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.

Objective:To explore the prognostic factors of elderly patients with community-acquired pneumonia-related sepsis and to construct a prediction model.Methods:The clinical data of elderly patients with community-acquired pneumonia-associated sepsis from October 2020 to October 2022 in the General Hospital of Ningxia Medical University from October 2020 to October 2022 were retrospectively included, and the clinical data of the two groups were divided into the modeling population and the validation population in the ratio of 7:3 by random number table method, and the clinical data of the two groups were compared. According to the 30-day outcomes of admission, the patients were divided into survival group and death group, and the independent risk factors for the prognosis of elderly patients with community-acquired pneumonia-related sepsis were screened out by LASSO regression and multivariate logistic regression analysis, and the nomogram prediction model was constructed by R software. The area under the curve (AUC), calibration curve and decision curve of the receiver operating characteristic curve were used to validate the nomogram prediction model in the modeling population and the validation population to judge its discrimination, calibration and clinical practicability.Results:A total of 472 patients were included, with 331 and 141 models and validations, respectively, indicating that the clinical data were comparable between the modeled and validated populations. LASSO regression and multivariate logistic regression analysis showed that pneumonia severity index (PSI) score and sequential organ failure assessment (SOFA) score were independent risk factors for the prognosis of elderly patients with community-acquired pneumonia-associated sepsis. The AUC of the modeled population prediction model was 0.984 (95% CI: 0.975-0.994), and the AUC of the validated population prediction model was 0.961 (95% CI: 0.926-0.996). The nomogram prediction model has good discrimination, calibration and clinical practicability in both the modeled and validated populations. Conclusions:The nomogram prediction model established in the study has high accuracy for early identification and risk of sepsis in elderly patients with CAP and can guide for clinicians to formulate personalized interventions.

Objective To evaluate and compare the impact of three root canal sealers(C-Root SP,iRoot SP,and AH-Plus Jet)on the sealing performance in single-cone root canal filling using micro-computed tomography(Micro-CT).Methods Ninety mandibular single-rooted extracted teeth meeting inclusion criteria were collected,prepared with Protaper Next up to X3,and randomly assigned to three experimental groups which were C-Root SP group(Group A),iRoot SP group(Group B),and AH-Plus Jet group(Group C).Subsequently,all three groups underwent single-cone root canal filling,followed by one week of oral environment simulation.The seal-ing performance of the three root canal sealers was evaluated by measuring the proportion of the root canal volume and its rate using Mi-cro-CT.Results In different segments of the roots(upper 1/3,middle,and lower),identifiable bubbles were observed in the Group A,Group B and Group C,with volumes of(0.15±0.03)mm3,(0.22±0.11)mm3,and(0.34±0.28)mm3,respectively.Overall,Group A exhibited the smallest bubble volume(0.15±0.03)mm3.Group A and B were similar,they were superior to group C.(P＜0.05)Nevertheless,in the apical 1/3 segment(0-3 mm),a significant statistical difference was observed between the bubble volumes of Group A(0.01±0.01)mm3 and Group C(0.12±0.08)mm3(P＜0.05),while no significant difference was observed compared to Group B.Conclusion The apical sealing performance of C-Root SP is similar to iRoot SP and superior to AH-Plus Jet.C-Root SP can be considered as a favorable choice for root canal sealer.

