Oral and maxillofacial tumor surgery is characterized by complex anatomical structures, extensive surgical trauma, and high demands for postoperative functional reconstruction. Perioperative complications and functional impairments significantly affect patients’ recovery process, quality of life, and long-term prognosis. Enhanced recovery after surgery (ERAS), grounded in evidence-based medicine, optimizes perioperative management through multidisciplinary collaboration and demonstrates substantial application potential in oral and maxillofacial surgery. Multiple prospective studies have confirmed that standardized airway management, goal-directed fluid and temperature management, and specialized ward-based care can shorten hospital stays, facilitate early enteral nutrition and ambulation, and reduce intensive care unit admission rates and postoperative complications. However, existing ERAS studies mainly focus on traditional clinical outcomes, with insufficient attention paid to functional recovery specific to patients with oral and maxillofacial tumors after surgery, including speech, swallowing, mastication, facial expression, and psychosocial function. Based on the structure-process-outcome quality evaluation model, this review summarizes the implementation pathways and evaluation framework of ERAS in oral and maxillofacial tumor surgery. Furthermore, integrating current international evidence and a large cohort study from our team evaluating a delayed extubation strategy in patients undergoing free flap reconstruction, we demonstrate that perioperative management aligned with ERAS principles can significantly shorten hospital stays, reduce postoperative complications, and decrease medical costs while maintaining safety. Future efforts should focus on specialized pathways for oral and maxillofacial surgery, strengthening long-term functional and quality-of-life follow-up, and exploring digital and precision rehabilitation tools to promote the transition of ERAS toward a comprehensive recovery model emphasizing functional restoration and social reintegration.

ObjectiveTo evaluate the clinical efficacy of Maxing Loushi decoction in the treatment of acute exacerbation of chronic obstructive pulmonary disease （AECOPD） with phlegm turbidity obstructing lung syndrome， and to investigate its effects on inflammatory factors， immune function， and the programmed death-1（PD-1）/programmed death-ligand 1 （PD-L1） signaling pathway. MethodsA randomized controlled study was conducted， enrolling 90 hospitalized patients with AECOPD and phlegm turbidity obstructing lung syndrome in the Respiratory and Emergency Departments of Dongzhimen Hospital， Beijing University of Chinese Medicine， from April 2024 to December 2024. Patients were randomly assigned to a control group and an observation group using a random number table， with 45 patients in each group. The control group received conventional Western medical treatment， while the observation group received additional Maxing Loushi decoction for 14 days. Clinical efficacy， COPD Assessment Test （CAT） score， modified Medical Research Council Dyspnea Scale （mMRC）， 6-minute walk test （6MWT）， serum inflammatory factors， T lymphocyte subsets， and serum PD-1/PD-L1 levels were compared between the two groups before and after treatment. ResultsThe total clinical effective rate was 78.57% （33/42） in the control group and 95.35% （41/43） in the observation group， with the observation group showing significantly higher efficacy than that of the control group. The difference was statistically significant （χ² = 5.136， P<0.05）. After treatment， both groups showed significant reductions in CAT and mMRC scores （P<0.05， P<0.01） and significant increases in 6MWT compared to baseline （P<0.01）. The observation group demonstrated significantly greater improvements than the control group in this regard. Levels of inflammatory markers including C-reactive protein （CRP）， procalcitonin （PCT）， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， monocyte chemoattractant protein-1（MCP-1）， and macrophage inflammatory protein-1α （MIP-1α） were significantly reduced in both groups （P<0.05， P<0.01）， with greater reductions in the observation group （P<0.05， P<0.01）. CD8⁺ levels were significantly reduced （P<0.01）， while CD3⁺， CD4⁺， and CD4⁺/CD8⁺ levels were significantly increased in both groups after treatment （P<0.05， P<0.01）， with more significant improvements observed in the observation group （P<0.05， P<0.01）. Serum PD-1 levels were reduced （P<0.05， P<0.01）， and PD-L1 levels were increased significantly in both groups after treatment （P<0.05， P<0.01）， with more pronounced changes in the observation group （P<0.05）. ConclusionMaxing Loushi decoction demonstrates definite therapeutic efficacy as an adjunctive treatment for patients with AECOPD and phlegm turbidity obstructing lung syndrome. It contributes to reducing serum inflammatory factors， improving immune function， and regulating the PD-1/PD-L1 signaling pathway.

