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.

Opportunities and challenges for ChatGPT and large language models in biomedicine and health.

ChatGPT has drawn considerable attention from both the general public and domain experts with its remarkable text generation capabilities. This has subsequently led to the emergence of diverse applications in the field of biomedicine and health. In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health. Specifically, we explore the areas of biomedical information retrieval, question answering, medical text summarization, information extraction and medical education and investigate whether LLMs possess the transformative power to revolutionize these tasks or whether the distinct complexities of biomedical domain presents unique challenges. Following an extensive literature survey, we find that significant advances have been made in the field of text generation tasks, surpassing the previous state-of-the-art methods. For other applications, the advances have been modest. Overall, LLMs have not yet revolutionized biomedicine, but recent rapid progress indicates that such methods hold great potential to provide valuable means for accelerating discovery and improving health. We also find that the use of LLMs, like ChatGPT, in the fields of biomedicine and health entails various risks and challenges, including fabricated information in its generated responses, as well as legal and privacy concerns associated with sensitive patient data. We believe this survey can provide a comprehensive and timely overview to biomedical researchers and healthcare practitioners on the opportunities and challenges associated with using ChatGPT and other LLMs for transforming biomedicine and health.

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.

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.

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.

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.

MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval.

MOTIVATION: Information retrieval (IR) is essential in biomedical knowledge acquisition and clinical decision support. While recent progress has shown that language model encoders perform better semantic retrieval, training such models requires abundant query-article annotations that are difficult to obtain in biomedicine. As a result, most biomedical IR systems only conduct lexical matching. In response, we introduce MedCPT, a first-of-its-kind Contrastively Pre-trained Transformer model for zero-shot semantic IR in biomedicine. RESULTS: To train MedCPT, we collected an unprecedented scale of 255 million user click logs from PubMed. With such data, we use contrastive learning to train a pair of closely integrated retriever and re-ranker. Experimental results show that MedCPT sets new state-of-the-art performance on six biomedical IR tasks, outperforming various baselines including much larger models, such as GPT-3-sized cpt-text-XL. In addition, MedCPT also generates better biomedical article and sentence representations for semantic evaluations. As such, MedCPT can be readily applied to various real-world biomedical IR tasks. AVAILABILITY AND IMPLEMENTATION: The MedCPT code and model are available at https://github.com/ncbi/MedCPT.

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.

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.

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.

An Organophosphorescence Probe with Ultralong Lifetime and Intrinsic Tissue Selectivity for Specific Tumor Imaging and Guided Tumor Surgery.

Organic phosphorescent materials are excellent candidates for use in tumor imaging. However, a systematic comparison of the effects of the intensity, lifetime, and wavelength of phosphorescent emissions on bioimaging performance has not yet been undertaken. This study addresses these gaps and reveals that longer lifetimes effectively increase the signal intensity, whereas longer wavelengths enhance the penetration depth. Conversely, a strong emission intensity with a short lifetime does not necessarily yield robust imaging signals. Building upon these findings, an organo-phosphorescent material with a lifetime of 0.94 s was designed for tumor imaging. Remarkably, the phosphorescent signals of various organic nanoparticles are nearly extinguished in blood-rich organs because of the quenching effect of iron ions. Moreover, for the first time, we demonstrated that iron ions universally quench the phosphorescence of organic room-temperature phosphorescent materials, which is an inherent property of such substances. Leveraging this property, both the normal liver and hepatitis tissues exhibit negligible phosphorescent signals, whereas liver tumors display intense phosphorescence. Therefore, phosphorescent materials, unlike chemiluminescent or fluorescent materials, can exploit this unique inherent property to selectively distinguish liver tumor tissues from normal tissues without additional modifications or treatments.

Adding Flying Wings: Butterfly-Shaped NIR-II AIEgens with Multiple Molecular Rotors for Photothermal Combating of Bacterial Biofilms.

