Large Language Models for Social Determinants of Health Information Extraction from Clinical Notes - A Generalizable Approach across Institutions.

The consistent and persuasive evidence illustrating the influence of social determinants on health has prompted a growing realization throughout the health care sector that enhancing health and health equity will likely depend, at least to some extent, on addressing detrimental social determinants. However, detailed social determinants of health (SDoH) information is often buried within clinical narrative text in electronic health records (EHRs), necessitating natural language processing (NLP) methods to automatically extract these details. Most current NLP efforts for SDoH extraction have been limited, investigating on limited types of SDoH elements, deriving data from a single institution, focusing on specific patient cohorts or note types, with reduced focus on generalizability. This study aims to address these issues by creating cross-institutional corpora spanning different note types and healthcare systems, and developing and evaluating the generalizability of classification models, including novel large language models (LLMs), for detecting SDoH factors from diverse types of notes from four institutions: Harris County Psychiatric Center, University of Texas Physician Practice, Beth Israel Deaconess Medical Center, and Mayo Clinic. Four corpora of deidentified clinical notes were annotated with 21 SDoH factors at two levels: level 1 with SDoH factor types only and level 2 with SDoH factors along with associated values. Three traditional classification algorithms (XGBoost, TextCNN, Sentence BERT) and an instruction tuned LLM-based approach (LLaMA) were developed to identify multiple SDoH factors. Substantial variation was noted in SDoH documentation practices and label distributions based on patient cohorts, note types, and hospitals. The LLM achieved top performance with micro-averaged F1 scores over 0.9 on level 1 annotated corpora and an F1 over 0.84 on level 2 annotated corpora. While models performed well when trained and tested on individual datasets, cross-dataset generalization highlighted remaining obstacles. To foster collaboration, access to partial annotated corpora and models trained by merging all annotated datasets will be made available on the PhysioNet repository.

A Study of Biomedical Relation Extraction Using GPT Models.

Relation Extraction (RE) is a natural language processing (NLP) task for extracting semantic relations between biomedical entities. Recent developments in pre-trained large language models (LLM) motivated NLP researchers to use them for various NLP tasks. We investigated GPT-3.5-turbo and GPT-4 on extracting the relations from three standard datasets, EU-ADR, Gene Associations Database (GAD), and ChemProt. Unlike the existing approaches using datasets with masked entities, we used three versions for each dataset for our experiment: a version with masked entities, a second version with the original entities (unmasked), and a third version with abbreviations replaced with the original terms. We developed the prompts for various versions and used the chat completion model from GPT API. Our approach achieved a F1-score of 0.498 to 0.809 for GPT-3.5-turbo, and a highest F1-score of 0.84 for GPT-4. For certain experiments, the performance of GPT, BioBERT, and PubMedBERT are almost the same.

Me-LLaMA: Foundation Large Language Models for Medical Applications.

Recent advancements in large language models (LLMs) such as ChatGPT and LLaMA have hinted at their potential to revolutionize medical applications, yet their application in clinical settings often reveals limitations due to a lack of specialized training on medical-specific data. In response to this challenge, this study introduces Me-LLaMA, a novel medical LLM family that includes foundation models - Me-LLaMA 13/70B, along with their chat-enhanced versions - Me-LLaMA 13/70B-chat, developed through continual pre-training and instruction tuning of LLaMA2 using large medical datasets. Our methodology leverages a comprehensive domain-specific data suite, including a large-scale, continual pre-training dataset with 129B tokens, an instruction tuning dataset with 214k samples, and a new medical evaluation benchmark (MIBE) across six critical medical tasks with 12 datasets. Our extensive evaluation using the MIBE shows that Me-LLaMA models achieve overall better performance than existing open-source medical LLMs in zero-shot, few-shot and supervised learning abilities. With task-specific instruction tuning, Me-LLaMA models outperform ChatGPT on 7 out of 8 datasets and GPT-4 on 5 out of 8 datasets. In addition, we investigated the catastrophic forgetting problem, and our results show that Me-LLaMA models outperform other open-source medical LLMs in mitigating this issue. Me-LLaMA is one of the largest open-source medical foundation LLMs that use both biomedical and clinical data. It exhibits superior performance across both general and medical tasks compared to other open-source medical LLMs, rendering it an attractive choice for medical AI applications. We release our models, datasets, and evaluation scripts at: https://github.com/BIDS-Xu-Lab/Me-LLaMA.

