Objective: The cross sectional study aimed to identify predominant co-occurrence patterns among six common mental health issues in college students, so as to provide empirical basis for designing targeted interventions. Methods: From October 2024, a total of 9 837 students from 4 universities in Xiangtan City, Hunan Province, participated in the current study by multistage random cluster sampling method. Participants completed self report measures, including the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder 7 item Scale (GAD-7), Young s Internet Addiction Diagnostic Questionnaire, the Adolescent Insomnia Symptom Self rating Scale, the Ottawa Self injury Inventory, and the Brief Community Assessment of Psychic Experiences Questionnaire. Demographic and co-occurrence characteristics were first compared using Chi square or trend Chi-square tests, followed by application of the Apriori algorithm to mine association rules for primary co-occurrence patterns. Results: The detection rate of co-occuring the common mental health issues was 46.44%. The detection rate was significantly higher in female than in male students (50.42%, 43.61%; χ 2=44.46) and in students from rural versus urban areas (47.22%, 44.60%; χ 2=5.67) (both P <0.05). Significant differences were observed among freshmen, sophomores, juniors, and seniors (46.63%, 48.35%, 45.05% , 43.66%, respectively; χ 2=9.22, P <0.05), although no statistically significant trend was detected ( χ 2 trend =3.75, P = 0.05 ). Association rule mining identified “anxiety + depression” “anxiety + psychotic experiences + depression” and “anxiety + sleep disorder + depression” as the combinations with the highest support. In addition, “anxiety+depression+Internet addiction+psychotic experiences =>sleep disorder (>= refered to the occurrence of the latter item under the condition that the former item occurs)” and “anxiety + depression+Internet addiction=>sleep disorder” were combinations with relatively high confidence. Conclusions Co-occurrence of these mental health issues among college students is high and exhibits diverse patterns. Strategies to address this burden should prioritize integrated interventions that target these specific combinations of factors.

Metastasis serves as an indicator of malignancy and is a biological characteristic of carcinomas. Epithelial-mesenchymal transition (EMT) plays a key role in the promotion of tumor invasion and metastasis and in the enhancement of tumor cell aggressiveness. Prostaglandin E synthase 3 (p23) is a cochaperone for heat shock protein 90 (HSP90). Our previous study showed that p23 is an HSP90-independent transcription factor in cancer-associated inflammation. The effect and mechanism of action of p23 on lung cancer metastasis are tested in this study. By utilizing cell models in vitro and mouse tail vein metastasis models in vivo, the results provide solid evidence that p23 is critical for promoting lung cancer metastases by regulating downstream CXCL1 expression. Rather than acting independently, p23 forms a complex with RNA-binding motif protein 14 (RBM14) to facilitate EMT progression in lung cancer. Therefore, our study provides evidence for the potential role of the RBM14-p23-CXCL1-EMT axis in the metastasis of lung cancer.

Objective:To develop an artificial intelligence (AI)-based automatic scoring model for magnetic resonance imaging-detected extramural vascular invasion (AI-mrEMVI) and evaluate its performance and prognostic value in patients with rectal cancer.Methods:In this multicenter retrospective cohort study, a total of 2 501 rectal cancer patients from seven centers between November 2012 and December 2020 were included and divided into completely independent training ( n=1 830) and validation ( n=671) cohorts. A nnUNet-based AI-mrEMVI scoring model was constructed. Manual mrEMVI scores assigned by two radiologists served as the reference standard for accessing the accuracy of the AI-mrEMVI scoring. Kaplan-Meier survival analysis and Cox regression were used to evaluate the prognostic stratification ability of the AI-mrEMVI scores. The concordance index (C-index) was calculated to evaluate prognostic performance. Results:In the validation cohort, the manual mrEMVI scores were 0-2 in 425 patients (63.3%), 3 in 89 (13.4%), and 4 in 157 (23.4%). The AI-mrEMVI model identified 0-2 in 375 patients (55.9%), 3 in 95 (14.2%), and 4 in 201 (30.0%), with an overall accuracy of 81.1% (544/671, 95% CI 77.9%-84.0%). The 3-year disease-free survival (DFS) rates for patients with AI-mrEMVI scores of 0-2, 3, and 4 were 85.2%, 70.0%, and 58.2%, respectively, and the 5-year overall survival (OS) rates were 87.2%, 81.6%, and 62.6%, respectively (DFS: χ2=48.74, P<0.001; OS: χ2=30.04, P<0.001). Multivariable Cox regression showed that for DFS, AI-mrEMVI scores of 3 and 4 were associated with hazard ratios ( HR) of 1.75 (95% CI 1.11-2.77, P=0.016) and 2.65 (95% CI 1.86-3.78, P<0.001), respectively. For OS, an AI-mrEMVI score of 4 was associated with an HR of 2.56 (95% CI 1.62-4.03, P<0.001). The C-index values of the AI-mrEMVI scoring model for predicting DFS and OS were 0.647 (95% CI 0.608-0.686) and 0.650 (95% CI 0.598-0.702), respectively. Conclusion:The proposed AI-mrEMVI automatic scoring model demonstrated high diagnostic accuracy and performed favorably in predicting DFS and OS prognostic risk in patients with rectal cancer.

