Objective: To explore the association between mental health and muscle strength among Chinese adolescents aged 13- 18, providing a theoretical foundation and intervention strategies for mental health promotion. Methods: Data were obtained from the 2019 Chinese National Survey on Students Constitution and Health, including 98 631 Chinese adolescents aged 13- 18. Psychological distress was assessed by using the Kessler Psychological Distress Scale (K10), and mental well being was measured with the Warwick-Edinburgh Mental Well being Scale (WEMWBS). Based on the gender and age specific Z scores of various test items [grip strength, standing long jump, pull ups (for males), and sit ups (for females)], muscle strength index (MSI) was constructed to evaluate the comprehensive level of muscle strength in adolescents. According to the Dual factor Model (DFM) of mental health, participants were categorized into four groups:troubled, symptomatic but content, vulnerable, and complete mental health. Gender differences were analyzed by using Chi-square tests, trends were tested with Cochran-Armitage tests, and multinomial Logistic regression models were applied to assess associations between muscle strength and mental health among adolescents. Results: In 2019, 37.4% of Chinese adolescents aged 13-18 were reported of high mental distress, and 59.9% were reported of low mental well being. Boys had significantly lower rates of high mental distress (35.3%) and low mental well being (55.6%) compared to girls (39.4%, 64.3%), and the differences were of statistical significance ( χ 2=176.13, 780.42, both P <0.05). In 2019, the rate of complete mental health among adolescents showed a downward trend with increasing age ( χ 2 trend = 258.47) and a gradual upward trend with increasing muscle strength levels ( χ 2 trend =123.14)，and both boys and girls exhibited similar trends ( χ 2 trend =103.83, 168.46; 57.00 , 67.34) (all P <0.05). The results of the unordered multiclass Logistic regression model showed that after controlling for confounding factors such as age and gender, when the completely pathological group as a reference, for every 1 unit increase in MSI in adolescents, the likelihood of being in a completely mental health state increased by 29% ( OR = 1.29); for every unit increase in the Z-score for pull ups, the likelihood of being in a completely mental health state increased by 6% ( OR =1.06) among boys; for every 1 unit increase in sit up Z score, the likelihood of being in a completely mental health state increased by 19% ( OR =1.19) among girls (all P <0.05). Conclusions The mental health status of Chinese adolescents is not good enough. Muscle strength is positively associated with mental health.

Objective: To systematically review the development and changes in mental health policies within the National Outline for Children s Development in China from 1992 to 2030, providing a reference basis for future formulation of mental health policies among children and adolescent in China. Methods: Based on the four editions of the National Outline for Children s Development in China across different periods from 1992 to 2030, word frequency analysis was used to reveal shifts in policy priorities, and an internationally recognized framework for adolescent health policy analysis was applied to conduct a textual review. Results: Word frequency analysis revealed that the term "psychological" appeared 6 times in the National Outline for Children s Development in China (2001-2010) but increased to 20 times in the National Outline for Children s Development in China (2021-2030) (abbreviated as the National Outline of 2021), while the term "health" rose from 4 times in the National Outline for Children s Development Plan in China in the 1990s to 68 times in the National Outline of 2021. The scope of mental health policy interventions expanded to encompass five key areas:health, safety, education, welfare and legal protection. Textual analysis highlighted that the policies of the National Outline for Children s Development in China were demand driven, prioritized vulnerable groups and continuously broadened their coverage, emphasizing sustainability and appropriateness, and monitoring/evaluation mechanisms. By 2023, 42.3% of primary schools and 64.8% of secondary schools employed full time mental health education teachers. However, the National Outline for Children s Development in China lacked direct evidence of children and adolescents participation in policy formulation, and publicly available mental health data disaggregated by age and gender remained limited. Conclusion Mental health policies of children and adolescents in China have evolved from nonexistence to gradual refinement, yet institutionalized channels for youth involvement in policy development and evaluation remain insufficient, and transparency in age and gender specific mental health data needs improvement.