Clinical utility of digital radiography dacryocystography for preoperative assessment in nasolacrimal duct obstruction prior to endoscopic dacryocystorhinostomy.

Purpose: To evaluate the clinical usefulness of digital radiography dacryocystography in patients with primary acquired nasolacrimal duct obstruction prior to endoscopic dacryocystorhinostomy. Methods: All dacryocystography images from 129 patients with primary acquired nasolacrimal duct obstruction were analyzed. Each group was assessed for postoperative epiphora severity using Munk's score via telephone follow-up three years post-surgery. Receiver operating characteristic (ROC) curve was plotted to obtain a suitable cutoff value of the transverse diameter of the lacrimal sac (LS), used to categorize LS size into small (≤4.350 mm) and large (>4.350 mm). Results: Analysis of the transverse diameter of the LS among 129 patients showed a negative correlation between it and Munk's score (r = -0.282, p = 0.001). There was a statistical difference between the surgical outcomes and the sizes of the LS (p = 0.041). The ROC curve analysis showed that the transverse diameter of the LS at 4.350 mm was the ideal cutoff value for the outcome of endoscopic dacryocystorhinostomy, with a sensitivity of 42.2 %, and specificity of 92.3 %. After adjusting for the age and sex, the small LS was associated with an increased risk of postoperative failed outcome (adjusted odds ratio [95 % CI]: 8.628 [1.074, 69.335]). Conclusion: The small LS was independently associated with the failed surgical outcome. Furthermore, the preoperative measurement of the LS transverse diameter serves as one of the reliable predictors for postoperative epiphora severity.

ß-Caryophyllene promotes the survival of random skin flaps by upregulating the PI3K/AKT signaling pathway.

BACKGROUND: Flap transplantation is a widely used plastic repair technique in surgical procedures, aimed at addressing skin defects resulting from diverse wounds and diseases. However, due to the insufficient blood supply after flap surgery, the occurrence of ischemia-reperfusion injury, and an excessive sterile inflammatory response, flaps frequently develop complications (e.g., partial or complete ischemic necrosis). These complications have adverse effects on wound healing and repair. ß-Caryophyllene (BCP) is a bicyclic sesquiterpene that is widely present in plants. It mitigates oxidative stress and inflammatory responses, demonstrates neuroprotective and analgesic properties, and serves a protective function in organs or tissues subjected to ischemia-reperfusion injury. However, no study has confirmed whether BCP can be used in the field of flap transplantation to improve the flap survival rate. METHODS: To assess the impact of BCP on random flap survival, we constructed a modified McFarlane random flap model on the rat. After 7 consecutive days of gavage with different doses of BCP, we measured the survival area ratio, angiogenesis, blood perfusion, tissue inflammation level, apoptosis-related protein levels, and the PI3K/AKT signaling pathway expression of the random flap. RESULTS: BCP treatment increased the survival area of the flap in a dose-dependent manner after random flap transplantation in rats. BCP mainly promoted the formation of tissue blood vessels, improved flap blood perfusion, limited the local inflammatory response, and reduced apoptosis. In addition, we demonstrated that BCP works primarily by promoting the PI3K/AKT signaling expression while enhancing the phosphorylation of AKT. Administration of wortmannin, a selective inhibitor of PI3K, eliminated the effects of BCP. CONCLUSION: BCP can promote the survival of random flaps by upregulating the PI3K/AKT signaling pathway, increasing tissue blood perfusion, and limiting the inflammatory response and apoptosis.

