Factors influencing mortality in COVID-19-associated mucormycosis: The international ID-IRI study.

The emergence of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a global pandemic. Concurrently, reports of mucormycosis cases surged, particularly during the second wave in India. This study aims to investigate mortality factors in COVID-19-associated mucormycosis (CAM) cases, exploring clinical, demographic, and therapeutic variables across mostly Asian and partly African countries. A retrospective, cross-sectional analysis of CAM patients from 22 medical centers across eight countries was conducted, focusing on the first 3 months post-COVID-19 diagnosis. Data collected through the ID-IRI included demographics, comorbidities, treatments, and outcomes. A total of 162 CAM patients were included. The mean age was 54.29 ± 13.04 years, with 54% male. Diabetes mellitus (85%) was prevalent, and 91% had rhino-orbital-cerebral mucormycosis. Surgical debridement was performed in 84% of the cases. Mortality was 39%, with advanced age (hazard ratio [HR] = 1.06, [P < .001]), rituximab use (HR = 21.2, P = .05), and diabetic ketoacidosis (HR = 3.58, P = .009) identified as risk factors. The mortality risk increases by approximately 5.6% for each additional year of age. Surgical debridement based on organ involvement correlated with higher survival (HR = 8.81, P < .001). The utilization of rituximab and diabetic ketoacidosis, along with advancing age, has been associated with an increased risk of mortality in CAM patients. A combination of antifungal treatment and surgical intervention has demonstrated a substantial improvement in survival outcomes.

Over a third of patients who developed mucormycosis after COVID-19 died. Older people, those on specific immunosuppressive treatments, and those with diabetic ketoacidosis had a higher risk of death. However, undergoing surgery as part of treatment significantly improved survival.

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.

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.

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.

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.

Facile synthesis of CuO/CdS heterostructure photocatalyst for the effective degradation of dye under visible light.

In this work, the photocatalytic property of p-type CuO was tailored by creating a heterojunction with n-type CdS. The CuO/CdS nanocomposite photocatalyst was synthesized by the ultrasound-assisted-wet-impregnation method and the physicochemical and optical properties of the catalysts were evaluated by using N2 physisorption, X-Ray Diffraction (XRD),X-Ray Photoelectron Spectroscopy (XPS), Raman spectroscopy, Transmission electron microscopy (TEM), Energy dispersive X-Ray (EDX) mapping, Field Emission Scanning Electron Microscope (FE-SEM), UV-Vis and photoluminescence spectroscopy experiments. Detailed characterization revealed the formation of a nanocomposite with a remarkable improvement in the charge carrier (electron/hole) separation. The photocatalytic degradation efficiencies of CuO and CuO/CdS were investigated for different dyes, for instance, rhodamine B (RhB), methylene blue (MLB), methyl blue (MB) and methyl orange (MO) under visible light irradiation. The obtained dye degradation efficiencies were ~93%, ~75%, ~83% and ~80%, respectively. The quantum yield for RhB degradation under visible light was 6.5 × 10-5. Reusability tests revealed that the CuO/CdS photocatalyst was recyclable up to four times. The possible mechanisms for the photocatalytic dye degradation over CuO/CdS nanocomposite were elucidated by utilizing various scavengers. Through these studies, it can be confirmed that the conduction band edges of CuO and CdS play a significant role in producing O2-. The produced O2- degraded the dye molecules in the bulk solution whereas the valence band position of CuO acted as the water oxidation site. In conclusion, the incorporation of CuO with CdS was demonstrated to be a viable strategy for the efficient photocatalytic degradation of dyes in aqueous solutions.

Engineered Tug-of-War Between Kinesin and Dynein Controls Direction of Microtubule Based Transport In Vivo.

Bidirectional transport of membrane organelles along microtubules (MTs) is driven by plus-end directed kinesins and minus-end directed dynein bound to the same cargo. Activities of opposing MT motors produce bidirectional movement of membrane organelles and cytoplasmic particles along MT transport tracks. Directionality of MT-based transport might be controlled by a protein complex that determines which motor type is active at any given moment of time, or determined by the outcome of a tug-of-war between MT motors dragging cargo organelles in opposite directions. However, evidence in support of each mechanisms of regulation is based mostly on the results of theoretical analyses or indirect experimental data. Here, we test whether the direction of movement of membrane organelles in vivo can be controlled by the tug-of-war between opposing MT motors alone, by attaching a large number of kinesin-1 motors to organelles transported by dynein to minus-ends of MTs. We find that recruitment of kinesin significantly reduces the length and velocity of minus-end-directed dynein-dependent MT runs, leading to a reversal of the overall direction of dynein-driven organelles in vivo. Therefore, in the absence of external regulators tug-of-war between opposing MT motors alone is sufficient to determine the directionality of MT transport in vivo.

Tyrosine phosphorylation of CD13 regulates inflammatory cell-cell adhesion and monocyte trafficking.

