OBJECTIVE: To evaluate the effect of palliative care on drug use, medical service utilization and medical expenditure of patients with advanced cancer. METHODS: A cohort of patients including pal-liative care and standard care was constructed using the medical records of the patients in Peking University Cancer Hospital from 2018 to 2020, and coarsened exact matching was used to match the two groups of patients. The average monthly opioid consumption, hospitalization rate, intensive care unit (ICU) rate and operation rate, and the average monthly total cost were selected to evaluate drug use, medical service utilization and medical expenditure. Chi-square test and Wilcoxon signed rank test were used to compare the differences between the two groups before and after exposure and the change in the palliative care group. The net impact of palliative care on the patients was calculated using the difference-in-differences analysis. RESULTS: In this study, 180 patients in the palliative care group and 3 101 patients in the stan-dard care group were finally included in the matching, and the matching effect of the two groups was good (L₁ < 0.1). Before and after exposure, the average monthly opioid consumption in the palliative care group was significantly higher than that in the standard care group (Before exposure: 0.3 DDD/person-month vs. 0.1 DDD/person-month, P < 0.01; After exposure: 0.7 DDD/person-month vs. 0.1 DDD/person-month, P < 0.01; DDD refers to defined daily dose), palliative care significantly increased the average monthly opioid consumption in the patients (0.3 DDD/person-month, P < 0.01). The hospitalization rate (48.9% vs. 74.3%, P < 0.01) and operation rate (3.9% vs. 8.8%, P < 0.01) of the patients in palliative care group were significantly lower than those in standard care group, and the ICU rate became similar between the two groups (1.1% vs. 1.6%, P=0.634). Palliative care significantly reduced the patients ' hospitalization rate (-25.6%, P < 0.01), ICU rate (-4.9%, P < 0.01) and operation rate (-14.5%, P < 0.01). Before and after exposure, the average monthly total costs of pal-liative care group were slightly higher than those of standard care group (Before exposure: 20 092.3 yuan vs. 19 132.8 yuan, P=0.725; After exposure: 9 719.8 yuan vs. 8 818.8 yuan, P=0.165). Palliative care increased the average monthly total cost by 2 208.8 yuan, but it was not statistically significant (P=0.316). CONCLUSION Palliative care can increase the opioid consumption in advanced cancer patients, reduce the rates of hospitalization, ICU and surgery, but has no significant effect on medical expenditure.
Humans ; Palliative Care/economics* ; Neoplasms/drug therapy* ; Analgesics, Opioid/economics* ; Male ; Female ; Middle Aged ; Aged ; Hospitalization/economics* ; Intensive Care Units/statistics & numerical data* ; Health Expenditures/statistics & numerical data* ; Adult ; Drug Utilization/statistics & numerical data* ; Patient Acceptance of Health Care/statistics & numerical data*

Gut microbial communities are likely remodeled in tandem with accumulated physiological decline during aging, yet there is limited understanding of gut microbiome variation in advanced age. Here, we performed a metagenomics-based enterotype analysis in a geographically homogeneous cohort of 367 enrolled Chinese individuals between the ages of 60 and 94 years, with the goal of characterizing the gut microbiome of elderly individuals and identifying factors linked to enterotype variations. In addition to two adult-like enterotypes dominated by Bacteroides (ET-Bacteroides) and Prevotella (ET-Prevotella), we identified a novel enterotype dominated by Escherichia (ET-Escherichia), whose prevalence increased in advanced age. Our data demonstrated that age explained more of the variance in the gut microbiome than previously identified factors such as type 2 diabetes mellitus (T2DM) or diet. We characterized the distinct taxonomic and functional profiles of ET-Escherichia, and found the strongest cohesion and highest robustness of the microbial co-occurrence network in this enterotype, as well as the lowest species diversity. In addition, we carried out a series of correlation analyses and co-abundance network analyses, which showed that several factors were likely linked to the overabundance of Escherichia members, including advanced age, vegetable intake, and fruit intake. Overall, our data revealed an enterotype variation characterized by Escherichia enrichment in the elderly population. Considering the different age distribution of each enterotype, these findings provide new insights into the changes that occur in the gut microbiome with age and highlight the importance of microbiome-based stratification of elderly individuals.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Bacteroides ; China ; Diabetes Mellitus, Type 2/microbiology* ; Escherichia coli/classification* ; Gastrointestinal Microbiome/genetics* ; Metagenomics ; East Asian People