ObjectiveTo evaluate the clinical efficacy of Maxing Loushi decoction in the treatment of acute exacerbation of chronic obstructive pulmonary disease （AECOPD） with phlegm turbidity obstructing lung syndrome， and to investigate its effects on inflammatory factors， immune function， and the programmed death-1（PD-1）/programmed death-ligand 1 （PD-L1） signaling pathway. MethodsA randomized controlled study was conducted， enrolling 90 hospitalized patients with AECOPD and phlegm turbidity obstructing lung syndrome in the Respiratory and Emergency Departments of Dongzhimen Hospital， Beijing University of Chinese Medicine， from April 2024 to December 2024. Patients were randomly assigned to a control group and an observation group using a random number table， with 45 patients in each group. The control group received conventional Western medical treatment， while the observation group received additional Maxing Loushi decoction for 14 days. Clinical efficacy， COPD Assessment Test （CAT） score， modified Medical Research Council Dyspnea Scale （mMRC）， 6-minute walk test （6MWT）， serum inflammatory factors， T lymphocyte subsets， and serum PD-1/PD-L1 levels were compared between the two groups before and after treatment. ResultsThe total clinical effective rate was 78.57% （33/42） in the control group and 95.35% （41/43） in the observation group， with the observation group showing significantly higher efficacy than that of the control group. The difference was statistically significant （χ² = 5.136， P<0.05）. After treatment， both groups showed significant reductions in CAT and mMRC scores （P<0.05， P<0.01） and significant increases in 6MWT compared to baseline （P<0.01）. The observation group demonstrated significantly greater improvements than the control group in this regard. Levels of inflammatory markers including C-reactive protein （CRP）， procalcitonin （PCT）， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， monocyte chemoattractant protein-1（MCP-1）， and macrophage inflammatory protein-1α （MIP-1α） were significantly reduced in both groups （P<0.05， P<0.01）， with greater reductions in the observation group （P<0.05， P<0.01）. CD8⁺ levels were significantly reduced （P<0.01）， while CD3⁺， CD4⁺， and CD4⁺/CD8⁺ levels were significantly increased in both groups after treatment （P<0.05， P<0.01）， with more significant improvements observed in the observation group （P<0.05， P<0.01）. Serum PD-1 levels were reduced （P<0.05， P<0.01）， and PD-L1 levels were increased significantly in both groups after treatment （P<0.05， P<0.01）， with more pronounced changes in the observation group （P<0.05）. ConclusionMaxing Loushi decoction demonstrates definite therapeutic efficacy as an adjunctive treatment for patients with AECOPD and phlegm turbidity obstructing lung syndrome. It contributes to reducing serum inflammatory factors， improving immune function， and regulating the PD-1/PD-L1 signaling pathway.

Objective To investigate the relationship between serum IL-8,homocysteine(Hey),C-reactive protein(CRP)levels and severity of coronary lesions in patients with coronary heart disease(CHD)complicated by chronic heart failure(CHF),as well as their prognostic value.Methods A total of 118 elderly eligible patients admitted to our hospital from January 2023 to September 2024 were prospectively recruited,and according to their Gensini score,they were divided into mild,moderate and severe stenosis groups(45,50 and 23 cases,respectively).Accord-ing to the clinical outcomes after follow-up,they were divided into a good prognosis group(82 cases)and a poor prognosis group(36 cases).The serum levels of IL-8,Hcy and CRP were com-pared among the three groups of different stenosis,and Pearson correlation analysis was used to examine the relationship between the three indicators and the severity of coronary lesions.Cox regression analysis was performed to identify factors influencing poor prognosis in patients with CHD and CHF,and receiver ROC curve analysis was conducted to assess the diagnostic value of serum IL-8,Hey,and CRP levels in predicting prognosis.Results The serum levels of IL-8,Hey and CRP were gradually decreased in the severe,moderate and mild stenosis groups in turn,with statistical significances(P＜0.05).Pearson correlation analysis indicated that serum IL-8,Hcy,and CRP levels were positively correlated with the severity of coronary lesions in the patients with CHD and CHF(r=0.364,0.355,0.372,P＜0.01).Significant differences were found in age,NT-proBNP,D-D,IL-8,Hcy,and CRP levels between the good and poor prognosis groups(P＜0.05).Cox regression analysis revealed that age,NT-proBNP,D-D,IL-8,Hcy,and CRP were influencing factors for poor prognosis in patients with CHD and CHF(P＜0.05).ROC curve ana-lysis showed that the AUC value of IL-8,Hey,CRP,and their combination in predicting prognosis was 0.698,0.714,0.723 and 0.899,respectively.Conclusion In the patients with CHD and CHF,serum IL-8,Hcy,and CRP levels are associated with the severity of coronary lesion and prognosis.Moreover,combining these three indicators has significant diagnostic value for predicting patient outcomes.

