Objective To investigate the therapeutic effects and mechanisms of electroacupuncture for stress urinary incontinence(SUI)in rats.Methods A total of 30 rats were randomly divided into three groups:normal group,model group,and electroacupuncture group[electroacupuncture at Shenshu(BL23)and Huiyang(BL35)acupoints],with 10 rats per group.Except for the normal group,a SUI model was established in the other two groups.After grouping interventions,the following assessments including sneeze test,urodynamic parameter measurements(including intravesical pressure),serum levels of lactate dehydrogenase(LDH)and creatine kinase(CK)were conducted,urethral histopathological change was detected by hematoxylin-eosin(HE)staining,and Western Blot was used to detect protein expression of transforming growth factor β1(TGF-β1),Smad2,Smad3,Smad7,typeⅠ collagen(Col Ⅰ),and type Ⅲ collagen(Col Ⅲ)in urethral tissues.Results Compared with the normal group,the model group exhibited significant increases in the number of sneeze test-positive rats,urine volume,residual urine volume,and serum LDH and CK levels,and protein expression of urethral Smad2 and Smad3,moreover,the model group showed significant decreases in urodynamic parameters[maximum bladder capacity(MBC),leak point pressure(LPP),abdominal leak point pressure(ALPP)],voiding efficiency,and protein expression of urethral TGF-β1,Smad7,ColⅠand Col Ⅲ,the differences being statistically significant(P＜0.05).In comparison to the model group,the electroacupuncture group demonstrated significant reductions in sneeze test-positive rats,urine volume,residual urine volume,serum LDH and CK levels,and protein expression of urethral Smad2 and Smad3,additionally,the electroacupuncture group displayed significant improvements in urodynamic parameters(MBC,LPP,ALPP)and voiding efficiency,upregulation of protein expression of urethral TGF-β1,Smad7,Col Ⅰ and Col Ⅲ,the differences being statistically significant(P＜0.05).Conclusion Electroacupuncture may ameliorate SUI by activating the TGF-β1/Smad pathway to promote pelvic collagen synthesis and reduce urethral injury,thus improving urinary function.

Objective:To evaluate the predictive value of the Controlling Nutritional Status (CONUT) score for severe acute pancreatitis (SAP).Methods:A total of 426 patients with acute pancreatitis (AP) admitted to the First Affiliated Hospital of Zhengzhou University between January and December 2024 were retrospectively reviewed. After applying exclusion criteria, 189 patients were included and classified into non-severe AP (NSAP) and SAP groups according to diagnostic criteria. Demographic characteristics (age, sex, underlying diseases), vital signs, CONUT score, Bedside Index for Severity in Acute Pancreatitis (BISAP) score, and laboratory parameters (including complete blood count, blood glucose, liver and kidney function, coagulation profile, amylase, and lipase) on admission were compared between the two groups. Binary logistic regression was used to identify independent risk factors associated with AP severity. Receiver operating characteristic (ROC) curves were generated to assess the predictive performance of each indicator by determining the area under the curve (AUC), sensitivity, specificity, and optimal cutoff values.Results:Compared with the NSAP group, the SAP group had significantly longer hospital stays, higher respiratory rates, and elevated levels of PCT, CRP, absolute neutrophil count, AST, GGT, PT, D-dimer, INR, fibrinogen, FDP, as well as higher CONUT, SIRS, and BISAP scores (all P < 0.05). In contrast, the NSAP group showed significantly higher red blood cell count, hemoglobin, absolute lymphocyte count, serum calcium, albumin, total cholesterol, prothrombin time activity, and PNI score (all P < 0.05). Binary logistic regression analysis identified CONUT score ( OR = 1.623, 95% CI: 1.048–2.512) and BISAP score ( OR = 19.608, 95% CI: 6.585–58.387) as independent risk factors for disease severity. The AUC values for predicting SAP using CONUT score, BISAP score, and their combination were 0.694, 0.815, and 0.864, respectively. Conclusions:The CONUT score is an early independent risk factor for SAP. Combining CONUT with BISAP scores provides better predictive performance for assessing the severity of acute pancreatitis.

