Numerous c-mesenchymal-epithelial transition (c-MET) inhibitors have been reported as potential anticancer agents. However, most fail to enter clinical trials owing to poor efficacy or drug resistance. To date, the scaffold-based chemical space of small-molecule c-MET inhibitors has not been analyzed. In this study, we constructed the largest c-MET dataset, which included 2,278 molecules with different structures, by inhibiting the half maximal inhibitory concentration (IC₅₀) of kinase activity. No significant differences in drug-like properties were observed between active molecules (1,228) and inactive molecules (1,050), including chemical space coverage, physicochemical properties, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles. The higher chemical diversity of the active molecules was downscaled using t-distributed stochastic neighbor embedding (t-SNE) high-dimensional data. Further clustering and chemical space networks (CSNs) analyses revealed commonly used scaffolds for c-MET inhibitors, such as M5, M7, and M8. Activity cliffs and structural alerts were used to reveal "dead ends" and "safe bets" for c-MET, as well as dominant structural fragments consisting of pyridazinones, triazoles, and pyrazines. Finally, the decision tree model precisely indicated the key structural features required to constitute active c-MET inhibitor molecules, including at least three aromatic heterocycles, five aromatic nitrogen atoms, and eight nitrogen-oxygen atoms. Overall, our analyses revealed potential structure-activity relationship (SAR) patterns for c-MET inhibitors, which can inform the screening of new compounds and guide future optimization efforts.

Numerous c-mesenchymal-epithelial transition(c-MET)inhibitors have been reported as potential anticancer agents.However,most fail to enter clinical trials owing to poor efficacy or drug resistance.To date,the scaffold-based chemical space of small-molecule c-MET inhibitors has not been analyzed.In this study,we constructed the largest c-MET dataset,which included 2,278 molecules with different struc-tures,by inhibiting the half maximal inhibitory concentration(IC50)of kinase activity.No significant differences in drug-like properties were observed between active molecules(1,228)and inactive mol-ecules(1,050),including chemical space coverage,physicochemical properties,and absorption,distri-bution,metabolism,excretion,and toxicity(ADMET)profiles.The higher chemical diversity of the active molecules was downscaled using t-distributed stochastic neighbor embedding(t-SNE)high-dimensional data.Further clustering and chemical space networks(CSNs)analyses revealed commonly used scaffolds for c-MET inhibitors,such as M5,M7,and M8.Activity cliffs and structural alerts were used to reveal"dead ends"and"safe bets"for c-MET,as well as dominant structural fragments consisting of pyr-idazinones,triazoles,and pyrazines.Finally,the decision tree model precisely indicated the key structural features required to constitute active c-MET inhibitor molecules,including at least three aromatic het-erocycles,five aromatic nitrogen atoms,and eight nitrogen-oxygen atoms.Overall,our analyses revealed potential structure-activity relationship(SAR)patterns for c-MET inhibitors,which can inform the screening of new compounds and guide future optimization efforts.

Cancer immunotherapy has great potential and is expected to become the mainstream method of cancer treatment.In the current application of cancer immunotherapy,immune checkpoint inhibitors(ICIs)have achieved remarkable results.The cur-rently widely used ICIs in clinical practice include inhibitors targeting cytotoxic T lymphocyte-associated antigen-4(CTLA-4),pro-grammed death-1(PD-1)and programmed death-ligand 1(PD-L1).In addition,new immunotherapies such as oncolytic viruses and chimeric antigen receptor T cells are gradually entering the clinical practice,and combination therapy related to ICIs has shown unique advantages.This article will focus on the current application status of ICIs,oncolytic viruses,and chimeric antigen receptor T cell ther-apies in solid tumors either their individual or combined forms.

