Microorganism-mediated biodegradation for effective management and/or removal of micro-plastics from the environment: a comprehensive review.

Micro- plastics (MPs) pose significant global threats, requiring an environment-friendly mode of decomposition. Microbial-mediated biodegradation and biodeterioration of micro-plastics (MPs) have been widely known for their cost-effectiveness, and environment-friendly techniques for removing MPs. MPs resistance to various biocidal microbes has also been reported by various studies. The biocidal resistance degree of biodegradability and/or microbiological susceptibility of MPs can be determined by defacement, structural deformation, erosion, degree of plasticizer degradation, metabolization, and/or solubilization of MPs. The degradation of microplastics involves microbial organisms like bacteria, mold, yeast, algae, and associated enzymes. Analytical and microbiological techniques monitor microplastic biodegradation, but no microbial organism can eliminate microplastics. MPs can pose environmental risks to aquatic and human life. Micro-plastic biodegradation involves fragmentation, assimilation, and mineralization, influenced by abiotic and biotic factors. Environmental factors and pre-treatment agents can naturally degrade large polymers or induce bio-fragmentation, which may impact their efficiency. A clear understanding of MPs pollution and the microbial degradation process is crucial for mitigating its effects. The study aimed to identify deteriogenic microorganism species that contribute to the biodegradation of micro-plastics (MPs). This knowledge is crucial for designing novel biodeterioration and biodegradation formulations, both lab-scale and industrial, that exhibit MPs-cidal actions, potentially predicting MPs-free aquatic and atmospheric environments. The study emphasizes the urgent need for global cooperation, research advancements, and public involvement to reduce micro-plastic contamination through policy proposals and improved waste management practices.

Prevalence of type 2 diabetes and pre-diabetes among pulmonary and extrapulmonary tuberculosis patients of Bangladesh: A cross-sectional study.

BACKGROUND: We aimed to determine the prevalence of type 2 diabetes (T2D) and pre-diabetes (pre-DM) among patients with pulmonary tuberculosis (PTB) and extrapulmonary tuberculosis (EPTB) in Bangladesh. We also examined the association between type of TB and hyperglycaemia as an adjunct to the primary objective. MATERIALS AND METHODS: This cross-sectional analytical study recruited 350 TB patients (175 PTB and 175 EPTB) from two tertiary care hospitals specialized for TB treatment. Oral glucose tolerance tests and fasting plasma glucose measurements were carried out for unknown glycaemic status and those with previously known diabetes, respectively. RESULTS: Overall, the prevalence of T2D and pre-DM was 19.1% (new 85.1%, old 14.9%) and 34.3%, respectively. Although the risk factors were highly prevalent among the patients with EPTB, a higher proportion of T2D (26.3%) and pre-DM (34.3%) was detected among the patients with PTB. The proportion of impaired fasting glucose was low in both groups, but a high trend of impaired glucose tolerance was observed across the groups, with a higher proportion (35.4%) in the PTB group. Both pre-DM and T2D showed significantly higher odds (pre-DM, AOR: 4.488; CI: 2.531-7.958; p < .001 and T2D, AOR: 4.280; CI: 2.305-7.946; p < .001) for having PTB. CONCLUSION: The prevalence of T2D and pre-DM was higher among the patients with PTB, and it (PTB) appeared as a predictor of hyperglycaemia. It indicates the primary intervention should target the patients with PTB to get the maximum benefit of screening to reduce the number of risk factors, disease burden and subsequent complications.

Leveraging intelligent agents for knowledge discovery from heterogeneous healthcare data repositories.

This paper presents a case for an intelligent agent based framework for knowledge discovery in a distributed healthcare environment comprising multiple heterogeneous healthcare data repositories. Data-mediated knowledge discovery, especially from multiple heterogeneous data resources, is a tedious process and imposes significant operational constraints on end-users. We demonstrate that autonomous, reactive and proactive intelligent agents provide an opportunity to generate end-user oriented, packaged, value-added decision-support/strategic planning services for healthcare professionals, manages and policy makers, without the need for a priori technical knowledge. Since effective healthcare is grounded in good communication, experience sharing, continuous learning and proactive actions, we use intelligent agents to implement an Agent based Data Mining Infostructure that provides a suite of healthcare-oriented decision-support/strategic planning services.