ObjectiveTo explore the construction of a machine learning model for the diagnosis of traditional Chinese medicine （TCM） syndromes in chronic cough and the optimization of this model using the Voting ensemble algorithm. MethodsA retrospective analysis was conducted using clinical data from 921 patients with chronic cough treated at the Respiratory Department of Dongfang Hospital， Beijing University of Chinese Medicine. After standardized processing， 84 clinical features were extracted to determine TCM syndrome types. A specialized dataset for TCM syndrome diagnosis in chronic cough was formed by selecting syndrome types with more than 50 cases. The synthetic minority over-sampling technique （SMOTE） was employed to balance the dataset. Four base models， logistic regression （LR）， decision tree （dt）， multilayer perceptron （MLP）， and Bagging， were constructed and integrated using a hard voting strategy to form a Voting ensemble model. Model performance was evaluated using accuracy， recall， precision， F1-score， receiver operating characteristic （ROC） curve， area under the curve （AUC）， and confusion matrix. ResultsAmong the 921 cases， six syndrome types had over 50 cases each， phlegm-heat obstructing the lung （294 cases）， wind pathogen latent in the lung （103 cases）， cold-phlegm obstructing the lung （102 cases）， damp-heat stagnating in the lung （64 cases）， lung yang deficiency （54 cases）， and phlegm-damp obstructing the lung （53 cases）， yielding a total of 670 cases in the specialized dataset. High-frequency symptoms among these patients included cough， expectoration， odor-induced cough， throat itchiness， itch-induced cough， and cough triggered by cold wind. Among the four base models， the MLP model showed the best diagnostic performance （test accuracy： 0.9104； AUC： 0.9828）. Compared with the base models， the Voting ensemble model achieved superior performance with an accuracy of 0.9289 on the training set and 0.9253 on the test set， showing a minimal overfitting gap of 0.0036. It also achieved the highest AUC （0.9836） in the test set， outperforming all base models. The model exhi-bited especially strong diagnostic performance for damp-heat stagnating in the lung （AUC： 0.9984） and wind pathogen latent in the lung （AUC： 0.9970）. ConclusionThe Voting ensemble algorithm effectively integrates the strengths of multiple machine learning models， resulting in an optimized diagnostic model for TCM syndromes in chronic cough with high accuracy and enhanced generalization ability.

Background: Twenty-four-hour urinary free cortisol (UFC) measurement is the initial diagnostic test for Cushing’s syndrome (CS). We compared UFC determination by both direct and extraction immunoassays using Abbott Architect, Siemens Atellica Solution, and Beckman DxI800 with liquid chromatography-tandem mass spectrometry (LC-MS/MS). In addition, we evaluated the value of 24-hr UFC measured by six methods for diagnosing CS. Methods: Residual 24-hr urine samples of 94 CS and 246 non-CS patients were collected.A laboratory-developed LC-MS/MS method was used as reference. UFC was measured by direct assays (D) using Abbott, Siemens, and Beckman platforms and by extraction assays (E) using Siemens and Beckman platforms. Method was compared using Passing–Bablok regression and Bland–Altman plot analyses. Cut-off values for the six assays and corresponding sensitivities and specificities were calculated by ROC analysis. Results: Abbott-D, Beckman-E, Siemens-E, and Siemens-D showed strong correlations with LC-MS/MS (Spearman coefficient r = 0.965, 0.922, 0.922, and 0.897, respectively), while Beckman-D showed weaker correlation (r = 0.755). All immunoassays showed proportionally positive bias. The areas under the curve were 0.975 for Abbott-D, 0.972 for LCMS/MS, 0.966 for Siemens-E, 0.948 for Siemens-D, 0.955 for Beckman-E, and 0.877 for Beckman-D. The cut-off values varied significantly (154.8–1,321.5 nmol/24 hrs). Assay sensitivity and specificity ranged from 76.1% to 93.2% and from 93.0% to 97.1%, respectively. Conclusions Commercially available immunoassays for measuring UFC show different levels of analytical consistency compared to LC-MS/MS. Abbott-D, Siemens-E, and Beckman-E have high diagnostic accuracy for CS.

