Patient outcomes following buprenorphine treatment for opioid use disorder: A retrospective analysis of the influence of patient- and prescriber-level characteristics in Massachusetts, USA.

BACKGROUND AND AIMS: Opioid use disorder (OUD) is treatable with buprenorphine/naloxone (buprenorphine), but many patients discontinue treatment prematurely. The aim of this study was to assess the influence of patient- and prescriber-level characteristics relative to several patient outcomes following the initiation of buprenorphine treatment for OUD. DESIGN: This was a retrospective observational investigation. We used the Public Health Data Warehouse from the Massachusetts Department of Public Health to construct a sample of patients who initiated buprenorphine treatment between 2015 and 2019. We attributed each patient to a prescriber based on information from prescription claims. We used multilevel models to assess the influence of patient- and prescriber-level characteristics on each outcome. SETTING: Massachusetts, USA. PARTICIPANTS: The study cohort comprised 37 955 unique patients and 2146 prescribers. Among patients, 64.6% were male, 52.6% were under the age of 35 and 82.2% were White, non-Hispanic. For insurance coverage, 72.1% had Medicaid. MEASUREMENTS: The outcome measures were poor medication continuity, treatment discontinuation and opioid overdose, all assessed within a 12-month follow-up period that began with a focal prescription for buprenorphine. Each patient had a single follow-up period. Poor medication continuity was defined as medication gaps totaling more than 7 days during the initial 180 days of buprenorphine treatment and treatment discontinuation was defined as having a medication gap for 2 consecutive months within the 12-month follow-up period. FINDINGS: The patient-level rates for poor medication continuity, treatment discontinuation and opioid overdose were 59.7% [95% confidence interval (CI) = 59.2-60.2], 57.4% (95% CI = 56.9-57.9) and 10.3% (95% CI = 10.0-10.6), respectively, with 1.1% (95% CI = 1.0-1.2) experiencing a fatal opioid overdose. At the patient level, after adjustment for covariates, adverse outcomes were associated with race/ethnicity as both Black, non-Hispanic and Hispanic patients had worse outcomes than did White, non-Hispanic patients (Black, non-Hispanic -- poor continuity: 1.50, 95% CI = 1.34-1.68; discontinuation: 1.44, 95% CI = 1.30-1.60; Hispanic -- poor continuity: 1.21, 95% CI = 1.12-1.31; discontinuation: 1.38, 95% CI = 1.28-1.48). Patients with insurance coverage through Medicaid also had worse outcomes than those with commercial insurance (poor continuity: 1.18, 95% CI = 1.11-1.26; discontinuation: 1.09, 95% CI = 1.03-1.16; overdose: 1.98, 95% CI = 1.75-2.23). Pre-treatment mental health conditions and other types of chronic illness were also associated with worse outcomes (History of mental health conditions -- poor continuity: 1.11, 95% CI = 1.06-1.17; discontinuation: 1.05, CI = 1.01-1.10; overdose: 1.47, 95% CI = 1.36-1.60; Chronic health conditions -- poor continuity: 1.15, 95% CI = 1.05-1.27; discontinuation: 1.15, 95% CI = 1.05-1.26; overdose: 1.83, 95% CI = 1.60-2.10; History of substance use disorder other than for opioids -- poor continuity: 1.54, 95% CI = 1.46-1.62; discontinuation: 1.54, 95% CI = 1.47-1.62; overdose: 1.93, 95% CI = 1.80-2.07). At the prescriber level, after adjustments for covariates, adverse outcomes were associated with clinical training, as primary care physicians had higher rates of adverse outcomes than psychiatrists (poor continuity: 1.12, 95% CI = 1.02-1.23; discontinuation: 1.04, 95% CI = 1.01-1.09). A larger prescriber panel size, based on number of patients being prescribed buprenorphine, was also associated with higher rates of adverse outcomes (poor continuity: 1.36, 95% CI = 1.27-1.46; discontinuation: 1.21, 95% CI = 1.14-1.28; overdose: 1.10, 95% CI = 1.01-1.19). Between 9% and 15% of the variation among patients for the outcomes was accounted for at the prescriber level. CONCLUSIONS: Patient- and prescriber-level characteristics appear to be associated with patient outcomes following buprenorphine treatment for opioid use disorder. In particular, patients' race/ethnicity and insurance coverage appear to be associated with substantial disparities in outcomes, and prescriber characteristics appear to be most closely associated with medication continuity during early treatment.

Application of Low-Temperature Air Plasma for the Enhancement of Defense Fabric's Self-Cleaning Property.

