Objective Domestic research institutions and researchers have established a wide variety of animal disease models and accumulated a wealth of specialized, distinctive, and targeted atlas data during the model development process. These atlas data are of great value for development and application. Therefore, it is necessary to develop a professional and complete digital atlas database platform for animal models, which can achieve the open sharing of animal model atlas data and the integration and optimization of atlas resources related to disease animal models held by relevant domestic institutions. Methods Based on the B/S architecture, the authors' institution built a digital atlas database of animal models, using Java as the main development language and Oracle database system along with related auxiliary tools. The database platform ran in a Linux environment and could be accessed by users through a web browser. At present, the data on this platform mainly came from the atlas resources submitted by animal model resource units within Guangdong Province. Results In August 2024, a digital atlas database platform for animal models was constructed based on the classification structure of three dimensions: systemic diseases, animal species, and resource units. This platform provided functions such as collection, management, retrieval, and viewing of atlas data. As of January 2025, four resource units had submitted 61 atlas data entries of animal models to the platform, totalling 610 data items. Conclusion The animal model digital atlas database platform has been constructed and put into preliminary use. Although the amount of data on the platform is still limited, it is capable of integrating and openly sharing animal model atlas data. It is believed that with the continuous enrichment of atlas data in the future, this platform is expected to provide important data support for the development of laboratory animal science and comparative medicine research, thereby promoting the efficient utilization of scientific research resources.

Objective Domestic research institutions and researchers have established a wide variety of animal disease models and accumulated a wealth of specialized, distinctive, and targeted atlas data during the model development process. These atlas data are of great value for development and application. Therefore, it is necessary to develop a professional and complete digital atlas database platform for animal models, which can achieve the open sharing of animal model atlas data and the integration and optimization of atlas resources related to disease animal models held by relevant domestic institutions. Methods Based on the B/S architecture, the authors' institution built a digital atlas database of animal models, using Java as the main development language and Oracle database system along with related auxiliary tools. The database platform ran in a Linux environment and could be accessed by users through a web browser. At present, the data on this platform mainly came from the atlas resources submitted by animal model resource units within Guangdong Province. Results In August 2024, a digital atlas database platform for animal models was constructed based on the classification structure of three dimensions: systemic diseases, animal species, and resource units. This platform provided functions such as collection, management, retrieval, and viewing of atlas data. As of January 2025, four resource units had submitted 61 atlas data entries of animal models to the platform, totalling 610 data items. Conclusion The animal model digital atlas database platform has been constructed and put into preliminary use. Although the amount of data on the platform is still limited, it is capable of integrating and openly sharing animal model atlas data. It is believed that with the continuous enrichment of atlas data in the future, this platform is expected to provide important data support for the development of laboratory animal science and comparative medicine research, thereby promoting the efficient utilization of scientific research resources.

Objective: To analyze the occurrence of unintentional injuries among college students and their association with sexual orientation and gender identity, so as to provide a targeted scientific basis for injury prevention measures and intervention strategies. Methods: From October 24 to November 18, 2023, a sample of 1 629 college students from two general universities in Beijing was selected using convenience sampling method. A questionnaire survey was conducted to collect information on the gender identity, sexual orientation and occurrence of unintentional injuries among college students in the past year. The Symptom Checklist-90 (SCL-90), Pittsburgh Sleep Quality Index (PSQI), Childhood Trauma Questionnaire (CTQ), and Delaware Bullying Victimization Scale-Student (DBVS-S) were used to assess mental health, sleep quality, childhood trauma, and dysfunctional impulsivity status. Analyses of sexual orientation and gender identity were conducted. The t-test and Chi square test were used for intergroup comparison,and multivariate Logistic regression analysis was used to examine risk factors for unintentional injuries among college students of different gender identities. Results: The incidence rate of unintentional injuries among college students was 16.94%, with boys (17.08%) being higher than girls (16.90%). Compared with those who did not experience unintentional injuries (5.28± 3.60 , 118.68±41.38), college students who experienced unintentional injuries had poorer sleep quality and mental health status ( 6.38 ±3.93, 135.59±50.96)( t =-3.92, -4.26); the differences in the incidence of unintentional injury among college students with non suicidal self injury, interpersonal violence, childhood trauma, and different sexual orientations and gender identities were all statistically significant ( χ 2=28.75, 75.18, 9.83, 16.20, 4.13) (all P <0.05). Multivariate Logistic regression analysis showed that after adjusting for age, gender and body mass index, non heterosexual orientation increased the risk of unintentional injuries ( OR=1.61, 95%CI =1.09-2.38), whereas existing non suicidal self injury behaviors ( OR=2.10, 95%CI =1.02-4.37) and poorer mental health status ( OR=1.54, 95%CI =1.05-2.27) increased the risk of unintentional injuries among non heterosexual college students (all P <0.05). Conclusions The incidence rate of unintentional injuries among college students is relatively high, with non heterosexual groups having increased risk of unintentional injuries. Mental health status and non suicidal self injury behaviors are important factors related to unintentional injuries among non heterosexual college students.

