Managing energy transition alongside environmental protection by making use of AI-led butanol powered SI engine optimization in compliance with SDGs.

Enormous consumption of fossil fuel resources has risked energy accessibility in the upcoming years. The price fluctuation and depletion rate of fossil fuels instigate the urgent need for searching their reliable substitute. The current study tries to address these issues by presenting butanol as a replacement for gasoline in SI engines at various speeds and loading conditions. The emission and performance parameters were ascertained for eight distinct butanol-gasoline fuel blends. The oxygenated butanol substantially increases engine efficiency and boosts power with lower fuel consumption. The carbon emissions were also observed to be lower in comparison with gasoline. Furthermore, the Artificial Intelligence (AI) approach was used in predicting engine performance running on the butanol blends. The correlation coefficients for the data training, validation, and testing were found to be 0.99986, 0.99942, and 0.99872, respectively. It was confirmed that the ANN predicted results were in accordance with the established statistical criteria. ANN was paired with Response Surface Methodology (RSM) technique to comprehend the influence of the sole design parameters along with their statistical interactions controlling the responses. Similarly, the R2 value of responses in case of RSM were close to unity and mean relative errors (MRE) were confined under specified range. A comparative study between ANN and RSM models unveiled that the ANN model should be preferred. Therefore, a joint utilization of the RSM and ANN can be more effective for reliable statistical interactions and predictions.

Understanding clinical outcomes and factors influencing mortality in intensive care unit patients with COVID-19-associated candidemia.

BACKGROUND: During the COVID pandemic, research has shown an increase in candidemia cases following severe COVID infection and the identification of risk factors associated with candidemia. However, there is a lack of studies that specifically explore clinical outcomes and mortality rates related to candidemia after COVID infection. OBJECTIVES: The aim of this international study was to evaluate the clinical outcomes and identify factors influencing mortality in patients who developed candidemia during their COVID infection. PATIENTS/METHODS: This study included adult patients (18 years of age or older) admitted to the intensive care unit (ICU) and diagnosed with COVID-associated candidemia (CAC). The research was conducted through ID-IRI network and in collaboration with 34 medical centres across 18 countries retrospectively, spanning from the beginning of the COVID pandemic until December 2021. RESULTS: A total of 293 patients diagnosed with CAC were included. The median age of the patients was 67, and 63% of them were male. The most common Candida species detected was C. albicans. The crude 30-day mortality rate was recorded at 62.4%. The logistic regression analysis identified several factors significantly impacting mortality, including age (odds ratio [OR] 1.04, 95% confidence interval [CI] 1.02-1.07, p < .0005), SOFA score (OR 1.307, 95% CI 1.17-1.45, p < .0005), invasive mechanical ventilation (OR 7.95, 95% CI 1.44-43.83, p < .017) and duration of mechanical ventilation (OR 0.98, 95% CI 0.96-0.99, p < .020). CONCLUSIONS: By recognising these prognostic factors, medical professionals can customise their treatment approaches to offer more targeted care, leading to improved patient outcomes and higher survival rates for individuals with COVID-associated candidemia.

Ferrocene-Boosted Nickel Sulfide Nanoarchitecture for Enhanced Alkaline Water Splitting.

Enhanced electrocatalysts that are cost-effective, durable, and derived from abundant resources are imperative for developing efficient and sustainable electrochemical water-splitting systems to produce hydrogen. Therefore, the design and development of non-noble-based catalysts with more environmentally sustainable alternatives in efficient alkaline electrolyzers are important. This work reports ferrocene (Fc)-incorporated nickel sulfide nanostructured electrocatalysts (Fc-NiS) using a one-step facile solvothermal method for water-splitting reactions. Fc-NiS exhibited exceptional electrocatalytic activity under highly alkaline conditions, evident from its peak current density of 345 mA cm-2 , surpassing the 153 mA cm-2 achieved by the pristine nickel sulfide (NiS) catalysts. Introducing ferrocene enhances electrical conductivity and facilitates charge transfer during water-splitting reactions, owing to the inclusion of iron metal. Fc-NiS exhibits a very small overpotential of 290 mV at 10 mA cm-2 and a Tafel slope of 50.46 mV dec-1 , indicating its superior charge transfer characteristics for the three-electron transfer process involved in water splitting. This outstanding electrocatalytic performance is due to the synergistic effects embedded within the nanoscale architecture of Fc-NiS. Furthermore, the Fc-NiS catalyst also shows a stable response for the water-splitting reactions. It maintains a steady current density with an 87% retention rate for 25 hours of continuous operation, indicating its robustness and potential for prolonged electrolysis processes.

