Impact of dosing schedules on performance of rotavirus vaccines in Ghana.

Background: Available live-oral rotavirus vaccines are associated with low to moderate performance in low- and middle-income settings. There is limited evidence relating to how the vaccine dosing schedule might be adjusted to improve vaccine performance in these settings. Methods: We used mathematical models fitted to rotavirus surveillance data for children <5 years of age from three different hospitals in Ghana (Korle-Bu Teaching Hospital in Accra, Komfo Anokye Teaching Hospital in Kumasi and War Memorial Hospital in Navrongo) to project the impact of rotavirus vaccination over a 10-year period (April 2012-March 2022). We quantified and compared the impact of the previous vaccination program in Ghana to the model-predicted impact for other vaccine dosing schedules across the three hospitals and the entire country, under different assumptions about vaccine protection. To project the rotavirus vaccine impact over Ghana, we sampled from the range of model parameters for Accra and Navrongo, assuming that these two settings represent the "extremes" of rotavirus epidemiology within Ghana. Results: For the previously implemented 6/10-week monovalent Rotarix vaccine (RV1) schedule, the model-estimated average annual incidence of moderate-to-severe rotavirus-associated gastroenteritis (RVGE) ranged between 1,151 and 3,002 per 100,000 people per year over the 10-year period for the three sites. Compared to no vaccination, the model-estimated median percentage reductions in RVGE ranged from 28-85% and 12-71% among children <1 year and <5 years of age respectively, with the highest and lowest percentage reductions predicted using model parameters estimated for Accra and Navrongo, respectively. The median predicted reductions in RVGE for the whole country ranged from 57-66% and 35-45% among children <1 year and <5 years of age, respectively. The 1/6/10- and 6/10/14-week schedules provided the best and comparable reductions in RVGE compared to the original 6/10-week schedule, whereas there was no improvement in impact for the 10/14-week schedule. Conclusions: We found that administering an additional dose of RV1 might be an effective strategy to improve rotavirus vaccine impact, particularly in settings with low vaccine effectiveness. The results could be extrapolated to other countries using a 2-dose vaccine schedule with low to moderate vaccine performance.

Effect of dietary supplementation of plantain herb, lemongrass and their combination on milk yield, immunity, liver enzymes, serum, and milk mineral status in dairy cows.

Objective: This research aimed to assess the effects of dried plantain herb, lemongrass, and their combination on milk yield, immunological, liver enzymatic, serum, and milk mineral status in dairy cows. Materials and Methods: Twenty cows were arbitrarily assigned to 4 diets. Cows were given a basal ration considered as control diet (CL-D) having 14.93% crude protein (CP)and 10.96 MJ ME per kg dry matter (DM). Each cow was given 100 gm plantain, 100 gm lemongrass, and 50 gm plantain + 50 gm lemongrass with CL-D and taken as plantain diet (PT-D), lemongrass diet (LG-D), and plantain-lemongrass diet (PL-D), daily for 63 days, respectively. Blood and milk samples were taken four times at an interval of 14 days. Data were analyzed using a two-way repeated measures analysis of covariance. Results: Better DM consumption and milk yield were observed in the PT-D and LG-D compared to the CL-D (p ≤ 0.05). LG-D improved the milk's total solids, protein, and fat compared to CL-D (p < 0.05). Substantially, herbal groups improved serum albumin and reduced globulin concentrations compared to CL-D. LG-D had the highest serum immunoglobulin G, while herbal groups effectively reduced the liver enzymes compared to CL-D. Herbal groups did not affect serum and milk's calcium and phosphorus concentrations, while LG-D and PL-D substantially improved serum and milk zinc concentrations. Conclusions: Both plantain and lemongrass improved dairy cows' DM consumption and milk yield. Plantain and/or lemongrass enhanced the immune system and liver health, but not serum and milk calcium and phosphorus level. Lemongrass and a combination of plantain and lemongrass increased the serum and milk zinc concentrations.