【Objective】 To examine the association between psychological behavioral problems and sleep problems in disabled children in Beijing, in order to provide reference for reducing the sleep problems of disabled children. 【Methods】 From September 2017 to February 2018, children with disabilities aged 6 to 17 years were selected by random sampling, and were investigated by a self-designed questionnaire to get their demographic characteristics.Pediatric Symptom Checklist-17 (PSC-17) was used to assess psychological and behavioral problems.The relationship between psychological behavior and sleep problems was analyzed by binary Logistic regression. 【Result】 Totally 650 children with disabilities were included in the study.The detection rates of sleep problems was significantly different in children with different genders, different school styles, different types of disabilities and different grades of disabilities (χ²=3.984, 4.437, 12.925, 18.104, P<0.001). There was statistically significant difference in the detection rate of sleep problems between children with and without psychological and behavioral problems (χ²=30.722, P<0.001).Psychological and behavioral problems were the main risk factors for sleep problems of disabled children (OR=2.540, 95%CI:1.740 - 3.709).The risk of sleep problems in day students with disabilities was 1.577 times as high as that of boarders with disabilities. 【Conclusions】 Psychological and behavioral problems of disabled children are related to sleep problems.Improving psychological and behavioral status can effectively prevent their sleep problems.

ObjectiveTo explore the effect of virtual reality on upper limb function in stroke patients through diffusion tensor imaging (DTI). MethodsFrom September, 2021 to March, 2023, 80 stroke patients in the Fuzhou Second General Hospital were randomly divided into control group (n = 40) and experimental group (n = 40). Both groups received routine rehabilitation, while the experimental group received virtual reality training additionally, for four weeks. They were assessed with Fugl-Meyer Assessment-Upper Extremities (FMA-UE) and Action Research Arm Test (ARAT) before treatment, after treatment and after four-week follow-up; and they were scaned with DTI to measure the fractional anisotropy (FA) and relative anisotropy (RA) of cerebral peduncle and posterior limb of inner capsule of the affected side before and after treatment. ResultsTwo cases dropped in each group. The FMA-UE and ARAT scores increased in both groups after treatment and follow-up (F > 2.790, P < 0.001), and increased more in the experimental group than in the control group (t > 2.297, P < 0.05). FA and RA in the posterior limb of inner capsule increased in both groups after treatment (t > 21.013, P < 0.001), and increased more in the experimental group (t > 2.006, P < 0.05). The d-value of FA of the posterior limb of internal capsule before and after treatment (ΔFA) was positively correlated with the d-value of FMA-UE score (r > 0.362, P < 0.05) in both groups, the ΔFA of the posterior limb of internal capsule was positively correlated with the d-value of ARAT score (r = 0.459, P < 0.01). ConclusionVirtual reality training can promote the recovery of upper limb function in stroke patients, which may associate with the conductivity of posterior limb of inner capsule.

