Contrasting neurofunctional correlates of face- and visuospatial-processing in children and adolescents with Williams syndrome: convergent results from four fMRI paradigms.

Understanding neurogenetic mechanisms underlying neuropsychiatric disorders such as schizophrenia and autism is complicated by their inherent clinical and genetic heterogeneity. Williams syndrome (WS), a rare neurodevelopmental condition in which both the genetic alteration (hemideletion of ~ twenty-six 7q11.23 genes) and the cognitive/behavioral profile are well-defined, offers an invaluable opportunity to delineate gene-brain-behavior relationships. People with WS are characterized by increased social drive, including particular interest in faces, together with hallmark difficulty in visuospatial processing. Prior work, primarily in adults with WS, has searched for neural correlates of these characteristics, with reports of altered fusiform gyrus function while viewing socioemotional stimuli such as faces, along with hypoactivation of the intraparietal sulcus during visuospatial processing. Here, we investigated neural function in children and adolescents with WS by using four separate fMRI paradigms, two that probe each of these two cognitive/behavioral domains. During the two visuospatial tasks, but not during the two face processing tasks, we found bilateral intraparietal sulcus hypoactivation in WS. In contrast, during both face processing tasks, but not during the visuospatial tasks, we found fusiform hyperactivation. These data not only demonstrate that previous findings in adults with WS are also present in childhood and adolescence, but also provide a clear example that genetic mechanisms can bias neural circuit function, thereby affecting behavioral traits.

The brain structure, inflammatory, and genetic mechanisms mediate the association between physical frailty and depression.

Cross-sectional studies have demonstrated strong associations between physical frailty and depression. However, the evidence from prospective studies is limited. Here, we analyze data of 352,277 participants from UK Biobank with 12.25-year follow-up. Compared with non-frail individuals, pre-frail and frail individuals have increased risk for incident depression independent of many putative confounds. Altogether, pre-frail and frail individuals account for 20.58% and 13.16% of depression cases by population attributable fraction analyses. Higher risks are observed in males and individuals younger than 65 years than their counterparts. Mendelian randomization analyses support a potential causal effect of frailty on depression. Associations are also observed between inflammatory markers, brain volumes, and incident depression. Moreover, these regional brain volumes and three inflammatory markers-C-reactive protein, neutrophils, and leukocytes-significantly mediate associations between frailty and depression. Given the scarcity of curative treatment for depression and the high disease burden, identifying potential modifiable risk factors of depression, such as frailty, is needed.

Epidemiology and Healthcare Expenditure for Skin Disease in Emergency Departments in Alberta, Canada.

BACKGROUND: There are limited data on the epidemiology and costs associated with managing dermatologic conditions in emergency departments (EDs). OBJECTIVE: To assess the incidence and mean cost per case of skin diseases in EDs in Alberta. METHODS: Alberta Health Services' Interactive Health Data Application was used to determine the epidemiology and costs associated with nonneoplastic dermatologic diseases in EDs in the province of Alberta, Canada, from 2018 to 2022. Skin conditions were identified using the International Classification of Disease 10th edition diagnostic groupings. RESULTS: Skin disease represented 3.59% of all ED presentations in Alberta in 2022. The total costs associated with managing dermatologic conditions have remained stable over time at approximately 15 million Canadian Dollars (CAD) annually, but the mean cost per case has risen from 188.88 (SD 15.42) in 2018 to 246.25 CAD (SD 27.47) in 2022 (7.59%/year). Infections of skin and subcutaneous tissue were the most expensive diagnostic grouping. The most common dermatologic diagnostic groupings presenting to the ED were infections of skin and subcutaneous tissue [mean age-standardized incidence rate (ASIR) of 143.67 per 100,000 standard population (SD 241.99)], urticaria and erythema [mean ASIR 33.57 per 100,000 standard population (SD 59.13)], and dermatitis and eczema [mean ASIR 18.59 per 100,000 standard population (SD 23.65)]. Cellulitis was both the most common and the costliest individual diagnosis. The majority of patients were triaged as less urgent or nonurgent. CONCLUSIONS: Skin disease represents a substantial public health burden in EDs. Further research into drivers of cost change and areas for cost savings is essential.