Health Literacy Among Non-Communicable Disease Service Seekers: A Nationwide Finding from Primary Health Care Settings of Bangladesh.

BACKGROUND: Considering the health literacy status of service seekers is crucial while developing programs and policies to improve service delivery in primary health care settings. OBJECTIVE: Our aim was to assess health literacy among adults seeking non-communicable disease (NCD)-related services in primary health care centers (PHC) of Bangladesh and identify its contributing factors and its preventive effect on risky behaviors. METHODS: In this cross-sectional study, 2,793 NCD service seekers were interviewed face-to-face from eight rural and three urban PHCs selected by a multi-stage random sampling method. We used the European Health Literacy Survey Questionnaire to collect data on health literacy. We applied logistic regression analysis to identify the contributing factors related to adequate health literacy. Odds ratios were used to calculate the preventive fraction of health literacy for NCD risk behaviors. KEY RESULTS: Limited health literacy was found among 43% of the respondents. Adequate health literacy was associated with younger age, male sex, having a formal education, living in an extended family, hailing from a high socioeconomic group, and attending urban PHC. After adjusting the sociodemographic factors, the prevalence of smoking, smokeless tobacco usage, and inadequate fruits and vegetables consumption among participants were found to be 25%, 51%, and 18% lower for people with sufficient health literacy. CONCLUSIONS: NCD service seekers have a high rate of inadequate health literacy. Adequate health literacy has the potential to lower the behavioral risk factors of NCDs. [HLRP: Health Literacy Research and Practice. 2024;8(1):e12-e20.].

PLAIN LANGUAGE SUMMARY: This study is the first to address the knowledge gap regarding the state of NCD-related health literacy in Bangladesh. The findings of this study can be used by policymakers to create initiatives that will improve the health literacy of people seeking primary health care for NCDs.

In-situ confining selenium within bubble - like carbon nanoshells for ultra-stable Li-Se batteries.

Lithium-selenium (Li-Se) batteries are promising energy storage devices. However, the long-term durability and high-rate performance of the Se cathode have been limited by significant volume expansion and the troublesome shuttle effect of polyselenides during repeated charging/discharging processes. To revolutionize these issues, we applied a top-down strategy through the in-situ trapping of amorphous Se within bubble-like carbon (BLC) frameworks, which can radically minimize the presence of surface-absorbed Se while enhancing Se loading capacity. This ingenious technique successfully encapsulates all Se species within carbon nanoshells, creating a distinct half-filled core-shell structure known as Se@void@BLC. This in-situ trapping approach ensures the efficient management of Se volume changes during repeated discharge and charge cycles. Moreover, an extraordinary Se loading capacity of up to 65.6 wt% is reached. Using the Se@void@BLC as cathode for Li-Se battery, we achieve a high initial Columbic efficiency of 84.2 %, a high reversible capacity of 585 mAh g-1, and an ultralow capacity decay of only 0.0037 % per cycle during 4000 cycles at 10 A g-1.

Migrant workers with COVID-19: a major challenge for Gulf Cooperation Council (GCC) countries to curb the spread of infection.

The six Gulf Cooperation Council (GCC) countries (Saudi Arabia, the United Arab Emirates, Bahrain, Kuwait, Oman and Qatar) host the majority of the estimated 23 million international migrants working in the Arab states. As the COVID-19 pandemic continues to evolve across the GCC states, the health authorities have reported a considerable number of non-national confirmed COVID-19 cases in the region. In Gulf countries, where more than half of the population are foreigners, migrant workers are more likely to contract and spread the disease due to numerous contributing factors. In this regard, unhygienic and overcrowded living conditions, barriers in accessing national or private health services, challenges in accessing accurate health information related to COVID-19, and lack of facemasks and hand hygiene facilities in their housing camps are the major factors that we identified and discuss in this paper. Moreover, we formulated specific recommendations for relevant authorities to overcome the challenges related to migrant workers during this pandemic situation. Because the migrant workers with COVID-19 infection could subsequently lead to more widespread community transmission, protecting this vulnerable group means reducing the risk of transmission for the entire population. It is essential to include migrant workers in all aspects of the response to COVID-19, such as prevention, detection, access to treatment, and containment measures.