Objective:To construct a prognostic predictive model for patients with moderate to severe traumatic brain injury (msTBI) based on regional cerebral oxygen saturation (rScO 2) and transcranial Doppler ultrasound (TCD) monitoring parameters and validate its effectiveness. Methods:A retrospective cohort study was conducted to analyze the clinical data of 161 patients with msTBI who were treated at Henan Provincial People′s Hospital from January 2021 to December 2022, including 104 males and 57 females, aged 19-76 years [(53.1±12.8)years]. Glasgow coma scale (GCS) score was 3-12 points [(7.0±1.9)points]. Both rScO 2 and TCD monitoring were performed. Based on the results of prognostic evaluation of patients with the modified Rankin scale (mRS) score at 90 days after discharge, the patients were divided into good prognosis group (mRS score≤3 points, n=88) and poor prognosis group (mRS score of 4-6 points, n=73). The following data of the two groups were collected: the general data, clinical data, rScO 2 monitoring parameters and TCD monitoring parameters. Univariate analysis was employed to compare the differences in the relevant prognostic indicators. Multivariate Logistic stepwise regression analysis was conducted to determine the predictors of poor prognostic outcomes in msTBI patients and regression equations were constructed. A nomogram predictive model based on regression equations was drawn with R language. The discriminability of the model was evaluated by drawing the receiver operating characteristic (ROC) curve, to calculate the area under the curve (AUC), sensitivity, specificity, and Jordan index of the model, and measuring the consistency index (C index). Hosmer-Lemeshow (H-L) goodness of fit test was conducted to evaluate the fit of the model, and the calibration curve was used to evaluate the calibration degree of the model. Decision curve analysis (DCA) was employed to evaluate the clinical benefit and applicability of the model. Results:There were significant differences between the two groups in the clinical data (cerebral hernia formation, GCS on admission, acute physiology and chronic health evaluation II (APACHE II) score on admission, Rotterdam CT score on admission, oxygenation index on admission, mean arterial pressure on admission), rScO 2 monitoring parameters (mean rScO 2, maximum rScO 2, rScO 2 variability), TCD monitoring parameters [peak systolic blood flow velocity (Vs), average blood flow velocity (Vm), pulse index (PI)] ( P<0.05 or 0.01). The results of multivariate Logistic stepwise regression analysis showed that cerebral hernia formation ( OR=9.28, 95% CI 3.40, 25.33, P<0.01), Rotterdam CT score on admission ( OR=1.92, 95% CI 1.32, 2.78, P<0.01), rScO 2 variability ( OR=4.66, 95% CI 1.74, 12.43, P<0.01), Vs ( OR=0.66, 95% CI 0.61, 0.75, P<0.01) and PI ( OR=20.07, 95% CI 4.17, 16.50, P<0.01) were predictive factors for poor prognosis in patients with msTBI. The regression equation was constructed with the forementioned 5 variables: Logit [ P/(1- P)]=2.23×"brain hernia formation"+0.65×"Rotterdam CT score on admission"+1.54×"rScO 2 variability"-0.42×"Vs"+3.00×"PI"-6.75. The AUC of prognostic predictive model of msTBI patients was 0.90 (95% CI 0.85, 0.95), with the sensitivity and specificity of 86.3% and 78.4%, Youden index of 0.65 and C index of 0.90. H-L goodness of fit test showed that the calibration degree of the predictive model was accurate ( χ2 =12.58, P>0.05). The average absolute error of the calibration curve was 0.025, showing that the calibration of the model was good. DCA results showed that this model had higher net return rate than the reference model within the probability range of risk threshold (20%-100%), with good clinical application value in evaluating the risk of poor prognosis of msTBI patients. Conclusion:The model constructed based on the combination of rScO 2 and TCD monitoring parameters (rScO 2 variability, Vs and PI) with multiple clinical indicators (cerebral hernia formation and Rotterdam CT score on admission) has good predictive performance for the prognosis of msTBI.