Nocebo effects in systemic therapies for adult plaque psoriasis: A systematic review and meta-analysis.

Introduction: The nocebo effect is defined as adverse outcomes secondary to negative patient expectations rather than the pharmacologic activity of an intervention. Nocebo effects can reduce treatment adherence and/or persistence. Therefore, nocebo effects in psoriasis need to be defined. Methods: A Cochrane systematic review was updated with a search of MEDLINE, Embase, and the CENTRAL Register of Controlled Trials for phase II - IV RCTs comparing systemic therapy versus placebo for patients with moderate-to-severe plaque psoriasis. Estimates were pooled using a random effects model, and heterogeneity was evaluated using the I2 statistic. The primary outcome was the pooled proportion of any adverse event (AE) and corresponding risk difference (RD) in patients randomized to placebo versus systemic therapy. Results: A total of 103 unique trials were identified enrolling 43,189 patients. The overall pooled AE rate in patients randomized to systemic therapy was 57.1% [95% CI: 54.7-59.5%] compared to 49.8% [95% CI: 47.1-52.4%] for placebo [RD 6.7% (95% CI: 4.6-8.9%), p < 0.00001, I2 = 75%]. Both biologic and non-biologic systemic therapy groups had a higher proportion of infectious AEs compared to placebo. No statistically significant RD in serious AEs or AEs leading to discontinuation was identified between systemic therapy and placebo groups. Discussion: Half of patients exposed to inert placebo in clinical trials of systemic psoriasis therapies experienced AEs, which may be explained by nocebo effects. These findings have important implications when counseling patients and designing future studies.

Investigating brain dynamics and their association with cognitive control in opioid use disorder using naturalistic and drug cue paradigms.

Objectives: Opioid use disorder (OUD) impacts millions of people worldwide. The prevalence and debilitating effects of OUD present a pressing need to understand its neural mechanisms to provide more targeted interventions. Prior studies have linked altered functioning in large-scale brain networks with clinical symptoms and outcomes in OUD. However, these investigations often do not consider how brain responses change over time. Time-varying brain network engagement can convey clinically relevant information not captured by static brain measures. Methods: We investigated brain dynamic alterations in individuals with OUD by applying a new multivariate computational framework to movie-watching (i.e., naturalistic; N=76) and task-based (N=70) fMRI. We further probed the associations between cognitive control and brain dynamics during a separate drug cue paradigm in individuals with OUD. Results: Compared to healthy controls (N=97), individuals with OUD showed decreased variability in the engagement of recurring brain states during movie-watching. We also found that worse cognitive control was linked to decreased variability during the rest period when no opioid-related stimuli were present. Conclusions: These findings suggest that individuals with OUD may experience greater difficulty in effectively engaging brain networks in response to evolving internal or external demands. Such inflexibility may contribute to aberrant response inhibition and biased attention toward opioid-related stimuli, two hallmark characteristics of OUD. By incorporating temporal information, the current study introduces novel information about how brain dynamics are altered in individuals with OUD and their behavioral implications.

Assessing the research landscape and clinical utility of large language models: a scoping review.