Diagnosis of behavioral symptoms as a predictor of institutionalization among Medicaid patients with dementia.

OBJECTIVES: Behavioral symptoms are commonly observed in the course of dementia. This study aimed to assess the association of the diagnosis of a cluster of behavioral symptoms (e.g., agitation, aggression, psychotic symptoms, and delirium/wandering) with the likelihood of subsequent institutionalization. METHODS: A retrospective cohort study of adults aged 65 and above diagnosed with dementia identified in the IBM® MarketScan® Multistate Medicaid database between October 01, 2015, and September 30, 2019, was conducted. The index date was defined as the first diagnosis date of dementia. The presence or absence of behavioral symptoms was identified in the 6 months prior to the index date (baseline). Institutionalization was evaluated 12 months (follow-up) post the index date. The association between diagnosed behavioral symptoms during the baseline period and institutionalization in the follow-up period was assessed using a multivariable logistic regression, adjusting for baseline sociodemographic and clinical characteristics. RESULTS: The study cohort included 40,714 patients with dementia. A diagnosis of behavioral symptoms was found among 2,067 (5.1%) patients during the baseline period. An increased likelihood of institutionalization was found during the follow-up among patients with agitation and aggression in baseline (OR = 1.51 (95% CI: 1.18-1.92)) compared to patients without these symptoms at baseline. Patients with psychotic symptoms in baseline had significantly higher odds of getting institutionalized during the follow-up compared to patients without psychotic symptoms in baseline (OR = 1.36 (95% CI: 1.20-1.54)). Similarly, patients with symptoms of delirium and wandering in baseline had a higher likelihood of institutionalization than patients without these symptoms at baseline (OR = 1.61 (95% CI: 1.30-1.99)). CONCLUSION: Several diagnosed behavioral symptoms were associated with a higher risk of institutionalization among older adults with dementia and should be considered when planning treatment strategies for the effective management of the condition.

A trend analysis of Black American women with cardiovascular disease and chronic medical conditions, sociodemographic factors from NHANES From 2011 to 2020.

BACKGROUND: Significant racial and gender differences exist in the prevalence of CVD in the United States. The goal of this study is to evaluate the prevalence of CVD among Black American women, the relationship between CVD and some medical conditions, and significant sociodemographic factors. METHODS: The researchers in this study used data from four cycles of the NHANES, carried out by the NCHS. 2011 to 2012, 2013 to 2014, 2015 to 2016, and 2017 to 2020 were the cycles that were chosen. The researchers used the survey package in the R programming language to examine the data. RESULTS: People with CVD problems 20 years of age and older were included in the analyses. Black American women experienced a considerable prevalence of CVD from 2011 to 2020. These women were more likely to report having completed no more schooling than the ninth grade, being widowed/divorced/separated, and having undergone a hysterectomy, as well as having a history of diabetes, asthma, obesity, arthritis, and depression. Black American women with CVD had a 3.8-fold increased risk of diabetes and a 5.6-fold increased risk of arthritis. CONCLUSION: This study shows that Black American women with CVD are more likely to have chronic illnesses such as hysterectomy, diabetes, asthma, obesity, arthritis, and depression. Black American women's cardiovascular risk profiles can be updated using the data from this study.

Assessment of biosecurity measures in commercial poultry farms of Rajshahi district in Bangladesh.