Self-cleaning textiles have the potential to revolutionize the lives of people like military personnel and hikers who spend extended periods of time in the sun and have restricted access to washing facilities. This research aims to develop the self-cleaning capability of defense uniforms by utilizing air plasma treatment and applying TiO2 nanocoating. Following plasma treatment of differing durations (2, 4, 6, 8, and 10 min, respectively), a pad-dry cure method was employed to apply a TiO2 coating to each sample, while keeping other processing parameters constant. SEM, Fourier transform infrared spectroscopy, ultraviolet protection factor (UVPF), energy-dispersive X-rays, and a water contact angle test were performed in order to validate the air plasma-induced surface modification. There was a gradual escalation in the rate of TiO2 absorption with an extension of the plasma treatment duration. Afterward, the samples were stained with various organic and inorganic compounds, including oil, ink, soil, and coffee, and subsequently exposed to sunlight for a period of 6 h. The samples demonstrated an enhanced cleaning effectiveness with increasing quantities of TiO2. The reflectance value and visual assessment of washed sample showed a reduced yet still present self-cleaning characteristic. The UVPF of the samples increased gradually as the duration of plasma treatment increased due to the UV absorption properties of TiO2, as validated by measuring the band gap energy.

Adhesion Mechanics and Detachment Dynamics of Vanishing Surface Layers on Hydrogels.

Cross-linked hydrogel surfaces exhibit reduced stiffness when polymerized against polymeric hydrophobic surfaces. As such, these layers play a critical role in contact mechanics, particularly exhibiting strong relative adhesion with colloidal probes when the contact area is small. This prevents the use of continuum models of adhesive soft contact. To connect mechanisms of stretch to the force response, depth-controlled nanoindentation experiments were conducted on polyacrylamide (pAAM) hydrogel samples using colloidal probe atomic force microscopy (AFM). The pAAM sample had a high water content of >90% and was molded against polyoxymethylene (POM) to create a more dilute surface layer with thickness ∼0.5 µm. Indentations to multiple depths between 50 nm and 1.25 µm were repeated 10 times each. First, the force drops during the unloading, and separation segments of each indentation were characterized. This described the detachment progression for increasing areas of contact, revealing that the pull-off force for a single chain was in the single-pN range. Second, the stretched polymer network was modeled as an array of parallel, linear springs. Assuming a constant areal chain density of α = 100 chains/µm2, the maximum force of adhesion was plotted versus the volume of chains stretched upward, and the average chain stiffness was calculated from a linear fit to be 22.8 × 10-6 N/m. A Weibull distribution analysis of detachment events revealed a dependence of chain stiffness on maximum indentation depth (dmax), with higher stiffness at shallower depths approaching kchain ≈ 20 × 10-6 N/m. These findings on adhesion mechanics between a vanishing hydrogel surface and probe can guide the development of multifunctional hydrogels for various biomedical applications.

Comparison of RNN-LSTM, TFDF and stacking model approach for weather forecasting in Bangladesh using historical data from 1963 to 2022.

Forecasting the weather in an area characterized by erratic weather patterns and unpredictable climate change is a challenging endeavour. The weather is classified as a non-linear system since it is influenced by various factors that contribute to climate change, such as humidity, average temperature, sea level pressure, and rainfall. A reliable forecasting system is crucial in several industries, including transportation, agriculture, tourism, & development. This study showcases the effectiveness of data mining, meteorological analysis, and machine learning techniques such as RNN-LSTM, TensorFlow Decision Forest (TFDF), and model stacking (including ElasticNet, GradientBoost, KRR, and Lasso) in improving the precision and dependability of weather forecasting. The stacking model strategy entails aggregating multiple base models into a meta-model to address issues of overfitting and underfitting, hence improving the accuracy of the prediction model. To carry out the study, a comprehensive 60-year meteorological record from Bangladesh was gathered, encompassing data on rainfall, humidity, average temperature, and sea level pressure. The results of this study suggest that the stacking average model outperforms the TFDF and RNN-LSTM models in predicting average temperature. The stacking average model achieves an RMSLE of 1.3002, which is a 10.906% improvement compared to the TFDF model. It is worth noting that the TFDF model had previously outperformed the RNN-LSTM model. The performance of the individual stacking model is not as impressive as that of the average model, with the validation results being better in TFDF.

Predictive landslide susceptibility modeling in the southeastern hilly region of Bangladesh: application of machine learning algorithms in Khagrachari district.

