The University of Texas Health Science Center at Houston (UTHealth) is a public academic health science center in Houston, Texas. It was created in 1972 by The University of Texas System Board of Regents. It is located in the Texas Medical Center, the largest medical center in the world. It is composed of six schools: McGovern Medical School, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, UTHealth School of Dentistry, Cizik School of Nursing, UTHealth School of Biomedical Informatics and UTHealth School of Public Health.
Background & Aims: The current prevalence of fatty liver disease (FLD) due to alcohol-associated (AFLD) and nonalcoholic (NAFLD) origins in US persons with HIV (PWH) is not well defined. We prospectively evaluated the burden of FLD and hepatic fibrosis in a diverse cohort of PWH. Approach & Results: Consenting participants in outpatient HIV clinics in 3 centers in the US underwent detailed phenotyping, including liver ultrasound and vibration-controlled transient elastography for controlled attenuation parameter and liver stiffness measurement. The prevalence of AFLD, NAFLD, and clinically significant and advanced fibrosis was determined. Univariate and multivariate logistic regression models were used to evaluate factors associated with the risk of NAFLD. Of 342 participants, 95.6% were on antiretroviral therapy, 93.9% had adequate viral suppression, 48.7% (95% CI 43%–54%) had steatosis by ultrasound, and 50.6% (95% CI 45%–56%) had steatosis by controlled attenuation parameter ≥263 dB/m. NAFLD accounted for 90% of FLD. In multivariable analysis, old age, higher body mass index, diabetes, and higher alanine aminotransferase, but not antiretroviral therapy or CD4+ cell count, were independently associated with increased NAFLD risk. In all PWH with fatty liver, the frequency of liver stiffness measurement 8–12 kPa was 13.9% (95% CI 9%–20%) and ≥12 kPa 6.4% (95% CI 3%–11%), with a similar frequency of these liver stiffness measurement cutoffs in NAFLD. Conclusions: Nearly half of the virally-suppressed PWH have FLD, 90% of which is due to NAFLD. A fifth of the PWH with FLD has clinically significant fibrosis, and 6% have advanced fibrosis. These data lend support to systematic screening for high-risk NAFLD in PWH.
Background: Safety and efficacy of roflumilast cream 0.15% for atopic dermatitis (AD) were demonstrated in two 4-week phase 3 trials.Objective: Evaluate long-term safety, tolerability, and efficacy of roflumilast cream 0.15% in AD.Methods: In this open-label extension (OLE) trial (INTEGUMENT-OLE; NCT04804605), patients aged ≥6 years who completed one of the 4-week phase 3 trials applied roflumilast for up to 52 weeks. After 4 weeks of once-daily application, patients who achieved Validated Investigator Global Assessment for AD (vIGA-AD) of clear (0) switched to twice-weekly (BIW) application to normal-appearing flare-prone areas (proactive treatment).Results: Among 657 patients treated, 36.7% reported adverse events, including 4.7% that were treatment related. Application site pain and stinging/burning that caused definite discomfort at any visit were reported for 0.5% and 0.4%-2.1% of patients, respectively. Patients who achieved vIGA-AD 0 and switched to proactive BIW application maintained vIGA-AD 0/1 (almost clear) for a median of 281 days (Kaplan-Meier estimate).Conclusion: Roflumilast cream 0.15% was well tolerated for up to 56 weeks. BIW application to normal-appearing flare-prone sites maintained improvement in AD signs and symptoms, showing that proactive treatment represents an alternative to the current standard practice of reactive treatment.
A critical factor in promoting quality practices in early care and education (ECE) settings is educator well-being, a multi-dimensional construct influenced by a variety of contextual factors. There has yet to be consensus on conceptualizing early childhood workforce well-being in a way that encompasses multiple factors and complex dynamics among them. Designed by an interdisciplinary group of scholars, this study aimed to develop a novel conceptual model, the Ecological Model of Holistic ECE Workforce Well-Being, that captures multiple layers of well-being. In addition, this new model was validated using an AI-assisted systematic review that identified 345 studies published between 1990 and 2023 for inclusion. We examined four core domains of ECE workforce well-being (i.e., physical, psychological, and professional well-being and health behaviors) and how these have been studied in the literature with regards to relational well-being and contextual factors. While our systematic review covers literature published between 1990 and 2023, it is noteworthy that there has been growing attention to ECE workforce well-being since 2019. However, most studies focused on one or two indicators of well-being using descriptive or correlational methods. This study provides future directions for ECE workforce well-being research and practice by identifying gaps in the literature that could support efforts to professionalize the ECE workforce and, ultimately, enhance the quality of care and learning for young children.
MOTIVATION:Lossless full text indexes are utilized in a myriad of applications in bioinformatics. The continuously decreasing cost of generating biological data has resulted in the need to build full text indexes on biological datasets of increasing size. Many compressed full text indexes have been developed to address this problem. In particular, run-length Burrows-Wheeler transform (RLBWT) based compressed full text indexes have seen wide development and adoption. However, the construction of these RLBWT-based compressed full text indexes is still computationally expensive, sometimes prohibitively so, even for current dataset sizes. RESULTS:Therefore, we present algorithms for the construction of RLBWT-based compressed full text indexes and their supporting data structures in compressed space. The algorithms have a space complexity of O(r) words and run in O(n) time for repetitive datasets, where r is the number of runs in the BWT, n is the length of the text, and repetitive datasets implies nr∈Ω(log n). We provide the first algorithm to compute LCP-related information for repetitive datasets in optimal time and O(r) space, greatly reducing memory requirements. The key idea behind this algorithm is the utilization of r samples of the inverse suffix array at regular intervals. For example, on the Human Pangenome Reference Consortium Release 2 dataset, this reduces peak memory from 2135 GiB to 170 GiB (12.6x reduction) compared to the previous best method (pfp-thresholds). AVAILABILITY AND IMPLEMENTATION:The implementation is available at https://github.com/ucfcbb/TeraTools.
Qualitative data poses a challenge for prevention science and public health, as it is critical to explain the context of communities, health, and behavior, yet collecting and analyzing qualitative data using traditional methods is time-intensive and requires extensive training. As artificial intelligence (AI) models have improved, there is a growing interest in using AI to code qualitative data quickly and reliably. This study compares the similarities and differences in methods and results of artificial intelligence (AI)-assisted qualitative analysis to traditional qualitative content analysis using data collected during the development of a city and county-based food plan. In total, 2820 community comments were collected across 43 community events in 27 zip codes across the region between March 2023 and January 2024. AI-assisted analysis was completed using a combination of a transcription app (Post-ItⓇ), GPT4 Plus, and GPT for Sheets with oversight from a public health practitioner. Traditional qualitative content analysis was completed with two trained coders who completed codebook development, reliability analysis, and full content coding. Both methods used deductive codes to represent key aspects of the food system and generated inductive codes to represent areas not included by the deductive food system codes. Results found that AI-assisted methods and traditional content analysis produced similar deductive coding results, while inductive coding results were less comparable across methods. Given that qualitative data has become a central part of prevention science, we believe with careful considerations, AI-assisted methods with intentional oversight have the potential to strengthen our ability to process large amounts of qualitative data.
