Quinic acid regulated TMA/TMAO-related lipid metabolism and vascular endothelial function through gut microbiota to inhibit atherosclerotic.

BACKGROUND: Quinic acid (QA) and its derivatives have good lipid-lowering and hepatoprotective functions, but their role in atherosclerosis remains unknown. This study attempted to investigate the mechanism of QA on atherogenesis in Apoe-/- mice induced by HFD. METHODS: HE staining and oil red O staining were used to observe the pathology. The PCSK9, Mac-3 and SM22a expressions were detected by IHC. Cholesterol, HMGB1, TIMP-1 and CXCL13 levels were measured by biochemical and ELISA. Lipid metabolism and the HMGB1-SREBP2-SR-BI pathway were detected by PCR and WB. 16 S and metabolomics were used to detect gut microbiota and serum metabolites. RESULTS: QA or low-frequency ABX inhibited weight gain and aortic tissue atherogenesis in HFD-induced Apoe-/- mice. QA inhibited the increase of cholesterol, TMA, TMAO, CXCL13, TIMP-1 and HMGB1 levels in peripheral blood of Apoe-/- mice induced by HFD. Meanwhile, QA or low-frequency ABX treatment inhibited the expression of CAV-1, ABCA1, Mac-3 and SM22α, and promoted the expression of SREBP-1 and LXR in the vascular tissues of HFD-induced Apoe-/- mice. QA reduced Streptococcus_danieliae abundance, and promoted Lactobacillus_intestinalis and Ileibacterium_valens abundance in HFD-induced Apoe-/- mice. QA altered serum galactose metabolism, promoted SREBP-2 and LDLR, inhibited IDOL, FMO3 and PCSK9 expression in liver of HFD-induced Apoe-/- mice. The combined treatment of QA and low-frequency ABX regulated microbe-related Glycoursodeoxycholic acid and GLYCOCHENODEOXYCHOLATE metabolism in HFD-induced Apoe-/- mice. QA inhibited TMAO or LDL-induced HCAECs damage and HMGB1/SREBP2 axis dysfunction, which was reversed by HMGB1 overexpression. CONCLUSIONS: QA regulated the gut-liver lipid metabolism and chronic vascular inflammation of TMA/TMAO through gut microbiota to inhibit the atherogenesis in Apoe-/- mice, and the mechanism may be related to the HMGB1/SREBP2 pathway.

PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge.

PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases and chemicals. It currently provides over one billion entity and relation annotations across approximately 36 million PubMed abstracts and 6 million full-text articles from the PMC open access subset, updated weekly. PubTator 3.0's online interface and API utilize these precomputed entity relations and synonyms to provide advanced search capabilities and enable large-scale analyses, streamlining many complex information needs. We showcase the retrieval quality of PubTator 3.0 using a series of entity pair queries, demonstrating that PubTator 3.0 retrieves a greater number of articles than either PubMed or Google Scholar, with higher precision in the top 20 results. We further show that integrating ChatGPT (GPT-4) with PubTator APIs dramatically improves the factuality and verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive set of features and tools that allow researchers to navigate the ever-expanding wealth of biomedical literature, expediting research and unlocking valuable insights for scientific discovery.

Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness.

Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.

Quality of Answers of Generative Large Language Models Versus Peer Users for Interpreting Laboratory Test Results for Lay Patients: Evaluation Study.