The ever-increasing threats of multidrug-resistant bacteria and their biofilm-associated infections have bred a desperate demand for alternative remedies to combat them. Near-infrared (NIR)-absorbing photothermal agent (PTAs)-mediated photothermal therapy (PTT) is particularly attractive for biofilm ablation thanks to its superiorities of noninvasive intervention, satisfactory antibacterial efficiency, and less likelihood to develop resistance. Herein, three butterfly-shaped aggregation-induced emission luminogens (AIEgens) with balanced nonradiative decay (for conducting PTT) and radiative decay (for supplying fluorescence in the NIR-II optical window) are rationally designed for imaging-assisted photothermal obliteration of bacterial biofilms. After being encapsulated into cationic liposomes, AIEgens-fabricated nanoparticles can eradicate a wide spectrum of biofilms formed by Gram-positive bacteria (methicillin-resistant Staphylococcus aureus and Enterococcus faecalis) and Gram-negative bacteria (Escherichia coli and Pseudomonas aeruginosa) upon an 808 nm laser irradiation. In vivo experiments firmly demonstrate that the NIR-II AIE liposomes with excellent biocompatibility perform well in both the P. aeruginosa biofilm-induced keratitis mouse model and the MSRA biofilm-induced skin infection mouse model.

Facile Synthesis of Self-Targeted Zn2+ -Gallic acid Nanoflowers for Specific Adhesion and Elimination of Gram-Positive Bacteria.

Transition metal ions are served as disinfectant thousand years ago. However, the in vivo antibacterial application of metal ions is strongly restricted due to its high affinity with proteins and lack of appropriate bacterial targeting method. Herein, for the first time, Zn2+ -gallic acid nanoflowers (ZGNFs) are synthesized by a facile one-pot method without additional stabilizing agents. ZGNFs are stable in aqueous solution while can be easily decomposed in acidic environments. Besides, ZGNFs can specifically adhere onto Gram-positive bacteria, which is mediated by the interaction of quinone from ZGNFs and amino groups from teichoic acid of Gram-positive bacteria. ZGNFs exhibit high bactericidal effect toward various Gram-positive bacteria in multiple environments, which can be ascribed to the in situ Zn2+ release on bacterial surface. Transcriptome studies reveal that ZGNFs can disorder basic metabolic processes of Methicillin-resistant Staphylococcus aureus (MRSA). Moreover, in a MRSA-induced keratitis model, ZGNFs exhibit long-term retention in the infected corneal site and prominent MRSA elimination efficacy due to the self-targeting ability. This research not only reports an innovative method to prepare metal-polyphenol nanoparticles, but also provides a novel nanoplatform for targeted delivery of Zn2+ in combating Gram-positive bacterial infections.

miR-19a-3p affected ox-LDL-induced SDC-1/TGF-ß1/Smad3 pathway in atherosclerosis.

Syndecan-1 (SDC-1) was a critical membrane proteoglycan and an important component of the glycocalyx in endothelial cells, but its role in atherosclerosis remains unknown. This study attempted to investigate the role of SDC-1 in atherosclerotic-related endothelial cell injury. Bioinformatics analyzed the differential miRNAs between atherosclerosis and healthy. Subjects with coronary atherosclerosis, which were diagnosed with intravascular atherosclerosis (IVUS), were enrolled as non-vulnerable plaque and vulnerable plaque in Changsha Central Hospital. Human aortic endothelial cells (HAECs) were induced by oxidized low-density lipoprotein (ox-LDL) to construct an in vitro model. A dual luciferase reporter assay was applied to analyze the target between miR-19a-3p and SDC-1. The cell proliferation and apoptosis were detected by CCK8 and flow cytometry, respectively. SDC-1 and cholesterol efflux was determined by ELISA. The expression of ATP-binding cassette (ABC) transports A1 (ABCA1), miR-19a-3p, ABCG1 and SDC-1 genes were detected by RT-qPCR. The expressions of SDC-1, ABCA1, ABCG1, TGF-ß1, Smad3 and p-Smad3 proteins were detected by western blot. Our results found that miR-19a-3p was down-regulated in atherosclerosis. ox-LDL decreased miR-19a-3p expression, increased cholesterol efflux and the expression of ABCA1, ABCG1 and SDC-1 in HAECs. Vulnerable plaque tissues in patients with coronary atherosclerosis showed palpable fibrous necrosis and calcification with elevated blood SDC-1 levels. miR-19a-3p could bind to SDC-1. Overexpression of miR-19a-3p promoted cell proliferation, inhibited apoptosis and cholesterol efflux, down-regulated the expression of SDC-1, ABCA1, ABCG1, TGF-ß1 and p-Smad3 proteins in ox-LDL-induced HAECs. In conclusion, miR-19a-3p targeting SDC-1 inhibited the ox-LDL-induced activation of the TGF-ß1/Smad3 pathway in HAECs.

