ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

OBJECTIVE To analyze the prototype components absorbed into blood and brain of Lithocarpus litseifolius leaves， so as to provide a reference for clarifying the pharmacological material basis of its prevention and treatment of central nervous system dis eases. METHODS The ethanol extract of L. litseifolius leaves， as well as the gastric lavage fluid and perfusion solution were prepared. Using rats as subjects， plasma samples of intestinal wall metabolism， intestinal flora metabolism and hepatic metabolism were prepared via in situ intestinal perfusion and closed intestinal loop method； while comprehensive metabolic plasma samples， brain tissue samples， and cerebrospinal fluid samples were collected after intragastric administration. UPLC-HRMS technology was utilized to analyze and identify chemical components and prototype components absorbed into blood and brain of L. litseifolius leaves. RESULTS A total of 66 chemical constituents were identified in L. litseifolius leaves， primarily consisting of flavonoids， organic acids， and others. A total of 16， 13， 11， and 5 prototype components were identified in intestinal wall metabolism， intestinal flora metabolism， hepatic metabolism， and comprehensive metabolic plasma samples， respectively. Additionally， 4 prototype components were detected in brain tissue and 9 in cerebrospinal fluid. Phloridzin， trilobatin， phloretin-2- O -malonyl hexoside， and phloretin were identified as common components across all sample types. CONCLUSIONS Prototype components absorbed into blood and brain of L. litseifolius leaves， such as phloridzin， trilobatin， phloretin， and other components may serve as the pharmacological material basis for their therapeutic effects on central nervous system diseases.

Allergic conjunctivitis is a common ocular inflammatory disease, with intense itching being the most typical and distressing symptom for patients. In recent years, with the in-depth study of the interaction between the nervous and immune systems, significant progress has been made in understanding the mechanism of itching in allergic conjunctivitis. This review elaborates on the neurobiological basis of itching in allergic conjunctivitis, with a focus on the complex dialogue between immune cells and sensory neurons, particularly the core role of the IL-33-ST2-CGRP signaling axis in mediating itching. Additionally, this article introduces new findings in genetic susceptibility research, including the identification of susceptibility genes for allergic conjunctivitis through transcriptome-wide association studies. The sensory nervous system not only transmits itch signals but also actively participates in the formation of antigen channels related to conjunctival goblet cells, thereby regulating the local uptake of allergens and the initiation of the immune response. Moreover, targeted novel therapeutic strategies offer hope for patients with refractory allergic conjunctivitis. Exploring the molecular and cellular mechanisms of itching in allergic conjunctivitis will provide a theoretical basis for the development of more effective treatment methods.

Background: The hypotension prediction index (HPI) is an algorithm designed to predict hypotension. Some studies have reported that HPI-guided hemodynamic management strategies decrease intraoperative hypotension and complications; however, the effect of HPI on reducing perioperative complications is controversial. This meta-analysis aimed to assess the efficacy of the HPI in reducing major complications and intraoperative hypotension. Methods: We conducted this meta-analysis according to the PRISMA statement and Cochrane Handbook guidelines. A comprehensive literature review was conducted to identify studies focusing on the efficacy of HPI-guided management in reducing intraoperative hypotension and postoperative complications. The PubMed, Embase, Scopus, and Web of Science databases were searched, and the resulting data were combined to calculate the pooled mean differences or risk ratios (RRs) with 95% CIs of both randomized controlled trials (RCTs) and retrospective studies, as appropriate. Heterogeneity and potential publication bias were also assessed. Results: Nineteen articles (12 RCTs and 7 retrospective studies) with 2570 recruited patients were included in this meta-analysis. The critical evaluation of the study quality revealed a low risk of bias in the included RCTs. Among the non-randomized trials, one was rated 7, two were rated 8, and the remaining four were rated 9 on the Newcastle-Ottawa Scale, indicating high quality and a low risk of bias. HPI-guided management significantly reduced intraoperative hypotension and associated major complications (RR = 0.79, 95% CI [0.69–0.90], I2 = 0, P < 0.001). Blood loss and length of hospital stay were comparable between the groups. Conclusions HPI-guided management significantly reduced intraoperative hypotension and major complications.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

