BACKGROUND: Although uncertainties exist regarding implementation, artificial intelligence-driven generative language models (GLMs) have enormous potential in medicine. Deployment of GLMs could improve patient comprehension of clinical texts and improve low health literacy.
OBJECTIVE: The goal of this study is to evaluate the potential of ChatGPT-3.5 and GPT-4 to tailor the complexity of medical information to patient-specific input education level, which is crucial if it is to serve as a tool in addressing low health literacy.
METHODS: Input templates related to 2 prevalent chronic diseases-type II diabetes and hypertension-were designed. Each clinical vignette was adjusted for hypothetical patient education levels to evaluate output personalization. To assess the success of a GLM (GPT-3.5 and GPT-4) in tailoring output writing, the readability of pre- and posttransformation outputs were quantified using the Flesch reading ease score (FKRE) and the Flesch-Kincaid grade level (FKGL).
RESULTS: Responses (n=80) were generated using GPT-3.5 and GPT-4 across 2 clinical vignettes. For GPT-3.5, FKRE means were 57.75 (SD 4.75), 51.28 (SD 5.14), 32.28 (SD 4.52), and 28.31 (SD 5.22) for 6th grade, 8th grade, high school, and bachelor\'s, respectively; FKGL mean scores were 9.08 (SD 0.90), 10.27 (SD 1.06), 13.4 (SD 0.80), and 13.74 (SD 1.18). GPT-3.5 only aligned with the prespecified education levels at the bachelor\'s degree. Conversely, GPT-4\'s FKRE mean scores were 74.54 (SD 2.6), 71.25 (SD 4.96), 47.61 (SD 6.13), and 13.71 (SD 5.77), with FKGL mean scores of 6.3 (SD 0.73), 6.7 (SD 1.11), 11.09 (SD 1.26), and 17.03 (SD 1.11) for the same respective education levels. GPT-4 met the target readability for all groups except the 6th-grade FKRE average. Both GLMs produced outputs with statistically significant differences (P<.001; 8th grade P<.001; high school P<.001; bachelors P=.003; FKGL: 6th grade P=.001; 8th grade P<.001; high school P<.001; bachelors P<.001) between mean FKRE and FKGL across input education levels.
CONCLUSIONS: GLMs can change the structure and readability of medical text outputs according to input-specified education. However, GLMs categorize input education designation into 3 broad tiers of output readability: easy (6th and 8th grade), medium (high school), and difficult (bachelor\'s degree). This is the first result to suggest that there are broader boundaries in the success of GLMs in output text simplification. Future research must establish how GLMs can reliably personalize medical texts to prespecified education levels to enable a broader impact on health care literacy.

The ingestion of debris by marine fauna is a growing threat to biodiversity. This study aimed to evaluate and characterize litter ingestion by odontocetes from the Western South Atlantic. Between 2018 and 2022, 154 stomachs from six species were collected from stranded individuals and incidental captures. Stomach contents were analyzed with the naked eye and items of anthropic origin found were counted and physically/chemically characterized. Generalized Linear Models were used to evaluate the influence of biological factors on the presence/absence of litter in stomachs, and for Pontoporia blainvillei only, the influence of these factors on the number of ingested items was also tested; additionally, a temporal analysis of ingestion was done for this species (1994-2022). A total of 156 items, mainly macro-sized plastics made of polypropylene, were found in 52 stomachs of four species: Tursiops spp. (FO% = 3.3%), Steno bredanensis (10.0%), Delphinus delphis (28.6%) and P. blainvillei (47.5%). The presence/absence of litter was explained only by species (χ2 = 28.29 and p < 0.001). For P. blainvillei, a threatened species in the region, the number of items was positively influenced by individual size (χ2 = 6.01 and p = 0.01) and sex (χ2 = 7.93 and p = 0.005). There was an increase in plastic ingestion by this species over the years (χ2 = 121.6 and p < 0.001) and it was estimated that 75% of P. blainvillei stomachs will contain plastic by 2040. The ingestion of litter by odontocetes from the Western South Atlantic was confirmed and the potential risks posed by this type of pollution were evidenced, especially since these species also face other anthropic pressures. These results further demonstrate the increasing threat of litter in the ocean and highlight the importance of circularity of plastics and proper waste management.

UNASSIGNED: High-throughput sequencing technology facilitates the quantitative analysis of microbial communities, improving the capacity to investigate the associations between the human microbiome and diseases. Our primary motivating application is to explore the association between gut microbes and obesity. The complex characteristics of microbiome data, including high dimensionality, zero inflation, and over-dispersion, pose new statistical challenges for downstream analysis.
UNASSIGNED: We propose a GLM-based zero-inflated generalized Poisson factor analysis (GZIGPFA) model to analyze microbiome data with complex characteristics. The GZIGPFA model is based on a zero-inflated generalized Poisson (ZIGP) distribution for modeling microbiome count data. A link function between the generalized Poisson rate and the probability of excess zeros is established within the generalized linear model (GLM) framework. The latent parameters of the GZIGPFA model constitute a low-rank matrix comprising a low-dimensional score matrix and a loading matrix. An alternating maximum likelihood algorithm is employed to estimate the unknown parameters, and cross-validation is utilized to determine the rank of the model in this study. The proposed GZIGPFA model demonstrates superior performance and advantages through comprehensive simulation studies and real data applications.

