Objective To explore the efficiency improvement in segmenting neural network with the application of Transformer + U-Net artificial intelligence (AI) and modeling with the application of Python scripts in three-dimensional (3D) printing brachytherapy. Methods A Transformer + U-Net AI neural network model was constructed, and Adam optimizer was used to ensure rapid gradient descent. Computed tomography or magnetic resonance imaging data of patients were standardized and processed as self-made data sets. The training set was used to train AI and the optimal result weight parameters were saved. The test set was used to evaluate the AI ability. Python programming language was used to write an automated script to obtain the output segmentation image and convert it to the STL file for import. The source applicator and needle could be automatically modeled. The time of automatic segmentation and modeling and the time of manual segmentation and modeling were entered by two people, and the difference was verified by paired t-test. Results Dice similarity coefficient (DSC), mean intersection over union (MIOU), and Hausdorff distance (HD95) were used for evaluation. DSC was 0.9341, MIOU was 0.8762, and mean HD95 was 2.516. The mean time of manual segmentation was 1187 s, and the mean time of AI segmentation was 145 s (P < 0.01). The mean time of manual modeling was 321 s, and the mean time of modeling with Python automated scripts was 18 s (P < 0.01). Conclusion The application of Transformer + U-Net automatic segmentation and Python automated scripts can effectively improve work efficiency in the 3D printing brachytherapy for cervical cancer.

Objective:To explore the factors influencing adherence to voice therapy among patients with voice disorders in China. Methods:Patients with voice disorders who visited the Voice Therapy Center at Sun Yat-sen Memorial Hospital, Sun Yat-sen University, from February to May 2022 were enrolled in the study. Adherence was assessed using the URICA-Voice scale, while influencing factors were assessed through the Voice Handicap Index（VHI） scale and a general information questionnaire. Correlation analysis was conducted using univariate and multivariate logistic regression analysis. Results:A total of 247 patients were included in the study, comprising 57 males（23.08%） and 190 females（76.92%）. The results revealed that: ①Female patients demonstrated higher likelihood of being in the contemplation stage（OR=0.22） compared to males. ②Patients with a monthly family income per capita>6 000 yuan were more likely to be in the contemplation stage than those with<3 000 yuan with an OR = 13.94. ③High vocal-demand occupations increased contemplation stage probability（OR=7.70） compared to moderate-demand occupations. ④Residence within 30-minute commute predicted action/maintenance stages（OR=7.14） versus≥60-minute commute. ⑤Patients whose occupations had high voice demands were more likely to be in the action and maintenance stages than those with average voice demands, with an OR of 16.20. Conclusion:Gender, monthly family income per capita, occupational voice demands, and distance to the hospital significantly impact the URICA-Voice compliance stages of patients. Patients who are female, have higher family income, have occupations with high voice demands, and live closer to the hospital exhibit higher compliance with voice training.
Humans ; Male ; Female ; Voice Disorders/therapy* ; Patient Compliance ; Voice Training ; Surveys and Questionnaires ; China ; Middle Aged ; Adult ; Voice Quality ; Logistic Models ; Aged

Objective:To analyze the epidemiological and clinical characteristics of respiratory syncytial virus (RSV) infection in children in Jiangsu Province from 2014 to 2023.Methods:The acute respiratory infection cases in children aged 0-14 years were selected from outpatient/emergency or inpatient departments in 2 surveillance sentinel hospitals, respectively, in Nanjing, Suzhou and Taizhou of Jiangsu from 1 July 2014 to 31 December 2023, and RSV nucleic acid test was conducted and the intensity of the RSV infection was accessed by WHO influenza epidemiological threshold method, and case information and clinical data were collected. χ2 test was used to compare the differences between groups, and the Bonferroni method was used for pairwise comparisons between groups. Results:In 4 946 cases of acute respiratory infections, the RSV positive rate was 8.21% (406/4 946), and the age M( Q1, Q3) of the cases was 1 (0, 3) years. The RSV positive rate was 10.92% (258/2 362) during 2014-2019 and 6.06% (118/1 948) during 2019-2023, the difference was significant ( χ2=31.74, P<0.001). RSV infection mainly occurred from October to March during 2014-2019, with the incidence peak in December and moderate or higher intensity. The seasonality of RSV infection was not obvious during 2019-2023, with low intensity. The RSV positive rate was highest in children in age group 0- years (17.85%, 151/846), and the positive rate declined gradually with age ( χ2=184.51, P<0.001). The RSV positive rate was higher in inpatient cases (9.84%, 244/2 480) than in outpatient/emergency cases (6.57%, 162/2 466) ( χ2=17.54, P<0.001). In the 155 RSV infection cases with complete clinical data, the clinical symptoms mainly included cough (99.35%, 154/155), fever (55.48%, 86/155), and shortness of breath (45.16%, 70/155). In the cases aged <6 months, the proportion of those with fever was low, but the proportion of those with shortness of breath, transferred to intensive care units, and receiving oxygen therapy were higher (all P<0.05). Children aged <6 months and those with underlying diseases were more likely to have severe RSV infection (all P<0.05). Conclusions:RSV infection in children in Jiangsu Province showed seasonal prevalence in winter from 2014 to 2019. Since 2020, the seasonal characteristics of the epidemic have changed, the epidemic period has been dispersed and the epidemic intensity has decreased. Infants <1 year old were at high risk for RSV infection, and those <6 months old and with underlying diseases might have severe infection.

