ObjectiveTo investigate the imaging features of calcium salt deposition and serological markers in patients with alveolar echinococcosis through a retrospective analysis， as well as independent risk factors for the degree of calcium salt deposition in lesions， and to provide a basis for assessing disease process. MethodsA retrospective analysis was performed for the imaging and clinical data of 107 patients with alveolar echinococcosis who were admitted to The First Affiliated Hospital of Shihezi University from December 2023 to June 2025， and according to the volume of calcium salt deposition， they were divided into non-deposition group with 16 patients， mild deposition group with 52 patients， moderate deposition group with 16 patients， and severe deposition group with 23 patients. A one-way analysis of variance or the Kruskal-Wallis H test was used for comparison of continuous data between groups， and the χ² test or Fisher’s exact test was used for comparison of categorical data between groups. The four groups were further combined into the low deposition group （no/mild deposition） and the high deposition group （moderate/severe deposition）. A binary logistic regression analysis was used to investigate the independent influencing factors for calcium salt deposition， and a predictive model was established. The receiver operating characteristic （ROC） curve was used to assess the predictive performance of the model， and the Bootstrap method was used for internal validation. ResultsThere were significant differences between the four groups in sex distribution， involvement of other sites， white blood cell count， lymphocyte percentage， fibrinogen， uric acid， sodium ion， chloride ion， and calcium ion （all P<0.05）. The univariate analysis showed that there were significant differences between the four groups in sex， involvement of other sites， white blood cell count， lymphocyte percentage， fibrinogen， alanine aminotransferase， albumin， creatinine， uric acid， sodium ion， chloride ion， and calcium ion （all P<0.1）. The multi-collinearity diagnosis showed that the VIF values for all continuous variables ranged from 1.104 to 1.760， suggesting that collinearity did not affect modeling. An ordinal logistic regression model was established based on sex， involvement of other sites， calcium ion， lymphocyte percentage， and uric acid. The multivariate analysis showed that lymphocyte percentage （odds ratio ［OR］=1.106， 95% confidence interval ［CI］： 1.041 — 1.174， P=0.001） and blood calcium level （OR=0.005， 95%CI： 0.000 —0.230， P=0.007） were independent influencing factors for the degree of calcium salt deposition. The regression equation was established as Logit（P）=8.231 + 0.100 × lymphocyte percentage -5.344 × calcium ion. The ROC curve analysis showed that the model had an area under the ROC curve of 0.716， with a Youden index of 0.353， a sensitivity of 1.000， and a specificity of 0.353. The Hosmer-Lemeshow test showed that the model had poor calibration （χ²=20.688， P=0.008）. The Bootstrap method with 1000 repeated samples showed that the estimated values of lymphocyte percentage （OR=1.106， 95%CI： 1.049 — 1.186， P=0.002） and calcium ion （OR=0.005， 95%CI： 0.000 — 0.214， P=0.010） were consistent with the original model， and the confidence intervals did not include 1， which further supported the reliability of the model. ConclusionBoth lymphocyte percentage and blood calcium level are independent influencing factors for calcium salt deposition in alveolar echinococcosis， and the degree of calcium salt deposition in alveolar echinococcosis lesions increases with the reduction in blood calcium level and the increase in lymphocyte percentage.

Objective To investigate the value of quantitative parameters of diffusion weighted imaging(DWI)based on mono-expo-nential model(MEM),diffusion kurtosis imaging(DKI)model,and stretched-exponential model(SEM)in predicting the efficacy of neoadjuvant therapy in locally advanced gastric cancer(LAGC).Methods Forty LAGC patients who underwent MRI examinations before neoadjuvant therapy and before radical surgery were prospectively enrolled.A radiologist delineated lesions on DWI images and acquired quantitative parameters before and after treatment,including lesion volume,apparent diffusion coefficient(ADC)of MEM,mean diffusivity(MD)and mean kurtosis(MK)of DKI model,distribution diffusion coefficient(DDC),and α of SEM.According to pathological tumor regression grade(TRG),the patients were stratified into good response group(TRG 0-1)and poor response group(TRG 2-3).The pre-treatment parameters and Δ of pre-and post-treatment parameters were compared between the two groups with Mann-Whitney U test;multivariate analysis was performed with binary logistic regression.Multiple DWI models and the combined model were established,and the prediction efficiency of each model was calculated.Results There was no significant differ-ence in each parameter before neoadjuvant therapy between the two groups(P>0.05).The delta of volume,ΔADC,ΔMD,and ΔDDC pre-and post-treatment were all statistically different between the two groups(P<0.05).The area under the curve(AUC)of ΔADC,ΔMD,and ΔDDC in predicting good response for LAGC were 0.900,0.806,and 0.762,respectively.The AUC of the combined model was 0.946.Conclusion Quantitative parameters of MEM,DKI model,and SEM can help predict the efficacy of neoadju-vant-treated LAGC patients.

