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Background: Tuberculous pleural effusion (TPE) is a challenging extrapulmonary manifestation of tuberculosis, with traditional diagnostic methods often involving invasive surgery and being time-consuming. While various machine learning and statistical models have been proposed for TPE diagnosis, these methods are typically limited by complexities in data processing and difficulties in feature integration. Therefore, this study aims to develop a diagnostic model for TPE using ChatGPT-4, a large language model (LLM), and compare its performance with traditional logistic regression and machine learning models. By highlighting the advantages of LLMs in handling complex clinical data, identifying interrelationships between features, and improving diagnostic accuracy, this study seeks to provide a more efficient and precise solution for the early diagnosis of TPE. Methods: We conducted a cross-sectional study, collecting clinical data from 109 TPE and 54 non-TPE patients for analysis, selecting 73 features from over 600 initial variables. The performance of the LLM was compared with logistic regression and machine learning models (k-Nearest Neighbors, Random Forest, Support Vector Machines) using metrics like area under the curve (AUC), F1 score, sensitivity, and specificity. Results: The LLM showed comparable performance to machine learning models, outperforming logistic regression in sensitivity, specificity, and overall diagnostic accuracy. Key features such as adenosine deaminase (ADA) levels and monocyte percentage were effectively integrated into the model. We also developed a Python package (https://pypi.org/project/tpeai/) for rapid TPE diagnosis based on clinical data. Conclusions: The LLM-based model offers a non-surgical, accurate, and cost-effective method for early TPE diagnosis. The Python package provides a user-friendly tool for clinicians, with potential for broader use. Further validation in larger datasets is needed to optimize the model for clinical application. [ABSTRACT FROM AUTHOR]

Background: Individuals with high autistic traits exhibit characteristics like those of individuals with autism, including impairments in sociability and communication skills. Whether individuals with high autistic traits exhibit less cooperation remains debated. Methods: This study employed the prisoner's dilemma game (PDG) to measure cooperation in 56 dyads, including 27 with high-low (HL) autistic traits and 29 with low-low (LL) autistic traits, using functional near-infrared spectroscopy (fNIRS) hyperscanning technique. Cognitive and emotional empathy were also measured. Results: Individuals with high autistic traits had a lower unilateral cooperation rate than did those with low autistic traits; The HL autistic dyads exhibited a lower mutual cooperation rate and reduced interpersonal brain synchronization (IBS) in the right inferior parietal lobule (r-IPL) and right temporoparietal junction (r-TPJ) compared with the LL autistic dyads; Individuals with high autistic traits had significantly lower cognitive empathy scores than did those with low autistic traits, and the cognitive empathy scores had a marginally significant positive correlation with the unilateral cooperation rate and a marginally significant negative correlation with the activation of the right inferior frontal gyrus (r-IFG); Emotional empathy scores did not significantly differ between the high and low autistic groups, and there was a significant positive correlation between emotional empathy scores and the activation of the r-IFG in individuals with high autistic traits. Conclusion: This study revealed abnormal cooperation in individuals with high autistic traits from unilateral and mutual behavior neural perspectives, potentially linked to a disability of cognitive empathy. [ABSTRACT FROM AUTHOR]

[Objective] This study aims to investigate the anti-depression effects of enzymatic hydrolysates of mulberry leaf flavonoids (EMLF). [Methods] EMLF was prepared and its components were analyzed by UPLC-MS. A zebrafish model of depression was induced by 40 mg/L reserpine. Six groups were designed, including the control, model, fluoxetine, L-EMLF (20 µg/mL), M-EMLF (40 µg/mL), and H-EMLF (80 µg/mL) groups. Behavioral tests, including the new tank test and the light-dark box test, were conducted. The structural changes in the brain tissue of zebrafish in each group were observed by HE staining. The mRNA levels of interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), tryptophan hydroxylase-2 (TPH-2), and 5-hydroxytryptamine 2A (5-HT2A) receptor in the brain tissue were determined by q-PCR. The activity of superoxide dismutase (SOD) and the level of reactive oxygen species (ROS) in the brain tissue of zebrafish were measured. Metabolomics analysis was performed to identify differential metabolites and associated metabolic pathways. [Results] UPLC-MS identified 10 flavonoid aglycone compounds. Reserpine (40 mg/L) induced depression-like behaviors in zebrafish, while EMLF alleviated the bottom-dwelling and dark-preference behaviors, restored the orderly arrangement of nerve cells in the midbrain region, and increased the cell number. EMLF treatment down-regulated the mRNA levels of IL-1β, TNF-α, and 5-HT2A while up-regulating the mRNA level of TPH-2. In addition, EMLF increased the SOD activity and reduced the ROS level. Metabolomics analysis revealed that EMLF significantly regulated 24 differential metabolites, primarily affecting purine metabolism, arginine and proline metabolism, tryptophan metabolism, and riboflavin metabolism. [Conclusion] EMLF effectively alleviated reserpine-induced depression in zebrafish, potentially by regulating the tryptophan metabolism, purine metabolism, and arginine and proline metabolism. [ABSTRACT FROM AUTHOR]