Objective:To evaluate the reliability and validity of the Chinese version of HEMO-FISS-QoL(HF-QoL) questionnaire (HF-QoL-C) in the Chinese population with hemorrhoids.Methods:From November 2021 to November 2022, a self-constructed general information questionnaire, HF-QoL-C, and the 36-item short form health survey (SF-36), Goligher classification, and Giordano severity of hemorrhoid symptom questionnaire (GSQ) were used to conduct a questionnaire survey on 760 hemorrhoid patients in the anorectal department of six hospitals. The data was analyzed for reliability and validity using SPSS 21.0 and AMOS 26.0 software.Results:The Cronbach's α coefficient of HF-QoL-C and its dimension ranged from 0.831 to 0.960, and the split coefficient was 0.832-0.915. Four common factors were extracted through principal component exploratory factor analysis. Confirmatory factor analysis indicated acceptable structural validity( χ2/ df=8.152, RSMEA=0.097, CFI=0.881, IFI=0.881, NFI=0.867). HF-QoL-C was correlated with SF36 and GSQ( r=-0.694, 0.501, both P<0.01). There were differences in the total score and dimensional scores of HF-QoL-C between surgical and drug treated patients, different grades of Goligher classification for hemorrhoidal disease, and different ranges of hemorrhoid prolapse (all P<0.001). No ceiling effect was found in the total score and the scores of each dimension(0.3%-2.0%). There was a floor effect in both psychological function and sexual activity dimensions (16.7%, 35.1%). Conclusion:HF-QoL-C has good reliability and validity, which can be used to measure the quality of life of Chinese hemorrhoid patients.

Purpose To evaluate the image quality improvement of deep learning iterative reconstruction(DLIR)on pediatric head CT images of head injury and to evaluate the performance of DLIR and conventional adaptive statistical iterative reconstruction-veo(ASIR-V)of noise and image texture of CT image in children's head trauma.Materials and Methods A total of 80 cases in Beijing Children's Hospital,Capital Medical University from December 7th to 11th 2020 of children's head low-dose CT were retrospectively selected.Scan voltage was 120 kV.Scan current was 150-220 mA.The raw data were reconstructed into 5 mm thick slice and 0.625 mm thin slice brain window and bone window images.50%ASIR-V and high weight DLIR images(DL-H)were reconstructed,respectively.A 4-point system was used to subjectively evaluate the display of sulcus,brain matter and bone.The number of lesions in each group was counted.The CT value and image noise values of gray matter and white matter were measured,and the contrast to noise ratio was calculated,then measured the blur metric index was measured in the same slice.The differences between the two image reconstruction methods were compared.Results Compared to 50%ASIR-V images,DL-H significantly improved the display ability of the sulcus and ventricles,as well as the display ability of the brain parenchyma(W=5.5-22.2,all P<0.05)in both slice thickness.There was no statistically significant difference in the display ability of the sulcus and ventricles between 5 mm 50%ASIR-V and 0.625 mm DL-H images(W=0.9,2.0,P=0.32,0.05,respectively).In terms of bone display ability,all images could achieve a maximum score of 4.0.A total of 35 lesions were found in 80 patients via 5 mm 50%ASIR-V and DL-H images,including 12 hemorrhagic lesions,1 intracranial gas,9 fractures,and 13 soft tissue swelling.In terms of objective evaluation,the noise level of DL-H images was significantly lower than that of 50%ASIR-V images(t=21.4-35.7,all P<0.05),and there was no statistically significant difference in noise and contrast noise ratio between 5 mm 50%ASIR-V and 0.625 mm DL-H images(t=1.7-2.2,all P≥0.05).The blur metric index showed that DL-H was superior to 50%ASIR-V images(t=6.1,10.0,both P<0.05),and there was no statistically significant difference in blur metric index between 0.625 mm DL-H and 5 mm 50%ASIR-V images(t=2.6,P=0.28).Conclusion DLIR can improve the CT image quality and image texture of children's head trauma,0.625 mm DL-H image quality is close to 5 mm 50%ASIR-V image,which can meet the diagnostic requirements,and possible to further reduce the radiation dose.