The application of biosecurity measures in the poultry sector are essential. Developing and implementing daily biosecurity activities are considered effective methods to prevent infectious diseases on poultry farms. Appropriate farm settings and management reduce the possibility of disease transmission between farms. This study aimed to assess the current biosecurity status of commercial poultry farms in the Rajshahi district of Bangladesh. A cross-sectional study was conducted from June to November 2021, involving 204 poultry farmers in the Rajshahi district, using a multistage sampling technique. Data were collected through face-to-face interviews using a semi-structured questionnaire. The statistically significant (p < 0.05) results found were: most farmers reported that the visitors could not access their farms (broiler vs. layer: 68.1%, 77.0%). A higher proportion of broilers compared to layer farms used company's supplied feed (81.2% vs. 66.7%). A higher proportion of layer (97.8%) farms cleaned the farm's litter within 24 h compared to broiler farms (72.5%). Broiler farms had better records animal mortality than layer farms (76.8% vs. 60.0%). More layer (96.3%) farms compared to broiler (87.0%) farms implemented a vaccination program. The findings of this study revealed that biosecurity measures are well-practiced and implemented in most broiler and layer farms in this region except in a small number of farms. Government authorities should strictly enforce and monitor biosecurity measures in farms that do not practice essential biosecurity measures.

Applied artificial intelligence: Acceptance-intention-purchase and satisfaction on smartwatch usage in a Ghanaian context.

Technology and its continuous advancement facilitate human beings to get rid of their criticality and limitation. Applied artificial intelligence (AAI) is one of the latest forms that delimited the limitation of human beings. Smartwatch acts as an applied artificial intelligence to assist various patients to check medical care without going to hospital and physicians. This (three) multiple-study research focused on the intention to use, purchase, and their satisfaction and spread positive word of mouth among others in the Ghanaian. To investigate these issues two renowned theories were underpinned- TAM theory and the Stimulus-Organism-Response (S-O-R). Total 550, 320, and 170 respondents were interviewed with Google forms due to COVID-19 using social media. AI-enabled smartwatch considering Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Perceived Credibility (PC), Perceived Self-Efficacy (PSE), and Perceived Financial Cost (PFC) were significant on intention to adoption and adoption intention on actual purchase. The final study showed device quality, its service level, their usage experience, perceived value, and the extent to which the satisfied customers made positive word of mouth to their friends and family, colleagues and followers. This research is significant in understanding the usage of AI-enabled smartwatches as a device doctor or electronic doctor (e-doctor).

Prospective analysis of febrile neutropenia patients with bacteraemia: the results of an international ID-IRI study.

OBJECTIVES: Bacteraemia during the course of neutropenia is often fatal. We aimed to identify factors predicting mortality to have an insight into better clinical management. METHODS: The study has a prospective, observational design using pooled data from febrile neutropenia patients with bacteraemia in 41 centres in 16 countries. Polymicrobial bacteraemias were excluded. It was performed through the Infectious Diseases-International Research Initiative platform between 17 March 2021 and June 2021. Univariate analysis followed by a multivariate binary logistic regression model was used to determine independent predictors of 30-d in-hospital mortality (sensitivity, 81.2%; specificity, 65%). RESULTS: A total of 431 patients were enrolled, and 85 (19.7%) died. Haematological malignancies were detected in 361 (83.7%) patients. Escherichia coli (n = 117, 27.1%), Klebsiellae (n = 95, 22% %), Pseudomonadaceae (n = 63, 14.6%), Coagulase-negative Staphylococci (n = 57, 13.2%), Staphylococcus aureus (n = 30, 7%), and Enterococci (n = 21, 4.9%) were the common pathogens. Meropenem and piperacillin-tazobactam susceptibility, among the isolated pathogens, were only 66.1% and 53.6%, respectively. Pulse rate (odds ratio [OR], 1.018; 95% confidence interval [CI], 1.002-1.034), quick SOFA score (OR, 2.857; 95% CI, 2.120-3.851), inappropriate antimicrobial treatment (OR, 1.774; 95% CI, 1.011-3.851), Gram-negative bacteraemia (OR, 2.894; 95% CI, 1.437-5.825), bacteraemia of non-urinary origin (OR, 11.262; 95% CI, 1.368-92.720), and advancing age (OR, 1.017; 95% CI, 1.001-1.034) were independent predictors of mortality. Bacteraemia in our neutropenic patient population had distinctive characteristics. The severity of infection and the way to control it with appropriate antimicrobials, and local epidemiological data, came forward. CONCLUSIONS: Local antibiotic susceptibility profiles should be integrated into therapeutic recommendations, and infection control and prevention measures should be prioritised in this era of rapidly increasing antibiotic resistance.

