-ray imaging is now utilized in clinics to diagnose COVID-19. Nonetheless, due to a spike in coronavirus and hospital doctors' overwhelming workload, developing an AI-based auto-COVID detection system with high accuracy has become imperative. On X-ray images, the diagnosis of COVID-19, non-COVID-19 non-COVID viral pneumonia, and other lung opacity can be challenging. This research utilized artificial intelligence (AI) to deliver high-accuracy automated COVID-19 detection from normal chest X-ray images. Further, this study extended to differentiate COVID-19 from normal, lung opacity and non-COVID viral pneumonia images. We have employed three distinct pre-trained models that are Xception, VGG19, and ResNet50 on a benchmark dataset of 21,165 X-ray images. Initially, we formulated the COVID-19 detection problem as a binary classification problem to classify COVID-19 from normal X-ray images and gained 97.5%, 97.5%, and 93.3% accuracy for Xception, VGG19, and ResNet50 respectively. Later we focused on developing an efficient model for multi-class classification and gained an accuracy of 75% for ResNet50, 92% for VGG19, and finally 93% for Xception. Although Xception and VGG19's performances were identical, Xception proved to be more efficient with its higher precision, recall, and f-1 scores. Finally, we have employed Explainable AI on each of our utilized model which adds interpretability to our study. Furthermore, we have conducted a comprehensive comparison of the model's explanations and the study revealed that Xception is more precise in indicating the actual features that are responsible for a model's predictions.This addition of explainable AI will benefit the medical professionals greatly as they will get to visualize how a model makes its prediction and won't have to trust our developed machine-learning models blindly.

Were hospitals with sustained high performance more successful at reducing mortality during the pandemic's second wave?

BACKGROUND: In 2019, the COVID-19 pandemic emerged. Variation in COVID-19 patient outcomes between hospitals was later reported. PURPOSE: This study aims to determine whether sustainers-hospitals with sustained high performance on Hospital Value-Based Purchasing Total Performance Score (HVBP-TPS)-more effectively responded to the pandemic and therefore had better patient outcomes. METHODOLOGY: We calculated hospital-specific risk-standardized event rates using deidentified patient-level data from the UnitedHealth Group Clinical Discovery Database. HVBP-TPS from 2016 to 2019 were obtained from Centers for Medicare & Medicaid Services. Hospital characteristics were obtained from the American Hospital Association Annual Survey Database (2019), and county-level predictors were obtained from the Area Health Resource File. We use a repeated-measures regression model assuming an AR(1) type correlation structure to test whether sustainers had lower mortality rates than nonsustainers during the first wave (spring 2020) and the second wave (October to December 2020) of the pandemic. RESULTS: Sustainers did not have significantly lower COVID-19 mortality rates during the first wave of the pandemic, but they had lower COVID-19 mortality rates during the second wave compared to nonsustainers. Larger hospitals, teaching hospitals, and hospitals with higher occupancy rates had higher mortality rates. CONCLUSION: During the first wave of the pandemic, mortality rates did not differ between sustainers and nonsustainers. However, sustainers had lower mortality rates than nonsustainers in the second wave, most likely because of their knowledge management capabilities and existing structures and resources that enable them to develop new processes and routines to care for patients in times of crisis. Therefore, a consistently high level of performance over the years on HVBP-TPS is associated with high levels of performance on COVID-19 patient outcomes. PRACTICE IMPLICATIONS: Investing in identifying the knowledge, processes, and resources that foster the dynamic capabilities needed to achieve superior performance in HVBP might enable hospitals to utilize these capabilities to adapt more effectively to future changes and uncertainty.

Identification of Potential Diuretic and Laxative Drug Candidates from Avicennia officinalis L. Bark through In Vivo Mice Model Studies and In Vitro Gas Chromatography-Mass Spectrometry and Molecular Docking Analysis.