Objective To explore the mechanism by which Sestrin2(SESN2)regulates autophagy activity of chondrocytes by mediating mammalian rapamycin target protein complex 1(mTORC1)signaling pathway.Methods The normal chondrocytes were treated with interleukin-1 β(IL-1β)to establish an osteoarthritis(OA)chondrocyte model,which was divided into the control group and the IL-1 β-treated group.Real-time quantitative PCR(qPCR)and Western blot were used to detect the expression levels of matrix metalloproteinase 13(MMP13),type Ⅱ collagen(COL2A1)and SESN2 in the two groups.The cell models of the chondrocyte overexpression SESN2 group and knockdown SESN2 group were obtained via cell transfection technology,and the expression levels of SESN2 in each group were detected by qPCR while those of SESN2,MMP13,COL2A1,mTORC1 pathway-related proteins and autophagy-related proteins in each group were detected by Western blot.The effects of SESN2 on cell proliferation and migration were detected by CCK-8 and cell scratch assay.Results(1)The expression level of MMP13 in the IL-1 β-treated group was significantly up-regulated,while the expression levels of COL2A1 and SESN2 were significantly decreased.(2)Compared with the control group,the expressions of p-mTORC1,ribosomal protein S6 kinase 1(S6K1),and MMP13 protein in OA chondrocytes in the overexpression group were significantly down-regulated,while the expressions of adenosine 5'-monophosphate(AMP)-activated protein kinase(AMPK)and chondroprotective gene COL2A1 were significantly increased,and the expression level of Beclin-1 and the ratio of microtubule associated protein 1 light chain 3-Ⅱ(LC3-Ⅱ)/(LC3-Ⅰ)were increased.Meanwhile,overexpression of SESN2 could up-regulate the proliferation and migration of chondrocytes,but the results were opposite after knockdown of SESN2.Conclusion SESN2 can enhance autophagy,proliferation and migration of chondrocytes by inhibiting mTORC1 pathway,which has provided data for revealing the pathogenesis of OA and exploring new therapeutic methods.

Objective:To observe any effect of supplementing applied behavior analysis with speech-and-language therapy in improving the language ability of children on the autism spectrum.Methods:A total of 60 children with an autism spectrum disorder were divided at random into an experimental group ( n=30) and a control group ( n=30). The control group accepted 3 hours of applied behavior analysis 5 days per week for 3 consecutive months. Over the same period the experiment group accepted 1.5 hours of applied behavior analysis and 1.5 hours of speech-language therapy. Before and after the intervention, sign-significate relations (S-S) and the language retardation examination were used to evaluate the language comprehension, expression, oral communication, the complexity of oral expression content, and the vocabulary of comprehension and expression of the two groups. Results:After the intervention, comprehension and expression in each stage of the S-S had improved significantly in both groups, but the average comprehension and language expression of the experimental group was significantly better than that of the control group. Oral communication improved significantly in both groups but complexity and the vocabulary used had improved significantly more in the experimental group.Conclusion:Supplementing applied behavioral analysis with speech-language therapy can improve the language ability of children on the autism spectrum significantly better than behavioral analysis alone.

Objective To explore the post-traumatic growth course for women with termination of pregnancy due to fetal abnormalities and analyze the promoting factors,in order to provide a basis for nursing and intervention tactics.Methods The purposive sampling and theoretical sampling methods were used to select 16 women who were diagnosed with fetal abnormalities and decided to terminate pregnancy in the obstetrics department of a tertiary care maternity hospital in Beijing from May 2021 to February 2022.Women were interviewed by semi-structured interviews,and the data was analyzed by Charmaz's constructing grounded theory paradigm.Results Women with termination of pregnancy due to fetal abnormalities underwent 4 stages of post-traumatic growth:traumatic stress period,rumination and seeking of help period,and acceptance internalization period,and post-traumatic growth period.The behavior of each period was influenced by the psychological experience of the corresponding period and influenced the experience of the next period.Extroverted personality,previous normal pregnancy and delivery experience,positive perception of events,positive self-identification,good social support,appropriate hospitalization environment and regulations,open mindedness towards death and religious belief,affected women's post-traumatic growth.Conclusion The post-traumatic growth course of women with termination of pregnancy due to fetal anomaly is a complex continuously sublimated process.Medical staff should pay attention to psychological and behavioral changes in women's post-traumatic growth,combine the factors that promote post-traumatic growth,and provide targeted guidance to help them successfully go through this difficult period and achieve personal growth.