Landslides pose a severe threat to people, buildings, and infrastructure. The rugged terrain of the Chattogram Hill Tract region in southeastern Bangladesh frequently experiences landslides, particularly during rainy seasons. This study provides a comparative analysis of innovative machine learning (ML) algorithms used for the purpose of landslide susceptibility (LS) mapping for the Khagrachari district of Bangladesh. The dataset for this study comprises 15 landslide conditioning factors and 127 landslide inventory points. The landslide inventory points included 71 landslide and 56 non-landslide points. Then, the data were split randomly into training data (70%) and testing data (30%). Three ML algorithms, namely random forest (RF), boosted regression trees (BRT), and k-nearest neighbor (KNN), were utilized to evaluate the LS zone. The models were validated using the area under the curve (AUC), overall accuracy, precision, and recall. Based on the AUC value, the BRT model demonstrated the highest performance with a value of 0.95, while the AUC values for RF and KNN were 0.91 and 0.86, respectively. Besides, overall accuracy, precision, and recall values (0.82, 0.81, and 0.86) also indicated BRT as the most effective model. The results showed that maximum rainfall and elevation were the most influential factors for both BRT and RF models. This research provides valuable insight into understanding the LS areas in Khagrachari, aiding in informed decision-making regarding landslide-related concerns in the region, and can be applied to the broader scale to develop effective planning and mitigation strategies for comprehensive disaster management and natural hazard response.

Marine natural compounds as potential CBP bromodomain inhibitors for treating cancer: an in-silico approach using molecular docking, ADMET, molecular dynamics simulations and MM-PBSA binding free energy calculations.

The cAMP-responsive element binding protein (CREB) binding protein (CBP), a bromodomain-containing protein, engages with multiple transcription factors and enhances the activation of many genes. CBP bromodomain acts as an epigenetic reader and plays an important role in the CBP-chromatin interaction which makes it an important drug target for treating many diseases. Though inhibiting CBP bromodomain was reported to have great potential in cancer therapeutics, approved CBP bromodomain inhibitor is yet to come. We utilized various in silico approaches like molecular docking, ADMET, molecular dynamics (MD) simulations, MM-PBSA calculations, and in silico PASS predictions to identify potential CBP bromodomain inhibitors from marine natural compounds as they have been identified as having distinctive chemical structures and greater anticancer activities. To develop a marine natural compound library for this investigation, Lipinski's rule of five was used. Sequential investigations utilizing molecular docking, ADMET studies, 100 ns MD simulations, and MM-PBSA calculations revealed that three marine compounds-ascididemin, neoamphimedine, and stelletin A-demonstrated superior binding affinity compared to the standard inhibitor, 69 A. These compounds also exhibited suitable drug-like properties, a favorable safety profile, and formed stable protein-ligand complexes. The in-silico PASS tool predicted that these compounds have significant potential for anticancer activity. Among them, ascididemin demonstrated the highest binding affinity in both molecular docking and MM-PBSA calculations, as well as a better stability profile in MD simulations. Hence, ascididemin can be a potential inhibitor of CBP bromodomain. However, in vitro and in vivo validation is required for further confirmation of these findings. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-024-00258-5.

Occurrence, characteristics and distribution of microplastics in commercial marine fishes of the Bay of Bengal.

The study aimed to assess and characterize microplastics (MPs) in muscles, guts, and gills of six commercially important marine fish from the Bay of Bengal. FTIR was utilized to identify MP's polymer compositions. A total 7085 MPs identified, where tuna exhibited the highest count and Bombay duck had the lowest. MPs abundance (MPs/g) was ranged from 1.56 ± 0.39 to 7.16 ± 1.36 in muscles, 1.91 ± 0.32 to 4.46 ± 0.75 in guts, and 2.36 ± 0.24 to 6.53 ± 1.58 in gills. The predominant MPs were 1-5 mm size (33.33-62.78 %), white/transparent color (18.45-54.63 %), filament shapes (75.00-94.71), and fiber types (73.21-94.71 %). FTIR revealed MPs 58.89 % polyethylene, 21.67 % polypropylene, 17.22 % polyester, and 2.22 % non-plastic compositions. Cluster analysis grouped two species with 50 % similarity, while PCA indicated significant variations among principal components (14-69.4 %) highlighting the dominance of fiber, particles, and 0.5-1.0 mm MPs in the fish tissues. The prevalence of MPs in seafood underscores measures to safeguard both the marine ecosystem and human health.

Mitigating Sociodemographic Bias in Opioid Use Disorder Prediction: Fairness-Aware Machine Learning Framework.