Commonly consumed processed packaged foods in Bangladesh are unhealthy and their nutrient contents are not in conformity with the label declaration.

The present study was undertaken to identify the major nutrient content in processed foods commonly consumed in Bangladesh, their label conformity healthiness, and percent daily nutrient contribution. Twenty-four nationally representative composite samples were analyzed using AOAC and other standard methods. Results were compared with label information using a restrictive approach and EU tolerance guidelines. The healthiness of the products was evaluated in terms of the Health Star Rating (HSR) scheme and the UK traffic light labeling system. Among the analyzed samples, fried pulse, chanachur, lozenge, and fried peas had the highest amount of protein, fat, carbohydrates, and dietary fiber, respectively. Biscuits and milk chocolate had high levels of trans fatty acids (TFA) and saturated fatty acids (SFA). It was observed that around half of the products lacked information about saturated fatty acid (46%), followed by total dietary fiber and trans-fat (38% each). Other information was missing in one-fifth of the products, namely protein (17%), total fat (17%), available carbohydrate (17%), energy (17%), sugar (21%), and salt (21%). Label compliance analysis according to the restrictive approach revealed that none of the products accurately reported the salt, sugar, saturated fat, and trans fat content on the label. According to the EU tolerance guideline, approximately half of the products had protein (58%), fat (54%), and carbohydrate (42%) levels that fell within the EU tolerance limit. However, only around one-third of the samples had sugar (21%), salt (38%), and saturated fat (33%) levels that met the EU tolerance limit. In terms of healthiness analysis, according to the HSR, the range of stars was between 0.5 and 2.5 of the foods where fried peas got the highest rating (2.5 stars), while in terms of the UK traffic light system, none of the samples got all green signals. The lozenge got green lights for fat, SFA, and salt contents. It was also found that consumption of 100 g of fried peas or pulse would exceed the acceptable daily limit of salt, sugar, and SFA compared to the daily maximum allowable intake for the 2000 kcal diet recommended by the WHO. However, according to the serving size, biscuits were major contributors of TFA, sugar, and SFA, whereas fried pulse was a key contributor of sodium/salt. Proper regulatory actions should be introduced to promote healthy processed foods with user-friendly front-of-the-pack labeling and monitor their quality to prevent non-communicable diseases (NCDs).

Factors Influencing the Medication Adherence in Adults With Attention-Deficit/Hyperactivity Disorder (ADHD) and Its Impact on Healthcare Utilization.

OBJECTIVE: This study aims to investigate the factors influencing medication adherence among adults with Attention-Deficit/Hyperactivity Disorder (ADHD) and impact of central nervous system stimulants (CNS) adherence on healthcare utilization (HCU). Methods: This was a cross-sectional study using Medical Expenditure Panel Survey 2013 to 2019, with participants (≥18 years of age) with ADHD and had at least one CNS prescription. Multivariate logistic and linear regression were utilized to evaluate the medication adherence and its impact on HCU, respectively. RESULT: Total 798 (10,718,005 weighted) ADHD patients, were mostly White (81%), aged between 18 and 25 (35%), and non-adherent to CNS (65%). The use of extended-release medications (OR = 1.51 [1.03, 2.23]) and new users (OR = 3.46 [2.12, 5.63], p ≤ .05) were positively associated with medication adherence. The adherent group utilized more outpatient visits (0.04 vs. 0.46) and prescription refills (18.38 vs. 31.25) compared to non-adherent. CONCLUSION: Our findings can be applied to improve the medication adherence, patient education, and optimize intervention for adults with ADHD.

Recognition of Pediatric Congenital Heart Diseases by Using Phonocardiogram Signals and Transformer-Based Neural Networks.