Replantation of traumatic amputation in children has its own characteristics. This consensus primarily focuses on the issues related to the treatment of traumatically amputated limb injuries in children. Organised along a timeline, the consensus summarises domestic and international clinical experiences in emergency care and injury assessment of traumatic limb amputation limbs, indications and contraindications for replantation surgery, principles and procedures of replantation surgery, postoperative medication and management, as well as rehabilitation in children. The aim of this consensus is to propose standardise the treatment protocols for limb replantation for children therefore to serve as a reference for clinical practitioners in medical practices, and further improve the treatment and care for the traumatic limb amputations in children.

Objective:To analyze the types and functions of CD34 + cells in full-thickness skin defect wounds of normal mice and diabetic mice by single-cell RNA sequencing. Methods:This study was an experimental study. The CD34 + cell lineage tracing mouse was produced, and the visualization of CD34 + cells under the fluorescent condition was realized. Six male CD34 + cell lineage tracing mice aged 7-8 weeks (designated as diabetic group) were intraperitoneally injected with streptozotocin to establish a diabetic model, and full-thickness skin defect wounds were prepared on their backs when they reached 13 weeks old. Another 6 male CD34 + cell lineage tracing mice aged 13 weeks (designated as control group) were also subjected to full-thickness skin defect wounds on their backs. On post-injury day (PID) 4, wound tissue was collected from 3 mice in control group and 2 mice in diabetic group, and digested to prepare single-cell suspensions. CD34 + cells were screened using fluorescence-activated cell sorting, followed by single-cell RNA sequencing. The Seurat 4.0.2 program in the R programming language was utilized for dimensionality reduction, visualization, and cell clustering analysis of CD34 + cell types, and to screen and annotate the marker genes for each CD34 + cell subpopulation. Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO) enrichment analysis was performed to analyze the differentially expressed genes (DEGs) of CD34 + fibroblasts (Fbs), smooth muscle cells (SMCs), keratinocytes (KCs), and chondrocyte-like cells (CLCs) in the wound tissue of two groups of mice for exploring cellular functions. Results:On PID 4, CD34 + cells in the wound tissue of both groups of mice were consisted of 7 cell types, specifically endothelial cells, Fbs, KCs, macrophages, T cells, SMCs, and CLCs. Among these, Fbs were further classified into 5 subpopulations. Compared with those in control group, the proportions of CD34 + endothelial cells, Fbs subpopulation 1, Fbs subpopulation 4, KCs, and CLCs in the wound tissue of mice were increased in diabetic group, while the proportions of CD34 + Fbs subpopulation 2, Fbs subpopulation 3, and SMCs were decreased. The marker genes for annotating CD34 + CLCs, endothelial cells, Fbs subpopulation 1, Fbs subpopulation 2, Fbs subpopulation 3, Fbs subpopulation 4, Fbs subpopulation 5, KCs, macrophages, SMCs, and T cells were respectively metastasis-associated lung adenocarcinoma transcript 1, fatty acid binding protein 4, Gremlin 1, complement component 4B, H19 imprinted maternally expressed transcript, Dickkopf Wnt signaling pathway inhibitor 2, fibromodulin, keratin 5, CD74 molecule, regulator of G protein signaling 5, and inducible T-cell co-stimulator molecule. KEGG and GO enrichment analysis revealed that, compared with those in control group, DEGs with significant differential expression (SDE) in CD34 + Fbs from the wound tissue of mice in diabetic group on PID 4 were significantly enriched in terms related to inflammatory response, extracellular matrix (ECM) organization, regulation of cell proliferation, and aging (with Pvalues all <0.05), DEGs with SDE in CD34 + SMCs were significantly enriched in terms related to cell migration, apoptotic process, positive regulation of transcription, and phagosome (with P values all <0.05), DEGs with SDE in CD34 + KCs were significantly enriched in terms related to mitochondrial function, transcription, and neurodegenerative diseases (with P values all <0.05), and DEGs with SDE in CD34 + CLCs were significantly enriched in terms related to rhythm regulation, ECM, and viral infection (with P values all <0.05). Conclusions:CD34 + cells display high heterogeneity in the healing process of full-thickness skin defect wounds in both normal mice and diabetic mice. The significantly enriched functions of DEGs with SDE in CD34 + cell subpopulations in the wound tissue of the two mouse groups are closely related to the wound healing process.

