ObjectiveTo develop a community-family management model for older adults with mild cognitive impairment (MCI) and to formulate detailed application specifications, and to fully leverage the initiative of communities and families under limited resource conditions, for achieving community-based early detection and early intervention for older adults with MCI. MethodsA systematic literature review was conducted to identify pertinent publications. Corpus-based research methodologies were employed to extract, refine, integrate and synthesize management elements, thereby establishing the specific content and service processes for each stage of the management model. Utilizing the 5W2H analytical framework, essential elements such as management stakeholders, target populations, content and methods for each stage were delineated. The model and its application guidelines were finalized through expert consultation and demonstration. ResultsAn expert evaluation of the management model yielded mean scores of 4.84, 4.32 and 4.84 for acceptability, feasibility and systematicity, respectively. By integrating the identified core elements with expert ratings and feedback, the final iteration of the community-family management model for older adults with MCI was formulated. This model comprised of five stages: screening and identification, comprehensive assessment, intervention planning, monitoring and referral pathways to ensure implementation, and enhanced support for communities, family members and caregivers. Additionally, it included 18 specific application guidelines. ConclusionThe proposed management model may theoretically help delay cognitive decline, improve cognitive function and potentially promote reversal from MCI to normal cognition. It may also enhance the awareness and coping capacity of older adults and their families, strengthen community healthcare professionals' ability to early identify and manage MCI.

Objective:To investigate the clinicopathological and molecular genetic characteristics of Crohn′s disease (CD).Methods:A retrospective analysis was conducted on 52 CD patients who underwent surgical resection at the First Affiliated Hospital of Nanjing Medical University between January 2014 and June 2023. Clinical presentations and histopathological features were assessed. Whole-genome sequencing was performed on 17 of the samples, followed by sequencing and pathway enrichment analyses. Immunohistochemistry was used to assess the expression of frequently mutated genes.Results:Among the 52 patients, 34 were males and 18 were females, male-to-female ratio was 1.9∶1.0, with a median age of 45 years at surgery and 35 years at diagnosis. According to the Montreal classification, A3 (51.9%,27/52), B2 (61.5%, 32/52), and L3 (50.0%,26/52) subtypes were the most predominant. Abdominal pain and diarrhea were the common symptoms. Histopathological features seen in all 52 patients included transmural inflammation, disruption of cryptal architecture, lymphoplasmacytic infiltration, varying degrees of submucosal fibrosis and thickening, increased enteric nerve fibers and neuronal proliferation. Mucosal defects, fissure ulcers, abscesses, pseudopolyps, and adenomatous proliferation were also observed in 51 (98.1%), 38 (73.1%), 28 (53.8%), 45 (86.5%), and 28 (53.8%) cases, respectively. Thirty-one (59.6%) cases had non-caseating granulomas, and 3 (5.8%) cases had intestinal mucosal glandular epithelial dysplasia. Molecular analysis showed that 12/17 CD patients exhibited mutations in at least one mucin family gene (MUC2, MUC3A, MUC4, MUC6, MUC12, MUC17), and MUC4 was the most frequently mutated in 7/17 of cases. Immunohistochemical stains showed reduced MUC4 expression in epithelial cells, with increased MUC4 expression in the epithelial surface, particularly around areas of inflammatory cell aggregation; and minimal expression in the lower half of the epithelium.Conclusions:CD exhibits diverse clinical and pathological features, necessitating a comprehensive multidimensional analysis for diagnosis. Mutations and expression alterations in mucin family genes, particularly MUC4, may play crucial roles in the pathogenesis of CD.