BACKGROUND: Although patients have easy access to their electronic health records and laboratory test result data through patient portals, laboratory test results are often confusing and hard to understand. Many patients turn to web-based forums or question-and-answer (Q&A) sites to seek advice from their peers. The quality of answers from social Q&A sites on health-related questions varies significantly, and not all responses are accurate or reliable. Large language models (LLMs) such as ChatGPT have opened a promising avenue for patients to have their questions answered. OBJECTIVE: We aimed to assess the feasibility of using LLMs to generate relevant, accurate, helpful, and unharmful responses to laboratory test-related questions asked by patients and identify potential issues that can be mitigated using augmentation approaches. METHODS: We collected laboratory test result-related Q&A data from Yahoo! Answers and selected 53 Q&A pairs for this study. Using the LangChain framework and ChatGPT web portal, we generated responses to the 53 questions from 5 LLMs: GPT-4, GPT-3.5, LLaMA 2, MedAlpaca, and ORCA_mini. We assessed the similarity of their answers using standard Q&A similarity-based evaluation metrics, including Recall-Oriented Understudy for Gisting Evaluation, Bilingual Evaluation Understudy, Metric for Evaluation of Translation With Explicit Ordering, and Bidirectional Encoder Representations from Transformers Score. We used an LLM-based evaluator to judge whether a target model had higher quality in terms of relevance, correctness, helpfulness, and safety than the baseline model. We performed a manual evaluation with medical experts for all the responses to 7 selected questions on the same 4 aspects. RESULTS: Regarding the similarity of the responses from 4 LLMs; the GPT-4 output was used as the reference answer, the responses from GPT-3.5 were the most similar, followed by those from LLaMA 2, ORCA_mini, and MedAlpaca. Human answers from Yahoo data were scored the lowest and, thus, as the least similar to GPT-4-generated answers. The results of the win rate and medical expert evaluation both showed that GPT-4's responses achieved better scores than all the other LLM responses and human responses on all 4 aspects (relevance, correctness, helpfulness, and safety). LLM responses occasionally also suffered from lack of interpretation in one's medical context, incorrect statements, and lack of references. CONCLUSIONS: By evaluating LLMs in generating responses to patients' laboratory test result-related questions, we found that, compared to other 4 LLMs and human answers from a Q&A website, GPT-4's responses were more accurate, helpful, relevant, and safer. There were cases in which GPT-4 responses were inaccurate and not individualized. We identified a number of ways to improve the quality of LLM responses, including prompt engineering, prompt augmentation, retrieval-augmented generation, and response evaluation.

Advancing entity recognition in biomedicine via instruction tuning of large language models.

MOTIVATION: Large Language Models (LLMs) have the potential to revolutionize the field of Natural Language Processing, excelling not only in text generation and reasoning tasks but also in their ability for zero/few-shot learning, swiftly adapting to new tasks with minimal fine-tuning. LLMs have also demonstrated great promise in biomedical and healthcare applications. However, when it comes to Named Entity Recognition (NER), particularly within the biomedical domain, LLMs fall short of the effectiveness exhibited by fine-tuned domain-specific models. One key reason is that NER is typically conceptualized as a sequence labeling task, whereas LLMs are optimized for text generation and reasoning tasks. RESULTS: We developed an instruction-based learning paradigm that transforms biomedical NER from a sequence labeling task into a generation task. This paradigm is end-to-end and streamlines the training and evaluation process by automatically repurposing pre-existing biomedical NER datasets. We further developed BioNER-LLaMA using the proposed paradigm with LLaMA-7B as the foundational LLM. We conducted extensive testing on BioNER-LLaMA across three widely recognized biomedical NER datasets, consisting of entities related to diseases, chemicals, and genes. The results revealed that BioNER-LLaMA consistently achieved higher F1-scores ranging from 5% to 30% compared to the few-shot learning capabilities of GPT-4 on datasets with different biomedical entities. We show that a general-domain LLM can match the performance of rigorously fine-tuned PubMedBERT models and PMC-LLaMA, biomedical-specific language model. Our findings underscore the potential of our proposed paradigm in developing general-domain LLMs that can rival SOTA performances in multi-task, multi-domain scenarios in biomedical and health applications. AVAILABILITY AND IMPLEMENTATION: Datasets and other resources are available at https://github.com/BIDS-Xu-Lab/BioNER-LLaMA.

Quality of Answers of Generative Large Language Models vs Peer Patients for Interpreting Lab Test Results for Lay Patients: Evaluation Study.

