Thyrotrope hyperplasia and hypertrophy are common responses to primary hypothyroidism. To understand the genetic regulation of these processes, we studied gene expression changes in the pituitaries of Cga(-/-) mice, which are deficient in the common α-subunit of TSH, LH, and FSH. These mice have thyrotrope hypertrophy and hyperplasia and develop thyrotrope adenoma. We report that cell proliferation is increased, but the expression of most stem cell markers is unchanged. The α-subunit is required for secretion of the glycoprotein hormone β-subunits, and mutants exhibit elevated expression of many genes involved in the unfolded protein response, consistent with dilation and stress of the endoplasmic reticulum. Mutants have elevated expression of transcription factors that are important in thyrotrope function, such as Gata2 and Islet 1, and those that stimulate proliferation, including Nupr1, E2f1, and Etv5. We characterized the expression and function of a novel, overexpressed gene, transcription elongation factor A (SII)-like 5 (Tceal5). Stable expression of Tceal5 in a pituitary progenitor cell line is sufficient to increase cell proliferation. Thus, Tceal5 may act as a proto-oncogene. This study provides a rich resource for comparing pituitary transcriptomes and an analysis of gene expression networks.
The Chikungunya virus (CHIKV) has been rapidly spreading throughout Latin America, utilizing pre-existing vectors to infiltrate the immunologically naïve populations. With the current rise of the Zika Virus, there is an urgent need for more rigorous vector control efforts to prevent further Zika breakout. We designed a school-based education module on CHIKV and mosquito prevention and presented it to the local students of ages of 6-18 in a rural town called Sudzal in Yucatan, Mexico. We distributed questionnaires before and after education to test the students’ knowledge of CHIKV and mosquito prevention. Chi-squared test was performed to determine the efficacy of the presentation in increasing their knowledge. The education presentation has proven to effectively educate the local residents in several critical methods of mosquito prevention, increasing the average test scores by 67% post-education. These include applying repellent, staying hydrated during recuperation, and cleaning indoor water containers to eliminate breeding sites (P<0.001). Furthermore, the questionnaire captured the residents’ behavioral patterns regarding CHIKV and mosquito prevention and identified cultural, ecological, and socioeconomic factors hindering effective implementation of vector control.
Synopsis Virtual prenatal support groups addressed heightened mental health, psychosocial support, and anticipatory guidance needs in pregnant patients during the COVID‐19 pandemic.
Sri Lanka is experiencing a rapid increase in the number of people with diabetes mellitus (DM) due to population growth and aging. Physician shortages, outdated technology, and insufficient health education have contributed to the difficulties associated with managing the burden of disease. New models of chronic disease management are needed to address the increasing prevalence of DM.Medical students, business students, and faculty members from the University of Michigan partnered with the Grace Girls’ Home, Trincomalee General Hospital, and Selvanayakapuram Central Hospital to identify and train diabetes-focused medical assistants (MAs) to collect and enter patient data and educate patients about their disease. Return visits to these MAs were encouraged so that patient progress and disease progression could be tracked longitudinally. Data entry was conducted through a cloud-based mechanism, facilitating patient management and descriptive characterization of the population. We implemented this pilot program in June 2016 in coordination with Trincomalee General Hospital and Selvanayakapuram Central Hospital. Over a 12-month period, 93 patients were systematically assessed by the medical assistants. All patients received education and were provided materials after the visit to better inform them about the importance of controlling their disease. Fifteen percent (14/93) of patients returned for follow-up consultation.Trained MAs have the potential to provide support to physicians working in congested health systems in low-resource settings. Public investment in training programs for MAs and greater acceptance by physicians and patients will be essential for handling the growing burden associated with chronic illnesses like DM. Trained MAs may also play a role in improved patient education and awareness regarding diabetes self-management.
ObjectiveTo develop a population-specific methodology for estimating glycaemic control that optimises resource allocation for patients with diabetes in rural Sri Lanka.DesignCross-sectional study.SettingTrincomalee, Sri Lanka.ParticipantsPatients with non-insulin-treated type 2 diabetes (n=220) from three hospitals in Trincomalee, Sri Lanka.Outcome measureCross-validation was used to build and validate linear regression models to identify predictors of haemoglobin A1c (HbA1c). Validation of models that regress HbA1c on known determinants of glycaemic control was thus the major outcome. These models were then used to devise an algorithm for categorising the patients based on estimated levels of glycaemic control.ResultsTime since last oral intake other than water and capillary blood glucose were the statistically significant predictors of HbA1c and thus included in the final models. In order to minimise type II error (misclassifying a high-risk individual as low-risk or moderate-risk), an algorithm for interpreting estimated glycaemic control was created. With this algorithm, 97.2% of the diabetic patients with HbA1c ≥9.0% were correctly identified.ConclusionsOur calibrated algorithm represents a highly sensitive approach for detecting patients with high-risk diabetes while optimising the allocation of HbA1c testing. Implementation of these methods will optimise the usage of resources devoted to the management of diabetes in Trincomalee, Sri Lanka. Further external validation with diverse patient populations is required before applying our algorithm more widely.
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