Objective On the basis of the theory of the tumor metastatic state, this work aims to explore the metastasis trajectories and peripheral immune response patterns in patients with non-small cell lung cancer (NSCLC) receiving vital qi-strengthening and toxin-eliminating therapy (Fuzheng Juandu) and reveal the value of peripheral immunity in assessing vital qi deficiency during metastasis. Methods A retrospective study design was adopted. Patients with NSCLC admitted from January 1, 2018, to October 31, 2025 were enrolled. Data on metastasis status and peripheral immune indices were collected at the follow-up time points of one, two, three, and five years. Group-based trajectory modeling was used to fit metastasis trajectories, and the independent predictors of trajectory grouping were analyzed through multifactor and multiclassification logistic regression. Results A total of 184 patients with NSCLC were included, and three different groups of metastasis trajectories were identified, namely, the low-risk stable (46.74%, n=86), low-risk rising (13.59%, n=25), and high-risk stable (39.67%, n=73) groups. Univariate analysis revealed substantial differences in peripheral immune indices among the three trajectory groups. Multivariate analysis showed that TNM stage, absolute neutrophil count, lymphocyte count, absolute monocyte count, platelet volume, SAA:CRP ratio, and NLR were independent predictors of the low-risk rising group, whereas TNM stage, absolute monocyte count, basophil count, SII, and NLR were independent predictors of the high-risk stable group. Conclusion Under Fuzheng Juandu therapy, patients with NSCLC exhibit three distinct metastasis trajectories, each corresponding to a specific peripheral immune response pattern. Elucidating these peripheral immune response patterns may facilitate the objective quantification of the degree of vital qi deficiency, prediction of future metastasis risk, and providing an important reference for the individualized clinical implementation of Fuzheng Juandu therapy.

This article investigates the collaborative management of metabolic dysfunction-associated fatty liver disease （MAFLD） and chronic kidney disease （CKD）. As major public health issues worldwide， MAFLD and CKD are closely related in terms of epidemiology， pathogenesis， and management strategies， and however， there are still many challenges in the multidisciplinary collaborative management of the two diseases. This article systematically elaborates on the epidemiology of MAFLD and CKD， summarizes their common risk factors such as metabolic disorder， genetic susceptibility， and active metabolites， and reviews the mutual screening strategies and combined management models based on noninvasive imaging， serum markers， FIB-4 score， and liver stiffness measurement. In addition， this article summarizes the advances in the application of lifestyle intervention and new drugs （such as GLP-1 receptor agonists and SGLT-2 inhibitors） and emphasizes the importance of multidisciplinary collaboration in improving the prognosis of patients. Due to the close association between MAFLD and CKD， their joint management is crucial， and therefore， it is necessary to establish a multidisciplinary collaboration mechanism and implement the measures of precise screening， comprehensive treatment， and long-term monitoring， so as to improve the prognosis of patients and reduce the risk of complications. Finally， this article proposes that in the future， more effective combined treatment regimens should be explored to expand the clinical options for the co-management of the liver and the kidney.

Objective To explore the influence of heparin sodium on early recovery after lumbar surgery and the risk factors of deep vein thrombosis(DVT)of lower extremities,and build a prediction model for DVT after lumbar surgery based on the risk factors.Methods A total of 276 patients who underwent lumbar surgery and were treated with heparin sodium in The First Affiliated Hospital of Naval Medical University from January 2020 to January 2023 were selected as research objects.Activated partial thromboplastin time(APTT),prothrombin time(PT)and functional indexes of lumbar spine were recorded before and after treatment.DVT of lower extremities was detected by ultrasound during postoperative hospitalization,and then the patients were divided into DVT group and non-DVT group.The clinical data and laboratory indicators of all patients were collected.The risk factors of DVT after lumbar surgery were analyzed by logistic regression analysis.A postoperative DVT risk prediction model based on Logistic regression analysis was constructed and validated.Results APTT and PT after operation were higher than those before operation(P<0.05).The score of Oswestry Disability Index(ODI)3 months after surgery was lower than that before surgery(t=30.661,P<0.05).The incidence of DVT was 14.86%(41/276).Blood transfusion,bed rest≥5 d,proportion of general anesthesia,intraoperative blood loss,body mass index(BMI)and D-dimer level in the DVT group were higher than those in the non-DVT group(P<0.05).Logistic regression analysis showed that intraoperative blood loss,BMI,abnormal D-dimer level,intraoperative blood transfusion,and bed rest≥5 d were all risk factors for DVT after lumbar surgery(P<0.05).logit(P)=9.762+1.425×intraoperative blood loss+1.212×BMI+0.856×intraoperative blood transfusion+1.105×bed rest+1.671×D-dimer.Hosmer-Lemeshow test showed that the model had a high goodness of fit(χ2=4.025,P=0.473).The sensitivity,specificity and area under curve(AUC)of the constructed prediction model for postoperative DVT were 91.80%,70.40%and 0.836(95%CI:0.698-0.948),respectively.Conclusion Heparin sodium can improve blood circulation and lumbar function and prolong clotting time in patients with lumbar surgery.Intraoperative blood loss,BMI,abnormal D-dimer,intraoperative blood transfusion,and bed rest≥5 d are all risk factors for the occurrence of DVT after lumbar surgery.The prediction model based on the above risk factors has high predictive value for the occurrence of DVT after lumbar surgery.