Objective To monitor the antifungal resistance of clinical isolates of Candida spp.in the East China region.Methods MALDI-TOF MS or molecular methods were used to re-identify the strains collected from January 2018 to December 2022.Antifungal susceptibility testing was performed using the broth microdilution method.The susceptibility test results were interpreted according to the breakpoints of 2022 Clinical and Laboratory Standards Institute(CLSI)documents M27 M44s-Ed3 and M57s-Ed4.Results A total of 3 026 strains of Candida were collected,65.33％of which were isolated from sterile body sites,mainly from blood(38.86％)and pleural effusion/ascites(10.21％).The predominant species of Candida were Candida albicans(44.51％),followed by Candida parapsilosis complex(19.46％),Candida tropicalis(13.98％),Candida glabrata(10.34％),and other Candida species(0.79％).Candida albicans showed overall high susceptibility rates to the 10 antifungal drugs tested(the lowest rate being 93.62％).Only 2.97％of the strains showed dose-dependent susceptibility(SDD)to fluconazole.Candida parapsilosis complex had a SDD rate of 2.61％and a resistance rate of 9.42％to fluconazole,and susceptibility rates above 90％to other drugs.Candida glabrata had a SDD rate of 92.01％and a resistance rate of 7.99％to fluconazole,resistance rates of 32.27％and 48.24％to posaconazole and voriconazole non-wild-type strains(NWT),respectively,and susceptibility rates above 90％to other drugs.Candida tropicalis had resistance rates of 29.55％and 26.24％to fluconazole and voriconazole,respectively,resistance rates of 76.60％and 21.99％to posaconazole and echinocandins non-wild-type strains(NWT),and a resistance rate of 2.36％to echinocandins.Conclusions The prevalence and species distribution of Candida spp.in the East China region are consistent with previous domestic and international reports.Candida glabrata exhibits certain degree of resistance to fluconazole,while Candida tropicalis demonstrates higher resistance to triazole drugs.Additionally,echinocandins resistance has emerged in Candida albicans,Candida glabrata,Candida tropicalis,and Candida parapsilosis.

Objective:To analyze the value of C-reactive protein to prealbumin ratio (CRP/PA) combined with neutrophil CD64 (nCD64) index in evaluating the severity and prognosis of mycoplasma pneumonia (MPP) in children.Methods:The clinical data of 98 children with MPP admitted to Changzhou Maternal and Child Health Hospital from March to December 2023 were retrospectively analyzed. According to the severity of the patients, they were divided into mild children (60 cases) and severe children (38 cases). After 7 days of routine treatment, the patients were divided into good prognosis group (72 cases) and poor prognosis group (26 cases). The CRP/PA and nCD64 indices of children with different severity of disease were compared, and the relationship between CRP/PA and severity of disease was analyzed. The clinical data of children with different prognosis outcomes were compared, and the influencing factors of prognosis were analyzed. The area under receiver operating characteristic (ROC) curve (AUC) was used to analyze the prognostic value of CRP/PA and nCD64 index.Results:CRP/PA[(1.05±0.21) vs (0.77±0.15)] and nCD64 index [(4.11±0.82) vs (2.96±0.59)] in severe children were higher than those in mild children (all P<0.05). Pearson analysis showed that CRP/PA and nCD64 index were positively correlated with clinical pulmonary infection score (CPIS) ( r=0.587, 0.574, all P<0.05). Univariate analysis showed that the age of the poor prognosis group [(4.81±0.96)years vs (6.67±1.33)years] was smaller than that of the good prognosis group. The severity of the disease was severe [65.38%(17/26) vs 29.17%(21/72)], complicated complications [46.15%(12/26) vs 18.06%(13/72)], CRP/PA[(1.23±0.25) vs (0.74±0.15)] and nCD64 index [(4.48±0.89) vs (3.02±0.61)] were higher than those in good prognosis group (all P<0.05). Binary logistic regression analysis showed that the severity of disease ( OR=4.251, 95% CI: 1.682-10.742), CRP/PA ( OR=3.857, 95% CI: 1.886-7.887), nCD64 index ( OR=3.161, 95% CI: 1.382-7.228) were prognostic factors (all P<0.05). ROC curve showed that the sensitivity and specificity of CRP/PA, nCD64 index and their combination in predicting prognosis were 76.90%, 73.10% and 88.50%, respectively, and 73.60%, 72.20% and 90.30%, respectively. The AUC values were 0.809, 0.794 and 0.923 (all P<0.001). Conclusions:CRP/PA and nCD64 index are significantly positively correlated with the severity of MPP in children, and they are the influencing factors for the prognosis of children. The combined detection of CRP/PA and nCD64 index has good predictive value for the prognosis of children.

