Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Pelvic and acetabular fractures are one of the serious traumatic diseases, leading to a high rate of disability and fatality. Their operative principles are anatomical repositioning and rigid fixation to achieve early functional exercise and avoid complications. The updating modern technology has made precision and minimally invasion a trend in orthopedic surgery. An increasingly number of new technologies has been applied in clinical surgery, such as three-dimensional printing, three-dimensional navigation, and orthopedic robotics, each with its own characteristics. Of them, three-dimensional printing technology is more advantageous in terms of reducing surgical cost and risk, enhancing surgical efficiency, achieving surgical precision and reducing radiation exposure, as evidenced by a large number of clinical case reports and randomized controlled trials. This paper summarizes the current situation and assesses the prospects of three-dimensional printing technology in the diagnosis and treatment of pelvic and acetabular fractures in order to provide reference for orthopedic colleagues.

Natural killer (NK) cells are essential in controlling cancer and infection. However, little is known about the dynamics of the transcriptional regulatory machinery during NK cell differen-tiation. In this study, we applied the assay of transposase accessible chromatin with sequencing (ATAC-seq) technique in a home-developed in vitro NK cell differentiation system. Analysis of ATAC-seq data illustrated two distinct transcription factor (TF) clusters that dynamically regulate NK cell differentiation. Moreover, two TFs from the second cluster, FOS-like 2 (FOSL2) and early growth response 2 (EGR2), were identified as novel essential TFs that control NK cell maturation and function. Knocking down either of these two TFs significantly impacted NK cell differentia-tion. Finally, we constructed a genome-wide transcriptional regulatory network that provides a bet-ter understanding of the regulatory dynamics during NK cell differentiation.

Objective To evaluate the cardioprotection induced by combination of dexmedetomidine and limb ischemic preconditioning in the patients undergoing coronary artery bypass grafting (CABG) with cardiopulmonary bypass (CPB).Methods Eighty American Society of Anesthesiologists physical starus Ⅱ or Ⅲ patients of both sexes,aged 52-64 yr,weighing 51-78 kg,with New York Heart Association Ⅱ or Ⅲ,scheduled for elective CABG with CPB,were divided into 4 groups (n =20 each) using a random number table method:control group (group C),limb ischemic preconditioning group (group L),dexmedetomidine group (group D) and dexmedetomidine plus limb ischemic preconditioning group (group DL).Limb ischemic preconditioning was induced by 3 cycles of 5-min unilateral lower limb ischemia followed by 5-min reperfusion starting from 30 min before aortic clamping in L and DL groups.Dexmedetomidine was injected via the central vein in a loading dose of 1 μg/kg after induction of anesthesia,followed by an infusion of 0.4 μg · kg-1 · h-1 until the end of operation in D and DL groups.Venous blood samples were obtained immediately before aortic clamping,at the end of CPB and at the end of operation for determination of plasma concentrations of cardiac troponin Ⅰ (cTnI) by enzyme-linked immunosorbent assay.Myocardial tissues were obtained from the right auricle immediately before aortic clamping and at the end of CPB for determination of the expression of Bcl-2 and Bax (by immunohistochemistry) and apoptosis index (AI) (using TUNEL).The restoration of spontaneous heart beat was recorded.Bcl-2/Bax ratio was calculated.Results Compared with group C,the plasma cTnI concentrations were significantly decreased,the Bcl-2 expression was up-regulated,the Bcl-2/Bax ratio was increased,Bax expression was down-regulated,and AI was decreased in the other three groups (P＜0.05).Compared with L and D groups,the plasma cT-nI concentrations were significantly decreased,the Bcl-2 expression was up-regulated,the Bcl-2/Bax ratio was increased,Bax expression was down-regulated,and AI was decreased in group DL (P＜0.05).The rate of restoration of spontaneous heart beat was significantly increased in group DL as compared with the other three groups (P＜0.05).Conclusion Combination of dexmedetomidine and limb ischemic preconditioning can mitigate myocardial injury,it provides better efficacy than either alone,and the mechanism is related to inhibiting cell apoptosis in the patients undergoing CABG with CPB.

