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Background: T1D is an autoimmune disease in which pancreatic islets of Langerhans are infiltrated by immune cells resulting in the specific destruction of insulin-producing islet beta cells. Our understanding of the factors leading to islet infiltration and the interplay of the immune cells with target beta cells is incomplete, especially in human disease. While murine models of T1D have provided crucial information for both beta cell and autoimmune cell function, the translation of successful therapies in the murine model to human disease has been a challenge. Scope of review: Here, we discuss current state of the art and consider knowledge gaps concerning the interface of the islet beta cell with immune infiltrates, with a focus on T cells. We discuss pancreatic and immune cell phenotypes and their impact on cell function in health and disease, which we deem important to investigate further to attain a more comprehensive understanding of human T1D disease etiology. Major conclusions: The last years have seen accelerated development of approaches that allow comprehensive study of human T1D. Critically, recent studies have contributed to our revised understanding that the pancreatic beta cell assumes an active role, rather than a passive position, during autoimmune disease progression. The T cell-beta cell interface is a critical axis that dictates beta cell fate and shapes autoimmune responses. This includes the state of the beta cell after processing internal and external cues (e.g., stress, inflammation, genetic risk) that that contributes to the breaking of tolerance by hyperexpression of human leukocyte antigen (HLA) class I with presentation of native and neoepitopes and secretion of chemotactic factors to attract immune cells. We anticipate that emerging insights about the molecular and cellular aspects of disease initiation and progression processes will catalyze the development of novel and innovative intervention points to provide additional therapies to individuals affected by T1D.

ObjectiveWe sought to characterize the performance of inpatient and outpatient computerized clinical decision support (CDS) alerts aimed at reducing inappropriate benzodiazepine and nonbenzodiazepine sedative medication prescribing in older adults 18 months after implementation.MethodsWe reviewed the performance of two CDS alerts in the outpatient and inpatient settings in 2019. To examine the alerts' effectiveness, we analyzed metrics including overall alert adherence, provider-level adherence, and reasons for alert trigger and override.ResultsIn 2019, we identified a total of 14,534 and 4,834 alerts triggered in the outpatient and inpatient settings, respectively. Providers followed only 1% of outpatient and 3% of inpatient alerts. Most alerts were ignored (68% outpatient and 60% inpatient), while providers selected to override the remaining alerts. In each setting, the top 2% of clinicians were responsible for approximately 25% of all ignored or overridden alerts. However, a small proportion of clinicians (2% outpatient and 4% inpatient) followed the alert at least half of the time and accounted for a disproportionally large fraction of the total followed alerts. Our analysis of the free-text comments revealed that many alerts were to continue outpatient prescriptions or for situational anxiety.ConclusionOur findings highlight the importance of evaluation of CDS performance after implementation. We found large variation in response to the inpatient and outpatient alerts, both with respect to follow and ignore rates. Reevaluating the alert design by providing decision support by indication may be more helpful and may reduce alert fatigue.

The formation of the mitotic checkpoint complex requires an intricate spatial coordination amongst the proteins Bub1, Mad1, Cdc20, and Mad2. Chen et al show that a structural flexibility in Mad1 plays an essential role in achieving this coordination.

Abstract Background Type 2 diabetes (T2D) is a critical healthcare challenge and priority in Qatar which is listed amongst the top 10 countries in the world, with its prevalence presently at 17% double the global average. MicroRNAs (miRNAs) are implicated in the pathogenesis of (T2D) and long-term microvascular complications including diabetic retinopathy (DR). Methods In this study, a T2D cohort that accurately matches the characteristics of the general population was employed to find microRNA (miRNA) signatures that are correlated with glycemic and β cell function measurements. Targeted miRNA profiling was performed in (471) T2D individuals with or without DR and (491) (non-diabetic) healthy controls from the Qatar Biobank. Discovery analysis identified 20 differentially expressed miRNAs in T2D compared to controls, of which miR-223-3p was significantly upregulated (fold change:5.16, p = 3.6e−02) and positively correlated with glucose and hemoglobin A1c (HbA1c) levels (p-value = 9.88e−04 and 1.64e−05, respectively), but did not show any significant associations with insulin or C-peptide. Accordingly, we performed functional validation using a miR-223-3p mimic (overexpression) under control and hyperglycemia-induced conditions in a zebrafish model. Results Over-expression of miR-223-3p alone was associated with significantly higher glucose (42.7 mg/dL, n = 75 vs 38.7 mg/dL, n = 75, p = 0.02) and degenerated retinal vasculature, and altered retinal morphology involving changes in the ganglion cell layer and inner and outer nuclear layers. Assessment of retinal angiogenesis revealed significant upregulation in the expression of vascular endothelial growth factor and its receptors, including kinase insert domain receptor. Further, the pancreatic markers, pancreatic and duodenal homeobox 1, and the insulin gene expressions were upregulated in the miR-223-3p group. Conclusion Our zebrafish model validates a novel correlation between miR-223-3p and DR development. Targeting miR-223-3p in T2D patients may serve as a promising therapeutic strategy to control DR in at-risk individuals.