Traditional Chinese medicine monomer has certain curative effects in tumor treatment,but its low targeting and difficulty in gathering at the tumor site limit its application.As a new type of drug delivery method,environmentally responsive nano drug delivery system has the advantages of targeted drug delivery,reducing drug side effects,and environmentally responsive release,and has been widely used in tumor treatment.In this paper,by consulting and sorting out the relevant literature,the general situation of traditional Chinese medicine monomers in the treatment of tumors,the classification of environment-responsive traditional Chinese medicine monomer nano drug delivery system,and its application in tumor treatment are summarized in three aspects,to provide ideas for solving the limitations of traditional Chinese medicine monomer in the treatment of tumor.

Objective:To investigate the clinical characteristics and correlation of sleep disturbances(SD)with cognitive impairment-associated cerebral small vascular disease(CSVD-CI)in elderly patients.Methods:In this cross-sectional study, 261 elderly CSVD-CI patients admitted to the Department of Neurology, Second Hospital of Tianjin Medical University between December 2019 and December 2021 were continuously enrolled.The Pittsburgh Sleep Index Scale(PSQI)was used to evaluate the overall sleep quality.Those with a PSQI score ≥7 was assigned to the CSVD-CI with sleep disturbances(CSVD-CI-SD)group, while those with a PSQI score <7 was assigned to the CSVD-CI without SD(CSVD-CI-NSD)group.The Montreal Cognitive Assessment(MoCA)was used to evaluate the cognitive function of patients with CSVD-CI, and scores on the overall cognitive function and various cognitive domains were compared between the CSVD-CI-SD group and the CSVD-CI-NSD group.Results:There were no significant differences between the CSVD-CI-SD group and the CSVD-CI-NSD group in sex ratio, age, education level and comorbidities( P>0.05). Compared with the CSVD-CI-NSD group, patients in the CSVD-CI-SD group took longer to fall asleep, had worse sleep efficiency, a shorter sleep duration, more obvious SD at night, worse sleep quality, more use of sleeping drugs, and more obvious daytime dysfunction(all P<0.05). Compared with the CSVD-CI-NSD group, the total MoCA score, attention score and orientation score in the CSVD-CI-SD group were significantly decreased( P<0.01). Correlation analysis results showed that the total MoCA score and attention in the CSVD-CI-SD group were negatively correlated with SD at night( r=-0.198, r=-0.115, P<0.05 for both), and orientation was negatively correlated with sleep quality( r=-0.170, P<0.05). Conclusions:The prevalence of SD is high in CSVD-CI patients, with CSVD-CI-SD patients showing more obvious overall cognitive, attention and orientation impairment in MoCA.Additionally, the total MoCA score and attention are negatively correlated with nighttime SD, and orientation is negatively correlated with sleep quality in CSVD-CI-SD patients.

Objective:To screen highly expressed inflammatory factors in diabetic nephropathy models using protein microarray, analyze differential genes and their regulatory networks, and predict potential therapeutic small molecular compounds.Methods:The inflammatory factor microarray was used to screen the inflammatory factors with the same tendency in the cell model and animal model of diabetic nephropathy. The differential genes screened by R language were enriched and analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG). STRING builds a protein interaction network online, Cytoscape software analyzes the core subnetwork, and Connectivity Map searches for and predicts small molecule compounds.Results:Diabetic nephropathy model was established using 16-week-old db/db mice and mesangial cells stimulated with high glucose, and the expression of C-X-C motif chemokine ligand 1(CXCL1) was elevated in both models. Multiple GEO datasets indicated a strong association between the high expression of CXCL1 and diabetic nephropathy. Specifically, GSE30122 showed an upregulation of 30 genes and a downregulation of 23 genes. GO enrichment analysis focused on biological processes such as humoral immunity and lipopolysaccharide response; While KEGG enrichment was mainly in pertussis and coagulation cascade pathways. CytoHubba identified 10 hub genes, such as ALB, LUM, and CXCL1. In addition, 10 small molecule compounds were predicted as potential therapeutic drugs using Connectivity Map.Conclusions:CXCL1 may serve as a key gene in the occurrence and development of diabetic nephropathy. ALB, LUM, CXCL1, MMP7, TGFBI, CCL2, S100A4, SOX9, VCAN, and CLU may participate in the regulatory network centered around CXCL1. There are 10 small molecular compounds demenestrating the potential to be therapeutic agents.