IMPORTANCE: Large language models (LLMs) like OpenAI's ChatGPT are powerful generative systems that rapidly synthesize natural language responses. Research on LLMs has revealed their potential and pitfalls, especially in clinical settings. However, the evolving landscape of LLM research in medicine has left several gaps regarding their evaluation, application, and evidence base. OBJECTIVE: This scoping review aims to (1) summarize current research evidence on the accuracy and efficacy of LLMs in medical applications, (2) discuss the ethical, legal, logistical, and socioeconomic implications of LLM use in clinical settings, (3) explore barriers and facilitators to LLM implementation in healthcare, (4) propose a standardized evaluation framework for assessing LLMs' clinical utility, and (5) identify evidence gaps and propose future research directions for LLMs in clinical applications. EVIDENCE REVIEW: We screened 4,036 records from MEDLINE, EMBASE, CINAHL, medRxiv, bioRxiv, and arXiv from January 2023 (inception of the search) to June 26, 2023 for English-language papers and analyzed findings from 55 worldwide studies. Quality of evidence was reported based on the Oxford Centre for Evidence-based Medicine recommendations. FINDINGS: Our results demonstrate that LLMs show promise in compiling patient notes, assisting patients in navigating the healthcare system, and to some extent, supporting clinical decision-making when combined with human oversight. However, their utilization is limited by biases in training data that may harm patients, the generation of inaccurate but convincing information, and ethical, legal, socioeconomic, and privacy concerns. We also identified a lack of standardized methods for evaluating LLMs' effectiveness and feasibility. CONCLUSIONS AND RELEVANCE: This review thus highlights potential future directions and questions to address these limitations and to further explore LLMs' potential in enhancing healthcare delivery.

Automated Paper Screening for Clinical Reviews Using Large Language Models: Data Analysis Study.

BACKGROUND: The systematic review of clinical research papers is a labor-intensive and time-consuming process that often involves the screening of thousands of titles and abstracts. The accuracy and efficiency of this process are critical for the quality of the review and subsequent health care decisions. Traditional methods rely heavily on human reviewers, often requiring a significant investment of time and resources. OBJECTIVE: This study aims to assess the performance of the OpenAI generative pretrained transformer (GPT) and GPT-4 application programming interfaces (APIs) in accurately and efficiently identifying relevant titles and abstracts from real-world clinical review data sets and comparing their performance against ground truth labeling by 2 independent human reviewers. METHODS: We introduce a novel workflow using the Chat GPT and GPT-4 APIs for screening titles and abstracts in clinical reviews. A Python script was created to make calls to the API with the screening criteria in natural language and a corpus of title and abstract data sets filtered by a minimum of 2 human reviewers. We compared the performance of our model against human-reviewed papers across 6 review papers, screening over 24,000 titles and abstracts. RESULTS: Our results show an accuracy of 0.91, a macro F1-score of 0.60, a sensitivity of excluded papers of 0.91, and a sensitivity of included papers of 0.76. The interrater variability between 2 independent human screeners was κ=0.46, and the prevalence and bias-adjusted κ between our proposed methods and the consensus-based human decisions was κ=0.96. On a randomly selected subset of papers, the GPT models demonstrated the ability to provide reasoning for their decisions and corrected their initial decisions upon being asked to explain their reasoning for incorrect classifications. CONCLUSIONS: Large language models have the potential to streamline the clinical review process, save valuable time and effort for researchers, and contribute to the overall quality of clinical reviews. By prioritizing the workflow and acting as an aid rather than a replacement for researchers and reviewers, models such as GPT-4 can enhance efficiency and lead to more accurate and reliable conclusions in medical research.

The use of large language models in medicine: proceeding with caution.

Large language models, like ChatGPT and Bard, have potential clinical applications due to their ability to generate conversational responses and encode medical knowledge. However, their clinical adoption faces challenges including hallucinations, lack of transparency, and lack of consistency. Ethicolegal concerns surrounding patient consent, legal liability, and data privacy further complicate matters. Despite their promise, an optimistic but cautious approach is essential for the safe integration of large language models into clinical settings.

Evaluating fluvoxamine for the outpatient treatment of COVID-19: A systematic review and meta-analysis.