Peripheral perfusion index in well newborns at 6 to 72 h of life at different altitudes: a multi-center study in China.

The purpose of this study is to obtain the reference range of peripheral perfusion index (PPI) of asymptomatic well newborns at 6 to 72 h of life at different altitudes. A population-based prospective cohort study was conducted in cities at different altitudes in China. Asymptomatic well newborns were enrolled consecutively from six hospitals with an altitude of 4 to 4200 m between February 1, 2020, and April 15, 2021. PPI was measured at 6, 12, 24, 48, and 72 h after birth on the right hand (pre-ductal) and either foot (post-ductal) using a Masimo SET Radical-7 oximeter. Fiftieth percentile reference curves of the pre- and post-ductal PPI values at 6-72 h after birth were generated using the Lambda Mu Sigma method. Linear mixed-effects regression was performed to determine the influence of different altitude levels on PPI values over different measurement time points. A total of 4257 asymptomatic well newborns were recruited for analysis. The median and quartile pre- and post-ductal PPI values at 6-72 h of life at different altitudes were 1.70 (1.20, 2.60) and 1.70 (1.10, 2.70) for all infants, 1.30 (1.10, 1.90) and 1.10 (0.88, 1.80) for infants at low altitude, 1.40 (1.00, 2.00) and 1.30 (0.99, 2.00) at mild altitudes, 1.90 (1.30, 2.50) and 1.80 (1.20, 2.70) at moderate altitudes, 1.80 (1.40, 3.50) and 2.20 (1.60, 4.30) for high altitudes, 3.20 (2.70, 3.70), and 3.10 (2.10, 3.30) for higher altitudes, respectively. Overall, both pre- and post-ductal PPI increased with altitude. The 50th percentile curves of pre- and post-ductal PPI values in well newborns at mild, low, moderate, and high altitudes were relatively similar, while the difference between the PPI curves of infants at higher altitudes and other altitudes was significantly different.  Conclusions: With the increase of altitude, pre- and post-ductal PPI of newborns increases. Our study obtained the PPI reference values of asymptomatic well newborns at 6 to 72 h after birth at different altitudes from 4 to ≥ 4000 m. What is Known: • Monitoring hemodynamics is very important to neonates. As an accurate and reliable hemodynamic monitoring index, PPI can detect irreversible damage caused by insufficient tissue perfusion and oxygenation early, directly, noninvasively, and continuously. What is New: • Our study obtained the PPI reference values of asymptomatic well newborns at 6 to 72 h after birth at different altitudes from 4 to ≥ 4000 m. With the increase of altitude, pre- and post-ductal PPI of newborns increase with statistical significance. Therefore, the values and disease thresholds of PPI for asymptomatic neonates should be modified according to altitudes.

Lonidamine liposomes to enhance photodynamic and photothermal therapy of hepatocellular carcinoma by inhibiting glycolysis.

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.

Effect of Polylactic Acid Membrane on Guided Bone Regeneration in Anterior Maxillary Implantation.

