ChatGPT: A Conceptual Review of Applications and Utility in the Field of Medicine.

Artificial Intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this narrative review, we evaluate the myriad applications of ChatGPT in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid, data-driven insights for diagnosis and treatment. We review the benefits of ChatGPT in personalized patient care, particularly in geriatric care, medication management, weight loss and nutrition, and physical activity guidance. We further delve into its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies. In the realm of medical education, we investigate the utility of ChatGPT as an information retrieval tool and personalized learning resource for medical students and professionals. There are numerous promising applications of ChatGPT that will likely induce paradigm shifts in healthcare practice, education, and research. The use of ChatGPT may come with several benefits in areas such as clinical decision making, geriatric care, medication management, weight loss and nutrition, physical fitness, scientific research, and medical education. Nevertheless, it is important to note that issues surrounding ethics, data privacy, transparency, inaccuracy, and inadequacy persist. Prior to widespread use in medicine, it is imperative to objectively evaluate the impact of ChatGPT in a real-world setting using a risk-based approach.

Genetic Foundation of Prostaglandin Metabolism Influences Patent Ductus Arteriosus Closure in Extremely Low Birth Weight Infants.

OBJECTIVE: Prostaglandins (PGs) play a major role in maintaining patency of the ductal arteriosus (DA). Pulmonary 15-hydroxyprostaglandin dehydrogenase (PGDH), which is ecoded by the hydroxyprostaglandin dehydrogenase (HPGD) gene, is the primary enzyme responsible for PG breakdown. Animal studies have shown HPGD-knockout mice have significantly higher prostaglandin E2 levels and no ductal remodeling. Functional variants of the HPGD gene that alter PG breakdown have not been studied in preterm infants with patent ductus arteriosus (PDA). STUDY DESIGN: This was an observational cohort study including extreme low birth weight (ELBW) infants classified as having spontaneous, medical, or procedural (transcatheter or surgical ligation) closure of their DA. Urine prostaglandin E metabolite (PGEM) levels were measured in ELBW infants following ibuprofen treatment using competitive ELISA. HPGD genetic variants rs8752, rs2612656, and rs9312555 were analyzed. Kruskal-Wallis, Fisher's exact, chi square, logistic regression, and Wilcoxon signed-rank tests were used; p < 0.05 was considered significant. RESULTS: Infants in the procedural closure group had a younger gestational age (GA). The incidence of spontaneous closure or medical closure was higher compared to procedural closure in the presence of any minor allele of rs8752 (67 and 27%, respectively; p = 0.01), when adjusted for GA and gender. Haplotype analysis of three variants of HPGD revealed differences when comparing the spontaneous and medical closure group to the procedural group (p < 0.05). Urinary PGEM levels dropped significantly in those ELBW infants who responded to ibuprofen (p = 0.003) in contrast to those who did not respond (p = 0.5). CONCLUSION: There was a different genotype distribution for the rs8752 genetic variant of the HPGD gene-as it relates to the mode of treatment for ELBW infants with PDA. We speculate that medical management in the presence of this variant facilitated additional PG breakdown, significantly abrogating the need for procedural closure. Additionally, differences in genotype and haplotype distributions implicate a specific HPGD genetic foundation for DA closure in ELBW infants. KEY POINTS: · PGs and their metabolism play a major role in PDA patency or closure.. · Genetic variants of the HPGD gene influence mode of treatment of PDA in ELBW infants.. · ELBW infants with PDA that responded to medical closure had significantly decreased urine PGEM levels..

Synergizing Safety: A Customized Approach to Curtailing Unplanned Extubations through Shared Decision-making in the NICU.

Background: Unplanned Extubation (UE) remains an important patient safety issue in the Neonatal Intensive Care Unit. Our SMART AIM was to decrease the rate of UE by 10% from the baseline from January to December 2022 by emphasizing collaboration among healthcare professionals and through the use of shared decision-making. Methods: We established an interdisciplinary Quality Improvement team composed of nurses, respiratory therapists, and physicians (MDs). The definition of UE was standardized. UE was audited using an apparent cause analysis form to discern associated causes and pinpoint areas for improvement. Interventions were implemented in a step-by-step fashion and reviewed monthly using the model for improvement. A shared decision-making approach fostered collaborative problem-solving. Results: Our baseline UE rate was 2.3 per 100 ventilator days. Retaping, general bedside care, and position change accounted for over 50% of the UE events in 2022. The rate of UE was reduced by 48% by the end of December 2022. We achieved special-cause variation by the end of March 2023. Conclusions: The sole education of medical and nursing providers about various approaches to decreasing unnecessary retaping was ineffective in reducing UE rates. Shared decision-making incorporating inputs from nurses, respiratory therapists, and MDs led to a substantial reduction in the UE rate and underscores the potential of systematic evaluation of risk factors combined with collaborative best practices.

