AIM:To investigate the effect of ranibizumab on blood flow density in different regions of the macula in patients with macular edema(ME)secondary to ischemic and non-ischemic branch retinal vein occlusion(BRVO).METHODS:This retrospective study enrolled patients with BRVO-ME who were treated at the hospital from September 2019 to March 2021. Patients were divided into ischemic and non-ischemic groups based on fundus findings. All patients received intravitreal injections of ranibizumab once monthly for three consecutive months. Best corrected visual acuity(BCVA), central macular thickness(CMT), and macular blood flow density were measured before treatment and at 1 d, 1 wk, 1 and 3 mo after treatment.RESULTS: A total of 46 patients(46 eyes)with BRVO-ME were included, comprising 21 eyes in the ischemic group(7 males, 14 females; mean age 55.81±10.36 y)and 25 eyes in the non-ischemic group(11 males, 14 females; mean age 54.84±9.81 y). At 3 mo after treatment, BCVA(LogMAR)in the non-ischemic group was superior to that in the ischemic group(0.19±0.19 vs 0.38±0.27, P=0.009). Analysis of CMT changes showed that the reduction amplitude in the ischemic group was significantly greater than that in the non-ischemic group at both 1 and 3 mo after treatment(all P<0.05). Blood flow densities in the whole, parafoveal, and perifoveal regions of the superficial capillary plexus(SCP), as well as in the whole and perifoveal regions of the deep capillary plexus(DCP), were significantly lower in ischemic patients than in non-ischemic patients, while blood flow density in the foveal region of DCP was significantly higher in the ischemic group(all P<0.05).CONCLUSION: Ranibizumab is effective for both types of patients. Non-ischemic patients have a better long-term visual prognosis, and the advantage may be related to better blood flow perfusion patterns in specific areas 3 mo after treatment. Monitoring changes in blood flow density in these areas can help provide personalized treatment for patients.

Objective:To construct a quality indicator system for pain nursing process in adult inpatients and to provide a scientific, objective and practical basis for the evaluation and management of pain nursing quality in clinical nursing.Methods:Based on the guidance framework of nursing procedures, a quality index system for pain nursing process in adult inpatients was preliminarily developed. Two rounds of expert questionnaires were conducted using the Delphi method, the analytic hierarchy process was used to analyze the weight of the indicators, and the semantic analysis of the indicator system was carried out with the nurses, and finally the quality indicator system for the pain nursing process in adult inpatients was constructed.Results:Both Delphi rounds attained 100% response rates, and the expert authority coefficients were 0.96 and 0.98 respectively, the familiarity coefficients were 0.95 and 0.98 respectively, both judgment basis coefficients were 0.98, and the Kendall′s coefficient of concordance were 0.187 and 0.451 respectively. The mean values of each indicator in semantic analysis ranged from 4.30 to 5.00, and the standard deviation was 0-1.06. The final constructed quality indicator system for the pain nursing process in adult inpatients included 5 primary-level indicators (pain assessment, pain nursing diagnosis, pain nursing plan, pain nursing measures, and pain nursing evaluation) and 28 secondary-level indicators.Conclusions:The quality indicator system for the pain nursing process is successfully constructed in adult inpatients, the method is scientific and reasonable, and the content is practical and reliable. It has guiding significance for evaluating the quality of pain nursing process for inpatients.

Objective:To identify the optimal machine learning model for predicting cholecystocardiac syndrome (CCS) in elderly patients and determine key predictive factors.Methods:A total of 150 elderly patients diagnosed as having CCS who presented with chest discomfort as the initial symptom were retrospectively included at Hangzhou First People's Hospital from July 2021 to December 2023, with 150 propensity score-matched controls (1∶1 matching for age, gender, smoking, and drinking history) with chest discomfort but without CCS during the same period. Demographic characteristics, vital signs at admission, laboratory test results, and electrocardiogram (ECG) findings of all patients were collected. The predictive performance of six machine learning models—gradient boosting, logistic regression, random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM) for early CCS recognition was compared. The model with the best overall performance was selected according to area under the curve (AUC), and its key predictive factors were identified based on feature importance ranking.Results:Among the six machine learning models, the RF model demonstrated the highest AUC (0.84) and lowest Brier score (0.165) and was therefore identified as the optimal model. In the independent test set, the RF model achieved an AUC of 0.87 (95% CI: 0.735-0.908), with a sensitivity of 86.7% (39/45), specificity of 80.0% (36/45), positive predictive value of 81.3% (39/48) and negative predictive value of 85.7% (36/42). The six most important predictive variables were procalcitonin (PCT), serum amyloid A (SAA), heart rate variability low-frequency to high-frequency ratio (LF/HF), percentage of successive normal RR intervals differing by >50 ms (PMN50), γ-glutamyltransferase/aspartate aminotransferase ratio (GGT/AST), and direct bilirubin (DBIL). Conclusion:The RF model can predict early risk of CCS in elderly patients, with the key predictive factors including PCT, SAA, LF/HF, PMN50, GGT/AST, and DBIL.