Background: Even though patients have easy access to their electronic health records and lab test results data through patient portals, lab results are often confusing and hard to understand. Many patients turn to online forums or question and answering (Q&A) sites to seek advice from their peers. However, the quality of answers from social Q&A on health-related questions varies significantly, and not all the responses are accurate or reliable. Large language models (LLMs) such as ChatGPT have opened a promising avenue for patients to get their questions answered. Objective: We aim to assess the feasibility of using LLMs to generate relevant, accurate, helpful, and unharmful responses to lab test-related questions asked by patients and to identify potential issues that can be mitigated with augmentation approaches. Methods: We first collected lab test results related question and answer data from Yahoo! Answers and selected 53 Q&A pairs for this study. Using the LangChain framework and ChatGPT web portal, we generated responses to the 53 questions from four LLMs including GPT-4, Meta LLaMA 2, MedAlpaca, and ORCA_mini. We first assessed the similarity of their answers using standard QA similarity-based evaluation metrics including ROUGE, BLEU, METEOR, BERTScore. We also utilized an LLM-based evaluator to judge whether a target model has higher quality in terms of relevance, correctness, helpfulness, and safety than the baseline model. Finally, we performed a manual evaluation with medical experts for all the responses of seven selected questions on the same four aspects. Results: Regarding the similarity of the responses from 4 LLMs, where GPT-4 output was used as the reference answer, the responses from LLaMa 2 are the most similar ones, followed by LLaMa 2, ORCA_mini, and MedAlpaca. Human answers from Yahoo data were scored lowest and thus least similar to GPT-4-generated answers. The results of Win Rate and medical expert evaluation both showed that GPT-4's responses achieved better scores than all the other LLM responses and human responses on all the four aspects (relevance, correctness, helpfulness, and safety). However, LLM responses occasionally also suffer from lack of interpretation in one's medical context, incorrect statements, and lack of references. Conclusions: By evaluating LLMs in generating responses to patients' lab test results related questions, we find that compared to other three LLMs and human answer from the Q&A website, GPT-4's responses are more accurate, helpful, relevant, and safer. However, there are cases that GPT-4 responses are inaccurate and not individualized. We identified a number of ways to improve the quality of LLM responses including prompt engineering, prompt augmentation, retrieval augmented generation, and response evaluation.

ROS-responsive & scavenging NO nanomedicine for vascular diseases treatment by inhibiting endoplasmic reticulum stress and improving NO bioavailability.

Vascular diseases seriously threaten human life and health. Exogenous delivery of nitric oxide (NO) represents an effective approach for maintaining vascular homeostasis during pathological events. However, the overproduction of reactive oxygen species (ROS) at vascular injury sites would react with NO to produce damaging peroxynitrite (ONOO-) species and limit the therapeutic effect of NO. Hence, we design a ROS-responsive NO nanomedicine (t-PBA&NO NP) with ROS scavenging ability to solve the dilemma of NO-based therapy. t-PBA&NO NP targets NO and anti-oxidant ethyl caffeate (ECA) to the injury sites via collagen IV homing peptide. The ROS-triggered ROS depletion and ECA release potently alleviate local oxidative stress via ROS scavenging, endoplasmic reticulum and mitochondrial regulation. It subsequently maximizes vascular modulation effects of NO, without production of harmful compounds, reactive nitrogen species (RNS). Therefore, it significantly increases competitiveness of human umbilical vein endothelial cells (HUVECs) over human aortic smooth muscle cells (HASMCs) both in vitro and in vivo. The strategy proved effective in inducing faster re-endothelialization, inhibiting neointimal formation and restoring vascular homeostasis. The synergy between ROS depletion and NO therapy served as a new inspiration for the treatment of cardiovascular diseases and other ROS-associated illnesses.

Onset of acute severe autoimmune hepatitis after severe acute respiratory syndrome coronavirus 2 infection: a case report.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can trigger autoimmune inflammation in the liver, leading to acute autoimmune hepatitis (AIH). We herein report a case involving a 39-year-old woman with a 23-day history of yellow skin and urine. Using the revised original scoring system of the International AIH Group, we definitively diagnosed the patient with acute severe AIH (AS-AIH). She began treatment with 80 mg/day intravenous methylprednisolone, which was gradually reduced and followed by eventual transition to oral methylprednisolone. The patient finally achieved a biochemical response after 30 days of therapy, and liver transplantation was avoided. Clinicians should be aware that the onset of AS-AIH after SARS-CoV-2 infection differs from the onset of conventional AIH with respect to its clinical and pathological features. Early diagnosis and timely glucocorticoid treatment are crucial in improving outcomes.

