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Abstract Fungi are very common infectious pathogens, which may cause invasive and potentially life-threatening infections. However, the efficacy of antifungal medications remains limited. Herein, a Cu2+-phenolic nanoflower is designed to combat fungal infections by combining cuproptosis and cell wall digestion. Firstly, protocatechuic acid (PA)-Cu2+ (PC) nanopetals are prepared by coordination interaction. Lywallzyme (Lyw) is then added to induce the self-assembly of PC to form Lyw loaded PC (PCW) nanoflowers. PCW nanoflowers can effectively adhere to fungal surface and Lyw can digest fungal cell walls to facilitate Cu2+ to penetrate into fungal interior, thereby exerting a synergistic fungicidal effect. PCW nanoflowers exhibit excellent fungicidal activity even in protein-rich and high-salt conditions, where dissociative Cu2+ completely loses fungicidal activity. Transcriptome sequencing analysis reveals that PCW can lead to fungal cuproptosis. The in vivo fungicidal effect of PCW nanoflowers is confirmed on a murine skin fungal infection model and a murine fungal keratitis model.

Abstract Background 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. Methods We use LLMs to generate responses that predict hospitalization, cost and mortality based on real patient cases. We manually examine the generated responses to identify biases. Results 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 racial and ethnic groups, and disparities in treatment recommendations, etc. Conclusions 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.

Abstract Large language models (LLMs) hold great promise in summarizing medical evidence. Most recent studies focus on the application of proprietary LLMs. Using proprietary LLMs introduces multiple risk factors, including a lack of transparency and vendor dependency. While open-source LLMs allow better transparency and customization, their performance falls short compared to the proprietary ones. In this study, we investigated to what extent fine-tuning open-source LLMs can further improve their performance. Utilizing a benchmark dataset, MedReview, consisting of 8161 pairs of systematic reviews and summaries, we fine-tuned three broadly-used, open-sourced LLMs, namely PRIMERA, LongT5, and Llama-2. Overall, the performance of open-source models was all improved after fine-tuning. The performance of fine-tuned LongT5 is close to GPT-3.5 with zero-shot settings. Furthermore, smaller fine-tuned models sometimes even demonstrated superior performance compared to larger zero-shot models. The above trends of improvement were manifested in both a human evaluation and a larger-scale GPT4-simulated evaluation.

Abstract 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.

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.

Abstract Oxide semimetals exhibiting both nontrivial topological characteristics stand as exemplary parent compounds and multiple degrees of freedom, offering a promise for the realization of novel electronic states. In this work, we report the structural and transport phase transition in an oxide semimetal, SrNbO3, achieved through effective anion doping. Notably, the resistivity increased by more than three orders of magnitude at room temperature upon nitrogen‐doping. The extent of electronic modulation in SrNbO3 is strongly correlated with misfit strain, underscoring its phase instability to both chemical doping and crystallographic symmetry variations. Using first‐principles calculations, we discern that elevating the level of nitrogen doping induces an upward shift in the conductive bands of SrNbO3−δNδ. Consequently, a transition from a metallic state to an insulating state becomes apparent as the nitrogen concentration reaches a threshold of 1/3. This investigation shows effective anion engineering in oxide semimetals, offering pathways for manipulating their physical properties.

The biomass production as well as the accumulation of secondary metabolites of plant is highly determined by the absorption of nutritional elements, in particular nitrogen (N) and phosphorus (P). Arbuscular mycorrhizal fungi (AMF) can absorb soluble P and transport it to plants, while phosphate solubilizing bacteria (PSB) can increase the content of solubilizing P in soil. Previous studies have identified the effects of either AMF or PSB inoculation on altering plant C:N:P stoichiometry, whether AMF interact with PSB in promoting plant growth and changing elemental concentration and composition of secondary metabolites by altering plant C:N:P stoichiometry remains ambiguous. In this study, we investigated the effects of inoculation of AMF, PSB, and their co-inoculation AMP (AMF and PSB) on the biomass growth, the C:N:P stoichiometry, the core microorganisms of rhizosphere soil, and the ginsenoside compositions of ginseng (Panax ginseng). The results showed that compared to control or single inoculation of AMF or PSB, co-inoculation of AMF and PSB significantly increased the AMF colonization rate on ginseng roots, increased the biomass of both above and under-ground parts of ginseng. Similarly, co-inoculation of AMF and PSB substantially increased the concentrations of N and P, reduced the ratios of C:P and N:P in the above-ground part of ginseng. The co-inoculation of AMF and PSB also increased concentrations of total ginsenosides and altered the compositions of ginsenosides in both the above and under-ground parts of ginseng. Analysis the rhizosphere microorganism showed that the co-inoculation of AMF and PSB recruited distinct core microorganisms that differ from the control and treatments with single inoculation of AMF or PSB. Our results suggested that PSB inoculation enhanced the positive effect of AMF in improving the absorption of nutrimental elements, altered the C:N:P stoichiometry and, ginsenosides concentration and composition of ginseng, influenced the plant rhizosphere microbial community. These findings offer valuable insights into enhancing plant biomass production and promoting secondary metabolites by improving the plant-fungi-bacterial relationships.

Abstract 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.