BACKGROUND This study evaluated the clinical efficacy of polylactic acid (PLA) membranes in guiding bone regeneration in anterior maxillary implants. MATERIAL AND METHODS Forty-eight participants with maxillary anterior tooth loss requiring implantation with guided bone regeneration were recruited and randomly divided into 2 groups (n=24): the PLA membrane (experimental) and the Bio-Gide membrane (control) group. Wound healing was observed at 1 week and 1 month postoperatively. Cone beam CT was performed immediately and at 6 and 36 months postoperatively. Soft-tissue parameters were measured at 18 and 36 months postoperatively. Implant stability quotient (ISQ) and patient satisfaction were separately evaluated at 6 and 18 months after the operation. The independent sample t test and chi-square test were preformed to analyze quantitative and descriptive statistics, respectively. RESULTS No implants were lost and there were no statistically significant differences in ISQ between the 2 groups. The labial bone plates in the experimental group exhibited a non-significantly greater extent of absorption than did the control group at 6 and 18 months postoperatively. Soft-tissue parameters did not reveal inferior results for the experimental group. Patients in both groups were satisfied. CONCLUSIONS PLA membranes are comparable to Bio-Gide in terms of the effectiveness and safety and can be used as a barrier membrane to guide bone regeneration in clinical practice.

Anthracnose of Brachybotrys paridiformis Caused by Colletotrichum siamense in Northeast China.

Brachybotrys paridiformis Maxim. ex Oliv. (Boraginaceae) is a perennial medicinal plant and vegetable that is cultivated commercially in China. Anthracnose is a devastating disease of B. paridiformis, with annual production losses exceeding 33% based on our survey. In July 2021, anthracnose of B. paridiformis was observed on 2-year-old plants in Shenyang city, Northeast China, which is the most important region for B. paridiformis cultivation. Round or irregular-shaped black spots were exhibited on leaves, with the leaf edges most commonly infected. As the necrosis expanded, the leaves withered and dropped; young leaves were generally not infected (Fig. 1). More than 40% of the plants in a 21-ha sampling field were infected in 2021. Symptomatic leaves (n = 20) were collected and the diseased tissue was cut into small pieces, immersed in 1% NaOCl for 2 min, rinsed three times with sterile water, and placed on acidified potato dextrose agar (PDA) in Petri dishes. After a 3-day incubation in darkness at 25 °C, 18 suspected single-pure morphologically identical Colletotrichum isolates were obtained and sequenced. Isolate SQZ9 was randomly selected and identified. Colonies on PDA were initially white, but gradually became pale brownish with a reverse side that was pale yellowish to pinkish. Aerial mycelia were grayish-white, dense, and cottony, with microsclerotia detected on some aging mycelia. The detected single-celled conidia (11.65-17.25 × 4.25-6.15 µm; n = 50) were fusiform to cylindrical with obtuse to slightly rounded ends. Appressoria were ovoid to clavate and medium brown. Setae were not observed. The morphological characteristics were similar to those of Colletotrichum spp. (Prihastuti et al. 2009; Weir et al. 2012). Initial BLAST searches of the GenBank database revealed the SQZ9 rDNA internal transcribed spacer region (OP389109, 566 bp), glyceraldehyde-3-phosphate dehydrogenase (OP407730, 260 bp), chitin synthase (OP407731, 301 bp), calmodulin (OP407732, 712 bp), actin (OP407733, 282 bp), glutamine synthetase (OP407734, 909 bp), ß-tublin (OP407735, 498 bp), and superoxide dismutase (OP407736, 396 bp) sequences were respectively 99%-100% similar to the C. siamense type strain JX010278, JX010019, JX009709, GQ856775, GQ856730, JX010100, JX010410, and JX010332 sequences (Carbone & Kohn 1999; Moriwaki & Tsukiboshi 2009; Stephenson et al. 1997). The SQZ9 identity was confirmed by constructing a phylogenetic tree combining all loci, which grouped the isolate and the C. siamense type strain in the same clade (Fig. 2). For pathogenicity tests, 15 healthy 2-year-old plants (3 plants per pot) were spray-inoculated with SQZ9 conidial suspension (1 × 105 conidia/mL) at 2 mL per plant. Same number of plants sprayed with water were used as control. This experiment was repeated twice. All plants were covered with clear plastic bags for 72 h to maintain high humidity and then placed in a greenhouse (29 °C, natural light, and 85% relative humidity). After six days, the inoculated leaves exhibited symptoms that were similar to those observed in the field, but the controls were symptomless. The same fungus was recovered from inoculated symptomatic leaves, and its identity was confirmed by sequencing and a phylogenetic analysis. This is the first report of C. siamense causing anthracnose on B. paridiformis in China. Future studies should assess the effectiveness of chemical and biological control measures for managing this disease.