ObjectiveTo investigate the distribution and trend of rehabilitation personnel of China Disabled Persons' Federation (CDPF) system and the people with disabilities (PWDs) using geographical gravity model. MethodsBased on ArcGIS and statistical data, the distribution of geographical center of gravity of the rehabilitation personnel of the CDPF system from 2011 to 2021 was analyzed. According to the economic development, the areas were divided into three regions, and the eastern region included eleven provincial units, the central region includes eight provincial units, and the western region included twelve provincial units. ResultsCompared with 2011, rehabilitation staffs per thousand PWDs increased at 107.5% in 2021, 81.1%, 114.2% and 174.1% for the eastern, central, and western regions, respectively; professional staffs increased at 190.5%, 148.8%, 284.6% and 280.6% for the eastern, central, and western regions, respectively; managerial staff increased at 80.0%, 46.8%, 554.3% and 128.1% for the eastern, central, and western regions, respectively. Compared with 2011, the geographical center of gravity of the rehabilitation personnel moved about 330.9 km in 2021, while the geographical center of gravity of the PWDs moved about 169.64 km. ConclusionThe rehabilitation personnel in the CDPF system is the most in the eastern region and least in the western region. The tracks of the geographical center of gravity of the three kind of rehabilitation personnel in the CDPF system are relatively consistent. The rehabilitation personnel in the eastern region are more concentrated than those in the western region, and the density of the PWDs is more westward than that of the rehabilitation personnel, and coordination is not a perfect match yet. It is necessary to strengthen the rehabilitation personnel allocation in the western region, to balance distribution of human resources for rehabilitation of PWDs among regions.

Humans ; Consensus ; Liver Neoplasms/therapy* ; China

Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma, with high clinical and biological heterogeneity. Only 60% of patients can benefit from standard immunochemotherapy. Looking for new clinical parameters and biomarkers to better classify and stratify the prognosis of DLBCL patients has been the focused area in recent years. This article reviews the classifications and their prognostic significances of DLBCL by analyzing the related studies of genome and transcriptome.

Objective:To compare the clinical, pathological features and prognosis of patients who underwent pancreaticoduodenectomy with standard or extended lymph node dissection for pancreatic ductal adenocarcinoma.Methods:A retrospective study was performed on 158 pancreatic head cancer patients who underwent radical resection at the First Affiliated Hospital of Nanjing Medical University from Jul 2017 to Feb 2019. The clinicopathological characteristics and prognosis between the standard dissection group and the extended dissection group were compared. The relationship between the number of examined lymph nodes, positive lymph nodes, and the lymph node ratio, together with their relationship with survival were analyzed.Results:Survival analysis showed no statistical difference in survival between the standard resection group and the extended resection group ( P=0.99). There were statistical differences in gender and age composition between the two group, but no significant differences in operation time, blood loss, or postoperative complications were found. Patients with less examined lymph nodes tended to be of stage N0. examined lymph nodes is positively correlated with positive lymph nodes but is not significantly correlated with lymph node ratio. Positive lymph nodes is strongly correlated with lymph node ratio. The location of lymph node metastasis was not survival-related. Conclusions:There is no prognostic difference between standard lymph node dissection and extended lymph node dissection in pancreatic cancinoma patients after Whipple procedure.

Objective:To investigate the relationship between clinicopathologic features and prognosis of pancreatic ductal adenocarcinoma located in the head of pancreas.Methods:A retrospective study was performed on 169 patients undergoing radical resection for pancreatic head cancer collected in the First Affiliated Hospital with Nanjing Medical University from January 2018 to April 2019. Univariate analysis and multivariate analysis were performed.Results:Patient′s age, tumor differentiation, tumor maximum diameter, resection margin (several resection margins including portal vein groove resection margin, posterior resection margin, and uncinate resection margin), number of positive lymph nodes, number of regional lymph node dissected, and some preoperative and postoperative indicators were associated with prognosis ( P<0.05). Direct tumor invasion of organs and surrounding tissues, perineural and vascular invasion, pathologic variants etc. had no statistical significance for survival time. Patient′s age, maximum tumor diameter, degree of differentiation, uncinate incision margin, number of regional lymph nodes dissected, and preoperative CA19-9 were independent factors affecting prognosis. Patients older than 74 years of age, with tumors larger than 3 cm in diameter, poorly differentiated, less than 7 regional lymph node dissected, positive uncinate margin, and preoperative CA19-9 higher than 1.5×10 5 U/L were independent risk factors in patients with pancreatic head cancer. Conclusions:Old age, tumor lager than 3 cm, poor differentiation, low examined lymph nodes, direct uncinate margin involvement and (or) with preoperative CA19-9 higher than 1.5×10 5 U/L are related to poor prognosis of head pancreatic cancer.