The phonocardiogram (PCG) or heart sound auscultation is a low-cost and non-invasive method to diagnose Congenital Heart Disease (CHD). However, recognizing CHD in the pediatric population based on heart sounds is difficult because it requires high medical training and skills. Also, the dependency of PCG signal quality on sensor location and developing heart in children are challenging. This study proposed a deep learning model that classifies unprocessed or raw PCG signals to diagnose CHD using a one-dimensional Convolution Neural Network (1D-CNN) with an attention transformer. The model was built on the raw PCG data of 484 patients. The results showed that the attention transformer model had a good balance of accuracy of 0.923, a sensitivity of 0.973, and a specificity of 0.833. The Receiver Operating Characteristic (ROC) plot generated an Area Under Curve (AUC) value of 0.964, and the F1-score was 0.939. The suggested model could provide quick and appropriate real-time remote diagnosis application in classifying PCG of CHD from non-CHD subjects.Clinical Relevance- The suggested methodology can be utilized to analyze PCG signals more quickly and affordably for rural doctors as a first screening tool before sending the cases to experts.

The association between polypharmacy and health-related quality of life among non-dialysis chronic kidney disease patients.

BACKGROUND AND OBJECTIVE: The United States government spends over $85 billion annually on treating non-dialysis chronic kidney disease (CKD). Patients with CKD are prescribed a multitude of medications to manage numerous comorbidities associated with CKD. Thus, this study aims to investigate the association between polypharmacy and health-related quality of life (HRQoL) in non-dialysis CKD patients. METHODS: This cross-sectional study utilized data from the Medical Expenditure Panel Survey (MEPS) from 2010 through 2019. We classified polypharmacy into three groups based on the number of medication classes: ≤ 4 (minor polypharmacy), 5 through 9 (major polypharmacy), and ≥ 10 (hyperpolypharmacy). To measure HRQoL, a Physical Component Summary (PCS) and a Mental Component Summary (MCS) were obtained from the 12-item Short-Form Health Survey version 2 and Veteran's Rand 12 item. We applied multivariable ordinary least squares regression to assess the association between polypharmacy and HRQoL in non-dialysis CKD patients. RESULTS: A total of 649 CKD patients (weighted n = 667,989) were included. Patients with minor polypharmacy, major polypharmacy, and hyperpolypharmacy were 22.27%, 48.24%, and 29.48%, respectively. Major polypharmacy and hyperpolypharmacy were significantly and negatively associated with lower PCS scores when compared with minor polypharmacy [Beta = -3.12 (95% CI: -3.62, -2.62), p-value<0.001; Beta = -4.13 (95CI: -4.74, -3.52), p-value<0.001]. Similarly, major polypharmacy and hyperpolypharmacy were significantly and negatively associated with lower MCS scores when compared to minor polypharmacy [Beta = -0.38 (95% CI: -0.55, -0.20), p-value<0.001; Beta = -1.70 (95% CI: -2.01, -1.40), p-value<0.001]. The top 5 classes of medications used by CKD patients were antihyperlipidemic (56.31%), beta-adrenergic blockers (49.71%), antidiabetics (42.14%), analgesics (42.17%), and diuretics (39.65%). CONCLUSION: Our study found that both major polypharmacy and hyperpolypharmacy were associated with lower HRQoL among non-dialysis CKD patients. This study highlights the need for further evaluation of the combination of medications taken by non-dialysis CKD patients to minimize unnecessary and inappropriate medication use.

Thiamine utilization and the lack of prescribing standardization: A critical examination.