Objective:To evaluate serum 25-hydroxyvitamin D 3 [25(OH)D 3] as a potential biomarker for amyotrophic lateral sclerosis (ALS) severity and to identify risk factors influencing ALS prognosis. Methods:This study included 217 ALS patients hospitalized at the Department of Neurology, First Medical Center, Chinese PLA General Hospital, between October 2018 and October 2021, who met the revised El Escorial diagnostic criteria. A cross-sectional analysis assessed differences in clinical indicators-including the ALS Functional Rating Scale-Revised (ALSFRS-R) and forced vital capacity percentage (FVC%)-across different serum 25(OH)D 3 levels. The correlation between 25(OH)D 3 levels and individual ALSFRS-R components was also examined. Conduct a prospective cohort study to identify independent risk factors affecting the survival time of ALS patients. Results:Among three groups categorized by serum 25(OH)D 3 levels, there were significant differences in the proportion of males ( χ2=10.51, P<0.05). Serum 25(OH)D 3 levels correlated positively with lower limb function scores in the ALSFRS-R ( r=0.05, P<0.05), but they were not identified as an independent risk factor for survival ( HR=0.98, 95% CI 0.93-1.04, P>0.05). In contrast, delayed diagnosis( HR=0.94, 95% CI 0.89-0.99, P<0.05) and reduced FVC%( HR=0.94, 95% CI 0.97-0.99, P<0.05) were independent predictors of shorter survival. Conclusion:Serum 25(OH)D 3 levels differ by gender distribution and may be linked to better lower limb function in ALS patients. However, their role in prolonging survival remains uncertain.

A retrospective analysis was conducted on 26 patients with relapsed and refractory multiple myeloma (RRMM) who were treated with carfilzomib-based regimens at Beijing Chaoyang Integrative Medicine Rescue and First Aid Hospital from July 2021 to May 2024. The median number of treatment cycles was 5 (range, 2-8). The overall response rate was 53.8% (14/26). The median follow-up duration was 9.5 months, and the median progression-free survival (PFS) was 8.6 months (range, 1.7-25.2 months). Patients without plasmacytoma recurrence ( n=17) had a PFS of 12.3 months. In contrast, patients with plasmacytoma recurrence ( n=9) had a PFS of 6.2 months. The difference in PFS between these two groups was statistically significant ( P=0.013). Age (over 65 vs. 65 or younger), and treatment line (more than three vs. three or fewer) did not significantly affect treatment efficacy (all P>0.05). Common adverse reactions of grade 3 or higher included hematologic adverse reactions in 10 cases, which improved with symptomatic treatment. Non-hematologic adverse reactions included pulmonary infection (2 cases), renal failure (1 case), and heart failure (1 case). In conclusion, carfilzomib-based regimens demonstrate good clinical efficacy and manageable safety in the treatment of RRMM.