ObjectiveTo investigate the risk factors for liver cancer in patients with chronic hepatitis B （CHB） in the Qidong Chronic Hepatitis B cohort， and to construct a nomogram model for predicting the risk of liver cancer in CHB patients. MethodsA structured questionnaire survey was conducted among the CHB patients， aged ≥18 years， who attended the outpatient service of Qidong Third People’s Hospital from January 1 to December 31， 2016. The onset of liver cancer was defined as the primary outcome， and the outcomes of the cohort were obtained from Qidong Cancer Registry. Baseline clinical features were compared ；between the liver cancer group and the non-liver cancer group. The independent-samples t test or the Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. The Cox regression model was used to analyze the risk factors for liver cancer in CHB patients and calculate their hazard ratio （HR） and 95% confidence interval （CI）； the variables with statistical significance in the univariate Cox regression analysis were included in the LASSO regression analysis， and then the variables obtained were included in the multivariate Cox regression analysis to establish a predictive model. The nomogram was used to visualize the complex model. The receiver operating characteristic （ROC） curve， index of concordance （C-index）， and the calibration curve were used to assess the predictive efficacy of the model， and the decision curve was used to evaluate the clinical practicability of the nomogram. ResultsA total of 1 479 CHB patients were selected， among whom 58 patients with a confirmed diagnosis of liver cancer， 15 with missing data on testing indicators， and 164 with missing data on important information in the questionnaire were excluded， and finally 1 242 subjects were included in the study. Up to December 31， 2023， there were 67 new cases of liver cancer after a median follow-up time of 7.71 years， and the incidence density of liver cancer was 729.78/100，000 person-years. There were significant differences between the liver cancer group and the non-liver cancer group in age， sex， educational level， liver cirrhosis， duration of liver cirrhosis， history of diabetes mellitus， albumin， total bilirubin （TBil）， direct bilirubin， aspartate aminotransferase， aspartate aminotransferase， gamma-glutamyl transpeptidase （GGT）， and alkaline phosphatase （all P<0.05）. The multivariate Cox regression analysis showed that the increase in age （HR=1.07， 95%CI： 1.05‍ ‍—‍ ‍1.10， P<0.001）， a relatively high level of TBil （HR=1.98， 95%CI： 1.15‍ ‍—‍ ‍3.42， P=0.014）， a relatively high level of GGT （HR=2.41， 95%CI： 1.43‍ ‍—‍ ‍4.08， P=0.001）， and a long duration of liver cirrhosis （HR=1.09， 95%CI： 1.02‍ ‍—‍ ‍1.15， P=0.009） were independent risk factors for liver cancer in CHB patients. A nomogram prediction model was constructed based on the above four indicators， with an area under the ROC curve of 0.790， 0.845， and 0.829， respectively， in predicting the risk of liver cancer in CHB patients at 1， 3， and 5 years， and the bootstrap resampling method was used for internal validation and showed a C-index of 0. 778. The calibration curve showed that the prediction model had good stability， and the decision curve showed that it had certain clinical practicability. ConclusionThe increase in age， relatively high levels of TBil and GGT， and a long duration of liver cirrhosis are independent risk factors for liver cancer in CHB patients， and the nomogram model constructed based on these factors has a good predictive value and can be used in clinical practice to help develop strategies for the long-term monitoring of liver cancer.

Objective To investigate the expression,synergistic relationship and clinical significance of alcohol de-hydrogenase(ADH1A)and vascular endothelial growth factor-A(VEGFA)in hepatocellular carcinoma(HCC).Methods The expression and correlation of ADH1A and VEGFA in HCC and adjacent normal tissues were ana-lyzed by GEPIA.TCGA and GSEA were used to analyze the pathway of ADH1A in HCC.The clinical and patho-logical data of 84 patients with HCC were collected,and 54 patients with paracancer normal tissue samples were se-lected as controls to analyze the correlation between ADH1A and VEGFA and clinicopathological parameters of HCC.Immunohistochemistry was used to detect the protein expression of ADH1A and VEGFA in cases and con-trols,and the correlation between the expression of ADH1A and VEGFA and the clinical progression and prognosis of patients with HCC was analyzed based on clinical pathological parameters and Kaplan-Meier.Results Bioinfor-matics analysis found that ADH1A was low-expressed in HCC and VEGFA was highly expressed in HCC,and there was a negative correlation between the two(P<0.001);immunohistochemical detection results showed that the expression of ADH1A in HCC tissue was lower than that in normal tissue adjacent to cancer(P<0.01)while the expression rate of VEGFA in HCC tissue was significantly higher than that of normal tissue adjacent to cancer(P<0.01);The recurrence rate of vascular thrombus and HCC patients in HCC group with high expression of ADH1A was lower(P<0.05).The proportion of tumor diameter>5 cm,high TNM stage,microsatellite and G2-G3 dif-ferentiation in HCC tissues in VEGFA high expression group was higher(P<0.05).Kaplan-Meier survival analy-sis showed that patients with high ADH1A expression and low VEGFA expression had a higher five-year survival rate.Conclusion Low expression of ADH1A and high expression of VEGFA in tumor tissues of patients with HCC indicate tumor progression and can be used as one of the prognostic evaluation indicators for patients with HCC.