Abstract Recent progress on the signatures of pressure-induced high-temperature superconductivity in Ruddlesden–Popper (RP) nickelates (La n+1Ni n O3n+1) has attracted growing interest in both theoretical calculations and experimental efforts. The fabrication of high-quality single-crystalline RP nickelate thin films is critical for possible reducing the superconducting transition pressure and advancing applications in microelectronics in the future. In this study, we report the observations of an active phase transition in RP nickelate films induced by misfit strain. We found that RP nickelate films favor the perovskite structure (n = ∞) under tensile strains, while compressive strains stabilize the La3Ni2O7 (n = 2) phase. The selection of distinct phases is governed by the strain dependent formation energy and electronic configuration. In compressively strained La3Ni2O7, we experimentally determined the e g splitting energy is ~0.2 eV and electrons prefer to occupy in-plane orbitals. First-principles calculations unveil a robust coupling between strain effects and the valence state of Ni ions in RP nickelates, suggesting a dual driving force for the inevitable phase co-existence transition in RP nickelates. Our work underscores the sensitivity of RP nickelate formation to epitaxial strain, presenting a significant challenge in fabricating pure-phase RP nickelate films. Therefore, special attention to stacking defects and grain boundaries between different RP phases is essential when discussing the pressure-induced superconductivity in RP nickelates.

Abstract 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.

Abstract Retrieval-based Clinical Decision Support (ReCDS) can aid clinical workflow by providing relevant literature and similar patients for a given patient. However, the development of ReCDS systems has been severely obstructed by the lack of diverse patient collections and publicly available large-scale patient-level annotation datasets. In this paper, we collect a novel dataset of patient summaries and relations called PMC-Patients to benchmark two ReCDS tasks: Patient-to-Article Retrieval (ReCDS-PAR) and Patient-to-Patient Retrieval (ReCDS-PPR). Specifically, we extract patient summaries from PubMed Central articles using simple heuristics and utilize the PubMed citation graph to define patient-article relevance and patient-patient similarity. PMC-Patients contains 167k patient summaries with 3.1 M patient-article relevance annotations and 293k patient-patient similarity annotations, which is the largest-scale resource for ReCDS and also one of the largest patient collections. Human evaluation and analysis show that PMC-Patients is a diverse dataset with high-quality annotations. We also implement and evaluate several ReCDS systems on the PMC-Patients benchmarks to show its challenges and conduct several case studies to show the clinical utility of PMC-Patients.

Abstract Background Nasopharyngeal carcinoma (NPC) is a relatively common type of cancer in Southern China, with local recurrence or distant metastases even after radical treatment; consequently, it is critical to identify the patients at higher risk for these events beforehand. This study aimed to assess the prognostic value of regional lymph node density (RLND) associated nomograms in NPC and to evaluate the utility of nomograms in risk stratification. Methods A total of 610 NPC patients without distant metastases (425 in the training and 185 in the validation cohort) were enrolled. The MRI-identified nodal features and clinical characteristics were documented, and the RLND was calculated. Cox analyses were conducted to identify prognostic-associated factors. Nomograms were generated based on the multivariate analysis results. The predictive accuracy and discriminative ability of the nomogram models were determined using the concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve; the results were compared with those of the tumor-node-metastasis (TNM) classification. Decision curve analysis (DCA) and C-index were used to assess the prognostic effect and added discriminative ability of RLND. We also estimated the optimal RLND-based nomogram score cut-off values for survival prediction. Results RLND was an independent predictor of overall survival (OS) and disease-free survival (DFS), with hazard ratios of 1.36 and 1.30, respectively. RLND was utilized in the construction of nomograms, alongside other independent prognostic factors. The RLND-based nomogram models presented a more effective discriminative ability than the TNM classification for predicting OS (C-index, 0.711 vs. 0.680) and DFS (C-index, 0.681 vs. 0.669), with favorable calibration and consistency. The comparison of C-index values between the nomogram models with and without RLND provided substantiation of the crucial role RLND plays in these models. DCA confirmed the satisfactory clinical practicability of RLND. Moreover, the nomograms were used to categorize the patients into three groups (high-, middle-, and low-risk), and the Kaplan–Meier curves showed significant differences in prognosis between them (p

Abstract Phototherapy, including photodynamic therapy (PDT) and photothermal therapy (PTT), has great promise in the treatment of cancer. However, there are many obstacles that can restrict the therapeutic efficacy of phototherapy. The hypoxic tumor microenvironment can restrict the production of reactive oxygen species (ROS) in PDT. As for PTT, the thermotolerance of cancer cells may lead to ineffective PTT. In this study, IR780 and glycolysis inhibitor lonidamine (LND)-encapsulated liposomes are prepared for photodynamic and photothermal therapy of hepatocellular carcinoma. IR780 can be used as a photosensitizer and photothermal agent for simultaneous PDT and PTT after being irradiated with 808 nm laser. LND can reduce the oxygen consumption of cancer cells by inhibiting glycolysis, which will relieve tumor hypoxia and produce more ROS for PDT. On the other hand, energy supply can be blocked by LND-induced glycolysis inhibition, which will inhibit the production of heat shock proteins (HSPs), reduce the thermotolerance of tumor cells, and finally enhance the therapeutic efficacy of PTT. The enhanced PTT is studied by measuring intracellular HSPs, ATP level, and mitochondrial membrane potential. The antitumor effect of IR780 and LND co-loaded liposomes is extensively investigated by in vitro and in vivo experiments. This research provides an innovative strategy to simultaneously enhance the therapeutic efficacy of PDT and PTT by inhibiting glycolysis, which is promising for future creative approaches to cancer phototherapy.

BackgroundAlthough 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. ObjectiveWe 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. MethodsWe 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. ResultsRegarding 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. ConclusionsBy 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.

Summary: 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.