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With increasing maternal cannabis use, there is a need to investigate the lasting impact of prenatal exposure to Δ9-tetrahydrocannabinol (THC), the main psychotropic compound in cannabis, on cognitive/memory function. The endocannabinoid system (ECS), which relies on polyunsaturated fatty acids (PUFAs) to function, plays a crucial role in regulating prefrontal cortical (PFC) and hippocampal network-dependent behaviors essential for cognition and memory. Using a rodent model of prenatal cannabis exposure (PCE), we report that male and female offspring display long-term deficits in various cognitive domains. However, these phenotypes were associated with highly divergent, sex-dependent mechanisms. Electrophysiological recordings revealed hyperactive PFC pyramidal neuron activity in both males and females, but hypoactivity in the ventral hippocampus (vHIPP) in males, and hyperactivity in females. Further, cortical oscillatory activity states of theta, alpha, delta, beta, and gamma bandwidths were strongly sex divergent. Moreover, protein expression analyses at postnatal day (PD)21 and PD120 revealed primarily PD120 disturbances in dopamine D1R/D2 receptors, NMDA receptor 2B, synaptophysin, gephyrin, GAD67, and PPARα selectively in the PFC and vHIPP, in both regions in males, but only the vHIPP in females. Lastly, using matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS), we identified region-, age-, and sex-specific deficiencies in specific neural PUFAs, namely docosahexaenoic acid (DHA) and arachidonic acid (ARA), and related metabolites, in the PFC and hippocampus (ventral/dorsal subiculum, and CA1 regions). This study highlights several novel, long-term and sex-specific consequences of PCE on PFC-hippocampal circuit dysfunction and the potential role of specific PUFA signaling abnormalities underlying these pathological outcomes.

We fit a power law distribution to US foodborne disease outbreaks to assess underdetection and underreporting. We predicted that 788 fewer than expected small outbreaks were identified annually during 1998-2017 and 365 fewer during 2018-2019, after whole-genome sequencing was implemented. Power law can help assess effectiveness of public health interventions.

Background We explored the association between health literacy and self-care behaviors among low-income patients with chronic kidney disease (CKD). Methods We used baseline data from the Kidney Awareness Registry and Education trial ( n  = 137 patients with CKD) and multivariable logistic regressions to cross-sectionally examine the association between health literacy, defined by a validated questionnaire, and healthy behaviors. Results Study participants had a mean age of 55 years, were racially diverse (6% White, 36% Hispanic, 43% Black, 15% Asian) and 26% had low health literacy. Over one-third (38%) had hypertension, 51% had diabetes, and 67% had CKD stage 3 or 4. Compared to individuals with adequate health literacy, those with low health literacy had non-statistically significant higher tobacco use (adjusted odds ratio [aOR] = 2.33; 95% CI 0.90–6.06) and lower consumption of sugary beverages (aOR = 0.50; 0.20-1.23) and statistically significant decreased fast food intake (aOR = 0.38; 0.16-0.93). Health literacy was not associated with differences in medication adherence (0.84; 0.38-1.89) or physical activity (aOR = 2.39; 0.54-10.53). Conclusions Health literacy was not uniformly associated with all self-care behaviors important for CKD management. A more nuanced understanding of the association of health literacy and self-care may be necessary to promote participation in behaviors known to slow CKD progression.

Ongoing symptoms might follow acute COVID-19. Using electronic health information, we compared pre‒ and post‒COVID-19 diagnostic codes to identify symptoms that had higher encounter incidence in the post‒COVID-19 period as sequelae. This method can be used for hypothesis generation and ongoing monitoring of sequelae of COVID-19 and future emerging diseases.

We describe characteristics of unplanned school closures (USCs) in the United States over two consecutive academic years during a non-pandemic period to provide context for implementation of school closures during a pandemic. From August 1, 2011 through June 30, 2013, daily systematic internet searches were conducted for publicly announced USCs lasting ≥ 1 day. The reason for closure and the closure dates were recorded. Information on school characteristics was obtained from the National Center for Education Statistics. During the two-year study period, 20,723 USCs were identified affecting 27,066,426 students. Common causes of closure included weather (79%), natural disasters (14%), and problems with school buildings or utilities (4%). Only 771 (4%) USCs lasted ≥ 4 school days. Illness was the cause of 212 (1%) USCs; of these, 126 (59%) were related to respiratory illnesses and showed seasonal variation with peaks in February 2012 and January 2013. USCs are common events resulting in missed school days for millions of students. Illness causes few USCs compared with weather and natural disasters. Few communities have experience with prolonged closures for illness.

In the United States, influenza season typically begins in October or November, peaks in February, and tapers off in April. During the winter holiday break, from the end of December to the beginning of January, changes in social mixing patterns, healthcare-seeking behaviors, and surveillance reporting could affect influenza-like illness (ILI) rates. We compared predicted with observed weekly ILI to examine trends around the winter break period. We examined weekly rates of ILI by region in the United States from influenza season 2003-2004 to 2012-2013. We compared observed and predicted ILI rates from week 44 to week 8 of each influenza season using the auto-regressive integrated moving average (ARIMA) method. Of 1,530 region, week, and year combinations, 64 observed ILI rates were significantly higher than predicted by the model. Of these, 21 occurred during the typical winter holiday break period (weeks 51-52); 12 occurred during influenza season 2012-2013. There were 46 observed ILI rates that were significantly lower than predicted. Of these, 16 occurred after the typical holiday break during week 1, eight of which occurred during season 2012-2013. Of 90 (10 HHS regions x 9 seasons) predictions during the peak week, 78 predicted ILI rates were lower than observed. Out of 73 predictions for the post-peak week, 62 ILI rates were higher than observed. There were 53 out of 73 models that had lower peak and higher post-peak predicted ILI rates than were actually observed. While most regions had ILI rates higher than predicted during winter holiday break and lower than predicted after the break during the 2012-2013 season, overall there was not a consistent relationship between observed and predicted ILI around the winter holiday break during the other influenza seasons.