BACKGROUND: Opioid use disorder (OUD) is a critical public health crisis in the United States, affecting >5.5 million Americans in 2021. Machine learning has been used to predict patient risk of incident OUD. However, little is known about the fairness and bias of these predictive models. OBJECTIVE: The aims of this study are two-fold: (1) to develop a machine learning bias mitigation algorithm for sociodemographic features and (2) to develop a fairness-aware weighted majority voting (WMV) classifier for OUD prediction. METHODS: We used the 2020 National Survey on Drug and Health data to develop a neural network (NN) model using stochastic gradient descent (SGD; NN-SGD) and an NN model using Adam (NN-Adam) optimizers and evaluated sociodemographic bias by comparing the area under the curve values. A bias mitigation algorithm, based on equality of odds, was implemented to minimize disparities in specificity and recall. Finally, a WMV classifier was developed for fairness-aware prediction of OUD. To further analyze bias detection and mitigation, we did a 1-N matching of OUD to non-OUD cases, controlling for socioeconomic variables, and evaluated the performance of the proposed bias mitigation algorithm and WMV classifier. RESULTS: Our bias mitigation algorithm substantially reduced bias with NN-SGD, by 21.66% for sex, 1.48% for race, and 21.04% for income, and with NN-Adam by 16.96% for sex, 8.87% for marital status, 8.45% for working condition, and 41.62% for race. The fairness-aware WMV classifier achieved a recall of 85.37% and 92.68% and an accuracy of 58.85% and 90.21% using NN-SGD and NN-Adam, respectively. The results after matching also indicated remarkable bias reduction with NN-SGD and NN-Adam, respectively, as follows: sex (0.14% vs 0.97%), marital status (12.95% vs 10.33%), working condition (14.79% vs 15.33%), race (60.13% vs 41.71%), and income (0.35% vs 2.21%). Moreover, the fairness-aware WMV classifier achieved high performance with a recall of 100% and 85.37% and an accuracy of 73.20% and 89.38% using NN-SGD and NN-Adam, respectively. CONCLUSIONS: The application of the proposed bias mitigation algorithm shows promise in reducing sociodemographic bias, with the WMV classifier confirming bias reduction and high performance in OUD prediction.

UBL3 overexpression enhances EV-mediated Achilles protein secretion in conditioned media of MDA-MB-231 cells.

Cancer cells communicate within the tumor microenvironment (TME) through extracellular vesicles (EVs), which act as crucial messengers in intercellular communication, transporting biomolecules to facilitate cancer progression. Ubiquitin-like 3 (UBL3) facilitates protein sorting into small EVs as a post-translational modifier. However, the effect of UBL3 overexpression in EV-mediated protein secretion has not been investigated yet. This study aimed to investigate the effect of UBL3 overexpression in enhancing EV-mediated Achilles protein secretion in MDA-MB-231 (MM) cells by a dual-reporter system integrating Akaluc and Achilles tagged with Ubiquitin where self-cleaving P2A linker connects Akaluc and Achilles. MM cells stably expressing Ubiquitin-Akaluc-P2A-Achilles (Ubi-Aka/Achi) were generated. In our study, both the bioluminescence of Ubiquitin-Akaluc (Ubi-Aka) and the fluorescence of Achilles secretion were observed. The intensity of Ubi-Aka was thirty times lower, while the Achilles was four times lower than the intensity of corresponding cells. The ratio of Ubi-Aka and Achilles in conditioned media (CM) was 7.5. They were also detected within EVs using an EV uptake luciferase assay and fluorescence imaging. To investigate the effect of the UBL3 overexpression in CM, Ubi-Aka/Achi was transiently transfected into MM-UBL3-KO, MM, and MM-Flag-UBL3 cells. We found that the relative fluorescence expression of Achilles in CM of MM-UBL3-KO, MM, and MM-Flag-UBL3 cells was 30 %, 28 %, and 45 %, respectively. These findings demonstrated that UBL3 overexpression enhances EV-mediated Achilles protein secretion in CM of MM cells. Targeting UBL3 could lead to novel therapies for cancer metastasis by reducing the secretion of pro-metastatic proteins, thereby inhibiting disease progression.

Genomic characterization of carbapenemase-producing Enterobacterales from Dhaka food markets unveils the spread of high-risk antimicrobial-resistant clones and plasmids co-carrying bla NDM and mcr-1.1.

Background: The transmission of carbapenemase-producing Enterobacterales (CPE) in the external environment, especially through food, presents a significant public health risk. Objectives: To investigate the prevalence and genetic characteristics of CPE in food markets of Dhaka, Bangladesh, using WGS. Methods: CPE isolates were obtained from different food and water samples collected from food markets in the southern part of Dhaka, Bangladesh. The isolates subsequently underwent molecular typing, WGS employing both short- and long-read sequencers, and plasmid analysis. Results: This study unveiled an extensive spread of CPE, with no significant difference in contamination rates observed in samples (N = 136), including meat (n = 8), fish (n = 5), vegetables (n = 36) or various food-washed water (n = 65) from markets near hospitals or residential areas. Thirty-eight Enterobacterales from 33 samples carried carbapenemase genes (bla NDM-1, -4, -7, bla KPC-2, bla OXA-181 or bla IMI-1). Among these, the high-risk Escherichia coli ST410 clone was the most prevalent and distributed across various locations. Furthermore, the identification of IncHI2 plasmids co-harbouring resistance genes like bla NDM-5 and mcr-1.1, without discernible epidemiological connections, is a unique finding, suggesting their widespread dissemination. Conclusions: The analysis unveils a dynamic landscape of CPE dissemination in food markets, underscored by the proliferation of novel IncHI2 hybrid plasmids carrying both colistin- and carbapenem-resistance genes. This illuminates the ever-evolving landscape of antimicrobial resistance in Dhaka, urging us to confront its emergent challenges.

