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.

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.

The active components in traditional Chinese medicine （TCM） generally have problems such as poor solubility and low bioavailability， which seriously restrict their clinical efficacy. In recent years， TCM nano-self-assembly based on the non-covalent interactions between molecules has become the research frontier in this field because it can spontaneously form an orderly structure and improve the insoluble components in the body. Moreover， TCM nano-self-assembly has shown unique advantages in the treatment of fibrosis， inflammatory and immune diseases， infectious diseases， and cancer. This article summarizes the research context， structural system， and potential as a pharmacological substance basis of TCM nano-self-assembly by reviewing recent published studies， with focuses on the application of TCM nano-self-assembly in the treatment of diseases from single components to complex systems. In addition， this article summarizes its advantages， mechanism characteristics， and main challenges faced. At present， this field still faces issues such as insufficient mechanism research， a lack of in-depth understanding of the competitive assembly rules of multiple components， difficulty in quality control， unclear in vivo processes and safety， and obstacles to clinical translation. To address these issues， this article proposes a series of solutions， including introducing molecular dynamics simulation and artificial intelligence to predict assembly behavior， using experimental techniques to analyze non-covalent interactions， establishing a comprehensive evaluation system with multiple indicators， using isotope labeling and in vivo imaging techniques to dynamically track in vivo processes， conducting standardized safety evaluation， promoting the filing of hospital preparations and new drug applications， and strengthening early communication between research and regulation. Finally， this article points out the future development directions， aiming to provide reference for subsequent research and clinical translation.

The active components in traditional Chinese medicine （TCM） generally have problems such as poor solubility and low bioavailability， which seriously restrict their clinical efficacy. In recent years， TCM nano-self-assembly based on the non-covalent interactions between molecules has become the research frontier in this field because it can spontaneously form an orderly structure and improve the insoluble components in the body. Moreover， TCM nano-self-assembly has shown unique advantages in the treatment of fibrosis， inflammatory and immune diseases， infectious diseases， and cancer. This article summarizes the research context， structural system， and potential as a pharmacological substance basis of TCM nano-self-assembly by reviewing recent published studies， with focuses on the application of TCM nano-self-assembly in the treatment of diseases from single components to complex systems. In addition， this article summarizes its advantages， mechanism characteristics， and main challenges faced. At present， this field still faces issues such as insufficient mechanism research， a lack of in-depth understanding of the competitive assembly rules of multiple components， difficulty in quality control， unclear in vivo processes and safety， and obstacles to clinical translation. To address these issues， this article proposes a series of solutions， including introducing molecular dynamics simulation and artificial intelligence to predict assembly behavior， using experimental techniques to analyze non-covalent interactions， establishing a comprehensive evaluation system with multiple indicators， using isotope labeling and in vivo imaging techniques to dynamically track in vivo processes， conducting standardized safety evaluation， promoting the filing of hospital preparations and new drug applications， and strengthening early communication between research and regulation. Finally， this article points out the future development directions， aiming to provide reference for subsequent research and clinical translation.

Objective: : The objective of this study was to assess the relationship between spermine synthase (SMS) expression, tumor occurrence, and prognosis in lower-grade gliomas (LGGs). Methods: : A total of 523 LGG patients and 1152 normal brain tissues were included as controls. Mann-Whitney U test was performed to evaluate SMS expression in the LGG group. Functional annotation analysis was conducted to explore the biological processes associated with high SMS expression. Immune cell infiltration analysis was performed to examine the correlation between SMS expression and immune cell types. The association between SMS expression and clinical and pathological features was assessed using Spearman correlation analysis. In vitro experiments were conducted to investigate the effects of overexpressing or downregulating SMS on cell proliferation, apoptosis, migration, invasion, and key proteins in the protein kinase B (AKT)/epithelialmesenchymal transition signaling pathway. Results: : The study revealed a significant upregulation of SMS expression in LGGs compared to normal brain tissues. High SMS expression was associated with certain clinical and pathological features, including older age, astrocytoma, higher World Health Organization grade, poor disease-specific survival, disease progression, non-1p/19q codeletion, and wild-type isocitrate dehydrogenase. Cox regression analysis identified SMS as a risk factor for overall survival. Bioinformatics analysis showed enrichment of eosinophils, T cells, and macrophages in LGG samples, while proportions of dendritic (DC) cells, plasmacytoid DC (pDC) cells, and CD8+ T cells were decreased. Conclusion : High SMS expression in LGGs may promote tumor occurrence through cellular proliferation and modulation of immune cell infiltration. These findings suggest the prognostic value of SMS in predicting clinical outcomes for LGG patients.