Long non-coding RNA MSL3P1 regulates CUL3 mRNA cytoplasmic transport and stability and promotes lung adenocarcinoma metastasis.

Lung adenocarcinoma (LUAD) is the most prevalent histological type of lung cancer. Previous studies have reported that specific long non-coding RNAs (lncRNAs) are involved in cancer development and progression. The phenotype and mechanism of ENST00000440028, named MSL3P1, a lncRNA which we referring to a cancer-testis gene with potential roles in tumorigenesis and progression, have not been reported. We found that MSL3P1 is overexpressed in LUAD tumor tissues, which is significantly associated with clinical characteristics, metastasis, and poor clinical prognosis. MSL3P1 promotes the metastasis of LUAD in vitro and in vivo. The enhancer reprogramming in LUAD tumor tissue is the major driver of the aberrantly expression of MSL3P1. Mechanistically, due to the competitive binding to CUL3 mRNA with ZFC3H1 protein (a protein involved in targeting polyadenylated RNA to exosomes and promoting the degradation of target mRNA), MSL3P1 can prevent the ZFC3H1-mediated RNA degradation of CUL3 mRNA and transport it to the cytoplasm. This activates the downstream epithelial-to-mesenchymal transition signaling pathway, and promote tumor invasion and metastasis. Implications: This study indicates that lncRNA MSL3P1 regulates CUL3 mRNA stability and promotes the metastasis and holds potential as a prognostic biomarker and therapeutic target in LUAD.

Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer.

Cervical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This research aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology. The AICCS system was trained and validated using various datasets, including retrospective, prospective, and randomized observational trial data, involving a total of 16,056 participants. It utilized two artificial intelligence (AI) models: one for detecting cells at the patch-level and another for classifying whole-slide image (WSIs). The AICCS consistently showed high accuracy in predicting cytology grades across different datasets. In the prospective assessment, it achieved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher AUC, specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity. Thus, AICCS holds promise as an additional tool for accurate and efficient cervical cancer screening.

Independent and interactive associations of heart rate and obesity with type 2 diabetes mellites: A population-based study.

BACKGROUND: Although obesity and heart rate (HR) were closely related to the prevalence and development of type 2 diabetes mllitus (T2DM), few studies have shown a co-association effect of them on T2DM. We aimed at assessing the interactive effects of HR and obesity with prevalence of T2DM in Chinese population, providing the exact cutpoint of the risk threshold for blood glucose with high HR. MATERIALS AND METHODS: In the Risk Evaluation of cAncers in Chinese diabeTic Individuals: a lONgitudinal study (REACTION) cohorts (N = 8398), the relationship between HR and T2DM was explored by linear regression, logistic regression, and restricted cubic spline, and odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Interaction terms between HR and body mass index (BMI) and HR and waist circumference (WC) were introduced into the logistic regression model. RESULTS: In those with HR > 88.0 beats/min, fasting plasma glucose and oral glucose tolerance tests were significantly correlated with HR, and the prevalence of T2DM was highly correlated with HR (all p < .05). There were interactive associations of HR and obesity in patients with T2DM with HR < 74 beats/min. CONCLUSION: High HR was in interaction with obesity, associating with prevalence of T2DM. The newly subdivided risk threshold for HR with T2DM might be HR > 88 beats/minute.

An Updated Simplified Severity Scale for Age-Related Macular Degeneration Incorporating Reticular Pseudodrusen: Age-Related Eye Disease Study Report Number 42.