OBJECTIVES: Thiamine is often prescribed for thiamine deficiency during hospitalization despite the lack of US-based clinical guidelines. This study aims to evaluate thiamine prescribing patterns and key characteristics associated with the deficiency to address gaps in care. METHODS: Data were obtained from electronic health records of hospitalized patients between September 1, 2021, and March 30, 2022. Alcohol use disorder (AUD) was defined by a positive Clinical Institute Withdrawal Assessment score or a positive serum alcohol level upon admission. Geriatric patients were defined as age ≥65. Cohort 1 was defined as: AUD, albumin <4 g/L, INR >1.5, and total bilirubin >3 mg/dL. Cohort 2 was defined as: age >65, albumin <4 g/L, hemoglobin <15 g/dL, and folate <4 ng/mL. A multivariable LASSO regression model was used to identify characteristics associated with higher thiamine dosing (>100 mg/day). RESULTS: Among 780 patients, 520 (66.7%) were identified as AUD, of which 265 (50.1%) were between the ages of 45-64 years. The AUD cohort was significantly different (p < 0.05) in the mean serum albumin 4.16 g/L (IQR: 3.8-4.5), AST 73.55 U/L (23.75-82.00), ALT 52.57 U/L (17.00-57.00), total bilirubin 0.98 (0.3-1.0), and INR 1.1 (0.99-1.12), compared to non-AUD patients with a mean serum albumin 3.75 g/L (3.3-4.2), AST 35.07 U/L (11.00-42.00), ALT 32.77 U/L (5.00-34.00), total bilirubin 0.89 (0.2-0.9), and INR 1.21 (1.0-1.22). In the geriatric cohort, 136 patients (17%) had a mean serum albumin 3.77 g/L (3.4-4.2), AST 38.66 U/L (14.0-41.0), ALT 29.36 U/L (9.0-37.0), total bilirubin 0.62 mg/dL (0.30-0.90), and direct bilirubin 0.12 mg/dL (0.00-0.20), compared to the non-geriatric cohort with a mean serum albumin 4.10 g/L (3.8-4.40), AST 66.44 U/L (21.0-75.0), ALT 50.03 U/L (16.00-53.75), total bilirubin 1.02 mg/dL (0.30-1.00), and direct bilirubin 0.31 mg/dL (0.00-0.20). In cohort 1, 40.6% patients were between 51 and 64 years old, (66.5%) male, and had a BMI <25 (36.4%). In cohort 2, 52.6% were between 65 and 70 years old, (57.9%) male, and had a BMI <25 (57.9%). Cohort 1 were prescribed a dose of 100 mg (47.7 %), oral (63.5%), intramuscular (18.2%), daily (58.9%), one-day duration (49.4%) most frequently. Cohort 2 were prescribed a dose of 100 mg (56.0%), oral (77.2%), daily (77.2%), one-day duration (29.8%) most frequently. The AUD was significantly associated with having a higher dosage (e.g., >100 mg) of thiamine prescribed per day OR 1.62 (1.11-2.37) (p < 0.01). CONCLUSIONS: This study confirms that thiamine prescribing patterns vary during hospitalization and suggest specific laboratory findings may aid in identifying cohorts associated with the deficiency.

Opioid-related polysubstance use and its effect on mortality and health resource utilization among trauma patients.

BACKGROUND: Pre-injury opioid use is common, but the effects of opioid-related polysubstance use on mortality and health resources utilization (HRU) have not been investigated yet. The objective of this study was to investigate the effects of opioid-related polysubstance use on mortality and HRU among patients in trauma centres in the US. METHODS: We conducted a retrospective cross-sectional study using the US National Trauma Databank from the year 2017 to 2019. Patients (≥ 18 years of age) who tested positive for opioids were included. Patients were analysed based on the number of substances used (i.e., opioids only, two substances (opioids + 1 substance), and three or more than three substances (opioids + ≥ 2 substances)), and polysubstance by type (i.e., opioids only, opioids and alcohol, opioids and stimulants, opioids and benzodiazepine, and other combinations). Multivariate logistic regression was used to determine the association between polysubstance use, mortality and HRU (i.e., need for hospital admission, ICU, and mechanical ventilation). RESULTS: Both polysubstance by number and type analyses showed that opioid-related polysubstance use was not significantly associated with mortality compared to opioids only. The odds of hospital admission were higher among the opioids and benzodiazepines group (OR 1.15, 95% CI 1.06-1.24, p < 0.01). The need for ICU was magnified using benzodiazepines and stimulants with opioids (OR 1.44, 95% CI 1.27-1.63, p < 0.01) when compared to the opioids only group. CONCLUSION: Opioid-related pre-injury polysubstance use was associated with higher HRU in trauma patients. The evidence can be used by policymakers and practitioners to improve patient outcomes in trauma centers.

