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 investigate the efficacy of low-methoxyl pectin (LMP) combined with early enteral nutrition (EEN) in patients with severe acute pancreatitis (SAP) complicated by acute kidney injury (AKI).Methods:A prospective study was conducted, enrolling 85 patients with SAP complicated by AKI admitted to the First Hospital of Qinhuangdao from January 2022 to December 2023. These patients were randomized into an LMP group ( n=42) and a control group ( n=43) using a random number table. The control group received traditional EEN, while the LMP group received traditional EEN combined with LMP. Nutritional indicators [total protein (TP), albumin (Alb), and prealbumin (PA)], inflammatory markers [C-reactive protein (CRP), interleukin (IL)-1, IL-6, IL-8, and tumor necrosis factor-α (TNF-α)], renal function indicators [creatinine (Cr), estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), uric acid (UA)], AKI stage, and fecal intestinal flora were compared between these two groups before and 7 days after intervention. Results:After 7 days of intervention, the TP, Alb, and PA levels significantly increased in both groups (all P<0.05); the increases in TP and PA were more significant in the LMP group (both P<0.05), whereas the increase in Alb did not differ significantly between groups ( P>0.05). The levels of CRP, IL-1, IL-6, IL-8, and TNF-α decreased significantly in both groups (all P<0.05),with more significant reductions in the LMP group (all P<0.001). The levels of Cr, eGFR, BUN, and UA improved significantly in both groups (all P<0.05),with more significant improvements in the LMP group (all P<0.05). The incidence of a higher AKI stage in the LMP group was significantly lower than that in the control group( P<0.05), and the incidence of a lower AKI stage was significantly higher than that in the control group ( P<0.001). The fecal counts of Bifidobacterium and Lactobacillus increased significantly while the fecal counts of Enterococcus and Enterobacter decreased significantly in both group (all P<0.001); notably, these changes in bacterial counts were significantly more pronounced in the LMP group ( P<0.001). Conclusions:EEN combined with LMP can significantly enhance nutritional status, reduce inflammatory response, and improve renal function in patients with SAP complicated by AKI.

Objective:To investigate the efficacy of low-methoxyl pectin (LMP) combined with early enteral nutrition (EEN) in patients with severe acute pancreatitis (SAP) complicated by acute kidney injury (AKI).Methods:A prospective study was conducted, enrolling 85 patients with SAP complicated by AKI admitted to the First Hospital of Qinhuangdao from January 2022 to December 2023. These patients were randomized into an LMP group ( n=42) and a control group ( n=43) using a random number table. The control group received traditional EEN, while the LMP group received traditional EEN combined with LMP. Nutritional indicators [total protein (TP), albumin (Alb), and prealbumin (PA)], inflammatory markers [C-reactive protein (CRP), interleukin (IL)-1, IL-6, IL-8, and tumor necrosis factor-α (TNF-α)], renal function indicators [creatinine (Cr), estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), uric acid (UA)], AKI stage, and fecal intestinal flora were compared between these two groups before and 7 days after intervention. Results:After 7 days of intervention, the TP, Alb, and PA levels significantly increased in both groups (all P<0.05); the increases in TP and PA were more significant in the LMP group (both P<0.05), whereas the increase in Alb did not differ significantly between groups ( P>0.05). The levels of CRP, IL-1, IL-6, IL-8, and TNF-α decreased significantly in both groups (all P<0.05),with more significant reductions in the LMP group (all P<0.001). The levels of Cr, eGFR, BUN, and UA improved significantly in both groups (all P<0.05),with more significant improvements in the LMP group (all P<0.05). The incidence of a higher AKI stage in the LMP group was significantly lower than that in the control group( P<0.05), and the incidence of a lower AKI stage was significantly higher than that in the control group ( P<0.001). The fecal counts of Bifidobacterium and Lactobacillus increased significantly while the fecal counts of Enterococcus and Enterobacter decreased significantly in both group (all P<0.001); notably, these changes in bacterial counts were significantly more pronounced in the LMP group ( P<0.001). Conclusions:EEN combined with LMP can significantly enhance nutritional status, reduce inflammatory response, and improve renal function in patients with SAP complicated by AKI.

Objective To analyze the reverse mechano-electric effect of the layered structure of articular cartilage and its influencing factors.Methods The cartilage samples were classified according to their physiological thickness(approximately 0.4 mm for the upper layer,1 mm for the middle layer,and 0.6 mm for the lower layer).Through a non-contact external electric field testing method,how different influencing factors affected the reverse mechano-electric effect of articular cartilage was analyzed.Results When the electric field spacing decreased,water content increased,and in vitro time decreased,the displacement of normal layered cartilage in a non-contact electric field increased by 18,10,15 μm,respectively.In the case of simulated arthritis defects,as the defect depth and radius increased,the overall deviation deflection of articular cartilage gradually decreased by about 7 μm.Conclusions The three-layer cartilage differed in their reverse mechano-electricity effects,showing the greatest deflection in the middle layer at 90％water content,under 7 mm electric field spacing,and after 12 hours ex vivo.