CD13 is a large cell surface peptidase expressed on the monocytes and activated endothelial cells that is important for homing to and resolving the damaged tissue at sites of injury. We showed previously that cross-linking of human monocytic CD13 with activating Abs induces strong adhesion to endothelial cells in a tyrosine kinase- and microtubule-dependent manner. In the current study, we examined the molecular mechanisms underlying these observations in vitro and in vivo. We found that cross-linking of CD13 on U937 monocytic cells induced phosphorylation of a number of proteins, including Src, FAK, and ERK, and inhibition of these abrogated CD13-dependent adhesion. We found that CD13 itself was phosphorylated in a Src-dependent manner, which was an unexpected finding because its 7-aa cytoplasmic tail was assumed to be inert. Furthermore, CD13 was constitutively associated with the scaffolding protein IQGAP1, and CD13 cross-linking induced complex formation with the actin-binding protein α-actinin, linking membrane-bound CD13 to the cytoskeleton, further supporting CD13 as an inflammatory adhesion molecule. Mechanistically, mutation of the conserved CD13 cytoplasmic tyrosine to phenylalanine abrogated adhesion; Src, FAK, and ERK phosphorylation; and cytoskeletal alterations upon Ab cross-linking. Finally, CD13 was phosphorylated in isolated murine inflammatory peritoneal exudate cells, and adoptive transfer of monocytic cell lines engineered to express the mutant CD13 were severely impaired in their ability to migrate into the inflamed peritoneum, confirming that CD13 phosphorylation is relevant to inflammatory cell trafficking in vivo. Therefore, this study identifies CD13 as a novel, direct activator of intracellular signaling pathways in pathophysiological conditions.

Large-scale proteomic characterization of melanoma expressed proteins reveals nestin and vimentin as biomarkers that can potentially distinguish melanoma subtypes.

Melanoma is an aggressive type of skin cancer, which accounts for only 4% of skin cancer cases but causes around 75% of skin cancer deaths. Currently, there is a limited set of protein biomarkers that can distinguish melanoma subtypes and provide an accurate prognosis of melanoma. Thus, we have selected and profiled the proteomes of five different melanoma cell lines from different stages of progression in comparison with a normal melanocytes using tandem mass spectrometry. We also profiled the proteome of a solid metastatic melanoma tumor. This resulted in the identification of 4758 unique proteins, among which ∼200-300 differentially expressed proteins from each set were found by quantitative proteomics. Correlating protein expression with aggressiveness of each melanoma cell line and literature mining resulted in the final selection of six proteins: vimentin, nestin, fibronectin, annexin A1, dipeptidyl peptidase IV, and histone H2A1B. Validation of nestin and vimentin using 40 melanoma samples revealed pattern of protein expression can help predict melanoma aggressiveness in different subgroups of melanoma. These results, together with the combined list of 4758 expressed proteins, provide a valuable resource for selecting melanoma biomarkers in the future for the clinical and research community.

A systematic review on green synthesis of silver nanoparticles using plants extract and their bio-medical applications.

Nanoparticles have recently become considered as a crucial player in contemporary medicine, with therapeutic uses ranging from contrast agents in imaging to carriers for the transport of drugs and genes into a specific target. Nanoparticles have the ability to have more precise molecular interactions with the human body in order to target specific cells and tissues with minimal adverse effects and maximal therapeutic outcomes. With the least number of side effects and the greatest possible therapeutic benefit, nanoparticles can target particular cells and tissues through more precise molecular interactions with the human body. The majority of global public health problems are now treated with green synthesized silver nanoparticles (AgNPs), which substantially affect the fundamental structure of DNA and proteins and thus display their antimicrobial action. AgNPs can inhibit the proliferation of tumor cells and induce oxidative stress. By inhibiting vascular endothelial growth factor (HIF)-1, pro-inflammatory mediators generated by silver nanoparticles are reduced, mucin hypersecretion is lessened, and gene activity is subsequently regulated to prevent infections. The biogenic synthesis of silver nanoparticles (AgNPs) using various plants and their applications in antibacterial, antifungal, antioxidant, anticancer, anti-inflammatory, and antidiabetic activities have been extensively discussed in this article. Also, because only natural substances are utilized in the manufacturing process, the particles that are created naturally are coated, stabilized, and play a vital role in these biomedical actions. The characterization of AgNPs, possibility of preparing AgNPSs with different shapes using biological method and their impact on functions and toxicities, impact of size, shape and other properties on AgNPs functions and toxicity profiles, limitations, and future prospects of green-mediated AgNPs have also been reported in this study. The major goal of this study is to provide readers with a comprehensive, informed, and up-to-date summary of the various AgNPs production and characterization methods and their under-investigational antioxidant, antibacterial, and anticancer, antidiabetic, antifungal and anti-inflammatory properties. This review provides instructions and suggestions for additional studies based on AgNPs. This evaluation also pushes researchers to look into natural resources like plant parts in order to create useful nanobiotechnology.

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.

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.

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.

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.].

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.

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.

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.

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.

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.

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.