PURPOSE: To update the Age-Related Eye Disease Study (AREDS) simplified severity scale for risk of late age-related macular degeneration (AMD), including incorporation of reticular pseudodrusen (RPD), and to perform external validation on the Age-Related Eye Disease Study 2 (AREDS2). DESIGN: Post hoc analysis of 2 clinical trial cohorts: AREDS and AREDS2. PARTICIPANTS: Participants with no late AMD in either eye at baseline in AREDS (n = 2719) and AREDS2 (n = 1472). METHODS: Five-year rates of progression to late AMD were calculated according to levels 0 to 4 on the simplified severity scale after 2 updates: (1) noncentral geographic atrophy (GA) considered part of the outcome, rather than a risk feature, and (2) scale separation according to RPD status (determined by validated deep learning grading of color fundus photographs). MAIN OUTCOME MEASURES: Five-year rate of progression to late AMD (defined as neovascular AMD or any GA). RESULTS: In the AREDS, after the first scale update, the 5-year rates of progression to late AMD for levels 0 to 4 were 0.3%, 4.5%, 12.9%, 32.2%, and 55.6%, respectively. As the final simplified severity scale, the 5-year progression rates for levels 0 to 4 were 0.3%, 4.3%, 11.6%, 26.7%, and 50.0%, respectively, for participants without RPD at baseline and 2.8%, 8.0%, 29.0%, 58.7%, and 72.2%, respectively, for participants with RPD at baseline. In external validation on the AREDS2, for levels 2 to 4, the progression rates were similar: 15.0%, 27.7%, and 45.7% (RPD absent) and 26.2%, 46.0%, and 73.0% (RPD present), respectively. CONCLUSIONS: The AREDS AMD simplified severity scale has been modernized with 2 important updates. The new scale for individuals without RPD has 5-year progression rates of approximately 0.5%, 4%, 12%, 25%, and 50%, such that the rates on the original scale remain accurate. The new scale for individuals with RPD has 5-year progression rates of approximately 3%, 8%, 30%, 60%, and 70%, that is, approximately double for most levels. This scale fits updated definitions of late AMD, has increased prognostic accuracy, seems generalizable to similar populations, but remains simple for broad risk categorization. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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.

Ophthalmic care may not align with patient need: An analysis on state-wide patient needs and provider density between 2008 and 2022.

PURPOSE: This study aims to assess the extent to which the demand for ophthalmologic care among patients at the state level is reflected in Google Trends data, serving as an indicator of patient desire in ophthalmology. METHODS: For each state, patient interest in ophthalmologic care was estimated using the Google Trends resource measuring web search and YouTube search rates for multiple ophthalmologic terms. We compared the change in search for ophthalmologic terms over time and used ordinary least squares regression to evaluate whether search interest for ophthalmologic terms was able to predict the rate of practicing ophthalmologists in each state. We also compare the changing rates of searches across the web and YouTube to evaluate the resources patients are most likely to utilize. RESULTS: From 2008 to 2022, web search rates for general ophthalmology related terms increased by 43.98%, while search interest for retinal specific terms increased by 19.51%. YouTube specific results for general ophthalmology terms increased by 55.83% while search for retinal terms fell by 58.48%. Ophthalmologic and retinal specific search interest was not significantly associated with either outcome. CONCLUSIONS: Our findings suggest that patient information needs, demographic elements, and the educational backgrounds of residents and fellows - those important factors - are surprisingly poorly correlated with ophthalmology provider density. Furthermore, we observed no noteworthy correlation between the search interest in ophthalmology and the overall density of ophthalmologists or retinal specialists. This implies that there is a pressing need to explore and implement strategies aimed at better aligning these influencing factors the choices made by ophthalmologists in selecting their practice locations to bridge the gap between healthcare availability and public interest.

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.

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.

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.

Improving large language models for clinical named entity recognition via prompt engineering.