ObjectiveTo investigate the accuracy of multiple discriminant analysis （MDA） versus fibrosis-4 （FIB-4） in assessing liver fibrosis degree in patients with HBV infection， as well as the possibility of MDA as an indicator for disease progression. MethodsA total of 263 patients with HBV infection who underwent liver biopsy in The First Affiliated Hospital of Guangxi Medical University from April 2010 to April 2024 were included， and their clinical data were collected. According to the results of pathological examination， they were divided into non-significant fibrosis group （F<2） with 126 patients and significant fibrosis group （F≥2） with 137 patients. The correlation of MDA and FIB-4 with liver fibrosis degree was analyzed， and MDA and FIB-4 were compared in terms of their accuracy in assessing significant liver fibrosis. A total of 62 patients completed follow-up， and according to the presence or absence of progression to liver cirrhosis at the last follow-up visit， they were divided into progressive group with 21 patients and non-progressive group with 41 patients； the efficacy of MDA and FIB-4 in diagnosing disease progression was analyzed and compared. The independent-samples t test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the Kruskal-Wallis H test was used for comparison between multiple groups， and the Bonferroni method was used for further comparison between two groups. The chi-square test was used for comparison of categorical data. The Spearman’s correlation coefficient was used for correlation analysis. The Wilcoxon signed rank sum test was used for the analysis of baseline data and data at the end of follow-up， and the binary Logistic regression analysis was used to investigate the influencing factors for progression to liver cirrhosis. The receiver operating characteristic （ROC） curve was used to investigate the diagnostic efficacy of indicators， the Z-test was used for comparison of the area under the ROC curve （AUC）， and the paired chi-square test was used for comparison of the sensitivity， specificity， and accuracy of the two indicators. ResultsThe correlation coefficient between FIB-4 and liver fibrosis degree was 0.378， while the correlation coefficient between MDA and liver fibrosis degree was -0.325 （both P<0.001）. FIB-4 had an AUC of 0.688， a sensitivity of 64.96%， a specificity of 68.87%， a positive predictive value of 67.42%， a negative predictive value of 63.36%， an accuracy of 65.40%， and a cut-off value of 1.01， while MDA had an AUC of 0.653， a sensitivity of 52.55%， a specificity of 78.57%， a positive predictive value of 72.73%， a negative predictive value of 60.37%， an accuracy of 65.02%， and a cut-off value of 0.29， suggesting that compared with FIB-4， MDA had a lower sensitivity （P=0.004） and a higher specificity （P=0.001）. The progressive group had a significantly higher age than the non-progressive group at baseline （t=2.611， P=0.011）. For the progressive group， there was an increase in FIB-4 and a reduction in MDA from baseline to the end of follow-up （both P<0.001）， while the non-progressive group showed no significant changes （both P>0.05）. The multivariate Logistic regression analysis showed that aspartate aminotransferase （odds ratio ［OR］=0.940， 95% confidence interval ［CI］： 0.885‍ ‍—‍ ‍0.998， P<0.05） and MDA （OR=0.445， 95%CI： 0.279‍ ‍—‍ ‍0.710， P<0.001） were independent influencing factors for disease progression. MDA had an AUC of 0.893 and an optimal cut-off value of -0.01 in diagnosing the disease progression of liver cirrhosis. ConclusionMDA has a comparable accuracy to FIB-4 in the diagnosis of significant liver fibrosis， and MDA<-0.01 has a high accuracy in diagnosing the progression of liver fibrosis to liver cirrhosis， which can help to reduce the need for liver biopsy in clinical practice.