CRISPR genome engineering and single-cell RNA sequencing have accelerated biological discovery. Single-cell CRISPR screens unite these two technologies, linking genetic perturbations in individual cells to changes in gene expression and illuminating regulatory networks underlying diseases. Despite their promise, single-cell CRISPR screens present considerable statistical challenges. We demonstrate through theoretical and real data analyses that a standard method for estimation and inference in single-cell CRISPR screens-\"thresholded regression\"-exhibits attenuation bias and a bias-variance tradeoff as a function of an intrinsic, challenging-to-select tuning parameter. To overcome these difficulties, we introduce GLM-EIV (\"GLM-based errors-in-variables\"), a new method for single-cell CRISPR screen analysis. GLM-EIV extends the classical errors-in-variables model to responses and noisy predictors that are exponential family-distributed and potentially impacted by the same set of confounding variables. We develop a computational infrastructure to deploy GLM-EIV across hundreds of processors on clouds (e.g. Microsoft Azure) and high-performance clusters. Leveraging this infrastructure, we apply GLM-EIV to analyze two recent, large-scale, single-cell CRISPR screen datasets, yielding several new insights.

Forest biomass plays a crucial role in the global carbon cycle as a significant contributor derived from both soil and trees. This study focuses on investigating tree carbon stock (TCS) and estimating aboveground biomass (AGB) based on elevation within the Srivilliputhur Wildlife Sanctuary forest, while also exploring the various factors that influence their contribution. Utilizing a non-destructive approach for carbon estimation, we found that the total tree biomass in this region ranged from 220.9 Mg/ha (in Z6) to 720.6 Mg/ha (Z2), while tree carbon stock ranged from 103.8 to 338.7 Mg/ha. While Kruskal-Wallis tests did not reveal a significant relationship (p = 0.09) between TCS and elevation, linear regression showed a weak correlation (R2 = 0.002, p < 0.05) with elevation. To delve deeper into the factors influencing TCS and biomass distribution, we employed a random forest (RF) machine learning algorithm, demonstrating that stand structural attributes, such as basal area (BA), diameter at breast height (DBH), and density, held a more prominent role than climatic variables, including temperature, precipitation, and slope. Generalized linear models (GLM) were also utilized, confirming that BA, mean DBH, and elevation significantly influenced AGB (p ≤ 0.001), with species richness, precipitation, and temperature having lower significance (p ≤ 0.01) comparatively. Overall, the RF model exhibited superior performance (R2 = 0.92, RMSE = 0.12) in terms of root mean square error (RMSE) compared to GLM (R2 = 0.88, RMSE = 0.35). These findings shed light on the intricate dynamics of biomass distribution and the importance of both stand structural and climatic factors in shaping forest ecosystems.

Understanding the responses of organisms to different environmental drivers is critical for improving ecosystem management and conservation. Estuarine ecosystems are under pressure from multiple anthropogenic stressors (e.g. increasing sediment and nutrient loads, pollution, climate change) that are affecting the functions and services these ecosystems provide. Here, we used long-term estuarine benthic invertebrate monitoring data (∼30 year time-series) to evaluate the responses of macrobenthic invertebrate communities and indicator species to climatic, oceanic, freshwater, and local environmental drivers in New Zealand estuaries. We aimed to improve our ability to predict ecosystem change and understand the effects of multiple environment drivers on benthic communities. Our analyses showed that the abundance and richness of macrobenthic fauna and four indicator taxa (bivalves known to have differing tolerances to sediment mud content: Austrovenus stutchburyi, Macomona liliana, Theora lubrica, and Arthritica bifurca) responded to unique combinations of multiple environmental drivers across sites and times. Macrobenthic responses were highly mixed (i.e., positive and negative) and site-dependent. We also show that responses of macrobenthic fauna were lagged and most strongly related to climatic and oceanic drivers. The way the macrobenthos responded has implications for predicting and understanding the ecological consequences of a rapidly changing environment and how we conserve and manage coastal ecosystems.

Herein, we explored the overall association between metal mixtures and lung functions in populations of varying ages and the relationship among the associated components. The 2007-2012 National Health and Nutrition Examination Survey data of 4382 American participants was analyzed, and generalized linear, elastic net, quantile g-computation, and Bayesian kernel machine regression models were used to evaluate the relationship between exposure to the metal mixture and lung function at various ages. The results of barium exposure at distinct stages revealed that children and adolescents exhibited greater lung function changes than those in adults and the elderly. Additionally, compared with children and adolescents, cadmium- and arsenic-containing metabolites contributed to nonconductive lung function changes in adults and the elderly exposed to metal mixtures. The results showed that the effects of exposure to metal mixtures on lung function in children and adolescents were predominantly caused by lead and barium. Altogether, children and adolescents were found to be more susceptible to metal-exposure-mediated lung function changes than adults and the elderly.