IMPORTANCE: The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models' performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets. OBJECTIVES: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance. MATERIALS AND METHODS: We evaluated these models on 2 clinical NER tasks: (1) to extract medical problems, treatments, and tests from clinical notes in the MTSamples corpus, following the 2010 i2b2 concept extraction shared task, and (2) to identify nervous system disorder-related adverse events from safety reports in the vaccine adverse event reporting system (VAERS). To improve the GPT models' performance, we developed a clinical task-specific prompt framework that includes (1) baseline prompts with task description and format specification, (2) annotation guideline-based prompts, (3) error analysis-based instructions, and (4) annotated samples for few-shot learning. We assessed each prompt's effectiveness and compared the models to BioClinicalBERT. RESULTS: Using baseline prompts, GPT-3.5 and GPT-4 achieved relaxed F1 scores of 0.634, 0.804 for MTSamples and 0.301, 0.593 for VAERS. Additional prompt components consistently improved model performance. When all 4 components were used, GPT-3.5 and GPT-4 achieved relaxed F1 socres of 0.794, 0.861 for MTSamples and 0.676, 0.736 for VAERS, demonstrating the effectiveness of our prompt framework. Although these results trail BioClinicalBERT (F1 of 0.901 for the MTSamples dataset and 0.802 for the VAERS), it is very promising considering few training samples are needed. DISCUSSION: The study's findings suggest a promising direction in leveraging LLMs for clinical NER tasks. However, while the performance of GPT models improved with task-specific prompts, there's a need for further development and refinement. LLMs like GPT-4 show potential in achieving close performance to state-of-the-art models like BioClinicalBERT, but they still require careful prompt engineering and understanding of task-specific knowledge. The study also underscores the importance of evaluation schemas that accurately reflect the capabilities and performance of LLMs in clinical settings. CONCLUSION: While direct application of GPT models to clinical NER tasks falls short of optimal performance, our task-specific prompt framework, incorporating medical knowledge and training samples, significantly enhances GPT models' feasibility for potential clinical applications.

Tetrahydropalmatine promotes random skin flap survival in rats via the PI3K/AKT signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Flap necrosis is the most common complication after flap transplantation, but its prevention remains challenging. Tetrahydropalmatine (THP) is the main bioactive component of the traditional Chinese medicine Corydalis yanhusuo, with effects that include the activation of blood circulation, the promotion of qi, and pain relief. Although THP is widely used to treat various pain conditions, its impact on flap survival is unknown. AIM OF THE STUDY: To explore the effect and mechanism of THP on skin flap survival. MATERIALS AND METHODS: In this study, we established a modified McFarlane flap model, and the flap survival rate was calculated after 7 days of THP treatment. Angiogenesis and blood perfusion were evaluated using lead oxide/gelatin angiography and laser Doppler, respectively. Flap tissue obtained from zone II was evaluated histopathologically, by hematoxylin and eosin staining, and in assays for malondialdehyde content and superoxide dismutase activity. Immunofluorescence was performed to detect interleukin (IL)-6, tumor necrosis factor (TNF)-α, hypoxia-inducible factor (HIF)-1α, Bcl-2, Bax, caspase-3, caspase-9, SQSTM1/P62, Beclin-1, and LC3 expression, and Western blot to assess PI3K/AKT signaling pathway activation and Vascular endothelial growth factor (VEGF) expression. The role played by the autophagy pathway in flap necrosis was examined using rapamycin, a specific inhibitor of mTOR. RESULTS: Experimentally, THP improved the survival rate of skin flaps, promoted angiogenesis, and improved blood perfusion. THP administration reduced the inflammatory response, oxidative stress, and apoptosis in addition to inhibiting autophagy via the PI3K/AKT/mTOR pathway. Rapamycin partially reversed these effects. CONCLUSION: THP promotes skin flap survival via the PI3K/AKT signaling pathway.

Rivaroxaban down-regulates pyroptosis and the TLR4/NF-κB/NLRP3 signaling pathway to promote flap survival.