Objective:To assess the accuracy of two-dimensional (2D) photographs in measuring esthetic parameters of the maxillary anterior teeth by comparing them with measurements obtained from three-dimensional (3D) dental models.Methods:A total of one hundred volunteers (49 males, 51 females, aged 18-23 years) were recruited from School and Hospital of Stomatology, Wuhan University from January to February 2024. 3D digital models of their dentitions were obtained using an intraoral scanner, and standardized frontal 2D intraoral photographs were captured with a digital camera. The lengths, widths and width/length ratio of the bilateral incisors, lateral incisors and canines were measured on both the 3D digital models and the 2D intraoral photographs. The width ratios of adjacent maxillary anterior were also calculated on the 2D intraoral photographs and the frontal view of 3D digital models.Results:The widths of lateral incisors [(5.85±0.60) mm] and canines [(4.73±0.71) mm] and the lengths of canines [(8.72±0.96) mm] in the 2D intraoral photographs were significantly lower than those in 3D digital models [(6.65±0.59), (7.76±0.60), (8.90±0.86) mm] ( t=-18.24, P<0.001; t=-54.43, P<0.001; t=-4.40, P<0.001), while there were no significant differences in the lengths and widths of the other teeth ( P>0.05). The width/length ratios measured from the 2D intraoral photographs for the lateral incisors and canines (0.74±0.08, 0.55±0.08) were significantly lower than those measured in the 3D digital models (0.84±0.09, 0.88±0.09) ( t=-19.68, P<0.001; t=-50.21, P<0.001), and the width/length ratio of the central incisors showed no significant difference between the two groups ( P>0.05). The width ratios of canines/lateral incisors and lateral incisors/central incisors measured on the 2D intraoral photographs (0.72±0.06, 0.85±0.11) were significantly smaller than those measured in the frontal view of 3D digital models (0.75±0.06, 0.89±0.11) ( t=-9.31, P<0.001; t=-6.58, P<0.001). Conclusions:There is a difference between 2D and 3D measurement results of teeth in the esthetic area and the magnitude of the difference varies with their position in the dental arch. When analyzing the measurement of the anterior teeth, it is necessary to choose the appropriate method according to the target tooth position.

Objective:This study aimed to explore the performance of renal resistive index (RRI), semiquantitative power Doppler ultrasound (PDU) score, and renal venous Doppler waveform (RVDW) pattern in predicting 28-day renal dysfunction in critically ill patients and establish nomogram model.Methods:This was a prospective, observational study. Critically ill patients admitted to the emergency intensive care unit (ICU) of Cangzhou Central Hospital from January 2018 to October 2022 were included. Patients underwent renal ultrasound examination to obtain RRI, PDU score and RVDW pattern within 24 h after ICU admission. The following clinical variables were collected during the renal ultrasound examination session, including heart rate, mean arterial pressure, type and dose of vasoactive drugs, oxygen therapy parameters, and average urine volume per hour derived from a period of 6 h prior to the ultrasound examination. The data on duration of AKI and mortality were recorded on the 28th day of follow-up. Patients were divided into 28-day normal renal function group and 28-day renal dysfunction group according to 28-day renal dysfunction. 28-days of renal dysfunction was defined as failure to achieve renal function recovery within 28 days of ICU admission. The difference of each index between the two groups was compared. Associated factors for 28-day renal dysfunction were determined by univariate and multivariate COX regression analyses. A nomogram was developed based on the independently factors associated with 28-day renal dysfunction. Survival receiver operator characteristic (ROC) curves were plotted to assess diagnostic performance in predicting 28-day renal dysfunction. Delong’s test was used to compare area under the curves (AUC) between each predictor.Results:187 patients were enrolled for the final analysis: 97 with no AKI, 48 with AKI stage 1, 24 with AKI stage 2, and 18 with AKI stage 3 upon enrollment. At 28-day follow up, 16 patients had renal dysfunction and 2 required continuous renal replacement therapy (CRRT). The multivariate COX regression showed that RVDW and SCr upon enrollment were the independent risk predictors. Nomogram based on RVDW and SCr upon enrollment showed the best performance in predicting 14-day renal dysfunction (AUC = 0.918, 95% CI:0.871-0.964, P<0.05), and the AUC was statistically significantly higher than single index (all P<0.05). Nomogram also showed the best performance in predicting 28-day renal dysfunction (AUC = 0.924, 95% CI:0.865-0.983, P<0.05), and the AUC was statistically significantly higher than single index (all P<0.05) except for SCr upon enrollment. The optimal cutoff for nomogram in predicting 28-day renal dysfunction was ≤89.5 (sensitivity, 81.2%; specificity, 90.6%; Youden index, 0.719). Kaplan-Meier analysis showed that the median duration of renal dysfunction in the groups with total nomogram score >85.9 and ≤85.9 was 0 and 22 days (HR=0.220, 95% CI:0.129-0.376, P<0.001). Conclusions:SCr and RVDW pattern within 24 h from ICU admission were independent factors associated with 28-day renal dysfunction in critically ill patients. The value of the nomogram model based on these two factors in predicting 28-day renal dysfunction is superior to each single intrarenal Doppler spectrum indicator and clinical indicator.