Transcriptomic Analyses and Experimental Validation Identified Immune-Related lncRNA-mRNA Pair MIR210HG-BPIFC Regulating the Progression of Hypertrophic Cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is a disease in which the myocardium of the heart becomes asymmetrically thickened, malformed, disordered, and loses its normal structure and function. Recent studies have demonstrated the significant involvement of inflammatory responses in HCM. However, the precise role of immune-related long non-coding RNAs (lncRNAs) in the pathogenesis of HCM remains unclear. In this study, we performed a comprehensive analysis of immune-related lncRNAs in HCM. First, transcriptomic RNA-Seq data from both HCM patients and healthy individuals (GSE180313) were reanalyzed thoroughly. Key HCM-related modules were identified using weighted gene co-expression network analysis (WGCNA). A screening for immune-related lncRNAs was conducted within the key modules using immune-related mRNA co-expression analysis. Based on lncRNA-mRNA pairs that exhibit shared regulatory microRNAs (miRNAs), we constructed a competing endogenous RNA (ceRNA) network, comprising 9 lncRNAs and 17 mRNAs that were significantly correlated. Among the 26 lncRNA-mRNA pairs, only the MIR210HG-BPIFC pair was verified by another HCM dataset (GSE130036) and the isoprenaline (ISO)-induced HCM cell model. Furthermore, knockdown of MIR210HG increased the regulatory miRNAs and decreased the mRNA expression of BPIFC correspondingly in AC16 cells. Additionally, the analysis of immune cell infiltration indicated that the MIR210HG-BPIFC pair was potentially involved in the infiltration of naïve CD4+ T cells and CD8+ T cells. Together, our findings indicate that the decreased expression of the lncRNA-mRNA pair MIR210HG-BPIFC was significantly correlated with the pathogenesis of the disease and may be involved in the immune cell infiltration in the mechanism of HCM.

Sorafenib-Encapsulated Liposomes to Activate Hypoxia-Sensitive Tirapazamine for Synergistic Chemotherapy of Hepatocellular Carcinoma.

Combination therapy with the synergistic effect is an effective way in cancer chemotherapy. Herein, an antiangiogenic sorafenib (SOR) and hypoxia-activated prodrug tirapazamine (TPZ)-coencapsulated liposome (LipTPZ/SOR) is prepared for chemotherapy of hepatocellular carcinoma (HCC). SOR is a multi-target tyrosine kinase inhibitor that can inhibit tumor cell proliferation and angiogenesis. The antiangiogenesis effect of SOR can reduce oxygen supply and aggravate tumor hypoxia, which is able to activate hypoxia-sensitive prodrug TPZ, exhibiting the synergistic antitumor effect. LipTPZ/SOR at different molar ratios of TPZ and SOR can significantly inhibit the proliferation of hepatocellular carcinoma cells. The mole ratio of TPZ and SOR was optimized to 2:1, which exhibited the best synergetic antitumor effect. The synergistic antitumor mechanism of SOR and TPZ was also investigated in vivo. After treated with SOR, the number of vessels was decreased, and the degree of hypoxia was aggravated in tumor tissues. What is more, in the presence of SOR, TPZ could be activated to inhibit tumor growth. The combination of TPZ and SOR exhibited an excellent synergistic antitumor effect. This research not only provides an innovative strategy to aggravate tumor hypoxia to promote TPZ activation but also paints a blueprint about a new nanochemotherapy regimen for the synergistic chemotherapy of HCC, which has excellent biosafety and bright clinical application prospects.

GeneGPT: augmenting large language models with domain tools for improved access to biomedical information.