Objective To analyze the reverse mechano-electric effect of the layered structure of articular cartilage and its influencing factors.Methods The cartilage samples were classified according to their physiological thickness(approximately 0.4 mm for the upper layer,1 mm for the middle layer,and 0.6 mm for the lower layer).Through a non-contact external electric field testing method,how different influencing factors affected the reverse mechano-electric effect of articular cartilage was analyzed.Results When the electric field spacing decreased,water content increased,and in vitro time decreased,the displacement of normal layered cartilage in a non-contact electric field increased by 18,10,15 μm,respectively.In the case of simulated arthritis defects,as the defect depth and radius increased,the overall deviation deflection of articular cartilage gradually decreased by about 7 μm.Conclusions The three-layer cartilage differed in their reverse mechano-electricity effects,showing the greatest deflection in the middle layer at 90％water content,under 7 mm electric field spacing,and after 12 hours ex vivo.

OBJECTIVE To investigate the distribution,drug resistance and molecular characteristics of the Staph-ylococcus aureus strains isolated from intensive care unit(ICU)environment in some hospitals of Shanghai Pudong New Area so as to provide bases for prevention and control of infections.METHODS The specimens were collected from ICU environment of 8 medical institutions in Pudong District in 2019 and 2023,the S.aureus strains were i-solated and identified.The drug susceptibility testing was performed by microbroth dilution method.The drug re-sistance genes and virulence genes were predicted based on virulence factors of bacterial pathogens,the compre-hensive antibiotic resistance database(mcmaster.ca)and PubMLST,multilocus sequence typing(MLST)and phylogenetic analysis were performed.RESULTS Totally 3408 samples were collected,and the isolated S.aureus strains accounted for 1.29％(44/3408);the isolated strains form both the shared articles and the articles that health care workers directly contact accounted for 20.45％.The drug resistance rate to penicillin was highest(72.73％,32/44);the strains were completely susceptible to vancomycin,rifampicin,sulfamethoxazole-trime-thoprim,daptomycin and nitrofurantoin.The methicillin-resistant Staphylococcus aureus(MRSA)strains accoun-ted for 25.00％.The multi-drug resistance rate was 40.91％.The strains carried 26 types of drug resistance genes in total,covering 9 categories;the carrying rate of fluroquinolones resistance gene nor A was 100.00％.The strains carried 12 types of enterotoxin genes,the carrying rate of sea gene was highest(50.00％,22/44),some of the strains carried lukS-PV,lukF-PV and tsst gene.There were 14 types of sequence type(ST),dominated by ST7(13/44,29.55％),and novel ST type ST9342 was discovered.The phylogenetic analysis showed that all of the strains were divided into 2 major branches,and the strains with the same ST type were phylogenetically close on the evolutionary tree.CONCLUSIONS The S.aureus strains are prevalent in ICU environment and are highly re-sistant to antibiotics.The strains exhibit various ST types,which,to a certain degree,is associated with the path-ogenicity.

OBJECTIVE To investigate the distribution,drug resistance and molecular characteristics of the Staph-ylococcus aureus strains isolated from intensive care unit(ICU)environment in some hospitals of Shanghai Pudong New Area so as to provide bases for prevention and control of infections.METHODS The specimens were collected from ICU environment of 8 medical institutions in Pudong District in 2019 and 2023,the S.aureus strains were i-solated and identified.The drug susceptibility testing was performed by microbroth dilution method.The drug re-sistance genes and virulence genes were predicted based on virulence factors of bacterial pathogens,the compre-hensive antibiotic resistance database(mcmaster.ca)and PubMLST,multilocus sequence typing(MLST)and phylogenetic analysis were performed.RESULTS Totally 3408 samples were collected,and the isolated S.aureus strains accounted for 1.29％(44/3408);the isolated strains form both the shared articles and the articles that health care workers directly contact accounted for 20.45％.The drug resistance rate to penicillin was highest(72.73％,32/44);the strains were completely susceptible to vancomycin,rifampicin,sulfamethoxazole-trime-thoprim,daptomycin and nitrofurantoin.The methicillin-resistant Staphylococcus aureus(MRSA)strains accoun-ted for 25.00％.The multi-drug resistance rate was 40.91％.The strains carried 26 types of drug resistance genes in total,covering 9 categories;the carrying rate of fluroquinolones resistance gene nor A was 100.00％.The strains carried 12 types of enterotoxin genes,the carrying rate of sea gene was highest(50.00％,22/44),some of the strains carried lukS-PV,lukF-PV and tsst gene.There were 14 types of sequence type(ST),dominated by ST7(13/44,29.55％),and novel ST type ST9342 was discovered.The phylogenetic analysis showed that all of the strains were divided into 2 major branches,and the strains with the same ST type were phylogenetically close on the evolutionary tree.CONCLUSIONS The S.aureus strains are prevalent in ICU environment and are highly re-sistant to antibiotics.The strains exhibit various ST types,which,to a certain degree,is associated with the path-ogenicity.

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 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.