Objective To investigate the relationship between APCV sign on SWI and END in pa-tients with acute anterior circulation cerebral infarction.Methods A retrospective study was con-ducted on 146 patients with acute anterior circulation cerebral infarction who were consecutively admitted in our hospital from May 2023 to March 2024.According to the results of SWI,they were divided into a negative APCV sign group(93 cases)and a positive APCV sign group(53 cases),and based on the progression of END or not within 3 d after admission,they were also assigned into a non-END group(97 cases)and an END group(49 cases).The general clinical data were compared between the negative and positive APCV sign groups and between the non-END and END groups.Multivariate logistic regression analysis was used to identify the risk factors for END and analyze the relationship between the occurrence of END and APCV sign in cerebral infarction.Results The positive APCV sign group had significantly larger proportions of hypertension and diabetes,higher glycosylated hemoglobin level and NIHSS score at admission,and elevated infarct volume than the negative APCV sign group(P＜0.01).Advanced age,higher ratios of hyperten-sion and diabetes,elevated NIHSS score,larger infarct volume and more APCV sign were ob-served in the END group than the non-END group(P＜0.05,P＜0.01).Multivariate logistic re-gression analysis showed that APCV sign was an independent risk factor for the occurrence of END in acute anterior circulation cerebral infarction after adjusting the variables,including age,hypertension,diabetes,N1HSS score,and infarct volume(OR=6.629,95％CI:1.799-24.428).Conclusion APCV sign is an independent risk factor for the occurrence of END in cerebral infarc-tion,and its appearance on the SWI sequence is an early marker in predicting the prognosis of cer-ebral infarction.

Objective To investigate the relationship between APCV sign on SWI and END in pa-tients with acute anterior circulation cerebral infarction.Methods A retrospective study was con-ducted on 146 patients with acute anterior circulation cerebral infarction who were consecutively admitted in our hospital from May 2023 to March 2024.According to the results of SWI,they were divided into a negative APCV sign group(93 cases)and a positive APCV sign group(53 cases),and based on the progression of END or not within 3 d after admission,they were also assigned into a non-END group(97 cases)and an END group(49 cases).The general clinical data were compared between the negative and positive APCV sign groups and between the non-END and END groups.Multivariate logistic regression analysis was used to identify the risk factors for END and analyze the relationship between the occurrence of END and APCV sign in cerebral infarction.Results The positive APCV sign group had significantly larger proportions of hypertension and diabetes,higher glycosylated hemoglobin level and NIHSS score at admission,and elevated infarct volume than the negative APCV sign group(P＜0.01).Advanced age,higher ratios of hyperten-sion and diabetes,elevated NIHSS score,larger infarct volume and more APCV sign were ob-served in the END group than the non-END group(P＜0.05,P＜0.01).Multivariate logistic re-gression analysis showed that APCV sign was an independent risk factor for the occurrence of END in acute anterior circulation cerebral infarction after adjusting the variables,including age,hypertension,diabetes,N1HSS score,and infarct volume(OR=6.629,95％CI:1.799-24.428).Conclusion APCV sign is an independent risk factor for the occurrence of END in cerebral infarc-tion,and its appearance on the SWI sequence is an early marker in predicting the prognosis of cer-ebral infarction.