BACKGROUND: Flap placement remains the primary method for wound repair, but postoperative ischemic flap necrosis is of major concern. This study explored whether rivaroxaban, a factor Xa inhibitor, enhanced flap survival. METHODS: Thirty-six rats were randomly divided into control, low-dose rivaroxaban (3 mg/kg/day), and high-dose rivaroxaban (7 mg/kg/day) groups. On postoperative day 7, the flap survival rate was analyzed and the average survival area calculated. After the rats were euthanized, immunological and molecular biological techniques were employed to assess vascular regeneration, pyroptosis, and inflammation. RESULTS: Rivaroxaban upregulated VEGF expression, in turn enhancing angiogenesis, and it downregulated IL-1ß, IL-6, and TNF-α expression, thereby mitigating inflammation. The drug also suppressed TLR4, NF-κB p65, NLRP3, caspase-1, and IL-18 syntheses, thus inhibiting pyroptosis. CONCLUSIONS: Rivaroxaban enhanced random flap survival by down-regulating the TLR4/NF-κB/NLRP3 signaling pathway to suppress pyroptosis, promoting vascular regeneration and inhibiting inflammation.

L-Borneol promotes skin flap survival by regulating HIF-1α/NF-κB pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: The clinical application of skin flaps in surgical reconstruction is frequently impeded by the occurrence of distant necrosis. L-Borneol exhibits myogenic properties in traditional Chinese medicine and is used in clinical settings to promote wound healing and conditions such as stroke. Nevertheless, the precise mechanism by which borneol exerts its protective effects on skin flap survival remains unclear. AIM OF THE STUDY: To explore the potential of L-borneol to promote skin flap survival and elucidate the underlying mechanisms. MATERIALS AND METHODS: Thirty-six male Sprague-Dawley rats were randomly divided into three groups: a high-dose (200 mg/kg L-borneol per day), a low-dose (50 mg/kg/day), and control group (same volume of solvent). In each rat, a modified rectangular McFarlane flap model measuring 3 × 9 cm was constructed. Daily intragastric administration of L-borneol or solvent was performed. The flap was divided into three square sections of equal size, namely Zone I (the proximal zone), Zone II (the intermediate zone), and Zone III (the distal zone). The survival rate was quantified, and the histological state of each flap was evaluated on the seventh day following the surgical procedure. The assessment of angiogenesis was conducted using lead oxide/gelatin angiography, whereas the evaluation of blood flow in the free flap was performed using laser Doppler flow imaging. Superoxide dismutase activity was detected using the water-soluble tetrazolium salt-8 method. The quantities of vascular endothelial growth factor, interleukin (IL)-1ß, IL-6, and tumour necrosis factor-α were determined using immunohistochemistry. The levels of nuclear transcription factor-κB, hypoxia-inducible factor-1, B-cell lymphoma-2 (BCL-2), and BCL-2-associated X (BAX) were determined by Western blotting technique. RESULTS: Flap survival rate significantly improved and neutrophil recruitment and release were enhanced after treatment with the compound. Angiogenesis was promoted. L-borneol protected against oxidative stress by increasing superoxide dismutase activity and decreasing malondialdehyde content. It downregulated the hypoxia-inducible factor nuclear transcription factor-κB pathway, leading to the inhibition of several inflammatory factors. Simultaneously, it facilitated the expression of vascular endothelial growth factor and BCL-2. CONCLUSION: The study shows that L-borneol may promote skin flap survival by inhibiting HIF-1α/NF-κB pathway.

Functional Brain Network Alterations in Patients With Systemic Lupus Erythematosus With Different Cognitive Function States: A Graph Theory Analysis Study.