MOTIVATION: While large language models (LLMs) have been successfully applied to various tasks, they still face challenges with hallucinations. Augmenting LLMs with domain-specific tools such as database utilities can facilitate easier and more precise access to specialized knowledge. In this article, we present GeneGPT, a novel method for teaching LLMs to use the Web APIs of the National Center for Biotechnology Information (NCBI) for answering genomics questions. Specifically, we prompt Codex to solve the GeneTuring tests with NCBI Web APIs by in-context learning and an augmented decoding algorithm that can detect and execute API calls. RESULTS: Experimental results show that GeneGPT achieves state-of-the-art performance on eight tasks in the GeneTuring benchmark with an average score of 0.83, largely surpassing retrieval-augmented LLMs such as the new Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as well as GPT-3 (0.16) and ChatGPT (0.12). Our further analyses suggest that: First, API demonstrations have good cross-task generalizability and are more useful than documentations for in-context learning; second, GeneGPT can generalize to longer chains of API calls and answer multi-hop questions in GeneHop, a novel dataset introduced in this work; finally, different types of errors are enriched in different tasks, providing valuable insights for future improvements. AVAILABILITY AND IMPLEMENTATION: The GeneGPT code and data are publicly available at https://github.com/ncbi/GeneGPT.

Hidden Flaws Behind Expert-Level Accuracy of GPT-4 Vision in Medicine.

Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study extends the current scope by conducting a comprehensive analysis of GPT-4V's rationales of image comprehension, recall of medical knowledge, and step-by-step multimodal reasoning when solving New England Journal of Medicine (NEJM) Image Challenges - an imaging quiz designed to test the knowledge and diagnostic capabilities of medical professionals. Evaluation results confirmed that GPT-4V performs comparatively to human physicians regarding multi-choice accuracy (81.6% vs. 77.8%). GPT-4V also performs well in cases where physicians incorrectly answer, with over 78% accuracy. However, we discovered that GPT-4V frequently presents flawed rationales in cases where it makes the correct final choices (35.5%), most prominent in image comprehension (27.2%). Regardless of GPT-4V's high accuracy in multi-choice questions, our findings emphasize the necessity for further in-depth evaluations of its rationales before integrating such multimodal AI models into clinical workflows.

PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge.

PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases, and chemicals. It currently provides over one billion entity and relation annotations across approximately 36 million PubMed abstracts and 6 million full-text articles from the PMC open access subset, updated weekly. PubTator 3.0's online interface and API utilize these precomputed entity relations and synonyms to provide advanced search capabilities and enable large-scale analyses, streamlining many complex information needs. We showcase the retrieval quality of PubTator 3.0 using a series of entity pair queries, demonstrating that PubTator 3.0 retrieves a greater number of articles than either PubMed or Google Scholar, with higher precision in the top 20 results. We further show that integrating ChatGPT (GPT-4) with PubTator APIs dramatically improves the factuality and verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive set of features and tools that allow researchers to navigate the ever-expanding wealth of biomedical literature, expediting research and unlocking valuable insights for scientific discovery.

Trichoderma spp. promotes ginseng biomass by influencing the soil microbial community.

Introduction: Ginseng (Panax ginseng C.A. Meyer) has multiple effects on human health; however, soil degradation seriously affects its yield. Trichoderma spp. play an important role in improving plant biomass by influencing the soil environment. Therefore, it is necessary to screen efficient Trichoderma strains that can increase ginseng biomass and determine their mechanisms. Methods: Herein, we selected six Trichoderma species (T. brevicompactum, T. velutinum, T. viridescens, T. atroviride, T. koningiopsis, and T. saturnisporum) isolated from ginseng rhizosphere soil, and evaluated their growth promoting effects on ginseng and their influence on the microbiome and chemical attributes of the ginseng rhizosphere soil. Results: Except for T. saturnisporum (F), compared with the control, the other five species increased ginseng biomass. In terms of chemical properties, the pH value, available potassium content, and available phosphorus content in the ginseng rhizosphere soil increased by 1.16-5.85%, 0.16-14.03%, and 3.92-38.64%, respectively, after root irrigation with spores of Trichoderma species. For the soil microbiome, fungal Chao1 and Ace richness indices decreased. Application of Trichoderma enhanced the relative level of Proteobacteria, but reduced the relative level of Ascomycota. At the genus level, application of Trichoderma enhanced the relative levels of Sphingomonas, Blastomonas, and Trichoderma, but reduced the relative level of Fusarium. Available K and available P were the most important elements that affected the structure of the bacterial community, while total K was the most influential element for the structure of the fungal community structure. Conclusion: The results indicated that the application of Trichoderma spp. could increase soil nutrients and regulate the structure and composition of the soil microbial community, thereby enhancing the biomass of ginseng. The results will provide guidance for soil improvement in ginseng cultivation.