Objective:To observe the clinical value of intracranial pressure (ICP) monitoring combined with target temperature management (TTM) in patients with acute anterior circulation ischemic stroke after mechanical thrombectomy.Methods:A prospective analysis was performed. Ninety-two patients with acute anterior circulation ischemic stroke who received mechanical thrombectomy from March 2019 to June 2022 in Department of Neurosurgery, He'nan Provincial People's Hospital were enrolled. Within 1-5 d of mechanical thrombectomy, these patients were randomly divided into observation group ( n=46) and control group ( n=46). The patients in observation group received comprehensive management for neurological critical illness through multimodal monitoring such as ICP real-time monitoring combined with TTM (controlling the core temperature at 33℃-35℃), while patients in control group received simple ICP real-time monitoring. ICP monitoring for both groups lasted for 5-7 d, and routine symptomatic support treatment was given. Stepwise treatment was adopted based on real-time changes of ICP. The differences in clinical data, ICP at different times, incidence of adverse events, length of hospital stay, mortality rate, and prognoses were compared between the 2 groups. Results:On the 2 nd, 3 rd, 4 th, and 5 th d of monitoring, the observation group had significantly decreased ICP compared with the control group ( P<0.05). Both observation group and control group had significantly increased ICP on the 2 nd, 3 rd, 4 th, and 5 th d of monitoring compared with that on the 1 st d of monitoring ( P<0.05). Compared with the control group, the observation group had statistically higher incidences of shivers and electrolyte disorders, and statistically lower incidences of unstable blood pressure, cerebral heart syndrome, septic shock, and cerebral hernia during hospitalization ( P<0.05). Compared with the control group, the observation group had significantly shortened hospital stay, and statistically lower modified Rankin scale (mRS) scores, higher Glasgow outcome scale-extended (GOS-E) scores, higher good prognosis rate, and lower mortality rate 6 months after mechanical thrombectomy ( P<0.05). Compared with the control group, the observation group had statistically lower incidences of postoperative cerebral hemorrhage conversion and recurrent cerebral infarction ( P<0.05). Kaplan-Meier survival analysis showed that the survival rate in the observation group was significantly higher than that in the control group ( P<0.05). Conclusion:ICP monitoring combined with TTM can reduce early complications, shorten hospital stay, reduce mortality, and improve long-term prognosis in patients with acute anterior circulation ischemic stroke after mechanical thrombectomy.

[摘 要] 目的：探讨卵巢癌组织中PD-L1与唾液酸结合性免疫球蛋白样凝集素15（Siglec-15）的关系及其临床意义以及两者对卵巢癌SKOV3细胞增殖、迁移及侵袭的影响。方法：收集2017年1月至2019年12月福建医科大学附属第二医院妇科50例手术切除的卵巢癌组织和配对输卵管组织的石蜡包埋标本，采用免疫组化染色Envision法检测癌组织和输卵管组织中PD-L1和Siglec-15的表达水平，Kaplan-Meier生存曲线和Logistic回归分析PD-L1和Siglec-15表达与患者预后的关系。利用瞬时转染技术在卵巢癌细胞SKOV3中分别转染si-PD-L1和si-NC，用qPCR和WB法检测SKOV3细胞中PD-L1的表达对Siglec-15的影响，用CCK-8及Transwell法验证PD-L1及Siglec-15表达对SKOV3细胞增殖、迁移及侵袭的影响。结果：50例卵巢癌组织中，PD-L1与Siglec-15均呈高表达（50.00%与42.00%）。PD-L1表达与肿瘤病理类型、有无腹水、淋巴结转移、FIGO分期及卵巢癌复发与否具有关联（均P<0.05）；Siglec-15表达与卵巢癌患者淋巴结转移及FIGO分期具有关联（均P<0.05）。成功构建PD-L1低表达SKOV3细胞株，降低PD-L1表达可使Siglec-15表达升高。结论：PD-L1和Siglec-15在卵巢癌组织中均有较高的阳性表达率，PD-L1是卵巢癌复发的独立风险因素。PD-L1和Siglec-15两者的表达呈负相关，降低PD-L1表达可使Siglec-15表达水平升高而抑制SKOV3细胞增殖、迁移和侵袭的能力。