Nationwide Analysis of the Outcomes and Mortality of Hospitalized Infants with Concomitant Diagnosis of COVID-19.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) generally causes milder illness in the pediatric population. However, infants represent a higher-risk population with evolving symptomatology and severity. There is a paucity of large population-based data on the impact of COVID-19 on hospitalized infants. STUDY DESIGN: In this large cohort study, the National Inpatient Sample database was queried for all infant hospital admissions between January and December 2020 in the United States, with and without a diagnosis of COVID-19 based on ICD-10-CM U07. The mortality and morbidity of infants with and without COVID-19 were evaluated. Parent-reported race and outcomes were also analyzed. RESULTS: A weighted total of 3,754,236 infants who were hospitalized were identified, of which 4,265 patients (0.11%) had a concomitant diagnosis of COVID-19. Infants with COVID-19 had similar mortality and extracorporeal membrane oxygenation utilization. Infants with concomitant COVID-19 had a higher rate of respiratory failure, congestive heart failure, acute kidney injury, and coagulopathy. Compared with Caucasian infants and Asian infants, Hispanic and African American infants were more likely to have COVID-19 hospital admissions than hospitalizations without COVID-19 diagnosis. Patients with lower median household income represented the majority of the COVID-19 hospitalization. The infants with COVID-19 were more likely to have Medicaid or Medicare insurance and less likely to have private insurance. CONCLUSION: In this large cohort of hospitalized infants with COVID-19, the infection was associated with complications, including respiratory failure and endotracheal intubations but not associated with a higher risk for mortality. Infants from racial minorities and lower socioeconomic strata carry the highest burden of COVID-19 infection. KEY POINTS: · Infants with COVID-19 represent a higher-risk group with evolving symptomatology and severity.. · Infants with COVID-19 had similar mortality rates and extracorporeal membrane oxygenation utilization as those without COVID-19.. · Racial minorities and lower socioeconomic strata carry the highest burden of COVID-19 infection..

Polygenic pathogen networks influence transcriptional plasticity in the Arabidopsis-Botrytis pathosystem.

Bidirectional flow of information shapes the outcome of the host-pathogen interactions and depends on the genetics of each organism. Recent work has begun to use co-transcriptomic studies to shed light on this bidirectional flow, but it is unclear how plastic the co-transcriptome is in response to genetic variation in both the host and pathogen. To study co-transcriptome plasticity, we conducted transcriptomics using natural genetic variation in the pathogen, Botrytis cinerea, and large-effect genetic variation abolishing defense signaling pathways within the host, Arabidopsis thaliana. We show that genetic variation in the pathogen has a greater influence on the co-transcriptome than mutations that abolish defense signaling pathways in the host. Genome-wide association mapping using the pathogens' genetic variation and both organisms' transcriptomes allowed an assessment of how the pathogen modulates plasticity in response to the host. This showed that the differences in both organism's responses were linked to trans-expression quantitative trait loci (eQTL) hotspots within the pathogen's genome. These hotspots control gene sets in either the host or pathogen and show differential allele sensitivity to the host's genetic variation rather than qualitative host specificity. Interestingly, nearly all the trans-eQTL hotspots were unique to the host or pathogen transcriptomes. In this system of differential plasticity, the pathogen mediates the shift in the co-transcriptome more than the host.

FindICI: Using machine learning to detect linguistic inconsistencies between code and natural language descriptions in infrastructure-as-code.

Linguistic anti-patterns are recurring poor practices concerning inconsistencies in the naming, documentation, and implementation of an entity. They impede the readability, understandability, and maintainability of source code. This paper attempts to detect linguistic anti-patterns in Infrastructure-as-Code (IaC) scripts used to provision and manage computing environments. In particular, we consider inconsistencies between the logic/body of IaC code units and their short text names. To this end, we propose FindICI a novel automated approach that employs word embedding and classification algorithms. We build and use the abstract syntax tree of IaC code units to create code embeddings used by machine learning techniques to detect inconsistent IaC code units. We evaluated our approach with two experiments on Ansible tasks systematically extracted from open source repositories for various word embedding models and classification algorithms. Classical machine learning models and novel deep learning models with different word embedding methods showed comparable and satisfactory results in detecting inconsistent Ansible tasks related to the top-10 used Ansible modules.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Positive Newborns of COVID-19 Mothers After Dyad-Care: A Case Series.

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in newborns is extremely rare, and there is a scarcity of research pertaining to epidemiology, clinical presentation, transmission, and prognosis in this population. We present five newborns who tested positive while colocating with their SARS-CoV-2 positive mothers from March 19 to May 15, 2020, at a large public hospital in Queens, New York that was severely affected by the coronavirus disease 2019 (COVID-19) pandemic. All the newborns subsequently tested negative and remained asymptomatic, including through median outpatient follow-up of three weeks.

Universal screening for SARS-CoV-2 infection among pregnant women at Elmhurst Hospital Center, Queens, New York.