Objective To construct a regional collaborative cloud-based treatment model treatment model for patients with severe road traffic injuries,and to preliminarily evaluate the differences in nursing timeliness indicators and outcomes.Methods The regional collaborative cloud-based treatment platform includes 4 ports,including public security traffic police,pre-hospital emergency center,regional trauma center triage,and regional trauma center resuscitation unit.This forms a standardized real-time interactive treatment process between regional medical services and police for patients with severe road traffic injuries.Using a concurrent control study design,241 patients with severe road traffic injuries admitted to the emergency department of a regional trauma center in Zhejiang Province from May 2022 to May 2024 were selected as the study subjects.Among them,120 patients treated with the regional real-time collaborative cloud-based treatment model were designated as an experimental group,while 121 patients treated with the original trauma care process were designated as a control group.The differences in timeliness indicators and outcomes between the 2 groups were compared.Results The study included 241 patients with severe trauma.After the application of the regional collaborative cloud-based treatment model,the time from the scene of the accident to the hospital,the proportion of information early waming,completion time of pre-examination and triage,waiting time of the trauma team,the time of the first CT,the length of multidisciplinary consultation,and the time for completing hospitalization procedures in the experimental group were shorter than those in the control group(P＜0.05),the proportion of information early waming in the experimental group was 100％(120/120),and the proportion of information early waming in the control group was 52.1％(63/121).The difference between the two groups was statistically significant(P＜0.001).The survival rate of the experimental group was 90.8％(109/120),and that of the control group was 86.0％(104/121).There was no significant difference between the two groups(x2=1.399,P=0.237).Conclusion The regional collaborative cloud-based treatment model improves the timeliness and standardization of the treatment of patients with severe road traffic injury,which has certain reference significance and promotion value.

Objective:To examine the clinical features and genetic profiles of patients with thyroid peroxidase(TPO) gene mutations and provide diagnostic guidance for clinicians.Methods:A retrospective review of four patients with TPO mutations treated at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, from January 2014 to December 2023. Data on demographics, clinical manifestation, genotypes, treatment, and prognosis of these patients were analyzed.Results:Two males and two females, aged 13 to 27 years at diagnosis, presented with goiter as the initial symptom, with three cases menifesting during puberty. Laboratory findings showed mildly elevated or upper-limit-normal serum thyroid-stimulating hormone(TSH) levels, significantly increased free triiodothyronine/free thyroxine(FT 3/FT 4) ratios, and elevated thyroglobulin(TG) levels. Genetic testing revealed compound heterozygous pathogenic or likely pathogenic TPO mutations. Despite regular levothyroxine(L-T 4) therapy, goiter persisted. Three patients required thyroidectomy due to cosmetic or compressive symptoms, with pathology showing follicular hyperplasia. Conclusion:TPO mutations are characterized by adolescent-onset goiter, elevated FT 3/FT 4 ratios, and normal to high TSH. Genetic testing confirms the diagnosis. L-T 4 offers limited improvement, and surgery is often needed.