Electronic Health Records (EHRs) contain vast clinical data that are difficult for providers to synthesize. Generative AI with Large Language Models (LLMs) can summarize records to reduce cognitive burden, but ensuring accuracy requires reliable evaluation. Human review is the gold standard but is costly and slow. To address this, we introduce and validate an automated LLM-based method to assess real-world EHR multi-document summaries. Benchmarking against the validated Provider Documentation Summarization Quality Instrument (PDSQI), our LLM-as-a-Judge framework demonstrated strong inter-rater reliability with human evaluators. GPT-o3-mini achieved an intraclass correlation coefficient of 0.818 (95% CI 0.772–0.854), a median score difference of 0 from humans, and completed evaluations in 22 seconds. Overall, reasoning models excelled in inter-rater reliability, particularly for evaluations requiring advanced reasoning and domain expertise, outperforming non-reasoning, task-trained, and multi-agent approaches. By automating high-quality evaluations, a medical LLM-as-a-Judge provides a scalable, efficient way to identify accurate, safe AI-generated clinical summaries.

Introduction Severe COVID-19 illness in adults has been linked to underlying medical conditions. This study identified frequent underlying conditions and their attributable risk of severe COVID-19 illness. Methods We used data from more than 800 US hospitals in the Premier Healthcare Database Special COVID-19 Release (PHD-SR) to describe hospitalized patients aged 18 years or older with COVID-19 from March 2020 through March 2021. We used multivariable generalized linear models to estimate adjusted risk of intensive care unit admission, invasive mechanical ventilation, and death associated with frequent conditions and total number of conditions. Results Among 4,899,447 hospitalized adults in PHD-SR, 540,667 (11.0%) were patients with COVID-19, of whom 94.9% had at least 1 underlying medical condition. Essential hypertension (50.4%), disorders of lipid metabolism (49.4%), and obesity (33.0%) were the most common. The strongest risk factors for death were obesity (adjusted risk ratio [aRR] = 1.30; 95% CI, 1.27–1.33), anxiety and fear-related disorders (aRR = 1.28; 95% CI, 1.25–1.31), and diabetes with complication (aRR = 1.26; 95% CI, 1.24–1.28), as well as the total number of conditions, with aRRs of death ranging from 1.53 (95% CI, 1.41–1.67) for patients with 1 condition to 3.82 (95% CI, 3.45–4.23) for patients with more than 10 conditions (compared with patients with no conditions). Conclusion Certain underlying conditions and the number of conditions were associated with severe COVID-19 illness. Hypertension and disorders of lipid metabolism were the most frequent, whereas obesity, diabetes with complication, and anxiety disorders were the strongest risk factors for severe COVID-19 illness. Careful evaluation and management of underlying conditions among patients with COVID-19 can help stratify risk for severe illness.

Importance Information on underlying conditions and severe COVID-19 illness among children is limited. Objective To examine the risk of severe COVID-19 illness among children associated with underlying medical conditions and medical complexity. Design, Setting, and Participants This cross-sectional study included patients aged 18 years and younger withInternational Statistical Classification of Diseases, Tenth Revision, Clinical Modification code U07.1 (COVID-19) or B97.29 (other coronavirus) during an emergency department or inpatient encounter from March 2020 through January 2021. Data were collected from the Premier Healthcare Database Special COVID-19 Release, which included data from more than 800 US hospitals. Multivariable generalized linear models, controlling for patient and hospital characteristics, were used to estimate adjusted risk of severe COVID-19 illness associated with underlying medical conditions and medical complexity. Exposures Underlying medical conditions and medical complexity (ie, presence of complex or noncomplex chronic disease). Main Outcomes and Measures Hospitalization and severe illness when hospitalized (ie, combined outcome of intensive care unit admission, invasive mechanical ventilation, or death). Results Among 43 465 patients with COVID-19 aged 18 years or younger, the median (interquartile range) age was 12 (4-16) years, 22 943 (52.8%) were female patients, and 12 491 (28.7%) had underlying medical conditions. The most common diagnosed conditions were asthma (4416 [10.2%]), neurodevelopmental disorders (1690 [3.9%]), anxiety and fear-related disorders (1374 [3.2%]), depressive disorders (1209 [2.8%]), and obesity (1071 [2.5%]). The strongest risk factors for hospitalization were type 1 diabetes (adjusted risk ratio [aRR], 4.60; 95% CI, 3.91-5.42) and obesity (aRR, 3.07; 95% CI, 2.66-3.54), and the strongest risk factors for severe COVID-19 illness were type 1 diabetes (aRR, 2.38; 95% CI, 2.06-2.76) and cardiac and circulatory congenital anomalies (aRR, 1.72; 95% CI, 1.48-1.99). Prematurity was a risk factor for severe COVID-19 illness among children younger than 2 years (aRR, 1.83; 95% CI, 1.47-2.29). Chronic and complex chronic disease were risk factors for hospitalization, with aRRs of 2.91 (95% CI, 2.63-3.23) and 7.86 (95% CI, 6.91-8.95), respectively, as well as for severe COVID-19 illness, with aRRs of 1.95 (95% CI, 1.69-2.26) and 2.86 (95% CI, 2.47-3.32), respectively. Conclusions and Relevance This cross-sectional study found a higher risk of severe COVID-19 illness among children with medical complexity and certain underlying conditions, such as type 1 diabetes, cardiac and circulatory congenital anomalies, and obesity. Health care practitioners could consider the potential need for close observation and cautious clinical management of children with these conditions and COVID-19.