A Federated Learning Model Based on Hardware Acceleration for the Early Detection of Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive illness with a slow start that lasts many years; the disease's consequences are devastating to the patient and the patient's family. If detected early, the disease's impact and prognosis can be altered significantly. Blood biosamples are often employed in simple medical testing since they are cost-effective and easy to collect and analyze. This research provides a diagnostic model for Alzheimer's disease based on federated learning (FL) and hardware acceleration using blood biosamples. We used blood biosample datasets provided by the ADNI website to compare and evaluate the performance of our models. FL has been used to train a shared model without sharing local devices' raw data with a central server to preserve privacy. We developed a hardware acceleration approach for building our FL model so that we could speed up the training and testing procedures. The VHDL hardware description language and an Altera 10 GX FPGA are utilized to construct the hardware-accelerator approach. The results of the simulations reveal that the proposed methods achieve accuracy and sensitivity for early detection of 89% and 87%, respectively, while simultaneously requiring less time to train than other algorithms considered to be state-of-the-art. The proposed algorithms have a power consumption ranging from 35 to 39 mW, which qualifies them for use in limited devices. Furthermore, the result shows that the proposed method has a lower inference latency (61 ms) than the existing methods with fewer resources.

Salt Intake Estimation from Urine Samples in South Asian Population: Scoping Review.

The World Health Organization recommended reducing one's salt intake below 5 g/day to prevent disability and death from cardiovascular and other chronic diseases. This review aimed to identify salt estimation at the population level in South Asian countries, namely Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka. We searched electronic databases and government websites for the literature and reports published between January 2011 and October 2021 and also consulted key informants for unpublished reports. We included studies that assessed salt intake from urinary sodium excretion, either spot urine or a 24 h urine sample, on a minimum of 100 samples in South Asian countries. We included 12 studies meeting the criteria after screening 2043 studies, out of which five followed nationally representative methods. This review revealed that salt intake in South Asian countries ranges from 6.7-13.3 g/day. The reported lowest level of salt intake was in Bangladesh and India, and the highest one was in Nepal. The estimated salt intake reported in the nationally representative studies were ranging from 8 g/day (in India) to 12.1 g/day (in Afghanistan). Salt consumption in men (8.9-12.5 g/day) was reported higher than in women (7.1-12.5 g/day). Despite the global target of population salt intake reduction, people in South Asian countries consume a much higher amount of salt than the WHO-recommended level.

Assessment of atrial septal defects using 3-dimensional transthoracic echocardiography prior to percutaneous device closure: first report from Bangladesh.

BACKGROUND: Secundum atrial septal defect (ASD) is treated following trans-catheter closure in alternative to surgical treatment. Per-intervention selection of device size with balloon occlusive diameter (BOD) often cause tearing or enlarging, causing arrhythmias and hypotension. We assessed the suitability of percutaneous device closure for ASD using 3-dimensional transthoracic echocardiography (3DTTE). OBJECTIVES: This study was conducted to investigate if 3DTTE could be an alternative of balloon sizing for selection of device size in atrial septal defect device closure. DESIGN: It was a cross-sectional comparative study. METHODS: This study was conducted at the department of Pediatric Cardiology, Bangabandhu Sheikh Mujib Medical University for a period of 2 years. Thirty-three purposively selected secundum ASD patients suitable for device closure were included in the study. Ethical permission was taken from the Institutional Review Board and written consent was taken from each patient's guardian. In this study, 3DTTE derived ASD diameter and BOD were compared with that of deployed device size using correlation analysis. RESULTS: Out of 33 patients, 63.6% were female and 36.4% were males had a mean age of 18.07 ± 14.58 years (range 2-55 years). Mean diameter of ASD measured by 2-dimensional (2D) and 3-dimensional (3D) echocardiography were 17.09 ± 6.08 mm and 21.30 ± 6.56 mm, respectively, yielding a significant difference (p < 0.001). 3D echocardiography derived ASDs diameter were highly correlated with device size than BOD and 2D echocardiography derived diameter (2D echocardiography: r = 0.796, p = <0.001, 3D echocardiography: r = 0.960, p = <0.001, BOD: r = 0.840, p = <0.001). CONCLUSION: 3DTTE can accurately measure ASD diameter and can be used as an alternate, effective, and safe method to select device size.