Alpha-Synuclein Interaction with UBL3 Is Upregulated by Microsomal Glutathione S-Transferase 3, Leading to Increased Extracellular Transport of the Alpha-Synuclein under Oxidative Stress.

Aberrant aggregation of misfolded alpha-synuclein (α-syn), a major pathological hallmark of related neurodegenerative diseases such as Parkinson's disease (PD), can translocate between cells. Ubiquitin-like 3 (UBL3) is a membrane-anchored ubiquitin-fold protein and post-translational modifier. UBL3 promotes protein sorting into small extracellular vesicles (sEVs) and thereby mediates intercellular communication. Our recent studies have shown that α-syn interacts with UBL3 and that this interaction is downregulated after silencing microsomal glutathione S-transferase 3 (MGST3). However, how MGST3 regulates the interaction of α-syn and UBL3 remains unclear. In the present study, we further explored this by overexpressing MGST3. In the split Gaussia luciferase complementation assay, we found that the interaction between α-syn and UBL3 was upregulated by MGST3. While Western blot and RT-qPCR analyses showed that silencing or overexpression of MGST3 did not significantly alter the expression of α-syn and UBL3, the immunocytochemical staining analysis indicated that MGST3 increased the co-localization of α-syn and UBL3. We suggested roles for the anti-oxidative stress function of MGST3 and found that the effect of MGST3 overexpression on the interaction between α-syn with UBL3 was significantly rescued under excess oxidative stress and promoted intracellular α-syn to extracellular transport. In conclusion, our results demonstrate that MGST3 upregulates the interaction between α-syn with UBL3 and promotes the interaction to translocate intracellular α-syn to the extracellular. Overall, our findings provide new insights and ideas for promoting the modulation of UBL3 as a therapeutic agent for the treatment of synucleinopathy-associated neurodegenerative diseases.

Prediction and assessment of deleterious and disease causing nonsynonymous single nucleotide polymorphisms (nsSNPs) in human FOXP4 gene: An in - silico study.

In humans, FOXP gene family is involved in embryonic development and cancer progression. The FOXP4 (Forkhead box protein P4) gene belongs to this FOXP gene family. FOXP4 gene plays a crucial role in oncogenesis. Single nucleotide polymorphisms are biological markers and common determinants of human diseases. Mutations can largely affect the function of the corresponding protein. Therefore, the molecular mechanism of nsSNPs in the FOXP4 gene needs to be elucidated. Initially, the SNPs of the FOXP4 gene were extracted from the dbSNP database and a total of 23124 SNPs was found, where 555 nonsynonymous, 20525 intronic, 1114 noncoding transcript, 334 synonymous were obtained and the rest were unspecified. Then, a series of bioinformatics tools (SIFT, PolyPhen2, SNAP2, PhD SNP, PANTHER, I-Mutant2.0, MUpro, GOR IV, ConSurf, NetSurfP 2.0, HOPE, DynaMut2, GeneMANIA, STRING and Schrodinger) were used to explore the effect of nsSNPs on FOXP4 protein function and structural stability. First, 555 nsSNPs were analyzed using SIFT, of which 57 were found as deleterious. Following, PolyPhen2, SNAP2, PhD SNP and PANTHER analyses, 10 nsSNPs (rs372762294, rs141899153, rs142575732, rs376938850, rs367607523, rs112517943, rs140387832, rs373949416, rs373949416 and rs376160648) were common and observed as deleterious, damaging and diseases associated. Following that, using I-Mutant2.0 and MUpro servers, 7 nsSNPs were found to be the most unstable. GOR IV predicted that these seven nsSNPs affect protein structure by altering the protein contents of alpha helixes, extended strands, and random coils. Following DynaMut2, 5 nsSNPs showed a decrease in the ΔΔG value compared with the wild-type and were found to be responsible for destabilizing the corresponding protein. GeneMANIA and STRING network revealed interaction of FOXP4 with other genes. Finally, molecular dynamics simulation analysis revealed consistent fluctuation in RMSD and RMSF values, Rg and hydrogen bonds in the mutant proteins compared with WT, which might alter the functional and structural stability of the corresponding protein. As a result, the aforementioned integrated comprehensive bioinformatic analyses provide insight into how various nsSNPs of the FOXP4 gene change the structural and functional properties of the corresponding protein, potentially proceeding with the pathophysiology of human diseases.