Objective:To investigate the definition and influencing factors of early recurrence after resection for synchronous colorectal cancer liver metastases (sCRLM).Methods:Patients with sCRLM in Department of Hepatobiliary Surgery, Cancer Hospital, Chinese Academy of Medical Sciences from December 2008 to December 2016 were included. Restricted cubic spline was used to determine the correlations between the time of recurrence and the long-term prognosis. The univariable and multivariable Cox was performed to measure the feasibility of recurrence within 6 months as the early recurrence. Then apply logistic regression, support vector machine, decision tree, random forest, artificial neural network and XGBoost, these machine learning algorithm to comprehensively rank the importance of every clinicopathological variable to early recurrence, and according to the comprehensively ranks, we introduced variables into the multivariable logistic regression model and observed the receiver operating characteristic curve (ROC) of the logistic regression model, based on the ROC area under curve, Akaike information criterion, and Bayesian information criterion, we identified the best performed variable combination and introduced them into the multivariate logistic regression analysis to confirm the independent risk factors for early recurrence. Subsequently, inverse probability weighting (IPTW) was performed on the therapy-associated independent risk factor to evaluate and validate its influence on the early recurrence of sCRLM patients after reducing the standardized mean difference of all covariates.Results:A total of 228 sCRLM patients who received resection were enrolled and followed up from 2.10 to 108.57 months. There were 142 males and 86 females, aged (55.89±0.67) years old. In 170 (74.6%) patients with recurrence, restricted cube analysis determined that the hazard ratio (HR) of disease free survival (DFS) and overall survival (OS) satisfies a linear relationship ( P<0.05), and Cox analysis indicated that 6 months as the time cutoff for defining early recurrence was feasible ( HR=3.405, 95% CI: 2.098-5.526, P<0.05). Early recurrence was occurred in 93 (40.79%) patients. The survival rate of patients in early recurrence group was significantly lower than that in the late recurrence group ( HR=3.405, 95% CI: 2.098-5.526, P<0.05, and the 5-year survival rate was 14.0% vs 52.0%). Comprehensive analysis of 6 machine learning algorithms identified that the total number of lymph node dissection >22 ( OR=0.258, 95% CI: 0.132-0.506, P<0.05) is an independent protective factor for early recurrence, while the number of liver metastases>3 ( OR=4.715, 95% CI: 2.467-9.011, P<0.05) and postoperative complications ( OR=2.334, 95% CI: 1.269-4.291, P<0.05) are independent risk factors. Finally, the IPTW analysis fully reduced the influence of covariate confounding influence via causal inference to prove lymph node dissection associated with early recurrence (IPTW OR=0.29, P<0.05), benefiting the DFS (IPTW HR=0.4887, P<0.05), but without influence on OS (IPTW HR=0.6951, P>0.05). Conclusion:Six months after sCRLM as the definition of early recurrence, it has significant feasibility. The long-term survival of patients with early recurrence is poor. The independent influencing factors of early recurrence after sCRLM are the total number of lymph node dissection, the number of liver metastases and postoperative complications disease.

Objective:To investigate the application value of machine learning algorithms and COX nomogram in the survival prediction of hepatocellular carcinoma (HCC) after resection.Methods:The retrospective and descriptive study was conducted. The clinicopathological data of 375 patients with HCC who underwent radical resection in the Cancer Hospital of Chinese Academy of Medical Sciences and Peking Union Medical College from January 2012 to January 2017 were collected. There were 304 males and 71 females, aged from 21 to 79 years, with a median age of 57 years. According to the random numbers showed in the computer, 375 patients were divided into training dataset consisting of 300 patients and validation dataset consisting of 75 patients, with a ratio of 8∶2. Machine learning algorithms including logistic regression (LR), supporting vector machine (SVM), decision tree (DT), random forest (RF), and artificial neural network (ANN) were used to construct survival prediction models for HCC after resection, so as to identify the optimal machine learning algorithm prediction model. A COX nomogram prediction model for predicting postoperative survival in patients with HCC was also constructed. Comparison of performance for predicting postoperative survival of HCC patients was conducted between the optimal machine learning algorithm prediction model and the COX nomogram prediction model. Observation indicators: (1) analysis of clinicopathological data of patients in the training dataset and validation dataset; (2) follow-up and survival of patients in the training