Objective:To investigate the factors influencing fall risk scores in elderly individuals.Methods:A total of 4 419 individuals were randomly selected using the cluster sampling method from Beijing, Nanning(Guangxi), and Yinchuan(Ningxia).Data on demographic characteristics and fall-related incidents were gathered and analyzed for their correlation with fall risk scores.Results:The fall risk score showed significant associations with various factors, such as the history of falls within one year( β=-3.607, 95% CI: -3.881 to -3.332), care methods( β=2.442, 95% CI: 2.226 to 2.658), exercise( β=0.714, 95% CI: 0.443 to 0.986), retirement( β=-0.585, 95% CI: -0.819 to -0.351), age( β=0.173, 95% CI: 0.159 to 0.187), and use of walking aids( β=-3.737, 95% CI: -4.054 to -3.421). Conclusions:Fall risk scores in older adults are influenced by a variety of factors.Factors such as no history of falls within the past year, living independently, engaging in physical activity, and being employed may contribute to lower fall risk scores in older adults.

OBJECTIVE To investigate the improvement effects and possible mechanism of 7-hydroxyethyl chrysin （7-HEC） on PC12 cell injury induced by hypobaric hypoxia. METHODS The rat adrenal pheochromocytoma cell line PC12 was cultured under low-pressure hypoxia （5%CO2， 94%N2， 1%O2， 54 004 Pa） to investigate the different concentrations of 7-HEC （100， 10， 1， 0.1， 0.01 μmol/L） on the survival rate of hypoxic cells； the effects of 7-HEC（1 μmol/L） on the contents of lactate dehydrogenase （LDH） and malondialdehyde （MDA）， superoxide dismutase （SOD） activity， apoptotic rate， cell cycle， and the expressions of cleaved caspase-3， Bcl-2 and Bax were detected. RESULTS Compared with control group， the survival rate of cells in hypobaric hypoxia group was decreased significantly （P＜0.01）； 10， 1， 0.1 μmol/L 7-HEC could reverse the decrease of cell survival rate caused by hypobaric hypoxia （P＜0.05 or P＜0.01）. Compared with control group， LDH content in supernatant， MDA content in cells， apoptotic rate， the proportion of cells at G1 stage and the protein expression of cleaved caspase-3 were increased significantly in hypobaric hypoxia group， while SOD activity in cells， the proportion of cells at S stage and G2 stage and Bcl-2/Bax ratio were decreased significantly （P＜0.05 or P＜0.01）. Compared with hypobaric hypoxia group， the contents of LDH and MDA， apoptotic rate， the proportion of cells at G1 stage and the expression of cleaved caspase-3 in 7-HEC group were decreased significantly， while SOD activity， the proportion of cells at G2 stage and Bcl-2/Bax ratio were increased significantly （P＜ 0.01）. CONCLUSIONS 7-HEC can significantly increase the survival rate of hypobaric hypoxia cells， reduce the LDH content in supernatant， improve cell cycle arrest， and reduce the rate of apoptosis. Its improvement effects on hypobaric hypoxia cell injury may be related to the inhibition of caspase-3/Bax/Bcl-2 pathway activation.