Objective This study aims to achieve high-yield functional expression of the human voltage-gated potassium channel K_V3.1 using an Escherichia coli cell-free protein synthesis system, thereby providing a novel synthetic approach for drug screening, structural analysis and functional characterization of K_V3.1. Methods K_V3.1 was expressed in an Escherichia coli cell-free protein synthesis system for 10 hours in the presence of peptide surfactant A₆K. The secondary structure of K_V3.1 was analyzed by circular dichroism spectroscopy. The potassium channel activity of the recombinant protein liposome K_V3.1-A₆K was investigated using fluorescent dyes Oxonol VI as indicators, which are capable of reflecting alterations in membrane potential. Results Soluble K_V3.1 protein was successfully synthesized, achieving a purified yield of up to 1.2 mg/mL via an Escherichia coli cell-free protein synthesis system. Circular dichroism spectroscopy revealed that K_V3.1 exhibited characteristic α-helical secondary structures. Membrane potential fluorescence assays demonstrated that the K_V3.1-A₆K proteoliposomes, which were reconstructed with surfactant peptide A₆K, exhibited remarkable potassium ion permeability. Conclusion This study successfully achieved high-yield expression of human K_V3.1 with activity using an Escherichia coli-based cell-free protein synthesis system. This innovative method not only significantly enhances the expression yield of K_V3.1, but also maintains its functional activity, thereby establishing a novel and efficient synthetic platform for drug screening and advancing our understanding of structure-function relationships in K_V3.1 research.
Humans ; Escherichia coli/metabolism* ; Shaw Potassium Channels/biosynthesis* ; Cell-Free System ; Circular Dichroism ; Protein Biosynthesis ; Recombinant Proteins/metabolism* ; Membrane Potentials ; Shab Potassium Channels

An automated identification method for adolescent schizophrenia based on brain electroencephalogram(EEG)entropy features is proposed for further improving the diagnostic accuracy of adolescent schizophrenia.The raw EEG signals are decomposed into 5 commonly used rhythm bands:Delta,Theta,Alpha,Beta,and Gamma.The permutation entropy,fuzzy entropy,and sample entropy are extracted from each rhythm band and then organized into a feature matrix structured by electrode location×frequency band.Finally,an ECA-CNN model integrating efficient channel attention(ECA)and convolutional neural network(CNN)is constructed for feature classification and realizing the automated identification of adolescent schizophrenia.The results demonstrate that the proposed ECA-CNN model has higher recognition accuracy than the traditional machine learning models,achieving an accuracy of 99.08%,a sensitivity of 99.27%,a specificity of 98.85%,a precision of 99.01%,a F1 score of 99.14%,and a Kappa coefficient of 0.9814.This study provides a new idea and method for the diagnosis of adolescent schizophrenia.

Objective To investigate the value of quantitative parameters of diffusion weighted imaging(DWI)based on mono-expo-nential model(MEM),diffusion kurtosis imaging(DKI)model,and stretched-exponential model(SEM)in predicting the efficacy of neoadjuvant therapy in locally advanced gastric cancer(LAGC).Methods Forty LAGC patients who underwent MRI examinations before neoadjuvant therapy and before radical surgery were prospectively enrolled.A radiologist delineated lesions on DWI images and acquired quantitative parameters before and after treatment,including lesion volume,apparent diffusion coefficient(ADC)of MEM,mean diffusivity(MD)and mean kurtosis(MK)of DKI model,distribution diffusion coefficient(DDC),and α of SEM.According to pathological tumor regression grade(TRG),the patients were stratified into good response group(TRG 0-1)and poor response group(TRG 2-3).The pre-treatment parameters and Δ of pre-and post-treatment parameters were compared between the two groups with Mann-Whitney U test;multivariate analysis was performed with binary logistic regression.Multiple DWI models and the combined model were established,and the prediction efficiency of each model was calculated.Results There was no significant differ-ence in each parameter before neoadjuvant therapy between the two groups(P>0.05).The delta of volume,ΔADC,ΔMD,and ΔDDC pre-and post-treatment were all statistically different between the two groups(P<0.05).The area under the curve(AUC)of ΔADC,ΔMD,and ΔDDC in predicting good response for LAGC were 0.900,0.806,and 0.762,respectively.The AUC of the combined model was 0.946.Conclusion Quantitative parameters of MEM,DKI model,and SEM can help predict the efficacy of neoadju-vant-treated LAGC patients.

