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.

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.

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.

OBJECTIVE: To investigate the anti-tumor effects of cinobufacini (CINO) on hepatocellular carcinoma (HCC) induced by des-gamma-carboxy-prothrombin (DCP) and to uncover the underlying mechanisms. METHODS: The inhibitory effect of CINO on HCC cell proliferation was evaluated using the cell counting kit-8 method, and the apoptosis rate was quantified using flow cytometry. Immunofluorescence and Western blot analyses were used to investigate the differential expression of proteins associated with cell growth, apoptosis, migration, and invasion pathways after CINO treatment. The therapeutic potential of CINO for HCC was confirmed, and the possibility of combining cinobufacini with c-Met inhibitor for the treatment of primary HCC was further validated by in vivo experiments. RESULTS: Under the induction of DCP, CINO inhibited the activity of HCC cells, induced apoptosis, and inhibited migration and invasion. Upon the induction of DCP, CINO regulated c-Met activation and the activation of the phosphatidylinositol-3 kinase/protein kinase B (PI3K/AKT) and mitogen-activated protein kinase kinase/extracellular signal-regulated kinase (MEK/ERK) pathways. In a mouse model of HCC, CINO exhibited significant antitumor effects by inhibiting the phosphorylation of c-Met and the downstream PI3K/AKT and MEK/ERK pathways in tumor tissues. CONCLUSIONS CINO inhibited HCC cell growth, promoted apoptosis, and suppressed HCC cell invasion and migration by targeting c-Met and PI3K/AKT and MEK/ERK signaling pathways under DCP induction.
Carcinoma, Hepatocellular/drug therapy* ; Proto-Oncogene Proteins c-met/metabolism* ; Liver Neoplasms/drug therapy* ; Signal Transduction/drug effects* ; Animals ; Humans ; Cell Movement/drug effects* ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Amphibian Venoms/therapeutic use* ; Cell Line, Tumor ; Neoplasm Metastasis ; Cell Survival/drug effects* ; Proto-Oncogene Proteins c-akt/metabolism* ; Phosphatidylinositol 3-Kinases/metabolism* ; Neoplasm Invasiveness ; Mice, Inbred BALB C ; Mice, Nude ; Mice ; Male ; Bufanolides/therapeutic use* ; Protein Precursors ; Prothrombin ; Biomarkers

Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

Periodontal bone defects, primarily caused by periodontitis, are highly prevalent in clinical settings and manifest as bone fenestration, dehiscence, or attachment loss, presenting a significant challenge to oral health. In regenerative medicine, harnessing developmental principles for tissue repair offers promising therapeutic potential. Of particular interest is the condensation of progenitor cells, an essential event in organogenesis that has inspired clinically effective cell aggregation approaches in dental regeneration. However, the precise cellular coordination mechanisms during condensation and regeneration remain elusive. Here, taking the tooth as a model organ, we employed single-cell RNA sequencing to dissect the cellular composition and heterogeneity of human dental follicle and dental papilla, revealing a distinct Platelet-derived growth factor receptor alpha (PDGFRA) mesenchymal stem/stromal cell (MSC) population with remarkable odontogenic potential. Interestingly, a reciprocal paracrine interaction between PDGFRA⁺ dental follicle stem cells (DFSCs) and CD31⁺ Endomucin⁺ endothelial cells (ECs) was mediated by Vascular endothelial growth factor A (VEGFA) and Platelet-derived growth factor subunit BB (PDGFBB). This crosstalk not only maintains the functionality of PDGFRA⁺ DFSCs but also drives specialized angiogenesis. In vivo periodontal bone regeneration experiments further reveal that communication between PDGFRA⁺ DFSC aggregates and recipient ECs is essential for effective angiogenic-osteogenic coupling and rapid tissue repair. Collectively, our results unravel the importance of MSC-EC crosstalk mediated by the VEGFA and PDGFBB-PDGFRA reciprocal signaling in orchestrating angiogenesis and osteogenesis. These findings not only establish a framework for deciphering and promoting periodontal bone regeneration in potential clinical applications but also offer insights for future therapeutic strategies in dental or broader regenerative medicine.
Receptor, Platelet-Derived Growth Factor alpha/metabolism* ; Humans ; Neovascularization, Physiologic/physiology* ; Dental Sac/cytology* ; Single-Cell Analysis ; Transcriptome ; Mesenchymal Stem Cells/metabolism* ; Bone Regeneration ; Animals ; Dental Papilla/cytology* ; Periodontium/physiology* ; Stem Cells/metabolism* ; Regeneration ; Angiogenesis