ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

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 explore the construction of an evaluation model for aortic root anatomy and calcium burden in patients with bicuspid aortic valve (BAV) stenosis before transcatheter aortic valve replacement (TAVR) based on deep learning (DL) algorithms.Methods:A retrospective collection of 362 BAV stenosis patients who underwent TAVR from September 2023 to May 2024 was performed. All patients underwent cardiac CT angiography. The patients were divided into training group ( n=104), internal validation group ( n=206), and external validation group ( n=52). A DL model was trained on the training dataset to assess aortic root anatomy and calcification burden. The evaluation included the segmentation accuracy of the algorithm, the measurement performance of key anatomical structures (i.e., valve leaflets and type-1 and type-2 fusion raphe), and calcification burden, as well as the measurement efficiency. Overall segmentation performance was assessed using the average Dice coefficient (ADC). The fine-scale segmentation quality was validated by the 95th-percentile Hausdorff distance (HD-95) and the average symmetric surface distance (ASSD). The consistency of the measurement results was assessed using the Pearson correlation coefficient and the intraclass correlation coefficient ( ICC) with a two-way mixed model for absolute agreement. In addition, the total time and total mouse movement distance required for manual assessment versus the DL model on the validation datasets were recorded and compared. Results:The algorithm demonstrated excellent segmentation performance on aortic root anatomical targets, achieving outstanding consistency within both internal and external validation datasets (0.955

Objective: To investigate the distribution of pupil diameter and its association with myopia in school age children, providing ideas into the mechanisms of the role of pupil diameter in the onset and development of myopia. Methods: Adopting a combination of stratified cluster random sampling and convenience sampling method, 3 839 children from six schools in Shandong Province were included in September 2021. Pupil diameters distribution was analyzed by age, sex, and myopic status. Pearson correlation analysis was used to assess the relationship between pupil diameter and cycloplegic spherical equivalent (SE), as well as axial length (AL) and other variables. Propensity score matching (PSM) was applied to match myopic and non myopic children at a 1∶1 ratio based on age and sex. A generalized linear model (GLM) was constructed with pupil diameter as the dependent variable to identify independent factors influencing pupil size and its association with myopia. Results: The mean pupil diameter of school age children was (5.77±0.80)mm. Pupil diameter exhibited a significant increasing trend with age ( F =49.34， P trend ＜ 0.01). Myopic children had a significantly larger mean pupil diameter [(6.10±0.73)mm] compared to non myopic children [(5.62±0.79)mm] with a statistically significant difference( t=18.10, P <0.01). Multivariable GLM analysis, adjusted for age, amplitude of accommodation, and uncorrected visual acuity, revealed a negative correlation between pupil diameter and cycloplegic SE (before PSM: β =-0.089, after PSM: β =-0.063, both P ＜0.01). Conclusions Myopic school age children exhibite larger pupil diameters than their non myopic counterparts. Pupil diameter may serve as a potential indicator for monitoring myopia development in school age children.

OBJECTIVE: Patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection frequently develop central nervous system damage, yet the mechanisms driving this pathology remain unclear. This study investigated the primary pathways and key factors underlying brain tissue damage induced by the SARS-CoV-2 beta variant (lineage B.1.351). METHODS: K18-hACE2 and C57BL/6 mice were intranasally infected with the SARS-CoV-2 beta variant. Viral replication, pathological phenotypes, and brain transcriptomes were analyzed. Gene Ontology (GO) analysis was performed to identify altered pathways. Expression changes of host genes were verified using reverse transcription-quantitative polymerase chain reaction and Western blot. RESULTS: Pathological alterations were observed in the lungs of both mouse strains. However, only K18-hACE2 mice exhibited elevated viral RNA loads and infectious titers in the brain at 3 days post-infection, accompanied by neuropathological injury and weight loss. GO analysis of infected K18-hACE2 brain tissue revealed significant dysregulation of genes associated with innate immunity and antiviral defense responses, including type I interferons, pro-inflammatory cytokines, Toll-like receptor signaling components, and interferon-stimulated genes. Neuroinflammation was evident, alongside activation of apoptotic and pyroptotic pathways. Furthermore, altered neural cell marker expression suggested viral-induced neuroglial activation, resulting in caspase 4 and lipocalin 2 release and disruption of neuronal molecular networks. CONCLUSION These findings elucidate mechanisms of neuropathogenicity associated with the SARS-CoV-2 beta variant and highlight therapeutic targets to mitigate COVID-19-related neurological dysfunction.
Animals ; COVID-19/genetics* ; Mice ; Brain/metabolism* ; Apoptosis ; Mice, Inbred C57BL ; SARS-CoV-2/physiology* ; Pyroptosis ; Gene Expression Profiling ; Transcriptome ; Male ; Female