In total, 109 fish species (24,996 individuals) were sampled around Guadeloupe and Martinique Islands from October 2021 to September 2022 to estimate the morphometric relationships between total length and weight (Length Weight Relationship: LWR) of each fish species according to potential spatial, temporal and sex differences. Of these species, this is the first time that the LWR was estimated in the Atlantic Ocean for 16 species. There is a significant relationship between length and weight for all tested species. For 83 tested species, the sex effect on the LWR showed significant sexual dimorphism for 24 species. Additionally, a link between the temporal effect and the reproduction period was tested for 68 species, of which 35 presented significant differences relative to the annual quarter of sampling. Finally, the geographical effect (i.e., the difference between samples from around Guadeloupe Island and those from Martinique Island) was significant for 60 species. This island effect was significant for 25 species.

BACKGROUND: Self-regulation in early childhood develops within a social context. Variations in such development can be attributed to inter-individual behavioral differences, which can be captured both as facets of temperament and across a normal:abnormal dimensional spectrum. With increasing emphasis on irritability as a robust early-life transdiagnostic indicator of broad psychopathological risk, linkage to neural mechanisms is imperative. Currently, there is inconsistency in the identification of neural circuits that underlie irritability in children, especially in social contexts. This study aimed to address this gap by utilizing a functional magnetic resonance imaging (fMRI) paradigm to investigate pediatric anger/frustration using social stimuli.
METHODS: Seventy-three children (M = 6 years, SD = 0.565) were recruited from a larger longitudinal study on irritability development. Caregivers completed questionnaires assessing irritable temperament and clinical symptoms of irritability. Children participated in a frustration task during fMRI scanning that was designed to induce frustration through loss of a desired prize to an animated character. Data were analyzed using both general linear modeling (GLM) and independent components analysis (ICA) and examined from the temperament and clinical perspectives.
RESULTS: ICA results uncovered an overarching network structure above and beyond what was revealed by traditional GLM analyses. Results showed that greater temperamental irritability was associated with significantly diminished spatial extent of activation and low-frequency power in a network comprised of the posterior superior temporal sulcus (pSTS) and the precuneus (p < .05, FDR-corrected). However, greater severity along the spectrum of clinical expression of irritability was associated with significantly increased extent and intensity of spatial activation as well as low- and high-frequency neural signal power in the right caudate (p < .05, FDR-corrected).
CONCLUSIONS: Our findings point to specific neural circuitry underlying pediatric irritability in the context of frustration using social stimuli. Results suggest that a deliberate focus on the construction of network-based neurodevelopmental profiles and social interaction along the normal:abnormal irritability spectrum is warranted to further identify comprehensive transdiagnostic substrates of the irritability.

UNASSIGNED: Despite the rising incidence rate of granulomatous lobular mastitis (GLM), uncertainties persist about its etiologic and predisposing factors to guide clinical treatment and early prevention. The objective of this study is to explore the predisposing factors for GLM.
UNASSIGNED: This case-control study was conducted from 2018 to 2021 at Beijing Hospital of Traditional Chinese Medicine, Capital Medical University. Patients with GLM (cases) were matched with healthy examinees (controls) in a 1:1 ratio according to gender and living area. We analyzed their demographic features and investigated 75 factors that may be relevant to GLM using a standard questionnaire. Univariate and multivariable binary conditional logistic regression analyses were used to compare the differences between the two groups and evaluate the predisposing factors that may induce GLM.
UNASSIGNED: There were 594 female GLM patients and 594 matched controls included in the study. The average age of the cases was 32.78 years (mainly 20 to 40). The incidence was high within five years after childbirth, and lesions were mainly in the unilateral breast. Univariate and multivariable conditional logistic regression analyses obtained six relevant factors and six high-risk factors. The six relevant factors included age, marriage, emotional abnormality, high prolactin, psychiatric drug intake, and sex hormone intake. Additionally, the independent high-risk factors for GLM included gestation, nipple invagination, blunt trauma, non-iatrogenic massage, lactation disorder, and nipple discharge (odds ratio (OR)=17.378, 8.518, 4.887, 3.116, 2.522, 1.685, P<0.05). Menopause was an independent protective factor (OR=0.249, P<0.05).
UNASSIGNED: The factors that increase milk and secretion production in the mammary duct are the main risk factors of GLM, especially when the nipples are invaginated. These factors can obstruct the duct and induce inflammation. Additionally, hormonal disorders, extrinsic trauma, and emotional abnormalities can accelerate the occurrence of GLM.