Objective:To construct of a computed tomography (CT) based radiomics model for predicting the prognosis of patients with gastric neuroendocrine neoplasm (GNEN) and inves-tigate its application value.Methods:The retrospective cohort study was conducted. The clinico-pathological data of 182 patients with GNEN who were admitted to 2 medical centers, including the First Affiliated Hospital of Zhengzhou University of 124 cases and the Affiliated Cancer Hospital of Zhengzhou University of 58 cases, from August 2011 to December 2020 were collected. There were 130 males and 52 females, aged 64(range, 56-70)years. Based on random number table, all 182 patients were divided into the training dataset of 128 cases and the validation dataset of 54 cases with a ratio of 7:3. All patients underwent enhanced CT examination. Observation indicators: (1) construction and validation of the radiomics prediction model; (2) analysis of prognostic factors for patients with GNEN in the training dataset; (3) construction and evaluation of the prediction model for prognosis of patients with GNEN. Measurement data with skewed distribution were represented as M(range), and comparison between groups was conducted using the Mann-Whitney U test. Count data were described as absolute numbers, and the chi-square test, corrected chi-square test or Fisher exact probability were used for comparison between groups. The Kaplan-Meier method was used to calculate survival rate and draw survival curve, and the Log-rank test was used for survival analysis. The COX regression model was used for univariate and multivariate analyses. The R software (version 4.0.3) glmnet software package was used for least absolute shrinkage and selection operator (LASSO)-COX regression analysis. The rms software (version 4.0.3) was used to generate nomogram and calibration curve. The Hmisc software (version 4.0.3) was used to calculate C-index values. The dca.R software (version 4.0.3) was used for decision curve analysis. Results:(1) Construction and valida-tion of the radiomics prediction model. One thousand seven hundred and eighty-one radiomics features were finally extracted from the 182 patients. Based on the feature selection using intra-group correlation coefficient >0.75, and the reduce dimensionality using LASSO-COX regression analysis, 14 non zero coefficient radiomics features were finally selected from the 1 781 radiomics features. The radiomics prediction model was constructed based on the radiomics score (R-score) of these non zero coefficient radiomics features. According to the best cutoff value of the R-score as -0.494, 128 patients in the training dataset were divided into 64 cases with high risk and 64 cases with low risk, 54 patients in the validation dataset were divided into 35 cases with high risk and 19 cases with low risk. The area under curve (AUC) of radiomics prediction model in predicting 18-, 24-, 30-month overall survival rate of patients in the training dataset was 0.83[95% confidence interval ( CI ) as 0.76-0.87, P<0.05], 0.84(95% CI as 0.73-0.91, P<0.05), 0.91(95% CI as 0.78-0.95, P<0.05), respectively. The AUC of radiomics prediction model in predicting 18-, 24-, 30-month overall survival rate of patients in the validation dataset was 0.84(95% CI as 0.75-0.92, P<0.05), 0.84 (95% CI as 0.73-0.91, P<0.05), 0.86(95% CI as 0.82-0.94, P<0.05), respectively. (2) Analysis of prognostic factors for patients with GNEN in the training dataset. Results of multivariate analysis showed gender, age, treatment method, tumor boundary, tumor T staging, tumor N staging, tumor M staging, Ki-67 index, CD56 expression were independent factors influencing prognosis of patients with GNEN in the training dataset ( P<0.05). (3) Construction and evaluation of the prediction model for prognosis of patients with GNEN. The clinical prediction model was constructed based on the independent factors influen-cing prognosis of patients with GNEN including gender, age, treatment method, tumor boundary, tumor T staging, tumor N staging, tumor M staging, Ki-67 index, CD56 expression. The C-index value of clinical prediction model in the training dataset and the validation dataset was 0.86 (95% CI as 0.82-0.90) and 0.80(95% CI as 0.72-0.87), respectively. The C-index value of radiomics prediction model in the training dataset and the validation dataset was 0.80 (95% CI as 0.74-0.86, P<0.05) and 0.75(95% CI as 0.66-0.84, P<0.05), respectively. The C-index value of clinical-radiomics combined prediction model in the training dataset and the validation dataset was 0.88(95% CI as 0.85-0.92) and 0.83 (95% CI as 0.77-0.89), respectively. Results of calibration curve show that clinical prediction model, radiomics prediction model and clinical-radiomics combined prediction model had good predictive ability. Results of decision curve show that the clinical-radiomics combined prediction model is superior to the clinical prediction model, radiomics prediction model in evaluating the prognosis of patients with GNEN. Conclusions:The predection model for predicting the prognosis of patients with GNEN is constructed based on 14 radiomics features after selecting. The prediction model can predict the prognosis of patients with GNEN well, and the clinical-radiomics combined prediction model has a better prediction efficiency.