Exploring the prevalence of antibiotic resistance patterns and drivers of antibiotics resistance of Salmonella in livestock and poultry-derived foods: a systematic review and meta-analysis in Bangladesh from 2000 to 2022.

Background: Antimicrobial resistance (AMR) is a severe public health problem that Bangladeshis are dealing with nowadays. However, we wanted to investigate the pooled prevalence of Salmonella and AMR in Salmonella strains isolated from livestock- and poultry-derived foods between 1 January 2000 and 31 August 2022. Methods: The metafor and metareg packages in the R programming language were used to conduct all analyses. We used a random-effect or fixed-effect model for pooled prevalence of Salmonella and AMR to Salmonella, depending on the heterogeneity test for each antibiotic. The heterogeneity was examined using stratified analyses, the meta-regression approach and sensitivity analysis. Results: The combined prevalence of Salmonella in livestock and poultry-derived food in Bangladesh is 37%, according to the 12-research considered (95% CI: 23%-52%). According to subgroup analysis, neomycin had the lowest prevalence of resistance (4%, 95% CI: 1%-13%), whereas tetracycline had the highest prevalence of resistance (81%, 95% CI: 53%-98%). According to univariate meta-analysis and correlation analysis, the prevalence of Salmonella increased with the study period (ß = 0.0179; 95% CI: 0.0059-0.0298, P = 0.0034; R2 = 46.11%) and without this, none of aforementioned variables was significantly associated with the detected heterogeneity and there was a positive relationship (r = 0.692, P = 0.001) between the Salmonella prevalence and study period. Conclusions: AMR is rising alarmingly in Bangladesh by livestock-derived food consumption. However, monitoring and evaluating antibiotic sensitivity trends and developing effective antibiotic regimens may improve Salmonella infection inhibition and control in Bangladesh. Policymakers should be concerned about food handling practices. Doctors should be concerned when using prescribing antibiotics.

Clinicopathological profile of peritoneal tuberculosis and a new scoring model for predicting mortality: an international ID-IRI study.

Existing literature about peritoneal tuberculosis (TBP) is relatively insufficient. The majority of reports are from a single center and do not assess predictive factors for mortality. In this international study, we investigated the clinicopathological characteristics of a large series of patients with TBP and determined the key features associated with mortality. TBP patients detected between 2010 and 2022 in 38 medical centers in 13 countries were included in this retrospective cohort. Participating physicians filled out an online questionnaire to report study data. In this study, 208 patients with TBP were included. Mean age of TBP cases was 41.4 ± 17.5 years. One hundred six patients (50.9%) were females. Nineteen patients (9.1%) had HIV infection, 45 (21.6%) had diabetes mellitus, 30 (14.4%) had chronic renal failure, 12 (5.7%) had cirrhosis, 7 (3.3%) had malignancy, and 21 (10.1%) had a history of immunosuppressive medication use. A total of 34 (16.3%) patients died and death was attributable to TBP in all cases. A pioneer mortality predicting model was established and HIV positivity, cirrhosis, abdominal pain, weakness, nausea and vomiting, ascites, isolation of Mycobacterium tuberculosis in peritoneal biopsy samples, TB relapse, advanced age, high serum creatinine and ALT levels, and decreased duration of isoniazid use were significantly related with mortality (p < 0.05). This is the first international study on TBP and is the largest case series to date. We suggest that using the mortality predicting model will allow early identification of high-risk patients likely to die of TBP.

High-resolution microbiome analysis reveals exclusionary Klebsiella species competition in preterm infants at risk for necrotizing enterocolitis.

