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Abstract This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies. 

Background:Few epidemiologic studies have examined the association of ambient heat with spontaneous abortion, a common and devastating pregnancy outcome. Methods:We conducted a case–crossover study nested within Pregnancy Study Online, a preconception cohort study (2013–2022). We included all participants reporting spontaneous abortion (N = 1,524). We defined the case window as the 7 days preceding the event and used time-stratified referent selection to select control windows matched on calendar month and day of week. Within each 7-day case and control window, we measured the mean, maximum, and minimum of daily maximum outdoor air temperatures. We fit splines to examine nonlinear relationships across the entire year and conditional logistic regression to estimate odds ratios (ORs) and 95% confidence interval (CI) of spontaneous abortion with increases in temperature during the warm season (May–September) and decreases during the cool season (November–March). Results:We found evidence of a U-shaped association between outdoor air temperature and spontaneous abortion risk based on year-round data. When restricting to warm season events (n = 657), the OR for a 10-percentile increase in the mean of lag 0–6 daily maximum temperatures was 1.1 (95% CI: 0.96, 1.2) and, for the maximum, 1.1 (95% CI: 0.99, 1.2). The OR associated with any extreme heat days (>95th county-specific percentile) in the preceding week was 1.2 (95% CI: 0.95, 1.5). Among cool season events (n = 615), there was no appreciable association between lower temperatures and spontaneous abortion risk. Conclusion:Our study provides evidence of an association between high outdoor temperatures and the incidence of spontaneous abortion. 

Abstract BackgroundThe target trial framework was developed as a strategy to design and analyze observational epidemiologic studies with the aim of reducing bias due to analytic decisions. It involves designing a hypothetical randomized trial to answer a question of interest and systematically considering how to use observational data to emulate each trial component. AimsThe primary aim of this paper is to provide a detailed example of the application of the target trial framework to a research question in oral epidemiology. Materials and MethodsWe describe the development of a hypothetical target trial and emulation protocol to evaluate the effect of preconception periodontitis treatment on time‐to‐pregnancy. We leverage data from Pregnancy Study Online (PRESTO), a preconception cohort, to ground our example in existing observational data. We discuss the decision‐making process for each trial component, as well as limitations encountered. ResultsOur target trial application revealed data limitations that precluded us from carrying out the proposed emulation. Implications for data quality are discussed and we provide recommendations for researchers interested in conducting trial emulations in the field of oral epidemiology. DiscussionThe target trial framework has the potential to improve the validity of observational research in oral health, when properly applied. ConclusionWe encourage the broad adoption of the target trial framework to the field of observational oral health research and demonstrate its value as a tool to identify directions for future research. 

Abstract INTRODUCTIONIdentification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. METHODSWe applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews ofn = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. RESULTSOur best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI‐to‐AD progression within 6 years. DISCUSSIONThe proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy‐to‐administer screening tool for MCI‐to‐AD progression prediction, facilitating development of remote assessment. HighlightsVoice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment.The study leveraged AI methods for speech recognition and processed the resulting text using language models.The developed AI‐powered pipeline can lead to fully automated assessment that could enable remote and cost‐effective screening and prognosis for Alzehimer's disease. 

Abstract The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology. 

IntroductionPredictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis. MethodsThis is a retrospective cohort study from a SafetyNet hospital’s electronic health records (EHR) from 2003-2016. The study population included 30,601 women aged 18-45 years without concurrent endocrinopathy who had any visit to Boston Medical Center for primary care, obstetrics and gynecology, endocrinology, family medicine, or general internal medicine. Four prediction outcomes were assessed for PCOS. The first outcome was PCOS ICD-9 diagnosis with additional model outcomes of algorithm-defined PCOS. The latter was based on Rotterdam criteria and merging laboratory values, radiographic imaging, and ICD data from the EHR to define irregular menstruation, hyperandrogenism, and polycystic ovarian morphology on ultrasound. ResultsWe developed predictive models using four machine learning methods: logistic regression, supported vector machine, gradient boosted trees, and random forests. Hormone values (follicle-stimulating hormone, luteinizing hormone, estradiol, and sex hormone binding globulin) were combined to create a multilayer perceptron score using a neural network classifier. Prediction of PCOS prior to clinical diagnosis in an out-of-sample test set of patients achieved an average AUC of 85%, 81%, 80%, and 82%, respectively in Models I, II, III and IV. Significant positive predictors of PCOS diagnosis across models included hormone levels and obesity; negative predictors included gravidity and positive bHCG. ConclusionMachine learning algorithms were used to predict PCOS based on a large at-risk population. This approach may guide early detection of PCOS within EHR-interfaced populations to facilitate counseling and interventions that may reduce long-term health consequences. Our model illustrates the potential benefits of an artificial intelligence-enabled provider assistance tool that can be integrated into the EHR to reduce delays in diagnosis. However, model validation in other hospital-based populations is necessary. 

ObjectivesTo evaluate the association between preconception contraceptive use and miscarriage. DesignProspective cohort study. SettingResidents of the United States of America or Canada, recruited from 2013 until the end of 2022. Participants13 460 female identified participants aged 21-45 years who were planning a pregnancy were included, of whom 8899 conceived. Participants reported data for contraceptive history, early pregnancy, miscarriage, and potential confounders during preconception and pregnancy. Main outcome measureMiscarriage, defined as pregnancy loss before 20 weeks of gestation. ResultsPreconception use of combined and progestin-only oral contraceptives, hormonal intrauterine devices, copper intrauterine devices, rings, implants, or natural methods was not associated with miscarriage compared with use of barrier methods. Participants who most recently used patch (incidence rate ratios 1.34 (95% confidence interval 0.81 to 2.21)) or injectable contraceptives (1.44 (0.99 to 2.12)) had higher rates of miscarriage compared with recent users of barrier methods, although results were imprecise due to the small numbers of participants who used patch and injectable contraceptives. ConclusionsUse of most contraceptives before conception was not appreciably associated with miscarriage rate. Individuals who used patch and injectable contraceptives had higher rates of miscarriage relative to users of barrier methods, although these results were imprecise and residual confounding was possible. 

Abstract Purpose of ReviewPreparing for pandemics requires a degree of interdisciplinary work that is challenging under the current paradigm. This review summarizes the challenges faced by the field of pandemic science and proposes how to address them. Recent FindingsThe structure of current siloed systems of research organizations hinders effective interdisciplinary pandemic research. Moreover, effective pandemic preparedness requires stakeholders in public policy and health to interact and integrate new findings rapidly, relying on a robust, responsive, and productive research domain. Neither of these requirements are well supported under the current system. SummaryWe propose a new paradigm for pandemic preparedness wherein interdisciplinary research and close collaboration with public policy and health practitioners can improve our ability to prevent, detect, and treat pandemics through tighter integration among domains, rapid and accurate integration, and translation of science to public policy, outreach and education, and improved venues and incentives for sustainable and robust interdisciplinary work. 

Abstract We examined the association between childhood adversity and fecundability (the per-cycle probability of conception), and the extent to which childhood social support modified this association. We used data from 6318 female participants aged 21-45 years in Pregnancy Study Online (PRESTO), a North American prospective preconception cohort study (2013-2022). Participants completed a baseline questionnaire, bimonthly follow-up questionnaires (until pregnancy or a censoring event), and a supplemental questionnaire on experiences across the life course including adverse childhood experiences (ACEs) and social support (using the modified Berkman-Syme Social Network Index [SNI]). We used proportional probabilities regression models to compute fecundability ratios (FRs) and 95% CIs, adjusting for potential confounders and precision variables. Adjusted FRs for ACE scores 1-3 and ≥4 vs 0 were 0.91 (95% CI, 0.85-0.97) and 0.84 (95% CI, 0.77-0.91), respectively. The FRs for ACE scores ≥4 vs 0 were 0.86 (95% CI, 0.78-0.94) among participants reporting high childhood social support (SNI ≥4) and 0.78 (95% CI, 0.56-1.07) among participants reporting low childhood social support (SNI <4). Our findings confirm results from 2 previous studies and indicate that high childhood social support slightly buffered the effects of childhood adversity on fecundability. 