With the continuous development of genomics and precision medicine,targeted therapy and immunotherapy targeting biomarkers have ush-ered in a new era of anti-tumor therapy.However,due to the heterogeneity of tumor cells and the variability of tumor microenvironment,there are still significant differences in response to the same drug even in patient populations with the same bio-marker enrichment.By combining omics data with drug sensitivity algorithms,the response of anti-tu-mor drugs can be predicted and transformed into personalized diagnosis and treatment strategies re-quired for precision medicine,which is expected to improve the effectiveness of anti-tumor drugs in clinical treatment.Currently,machine learning is one of the commonly used modeling algorithms for predicting the response of anti-tumor drugs.How-ever,due to differences in input data and algorithm construction methods,there is currently a lack of comprehensive literature review in this field.There-fore,this article provides a review of machine learning algorithms for predicting anti-tumor drug responses,summarizing publicly available cell ge-nome characterization datasets,machine learning algorithms,and evaluation indicators in drug re-sponse prediction,as well as the current situation and challenges faced in clinical applications,in or-der to provide methodological references for the main research problems and potential solutions of machine learning algorithms in the field of drug re-sponse prediction.

Objectives:To analyze whether there is a causal relationship between plasma proteins and the risk of coronary atherosclerosis(CAS)based on a two-sample Mendelian randomization(MR)analysis and to identify potential therapeutic targets for CAS.Methods:Single nucleotide polymorphisms(SNP)associated with plasma proteins from the UK Biobank Pharmacoproteomics Program(UKB-PPP)database were used as instrumental variables and outcome data were obtained from genome-wide association study databases.The Wald ratio method and inverse variance weighted(IVW)method in two-sample MR were employed as the primary approaches to assess the causal relationship between plasma proteins and CAS.Colocalization analysis was conducted as a sensitivity analysis to ensure the robustness of the MR findings.Results:A total of 132 plasma proteins were found to have causal associations with an increased risk of CAS.Colocalization analysis revealed that 12 plasma proteins shared genetic variants with CAS.Among them,Proprotein convertase subtilise/kexin type 9(PCSK9)(OR=1.23,95%CI:1.15-1.32,P<0.01)and neurocan(NCAN)(OR=1.35,95%CI:1.21-1.52,P<0.01)exhibited posterior probability of hypothesis4(PPH4)values<0.80 in the colocalization analysis,indicating strong support for colocalization and suggesting their potential as primary plasma protein drug targets for CAS.Conclusions:PCSK9 is associated with an increased risk of CAS and is confirmed as a therapeutic target for CAS.NCAN emerges as another potential therapeutic target for CAS.

The study and control of elemental impurities are crucial for ensuring the quality and safety of drugs.The ICH has published the Q3D guideline as a globally harmonised approach for the research and control of elemental impurities in drugs.In accordance with the requirements of the ICH Q3D guideline for risk assessment and control of elemental impurities,how to carry out the development and validation of detecting methods for elemental impurities is important to analysts.In this research,the key points of ICP-AES and ICP-MS method development are summarized,including the determination of the types and limits of the elements to be measured,the selection of pretreatment methods,interferences and corrections;the validation requirements for the two methods in ICH Q2(R2)and different pharmacopoeial general rules are analyzed,and the evaluation methods of each validation experiments are compared in detail.This paper can provide a reference for the development and validation of detecting methods for elemental impurities,and research ideas for related research workers.

With the continuous development of genomics and precision medicine,targeted therapy and immunotherapy targeting biomarkers have ush-ered in a new era of anti-tumor therapy.However,due to the heterogeneity of tumor cells and the variability of tumor microenvironment,there are still significant differences in response to the same drug even in patient populations with the same bio-marker enrichment.By combining omics data with drug sensitivity algorithms,the response of anti-tu-mor drugs can be predicted and transformed into personalized diagnosis and treatment strategies re-quired for precision medicine,which is expected to improve the effectiveness of anti-tumor drugs in clinical treatment.Currently,machine learning is one of the commonly used modeling algorithms for predicting the response of anti-tumor drugs.How-ever,due to differences in input data and algorithm construction methods,there is currently a lack of comprehensive literature review in this field.There-fore,this article provides a review of machine learning algorithms for predicting anti-tumor drug responses,summarizing publicly available cell ge-nome characterization datasets,machine learning algorithms,and evaluation indicators in drug re-sponse prediction,as well as the current situation and challenges faced in clinical applications,in or-der to provide methodological references for the main research problems and potential solutions of machine learning algorithms in the field of drug re-sponse prediction.