Objective:To analyze the changes of serum metabolites in patients with acute pancreatitis (AP) by non-targeted metabolomics method.Methods:Serum samples and clinical data of 15 AP patients hospitalized in the Affiliated Hospital of Jiangnan University from August to September 2024 were collected and included in the AP group, including 9 males and 6 females, aged (55.4±15.3) years. The serum and clinical data of 25 patients with colon polyps in the same hospital during the same period of time were collected, including 15 males and 10 females, aged (61.2±11.5) years, and were included in the control group. Serum metabolomic detection was performed using the ultra-high performance liquid chromatography tandem Fourier transform mass spectrometer. The modeling method was orthogonal partial least square discriminant analysis, and principal component analysis was performed on the data matrix to screen the differential metabolites in serum of AP patients. The Kyoto Encyclopedia database of Genes and Genomes was used to annotate differential metabolites, and the pathway of differential metabolite enrichment was analyzed by software.Results:The principal component analysis showed that the contribution ratio of the first principal component was 15.1%, the proportion of the second principal component was 10.8%, and the total proportion of the two was 25.9%. In principal component analysis, two groups of samples can be clearly distinguished and show obvious clustering characteristics. According to the analysis of OPLS-DA model, there were significant differences in serum metabolic profiles between AP group and control group. There were 683 differentially expressed metabolites between the two groups, with 367 differentially expressed metabolites up-regulated compared with the control group and 316 differentially expressed metabolites down-regulated compared with the control group. It is mainly Phosphatidic Acid (Lte4/8: 0) (+ 218%), Omeprazole Sulphone (-38%), and 2-(Propylthio) Nicotinic Acid (2-propyl thionicotinic acid) (-58%), Gein (salicyricetin) (-47%) and so on. Pathway enrichment analysis showed that the differential metabolites in AP patients were mainly concentrated in citric acid cycle, arginine biosynthesis and glycerophospholipid metabolism pathways.Conclusion:Serum metabolites in AP patients change significantly, including citric acid cycle, arginine biosynthesis, glycerophospholipid metabolism.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Objective:To investigate the association between peripheral immune function status and lung cancer metastasis,and to identify peripheral blood immune biomarkers for'Deficiency of Vital Qi'assessment in lung cancer metastasis.Methods:A retrospective analysis was conducted on peripheral blood immune markers collected before treatment from lung cancer patients admitted into Shanghai Municipal Hospital of Traditional Chinese Medicine,affiliated to Shanghai University of Traditional Chinese Medicine,between March 2023 and April 2025.Patients were categorized into the non-metastatic and the metastatic groups based on the presence of distant metastasis,and the differences in the expressions of immune cells and cytokines between groups were compared.Peripheral blood immune markers with P<0.05 in univariate analysis were incorporated into a multivariate binary logistic regression model to identify independent predictors of lung cancer metastasis.Results:A total of 193 lung cancer patients were included(101 in the non-metastatic group and 92 in the metastatic group).There were no statistically significant differences between the two groups in terms of gender,age,smoking history,drinking history,or pathological type(all P>0.05).Univariate analysis revealed significant differences in multiple immune markers between the non-metastatic and metastatic groups(all P<0.05),including:lymphocyte count,CD3+,CD4+,and CD8+T,CD19+B cells,absolute counts of CD3-CD16+CD56+NK cells,percentages of Treg cells,CD8+CD28+Treg cells,G-MDSC,and CD3-CD16+CD56+dim NK cells,and levels of cytokine IL-1β,IL-6,and IL-10.Binary logistic regression analysis of differential indicators suggested that the percentage of Treg cells and CD8+CD28+Treg cells in peripheral blood were independent predictors of distant metastasis in lung cancer(OR=1.193,95%CI[1.047,1.36],P<0.01;OR=0.978,95%CI[0.957,0.999],P<0.05).Conclusion:Peripheral blood immune dysfunction is the biological basis for'qi deficiency'in lung cancer metastasis.This study quantitatively demonstrates the correlation between peripheral immune function status and lung cancer metastasis,providing empirical evidence for the theories of'qi deficiency and hidden toxicity'and'metastatic state of tumors'.