BACKGROUND: Universal screening for SARS-CoV-2 infection on Labor and Delivery (L&D) units is a critical strategy to manage patient and health worker safety, especially in a vulnerable high-prevalence community. We describe the results of a SARS-CoV-2 universal screening program at the L&D Unit at Elmhurst Hospital in Queens, NY, a 545-bed public hospital serving a diverse, largely immigrant and low-income patient population and an epicenter of the global pandemic. METHODS AND FINDINGS: We conducted a retrospective cross-sectional study. All pregnant women admitted to the L&D Unit of Elmhurst Hospital from March 29, 2020 to April 22, 2020 were included for analysis. The primary outcomes of the study were: (1) SARS-CoV-2 positivity among universally screened pregnant women, stratified by demographic characteristics, maternal comorbidities, and delivery outcomes; and (2) Symptomatic or asymptomatic presentation at the time of testing among SARS-CoV-2 positive women. A total of 126 obstetric patients were screened for SARS-CoV-2 between March 29 and April 22. Of these, 37% were positive. Of the women who tested positive, 72% were asymptomatic at the time of testing. Patients who tested positive for SARS-CoV-2 were more likely to be of Hispanic ethnicity (unadjusted difference 24.4 percentage points, CI 7.9, 41.0) and report their primary language as Spanish (unadjusted difference 32.9 percentage points, CI 15.8, 49.9) than patients who tested negative. CONCLUSIONS: In this retrospective cross-sectional study of data from a universal SARS-Cov-2 screening program implemented in the L&D unit of a safety-net hospital in Queens, New York, we found over one-third of pregnant women testing positive, the majority of those asymptomatic. The rationale for universal screening at the L&D Unit at Elmhurst Hospital was to ensure safety of patients and staff during an acute surge in SARS-Cov-2 infections through appropriate identification and isolation of pregnant women with positive test results. Women were roomed by their SARS-CoV-2 status given increasing space limitations. In addition, postpartum counseling was tailored to infection status. We quickly established discharge counseling and follow-up protocols tailored to their specific social needs. The experience at Elmhurst Hospital is instructive for other L&D units serving vulnerable populations and for pandemic preparedness.

Transposable element insertions shape gene regulation and melanin production in a fungal pathogen of wheat.

BACKGROUND: Fungal plant pathogens pose major threats to crop yield and sustainable food production if they are highly adapted to their host and the local environment. Variation in gene expression contributes to phenotypic diversity within fungal species and affects adaptation. However, very few cases of adaptive regulatory changes have been reported in fungi and the underlying mechanisms remain largely unexplored. Fungal pathogen genomes are highly plastic and harbor numerous insertions of transposable elements, which can potentially contribute to gene expression regulation. In this work, we elucidated how transposable elements contribute to variation in melanin accumulation, a quantitative trait in fungi that affects survival under stressful conditions. RESULTS: We demonstrated that differential transcriptional regulation of the gene encoding the transcription factor Zmr1, which controls expression of the genes in the melanin biosynthetic gene cluster, is responsible for variation in melanin accumulation in the fungal plant pathogen Zymoseptoria tritici. We show that differences in melanin levels between two strains of Z. tritici are due to two levels of transcriptional regulation: (1) variation in the promoter sequence of Zmr1 and (2) an insertion of transposable elements upstream of the Zmr1 promoter. Remarkably, independent insertions of transposable elements upstream of Zmr1 occurred in 9% of Z. tritici strains from around the world and negatively regulated Zmr1 expression, contributing to variation in melanin accumulation. CONCLUSIONS: Our studies identified two levels of transcriptional control that regulate the synthesis of melanin. We propose that these regulatory mechanisms evolved to balance the fitness costs associated with melanin production against its positive contribution to survival in stressful environments.

Widespread signatures of selection for secreted peptidases in a fungal plant pathogen.

BACKGROUND: Fungal plant pathogens secrete a large arsenal of hydrolytic enzymes during the course of infection, including peptidases. Secreted peptidases have been extensively studied for their role as effectors. In this study, we combined transcriptomics, comparative genomics and evolutionary analyses to investigate all 39 secreted peptidases in the fungal wheat pathogen Zymoseptoria tritici and its close relatives Z. pseudotritici and Z. ardabiliae. RESULTS: RNA-seq data revealed that a majority of the secreted peptidases displayed differential transcription during the course of Z. tritici infection, indicative of specialization for different stages in the life cycle. Evolutionary analyses detected widespread evidence of adaptive evolution acting on at least 28 of the peptidases. A few peptidases displayed lineage-specific rates of molecular evolution, suggesting altered selection pressure in Z. tritici following host specialization on domesticated wheat. The peptidases belonging to MEROPS families A1 and G1 emerged as a particularly interesting group that may play key roles in host-pathogen co-evolution, host adaptation and pathogenicity. Sister genes in the A1 and G1 families showed accelerated substitution rates after gene duplications. CONCLUSIONS: These results suggest widespread evolution of secreted peptidases leading to novel gene functions, consistent with predicted models of "escape from adaptive conflict" and "neo-functionalization". Our analyses identified candidate genes worthy of functional analyses that may encode effector functions, for example by suppressing plant defenses during the biotrophic phase of infection.