SeniorSentry: Correlation and Mutual Information-Based Contextual Anomaly Detection for Aging in Place.

With the ever-growing reliance on IoT-enabled sensors to age in place, a need arises to protect them from malicious actors and detect malfunctions. In an IoT smart home, it is reasonable to hypothesize that sensors near one another can exhibit linear or nonlinear correlations. If substantiated, this property can be beneficial for constructing relationship trends between the sensors and, consequently, detecting attacks or other anomalies by measuring the deviation of their readings against these trends. In this work, we confirm the presence of correlations between co-located sensors by statistically analyzing two public smart-home datasets and a dataset we collected from our experimental setup. Additionally, we leverage the sliding window approach and supervised machine learning to develop a contextual-anomaly-detection model. This model reaches a true positive rate of 89.47% and a false positive rate of 0%. Our work not only substantiates the correlations but also introduces a novel anomaly-detection technique to enhance security in IoT smart homes.

Rational Design of N-Doped Carbon-Coated Cobalt Nanoparticles for Highly Efficient and Durable Photothermal CO2 Conversion.

Photothermal CO2 hydrogenation to high-value-added chemicals and fuels is an appealing approach to alleviate energy and environmental concerns. However, it still relies on the development of earth-abundant, efficient, and durable catalysts. Here, the design of N-doped carbon-coated Co nanoparticles (NPs), as a photothermal catalyst, synthesized through a two-step pyrolysis of Co-based ZIF-67 precursor, is reported. Consequently, the catalyst exhibits remarkable activity and stability for photothermal CO2 hydrogenation to CO with a 0.75 mol gcat -1 h-1 CO production rate under the full spectrum of light illumination. The high activity and durability of these Co NPs are mainly attributed to the synergy of the attuned size of Co NPs, the thickness of carbon layers, and the N doping species. Impressively, the experimental characterizations and theoretical simulations show that such a simple N-doped carbon coating strategy can effectively facilitate the desorption of generated CO and activation of reactants due to the strong photothermal effect. This work provides a simple and efficient route for the preparation of highly active and durable nonprecious metal catalysts for promising photothermal catalytic reactions.

Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network.

Heart disease is a significant public health problem, and early detection is crucial for effective treatment and management. Conventional and noninvasive techniques are cumbersome, time-consuming, inconvenient, expensive, and unsuitable for frequent measurement or diagnosis. With the advance of artificial intelligence (AI), new invasive techniques emerging in research are detecting heart conditions using machine learning (ML) and deep learning (DL). Machine learning models have been used with the publicly available dataset from the internet about heart health; in contrast, deep learning techniques have recently been applied to analyze electrocardiograms (ECG) or similar vital data to detect heart diseases. Significant limitations of these datasets are their small size regarding the number of patients and features and the fact that many are imbalanced datasets. Furthermore, the trained models must be more reliable and accurate in medical settings. This study proposes a hybrid one-dimensional convolutional neural network (1D CNN), which uses a large dataset accumulated from online survey data and selected features using feature selection algorithms. The 1D CNN proved to show better accuracy compared to contemporary machine learning algorithms and artificial neural networks. The non-coronary heart disease (no-CHD) and CHD validation data showed an accuracy of 80.1% and 76.9%, respectively. The model was compared with an artificial neural network, random forest, AdaBoost, and a support vector machine. Overall, 1D CNN proved to show better performance in terms of accuracy, false negative rates, and false positive rates. Similar strategies were applied for four more heart conditions, and the analysis proved that using the hybrid 1D CNN produced better accuracy.