Objective: To analyze the epidemiological characteristics of mumps in Shanxi Province from 2014 to 2023, so as to provide scientific evidence for targeted prevention strategies. Methods: Mumps case data in Shanxi Province were obtained from the China Information System for Disease Prevention and Control. Descriptive epidemiological analysis and age-period-cohort (APC) analysis were carried out on the reported incidence of mumps from 2014 to 2023. Results: A total of 44 360 mumps cases were reported in Shanxi Province from 2014 to 2023, with an average annual incidence rate of 11.78/100 000. The incidence rates were high during 2017-2019, which were 21.00/100 000, 16.76/100 000, and 19.51/100 000, respectively. Males had a higher incidence rate (13.50/100 000) than females (9.98/100 000). Children aged 5-9 years were the most affected group, accounting for 47.29% of total cases. In 2017 and 2019, incidence rates among the 5-15-year-old group were particularly high, reaching 155.08/100 000 and 131.78/100 000, respectively. The APC model age effect, period effect and cohort effect of the reported incidence rate in the high-incidence population aged 0-20 years all had statistical significance ( P <0.05). The age-relative risk ( RR ) decreased from 1.75 in the 0-year-old group to 0.33 in the 20-year-old group, and the birth cohort RR decreased from 2.58 in 1994 to 0.26 in 2023. The morbidity risk of the population aged 0-20 years showed a trend of first increasing and then decreasing over time, among which it was the highest in 2017 ( RR =1.23) and the lowest in 2023 ( RR =0.29). Conclusions Shanxi exhibits cyclical mumps epidemics, with school-aged children as the high-risk population. School health management work should be carried out, and the surveillance of mumps in high-risk areas and the routine vaccination of two doses of mumps-containing vaccines for eligible children should be strengthened.

Objective:To explore the construction of an evaluation model for aortic root anatomy and calcium burden in patients with bicuspid aortic valve (BAV) stenosis before transcatheter aortic valve replacement (TAVR) based on deep learning (DL) algorithms.Methods:A retrospective collection of 362 BAV stenosis patients who underwent TAVR from September 2023 to May 2024 was performed. All patients underwent cardiac CT angiography. The patients were divided into training group ( n=104), internal validation group ( n=206), and external validation group ( n=52). A DL model was trained on the training dataset to assess aortic root anatomy and calcification burden. The evaluation included the segmentation accuracy of the algorithm, the measurement performance of key anatomical structures (i.e., valve leaflets and type-1 and type-2 fusion raphe), and calcification burden, as well as the measurement efficiency. Overall segmentation performance was assessed using the average Dice coefficient (ADC). The fine-scale segmentation quality was validated by the 95th-percentile Hausdorff distance (HD-95) and the average symmetric surface distance (ASSD). The consistency of the measurement results was assessed using the Pearson correlation coefficient and the intraclass correlation coefficient ( ICC) with a two-way mixed model for absolute agreement. In addition, the total time and total mouse movement distance required for manual assessment versus the DL model on the validation datasets were recorded and compared. Results:The algorithm demonstrated excellent segmentation performance on aortic root anatomical targets, achieving outstanding consistency within both internal and external validation datasets (0.955

OBJECTIVE:The quality of systematic reviews/meta-analyses directly affects the reliability of clinical decision-making basis.Currently,there is no literature quality research on the systematic reviews/meta-analyses of platelet rich plasma versus hyaluronic acid in the treatment of knee osteoarthritis both domestically and internationally.This article will comprehensively evaluate the methodological quality,reporting quality,and evidence quality of the systematic reviews/meta-analyses of platelet rich plasma versus hyaluronic acid in the treatment of knee osteoarthritis.METHODS:Computer searches were conducted on CNKI,WanFang Data,VIP,CBM,PubMed,Embase,and The Cochrane Library.From the establishment of the database until January 24,2024,all systematic reviews/meta-analyses treated with platelet rich plasma versus hyaluronic acid for knee osteoarthritis were collected.Two evaluators independently conducted literature screening and data extraction,and used the AMSTAR 2,PRISMA 2020,and GRADE systems to evaluate and summarize the methodological,reporting,and evidence quality of the included systematic reviews/meta-analyses.RESULTS:A total of 18 qualified systematic reviews/meta-analyses were included,and the results showed that the efficacy and safety of platelet rich plasma might be better than that of hyaluronic acid.The methodological quality of all 18 studies was extremely low;4 reports had poor quality and relatively serious information defects,while 14 reports had moderate quality and some information defects.Among the 275 outcome measures of 16 systematic reviews/meta-analyses,there were 9 medium quality evidence,90 low-quality evidence,and 176 extremely low-quality evidence,with no high-quality evidence.CONCLUSION:At present,the quality of systematic reviews/meta-analyses literature on the treatment of knee osteoarthritis with platelet rich plasma versus hyaluronic acid is relatively low.In the future,the authors of the systematic reviews need to strictly follow the entries of quality evaluation tools such as AMSTAR 2 and PRISMA 2020 in terms of plan registration,research type explanation,retrieval strategy,exclusion list,research site and funding source,bias risk analysis,publication bias evaluation,and public information acquisition,and conduct evidence quality evaluation on the combined results of the systematic reviews/meta-analyses to provide more reliable and rigorous evidence-based basis for clinical practice.