Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective To analyze the main factors influencing the management effect of rural epilepsy prevention and control projects in Hubei Province, and to provide reference for further improving the management effect. Methods According to the phenobarbital treatment and management plan of the rural epilepsy project, the target population was screened and reviewed, and patients who met the inclusion criteria were enrolled into the project management. Regular follow-up visits, free drug treatment, health education and other measures were carried out, and all relevant information was collected and integrated into the survey data. After the data was reviewed level by level, SPSS20.0 software was used for statistical analysis. Results From January 1, 2015 to December 31, 2020, among patients treated and managed with phenobarbital in 6 project counties, 1430 patients were treated and managed for more than 1 year, of whom 1119 (78.25%) had no seizures or had more than 75% reduction in the number of seizures during the observation period. Compared with other age groups, the age group of 65 years and above had the highest markedly effective/effective ratio (95.77%). From the point of follow-up, the markedly effective/effective ratio of 5 years and above was the highest (91.51%). Compared with those who received no treatment prior to enrollment and those who received regular treatment, the patients receiving informal treatment had the lowest markedly effective/effective ratio (82.43%). 1213 cases (84.83%) had good compliance during the observation period, of whom 1062 cases (87.55%) had a reduction in the number of seizures by more than 50% compared with that before treatment. Univariate analysis showed that the age of patients, the length of follow-up, the treatment status before enrollment, the average daily dose of phenobarbital and the compliance of patients all had an impact on the management effect, and the difference was statistically significant (P<0.05). Multivariate analysis showed that the markedly effective/effective rate of patients in the age group of 65 years and above was 6.749 times that of the younger age group. Receiving informal treatment prior to enrollment was a risk factor for difficult-to-control epilepsy. The markedly effective/effective rate of patients receiving informal treatment was 0.29 times that of patients never receiving treatment. Good compliance was a protective factor for epilepsy control, and the markedly effective/effective rate of patients with good compliance was 2.058 times that of patients with poor compliance. Conclusion The epilepsy prevention and management project in rural areas has a significant effect on seizure control. Early treatment, standardized treatment, and improvement of treatment compliance are effective ways to improve the management effect of epilepsy patients.

Background and objective Transcription factor(TF)can bind specific sequences that either promotes or represses the transcription of target genes,and exerts important effects on tumorigenesis,migration,invasion.Staphylococcal nuclease-containing structural domain 1(SND1),which is a transcriptional co-activator,is considered as a promising target for tumor therapy.However,its role in lung adenocarcinoma(LUAD)remains unclear.This study aims to explore the role of SND1 in LUAD.Methods Data from The Cancer Genome Atlas(TCGA),Gene Expression Omnibus(GEO),Clinical Pro-teomic Tumor Analysis Consortium(CPTAC),and Human Protein Atlas(HPA)database was obtained to explore the associa-tion between SND1 and the prognosis,as well as the immune cell infiltration,and subcellular localization in LUAD tissues.Furthermore,the functional role of SND1 in LUAD was verified in vitro.EdU assay,CCK-8 assay,flow cytometry,scratch assay,Transwell assay and Western blot were performed.Results SND1 was found to be upregulated and high expression of SND1 is correlated with poor prognosis of LUAD patients.In addition,SND1 was predominantly present in the cytoplasm of LUAD cells.Enrichment analysis showed that SND1 was closely associated with the cell cycle,as well as DNA replication,and chro-mosome segregation.Immune infiltration analysis showed that SND1 was closely associated with various immune cell popula-tions,including T cells,B cells,cytotoxic cells and dendritic cells.In vitro studies demonstrated that silencing of SND1 inhib-ited cell proliferation,invasion and migration of LUAD cells.Besides,cell cycle was blocked at G,phase by down-regulating SND1.Conclusion SND1 might be an important prognostic biomarker of LUAD and may promote LUAD cells proliferation and migration.

Chronic obstructive pulmonary disease(COPD)has become a common chronic disease in the adult in recent years,and more attention has been gradually paid to its prevention and treatment.This paper reviewed the clinical studies about the effect of traditional Chinese medicine(TCM)exercise therapy on COPD,and indicated that TCM exercise therapy can improve the rehabilitation of COPD patients.TCM exercise therapies,such as Tai Chi,Wuqinxi,Baduanjin,and Liuzijue,have been shown to significantly improve lung function,inflammation levels,and exercise capacity in COPD patients in recent studies.Tai Chi significantly improves patient's respiratory problems although lung function indices are not changed.Liuzijue enhances the strength and endurance of respiratory muscles and limbs.Baduanjin helps to improve patient's cognitive and emotional states.Overall,TCM exercise therapy provides an effective rehabilitation option for COPD patients.However,more clinical controlled trials are needed to further confirm their effectiveness and to develop appropriate rehabilitation programs for COPD patients.