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System.

Advancements in digital communications that permit remote patient visits and condition monitoring can be attributed to a revolution in digital healthcare systems. Continuous authentication based on contextual information offers a number of advantages over traditional authentication, including the ability to estimate the likelihood that the users are who they claim to be on an ongoing basis over the course of an entire session, making it a much more effective security measure for proactively regulating authorized access to sensitive data. Current authentication models that rely on machine learning have their shortcomings, such as the difficulty in enrolling new users to the system or model training sensitivity to imbalanced datasets. To address these issues, we propose using ECG signals, which are easily accessible in digital healthcare systems, for authentication through an Ensemble Siamese Network (ESN) that can handle small changes in ECG signals. Adding preprocessing for feature extraction to this model can result in superior results. We trained this model on ECG-ID and PTB benchmark datasets, achieving 93.6% and 96.8% accuracy and 1.76% and 1.69% equal error rates, respectively. The combination of data availability, simplicity, and robustness makes it an ideal choice for smart healthcare and telehealth.

Metal contamination in soil and vegetables around Savar tannery area, Dhaka, Bangladesh: A preliminary study for risk assessment.

Monitoring of heavy metal concentrations in soil and their accumulation in vegetables grown in the newly shifted tannery area of Savar, Bangladesh, is crucial for human health. Heavy metals (i.e., Cr, Pb, Cu, Zn, Ni, and Cd) concentrations in soil and vegetable samples were determined by atomic absorption spectrophotometer (AAS). In soil, 3220 mg/kg Cr was observed, which was 32-fold greater than the WHO/FAO recommended limit. Ecological risk indices such as the contamination factor, enrichment factor, pollution load index, and geoaccumulation index showed metal levels as moderately to very highly contaminated. The non-carcinogenic risk (NCR) was found to be higher, and the carcinogenic risk (CR) exceeded the acceptable value 1 × 10-6 and posed greater risks to children than adults, especially for Cr in soil. The main exposure pathway for soil metals was 97.8-99.9% due to oral ingestion. The concentration of heavy metals especially Cr, Pb, Zn, and Cd, in vegetables was alarming as they crossed the safety limit. The calculated mean hazard index (8.71) for vegetable samples showed elevated levels of potential NCR, while CR for Cr and Cd, exceeded the acceptable limit of 1 × 10-6, indicating the probability of cancer risk to humans through the consumption of vegetables. This study revealed a to-and-fro analysis of the present scenario of the tannery area, giving importance to human health and the environment.

Medication Regimen Complexity Index Score at Admission as a Predictor of Inpatient Outcomes: A Machine Learning Approach.

BACKGROUND: In the intensive care unit, traditional scoring systems use illness severity and/or organ failure to determine prognosis, and this usually rests on the patient's condition at admission. In spite of the importance of medication reconciliation, the usefulness of home medication histories as predictors of clinical outcomes remains unexplored. METHODS: A retrospective cohort study was conducted using the medical records of 322 intensive care unit (ICU) patients. The predictors of interest included the medication regimen complexity index (MRCI) at admission, the Acute Physiology and Chronic Health Evaluation (APACHE) II, the Sequential Organ Failure Assessment (SOFA) score, or a combination thereof. Outcomes included mortality, length of stay, and the need for mechanical ventilation. Machine learning algorithms were used for outcome classification after correcting for class imbalances in the general population and across the racial continuum. RESULTS: The home medication model could predict all clinical outcomes accurately 70% of the time. Among Whites, it improved to 80%, whereas among non-Whites it remained at 70%. The addition of SOFA and APACHE II yielded the best models among non-Whites and Whites, respectively. SHapley Additive exPlanations (SHAP) values showed that low MRCI scores were associated with reduced mortality and LOS, yet an increased need for mechanical ventilation. CONCLUSION: Home medication histories represent a viable addition to traditional predictors of health outcomes.