Owing to its inherent advantages—such as being non-destructive, rapid, highly molecule-specific, and minimally interfered with by moisture—Raman spectroscopy has been widely adopted in the fields of skin barrier function assessment, monitoring the transdermal penetration of active cosmetic ingredients, and the identification and quality control of cosmetic products. Despite these strengths, the practical application of this technique faces a significant bottleneck: the strong fluorescence background generated by endogenous skin components and exogenous cosmetic additives. Endogenous skin substances, such as structural proteins (e.g., collagen and elastin), metabolic coenzymes (e.g., nicotinamide adenine dinucleotide), and pigments (e.g., melanin), together with exogenous cosmetic constituents like organic colorants, chemical sunscreens, and fragrances, often possess strong absorption and emission characteristics. When excited by lasers, these components produce a fluorescence background that can be 10⁶ to 10⁸ times stronger than the Raman scattering signals, effectively masking the inherently weak vibrational fingerprint information. In recent years, driven by the rapid development of optoelectronic hardware and artificial intelligence algorithms, fluorescence suppression strategies have evolved from isolated, single-method approaches into comprehensive, multi-level synergistic systems. These systems are categorized into three distinct tiers: sample preparation, signal acquisition, and data processing. At the sample preparation level, techniques such as photobleaching and surface-enhanced Raman spectroscopy (SERS) are employed to eliminate or bypass the generation of fluorescence at the source. At the signal acquisition level, instrumental improvements—including the use of long-wavelength near-infrared excitation (typically 785 nm or 1 064 nm), confocal spatial filtering, and shifted excitation Raman difference spectroscopy (SERDS)— are utilized to physically isolate Raman signals from the fluorescence background. Furthermore, at the data processing level, numerical baseline correction methods such as polynomial fitting, penalized least squares (e.g., airPLS, arPLS), wavelet transform, and derivative algorithms are increasingly integrated into the analytical pipeline to extract Raman spectral features from mixed signals without increasing hardware costs or acquisition time. This review provides a systematic categorization and critical evaluation of these fluorescence suppression methods, detailing their underlying principles, technical advantages, and inherent limitations in diverse experimental setups. By focusing on critical application scenarios—including skin barrier assessment, percutaneous absorption monitoring, the routine quality control of cosmetics, and the emerging field of portable on-site detection—this paper explores the current state of technique selection and optimization. Finally, the article discusses future development trends, emphasizing the necessity of constructing adaptive, tiered suppression strategies, developing intelligent and automated data processing algorithms, and promoting the integration of portable, multi-modal diagnostic devices. The objective of this review is to provide a comprehensive technical reference to facilitate the transition of Raman spectroscopy from a specialized laboratory tool into a routine, robust analytical platform for advancements in skin science and cosmetic research.

Clinical practice guidelines serve as a crucial bridge linking research evidence, clinical practice, and health policy decision-making. However, the guideline domain continues to face persistent challenges, including duplicate guideline development, variable methodological quality, lack of transparent reporting, insufficient implementation and translation, and delayed updates. Focusing solely on "how to develop a high-quality guideline" is no longer sufficient to meet the modern healthcare system's demands for timely, trustworthy, actionable, and dynamically updated recommendations. The emergence of the guideline ecosystem marks a shift in guideline research and practice from a linear development process toward a more systematic governance model. Rooted in the evidence ecosystem, the guideline ecosystem has its own distinct functional positioning, encompassing recommendation formulation, clinical implementation, and life-cycle updating. Looking ahead, China should leverage guideline registries, integrated quality assessment systems, implementation science, dynamic updating mechanisms, and artificial intelligence technologies to promote the transformation of its guideline system from scale expansion to an ecosystem-based model characterized by high quality and sustainability.