dataset and validation dataset; (3) construction and evaluation of machine learning algorithm prediction models; (4) construction and evaluation of COX nomogram prediction model; (5) evaluation of prediction performance between RF machine learning algorithm prediction model and COX nomogram prediction model. Follow-up was performed using outpatient examination or telephone interview to detect survival of patients up to December 2019 or death. Measurement data with normal distribution were expressed as Mean± SD, and comparison between groups was analyzed by the paired t test. Measurement data with skewed distribution were expressed as M ( P25, P75) or M (range), and comparison between groups was analyzed by the Mann-Whitney U test. Count data were represented as absolute numbers. Comparison between groups was performed using the chi-square test when Tmin ≥5 and N ≥40, using the calibration chi-square test when 1≤ Tmin ≤5 and N ≥40, and using Fisher exact probability when Tmin <1 or N <40. The Kaplan-Meier method was used to calculate survival rate and draw survival curve. The COX proportional hazard model was used for univariate analysis, and variables with P<0.2 were included for the Lasso regression analysis. According to the lambda value, variables affecting prognosis were screened for COX proportional hazard model to perform multivariate analysis. Results:(1) Analysis of clinicopathological data of patients in the training dataset and validation dataset: cases without microvascular invasion or with microvascular invasion, cases without liver cirrhosis or with liver cirrhosis of the training dataset were 292, 8, 105, 195, respectively, versus 69, 6, 37, 38 of the validation dataset, showing significant differences between the two groups ( χ2=4.749, 5.239, P<0.05). (2) Follow-up and survival of patients in the training dataset and validation dataset: all the 375 patients received follow-up. The 300 patients in the training dataset were followed up for 1.1-85.5 months, with a median follow-up time of 50.3 months. Seventy-five patients in the validation dataset were followed up for 1.0-85.7 months, with a median follow-up time of 46.7 months. The postoperative 1-, 3-year overall survival rates of the 375 patients were 91.7%, 79.5%. The postoperative 1-, 3-year overall survival rates of the training dataset were 92.0%, 79.7%, versus 90.7%, 81.9% of the validation dataset, showing no significant difference in postoperative survival between the two groups ( χ2=0.113, P>0.05). (3) Construction and evaluation of machine learning algorithm prediction models. ① Selection of the optimal machine learning algorithm prediction model: according to information divergence of variables for prediction of 3 years postoperative survival of HCC, five machine learning algorithms were used to comprehensively rank the variables of clinicopathological factors of HCC, including LR, SVM, DT, RF, and ANN. The main predictive factors were screened out, as hepatitis B e antigen (HBeAg), surgical procedure, maximum tumor diameter, perioperative blood transfusion, liver capsule invasion, and liver segment Ⅳ invasion. The rank sequence 3, 6, 9, 12, 15, 18, 21, 24, 27, 29 variables of predictive factors were introduced into 5 machine learning algorithms in turn. The results showed that the area under curve (AUC) of the receiver operating charateristic curve of LR, SVM, DT, and RF machine learning algorithm prediction models tended to be stable when 9 variables are introduced. When more than 12 variables were introduced, the AUC of ANN machine learning algorithm prediction model fluctuated significantly, the stability of AUC of LR and SVM machine learning algorithm prediction models continued to improve, and the AUC of RF machine learning algorithm prediction model was nearly 0.990, suggesting RF machine learning algorithm prediction model as the optimal machine learning algorithm prediction model. ② Optimization and evaluation of RF machine learning algorithm prediction model: 29 variables of predictive factors were sequentially introduced into the RF machine learning algorithm to construct the optimal RF machine learning algorithm prediction model in the training dataset. The results showed that when 10 variables were introduced, results of grid search method showed 4 as the optimal number of nodes in DT, and 1 000 as the optimal number of DT. When the number of introduced variables were not less than 10, the AUC of RF machine learning algorithm prediction model was about 0.990. When 10 variables were introduced, the RF machine learning algorithm prediction model had an AUC of 0.992 for postoperative overall survival of 3 years, a sensitivity of 0.629, a specificity of 0.996 in the training dataset, an AUC of 0.723 for postoperative overall survival of 3 years, a sensitivity of 0.177, a specificity of 0.948 in the validation dataset. (4) Construction and evaluation of COX nomogram prediction model. ① Analysis of postoperative survival factors of HCC patients in the