Objective To propose a motion performance-based adaptive assistance strategy to balance the differences in movement intensity caused by varying motor abilities in dual-person upper limb rehabilitation training. Methods A competitive interactive task suitable for dual-person rehabilitation training was designed.Two kinematic evaluation metrics were introduced,and fuzzy logic was applied to comprehensively assess the patients'motor performance.An adaptive control system framework was constructed to dynamically adjust the robot's assistance level through a difference-based adaptive step-size piecewise function.Four healthy persons were recruited ran-domly to test the system. Results The proposed strategy could adaptively adjust the level of assistance based on the participants'motor perfor-mance,balancing the differences in motor abilities between them and enabling both parties to engage at a similar competitive level. Conclusion The motion performance-based adaptive assistance strategy and adaptive control system framework may bal-ance the skill levels between participants with different motor abilities,avoiding the imbalance of training intensi-ty during dual-person interactive rehabilitation.

Objective:To estimate the longitudinal association between serum lipid biomarkers and the development of cardiometabolic multimorbidity (CMM) in middle-aged and old adults (≥45) in China, while examining effect differences among degree of dyslipidemia aggregation and various dyslipidemia combination patterns.Methods:Based on data from the China Health and Retirement Longitudinal Study (2011-2018), logistic regression analysis was used to evaluate the associations of TC, LDL-C, HDL-C, TG (4 forms of dyslipidemias), degree and pattern of dyslipidemia combination with CMM. We also used restricted cubic splines to show the dose-response associations between 4 lipid biomarkers and CMM development.Results:Of the 6 522 participants included, 590 (9.05%) developed CMM. After adjusting for covariates, all 4 forms of dyslipidemias were positively associated with CMM development (high TC: OR=1.33, 95% CI: 1.03-1.71; high LDL-C: OR=1.35, 95% CI: 1.05-1.75; low HDL-C: OR=1.45, 95% CI: 1.19-1.77; high TG: OR=1.50, 95% CI: 1.20-1.88). The U-shaped dose-response relationship between LDL-C and CMM development was observed ( P for non-linear =0.022). The odds of CMM increased with the increase of dyslipidemias forms, which was highest in those with ≥3 forms of dyslipidemias ( OR=2.02, 95% CI: 1.33-3.06). In various dyslipidemia form combinations, the possibility of CMM development was highest in those with high TC, high LDL-C and low HDL-C ( OR=3.54, 95% CI: 1.40-8.67). High TC and high LDL-C were significantly associated with CMM development in people without cardiometabolic diseases. Low HDL-C was positively associated with diabetes and CMM development in participants without cardiometabolic diseases, cardiovascular disease (CVD) followed by diabetes, and diabetes followed by CVD. High TG was positively associated with diabetes and CMM in participants without cardiometabolic diseases, and diabetes followed by CVD. Conclusions:A total of 4 forms of dyslipidemia were all independently associated with CMM development in middle-aged and old adults in China. The dose-response relationship between LDL-C level and CMM development was U-shaped. The aggregation of 4 forms of dyslipidemia were associated with the development of CMM. Low HDL-C and high TG were significantly associated with multiple patterns of cardiometabolic diseases development.

Objective To identify the influencing factors of operation time of hand-assisted laparoscopic living donor nephrectomy, and to analyze the relationship between influencing factors and the severity of postoperative complications. Methods Clinical data of 91 donors who underwent hand-assisted laparoscopic nephrectomy were retrospectively analyzed. The correlation between preoperative baseline data of donors and operation time was analyzed. The relationship between operation time and postoperative complications was assessed and the threshold of operation time was determined. Results Multiple donor renal arteries, thick perirenal and posterior renal fat, metabolic syndrome, high Mayo adhesive probability (MAP) score and Clavien-Dindo score prolonged the operation time. By analyzing the receiver operating characteristic (ROC) curve, we found that when the operation time was ≥138 min, the incidence of postoperative complications of donors was significantly increased (P<0.05). Conclusions For donors with multiple renal arteries, thick perirenal and posterior renal fat, metabolic syndrome and high MAP score and Clavien-Dindo score, experienced surgeons should be selected to make adequate preoperative preparation and pay close attention after surgery, so as to timely detect postoperative complications and reduce the severity of complications, enhance clinical prognosis of the donors.