Weight Gain in People with HIV: The Role of Demographics, Antiretroviral Therapy, and Lifestyle Factors on Weight.

We explored factors associated with weight gain among people with HIV (PWH) on antiretroviral therapy (ART) at The Ohio State University Wexner Medical Center (OSUWMC). This was a retrospective cohort study of adult PWH on ART for ≥3 months. Patients with CD4+ T cell count <200 cells/mm3, viral load >200 copies/mL, history of malignancy, or pregnancy were excluded. Eight hundred seventy patients met criteria. The primary outcome was percent weight change over the follow-up period (Δ = relative effects). The secondary outcome was the odds of ≥5 kg weight gain over the study period. The effects of concurrent medications, medical comorbidities, ART combinations, and lifestyle behaviors on these outcomes were modeled using mixed effects regression analyses. Over a mean follow-up of 1.86 years, the study population gained a mean percent weight of 2.12% ± 0.21% (p < .001) with the odds of ≥5 kg weight gain of 0.293 (p < .001). Males gained an average of 1.88% ± 0.22% over follow up, while females gained an average of 3.37% ± 0.51% over follow up (p = .008 for the difference). In regression models, combination therapy with tenofovir alafenamide (TAF) and integrase strand transfer inhibitor (INSTI) containing regimens was associated with an increase in weight over the study period (Δ = 2.14% ± 0.45%, p < .001 and Δ = 1.09% ± 0.39%, p = .005, respectively). Increasing age was significantly associated with a decrease in percent weight change over the study period (Δ = -0.68% ± 0.18% per year, p < .001). Self-reported improvement in diet was associated with a decrease in weight change (Δ = -1.99% ± 0.47%, p ≤ .001) and reduced odds of ≥5 kg weight gain (odds ratio = 0.70, 95% confidence interval = 0.50-0.97, p = .03). Factors associated with weight gain include therapy with TAF and INSTI. Diet may play an influential role in attenuating weight gain in PWH.

Engineered bacterial swarm patterns as spatial records of environmental inputs.

A diverse array of bacteria species naturally self-organize into durable macroscale patterns on solid surfaces via swarming motility-a highly coordinated and rapid movement of bacteria powered by flagella. Engineering swarming is an untapped opportunity to increase the scale and robustness of coordinated synthetic microbial systems. Here we engineer Proteus mirabilis, which natively forms centimeter-scale bullseye swarm patterns, to 'write' external inputs into visible spatial records. Specifically, we engineer tunable expression of swarming-related genes that modify pattern features, and we develop quantitative approaches to decoding. Next, we develop a dual-input system that modulates two swarming-related genes simultaneously, and we separately show that growing colonies can record dynamic environmental changes. We decode the resulting multicondition patterns with deep classification and segmentation models. Finally, we engineer a strain that records the presence of aqueous copper. This work creates an approach for building macroscale bacterial recorders, expanding the framework for engineering emergent microbial behaviors.

Educational, psychosocial, and clinical impact of SARS-CoV-2 (COVID-19) pandemic on medical students in the United States.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic altered education, exams, and residency applications for United States medical students. AIM: To determine the specific impact of the pandemic on US medical students and its correlation to their anxiety levels. METHODS: An 81-question survey was distributed via email, Facebook and social media groups using REDCapTM. To investigate risk factors associated with elevated anxiety level, we dichotomized the 1-10 anxiety score into low (≤ 5) and high (≥ 6). This cut point represents the 25th percentile. There were 90 (29%) shown as low anxiety and 219 (71%) as high anxiety. For descriptive analyses, we used contingency tables by anxiety categories for categorical measurements with chi square test, or mean ± STD for continuous measurements followed by t-test or Wilcoxson rank sum test depending on data normality. Least Absolute Shrinkage and Selection Operator was used to select important predictors for the final multivariate model. Hierarchical Poisson regression model was used to fit the final multivariate model by considering the nested data structure of students clustered within State. RESULTS: 397 medical students from 29 states were analyzed. Approximately half of respondents reported feeling depressed since the pandemic onset. 62% of participants rated 7 or higher out of 10 when asked about anxiety levels. Stressors correlated with higher anxiety scores included "concern about being unable to complete exams or rotations if contracting COVID-19" (RR 1.34; 95%CI: 1.05-1.72, P = 0.02) and the use of mental health services such as a "psychiatrist" (RR 1.18; 95%CI: 1.01-1.3, P = 0.04). However, those students living in cities that limited restaurant operations to exclusively takeout or delivery as the only measure of implementing social distancing (RR 0.64; 95%CI: 0.49-0.82, P < 0.01) and those who selected "does not apply" for financial assistance available if needed (RR 0.83; 95%CI: 0.66-0.98, P = 0.03) were less likely to have a high anxiety. CONCLUSION: COVID-19 significantly impacted medical students in numerous ways. Medical student education and clinical readiness were reduced, and anxiety levels increased. It is vital that medical students receive support as they become physicians. Further research should be conducted on training medical students in telemedicine to better prepare students in the future for pandemic planning and virtual healthcare.

Two New Plasmid Post-segregational Killing Mechanisms for the Implementation of Synthetic Gene Networks in Escherichia coli.

Plasmids are the workhorse of both industrial biotechnology and synthetic biology, but ensuring they remain in bacterial cells is a challenge. Antibiotic selection cannot be used to stabilize plasmids in most real-world applications, and inserting dynamical gene networks into the genome remains challenging. Plasmids have evolved several mechanisms for stability, one of which, post-segregational killing (PSK), ensures that plasmid-free cells do not survive. Here we demonstrate the plasmid-stabilizing capabilities of the axe/txe toxin-antitoxin system and the microcin-V bacteriocin system in the probiotic bacteria Escherichia coli Nissle 1917 and show that they can outperform the commonly used hok/sok. Using plasmid stability assays, automated flow cytometry analysis, mathematical models, and Bayesian statistics we quantified plasmid stability in vitro. Furthermore, we used an in vivo mouse cancer model to demonstrate plasmid stability in a real-world therapeutic setting. These new PSK systems, plus the developed Bayesian methodology, will have wide applicability in clinical and industrial biotechnology.

Consumer-facing Diagnostic Sensors in a Patient with Implantable Cardioverter-Defibrillator.

Since its introduction in 2015, the Apple Watch (Apple Inc., Cupertino, CA, USA) has been purchased by an estimated 60 million consumers and boasts algorithms cleared by the United States Food and Drug Administration able to detect bradycardia, tachycardia, and atrial fibrillation, with the newest version of the device also allowing for real-time electrocardiogram acquisition. This case offers potentially the first demonstration of an Apple Watch correctly detecting atrial fibrillation with an implantable cardioverter-defibrillator confirming the accuracy of the detection from stored electrograms.

Advances in bacterial cancer therapies using synthetic biology.

Synthetic biology aims to apply engineering principles to biology by modulating the behavior of living organisms. An emerging application of this field is the engineering of bacteria as a cancer therapy by the programming of therapeutic, safety, and specificity features through genetic modification. Here, we review progress in this engineering including the targeting of bacteria to tumors, specific sensing and response to tumor microenvironments, remote induction methods, and controllable release of therapeutics. We discuss the most prominent bacteria strains used and their specific properties and the types of therapeutics tested thus far. Finally, we note current challenges, such as genetic stability, that researchers must address for successful clinical implementation of this novel therapy in humans.