Zoonotic linkage and environmental contamination of Methicillin-resistant Staphylococcus aureus (MRSA) in dairy farms: A one health perspective.

Methicillin-Resistant Staphylococcus aureus (MRSA) is a ubiquitous public health challenge, with its prevalence in human, animal, and environmental interfaces posing significant concerns. This study aimed to characterize and detect the zoonotic linkages of MRSA within the cow-environment-human interfaces in dairy farms to address the One Health perspective. A comprehensive investigation, involving 636 samples (an equal number of raw milk and cow nasal swab samples, along with varying numbers of human nasal swab and environmental samples), revealed an overall MRSA prevalence of 13.4% (n = 271/636). Notably, environmental samples exhibited the highest prevalence (19.3%), emphasizing the potential role of farm surroundings in MRSA transmission, while the lowest prevalence was found in raw milk at 11.8% (n = 31/263). The prevalence in cow nasal swabs and human nasal swabs was 13.3% (n = 35/263) and 15.1% (n = 8/53), respectively. Multiplex PCR analysis revealed the presence of different Staphylococcal enterotoxin (SEa, SEb, SEc, and SEd), and exfoliative toxin-producing genes (Eta, Etb) within the MRSA isolates underlining their potential to induce public health threats. All MRSA isolates exhibited complete resistance to Oxacillin (100%) and Amoxicillin (100%), while the highest sensitivity was observed for Vancomycin (85.8%). Furthermore, these MRSA strains demonstrated varying degrees of resistance to other commonly used antimicrobial drugs, including Cefoxitin (75.3%), Ceftarolin (71.2%), Sulfamethoxazole-Trimethoprim (63.5%), Ciprofloxacin (60%), and Gentamicin (49.5%). Detection of MRSA in cow, human, and environmental samples within the same farm vicinity highlights the risk of zoonotic transmission of MRSA from cows to humans through environmental interfaces. Phylogenetic analysis of the mecA gene in MRSA isolates from all sources within the same farm revealed a high similarity index (>84%) among them suggesting a shared evolutionary origin. Moreover, the MRSA isolates from milk samples showed a close evolutionary relationship with isolates from Kenya and Brazil, while the isolates from humans and the environment displayed noticeable resemblance to isolates from several Asian countries. The findings emphasize the importance of collaborative efforts under the One Health framework to address this multifaceted issue and ensure the safety of our food supply and public health.

Responsible AI for cardiovascular disease detection: Towards a privacy-preserving and interpretable model.

BACKGROUND AND OBJECTIVE: Cardiovascular disease (CD) is a major global health concern, affecting millions with symptoms like fatigue and chest discomfort. Timely identification is crucial due to its significant contribution to global mortality. In healthcare, artificial intelligence (AI) holds promise for advancing disease risk assessment and treatment outcome prediction. However, machine learning (ML) evolution raises concerns about data privacy and biases, especially in sensitive healthcare applications. The objective is to develop and implement a responsible AI model for CD prediction that prioritize patient privacy, security, ensuring transparency, explainability, fairness, and ethical adherence in healthcare applications. METHODS: To predict CD while prioritizing patient privacy, our study employed data anonymization involved adding Laplace noise to sensitive features like age and gender. The anonymized dataset underwent analysis using a differential privacy (DP) framework to preserve data privacy. DP ensured confidentiality while extracting insights. Compared with Logistic Regression (LR), Gaussian Naïve Bayes (GNB), and Random Forest (RF), the methodology integrated feature selection, statistical analysis, and SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for interpretability. This approach facilitates transparent and interpretable AI decision-making, aligning with responsible AI development principles. Overall, it combines privacy preservation, interpretability, and ethical considerations for accurate CD predictions. RESULTS: Our investigations from the DP framework with LR were promising, with an area under curve (AUC) of 0.848 ± 0.03, an accuracy of 0.797 ± 0.02, precision at 0.789 ± 0.02, recall at 0.797 ± 0.02, and an F1 score of 0.787 ± 0.02, with a comparable performance with the non-privacy framework. The SHAP and LIME based results support clinical findings, show a commitment to transparent and interpretable AI decision-making, and aligns with the principles of responsible AI development. CONCLUSIONS: Our study endorses a novel approach in predicting CD, amalgamating data anonymization, privacy-preserving methods, interpretability tools SHAP, LIME, and ethical considerations. This responsible AI framework ensures accurate predictions, privacy preservation, and user trust, underscoring the significance of comprehensive and transparent ML models in healthcare. Therefore, this research empowers the ability to forecast CD, providing a vital lifeline to millions of CD patients globally and potentially preventing numerous fatalities.

An explainable machine learning framework for predicting the risk of buprenorphine treatment discontinuation for opioid use disorder among commercially insured individuals.