An automated identification method for adolescent schizophrenia based on brain electroencephalogram(EEG)entropy features is proposed for further improving the diagnostic accuracy of adolescent schizophrenia.The raw EEG signals are decomposed into 5 commonly used rhythm bands:Delta,Theta,Alpha,Beta,and Gamma.The permutation entropy,fuzzy entropy,and sample entropy are extracted from each rhythm band and then organized into a feature matrix structured by electrode location×frequency band.Finally,an ECA-CNN model integrating efficient channel attention(ECA)and convolutional neural network(CNN)is constructed for feature classification and realizing the automated identification of adolescent schizophrenia.The results demonstrate that the proposed ECA-CNN model has higher recognition accuracy than the traditional machine learning models,achieving an accuracy of 99.08%,a sensitivity of 99.27%,a specificity of 98.85%,a precision of 99.01%,a F1 score of 99.14%,and a Kappa coefficient of 0.9814.This study provides a new idea and method for the diagnosis of adolescent schizophrenia.

Objective:To screen the characteristic genes of early-onset pre-eclampsia (EOSP) and to analyze their association with immune cell infiltration based on bioinformatics analysis and machine learning methods.Methods:In the Gene Expression Omnibus (GEO) database, the mRNA sequences of placental tissues from women with EOSP and normal pregnancy were retrieved using the term "early-onset pre-eclampsia". The R language was used for background correction, standardization, summarization, and probe quality control. Annotation packages were downloaded for ID conversion and the expression matrices were extracted. The differentially expressed genes (DEGs) between the EOSP and the normal pregnancy in the metadata were analyzed after correcting for batch effects using the limma package. Characteristic genes were identified through the support vector machine (SVM) -recursive feature elimination (RFE) method and the LASSO regression model. The area under the curve (AUC) was calculated to judge the diagnostic efficiency of the characteristic genes. Placental tissues were retrospectively collected for verification from 15 patients with EOSP and 15 with normal pregnancy who were delivered at Beijing Obstetrics and Gynecology Hospital, Capital Medical University from January 1, 2022, to February 28, 2023. The expression of characteristic genes was verified using quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot, which were further validated in the validation dataset. Finally, the CIBERSORT algorithm was used to analyze the relative proportion of infiltrating immune cell in EOSP. A t-test was used for differential analysis. Results:Three gene datasets were downloaded, including GSE44711 (eight cases each for EOSP and normal pregnancy), GSE74341 (seven cases for EOSP and five cases for normal pregnancy), and GSE190639 (13 cases each for EOSP and normal pregnancy). A total of 29 DEGs were screened after combining the GSE44711 and GSE74341 datasets, including 27 upregulated and two downregulated genes. Gene ontology enrichment analysis showed that these genes are mainly involved in the secretion of gonadotropins, female pregnancy, regulation of endocrine processes, secretion of endocrine hormones, and negative regulation of hormone secretion. Eight characteristic genes ( EBI3, HTRA4, TREML2, TREM1, NTRK2, ANKRD37, CST6, and ARMS2) were screened using the LASSO regression algorithm combined with SVM-RFE algorithm and the expression differences of these characteristic genes were verified as statistically significant by qRT-PCR and Western blot (all P<0.05, except for CST6). Logistic regression algorithm showed that the AUC (95% CI) of TREML2, ANKRD37, NTRK2, TREM1, HTRA4, EBI3, and ARMS2 were 0.979 (0.918-1.000), 0.969 (0.897-1.000), 0.969 (0.892-1.000), 0.979 (0.918-1.000), 0.990 (0.954-1.000), 0.990 (0.954-1.000), and 0.903 (0.764-1.000). Immune cell infiltration analysis indicated that the infiltration ratio of M2 macrophages in the placental tissue from EOSP was significantly lower than that in the normal pregnancy (0.167±0.074 vs. 0.462±0.091, P=0.002), but the infiltration ratios of monocytes and eosinophils were significantly higher (0.201±0.004 vs. 0.085±0.006; 0.031±0.001 vs. 0.001±0.000, both P<0.05). The correlation analysis between characteristic genes and infiltrating immune cells found that the seven characteristic genes were closely related to the immune cells (all P<0.05). Conclusion:Seven characteristic genes that are critical for the prediction and early diagnosis of EOSP are screened using bioinformatics analysis and machine-learning algorithms in this study, which provides new research targets and a basis for the prevention and treatment of preeclampsia in the future.