Objective:To understand and explore the incidence and influencing factors of malnutrition in patients with inflammatory bowel disease.Methods:This study was a cross-sectional study. Patients with inflammatory bowel disease hospitalized in the Department of Gastroenterology of a tertiary hospital in Shenzhen from March 1 to August 31, 2021 were enrolled. Indicators related to nutrition and clinical outcome were collected, including height, weight, body mass index (BMI), nutritional risk screening (NRS 2002) results, malnutrition diagnosis as per Global Leadership Initiative on Malnutrition (GLIM) criteria and Pittsburgh Sleep Quality Index (PSQI). Chi-square test, t-test and Wilcoxon rank sum test were used as appropriate for univariate analysis, and binary logistic regression analysis was used for multivariate analysis.Results:A total of 188 patients were included in this survey. There were 145 (77.1%) patients with no malnutrition, 38 (20.2%) with moderate malnutrition, and 5 (2.7%) with severe malnutrition according to GLIM criteria. In the subgroup of 47 ulcerative colitis patients, 12 (25.5%) were with moderate malnutrition and 3 (3.4%) were with severe malnutrition. In the subgroup of 141 Crohn's disease patients, 26 (18.4%) were with moderate malnutrition and 2 (1.4%) were with severe malnutrition. When divided by the presence or absence of malnutrition, there were statistically significant differences in age ( t = -2.237, P = 0.026), disease stage ( χ 2 = 22.299, P < 0.001), history of digestive tract resection ( χ 2 = 6.890, P = 0.009), intestinal infection ( χ 2 = 4.010, P = 0.045), gastrointestinal symptoms ( χ 2 =11.884, P = 0.001), hemoglobin ( t = 5.160, P < 0.001), serum albumin ( t = 3.96, P < 0.001), prealbumin ( t = 5.061, P < 0.001) and PSQI scores ( t = -4.744, P < 0.001). Multivariate analysis showed that the stage of disease, history of partial resection of digestive tract, gastrointestinal symptoms, hemoglobin, prealbumin and PSQI scores were the main influencing factors of malnutrition. Conclusions:IBD patients at older age, at active stage, and with history of partial digestive tract resection, intestinal infection, gastrointestinal symptoms, low hemoglobin, low serum albumin, low prealbumin and poor sleep quality are more likely to develop malnutrition. Timely intervention should be carried out to improve the nutritional status of these patients.

Objective:To investigate the value of a deep learning method based on MobileNet in classification of bedside chest radiograph and improvement of the work efficiency.Methods:A total of 6, 320 bedside chest radiographs from January 2017 to December 2022 in the Second Peoples′ Hospital of Changzhou were retrospectively collected. The included cases were divided into normal group (885 images), pneumonia group (1 927 images), pleural effusion group (373 images), and pneumonia with pleural effusion group (3 135 images). Three hundred and fifty images were selected as a validation set, while the remaining images were divided into a train set (4 775 images) and a test set (1 195 images) using simple randomization, by 8∶2 ratio. Two lightweight convolutional neural network models (MobileNetV1 and MobileNetV2) were used to construct a bedside chest radiograph classification model, based on which two fine-tuning strategies were designed. Four models were generated namely MobileNetV1_False (V1_False), MobileNetV1_True (V1_True), MobileNetV2_False (V2_False) and MobileNetV2_True (V2_True). In the first stage, a binary classification model was established to divide the images into normal and lesion groups; then a four-class classification model was established in the second stage, with which the images were divided into four groups: normal, pneumonia, pleural effusion and pneumonia with pleural effusion. Metrics for model performance evaluation including accuracy (Ac), precision (Pr), recall rate (Rc), F1 score (F1) and area under the receiver operating characteristic curve (AUC) were calculated.Results:In both the first and second stages, V1_True and V2_True had higher Ac, Pr, Rc, and F1 than V1_False and V2_False in both the training set and validation set; and the V1_True model outperformed the other three models in classification. The classification Ac of the V1_True model in the validation set was higher than that of radiologists in the first stage [95.71% (335/350) vs. 90.29% (316/350)] and in the second stage [93.43% (327/350) vs. 87.14% (305/350)]. The recognition time of V1_True model′s in the validation set of 350 bedside chest radiographs was significantly less than that of the radiologists (mean: 17 s vs. 300 min).Conclusions:V1_True is an optimal MobileNet model for classifying bedside chest radiographs. The application of this model in clinical practice may help to accurately identify the information of lung lesions from bedside chest radiographs in time, and may improve the work efficiency in the radiology department.