Objective: To investigate the methylation status and expression of FOXP3 in CD4+ T cells of patients with chronic hepatitis B (CHB). Methods: Peripheral blood mononuclear cells (PBMCs) from 59 CHB patients and 22 healthy controls (HC) were collected. The percentage of CD4+ CD25+ Foxp3+ Tregs in CD4+ T cells was estimated by flow cytometry. FOXP3 expression was measured by quantitative real time-polymerase chain reaction (RT-qPCR) and Western blotting. The methylation status of FOXP3 was determined by methylation-specific polymerase chain reaction. Results: The percentage of CD4+ CD25+ pFoxp3+ Tregs in CD4+ T cells, FOXP3 mRNA and protein expression levels were significantly higher in patients with CHB than HCs (P<0.05). Meanwhile, the methylation frequency of FOXP3 was significantly lower in CHB patients than HCs (P<0.05). FOXP3 mRNA levels and the percentage of CD4+ CD25+ Foxp3+ Tregs were significantly lower (P<0.05) in patients with gene methylation than those without. Conclusions Aberrant demethylation of FOXP3 gene existed in CD4+ T cells of CHB, which contributed to an elevation in FOXP3 expression and percentage of CD4+ CD25+ Foxp3+ Tregs. It might provide a new target for prevention and treatment of CHB.

Normal feed and stone-leading feed were used respectively to raise guinea pigs in the control group and stone-causing group. The dynamic changes of total cholesterol, mucoprotein, total phospholipid and total cholic acid were measured during various raising periods. The formation of crystal nucleus and the growth of gallstone were studied under polarzing microscope. It was found that the contents of total cholesterol, mucoprotein, total phospholipid and total cholic acid in the gallbladder bile of control group were nearly the same during the whole feeding process, and no shaped stone crystal was formed. In the stone-causing group, however, the contents of total cholesterol and mucoprotein gradually went up and the contents of total phospholipid and total cholic acid gradually went down. After 10 days' feeding, significant difference was seen,and after 25 days' feeding, highly significant difference was noted. With the increase of feeding days, the liquid crystal vesicles in the bile increased, became bigger, gathered in strings, and then formed liquid crystal cells. The stone crystal growth along these nuclei of bile liquid crystal cells spread out rapidly, and the micro-crystal grains formed further in number. It was shown that, during the process of gallbladder stone formation, bile liquid crystal cells form a basic kind of nucleus, and the gathering and merging of bile liquid crystal vesicles be the key to crystal growth. So cholesterol and mucoprotein play the role of nucleation-leading factors in enhancing the gathering and merging of liquid crystal vesicles, and phospholipid and cholate play the role of anti-nucleation during the formation of gallbladder stone.
Animals ; Cholesterol ; metabolism ; Crystallization ; Female ; Gallstones ; chemically induced ; metabolism ; Guinea Pigs ; Male ; Mucoproteins ; metabolism ; Phospholipids ; metabolism ; Random Allocation ; Taurocholic Acid ; metabolism

OBJECTIVETo study the relation between the expression of transcription factor T-bet/GATA3 and Th1/Th2 type cytokines in peripheral blood mononuclear cells (PBMC) from lung cancer patients and their interference by the traditional Chinese herbal medicine.

METHODSThe gene expression of Th1/Th2 type cytokine IFN gamma, IL-2, IL-4, IL-6, IL-10, transcription factor T-bet/GATA3 and tumor tissue specific mRNA CEA, CK19 in PBMC from lung cancer patients were detected by reverse transcription-polymerase chain reaction RT-PCR. Meanwhile, the change of IFN gamma, IL-4, T-bet and GATA3 in PBMC before and after being cultured with the traditional Chinese herbal medicine-Astragulus and Tetramethylpyrazine was also observed.

RESULTSPredominant expression of Th2 type cytokines was detected in 42 lung cancer patients. The positive rates of IL-4, IL-6, IL-10, IFN gamma and IL-2 were 27/42, 24/42, 31/42, 4/42 and 5/42, respectively. But, the positive rates of transcription factor T-bet and GATA3 were 16/42 and 34/42. Moreover, the expression intensity of T-bet was lower in the CEA and CK19 positive patients than the negative ones. On the contrary, the expression intensity of GATA3 was significantly higher in the same patients.

CONCLUSIONPredominant expression of Th2 type cytokines may be related to lower expression of T-bet or higher expression of GATA3. This condition can be interfered by the traditional Chinese herbal medicine-Astragulus and Tetramethylpyrazine.

Cytokines ; blood ; drug effects ; DNA-Binding Proteins ; biosynthesis ; genetics ; Drugs, Chinese Herbal ; pharmacology ; GATA3 Transcription Factor ; Gene Expression ; drug effects ; Humans ; Lung Neoplasms ; blood ; genetics ; Medicine, Chinese Traditional ; RNA, Messenger ; biosynthesis ; drug effects ; T-Box Domain Proteins ; Th1 Cells ; drug effects ; immunology ; Th2 Cells ; drug effects ; immunology ; Trans-Activators ; biosynthesis ; genetics ; Transcription Factors ; biosynthesis ; genetics