BACKGROUND:Calcaneal defects are common in clinical practice.It is difficult for surgeons to evaluate the effect of calcaneal reconstruction due to the complex anatomical structure and motor function of the heel.Finite element analysis has become an effective method for biomechanical behavior simulation and numerical analysis. OBJECTIVE:To compare the clinical effect and biomechanical characteristics of total calcaneal reconstruction with the Ⅱ-shaped and V-shaped fibular flap. METHODS:CT images of one left foot of a healthy 50-year-old male were acquired.Mimics software was used to obtain the preliminary three-dimensional model.Geomagic software was used to trim and curve the model.The model was imported into Solidworks software to simulate calcaneal reconstruction and complete the pre-processing of finite element calculation.Finally,Ansys software was used to solve the problem.The simulation results were compared with previous literature results to verify the effectiveness of the model.The surgical effect and biomechanical characteristics of the foot in different gait phases based on the simulated stress results were analyzed. RESULTS AND CONCLUSION:(1)Both Ⅱ-shaped and V-shaped fibular flaps could be used to reconstruct completely missing calcaneus,which could restore the length,width and height of normal calcaneus,and fill up the missing calcaneus bone.(2)Compared with the normal calcaneus,both configurations of fibular flaps showed a tendency for over-concentration of stress after loading.The normal calcaneus stress was mostly concentrated around the calcaneus nodule,the subtalar process and the calcaneus groove,while the stress of the two fibular flaps was mostly concentrated at the junction between the bone flap with the talus and cuboid bones.(3)The maximum stress of calcaneus was different between the two models and normal calcaneus under different simulation conditions,with statistically significant differences(P＜0.05).Compared with the V-shaped fibular flaps,Ⅱ-shaped fibular flaps had less force change in different gaits and were closer to the normal calcaneus.The V-shaped fibular flap bore excessive stress during the period of push-off,and the grafted bone material may yield under this condition and have the risk of fractures.

Background::The potential impact of pre-existing coronary artery stenosis (CAS) on acute pulmonary embolism (PE) episodes remains underexplored. This study aimed to investigate the association between pre-existing CAS and the elevation of high-sensitivity cardiac troponin I (hs-cTnI) levels in patients with PE.Methods::In this multicenter, prospective case-control study, 88 cases and 163 controls matched for age, sex, and study center were enrolled. Cases were patients with PE with elevated hs-cTnI. Controls were patients with PE with normal hs-cTnI. Coronary artery assessment utilized coronary computed tomographic angiography or invasive coronary angiography. CAS was defined as ≥50% stenosis of the lumen diameter in any coronary vessel >2.0 mm in diameter. Conditional logistic regression was used to evaluate the association between CAS and hs-cTnI elevation.Results::The percentage of CAS was higher in the case group compared to the control group (44.3% [39/88] vs. 30.1% [49/163]; P = 0.024). In multivariable conditional logistic regression model 1, CAS (adjusted odds ratio [OR], 2.680; 95% confidence interval [CI], 1.243–5.779), heart rate >75 beats/min (OR, 2.306; 95% CI, 1.056–5.036) and N-terminal pro-B type natriuretic peptide (NT-proBNP) >420 pg/mL (OR, 12.169; 95% CI, 4.792–30.900) were independently associated with elevated hs-cTnI. In model 2, right CAS (OR, 3.615; 95% CI, 1.467–8.909) and NT-proBNP >420 pg/mL (OR, 13.890; 95% CI, 5.288–36.484) were independently associated with elevated hs-cTnI. Conclusions::CAS was independently associated with myocardial injury in patients with PE. Vigilance towards CAS is warranted in patients with PE with elevated cardiac troponin levels.