Humans ; Transcranial Magnetic Stimulation ; Pain Management ; Psychotic Disorders/therapy* ; Depressive Disorder, Major ; Treatment Outcome ; Transcranial Direct Current Stimulation

Schizophrenia is a serious mental disease. The diagnosis of schizophrenia so far relies heavily on subjective evidence, including self-reported experiences by patients, manifestations described by relatives, and abnormal behaviors assessed by psychiatrists. The diagnosis, monitoring of the disease progression and therapy efficacy assessment are challenging due to the lack of established laboratory biomarkers. Based on the current literature, clinical consensus, guidelines, and expert recommendations, this review highlighted evidence-based potential laboratory biomarkers for the diagnosis of schizophrenia, including genetic biomarkers, neurotransmitters, neurodevelopmental-related proteins, and intestinal flora, and discussed the potential future directions for the application of these biomarkers in this field, aiming to provide an objective basis for the use of these biomarkers in the early and accurate diagnosis, treatment, and prognosis and rehabilitation assessment of schizophrenia.

Objective:To investigate the preoperative risk factors affecting early extremity blood supply after repair of major arterial injury so as to provide clues for prevention of limb ischemia.Methods:The clinical data were retrospectively analyzed of the 139 patients (140 extremities) with major extremity arterial injury who had been admitted to Department of Microsurgery, Orthopaedic Trauma and Hand Surgery, The First Hospital Affiliated to Sun Yat-sen University from January 2003 to December 2019. There were 112 males and 27 females, with a mean age of 30 (20, 44) years. The primary outcome was the early status of blood supply to the injured extremity (48 hours after surgery). Univariate analysis was conducted of such factors as gender, age, ischemia time, injury mechanism, injury site, fracture, soft tissue lesion, and duration of surgery. The significant factors ( P<0.1) were then analyzed by logistic regression, and P<0.05 was considered statistically significant. Results:Ischemia happened in 44 (31.4%, 44/140) extremities within 48 hours after surgery. There were significant differences in injury mechanism, ischemia time, fracture, and soft tissue lesion between patients with and without postoperative extremity ischemia ( P<0.05). Logistic regression analysis indicated that blunt injury ( OR=5.639, 95% CI: 1.068 to 29.761, P=0.042) and soft tissue lesion ( OR=12.568, 95% CI: 3.402 to 46.431, P<0.001) were significant preoperative risk factors affecting the early blood supply after repair of major extremity arterial injury. Conclusion:As blunt injury and soft tissue defect are preoperative risk factors for early extremity ischemia after repair of major extremity arterial injury, surgeons should pay more attention to them when assessing patients and making repair protocols.