OBJECTIVE: The study aimed to investigate the characteristics of brain functional network disruption in patients with systemic lupus erythematosus (SLE) with different cognitive function states by using graph theory analysis and to explore their relationship with clinical data and neuropsychiatric scales. METHODS: Resting-state functional magnetic resonance imaging data were collected from 38 female SLE patients and 44 healthy controls. Based on Montreal Cognitive Assessment (MoCA) scores, SLE patients were divided into a high MoCA group (MoCA-H; MoCA score, ≥26) and a low MoCA group (MoCA-L; MoCA score, <26). The matrix of resting-state functional brain networks of subjects in the 3 groups was constructed by using the graph theory approach. The topological properties of the functional brain networks, including global and local metrics, in the 3 groups were calculated. The differences in the topological properties of networks between the 3 groups were compared. In addition, Spearman correlation analysis was used to explore the correlation between altered topological properties of brain networks and clinical indicators, as well as neuropsychiatric scales in SLE patients in the MoCA-L group. RESULTS: At the global level, in the sparsity threshold range of 0.10 to 0.34, the values of small-world properties were greater than 1 in all 3 groups, indicating that functional brain networks of both 3 groups had small-world properties. There were statistically significant differences in the characteristic path length, global, and local efficiency between 3 groups ( F = 3.825, P = 0.0260; F = 3.722, P = 0.0285; and F = 3.457, P = 0.0364, respectively). Systemic lupus erythematosus patients in the MoCA-L group showed increased characteristic path length ( t = 2.816, P = 0.00651), decreased global ( t = -2.729, P = 0.00826), and local efficiency ( t = -2.623, P = 0.0109) compared with healthy controls. No statistically significant differences in local metrics were found between the MoCA-H group and the healthy control, MoCA-L groups. At the local level, there was statistically significant difference in the node efficiency among the 3 groups ( P < 0.05 after Bonferroni correction). Compared with healthy controls, SLE patients in the MoCA-L group showed decreased node efficiency in left anterior cingulate paracingulate gyrus, bilateral putamen, bilateral pallidum, and left Heschl gyrus. No statistically significant differences in the local metrics were found between the MoCA-H, MoCA-L, and healthy control groups. Correlation analysis in SLE patients in the MoCA-L group showed that the characteristic path length was positively correlated with C4 levels ( r = 0.587, P = 0.007), the global and local efficiencies were negatively correlated with C4 levels ( r = -0.599, P = 0.005; r = -0.599, P = 0.005, respectively), and the node efficiency in the bilateral putamen was negatively correlated with C4 levels ( r = -0.611, P = 0.004; r = -0.570, P = 0.009). The node efficiency in the left pallidum was negatively correlated with disease duration ( r = -0.480, P = 0.032). The node efficiency in the left Heschl gyrus was negatively correlated with IgM levels ( r = -0.478, P = 0.033). No correlation was noted between other network metrics, clinical indicators, and neuropsychological scales. CONCLUSIONS: The topological properties of functional brain networks were disrupted in SLE patients with low MoCA scores, suggesting that altered topological properties of the brain networks were associated with cognitive function in SLE patients. Correlation between altered topological properties of the brain networks and clinical indicators was noted in SLE patients with low MoCA scores, suggesting that altered topological properties of brain networks in SLE patients may have clinical significance as imaging markers for monitoring disease changes in patients with SLE.

Tetrandrine promotes the survival of the random skin flap via the PI3K/AKT signaling pathway.

Flaps are mainly used for wound repair. However, postoperative ischemic necrosis of the distal flap is a major problem, which needs to be addressed urgently. We evaluated whether tetrandrine, a compound found in traditional Chinese medicine, can prolong the survival rate of random skin flaps. Thirty-six rats were randomly divided into control, low-dose tetrandrine (25 mg/kg/day), and high-dose tetrandrine (60 mg/kg/day) groups. On postoperative Day 7, the flap survival and average survival area were determined. After the rats were sacrificed, the levels of angiogenesis, apoptosis, and inflammation in the flap tissue were detected with immunology and molecular biology analyses. Tetrandrine increased vascular endothelial growth factor and Bcl-2 expression, in turn promoting angiogenesis and anti-apoptotic processes, respectively. Additionally, tetrandrine decreased the expression of Bax, which is associated with the induction of apoptosis, and also decreased inflammation in the flap tissue. Tetrandrine improved the survival rate of random flaps by promoting angiogenesis, inhibiting apoptosis, and reducing inflammation in the flap tissue through the modulation of the PI3K/AKT signaling pathway.