Objective:To investigate the association of exposure to PM 2.5 and its constituents during pregnancy and fetal growth and to further identify critical windows of exposure for fetal growth. Methods:We included 4 089 mother-child pairs from the Jiangsu Birth Cohort Study between January 2016 and October 2019. Data of general characteristics, clinical information, daily average PM 2.5 exposure, and its constituents during pregnancy were collected. Fetal growth parameters, including head circumference (HC), abdominal circumference (AC), and femur length (FL), were measured by ultrasound after 20 weeks of gestation, and then estimated fetal weight (EFW) was calculated. Generalized linear mixed models were adopted to examine the associations of prenatal exposure to PM 2.5 and its constituents with fetal growth. Distributed lag nonlinear models were used to identify critical exposure windows for each outcome. Results:A 10 μg/m 3 increase in PM 2.5 exposure during pregnancy was associated with a decrease of 0.025 ( β=-0.025, 95% CI: -0.048- -0.001) in HC Z-score, 0.026 ( β=-0.026, 95% CI: -0.049- -0.003) in AC Z-score, and 0.028 ( β=-0.028, 95% CI:-0.052--0.004) in EFW Z-score, along with an increased risk of 8.5% ( RR=1.085, 95% CI: 1.010-1.165) and 13.5% ( RR=1.135, 95% CI: 1.016-1.268) for undergrowth of HC and EFW, respectively. Regarding PM 2.5 constituents, prenatal exposure to black carbon, organic matter, nitrate, sulfate (SO 42-) and ammonium consistently correlated with decreased HC Z-score. SO 42- exposure was also associated with decreased FL Z-scores. In addition, we found that gestational weeks 2-5 were critical windows for HC, weeks 4-13 and 19-40 for AC, weeks 4-13 and 23-37 for FL, and weeks 4-12 and 20-40 for EFW. Conclusions:Our findings demonstrated that exposure to PM 2.5 and its constituents during pregnancy could adversely affect fetal growth and the critical windows for different fetal growth parameters are not completely consistent.

Objective:To investigate the feasibility and accuracy of computer vision-based artificial intelligence technology in detecting and recognizing instruments and organs in the scenario of radical laparoscopic gastrectomy for gastric cancer.Methods:Eight complete laparoscopic distal radical gastrectomy surgery videos were collected from four large tertiary hospitals in China (First Medical Center of Chinese PLA General Hospital [three cases], Liaoning Cancer Hospital [two cases], Liyang Branch of Jiangsu Province People's Hospital [two cases], and Fudan University Shanghai Cancer Center [one case]). PR software was used to extract frames every 5–10 seconds and convert them into image frames. To ensure quality, deduplication was performed manually to remove obvious duplication and blurred image frames. After conversion and deduplication, there were 3369 frame images with a resolution of 1,920×1,080 PPI. LabelMe was used for instance segmentation of the images into the following 23 categories: veins, arteries, sutures, needle holders, ultrasonic knives, suction devices, bleeding, colon, forceps, gallbladder, small gauze, Hem-o-lok, Hem-o-lok appliers, electrocautery hooks, small intestine, hepatogastric ligaments, liver, omentum, pancreas, spleen, surgical staplers, stomach, and trocars. The frame images were randomly allocated to training and validation sets in a 9:1 ratio. The YOLOv8 deep learning framework was used for model training and validation. Precision, recall, average precision (AP), and mean average precision (mAP) were used to evaluate detection and recognition accuracy.Results:The training set contained 3032 frame images comprising 30 895 instance segmentation counts across 23 categories. The validation set contained 337 frame images comprising 3407 instance segmentation counts. The YOLOv8m model was used for training. The loss curve of the training set showed a smooth gradual decrease in loss value as the number of iteration calculations increased. In the training set, the AP values of all 23 categories were above 0.90, with a mAP of 0.99, whereas in the validation set, the mAP of the 23 categories was 0.82. As to individual categories, the AP values for ultrasonic knives, needle holders, forceps, gallbladders, small pieces of gauze, and surgical staplers were 0.96, 0.94, 0.91, 0.91, 0.91, and 0.91, respectively. The model successfully inferred and applied to a 5-minutes video segment of laparoscopic gastroenterostomy suturing.Conclusion:The primary finding of this multicenter study is that computer vision can efficiently, accurately, and in real-time detect organs and instruments in various scenarios of radical laparoscopic gastrectomy for gastric cancer.