Background: Avicennia officinalis is a medicinal plant that has traditionally been used as a diuretic, anti-infective, and antiasthmatic. Our investigation was designed to explore the diuretic and laxative potentials of different fractions of this plant's bark extract as well as the identification of possible drug candidates for the activity. Methods: Collected bark was extracted in ethanol and fractionated in different polar and nonpolar solvents, i.e., water, chloroform, ethyl acetate, and n-hexane. Phytoconstituents were identified following the published protocols and gas chromatography-mass spectrometry (GC-MS). In the diuretic test, Na+ and K+ ions were measured using a flame photometer whereas the Cl- ion content was measured by titrimetric method against AgNO3. In the laxative test, feces amount and consistency were also measured. Molecular docking analysis was conducted using the "Vina Wizard" program in PyRx-Python Prescription 0.8. Results: Phytochemical analysis indicated that alkaloids, tannins, flavonoids, saponins, glycosides, and terpenoids were detected in the most bioactive crude extracts, whereas alkaloids, terpenoids, saponins, and gums were found in bioactive n-hexane fraction and steroids, glycosides, and terpenoids were found positive in chloroform fraction. Almost all the fractions demonstrated a dose-dependent increment of stool production with a soft consistency; however, the chloroform fraction was found to be the most active (p < 0.001). The crude extract and n-hexane fractions significantly increased (p < 0.01) the urinary output at the dose of 200 and 400 mg/kg. The concentrations of Na+, K+, and Cl- in collected urine were found to be more compared with the control group. The GC-MS analysis identified seven compounds in bioactive n-hexane fraction (phenolic and ester-type mainly) whereas seven other compounds (acidic and ester-type mainly) were identified in chloroform fraction. In molecular docking, two drug candidates of this extract (2,4-bis(2-phenylpropan-2-yl)phenol and 2-[4-[2-(dimethylamino)-2-oxo-1,1-diphenylethyl]phenyl]-2-phenylacetic acid) showed excellent binding affinity with the receptor compared with furosemide. Conclusion: A. officinalis bark might be a potential source of bioactive compounds for treating hypertension, edema, and constipation.

Is there an association between hospital staffing levels and inpatient-COVID-19 mortality rates?

OBJECTIVE: This study aims to investigate the relationship between RNs and hospital-based medical specialties staffing levels with inpatient COVID-19 mortality rates. METHODS: We relied on data from AHA Annual Survey Database, Area Health Resource File, and UnitedHealth Group Clinical Discovery Database. In phase 1 of the analysis, we estimated the risk-standardized event rates (RSERs) based on 95,915 patients in the UnitedHealth Group Database 1,398 hospitals. We then used beta regression to analyze the association between hospital- and county- level factors with risk-standardized inpatient COVID-19 mortality rates from March 1, 2020, through December 31, 2020. RESULTS: Higher staffing levels of RNs and emergency medicine physicians were associated with lower COVID-19 mortality rates. Moreover, larger teaching hospitals located in urban settings had higher COVID-19 mortality rates. Finally, counties with greater social vulnerability, specifically in terms of housing type and transportation, and those with high infection rates had the worst patient mortality rates. CONCLUSION: Higher staffing levels are associated with lower inpatient mortality rates for COVID-19 patients. More research is needed to determine appropriate staffing levels and how staffing levels interact with other factors such as teams, leadership, and culture to impact patient care during pandemics.

Vision transformer and explainable transfer learning models for auto detection of kidney cyst, stone and tumor from CT-radiography.

Renal failure, a public health concern, and the scarcity of nephrologists around the globe have necessitated the development of an AI-based system to auto-diagnose kidney diseases. This research deals with the three major renal diseases categories: kidney stones, cysts, and tumors, and gathered and annotated a total of 12,446 CT whole abdomen and urogram images in order to construct an AI-based kidney diseases diagnostic system and contribute to the AI community's research scope e.g., modeling digital-twin of renal functions. The collected images were exposed to exploratory data analysis, which revealed that the images from all of the classes had the same type of mean color distribution. Furthermore, six machine learning models were built, three of which are based on the state-of-the-art variants of the Vision transformers EANet, CCT, and Swin transformers, while the other three are based on well-known deep learning models Resnet, VGG16, and Inception v3, which were adjusted in the last layers. While the VGG16 and CCT models performed admirably, the swin transformer outperformed all of them in terms of accuracy, with an accuracy of 99.30 percent. The F1 score and precision and recall comparison reveal that the Swin transformer outperforms all other models and that it is the quickest to train. The study also revealed the blackbox of the VGG16, Resnet50, and Inception models, demonstrating that VGG16 is superior than Resnet50 and Inceptionv3 in terms of monitoring the necessary anatomy abnormalities. We believe that the superior accuracy of our Swin transformer-based model and the VGG16-based model can both be useful in diagnosing kidney tumors, cysts, and stones.

Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites.

Integrating real-world data (RWD) from several clinical sites offers great opportunities to improve estimation with a more general population compared to analyses based on a single clinical site. However, sharing patient-level data across sites is practically challenging due to concerns about maintaining patient privacy. We develop a distributed algorithm to integrate heterogeneous RWD from multiple clinical sites without sharing patient-level data. The proposed distributed conditional logistic regression (dCLR) algorithm can effectively account for between-site heterogeneity and requires only one round of communication. Our simulation study and data application with the data of 14,215 COVID-19 patients from 230 clinical sites in the UnitedHealth Group Clinical Research Database demonstrate that the proposed distributed algorithm provides an estimator that is robust to heterogeneity in event rates when efficiently integrating data from multiple clinical sites. Our algorithm is therefore a practical alternative to both meta-analysis and existing distributed algorithms for modeling heterogeneous multi-site binary outcomes.

Assessment of fear, anxiety, obsession and functional impairment due to COVID-19 amongst health-care workers and trainees: A cross-sectional study in Nepal.

BACKGROUND: The emergence of the COVID-19 epidemic threw the world into turmoil. The medical community bore the brunt of the pandemic's toll. Long work hours, and a lack of personal protective equipment (PPE) and social support all had an influence on mental health. METHODS: This cross-sectional study was conducted among Lumbini Medical College Teaching Hospital students and employees in Palpa, Nepal. Data entailing their demographic details, pre-existing comorbidities, or death in the family due to COVID-19 was collected using a self-administered survey. In addition, the level of fear, anxiety, obsession, and functional impairment due to COVID-19 was recorded using previously validated respective scales. RESULTS: In total, 403 health-care workers and trainees participated in our study. The mean age of the study participants was 23±4 years, and more than half of them (n=262, 65%) were females. A significant association was found between fear score with age (p-value=0.04), gender (p-value <0.01) and occupation (p-value<0.001). The participants suffering from chronic diseases (p-value=0.36), were not found to be significantly obsessed with COVID-19. Age (p-value=0.34), was not found to be significantly associated with higher anxiety levels. Nursing students suffered from a significantly greater functional impairment than other health-care professionals (mean rank score=269.15, p-value < 0.001). A moderately positive correlation was observed between fear, anxiety, obsession, and functional impairment scales. CONCLUSION: This study revealed various socio-demographic characteristics as risk factors for psychological stress in the people related to the health-care profession of Nepal during the COVID-19 pandemic. A viable answer to this quandary might be adequate psychosocial intervention by health-care authorities, increased social support, and the introduction of better mental health management measures for the front-line health-care workers.

Hydrogel Nanoarchitectonics: An Evolving Paradigm for Ultrasensitive Biosensing.

The integration of nanoarchitectonics and hydrogel into conventional biosensing platforms offers the opportunities to design physically and chemically controlled and optimized soft structures with superior biocompatibility, better immobilization of biomolecules, and specific and sensitive biosensor design. The physical and chemical properties of 3D hydrogel structures can be modified by integrating with nanostructures. Such modifications can enhance their responsiveness to mechanical, optical, thermal, magnetic, and electric stimuli, which in turn can enhance the practicality of biosensors in clinical settings. This review describes the synthesis and kinetics of gel networks and exploitation of nanostructure-integrated hydrogels in biosensing. With an emphasis on different integration strategies of hydrogel with nanostructures, this review highlights the importance of hydrogel nanostructures as one of the most favorable candidates for developing ultrasensitive biosensors. Moreover, hydrogel nanoarchitectonics are also portrayed as a promising candidate for fabricating next-generation robust biosensors.