Objective:To investigate the predictive value of procalcitonin to platelet ratio (PPR) and C-reactive protein to albumin ratio (CAR) in severe acute pancreatitis (SAP) and the value of SAP and concomitant acute liver injury (ALI).Methods:Total of 195 patients with AP from June 2021 to December 2022 from 374 patients were screened for inclusion in the study and were divided into non-severe acute pancreatitis (NSAP) and SAP groups. The ALI group was divided into non-acute liver injury (NALI) and ALI groups according to ALI criteria, and then into hepatocellular ALI subgroup, cholangiocellular ALI subgroup and mixed ALI subgroup. Laboratory tests for procalcitonin (PCT), C-reactive protein (CRP), albumin and platelet (PLT) were completed within 48 h. Risk factors for SAP, ALI and each subgroup of ALI were analysed by binary logistic regression. Subject work characteristic (ROC) curves were plotted and the optimal thresholds for PPR and CAR were calculated. The predictive value of PPR, CAR and their combination for SAP, ALI and each type of ALI was determined.Results:The AUCs for predicting SAP by plotting ROC curves and calculating the bedside index score of acute pancreatitis severity (BISAP score), PPR, CAR, PPR combined with CAR, PPR combined with BISAP score, CAR combined with BISAP score and combined PPR, CAR and BISAP score were 0.82, 0.85, 0.79 and 0.86. The areas under the ROC curves for PPR, CAR and combined prediction of ALI were 0.81, 0.85 and 0.88, respectively; the areas under the ROC curves for PPR, CAR and combined prediction of hepatocellular ALI were 0.93, 0.77 and 0.92, respectively; and the areas under the ROC curves for PPR, CAR and combined prediction of cholangiocellular ALI were 0.76, 0.76 and 0.77, respectively. The area under the ROC curves for PPR, CAR and combined prediction of mixed ALI were 0.83, 0.76 and 0.82Conclusions:Elevated PPR and CAR are risk factors for SAP and for the development of ALI in AP. PPR has better predictive value than CAR for hepatocellular and mixed ALI, and CAR has better predictive value than PPR for cholangiocellular ALI.

Objective:To investigate the value of fibrinogen to albumin ratio (FAR), creatinine to albumin ratio (CAR) and platelet to lymphocyte ratio (PLR) in predicting the poor prognosis of hyperlipidemic acute pancreatitis (HLAP).Methods:Clinical data of HLAP patients admitted to the hospital from January 2021 to January and December 2023 were retrospectively collected. According to the prognosis, the patients were divided into two groups: good prognosis group and poor prognosis group.The independent risk factors of HLAP in different prognostic groups were obtained by multivariate Logistic regression analysis. Receiver operating characteristic (ROC) curves were plotted to evaluate the prognostic value of FAR, CAR and PLR alone and in combination.Results:A total of 118 patients with HLAP were included, including 69 patients with good prognosis and 49 patients with poor prognosis.The difference of heart rate, lymphocyte, triglyceride, albumin, creatinine, urea nitrogen, blood calcium, blood glucose, C-reactive protein, procalcitonin, fibrinogen, FAR, CAR, PLR, Bedside indicator of acute pancreatitis Severity score, Acute Physiology and Chronic Health status score, hospitalization time assessment between the two groups was statistically significant ( P<0.05). Multivariate Logistic regression analysis showed that FAR (odds ratio ( OR) = 25.949, 95% confidence interval (95% CI):3.190 ~ 211.080, P = 0.002), CAR ( OR = 1.453, 95% CI:1.095 ~ 1.928, P = 0.010) and PLR ( OR = 1.005, 95% CI: 1.001 ~ 1.009, P = 0.020) were independent risk factors for poor prognosis in HLAP patients. ROC curve analysis showed that the area under the ROC curve (AUC) of FAR, CAR and PLR to predict poor prognosis of HLAP patients were 0.823, 0.781 and 0.652, respectively.The AUC of FAR combined with CAR, FAR combined with PLR and CAR combined with PLR were 0.840, 0.845 and 0.849, respectively.The combined ability of FAR, CAR and PLR to predict poor prognosis in HLAP patients was (AUC=0.875,95% CI:0.814 ~ 0.937). When the cut-off value was 0.387, the sensitivity was 83.7%, and the specificity was 79.7%. Conclusions:The prognostic value of FAR, CAR and PLR in HLAP patients is better than that of single or pairwise combination.