Intestinal colonization with Klebsiella has been linked to necrotizing enterocolitis (NEC), but methods of analysis usually failed to discriminate Klebsiella species or strains. A novel ~ 2500-base amplicon (StrainID) that spans the 16S and 23S rRNA genes was used to generate amplicon sequence variant (ASV) fingerprints for Klebsiella oxytoca and Klebsiella pneumoniae species complexes (KoSC and KpSC, respectively) and co-occurring fecal bacterial strains from 10 preterm infants with NEC and 20 matched controls. Complementary approaches were used to identify cytotoxin-producing isolates of KoSC. Klebsiella species colonized most preterm infants, were more prevalent in NEC subjects versus controls, and replaced Escherichia in NEC subjects. Single KoSC or KpSC ASV fingerprinted strains dominated the gut microbiota, suggesting exclusionary Klebsiella competition for luminal resources. Enterococcus faecalis was co-dominant with KoSC but present infrequently with KpSC. Cytotoxin-producing KoSC members were identified in most NEC subjects and were less frequent in controls. Few Klebsiella strains were shared between subjects. We conclude that inter-species Klebsiella competition, within an environment of KoSC and E. faecalis cooperation, appears to be an important factor for the development of NEC. Preterm infants seem to acquire Klebsiella primarily through routes other than patient-to-patient transmission.

Development and Temporal Validation of an Electronic Medical Record-Based Insomnia Prediction Model Using Data from a Statewide Health Information Exchange.

This study aimed to develop and temporally validate an electronic medical record (EMR)-based insomnia prediction model. In this nested case-control study, we analyzed EMR data from 2011-2018 obtained from a statewide health information exchange. The study sample included 19,843 insomnia cases and 19,843 controls matched by age, sex, and race. Models using different ML techniques were trained to predict insomnia using demographics, diagnosis, and medication order data from two surveillance periods: -1 to -365 days and -180 to -365 days before the first documentation of insomnia. Separate models were also trained with patient data from three time periods (2011-2013, 2011-2015, and 2011-2017). After selecting the best model, predictive performance was evaluated on holdout patients as well as patients from subsequent years to assess the temporal validity of the models. An extreme gradient boosting (XGBoost) model outperformed all other classifiers. XGboost models trained on 2011-2017 data from -1 to -365 and -180 to -365 days before index had AUCs of 0.80 (SD 0.005) and 0.70 (SD 0.006), respectively, on the holdout set. On patients with data from subsequent years, a drop of at most 4% in AUC is observed for all models, even when there is a five-year difference between the collection period of the training and the temporal validation data. The proposed EMR-based prediction models can be used to identify insomnia up to six months before clinical detection. These models may provide an inexpensive, scalable, and longitudinally viable method to screen for individuals at high risk of insomnia.

Corrigendum to "Financial and functionality analysis of biogas a plant in Bangladesh" [Heliyon 8(9), September 2022, Article e10727].

[This corrects the article DOI: 10.1016/j.heliyon.2022.e10727.].

Tuft cells - the immunological interface and role in disease regulation.

Tuft cells, also known as taste chemosensory cells, accumulate during parasite colonization or infection and have powerful immunomodulatory effects on substances that could be detrimental, as well as possible anti-inflammatory or antibacterial effects. Tuft cells are the primary source of interleukin (IL)-25. They trigger extra Innate lymphoid type-2 cells (ILC2) in the intestinal lamina propria to create cytokines (type 2); for instance, IL-13, which leads to an increase in IL-25. As tuft cells can produce biological effector molecules, such as IL-25 and eicosanoids involved in allergy (for example, cysteinyl leukotrienes and prostaglandin D2) and the neurotransmitter acetylcholine. Following parasite infection, tuft cells require transient receptor potential cation channel subfamily M member 5 (TRPM5)-dependent chemosensation to produce responses. Secretory tuft cells provide a physical mucus barrier against the external environment and therefore have vital defensive roles against diseases by supporting tissue maintenance and repair. In addition to recent research on tuft cells, more studies are required to understand the distribution, cell turnover, molecular characteristics, responses in various species, involvement in immunological function across tissues, and most importantly, the mechanism involved in the control of various diseases.

Construction of copy number variation landscape and characterization of associated genes in a Bangladeshi cohort of neurodevelopmental disorders.