Magnetized plasma interactions are ubiquitous in astrophysical and laboratory plasmas. Various physical effects have been shown to be important within colliding plasma flows influenced by opposing magnetic fields, however, experimental verification of the mechanisms within the interaction region has remained elusive. Here we discuss a laser-plasma experiment whereby experimental results verify that Biermann battery generated magnetic fields are advected by Nernst flows and anisotropic pressure effects dominate these flows in a reconnection region. These fields are mapped using time-resolved proton probing in multiple directions. Various experimental, modelling and analytical techniques demonstrate the importance of anisotropic pressure in semi-collisional, high-β plasmas, causing a reduction in the magnitude of the reconnecting fields when compared to resistive processes. Anisotropic pressure dynamics are crucial in collisionless plasmas, but are often neglected in collisional plasmas. We show pressure anisotropy to be essential in maintaining the interaction layer, redistributing magnetic fields even for semi-collisional, high energy density physics (HEDP) regimes.

Objective: According to modern medicine sciences, new term “diabesity” is being coined to explain the interdependency and relationship of diabetes and obesity. Diabesity is diabetes occurring in the context of obesity. The terms such as Prameha and Sthoulya are used to explain the above conditions in Ayurveda. Both of these conditions have many etiological factors in common. The basic principles of management of all the diseases in Ayurveda include Aushadha (~medications), Aahara (~diet), and Vihaara (~lifestyle). It is observed that Pathya-Aahara-Vihaara (~balanced diet and lifestyle) can help in reversal of such diseases. Data Source: Combined Pathya-Aahara-Vihaara regimen as prescribed for both Prameha and Sthoulya can help in effective management of the condition. This includes the study of suggested Pathya-Aahara-Vihaara from almost all the Aahara Varga (~food groups) mentioned in Data Source and Review. Review Methods: Information regarding the etiological factors and Pathya-Aahara-Vihaara according to different Samhita ranging from Brihattrayee (Charaka Samhita, Sushruta Samhita, Ashtang Sangraha, Ashtang Hridaya) to Laghutrayee (Bhavaprakash, Sharangdhar Samhita, Madhav Nidana), Bhaishajya Ratnavali, and Yogaratnakar. In this article, we mainly focus on the Pathya-Aahara-Vihaara that Sthula Pramehi (~obese diabetic) or any person suffering from diabesity can follow to prevent control and revert the diseases. Conclusion: Diabesity can be correlated with conditions such as Prameha and Sthoulya according to Ayurveda, and there was similarity in etiological factors of both conditions. Combined Pathya-Aahara-Vihaara can help in managing the diabesity like condition.

Abstract Background Understanding, characterizing, and quantifying human exposures to environmental chemicals is critical to protect public health. Exposure assessments are key to determining risks to the general population and for specific subpopulations given that exposures differ between groups. Exposure data are also important for understanding where interventions, including public policies, should be targeted and the extent to which interventions have been successful. In this review, we aim to show how inadequacies in exposure assessments conducted by polluting industries or regulatory agencies have led to downplaying or disregarding exposure concerns raised by communities; that underestimates of exposure can lead regulatory agencies to conclude that unacceptable risks are, instead, acceptable, allowing pollutants to go unregulated; and that researchers, risk assessors, and policy makers need to better understand the issues that have affected exposure assessments and how appropriate use of exposure data can contribute to health-protective decisions. Methods We describe current approaches used by regulatory agencies to estimate human exposures to environmental chemicals, including approaches to address limitations in exposure data. We then illustrate how some exposure assessments have been used to reach flawed conclusions about environmental chemicals and make recommendations for improvements. Results Exposure data are important for communities, public health advocates, scientists, policy makers, and other groups to understand the extent of environmental exposures in diverse populations. We identify four areas where exposure assessments need to be improved due to systemic sources of error or uncertainty in exposure assessments and illustrate these areas with examples. These include: (1) an inability of regulatory agencies to keep pace with the increasing number of chemicals registered for use or assess their exposures, as well as complications added by use of ‘confidential business information’ which reduce available exposure data; (2) the failure to keep assessments up-to-date; (3) how inadequate assumptions about human behaviors and co-exposures contribute to underestimates of exposure; and (4) that insufficient models of toxicokinetics similarly affect exposure estimates. Conclusion We identified key issues that impact capacity to conduct scientifically robust exposure assessments. These issues must be addressed with scientific or policy approaches to improve estimates of exposure and protect public health.