This systematic review and meta-analysis of randomised controlled trials (RCTs) aimed to evaluate the efficacy, safety, and tolerability of fluvoxamine for the outpatient management of COVID-19. We conducted this review in accordance with the PRISMA 2020 guidelines. Literature searches were conducted in MEDLINE, EMBASE, International Pharmaceutical Abstracts, CINAHL, Web of Science, and CENTRAL up to 14 September 2023. Outcomes included incidence of hospitalisation, healthcare utilization (emergency room visits and/or hospitalisation), mortality, supplemental oxygen and mechanical ventilation requirements, serious adverse events (SAEs) and non-adherence. Fluvoxamine 100 mg twice a day was associated with reductions in the risk of hospitalisation (risk ratio [RR] 0.75, 95% confidence interval [CI] 0.58-0.97; I 2  = 0%) and reductions in the risk of healthcare utilization (RR 0.68, 95% CI 0.53-0.86; I 2  = 0%). While no increased SAEs were observed, fluvoxamine 100 mg twice a day was associated with higher treatment non-adherence compared to placebo (RR 1.61, 95% CI 1.22-2.14; I 2  = 53%). In subgroup analyses, fluvoxamine reduced healthcare utilization in outpatients with BMI ≥30 kg/m2 , but not in those with lower BMIs. While fluvoxamine offers potential benefits in reducing healthcare utilization, its efficacy may be most pronounced in high-risk patient populations. The observed non-adherence rates highlight the need for better patient education and counselling. Future investigations should reassess trial endpoints to include outcomes relating to post-COVID sequelaes. Registration: This review was prospectively registered on PROSPERO (CRD42023463829).

Network state dynamics underpin craving in a transdiagnostic population.

Emerging fMRI brain dynamic methods present a unique opportunity to capture how brain region interactions across time give rise to evolving affective and motivational states. As the unfolding experience and regulation of affective states affect psychopathology and well-being, it is important to elucidate their underlying time-varying brain responses. Here, we developed a novel framework to identify network states specific to an affective state of interest and examine how their instantaneous engagement contributed to its experience. This framework investigated network state dynamics underlying craving, a clinically meaningful and changeable state. In a transdiagnostic sample of healthy controls and individuals diagnosed with or at risk for craving-related disorders (N=252), we utilized connectome-based predictive modeling (CPM) to identify craving-predictive edges. An edge-centric timeseries approach was leveraged to quantify the instantaneous engagement of the craving-positive and craving-negative networks during independent scan runs. Individuals with higher craving persisted longer in a craving-positive network state while dwelling less in a craving-negative network state. We replicated the latter results externally in an independent group of healthy controls and individuals with alcohol use disorder exposed to different stimuli during the scan (N=173). The associations between craving and network state dynamics can still be consistently observed even when craving-predictive edges were instead identified in the replication dataset. These robust findings suggest that variations in craving-specific network state recruitment underpin individual differences in craving. Our framework additionally presents a new avenue to explore how the moment-to-moment engagement of behaviorally meaningful network states supports our changing affective experiences.

Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available.

Open-source, publicly available neuroimaging datasets - whether from large-scale data collection efforts or pooled from multiple smaller studies - offer unprecedented sample sizes and promote generalization efforts. Releasing data can democratize science, increase the replicability of findings, and lead to discoveries. Partly due to patient privacy, computational, and data storage concerns, researchers typically release preprocessed data with the voxelwise time series parcellated into a map of predefined regions, known as an atlas. However, releasing preprocessed data also limits the choices available to the end-user. This is especially true for connectomics, as connectomes created from different atlases are not directly comparable. Since there exist several atlases with no gold standards, it is unrealistic to have processed, open-source data available from all atlases. Together, these limitations directly inhibit the potential benefits of open-source neuroimaging data. To address these limitations, we introduce Cross Atlas Remapping via Optimal Transport (CAROT) to find a mapping between two atlases. This approach allows data processed from one atlas to be directly transformed into a connectome based on another atlas without the need for raw data access. To validate CAROT, we compare reconstructed connectomes against their original counterparts (i.e., connectomes generated directly from an atlas), demonstrate the utility of transformed connectomes in downstream analyses, and show how a connectome-based predictive model can generalize to publicly available data that was processed with different atlases. Overall, CAROT can reconstruct connectomes from an extensive set of atlases - without needing the raw data - allowing already processed connectomes to be easily reused in a wide range of analyses while eliminating redundant processing efforts. We share this tool as both source code and as a stand-alone web application (http://carotproject.com/).