OBJECTIVES: Buprenorphine is an effective evidence-based medication for opioid use disorder (OUD). Yet premature discontinuation undermines treatment effectiveness, increasing the risk of mortality and overdose. We developed and evaluated a machine learning (ML) framework for predicting buprenorphine care discontinuity within 12 months following treatment initiation. METHODS: This retrospective study used United States (US) 2018-2021 MarketScan commercial claims data of insured individuals aged 18-64 who initiated buprenorphine between July 2018 and December 2020 with no buprenorphine prescriptions in the previous six months. We measured buprenorphine prescription discontinuation gaps of ≥30 days within 12 months of initiating treatment. We developed predictive models employing logistic regression, decision tree classifier, random forest, extreme gradient boosting, Adaboost, and random forest-extreme gradient boosting ensemble. We applied recursive feature elimination with cross-validation to reduce dimensionality and identify the most predictive features while maintaining model robustness. For model validation, we used several statistics to evaluate performance, such as C-statistics and precision-recall curves. We focused on two distinct treatment stages: at the time of treatment initiation and one and three months after treatment initiation. We employed SHapley Additive exPlanations (SHAP) analysis that helped us explain the contributions of different features in predicting buprenorphine discontinuation. We stratified patients into risk subgroups based on their predicted likelihood of treatment discontinuation, dividing them into decile subgroups. Additionally, we used a calibration plot to analyze the reliability of the models. RESULTS: A total of 30,373 patients initiated buprenorphine and 14.98% (4551) discontinued treatment. C-statistic varied between 0.56 and 0.76 for the first-stage models including patient-level demographic and clinical variables. Inclusion of proportion of days covered (PDC) measured after one month and three months following treatment initiation significantly increased the models' discriminative power (C-statistics: 0.60 to 0.82). Random forest (C-statistics: 0.76, 0.79 and 0.82 with baseline predictors, one-month PDC and three-months PDC, respectively) outperformed other ML models in discriminative performance in all stages (C-statistics: 0.56 to 0.77). Most influential risk factors of discontinuation included early stage medication adherence, age, and initial days of supply. CONCLUSION: ML algorithms demonstrated a good discriminative power in identifying patients at higher risk of buprenorphine care discontinuity. The proposed framework may help healthcare providers optimize treatment strategies and deliver targeted interventions to improve buprenorphine care continuity.

In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene.

Single nucleotide polymorphisms (SNPs) are one of the most common determinants and potential biomarkers of human disease pathogenesis. SNPs could alter amino acid residues, leading to the loss of structural and functional integrity of the encoded protein. In humans, members of the minichromosome maintenance (MCM) family play a vital role in cell proliferation and have a significant impact on tumorigenesis. Among the MCM members, the molecular mechanism of how missense SNPs of minichromosome maintenance complex component 6 (MCM6) contribute to DNA replication and tumor pathogenesis is underexplored and needs to be elucidated. Hence, a series of sequence and structure-based computational tools were utilized to determine how mutations affect the corresponding MCM6 protein. From the dbSNP database, among 15,009 SNPs in the MCM6 gene, 642 missense SNPs (4.28%), 291 synonymous SNPs (1.94%), and 12,500 intron SNPs (83.28%) were observed. Out of the 642 missense SNPs, 33 were found to be deleterious during the SIFT analysis. Among these, 11 missense SNPs (I123S, R207C, R222C, L449F, V456M, D463G, H556Y, R602H, R633W, R658C, and P815T) were found as deleterious, probably damaging, affective and disease-associated. Then, I123S, R207C, R222C, V456M, D463G, R602H, R633W, and R658C missense SNPs were found to be highly harmful. Six missense SNPs (I123S, R207C, V456M, D463G, R602H, and R633W) had the potential to destabilize the corresponding protein as predicted by DynaMut2. Interestingly, five high-risk mutations (I123S, V456M, D463G, R602H, and R633W) were distributed in two domains (PF00493 and PF14551). During molecular dynamics simulations analysis, consistent fluctuation in RMSD and RMSF values, high Rg and hydrogen bonds in mutant proteins compared to wild-type revealed that these mutations might alter the protein structure and stability of the corresponding protein. Hence, the results from the analyses guide the exploration of the mechanism by which these missense SNPs of the MCM6 gene alter the structural integrity and functional properties of the protein, which could guide the identification of ways to minimize the harmful effects of these mutations in humans.

Effects of Commercial Probiotics on the Growth Performance, Intestinal Microbiota and Intestinal Histomorphology of Nile Tilapia (Oreochromis niloticus) Reared in Biofloc Technology (BFT).