Objective To investigate the effect of berberine and its mechanism in delaying silica-induced lung injury via network pharmacology and molecular docking, followed by verification with animal experiments. Methods i) Targets of berberine and silicosis were obtained from multiple databases, and the common targets were identified. A protein-protein interaction network was constructed using the STRING database, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the common targets via the DAVID database. Molecular docking was conducted using AutoDock software for validation. ii) Specific pathogen-free Wistar rats were randomly divided into five groups, with six rats in each group. Rats in the model group, pirfenidone group, and low- and high-dose berberine groups received a single non-exposure intratracheal instillation of 1.00 mL silica suspension at a mass concentration of 100 g/L, whereas rats in the control group received an equal volume of 0.9% sodium chloride solution. Rats in the pirfenidone group were administered with pirfenidone solution at a dose of 100 mg/kg body weight, and rats in the low- and high-dose berberine groups were administered with berberine solutions at doses of 100 and 200 mg/kg body weight, respectively. The remaining two groups received equal volumes of 0.9% sodium chloride solution. All treatments were administered by gavage once daily for 28 consecutive days from 24 hours after silica exposure. After intervention, rats in each group were sacrificed. Histopathological changes in lung tissues were observed, and the lung organ coefficient was detected. Serum malondialdehyde (MDA) was detected by colorimetry. Serum inflammatory cytokine were detected by enzyme-linked immunosorbent assay. The expression of fibrosis markers in lung tissues was detected by immunohistochemistry. The protein expression of phosphatidylinositol 3-kinase (PI3K), protein kinase B (AKT), and phosphorylated AKT (p-AKT) in lung tissues was determined by Western blot. Results i) Network pharmacology analysis identified 46 potential targets. The core targets included AKT1, tumor protein p53, caspase-3, matrix metalloproteinase-9, and albumin. GO enrichment analysis revealed that the targets related to the anti-fibrotic effect of berberine against silicosis were mainly enriched in biological processes including signal transduction, negative regulation of apoptosis, extracellular matrix degradation, and response to reactive oxygen species. KEGG enrichment analysis showed that berberine exerted anti-silicosis effects through multiple signaling pathways, such as the PI3K/AKT pathway, hypoxia-inducible factor-1 pathway, and advanced glycation end product (AGE)-receptor for AGE pathway. ii) Histopathological examination revealed that rats in the model group presented typical pulmonary fibrotic lesions. Pathological lung injury of rats was alleviated in all three intervention groups (pirfenidone group, low- and high-dose berberine group) compared with the model group. The improvement in the high-dose berberine group was superior to that in the low-dose berberine group and was comparable with that in the pirfenidone group. rats in the model group showed increased lung organ coefficient, serum MDA, interleukin-1β (IL-1β), and tumor necrosis factor-α (TNF-α) (all P<0.05), as well as increased expression of typeⅠcollagen (COLⅠ), α-smooth muscle actin (α-SMA) and PI3K, and p-AKT/AKT ratio in lung tissues (all P<0.05) when compared with the control group. The above indicators were lower in rats in both the low- and high-dose berberine groups than those in the model group (all P<0.05). Serum MDA, IL-1β, and TNF-α, as well as relative expression of COLⅠ and α-SMA in lung tissues, were lower in the high-dose berberine group than in the low-dose berberine group (all P<0.05). Conclusion Berberine may alleviate pulmonary inflammation and fibrosis in rats through regulation of the PI3K/AKT signaling pathway, thereby delaying the progression of silicosis.

Objective To evaluate the performance of lightweight Chinese large language models (LLMs) in answering specialized lung cancer questions, and to explore the impact of retrieval-augmented generation (RAG) on model performance. Methods Eleven lightweight Chinese LLMs with parameter sizes ranging from 7B to 32B were included. A lung cancer-specific evaluation dataset consisting of 200 questions [100 A1-type (basic knowledge) and 100 A2-type (clinical case) questions], constructed based on clinical guidelines and thoracic surgery textbooks, was used for assessment. Model performance was evaluated under two conditions (with and without RAG). Accuracy was used to assess model performance, and response latency was recorded to reflect inference efficiency. An accuracy–latency scatter plot was constructed for descriptive analysis of overall model performance. Results All models successfully completed the evaluation. With the introduction of RAG, the overall average accuracy improved from 61.68% to 76.36%. Smaller models demonstrated the most significant improvement (e.g., the accuracy of DeepSeek-7B increased from 32.50% to 60.00%, P<0.001). The average response latency increased from 12.58 s to 13.80 s. The Qwen3 series showed the best overall performance, and Qwen3-32B achieved the highest accuracy under both conditions (76.50% and 84.00%, respectively). After RAG integration, performance differences among model families were markedly reduced. Based on the accuracy-latency trade-off, Qwen3-32B achieved the best balance between accuracy and response latency under the baseline condition, whereas Qwen3-14B demonstrated superior overall performance in terms of accuracy, latency, and computational cost after RAG integration. Conclusion The integration of RAG technology improves the ability of lightweight Chinese LLMs to answer specialized lung cancer questions. Under the dual practical constraints of limited computational resources and medical data security requirements, the "lightweight model+RAG" technical framework may represent a promising deployment solution.