Abstract STUDY QUESTIONTo what extent is male fatty acid intake associated with fecundability among couples planning pregnancy? SUMMARY ANSWERWe observed weak positive associations of male dietary intakes of total and saturated fatty acids with fecundability; no other fatty acid subtypes were appreciably associated with fecundability. WHAT IS KNOWN ALREADYMale fatty acid intake has been associated with semen quality in previous studies. However, little is known about the extent to which male fatty acid intake is associated with fecundability among couples attempting spontaneous conception. STUDY DESIGN, SIZE, DURATIONWe conducted an internet-based preconception prospective cohort study of 697 couples who enrolled during 2015–2022. During 12 cycles of observation, 53 couples (7.6%) were lost to follow-up. PARTICIPANTS/MATERIALS, SETTING, METHODSParticipants were residents of the USA or Canada, aged 21–45 years, and not using fertility treatment at enrollment. At baseline, male participants completed a food frequency questionnaire from which we estimated intakes of total fat and fatty acid subtypes. We ascertained time to pregnancy using questionnaires completed every 8 weeks by female participants until conception or up to 12 months. We used proportional probabilities regression models to estimate fecundability ratios (FRs) and 95% CIs for the associations of fat intakes with fecundability, adjusting for male and female partner characteristics. We used the multivariate nutrient density method to account for energy intake, allowing for interpretation of results as fat intake replacing carbohydrate intake. We conducted several sensitivity analyses to assess the potential for confounding, selection bias, and reverse causation. MAIN RESULTS AND THE ROLE OF CHANCEAmong 697 couples, we observed 465 pregnancies during 2970 menstrual cycles of follow-up. The cumulative incidence of pregnancy during 12 cycles of follow-up after accounting for censoring was 76%. Intakes of total and saturated fatty acids were weakly, positively associated with fecundability. Fully adjusted FRs for quartiles of total fat intake were 1.32 (95% CI 1.01–1.71), 1.16 (95% CI 0.88–1.51), and 1.43 (95% CI 1.09–1.88) for the second, third, and fourth vs the first quartile, respectively. Fully adjusted FRs for saturated fatty acid intake were 1.21 (95% CI 0.94–1.55), 1.16 (95% CI 0.89–1.51), and 1.23 (95% CI 0.94–1.62) for the second, third, and fourth vs the first quartile, respectively. Intakes of monounsaturated, polyunsaturated, trans-, omega-3, and omega-6 fatty acids were not strongly associated with fecundability. Results were similar after adjustment for the female partner’s intakes of trans- and omega-3 fats. LIMITATIONS, REASONS FOR CAUTIONDietary intakes estimated from the food frequency questionnaire may be subject to non-differential misclassification, which is expected to bias results toward the null in the extreme categories when exposures are modeled as quartiles. There may be residual confounding by unmeasured dietary, lifestyle, or environmental factors. Sample size was limited, especially in subgroup analyses. WIDER IMPLICATIONS OF THE FINDINGSOur results do not support a strong causal effect of male fatty acid intakes on fecundability among couples attempting to conceive spontaneously. The weak positive associations we observed between male dietary fat intakes and fecundability may reflect a combination of causal associations, measurement error, chance, and residual confounding. STUDY FUNDING/COMPETING INTEREST(S)The study was funded by the National Institutes of Health, grant numbers R01HD086742 and R01HD105863. In the last 3 years, PRESTO has received in-kind donations from Swiss Precision Diagnostics (home pregnancy tests) and Kindara.com (fertility app). L.A.W. is a consultant for AbbVie, Inc. M.L.E. is an advisor to Sandstone, Ro, Underdog, Dadi, Hannah, Doveras, and VSeat. The other authors have no competing interests to report. TRIAL REGISTRATION NUMBERN/A.