Introduction: Copy number variations (CNVs) play a critical role in the pathogenesis of neurodevelopmental disorders (NDD) among children. In this study, we aim to identify clinically relevant CNVs, genes and their phenotypic characteristics in an ethnically underrepresented homogenous population of Bangladesh. Methods: We have conducted chromosomal microarray analysis (CMA) for 212 NDD patients with male to female ratio of 2.2:1.0 to identify rare CNVs. To identify candidate genes within the rare CNVs, gene constraint metrics [i.e., "Critical-Exon Genes (CEGs)"] were applied to the population data. Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) was followed in a subset of 95 NDD patients to assess the severity of autism and all statistical tests were performed using the R package. Results: Of all the samples assayed, 12.26% (26/212) and 57.08% (121/212) patients carried pathogenic and variant of uncertain significance (VOUS) CNVs, respectively. While 2.83% (6/212) patients' pathogenic CNVs were found to be located in the subtelomeric regions. Further burden test identified females are significant carriers of pathogenic CNVs compared to males (OR = 4.2; p = 0.0007). We have observed an increased number of Loss of heterozygosity (LOH) within cases with 23.85% (26/109) consanguineous parents. Our analyses on imprinting genes show, 36 LOH variants disrupting 69 unique imprinted genes and classified these variants as VOUS. ADOS-2 subset shows severe social communication deficit (p = 0.014) and overall ASD symptoms severity (p = 0.026) among the patients carrying duplication CNV compared to the CNV negative group. Candidate gene analysis identified 153 unique CEGs in pathogenic CNVs and 31 in VOUS. Of the unique genes, 18 genes were found to be in smaller (<1 MB) focal CNVs in our NDD cohort and we identified PSMC3 gene as a strong candidate gene for Autism Spectrum Disorder (ASD). Moreover, we hypothesized that KMT2B gene duplication might be associated with intellectual disability. Conclusion: Our results show the utility of CMA for precise genetic diagnosis and its integration into the diagnosis, therapy and management of NDD patients.

Modeling acute care utilization: practical implications for insomnia patients.

Machine learning models can help improve health care services. However, they need to be practical to gain wide-adoption. In this study, we investigate the practical utility of different data modalities and cohort segmentation strategies when designing models for emergency department (ED) and inpatient hospital (IH) visits. The data modalities include socio-demographics, diagnosis and medications. Segmentation compares a cohort of insomnia patients to a cohort of general non-insomnia patients under varying age and disease severity criteria. Transfer testing between the two cohorts is introduced to demonstrate that an insomnia-specific model is not necessary when predicting future ED visits, but may have merit when predicting IH visits especially for patients with an insomnia diagnosis. The results also indicate that using both diagnosis and medications as a source of data does not generally improve model performance and may increase its overhead. Based on these findings, the proposed evaluation methodologies are recommended to ascertain the utility of disease-specific models in addition to the traditional intra-cohort testing.

Economic outcomes associated with diagnosed behavioral symptoms among patients with dementia in the United States: a health care claims database analysis.

BACKGROUND: Behavioral symptoms are common in patients with dementia. However, there is limited evidence of their economic burden. Among commercially insured patients with dementia in the United States, this study assessed the prevalence of diagnosed behavioral symptoms and whether healthcare resources utilization and costs were associated with these symptoms. METHODS: This retrospective observational study was conducted using the IBM® MarketScan® Commercial Claims and Encounters and Medicare Supplemental database from October 1, 2015, to September 30, 2019. Diagnoses of dementia and behavioral symptoms were identified using the International Classification of Diseases, 10th Modification codes. To test differences in patient characteristics among those with and without diagnosed behavioral symptoms, t-tests were used for continuous variables, and chi-square tests were used for categories. Generalized linear models were used to compare healthcare resource utilization and costs between patients with and without diagnosed behavioral symptoms, adjusted for baseline characteristics. RESULTS: Of the 62,901 patients with dementia included in the analysis, 16.5% had diagnosed behavioral symptoms 12 months post dementia diagnosis. Patients with diagnosed behavioral symptoms used more health care resources (mean annual pharmacy visits per patient: 39.83 vs. 33.08, mean annual outpatient visits per patient: 24.20 vs. 16.94, mean annual inpatient visits per patient: 0.98 vs. 0.47, mean annual ER visits per patient: 2.45 vs. 1.21) and incurred higher cost of care than those without diagnosed behavioral symptoms (mean annual total health care costs per patients: $63,268 versus $33,383). Inpatient care was the most significant contributor to total costs (adjusted annual mean cost per patient: $28,195 versus $12,275). CONCLUSION: Behavioral symptoms were significantly associated with higher healthcare resource utilization and costs among patients with dementia. Further research is warranted to address the unmet medical needs of this patient population.