L-theanine has been shown to have a therapeutic effect on depression.However,whether L-theanine has an excellent preventive effect on depression in children and adolescents and what its mechanism is have not been well explained.Given the complexity of the pathogenesis of depression,this study investigated the preventive effect and mechanism of L-theanine on depression in juvenile rats by combining serum and hippocampal metabolomic strategies.Behavioral tests,hippocampal tissue sections,and serum and hippocampal biochemical indexes were studied,and the results confirmed the preventive effect of L-theanine.Untargeted reversed-phase liquid chromatography-quadrupole-time-of-flight mass spec-trometry and targeted hydrophilic interaction liquid chromatography-triple quadrupole mass spec-trometry were developed to analyze the metabolism changes in the serum and hippocampus to screen for potential biomarkers related to L-theanine treatment.The results suggested that 28 abnormal me-tabolites in the serum and hippocampus that were considered as potential biomarkers returned to near-normal levels after L-theanine administration.These biomarkers were involved in various metabolic pathways,mainly including amino acid metabolism and lipid metabolism.The levels of amino acids and neurotransmitters in the phenylalanine,tryptophan,and glutamic acid pathways were significantly reduced after L-theanine administration compared with chronic unpredictable mild stress-induced rats.In summary,L-theanine had a significant preventive effect on depression and achieved its preventive results on depression by regulating various aspects of the body,such as amino acids,lipids,and inflammation.This research systematically analyzed the mechanism of L-theanine in preventing depression and laid the foundation for applying L-theanine to prevent depression in children and adolescents.

Medical Physics is an interdiscipline which is formed by applying the basic principles, methods, and techniques of physics to clinical medical research such as prevention, diagnosis, and treatment of human diseases, and it is a compulsory professional basic course for medical students. However, there are many medical students reflect that the content of this course is obscure and difficult to understand. Teaching effect is not ideal. The main reasons are that the teaching method is single and the reference materials are few. Based on the actual situation of the course, Hebei Medical University, China promotes formative evaluation from the content of teaching materials, teaching means, and other aspects. At the same time, by combining with the Internet, the digital construction has been realized, deepening the reform of the Medical Physics curriculum, and remarkable results have been achieved.

Objective:To analyze the influencing factors of catheter-related bloodstream infection (CRBSI) in chronic renal failure (CRF) hemodialysis patients and to construct a risk prediction model.Methods:Using the convenient sampling method, a total of 90 CRF patients who underwent hemodialysis in International Medical Department of Peking Union Medical College Hospital, Chinese Academy of Medical Sciences from May 2018 to May 2021 were selected as the research objects. The gender, age, catheter placement site, catheter type, catheter retention time, catheter care times, tube sealing method, strict hand hygiene, combination with diabetes, hemoglobin, serum albumin, dialysis time, immunosuppressant and other data of the patients were analyzed. Univariate analysis and multivariate Logistic regression were used to analyze the influencing factors of CRBSI in CRF patients and a prediction model was established. Hosmer-Lemeshow (H-L) test and area under the receiver operating characteristic curve ( AUC) were used to verify the fitting effect and discrimination of the model. Results:Among the 90 CRF hemodialysis patients, 16 patients developed CRBSI, with an incidence rate of 17.78% (16/90) . The results of univariate analysis showed that age, catheter placement site, catheter type, catheter retention time, catheter nursing times, strict implementation of hand hygiene, combination with diabetes mellitus, hemoglobin, and serum albumin were the influencing factors of CRBSI in CRF patients ( P<0.05) . Multivariate Logistic regression analysis showed that femoral vein, temporary catheter, catheter retention time greater than 2 weeks, not strictly implementing hand hygiene, complicated with diabetes and serum albumin less than 30 g/L were independent risk factors for CRBSI in CRF hemodialysis patients ( P<0.05) . The H-L test results showed that the model had good goodness of fit (χ 2=7.978, P=0.436) . The receiver operating characteristic curve analysis showed that the AUC of the model was 0.889, the best cut-off value was 0.732, the sensitivity was 81.30% and the specificity was 91.90%. Conclusions:There are many risk factors for CRBSI in RF hemodialysis patients. The establishment of the early warning model is conducive to the early risk assessment of CRBSI occurrence and the formulation of countermeasures to reduce the actual incidence of CRBSI.