First Report of Calonectria montana Causing Leaf Spot on Ligularia fischeri in China.

Ligularia fischeri (Ledeb.) is a perennial herbal plant of Compositae that is cultivated commercially in China as a medicinal, ornamental, and edible plant. Leaf spots were observed in 2-year-old L. fischeri in Benxi County of northeast China, in August 2021. Irregular reddish brown spots ranging from 3 to 11 mm were observed on infected leaves, and each leaf had dozens of spots (Fig. 1). As the disease progressed, the diseased spots withered and the centers fell out, and multiple lesions merge into large diseased spots, causing leaf wilting. The roots and stem bases were not infected during the reproductive stage. More than 37% of the plants in a 18 ha field were infected in 2021. The ten diseased leaves were collected and cut into small (3-5 mm) pieces, which were surface-disinfested by immersing into 1% NaOCl for 2 min and rinsing with sterile distilled water three times. The leaf pieces were then placed on acidified potato dextrose agar (PDA) in petri plates and incubated in the dark at 25°C. Twenty isolates with the same morphological characteristics were obtained. Isolates were further purified by starting a new colony for each isolate from a single spore collected from water agar. Isolate TYTW7 was randomly selected for identification and pathogenicity testing. It grew rapidly and produced profuse aerial mycelia with a carmine red underside. The conidiophores had many fertile branches and formed an elongated stipe with a sphaeropedunculate vesicle at the tip. The one-septate conidia were cylindrical and almost straight with parallel walls and rounded ends. Their sizes ranged from 30.35 to 51.76 × 2.93 to 5.01 µm (n = 100) and the pathogens were initially identified as Calonectria sp. (Crous 2002; Crous et al. 2004; Lombard et al. 2015, 2016). Further confirmation of the identification was determined according to published method (Liu and Chen 2017; Shao and Li 2021). The partial gene regions including the translation elongation factor 1-alpha (GenBank accession no. OP290551), histone H3 (OP290552), calmodulin (OP290553) and ß-tubulin (OP290554) were obtained, and BLAST searches showed 99-100% homology with the ex-type culture CERC 8952 (MF527049, MF527065, MF527081 and MF527107) and phylogenetic analysis combining all loci revealed that the isolate TYTW7 and the type strain of Ca. montana clustered in one group (Fig. 2). Based on morphological characteristics and phylogenetic analysis, isolate TYTW7 was identified as Ca. Montana. Healthy 2-year-old plants were used for the pathogenicity test. A spore suspension (1×105 spores/mL water) was used to inoculate three host plants; sterile water was sprayed on the same number plants serving as a control. The experiment was repeated three times. All plants were incubated at 27±2°C (12h photoperiod) and were evaluated after seven days. The inoculated plants showed lesions on the leaf surface, similar to those in the field, and the control remained symptomless. The pathogens were successfully reisolated and identified by sequencing, and no pathogens were isolated from symptomless control plants. To our knowledge, this is the first report of Ca. montana causing L. fischeri leaf spot. The disease poses a threat to the production and more control strategies are needed on management options to minimize losses.