training dataset. Results of univariate analysis showed that HBeAg, alpha fetoprotein (AFP), preoperative blood transfusion, maximum tumor diameter, liver capsule invasion, and degree of tumor differentiation were related factors for postoperative survival of HCC patients [ hazard ratio ( HR)=1.958, 1.878, 2.170, 1.188, 2.052, 0.222, 95% confidence interval ( CI): 1.185-3.235, 1.147-3.076, 1.389-3.393, 1.092-1.291, 1.240-3.395, 0.070-0.703, P<0.05]. Clinico-pathological data with P<0.2 were included for Lasso regression analysis, and the results showed that age, HBeAg, AFP, surgical procedure, perioperative blood transfusion, maximum tumor diameter, tumor located at liver segment Ⅴ or Ⅷ, liver capsule invasion, and degree of tumor differentiation as high differentiation, moderate-high differentiation, moderate differentiation, moderate-low differentiation were related factors for postoperative survival of HCC patients. The above factors were included for further multivariate COX analysis, and the results showed that HBeAg, surgical procedure, maximum tumor diameter were independent factors affecting postoperative survival of HCC patients ( HR=1.770, 8.799, 1.142, 95% CI: 1.049- 2.987, 1.203-64.342, 1.051-1.242, P<0.05). ② Construction and evaluation of COX nomogram prediction model: the clinicopathological factors of P≤0.1 in the COX multivariate analysis were induced to Rstudio software and rms software package to construct COX nomogram prediction model in the training dataset. The COX nomogram prediction model for predicting postoperative overall survival had an consistency index of 0.723 (se=0.028), an AUC of 0.760 for postoperative overall survival of 3 years in the training dataset, an AUC of 0.795 for postoperative overall survival of 3 years in the validation dataset. The verification of the calibration plot in the training dataset showed that the COX nomogram prediction model had a good prediction performance for postoperative survival. COX nomogram score=0.627 06×HBeAg (normal=0, abnormal=1)+ 0.134 34×maximum tumor diameter (cm)+ 2.107 58×surgical procedure (laparoscopy=0, laparotomy=1)+ 0.545 58×perioperative blood transfusion (without blood transfusion=0, with blood transfusion=1)-1.421 33×high differentiation (non-high differentiation=0, high differentiation=1). The COX nomogram risk scores of all patients were calculated. Xtile software was used to find the optimal threshold of COX nomogram risk scores. Patients with risk scores ≥2.9 were assigned into high risk group, and patients with risk scores <2.9 were assigned into low risk group. Results of Kaplan-Meier overall survival curve showed a significant difference in the postoperative overall survival between low risk group and high risk group of the training dataset ( χ2=33.065, P<0.05). There was a significant difference in the postoperative overall survival between low risk group and high risk group of the validation dataset ( χ2=6.585, P<0.05). Results of further analysis by the decision-making curve showed that COX nomogram prediction model based on the combination of HBeAg, surgical procedure, perioperative blood transfusion, maximum tumor diameter, and degree of tumor differentiation was superior to any of the above individual factors in prediction performance. (5) Evaluation of prediction performance between RF machine learning algorithm prediction model and COX nomogram prediction model: prediction difference between two models was investigated by analyzing maximun tumor diameter (the important variable shared in both models), and by comparing the predictive error curve of both models. The results showed that the postoperative 3-year survival rates predicted by RF machine learning algorithm prediction model and COX nomogram prediction model were 77.17% and 74.77% respectively for tumor with maximum diameter of 2.2 cm ( χ2=0.182, P>0.05), 57.51% and 61.65% for tumor with maximum diameter of 6.3 cm ( χ2=0.394, P>0.05), 51.03% and 27.52% for tumor with maximum diameter of 14.2 cm ( χ2=12.762, P<0.05). With the increase of the maximum tumor diameter, the difference in survival rates predicted between the two models turned larger. In the validation dataset, the AUC for postoperative overall survival of 3 years of RF machine learning algorithm prediction model and COX nomogram prediction model was 0.723 and 0.795, showing a significant difference between the two models ( t=3.353, P<0.05). Resluts of Bootstrap cross-validation for prediction error showed that the integrated Brier scores of RF machine learning algorithm prediction model and COX nomogram prediction model for predicting 3-year survival were 0.139 and 0.134, respectively. The prediction error of COX nomogram prediction model was lower than that of RF machine learning algorithm prediction model. Conclusion:Compared with machine learning algorithm prediction models, the COX nomogram prediction model performs better in predicting 3 years postoperative survival of HCC, with fewer variables, which is easy for clinical use.