Objective:To screen the optimal machine learning model for predicting the recurrence condition of hepatocellular carcinoma (HCC) at different time points post-surgery, based on the cutoff value of the Ki67 positive proliferation index condition calculated from recurrence-free survival and combined with various clinical features.Methods:retrospective study included initially treated patients with solitary HCC who underwent radical surgery at the Fifth Medical Center of the PLA General Hospital from January 2013 to March 2023. Data included general clinical data, preoperative laboratory parameters, and surgical pathology information about the subjects. The postoperative recurrence status was assessed by querying the medical record system or by telephone follow-up. The Ki67 positive index cutoff value was determined by the X-tile software based on the patient's recurrence-free survival status and time analysis. Survival rates were calculated using the Kaplan-Meier method, and survival curves were plotted. The study population was randomly divided into training and testing groups in a 7:3 ratio using a computer-generated random number method. The minimum redundancy maximum relevance (mRMR) method was used for feature variable selection. Predictive models for postoperative HCC recurrence conditions in patients with HCC were constructed using random forest, support vector machine, logistic regression, and gradient boosting decision tree machine learning algorithms. Inter-group comparisons for continuous data were performed using the t-test or Mann-Whitney U test. Inter-group comparisons of enumeration data were performed using the Pearson χ2 test, continuity-corrected χ2 test, or Fisher's exact test. Results:The cutoff values for the Ki67 positivity index were 0.3 and 0.5 in 510 cases, with a follow-up time ranging from 1.2 to 11.4 years (median: 6.2 years). The recurrence-free survival time was between 1 and 135 months (median: 32 months), with recurrence-free survival rates post-surgery at 1, 2, 3, and 5 years were 87.5%, 77.1%, 61.2%, and 54.5%, respectively. The top five variables predicted HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years, in accordance with information obtained by the mRMR screen out. The Ki67 positivity index screened a successfully constructed machine learning model to predict HCC recurrence and non-recurrence conditions following surgical follow-up at 6 months, 1 year, 2 years, and beyond 2 years. The machine learning model based on the gradient boosting decision tree algorithm had the best prediction performance among them (areas under the receiver operating characteristic curves for predicting HCC recurrence within six months in the training and validation sets were 0.996 and 0.946, and accuracies were 0.972 and 0.935, respectively).Conclusion:A machine learning model was successfully constructed using the Ki67 positivity index combined with four readily available clinical features to predict HCC recurrence. The machine learning model based on the gradient boosting decision tree algorithm demonstrated the best performance in terms of predicting HCC recurrence within six months after surgery.

Objective:To analyze the changes of serum metabolites in patients with acute pancreatitis (AP) by non-targeted metabolomics method.Methods:Serum samples and clinical data of 15 AP patients hospitalized in the Affiliated Hospital of Jiangnan University from August to September 2024 were collected and included in the AP group, including 9 males and 6 females, aged (55.4±15.3) years. The serum and clinical data of 25 patients with colon polyps in the same hospital during the same period of time were collected, including 15 males and 10 females, aged (61.2±11.5) years, and were included in the control group. Serum metabolomic detection was performed using the ultra-high performance liquid chromatography tandem Fourier transform mass spectrometer. The modeling method was orthogonal partial least square discriminant analysis, and principal component analysis was performed on the data matrix to screen the differential metabolites in serum of AP patients. The Kyoto Encyclopedia database of Genes and Genomes was used to annotate differential metabolites, and the pathway of differential metabolite enrichment was analyzed by software.Results:The principal component analysis showed that the contribution ratio of the first principal component was 15.1%, the proportion of the second principal component was 10.8%, and the total proportion of the two was 25.9%. In principal component analysis, two groups of samples can be clearly distinguished and show obvious clustering characteristics. According to the analysis of OPLS-DA model, there were significant differences in serum metabolic profiles between AP group and control group. There were 683 differentially expressed metabolites between the two groups, with 367 differentially expressed metabolites up-regulated compared with the control group and 316 differentially expressed metabolites down-regulated compared with the control group. It is mainly Phosphatidic Acid (Lte4/8: 0) (+ 218%), Omeprazole Sulphone (-38%), and 2-(Propylthio) Nicotinic Acid (2-propyl thionicotinic acid) (-58%), Gein (salicyricetin) (-47%) and so on. Pathway enrichment analysis showed that the differential metabolites in AP patients were mainly concentrated in citric acid cycle, arginine biosynthesis and glycerophospholipid metabolism pathways.Conclusion:Serum metabolites in AP patients change significantly, including citric acid cycle, arginine biosynthesis, glycerophospholipid metabolism.

Primary hepatic cancer is one of the major problems that need to be solved urgently in the field of public health, seriously endangering the life and health of Chinese people. Its treatment mode is multidisciplinary participation and synergy of multiple therapeutic methods. Even though there are many common clinical treatments for liver cancer in China, its therapeutic outcome is still unsatisfactory. yttrium-90 has been applied for more than 20 years, and a large amount of foreign clinical data have been accumulated. Combining the latest literature and clinical practice, the authors describe the clinical application and research progress of yttrium-90 micro-sphere selective internal radiation therapy in primary liver cancer.