Circulating metabolomic markers linking diabetic kidney disease and incident cardiovascular disease in type 2 diabetes: analyses from the Hong Kong Diabetes Biobank.

AIMS/HYPOTHESIS: The aim of this study was to describe the metabolome in diabetic kidney disease (DKD) and its association with incident CVD in type 2 diabetes, and identify prognostic biomarkers. METHODS: From a prospective cohort of individuals with type 2 diabetes, baseline sera (N=1991) were quantified for 170 metabolites using NMR spectroscopy with median 5.2 years of follow-up. Associations of chronic kidney disease (CKD, eGFR<60 ml/min per 1.73 m2) or severely increased albuminuria with each metabolite were examined using linear regression, adjusted for confounders and multiplicity. Associations between DKD (CKD or severely increased albuminuria)-related metabolites and incident CVD were examined using Cox regressions. Metabolomic biomarkers were identified and assessed for CVD prediction and replicated in two independent cohorts. RESULTS: At false discovery rate (FDR)<0.05, 156 metabolites were associated with DKD (151 for CKD and 128 for severely increased albuminuria), including apolipoprotein B-containing lipoproteins, HDL, fatty acids, phenylalanine, tyrosine, albumin and glycoprotein acetyls. Over 5.2 years of follow-up, 75 metabolites were associated with incident CVD at FDR<0.05. A model comprising age, sex and three metabolites (albumin, triglycerides in large HDL and phospholipids in small LDL) performed comparably to conventional risk factors (C statistic 0.765 vs 0.762, p=0.893) and adding the three metabolites further improved CVD prediction (C statistic from 0.762 to 0.797, p=0.014) and improved discrimination and reclassification. The 3-metabolite score was validated in independent Chinese and Dutch cohorts. CONCLUSIONS/INTERPRETATION: Altered metabolomic signatures in DKD are associated with incident CVD and improve CVD risk stratification.

PubMed and beyond: biomedical literature search in the age of artificial intelligence.

Biomedical research yields vast information, much of which is only accessible through the literature. Consequently, literature search is crucial for healthcare and biomedicine. Recent improvements in artificial intelligence (AI) have expanded functionality beyond keywords, but they might be unfamiliar to clinicians and researchers. In response, we present an overview of over 30 literature search tools tailored to common biomedical use cases, aiming at helping readers efficiently fulfill their information needs. We first discuss recent improvements and continued challenges of the widely used PubMed. Then, we describe AI-based literature search tools catering to five specific information needs: 1. Evidence-based medicine. 2. Precision medicine and genomics. 3. Searching by meaning, including questions. 4. Finding related articles with literature recommendation. 5. Discovering hidden associations through literature mining. Finally, we discuss the impacts of recent developments of large language models such as ChatGPT on biomedical information seeking.

Unmasking and Quantifying Racial Bias of Large Language Models in Medical Report Generation.

Large language models like GPT-3.5-turbo and GPT-4 hold promise for healthcare professionals, but they may inadvertently inherit biases during their training, potentially affecting their utility in medical applications. Despite few attempts in the past, the precise impact and extent of these biases remain uncertain. Through both qualitative and quantitative analyses, we find that these models tend to project higher costs and longer hospitalizations for White populations and exhibit optimistic views in challenging medical scenarios with much higher survival rates. These biases, which mirror real-world healthcare disparities, are evident in the generation of patient backgrounds, the association of specific diseases with certain races, and disparities in treatment recommendations, etc. Our findings underscore the critical need for future research to address and mitigate biases in language models, especially in critical healthcare applications, to ensure fair and accurate outcomes for all patients.

Crosstalk of HDAC4, PP1, and GSDMD in controlling pyroptosis.