Objective:To investigate the feasibility and accuracy of computer vision-based artificial intelligence technology in detecting and recognizing instruments and organs in the scenario of radical laparoscopic gastrectomy for gastric cancer.Methods:Eight complete laparoscopic distal radical gastrectomy surgery videos were collected from four large tertiary hospitals in China (First Medical Center of Chinese PLA General Hospital [three cases], Liaoning Cancer Hospital [two cases], Liyang Branch of Jiangsu Province People's Hospital [two cases], and Fudan University Shanghai Cancer Center [one case]). PR software was used to extract frames every 5–10 seconds and convert them into image frames. To ensure quality, deduplication was performed manually to remove obvious duplication and blurred image frames. After conversion and deduplication, there were 3369 frame images with a resolution of 1,920×1,080 PPI. LabelMe was used for instance segmentation of the images into the following 23 categories: veins, arteries, sutures, needle holders, ultrasonic knives, suction devices, bleeding, colon, forceps, gallbladder, small gauze, Hem-o-lok, Hem-o-lok appliers, electrocautery hooks, small intestine, hepatogastric ligaments, liver, omentum, pancreas, spleen, surgical staplers, stomach, and trocars. The frame images were randomly allocated to training and validation sets in a 9:1 ratio. The YOLOv8 deep learning framework was used for model training and validation. Precision, recall, average precision (AP), and mean average precision (mAP) were used to evaluate detection and recognition accuracy.Results:The training set contained 3032 frame images comprising 30 895 instance segmentation counts across 23 categories. The validation set contained 337 frame images comprising 3407 instance segmentation counts. The YOLOv8m model was used for training. The loss curve of the training set showed a smooth gradual decrease in loss value as the number of iteration calculations increased. In the training set, the AP values of all 23 categories were above 0.90, with a mAP of 0.99, whereas in the validation set, the mAP of the 23 categories was 0.82. As to individual categories, the AP values for ultrasonic knives, needle holders, forceps, gallbladders, small pieces of gauze, and surgical staplers were 0.96, 0.94, 0.91, 0.91, 0.91, and 0.91, respectively. The model successfully inferred and applied to a 5-minutes video segment of laparoscopic gastroenterostomy suturing.Conclusion:The primary finding of this multicenter study is that computer vision can efficiently, accurately, and in real-time detect organs and instruments in various scenarios of radical laparoscopic gastrectomy for gastric cancer.

Objective:To assess the predictive value of dosiomics in predicting the occurrence of bone marrow suppression (BMS) in patients with pelvic tumors during radiotherapy.Methods:A retrospective analysis was conducted on the clinical data and radiotherapy planning documents of 129 patients with pelvic region tumors who underwent radiotherapy at the First Affiliated Hospital of Nanjing Medical University from January 2019 to January 2023. The region of interest (ROI) was outlined for bone marrow in the pelvic region by Accu Contour software in planning CT, and the ROI was exported together with the dose distribution file. According to a stratified randomization grouping method, the patients were divided into the training set and test set in an 8 vs. 2 ratio. The dosiomic features were extracted from the ROI, and the two independent samples t-test and the least absolute shrinkage and selection operator (LASSO) algorithm was employed to identify the best predictive characteristics. Subsequently, the dosiomic scores were calculated. Clinical predictors were identified through both univariant and multivariate logistic regression analyses. Predictive models were constructed by using clinical predictors alone and combining clinical predictors and dosiomic scores. The efficacy of predictive model was assessed by plotting the receiver operating characteristic (ROC) curve and evaluating its performance through the area under the ROC curve (AUC), the calibration curve, and decision curve analysis (DCA). Results:Fourteen dosiomic features that showed a strong correlation with the occurrence of BMS were screened and utilized to calculate the dosiomic scores. Based on both univariant and multivariate logistic regression analyses, chemotherapy, planning target volume (PTV) and V 5 Gy were identified as clinical predictors. According to the combined model, the AUC values for the training set and test set were 0.911 and 0.868, surpassing those of the clinical model (AUC=0.878 and 0.824). Furthermore, the analysis of both the calibration curve and DCA suggested that the combined model had higher calibration and net clinical benefit. Conclusion:The combined model has a high diagnostic value for predicting BMS in patients with pelvic tumors during radiotherapy.