Resistance to RAF- and MEK-targeted therapy is a major clinical challenge. RAF and MEK inhibitors are initially but only transiently effective in some but not all patients with BRAF gene mutation and are largely ineffective in those with RAS gene mutation because of resistance. Through a genetic screen in BRAF-mutant tumor cells, we show that the Hippo pathway effector YAP (encoded by YAP1) acts as a parallel survival input to promote resistance to RAF and MEK inhibitor therapy. Combined YAP and RAF or MEK inhibition was synthetically lethal not only in several BRAF-mutant tumor types but also in RAS-mutant tumors. Increased YAP in tumors harboring BRAF V600E was a biomarker of worse initial response to RAF and MEK inhibition in patients, establishing the clinical relevance of our findings. Our data identify YAP as a new mechanism of resistance to RAF- and MEK-targeted therapy. The findings unveil the synthetic lethality of combined suppression of YAP and RAF or MEK as a promising strategy to enhance treatment response and patient survival.

Background: The causes of migraine remain unclear. Evidence suggests that the MAPK and PI3K/Akt signaling pathways play a role in migraine pathogenesis. However, studies on genetic polymorphisms in the two pathways associated with migraine are still limited. Methods: This study included 226 migraineurs and 452 age‐ and sex‐matched nonmigraine control individuals. Genotyping of 31 Single Nucleotide Polymorphisms (SNPs) in 21 genes was performed. The relationship between migraine and gene polymorphisms was analyzed by using logistic regression. SNP–SNP interactions were examined by a generalized multifactor dimension reduction (GMDR) approach. The possible role of SNPs was evaluated with gene expression data from the GTEx database. Results: The RASGRP2‐rs2230414 GT genotype was associated with decreased migraine risk compared with the wild‐type GG genotype [ORadj (95% CI): 0.674(0.458–0.989)]. PIK3R1‐rs3730089 was associated with migraine in the recessive model [ORadj (95% CI): 1.446(1.004–2.083)]. The CACNA1H‐rs61734410 CT genotype was associated with migraine risk [ORadj (95% CI): 1.561(1.068–2.281)]. One significant two‐way SNP–SNP interaction was found (PRKCA rs2228945‐BDNF rs6265) (p = 0.0107). Significant eQTL and sQTL signals were observed for the SNP rs2230414. Conclusions: This is the first study to systematically reveal significant associations between MAPK and PI3K/Akt signaling pathway‐related gene polymorphisms and migraine risk. [ABSTRACT FROM AUTHOR]

Oncogenic mutations in the BRAF kinase occur in 6-8% of nonsmall cell lung cancers (NSCLCs), accounting for more than 90,000 deaths annually worldwide. The biological and clinical relevance of these BRAF mutations in NSCLC is incompletely understood. Here we demonstrate that human NSCLC cells with BRAF(V600E), but not other BRAF mutations, initially are sensitive to BRAF-inhibitor treatment. However, these BRAF(V600E) NSCLC cells rapidly acquire resistance to BRAF inhibition through at least one of two discrete molecular mechanisms: (i) loss of full-length BRAF(V600E) coupled with expression of an aberrant form of BRAF(V600E) that retains RAF pathway dependence or (ii) constitutive autocrine EGF receptor (EGFR) signaling driven by c-Jun-mediated EGFR ligand expression. BRAF(V600E) cells with EGFR-driven resistance are characterized by hyperphosphorylated protein kinase AKT, a biomarker we validated in BRAF inhibitor-resistant NSCLC clinical specimens. These data reveal the multifaceted molecular mechanisms by which NSCLCs establish and regulate BRAF oncogene dependence, provide insights into BRAF-EGFR signaling crosstalk, and uncover mechanism-based strategies to optimize clinical responses to BRAF oncogene inhibition.