A meta-analysis of COVID-19 vaccines acceptance among black/African American.

The COVID-19 pandemic had harmed Black/African Americans disproportionately. Mortality and morbidity can reduce by increasing vaccination acceptability and availability. We conducted a meta-analysis of 20 studies that show the prevalence of Black/African Americans who embrace COVID-19 vaccination between 2020 and September, 2022. Investigations conducted before and after the availability of COVID-19 vaccines found the vaccinations effective. The heterogeneity was examined using stratified analyses, the meta-regression approach, and sensitivity analysis in R programming language. This meta-analysis showed that the overall COVID-19 vaccine hesitancy among Black/African Americans is 35% (95% CI: 26%-45%). That means 65% of Black African Americans received vaccines without any hesitancy. According to correlation analysis, there was a negative relationship (r = -0.392, P = 0.021) between the prevalence of vaccine hesitancy and the survey year. Evidence suggests ethnic health disparities in Black/African Americans were for lower socioeconomic status. Some initiatives had to address health disparities, while ethnicity had not consistently been a focus. Only vaccines can prevent COVID-19 like infectious diseases. Policy makers and health educators should concern on vaccine acceptance or hesitancy related programs among Black/African American.

Financial and functionality analysis of a biogas plant in Bangladesh.

Installation of a biogas plant in a rural region has become a viable alternative energy source with a variety of health and environmental advantages. Though Bangladesh has enormous resources for biogas production, biogas energy production is infancy stage in Bangladesh. The study aims to explore the economic aspect of household-level biogas plants and determine the relationship between biogas plant functionality and different factors. For doing this, 300 biogas plant owners were interviewed from fifteen Upazilla in Bangladesh and respondents were chosen by a two-stage random sampling technique. The study shows by measuring partial budgeting, USD 294.80 per year can be earned by a family by introducing biogas plant. Cost-benefit analysis showed that a small biogas plant (USD 143.07/year) was most profitable, followed by a large biogas plant (USD 142.17/year). In discounted cost-benefit analysis, medium size biogas plant was found to be the most beneficial investment, followed by a small size biogas plant. Average NPV, BCR, PBP, and IRR of Biogas plant were USD 1629.11, 1.77, 2.93, and 48% with subsidy where USD 1525.25, 1.77, 3.75, and 43% without subsidy. The measurement of carbon trading also highlights the economic benefit of a biogas plant in Bangladesh. The bivariate relationship between the functionality of biogas plants with different factors highlights that higher educated, trained plant owners with quality mason and follow up services ensured the efficient operation of the biogas plant.

Factors associated with Facebook addiction among university students amid the COVID-19 pandemic: Findings from an online cross-sectional survey.

BACKGROUND: Facebook addiction (FA) has been suggested as a potential behavioral addiction. There is a severe lack of research evidence regarding the Facebook addiction behavior among university students during the ongoing COVID-19 pandemic. The aim of this study was to determine factors associated with Facebook addiction among Bangladeshi university students. METHODS: A cross-sectional online survey was conducted among 2,161 Bangladeshi university students during the COVID-19 pandemic from June 2021 to September 2021. A well fitted regression model in R programming language was used for this study. RESULTS: Female respondents and those whose family monthly income was <25,000 BDT were more addicted to Facebook than other respondents. Respondents who lost a family member or a relative to COVID-19, engaged in physical activities (exercise) during the pandemic, used Facebook for work purposes or used Facebook to relieve daily stress were more addicted to Facebook. CONCLUSION: Overuse of social media is problematic as it can trigger several mental health symptoms, especially among students. Adequate and effective interventions are required to educate students about the dangers of Facebook addiction and to provide an alternative, healthy options.