Gasdermin D (GSDMD) functions as a pivotal executor of pyroptosis, eliciting cytokine secretion following cleavage by inflammatory caspases. However, the role of posttranslational modifications (PTMs) in GSDMD-mediated pyroptosis remains largely unexplored. In this study, we demonstrate that GSDMD can undergo acetylation at the Lysine 248 residue, and this acetylation enhances pyroptosis. We identify histone deacetylase 4 (HDAC4) as the specific deacetylase responsible for mediating GSDMD deacetylation, leading to the inhibition of pyroptosis both in vitro and in vivo. Deacetylation of GSDMD impairs its ubiquitination, resulting in the inhibition of pyroptosis. Intriguingly, phosphorylation of HDAC4 emerges as a critical regulatory mechanism promoting its ability to deacetylate GSDMD and suppress GSDMD-mediated pyroptosis. Additionally, we implicate Protein phosphatase 1 (PP1) catalytic subunits (PP1α and PP1γ) in the dephosphorylation of HDAC4, thereby nullifying its deacetylase activity on GSDMD. This study reveals a complex regulatory network involving HDAC4, PP1, and GSDMD. These findings provide valuable insights into the interplay among acetylation, ubiquitination, and phosphorylation in the regulation of pyroptosis, offering potential targets for further investigation in the field of inflammatory cell death.

Differential effects of specific emotions on spatial decision-making: evidence from cross-frequency functionally independent brain networks.

Emotions significantly shape the way humans make decisions. However, the underlying neural mechanisms of this influence remain elusive. In this study, we designed an experiment to investigate how emotions (specifically happiness, fear, and sadness) impact spatial decision-making, utilizing EEG data. To address the inherent limitations of sensor-level investigations previously conducted, we employed standard low-resolution brain electromagnetic tomography and functional independent component analysis to analyze the EEG data at the cortical source level. Our findings showed that across various spectral-spatial networks, positive emotion activated the decision-making network in the left middle temporal gyrus and inferior temporal gyrus, in contrast to negative emotions. We also identified the common spectral-spatial networks and observed significant differences in network strength across emotions. These insights further revealed the important role of the gamma-band prefrontal network. Our research provides a basis for deciphering the roles of brain networks in the impact of emotions on decision-making.

Serine protease homolog pairs CLIPA4-A6, A4-A7Δ, and A4-A12 act as cofactors for proteolytic activation of prophenoloxidase-2 and -7 in Anopheles gambiae.

Phenoloxidase (PO) catalyzed melanization and other insect immune responses are mediated by serine proteases (SPs) and their noncatalytic homologs (SPHs). Many of these SP-like proteins have a regulatory clip domain and are called CLIPs. In most insects studied so far, PO precursors are activated by a PAP (i.e., PPO activating protease) and its cofactor of clip-domain SPHs. Although melanotic encapsulation is a well-known refractory mechanism of mosquitoes against malaria parasites, it is unclear if a cofactor is required for PPO activation. In Anopheles gambiae, CLIPA4 is 1:1 orthologous to Manduca sexta SPH2; CLIPs A5-7, A12-14, A26, A31, A32, E6, and E7 are 11:4 orthologous to M. sexta SPH1a, 1b, 4, and 101, SPH2 partners in the cofactors. Here we produced proCLIPs A4, A6, A7Δ, A12, and activated them with CLIPB9 or M. sexta PAP3. A. gambiae PPO2 and PPO7 were expressed in Escherichia coli for use as PAP substrates. CLIPB9 was mutated to CLIPB9Xa by including a Factor Xa cleavage site. CLIPA7Δ was a deletion mutant with a low complexity region removed. After PAP3 or CLIPB9Xa processing, CLIPA4 formed a high Mr complex with CLIPA6, A7Δ or A12, which assisted PPO2 and PPO7 activation. High levels of specific PO activity (55-85 U/µg for PO2 and 1131-1630 U/µg for PO7) were detected in vitro, indicating that cofactor-assisted PPO activation also occurs in this species. The cleavage sites and mechanisms for complex formation and cofactor function are like those reported in M. sexta and Drosophila melanogaster. In conclusion, these data suggest that the three (and perhaps more) SPHI-II pairs may form cofactors for CLIPB9-mediated activation of PPOs for melanotic encapsulation in A. gambiae.