Objective:To explore the features of levels of lung cancer biomarkers in peripheral blood of adults in Beijing and surrounding areas, and establish personalized reference intervals for these biomarkers.Methods:A cross sectional study was conducted. The lung cancer biomarker data, including carcinoembryonic antigen (CEA), cytokeratin 19 fragment antigen 21-1 (CYFRA21-1), neuron specific enolase (NSE), progastrin-releasing peptide (ProGRP), and squamous cell carcinoma antigen (SCC-Ag), collected from adults who underwent cancer prevention examinations at the Cancer Hospital of the Chinese Academy of Medical Sciences from July 2021 to July 2022 were retrospectively analyzed. The interquartile range method was used to eliminate outliers, and the P95 value was calculated. Upper limit of 5 lung cancer biomarkers in different gender and age groups were obtained by referring to the reference intervals of quantitative analytes in the clinical laboratory (WS/T 402-2024). By analyzing the data of 208 adults who underwent cancer prevention physical examinations at the same center in June 2021 and 140 patients with benign lung masses confirmed by surgical resection pathology from January 2016 to June 2022, the established reference intervals for biomarkers were validated. Results:Two thousand six hundred and twenty-six cases of apparently healthy physical examiners were included for constructing reference intervals, including 1 456 males (55.4%) and 1 170 females (44.6%); the age range was 20-88 years old. The serum levels [ M ( Q1, Q3)] of CEA, NSE, ProGRP, SCC-Ag and CYFRA21-1 in 2 626 cases were 1.63 (1.07, 2.43) ng/ml, 13.08 (11.44, 14.77) ng/ml, 34.93 (29.02, 42.19) pg/ml, 0.80 (0.60, 1.00) ng/ml and 1.96 (1.48, 2.63) ng/ml, respectively. The serum levels of CEA [1.88 (1.22, 2.76) ng/ml vs. 1.41 (0.93, 2.02) ng/ml], NSE [13.31 (11.87, 15.00) ng/ml vs. 12.69 (10.96, 14.53) ng/ml], SCC-Ag [0.9 (0.7, 1.1) ng/ml vs. 0.7 (0.6, 0.9) ng/ml], and CYFRA21-1 [2.02 (1.53, 2.71) ng/ml vs. 1.87 (1.40, 2.51) ng/ml] in males were higher than those in females, and ProGRP [34.00 (28.25, 41.55) pg/ml vs. 36.12 (29.97, 42.98) pg/ml] was lower than that in females, and the differences were statistically significant (all P < 0.001). There were statistically significant differences in serum CEA levels between the groups of ≤ 40 years old (458 cases), >40-50 years old (827 cases), >50-60 years old (783 cases), >60-70 years old (412 cases), and >70 years old (146 cases) in pairwise comparison (all P < 0.05). Except for the age groups of ≤ 40 years old and >40-50 years old and the age groups of >60-70 years old and >70 years old, there were statistically significant differences in serum NSE levels among the other age groups in pairwise comparison (all P < 0.05). There were statistically significant differences in serum ProGRP levels between the 5 age groups (all P < 0.05). There were statistically significant differences when comparing the serum SCC-Ag level in the >40-50 age group, >50-60 age group and >60-70 age group with that in the ≤40 age group and >70 age group, respectively (all P < 0.05). However, there was no statistically significant difference between the other age groups in pairwise comparison (all P > 0.05). There were statistically significant differences in serum CYFRA21-1 levels between the 5 age groups (all P < 0.05). When gender and age were not distinguished, the P95 values of serum CEA, NSE, ProGRP, SCC-Ag and CYFRA21-1 levels were 4.44 ng/ml, 16.61 ng/ml, 57.65 pg/ml, 1.50 ng/ml, and 4.21 ng/ml, respectively. Considering gender and age, except for the >70 age group with no statistically significant difference in the P95 value of serum CEA level between males and females ( P > 0.05), the P95 value of serum CEA level in males was higher than that in females in all other age groups (all P < 0.001); the P95 values of serum CEA level in both males and females increased with age, but showed a decreasing trend in males over the age of 70. The P95 value of serum NSE level in males was higher than that in females in the age groups of ≤ 40 years and >40-50 years (both P < 0.05), while there was no statistically significant difference in the P95 value of serum NSE level between males and females in other age groups (all P > 0.05). The P95 values of serum NSE level in both males and females decreased firstly and increased later with age, reaching their highest levels at the age of >70. The P95 values of serum ProGRP level in females aged ≤ 40 and >50-60 were higher than those in males (both P < 0.05), while there was no statistically significant difference in the P95 value of serum ProGRP level between genders in other age groups (all P > 0.05); the P95 values of serum ProGRP level in both males and females increased with age. There was no statistically significant difference in the P95 value of serum SCC-Ag level between males and females in the ≤ 40 age group ( P > 0.05), while the P95 value of serum SCC-Ag level in males was higher than that in females in all other age groups (all P < 0.05). The P95 values of serum SCC-Ag level in males increased with age, while they were stable in females. There was no statistically significant difference in the P95 value of serum CYFRA21-1 level between males and females in the >60-70 age group ( P > 0.05), while the P95 value of serum CYFRA21-1 level in males was higher than those in females in all other age groups (all P < 0.05); the P95 values of serum CYFRA21-1 level in both males and females increased with age. Based on data from 2 626 apparently healthy physical examiners, reference intervals for the levels of 5 lung cancer biomarkers were constructed in different age groups of different genders. Validation was conducted on 208 physical examiners and 140 patients with benign lung lesions, and it was found that the compliance rate of using newly created reference intervals for different gender and age groups to interpret detection results was >90%, and the validation was passed. Conclusions:There are gender and age differences in the reference intervals of CEA, CYFRA21-1, NSE, ProGRP, and SCC-Ag in peripheral blood of adults in Beijing and surrounding areas. The constructed reference intervals of gender and age for biomarkers have been validated and shown good results, providing reference for optimizing the clinical application of lung cancer-related biomarkers.

Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.

Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.

Objective:To explore the significance of any correlation between the cost of the rehabilitation provided to stroke survivors during their hospitalization and the functional levels attained, and to analyze factors influencing that correlation.Methods:The International Classification of Functioning, Disability and Health Rehabilitation Set (ICF-RS) was used to evaluate the functioning of 304 stroke survivors on their days of hospital admission and discharge, as well as on their 10th day in hospital. The cost of their rehabilitation was computed, and demographic and clinical data were collected. A generalized estimation equation was used to analyze the changes in dysfunction with time and its risk factors. The relationship between functional levels and rehabilitation cost and its influencing factors were analyzed.Results:Length of stay, age≥60 and hemorrhagic stroke were significant risk factors for greater dysfunction among the stroke survivors. On the 10th day in hospital and the day before discharge (the 18th day), the frequency of severe dysfunction had decreased. The significant predictors of increased cost were severe or moderate dysfunction, the stage of stroke (sub-acute stage), and non-first rehabilitation.Conclusion:Functional level is a useful predictor of rehabilitation cost. It is influenced by the stage of stroke and non-first rehabilitation.