Objective:To investigate the feasibility of coronary CT angiography(CCTA)-feature tracking(FT) for assessing global and regional myocardial strain in patients with heart failure(HF).Methods:From July 2019 to December 2020, twenty-five patients diagnosed with HF from Peking Union Medical College Hospital were prospectively enrolled into the study. All patients underwent retrospective electrocardiogram-gated CCTA and cardiac MR (CMR) imaging within 7 days. CCTA-FT and CMR-FT were undertaken using cvi 42 dedicated commercial software to measure global and regional strain parameters, including global peak radial strain (GPRS), global peak circumferential strain (GPCS) and global peak longitudinal strain (GPLS), as well as peak radial strain (PRS), peak circumferential strain (PCS) and peak longitudinal strain (PLS) of left ventricular basal segment, middle segment and apical segment. Conventional left ventricular functional parameters were also calculated, including left ventricular ejection fraction (LVEF), left ventricular stroke volume (LVSV) and left ventricular mass index (LVMI). Paired t test or Wilcoxon signed-rank test was used to compare the differences of measurements between CCTA group and CMR group. Pearson or Spearman correlation analysis was used to analyze the correlation between the two groups. Inter-and intra-observer consistence in CCTA group was evaluated by intraclass correlation coefficient (ICC) analysis. Results:The effective radiation dose of CCTA examination was 6.00 (4.86,7.63) mSv. Inter-and intra-observer consistence in CCTA group was excellent, and the ICC value was 0.85-0.98. In the overall strain parameters, GPCS in CCTA group[-8.10%(-10.32%, -5.20%)] was significantly lower than that of CMR group[-8.49%(-13.79%, -5.95%)] ( Z=-2.15, P=0.031). There was no significant difference in GPRS and GPRS between the two measurement methods ( P>0.05). Strong correlations were observed between GPRS, GPCS and GPLS ( r=0.65, 0.63, 0.71,all P<0.001). For local strain parameters, PCS in the middle segment and apical segment of CCTA group were lower than those of CMR group ( Z=-2.17, -2.62, all P<0.05). There were no significant differences in PCS of basal segment, PRS and PLS of all segments between groups (all P>0.05). The PCS and PLS of basal segment, PRS of middle segment and PRS of apical segment were moderately correlated ( r=0.46, 0.52, 0.58, 0.53, P<0.05); The other local strain parameters were strongly correlated, the range of r value was from 0.64 to 0.70 (all P<0.001). For left ventricular functional parameters, LVEF, LVSV and LVMI showed no significant differences between groups ( P>0.05), and the correlation was extremely strong ( r=0.90, 0.89, 0.96, all P<0.001). Conclusions:The repeatability of CCTA-FT technique in measuring myocardial strain was good, and the correlation of parameters measured by CCTA-FT technique and CMR-FT technique was excellent. Therefore, CCTA-FT technique can be used as a new noninvasive and simple method to evaluate myocardial motor function.

BACKGROUND: Lung cancer is a malignant with high incidence and mortality and adenocarcinoma is among the most popular subtypes. Epidermal growth factor receptor (EGFR) mutation is one of the most important driver mutations for lung adenocarcinoma and EGFR-tyrosine kinase inhibitor (TKI) will benefit those patients with sensitive EGFR mutations. Recently, immune checkpoint inhibitor (ICI) therapy, provide a new breakthrough treatment for lung cancer patients. Whereas immunotherapy as an emerging treatment does not benefit patients with EGFR mutations, for which mechanistic studies are poorly defined and focused on the link of EGFR mutations and programmed cell death-ligand 1 (PD-L1) expression, we speculate that the different immune microenvironment associated with the two classes of patients. METHODS: Lung adenocarcinoma datasets were collected from the Cancer Genome Atlas (TCGA) database, and clinical information and gene expression profiles were downloaded. The immune related lymphocyte infiltration in TCGA database were generated through timer 2.0 GSEA was used to analyze the difference of pathway expression between EGFR mutant patients and wild type patients. RESULTS: EGFR mutation was more frequently among women and never smokers. Immunoinfiltration analysis showed that patients with EGFR mutation tends to have more tumor associated fibroblasts, common myeloid progenitor cells, hematopoietic stem cells, effector CD4⁺ T cells and natural killer T cells infiltration, and less memory B cells, naïve B cells, plasma B cells, plasmacytoid dendritic cells, memory CD4⁺ T cells, CD4⁺ helper T cells 2, naive CD8⁺ T cells, CD8⁺ T cells and central memory CD8⁺ T cells infiltration. Moreover, patients with more infiltration of CD8⁺ T cells, natural killer T cells, memory B cells and hematopoietic stem cells, tends have better prognosis (Log-rank test, P=0.017, 0.0093, 0.018, 0.016). However, the patients with more CD4⁺ T th2 infiltration in the tumor tends to have worse prognosis (Log-rank test, P=0.016). Furthermore, the results of gene set enrichment analysis showed that compared with the lung adenocarcinoma patients with EGFR wild type, the three pathways positive regulation of natural killer (NK) cell-mediated immune response to tumor cells, NK cell activation involved in immune response, and NK cell-mediated immune response to tumor cells related to natural killer cells in patients with EGFR mutation were down regulated, while the pathway the positive regulation of cytokine secretion involved in immune response was up-regulated in EGFR mutation patients. CONCLUSIONS The tumour microenvironment of patients with EGFR mutations lacks potent tumour killing effector cells and appears dysfunctional with effector cells. This may be a potential reason for the poor efficacy of immunotherapy in patients with EGFR mutations.