OBJECTIVE: To explore the regulatory mechanism of human hepatocyte apoptosis induced by lysosomal membrane protein Sidt2 knockout. METHODS: The Sidt2 knockout (Sidt2^-/-) cell model was constructed in human hepatocyte HL7702 cells using Crispr-Cas9 technology.The protein levels of Sidt2 and key autophagy proteins LC3-II/I and P62 in the cell model were detected using Western blotting, and the formation of autophagosomes was observed with MDC staining.EdU incorporation assay and flow cytometry were performed to observe the effect of Sidt2 knockout on cell proliferation and apoptosis.The effect of chloroquine at the saturating concentration on autophagic flux, proliferation and apoptosis of Sidt2 knockout cells were observed. RESULTS: Sidt2^-/- HL7702 cells were successfully constructed.Sidt2 knockout significantly inhibited the proliferation and increased apoptosis of the cells, causing also increased protein expressions of LC3-II/I and P62(P < 0.05) and increased number of autophagosomes.Autophagy of the cells reached a saturated state following treatment with 50 μmol/L chloroquine, and at this concentration, chloroquine significantly increased the expressions of LC3B and P62 in Sidt2^-/- HL7702 cells. CONCLUSION Sidt2 gene knockout causes dysregulation of the autophagy pathway and induces apoptosis of HL7702 cells, and the latter effect is not mediated by inhibiting the autophagy-lysosomal pathway.
Humans ; Lysosome-Associated Membrane Glycoproteins/metabolism* ; Autophagy ; Apoptosis ; Hepatocytes ; Lysosomes/metabolism* ; Chloroquine/pharmacology* ; Nucleotide Transport Proteins/metabolism*

Objective:To assess the effectiveness of a model created using clinical features and preoperative chest CT imaging features in predicting the chronic obstructive pulmonary disease (COPD) among patients diagnosed with lung cancer.Methods:A retrospective analysis was conducted on clinical (age, gender, smoking history, smoking index, etc.) and imaging (lesion size, location, density, lobulation sign, etc.) data from 444 lung cancer patients confirmed by pathology at the Second Affiliated Hospital of Naval Medical University between June 2014 and March 2021. These patients were randomly divided into a training set (310 patients) and an internal test set (134 patients) using a 7∶3 ratio through the random function in Python. Based on the results of pulmonary function tests, the patients were further categorized into two groups: lung cancer combined with COPD and lung cancer non-COPD. Initially, univariate analysis was performed to identify statistically significant differences in clinical characteristics between the two groups. The variables showing significance were then included in the logistic regression analysis to determine the independent factors predicting lung cancer combined with COPD, thereby constructing the clinical model. The image features underwent a filtering process using the minimum absolute value convergence and selection operator. The reliability of these features was assessed through leave-P groups-out cross-validation repeated five times. Subsequently, a radiological model was developed. Finally, a combined model was established by combining the radiological signature with the clinical features. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) curves were plotted to evaluate the predictive capability and clinical applicability of the model. The area under the curve (AUC) for each model in predicting lung cancer combined with COPD was compared using the DeLong test.Results:In the training set, there were 182 cases in the lung cancer combined with COPD group and 128 cases in the lung cancer non-COPD group. The combined model demonstrated an AUC of 0.89 for predicting lung cancer combined with COPD, while the clinical model achieved an AUC of 0.82 and the radiological model had an AUC of 0.85. In the test set, there were 78 cases in the lung cancer combined with COPD group and 56 cases in the lung cancer non-COPD group. The combined model yielded an AUC of 0.85 for predicting lung cancer combined with COPD, compared to 0.77 for the clinical model and 0.83 for the radiological model. The difference in AUC between the radiological model and the clinical model was not statistically significant ( Z=1.40, P=0.163). However, there were statistically significant differences in the AUC values between the combined model and the clinical model ( Z=-4.01, P=0.010), as well as between the combined model and the radiological model ( Z=-2.57, P<0.001). DCA showed the maximum net benifit of the combined model. Conclusion:The developed synthetic diagnostic combined model, incorporating both radiological signature and clinical features, demonstrates the ability to predict COPD in patients with lung cancer.