Journal of the Indian Institute of Science最新文献

Bioaerosols play essential roles in the atmospheric environment and can affect human health. With a few exceptions (e.g., farm or rainforest environments), bioaerosol samples from wide-ranging environments typically have a low biomass, including bioaerosols from indoor environments (e.g., residential homes, offices, or hospitals), outdoor environments (e.g., urban or rural air). Some specialized environments (e.g., clean rooms, the Earth’s upper atmosphere, or the international space station) have an ultra-low-biomass. This review discusses the primary sources of bioaerosols and influencing factors, the recent advances in air sampling techniques and the new generation sequencing (NGS) methods used for the characterization of low-biomass bioaerosol communities, and challenges in terms of the bias introduced by different air samplers when samples are subjected to NGS analysis with a focus on ultra-low biomass. High-volume filter-based or liquid-based air samplers compatible with NGS analysis are required to improve the bioaerosol detection limits for microorganisms. A thorough understanding of the performance and outcomes of bioaerosol sampling using NGS methods and a robust protocol for aerosol sample treatment for NGS analysis are needed. Advances in NGS techniques and bioinformatic tools will contribute toward the precise high-throughput identification of the taxonomic profiles of bioaerosol communities and the determination of their functional and ecological attributes in the atmospheric environment. In particular, long-read amplicon sequencing, viability PCR, and meta-transcriptomics are promising techniques for discriminating and detecting pathogenic microorganisms that may be active and infectious in bioaerosols and, therefore, pose a threat to human health.

Gestational diabetes mellitus (GDM) is defined as abnormal glucose tolerance recognized first during pregnancy. Presence of GDM has been associated with obstetric and neonatal complications and has been recognized as a risk factor for future risk of diabetes and obesity in both mother and baby. The factors that play a role in development of GDM, include age, obesity, family history of diabetes, and previous history of GDM. Ethnicity, multi-fetal pregnancies, and presence of polycystic ovaries are some other risk factors for GDM. There are several screening and diagnostic criteria for GDM. The most widely accepted is the International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria which recommends a single step 75 g oral glucose tolerance test at 24–28 weeks. To ensure normal glucose levels and reduce mortality and morbidity due to GDM, management of GDM during pregnancy and postpartum follow up are very essential. In this review, we present some key issues related to postpartum testing and have highlighted some strategies to tackle the same. We also present some learnings from the Women in India with GDM Strategy project, which developed a model of care for GDM in resource constrained settings. As part of the implementation of the model of care, several strategies were adopted that led to a 95.8% postpartum follow up of women with GDM.

Diabetes is a common disease in India; however, many myths about the disease are still prevalent even amongst educated people in the country. This popular science article aims to provide an in-depth look at the different kinds of diabetes in India to people with no medical background.

The increasing prevalence of diabetes has led to reducing hyperglycemia through mechanisms other than the conventional mechanism, such as α-amylase and α-glucosidase inhibition. In recent years, medicinal drugs focusing on inhibiting dipeptidyl peptidase IV (DPP IV) and protein tyrosine phosphatase 1B (PTP1B) enzymes have emerged and are being used for type 2 diabetes management. DPP IV inhibitors reduce blood glucose levels by preventing the degradation of incretin hormones such as glucagon-like peptide and glucose-dependent insulinotropic polypeptide. PTP1B has also been known to play a crucial role in reducing insulin resistance and is one of the most promising targets for managing Type 2 diabetes. Inhibition of these two enzymes is also expected to benefit other metabolic conditions such as cancer, obesity, lowered immunity, etc. The existing synthetic DPP IV and PTP1B inhibitors have been known to cause side effects. Inhibitors from natural sources are expected to be safer. The search for PTP1B inhibitors is especially necessary since the primary treatment for type 2 diabetes is to reduce insulin resistance. None of the existing PTP1B inhibitors are clinically well-approved to date. Hence, searching for antihyperglycemic components from natural sources such as foods has become a pressing need. This review has attempted to collate and analyze the existing scientific evidence to identify plant foods and their phytochemicals with in vitro and in vivo DPP IV and PTP1B inhibitory activity comprehensively. With further scientific validation and safety studies, the identified phytochemicals could be used for pharmacological applications. The foods and their extracts could be advantageous in formulating functional foods and diets suitable for type 2 diabetes, along with other physiological benefits.

The effect of an increasing diabetes population has resulted in escalated costs and overburdened physicians. The increase in cost is not due to the disease per se, but because of its largely preventable complications. Patient-friendly technologies are proven to significantly reduce complications and thereby cost, but seldom practised. Telemedicine is increasingly being utilized in diabetology to improve access to health care, quality of care, and clinical/psychosocial outcomes in patients with diabetes (PWD). In PWD, patient–physician interactions are essential for improving health outcomes and preventing long-term complications. Smartphones are one of the basic modalities for telemedicine application. Mobile phone messaging applications, including text messaging and multimedia message service, could offer a convenient and cost-effective way to support desirable health behaviors. There are diabetes-related mobile apps mainly focusing on self-management of diabetes, lifestyle modification, and medication adherence motivation. With the widespread availability of high-speed Internet, remote monitoring has also become popular. Home monitoring of blood glucose and blood pressure, wearable devices, and continuous glucose monitoring also play a vital role in bringing down the long‑term vascular complications of diabetes and thereby reduce the overall cost and improve the quality of life of patients. There are hundreds of tech platforms for diabetes management, of which only a few with proven efficacy and safety are recommended by physicians.

Development, validation, and testing of algorithms for artificial pancreas (AP) systems require mathematical models for the glucose–insulin dynamics inside the body. These physiological models have been extensively studied over the past decades. Two broad types of models are available in diabetic research, each with its own unique purpose: (i) minimal models, which are relatively simple but still manages to capture the macroscopic behavior of the glucose–insulin dynamics of the body, and (ii) high-fidelity models, which are complex and precisely describe the internal dynamics of the glucose–insulin interaction in the body. The minimal models are primarily utilized for control algorithm synthesis, whereas the high-fidelity models are used as platforms for testing and validating AP systems. The most well-known variants of these physiological models are discussed in detail. In addition to these systems, data-driven models such as the auto-regressive moving average with exogenous inputs (ARMAX) models are also used widely in control algorithm synthesis for AP systems. High-fidelity models are utilized for simulating virtual diabetic patients for in silico testing and validation of artificial pancreas systems. Two currently available high-fidelity models are reviewed in this paper for completeness, including the Type-1 diabetes mellitus (T1DM) simulator approved by the food and drug administration of USA. Models accounting for exercise and also glucagon infusion (for dual-hormone AP systems) are also included, which are essential in developing control algorithms with better autonomy and minimal risk.