A growing body of research points out that gut microbiota plays a key role in tumor immunotherapy. By optimizing the composition of intestinal microbiota, it is possible to effectively improve immunotherapy resistance and enhance its therapeutic effect. This article comprehensively analyzes the mechanism of intestinal microbiota influencing tumor immunotherapy resistance, expounds the current strategies for targeted regulation of intestinal microbiota, such as traditional Chinese medicine and plant components, fecal microbiota transplantation, probiotics, prebiotics and dietary therapy, and explores the potential mechanisms of these strategies to improve patients' resistance to tumor immunotherapy. At the same time, the article also briefly discusses the prospects and challenges of targeting intestinal microbiota to improve tumor immunotherapy resistance, which provides a reference for related research to help the strategy research of reversing tumor immunotherapy resistance.

A 20-year-old male patient presented to the Department of Dermatology of Peking Union Medical College Hospital with complaints of an 8-year history of facial scarring, swelling of the lower limbs, and a 4-year history of scalp thickening. Physical examination showed thickening furrowing wrinkling of the skin on the face and behind the ears, ciliary body hirsutism, blepharoptosis, and cutis verticis gyrate. Both lower limbs were swollen, especially the knees and ankles. The skin of the palms and soles of the feet was keratinized and thickened. Laboratory examination using bone and joint X-ray showed periostosis of the proximal middle phalanges and metacarpals of both hands, distal ulna and radius, tibia and fibula, distal femurs, and metatarsals.Genetic testing revealed two variants in SLCO2A1, c.1658T > A and c.96+4A > C.Through multidisciplinary consultation, we confirmed the diagnosis of pachydermoperiostosis.

Non-typeable Haemophilus influenzae(NTHi) is a non-encapsulated, Gram-negative coccobacillus that commonlycolonizes in human upper respiratory tract and can cause invasive infections in immunocompromised hosts. In recent years,with the widespread implementation of Haemophilus influenzae type b(Hib) vaccination, NTHi has emerged as the predominantpathogenic strain of Haemophilus influenzae infections globally. Notably, the clinical isolation rate of NTHi has shown aprogressive increase worldwide, accompanied by rising resistance to β-lactams and other antibiotics. NTHi frequently formspolymicrobial infections with pathogens such as Streptococcus pneumoniae and Moraxella catarrhalis, and contributes tovarious diseases such as otitis media, meningitis, and acute exacerbations of chronic obstructive pulmonary disease(COPD)through virulence mechanisms including biofilm formation and immune evasion. At present, antibiotics remain the maindrugs for NTHi infection management, while increasing antimicrobial resistance has driven vaccine development as a criticalresearch priority. Promising vaccine candidates based on antigens such as outer membrane proteins and adhesins, havedemonstrated favorable immunogenicity and protective efficacy, but further optimization is required to enhance cross-protectiveimmunity. In this paper, the epidemic status, related diseases and vaccine research progress of NTHi are summarized, aimingto provide references for the prevention and control of NTHi infection.

Objective By combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. Methods A retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results A total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. Conclusion The combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.

Objective To develop and validate a prediction model for risk of amputation in patients with diabetic foot ulcers(DFU)based on systematic review and meta-analysis.Methods The studies on the risk factors of amputation in DFU patients was retrieved by using subject words+free words.After screening,37 cohort studies were finally included,and the Newcastle-Ottawa scale(NOS)was used for quality evaluation.Meta-analysis was performed on the risk factors of amputation in DFU.Then a prediction model for DFU amputation risk were constructed based on the statistically significant risk factors in the meta-analysis.The corresponding β value was calculated based on the combined odds ratio(OR)value of each risk factor,and each risk factor was scored to establish a scoring system model.The clinical data of 453 DFU patients hospitalized in our department from 2021 to 2023 were collected as a validation cohort.Receiver operating characteristic(ROC)curve analysis was used to evaluate the model performance.The area under the curve(AUC)was calculated,and the optimal cutoff score was determined by calculation of the maximum Youden index through sensitivity and specificity.Results Our meta-analysis showed a cumulative amputation rate of approximately 34.65%in 11 779 DFU patients.The final risk prediction models include gangrene[OR=11.92(5.86～24.24)],ulcer depth[OR=4.93(2.52～9.64)],osteomyelitis[OR=3.19(2.36～4.29)],previous amputation history[OR=3.19(2.00～5.09)]and lower extremity arterial disease[OR=3.10(2.31～4.17)].According to the weights of each risk factor,the total score of the model is 76,and the optimal cut-off score is 36.5.The prediction model performed well,with an AUC value of 0.864(0.824,0.903),a sensitivity of 0.743,a specificity of 0.859,and an accuracy rate of 83.00%.Conclusion A prediction model for DFU amputation risk is developed based on risk factor scoring,and has good discrimination and calibration,providing effective scientific basis for clinical research and clinical decision-making related to DFU amputation.