Abstract The manufacture and production of industrial chemicals continues to increase, with hundreds of thousands of chemicals and chemical mixtures used worldwide, leading to widespread population exposures and resultant health impacts. Low-wealth communities and communities of color often bear disproportionate burdens of exposure and impact; all compounded by regulatory delays to the detriment of public health. Multiple authoritative bodies and scientific consensus groups have called for actions to prevent harmful exposures via improved policy approaches. We worked across multiple disciplines to develop consensus recommendations for health-protective, scientific approaches to reduce harmful chemical exposures, which can be applied to current US policies governing industrial chemicals and environmental pollutants. This consensus identifies five principles and scientific recommendations for improving how agencies like the US Environmental Protection Agency (EPA) approach and conduct hazard and risk assessment and risk management analyses: (1) the financial burden of data generation for any given chemical on (or to be introduced to) the market should be on the chemical producers that benefit from their production and use; (2) lack of data does not equate to lack of hazard, exposure, or risk; (3) populations at greater risk, including those that are more susceptible or more highly exposed, must be better identified and protected to account for their real-world risks; (4) hazard and risk assessments should not assume existence of a “safe” or “no-risk” level of chemical exposure in the diverse general population; and (5) hazard and risk assessments must evaluate and account for financial conflicts of interest in the body of evidence. While many of these recommendations focus specifically on the EPA, they are general principles for environmental health that could be adopted by any agency or entity engaged in exposure, hazard, and risk assessment. We also detail recommendations for four priority areas in companion papers (exposure assessment methods, human variability assessment, methods for quantifying non-cancer health outcomes, and a framework for defining chemical classes). These recommendations constitute key steps for improved evidence-based environmental health decision-making and public health protection.

Abstract The strength of social relations has been shown to affect an individual’s access to opportunities. To date, however, the correspondence between tie strength and population’s economic prospects has not been quantified, largely because of the inability to operationalise strength based on Granovetter’s classic theory. Our work departed from the premise that tie strength is a unidimensional construct (typically operationalized with frequency or volume of contact), and used instead a validated model of ten fundamental dimensions of social relationships grounded in the literature of social psychology. We built state-of-the-art NLP tools to infer the presence of these dimensions from textual communication, and analyzed a large conversation network of 630K geo-referenced Reddit users across the entire US connected by 12.8M social ties created over the span of 7 years. We found that unidimensional tie strength is only weakly correlated with economic opportunities ( $$R^2=0.30$$ R 2 = 0.30 ), while multidimensional constructs are highly correlated ( $$R^2=0.62$$ R 2 = 0.62 ). In particular, economic opportunities are associated to the combination of: (i) knowledge ties, which bridge geographically distant groups, facilitating the knowledge dissemination across communities; and (ii) social support ties, which knit geographically close communities together, and represent dependable sources of social and emotional support. These results point to the importance of developing high-quality measures of tie strength in network theory.

Abstract Background Leukocyte telomere length (LTL) is suggested to be a biomarker of biological age and reported to be associated with metabolic diseases such as type 2 diabetes. Glucose metabolic traits including glucose and insulin levels have been reported to be associated with LTL in adulthood. However, there is relatively little research focusing on children’s LTL and the association with prenatal exposures. This study investigates the relationship between maternal and offspring glucose metabolism with offspring LTL in early life. Methods This study included 882 mother-child pairs from the HAPO Hong Kong Field Centre, with children evaluated at age 7.0 ± 0.4 (mean ± SD) years. Glucose metabolic traits including maternal post-load glucose during pregnancy, children’s glucose and insulin levels, and their derived indices at follow-up were measured or calculated. Offspring LTL was assessed using real-time polymerase chain reaction. Results Sex- and age-adjusted children’s LTL was found to be associated with children’s HOMA-IR (β=−0.046 ± 0.016, p=0.005). Interestingly, both children’s and maternal post-load glucose levels were positively associated with children’s LTL. However, negative associations were observed between children’s LTL and children’s OGTT insulin levels. In addition, the LTL in females was more strongly associated with pancreatic beta-cell function whilst LTL in males was more strongly associated with OGTT glucose levels. Conclusions Our findings suggest a close association between maternal and offspring glucose metabolic traits with early life LTL, with the offspring sex as an important modifier of the disparate relationships in insulin production and response.