OBJECTIVE To investigate the predictive value of prognostic nutritional index(PNI) and lymphocyte-monocyte ratio(LMR) in severe radiotherapy-induced oral mucositis(RIOM) during treatment of patients with head and neck cancer,and to construct a risk prediction model and test the prediction effect. METHODS A total of 502 patients with head and neck cancer who underwent radiotherapy were recruited from September 2021 to October 2023 in Xiangya Hospital Central South University. The participants were randomly divided into training group and validation group at a ratio of 7:3. According to whether severe RIOM occurred,they were divided into severe RIOM group and non-severe RIOM group. Univariate analysis and logistic regression analysis were used to screen the risk factors of severe RIOM. The receiver operating characteristic(ROC) curve was used to evaluate its prediction effect and R4.3.2 software was used to draw nomograms and decision curve. RESULTS The risk prediction model for patients with head and neck cancer during treatment had five factors,including the number of comorbidities(OR=2.221,95％CI=1.185-4.165),surgical history(OR=2.938,95％CI=1.393-6.198),the degree of tumor differentiation(OR=1.511,95％CI=1.090-2.094),PNI(OR=0.892,95％CI=0.852-0.934),LMR(OR=0.512,95％CI=0.254-1.030). Model formula:Y=2.102+0.413×degree of differentiation+0.798×number of comorbidities+1.078×surgical history-0.114×PNI-0.669×LMR. The validation results of the prediction model showed that the area under the ROC curve of the training group was 0.847(P<0.001),the area under the curve of the validation group was 0.808(P<0.001),and the P values of the Hosmer-Lemeshow test of the modeling group and the validation group were both greater than 0.05. The decision curve was above the reference line within most of the high-risk thresholds. CONCLUSION The risk prediction model constructed in this study has good effect,which can predict the risk of severe RIOM during radiotherapy in patients with head and neck cancer,providing the reference for taking preventive intervention measures for high-risk patients.

Objective To investigate the prevalence and severity of symptoms and to construct symptom networks in head and neck cancer patients during treatment to identify core symptoms and symptom clusters.Methods 366 patients who were hospitalized in 3 tertiary hospitals in Changsha were selected using convenience sampling from March to October 2022 and asked to complete the M.D.Anderson Symptom Inventory-Head & Neck.Exploratory factors analysis was used to extract the symptom clusters,and R packages were used to construct the symptom severity network and symptom clusters network.The centrality indexes of the networks,including strength,closeness,and betweenness,were analyzed to identify core symptoms and core symptom cluster.Results The most common symptoms in head and neck cancer patients during treatment were dry mouth(93.44％),fatigue(89.07％),loss of appetite(86.34％),and difficulty swallowing or chewing(85.79％),and the most severe symptoms were dry mouth,loss of appetite,oral or pharyngeal mucus,and difficulty swallowing or chewing.4 symptom clusters were extracted,namely oral-pharyngeal,gastrointestinal,emotional-sleep,and sickness-sensing behavioral,which could explain 67.415％of the total variance.In the symptom severity network,oral or pharyngeal mucus(rs=9.60)was a symptom with the highest strength.In the symptom clusters network,oral or pharyngeal mucus(rs=1.20),nausea(rs=1.00),fatigue(rs=1.10),and drowsiness(rs=0.97)were the symptoms with the highest strength across 4 symptom clusters.Conclusion Oral or pharyngeal mucus,nausea,fatigue,and drowsiness are the core symptoms of symptom clusters in head and neck cancer patients during treatment.Oral-pharyngeal symptom cluster is the core symptom cluster.It is recommended that clinical staff should develop interventions based on the core symptoms and symptom cluster to implement precise symptom management and improve symptom management efficiency.

Esophageal squamous cell carcinoma (ESCC) is one of the leading causes of cancer death worldwide. It is urgent to develop new drugs to improve the prognosis of ESCC patients. Here, we found benzydamine, a locally acting non-steroidal anti-inflammatory drug, had potent cytotoxic effect on ESCC cells. Benzydamine could suppress ESCC proliferation in vivo and in vitro. In terms of mechanism, CDK2 was identified as a target of benzydamine by molecular docking, pull-down assay and in vitro kinase assay. Specifically, benzydamine inhibited the growth of ESCC cells by inhibiting CDK2 activity and affecting downstream phosphorylation of MCM2, c-Myc and Rb, resulting in cell cycle arrest. Our study illustrates that benzydamine inhibits the growth of ESCC cells by downregulating the CDK2 pathway.
Humans ; Benzydamine ; Esophageal Neoplasms/drug therapy* ; Esophageal Squamous Cell Carcinoma/drug therapy* ; Molecular Docking Simulation ; Phosphorylation ; Cell Proliferation ; Cell Line, Tumor ; Apoptosis ; Cyclin-Dependent Kinase 2