Though different types of commercial probiotics are supplemented in biofloc technology (BFT), very little information is available on their effects on the farmed fish. Therefore, this study focused on evaluating the effects of three most commonly used commercial probiotics on the growth performance, intestinal histomorphology, and intestinal microbiota of Nile tilapia (Oreochromis niloticus) reared in BFT. Tilapia fry, with an average weight of 3.02 ± 0.50 g, were stocked at a density of 60 fry/0.2 m3, and cultured for 90 days. Three commercial probiotics were administered, with three replications for each: a single-genus multi-species probiotic (Bacillus spp.) (T1), a multi-genus multi-species probiotic (Bacillus sp., Lactobacillus sp., Nitrosomonas sp., Nitrobacter sp.) (T2), and a multi-species probiotic (Bacillus spp.) combined with enzymes including amylase, protease, cellulase, and xylanase (T3). The results showed significant variations in growth and feed utilization, with T3 outperforming other treatments in terms of weight gain, liver weight, and intestine weight. Adding Bacillus spp. with enzymes (T3) to water significantly increased the histomorphological parameters (villi length, villi depth, crypt depth, muscle thickness, intestinal thickness) as well as microbes (total viable count and total lactic acid bacteria) of intestine of fish compared to T1 and T2, leading to improved digestion and absorption responses. It is concluded that the supplementation of commercial probiotics has potential benefits on farmed fish species in BFT.

Hazard-mapping and health risk analysis of iron and arsenic contamination in the groundwater of Sylhet district.

This study investigates groundwater contamination by arsenic and iron and its health implications within the Sylhet district in Bangladesh. Utilizing geographic information system (GIS) and inverse distance weighting (IDW) methods, hazard maps have been developed to evaluate contamination risk across various upazilas. The findings show significant arsenic and iron pollution, particularly in the northwestern part of the district. In about 50% of the area, especially in Jaintiapur, Zakiganj, Companiganj, and Kanaighat where arsenic levels surpass 0.05 mg/L which is the standard limit of Bangladesh. Iron levels peak at 13.83 mg/L, severely impacting 45% of the region, especially in Gowainghat, northeastern Jaintiapur, Zakigonj, and Golabganj. The study employs USEPA health risk assessment methods to calculate the hazard quotient (HQ) and hazard index (HI) for both elements via oral and dermal exposure. Results indicate that children face greater noncarcinogenic and carcinogenic risks than adults, with oral HI showing significant risk in Balagonj and Bishwanath. Dermal adsorption pathways exhibit comparatively lower risks. Cancer risk assessments demonstrate high carcinogenic risks from oral arsenic intake in all areas. This comprehensive analysis highlights the urgent need for effective groundwater management and policy interventions in the Sylhet district to mitigate these health risks and ensure safe drinking water.

Comparison of hypoxia- and hyperoxia-induced alteration of epigene expression pattern in lungs of Pleurodeles waltl and Mus musculus.

Epigenetics is an emerging field of research because of its involvement in susceptibility to diseases and aging. Hypoxia and hyperoxia are known to be involved widely in various pathophysiologies. Here, we compared the differential epigene expression pattern between Pleurodeles waltl and Mus musculus (commonly known as Iberian ribbed newt and mouse, respectively) exposed to hypoxia and hyperoxia. Adult healthy newts and mice were exposed to normobaric hypoxia (8% O2) and hyperoxia (80% O2) for 2 hours. We collected the lungs and analyzed the expression of hypoxia-inducible factor 1 alpha (Hif1α) and several key epigenes from DNA methyltransferase (DNMT) family, histone deacetylase (HDAC) family, and methyl-CpG binding domain (MBD) family. The exposure to hypoxia significantly increased the mRNA levels of DNA methyltransferase 3 alpha (Dnmt3α), methyl-CpG binding domain protein 2 (Mbd2), Mbd3, and histone deacetylase 2 (Hdac2) in lungs of newts, but decreased the mRNA levels of DNA methyltransferase 1 (Dnmt1) and Dnmt3α in lungs of mice. The exposure to hyperoxia did not significantly change the expression of any gene in either newts or mice. The differential epigene expression pattern in response to hypoxia between newts and mice may provide novel insights into the prevention and treatment of disorders developed due to hypoxia exposure.

SalivaDirect: an alternative to a conventional RNA extraction protocol for molecular detection of SARS-CoV-2 in a clinical setting.

IMPORTANCE: Affordable and accessible tests for COVID-19 allow for timely disease treatment and pandemic management. SalivaDirect is a faster and easier method to implement than NPS sampling. Patients can self-collect saliva samples at home or in other non-clinical settings without the help of a healthcare professional. Sample processing in SalivaDirect is less complex and more adaptable than in conventional nucleic acid extraction methods. We found that SalivaDirect has good diagnostic performance and is ideal for large-scale testing in settings where supplies may be limited or trained healthcare professionals are unavailable.