Lipid Droplets/metabolism* ; Bacteria ; Lipid Metabolism

Objective:To analyze the prognostic risk factors of patients with traumatic pancreatitis (TP) and establish an early combined prediction of multiple indicators model for TP.Methods:Patients admitted to the ICU of the First Affiliated Hospital of Zhengzhou University from June 2017 to June 2022 were collected retrospectively. Based on their prognosis, the patients were divided into two groups: the good prognosis group and the poor prognosis group. The general data such as sex, age, underlying diseases, Glasgow Coma Scale (GCS), acute physiology and chronic health evaluationⅡ (APACHEⅡ), injury severity score (ISS), bedside index for severity in acute pancreatitis (BISAP), and clinical test indices such as blood routine, blood coagulation, blood gas analysis, and liver and kidney function at admission were compared between the two groups. Univariate analysis and multivariate logistic regression analysis were used to screen the early independent predictors of poor prognosis of TP, and the prediction model of TP was established by combining all of the independent indicators. The receiver operating characteristic (ROC) curve of each independent predictor and prediction model was drawn, and the area under the curve (AUC), sensitivity, specificity, and optimal cut-off value were calculated to examine the diagnostic impact of each independent predictor and the combined prediction model.Results:There were statistically significant differences in the complication rate of mental disorders, GCS, APACHE II, combined craniocerebral injury, combined chest injury, activated partial thromboplastin time, fibrin(pro)degradation products, lactate, aspartate aminotransferase, glomerular filtration rate, amylase, lipase, NT-proBNP, myoglobin, procalcitonin, ISS, and BISAP between the good and poor prognosis groups (all P<0.05). Multivariate logistic regression analysis showed that lactate ( OR=1.636, 95% CI: 1.046-2.559), lipase ( OR=1.005, 95% CI: 1.001-1.008), and ISS ( OR=1.161, 95% CI: 1.064-1.266) were independent risk factors influencing the prognosis of patients with TP. Based on the risk factors listed above, a prediction model was created: Logit P=-9.260+0.492×lactate+0.005×lipase+0.149×ISS, and the ROC curve was plotted. The AUC curve of the prediction model was 0.96 (95% CI: 0.91-1.00). Conclusions:Lactate, lipase, and ISS are early independent risk factors associated with the prognosis of TP. Their combined multi-indicator prediction model has an excellent clinical prediction effect, which can provide a clinical reference for early prediction and treatment of TP.

OBJECTIVE: To investigate the predictive value of pancreatitis activity scoring system (PASS) combined with Neutrophil to lymphocyte ratio (NLR) and C-reactive protein (CRP) for infected pancreatic necrosis (IPN) in patients with severe acute pancreatitis (SAP). METHODS: Clinical data of SAP patients admitted to the First Affiliated Hospital of Zhengzhou University from January 2020 to January 2023 were retrospectively collected, including basic information, vital signs at admission, first laboratory indexes within 48 hours of admission. The PASS scores at admission and 24, 48 and 72 hours after admission were calculated. According to the diagnostic criteria of IPN, the patients were divided into the non-IPN group and the IPN group, and the independent risk factors of SAP complicating IPN were determined by using univariate analysis and multifactorial Logistic regression. The receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of NLR, CRP, and PASS score, alone and in combination for IPN in patients with SAP. RESULTS: A total of 149 SAP patients were enrolled, including 102 in the non-IPN group and 47 in the IPN group. The differences in PASS score at each time point, NLR, CRP, procalcitonin (PCT), blood urea nitrogen, blood chloride, and days of hospitalization between the two groups were statistically significant. Multifactorial Logistic regression analysis showed that 72 hours admission PASS score [odds ratio (OR) = 1.034, 95% confidence interval (95%CI) was 1.005-1.065, P = 0.022], NLR (OR = 1.284, 95%CI was 1.139-1.447, P = 0.000), and CRP (OR = 1.015, 95%CI was 1.006-1.023, P = 0.001) were independent risk factors for IPN in patients with SAP. ROC curve analysis showed that the area under the ROC curve (AUC) of the PASS score at 72 hours of admission, NLR, and CRP alone in predicting IPN in SAP patients were 0.828, 0.771, and 0.701, respectively. The AUC of NLR combined with CRP, PASS combined with NLR, and PASS combined with CRP were 0.818, 0.895, and 0.874, respectively. The combination of PASS score at 72 hours after admission, NLR, and CRP had a better predictive ability for IPN in patients with SAP (AUC = 0.922, 95%CI was 0.877-0.967), and the sensitivity was 72.3% when the cut-off value was 0.539. CONCLUSIONS The predictive value of the PASS score at 72 hours after admission, NLR and CRP in combination for IPN in SAP patients is better than that of the combination of each two and individual detection and has better test efficacy.
Humans ; Pancreatitis, Acute Necrotizing/diagnosis* ; C-Reactive Protein/metabolism* ; Acute Disease ; Neutrophils/metabolism* ; Retrospective Studies ; ROC Curve ; Lymphocytes ; Prognosis