ß-Ketoadipic acid production from poly(ethylene terephthalate) waste via chemobiological upcycling.

The upcycling of poly(ethylene terephthalate) (PET) waste can simultaneously produce value-added chemicals and reduce the growing environmental impact of plastic waste. In this study, we designed a chemobiological system to convert terephthalic acid (TPA), an aromatic monomer of PET, to ß-ketoadipic acid (ßKA), a C6 keto-diacid that functions as a building block for nylon-6,6 analogs. Using microwave-assisted hydrolysis in a neutral aqueous system, PET was converted to TPA with Amberlyst-15, a conventional catalyst with high conversion efficiency and reusability. The bioconversion process of TPA into ßKA used a recombinant Escherichia coli ßKA expressing two conversion modules for TPA degradation (tphAabc and tphB) and ßKA synthesis (aroY, catABC, and pcaD). To improve bioconversion, the formation of acetic acid, a deleterious factor for TPA conversion in flask cultivation, was efficiently regulated by deleting the poxB gene along with operating the bioreactor to supply oxygen. By applying two-stage fermentation consisting of the growth phase in pH 7 followed by the production phase in pH 5.5, a total of 13.61 mM ßKA was successfully produced with 96% conversion efficiency. This efficient chemobiological PET upcycling system provides a promising approach for the circular economy to acquire various chemicals from PET waste.

Altered Brain Dynamics Across Bipolar Disorder and Schizophrenia During Rest and Task Switching Revealed by Overlapping Brain States.

BACKGROUND: Individuals with bipolar disorder (BD) and schizophrenia (SCZ) show aberrant brain dynamics (i.e., altered recruitment or traversal through different brain states over time). Existing investigations of brain dynamics typically assume that one dominant brain state characterizes each time point. However, as multiple brain states likely are engaged at any given moment, this approach can obscure alterations in less prominent but critical brain states. Here, we examined brain dynamics in BD and SCZ by implementing a novel framework that simultaneously assessed the engagement of multiple brain states. METHODS: Four recurring brain states were identified by applying nonlinear manifold learning and k-means clustering to the Human Connectome Project task-based functional magnetic resonance imaging data. We then assessed moment-to-moment state engagement in 2 independent samples of healthy control participants and patients with BD or SCZ using resting-state (N = 336) or task-based (N = 217) functional magnetic resonance imaging data. Relative state engagement and state engagement variability were extracted and compared across groups using multivariate analysis of covariance, controlling for site, medication, age, and sex. RESULTS: Our framework identified dynamic alterations in BD and SCZ, while a state discretization approach revealed no significant group differences. Participants with BD or SCZ showed reduced state engagement variability, but not relative state engagement, across multiple brain states during resting-state and task-based functional magnetic resonance imaging. We found decreased state engagement variability in older participants and preliminary evidence suggesting an association with avolition. CONCLUSIONS: Assessing multiple brain states simultaneously can reflect the complexity of aberrant brain dynamics in BD and SCZ, providing a more comprehensive understanding of the neural mechanisms underpinning these conditions.

Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.

Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in children of these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief primer and systematic review on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline ethical and future considerations for neuroimaging researchers interested in predicting health outcomes in early life, including researchers who may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning has provided a foundation for accelerating the prediction of early-life trajectories across the full spectrum of illness and health.

Efficacy and safety of selective serotonin reuptake inhibitors in COVID-19 management: a systematic review and meta-analysis.

BACKGROUND: The efficacy of selective serotonin reuptake inhibitors (SSRIs) in the treatment of acute COVID-19 is still under investigation, with conflicting results reported from randomized controlled trials (RCTs). Different dosing regimens may have contributed to the contradictory findings. OBJECTIVES: To evaluate the efficacy and safety of SSRIs and the effect of different dosing regimens on the treatment of acute COVID-19. DATA SOURCES: Seven databases were searched from January 2020 to December 2022. Trial registries, previous reviews, and preprint servers were hand-searched. STUDY ELIGIBILITY CRITERIA: RCTs and observational studies with no language restrictions. PARTICIPANTS: COVID-19 inpatients/outpatients. INTERVENTIONS: SSRIs prescribed after diagnosis were compared against a placebo or standard of care. ASSESSMENT OF RISK OF BIAS: Risk of bias was rated using the revised Cochrane Risk of Bias Tool for Randomized Trials version 2.0 and Risk of Bias in Non-Randomized Studies of Interventions. METHODS OF DATA SYNTHESIS: Outcomes were mortality, hospitalization, composite of hospitalization/emergency room visits, hypoxemia, requirement for supplemental oxygen, ventilator support, and serious adverse events. RCT data were pooled in random-effects meta-analyses. Observational findings were narratively described. Subgroup analyses were performed on the basis of SSRI dose, and sensitivity analyses were performed excluding studies with a high risk of bias. The Grading of Recommendations, Assessment, Development and Evaluations framework was used to assess the quality of evidence. RESULTS: Six RCTs (N = 4197) and five observational studies (N = 1156) were included. Meta-analyses associated fluvoxamine with reduced mortality (risk ratio, 0.72; 95% CI, 0.63-0.82) and hospitalization (risk ratio, 0.79; 95% CI, 0.64-0.99) on the basis of moderate quality of evidence. Medium-dose fluvoxamine (100 mg twice a day) was associated with reduced mortality, hospitalization, and composite of hospitalization/emergency room visits, but low-dose fluvoxamine (50 mg twice a day) was not. Fluvoxamine was not associated with increased serious adverse events. Observational studies support the use of fluvoxamine and highlight fluoxetine as a possible alternative to SSRIs for the treatment of COVID-19. DISCUSSION: Fluvoxamine remains a candidate pharmacotherapy for treating COVID-19 in outpatients. Medium-dose fluvoxamine may be preferable over low-dose fluvoxamine.

Interactions of Caenorhabditis elegans ß-tubulins with the microtubule inhibitor and anthelmintic drug albendazole.

Parasitic nematodes are major human and agricultural pests, and benzimidazoles are amongst the most important broad-spectrum anthelmintic drug class used for their control. Benzimidazole resistance is now widespread in many species of parasitic nematodes in livestock globally and an emerging concern for the sustainable control of human soil-transmitted helminths. ß-tubulin is the major benzimidazole target, although other genes may influence resistance. Among the 6 Caenorhabditis elegans ß-tubulin genes, loss of ben-1 causes resistance without other apparent defects. Here, we explored the genetics of C. elegans ß-tubulin genes in relation to the response to the benzimidazole derivative albendazole. The most highly expressed ß-tubulin isotypes, encoded by tbb-1 and tbb-2, were known to be redundant with each other for viability, and their products are predicted not to bind benzimidazoles. We found that tbb-2 mutants, and to a lesser extent tbb-1 mutants, were hypersensitive to albendazole. The double mutant tbb-2 ben-1 is uncoordinated and short, resembling the wild type exposed to albendazole, but the tbb-1 ben-1 double mutant did not show the same phenotypes. These results suggest that tbb-2 is a modifier of albendazole sensitivity. To better understand how BEN-1 mutates to cause benzimidazole resistance, we isolated mutants resistant to albendazole and found that 15 of 16 mutations occurred in the ben-1 coding region. Mutations ranged from likely nulls to hypomorphs, and several corresponded to residues that cause resistance in other organisms. Null alleles of ben-1 are albendazole-resistant and BEN-1 shows high sequence identity with tubulins from other organisms, suggesting that many amino acid changes could cause resistance. However, our results suggest that missense mutations conferring resistance are not evenly distributed across all possible conserved sites. Independent of their roles in benzimidazole resistance, tbb-1 and tbb-2 may have specialized functions as null mutants of tbb-1 or tbb-2 were cold or heat sensitive, respectively.