Objective:To examine the influence of sarcopenia on the mid-term clinical outcomes of percutaneous vertebroplasty(PVP) in the management of osteoporotic vertebral compression fractures(OVCF).Methods:A retrospective case analysis method was adopted to analyze the clinical data of 102 patients with OVCF who underwent PVP in Beijing Daxing District Hospital of Integrated Chinese and Western Medicine from September 2020 to September 2022. Among them, there were 19 males and 83 females; the patients was (74.31±6.99) years, aged from 61 to 89 years. Using the skeletal muscle index (SMI) at the L3 vertebral level as the basis for grouping, with the diagnostic criteria of SMI<24.4 cm 2/m 2 for males and SMI<23.3 cm 2/m 2 for females. The 102 patients were divided into the sarcopenia group ( n=42) and the non-sarcopenia group ( n=60). The visual analog scale(VAS) scores for low back pain were recorded before surgery and at 3 days, 6 months, 1 year, 3 years after surgery; The oswestry disability index(ODI) was recorded at 6 months, 1 year, and 3 years after surgery; Bone mineral density (BMD) T-scores were recorded at 1 year and 3 years after surgery, and complications such as adjacent vertebral fractures were recorded. Measurement data with normal distribution were expressed as mean±standard deviation( ± s), and comparisons between groups were performed using the t-test. Count data were expressed as case and percentage (%), and comparisons between groups were performed using the chi-square test or Fisher′s exact test. Results:All patients in this study successfully completed the surgery and were followed up for 3 years. Compared with the preoperative baseline levels, the VAS for low back pain and ODI of patients in both groups decreased significantly at all postoperative time points, with statistically significant differences ( P<0.05). For patients in the sarcopenia group, the VAS for low back pain at 3 days, 6 months, 1 year, and 3 years after surgery were (2.50±1.04), (2.45±0.80), (2.17±0.79), (1.90±0.76) points, respectively. Those in the non-sarcopenia group were(2.02±0.68), (1.85±0.80), (1.78±0.61), (1.57±0.62) points, respectively, with a statistically significant difference between the two groups ( P<0.05). The ODI scores of the sarcopenia group at 6 months, 1 year, and 3 years postoperatively were (23.76±3.88)%, (23.05±4.33)%, (22.51±4.49)%, while those of the non-sarcopenia group were (21.68±4.51)%, (20.78±4.60)%, (20.30±4.44)%. The difference between the two groups was statistically significant ( P<0.05). The T-scores of femoral neck bone mineral density in the sarcopenia group at 1 year and 3 years after surgery were (-2.78±0.38), (-2.71±0.41), and those in the non-sarcopenia group were (-2.63±0.35), (-2.53±0.34), with a statistically significant intergroup difference ( P<0.05). For the T-scores of lumbar spine bone mineral density, the sarcopenia group had (-3.36±0.58), (-3.47±0.68) at 1 year and 3 years postoperatively, compared with (-3.12±0.59), (-2.91±0.53) in the non-sarcopenia group. The difference between the two groups was statistically significant ( P<0.05). Regarding the incidence of postoperative complications such as adjacent vertebral fractures, the sarcopenia group had a significantly higher incidence (19.0%) than the non-sarcopenia group (3.3%), with a statistically significant difference between the groups ( P<0.05). Conclusions:OVCF patients with sarcopenia who undergo PVP have poorer mid-term postoperative efficacy than those without sarcopenia, and they also face an increased risk of postoperative complications. Early screening and active intervention for patients with sarcopenia will help improve their clinical efficacy.

Objective:To explore the relief effect and mechanism of electroacupuncture in rats with knee osteoarthritis (KOA) based on the NF-κB/NLRP3 signaling pathway.Methods:SD rats were divided into a blank control group, a model group, an electroacupuncture group, and an electroacupuncture+PMA group using a random number table method, with 10 rats in each group. Except for the blank control group, all other groups were used to prepare KOA models. After successful modeling, rats in the electroacupuncture group and electroacupuncture+PMA group were subjected to electroacupuncture stimulation on both sides of the "Yanglingquan"(GB34), "Neixiyan"(EX LE4), and "Waixiyan"(EX-LE5); the electroacupuncture+PMA group received intraperitoneal injection of 5 mg/kg PMA once a day for 11 consecutive days of intervention; the blank control group and model group did not receive any intervention. Thermal foot contraction latency (TWL) and mechanical foot contraction threshold (MWT) were detected. HE staining was used to observe the inflammation and damage of synovial tissue and knee articular cartilage tissue. ELISA was used to detect the levels of calcitonin gene-related peptide (CGRP), pain mediator substance P (SP) and IL-6 in serum. The expressions of CGRP, iNOS proteins in dorsal root ganglion were detected by immunofluorescence assay. The expressions of CGRP, iNOS, p-p65/p65, NLRP3, Caspase-1, ASC proteins in dorsal root ganglia were detected by Western blot.Results:Compared with the model group, the TWL and MWT of the rats in the electroacupuncture group (3, 6, 9 and 11 days) significantly increased ( P<0.05), the pathological damage of synovial tissue and knee articular cartilage tissue were reduced, serum CGRP, SP, IL-6 levels significantly decreased ( P<0.05), and the expressions of CGRP, iNOS, p-p65/p65, NLRP3, Caspase-1, ASC proteins in dorsal root ganglion significantly decreased ( P<0.05); PMA significantly inhibited the effect of electrolysis on KOA rats ( P<0.05). Conclusion:Electroacupuncture can alleviate pain symptoms in KOA rats, and the mechanism may be related to the inhibition of the activation of NF-κB/NLRP3 signaling pathway.

Objective:To construct a quality indicator system for pain nursing process in adult inpatients and to provide a scientific, objective and practical basis for the evaluation and management of pain nursing quality in clinical nursing.Methods:Based on the guidance framework of nursing procedures, a quality index system for pain nursing process in adult inpatients was preliminarily developed. Two rounds of expert questionnaires were conducted using the Delphi method, the analytic hierarchy process was used to analyze the weight of the indicators, and the semantic analysis of the indicator system was carried out with the nurses, and finally the quality indicator system for the pain nursing process in adult inpatients was constructed.Results:Both Delphi rounds attained 100% response rates, and the expert authority coefficients were 0.96 and 0.98 respectively, the familiarity coefficients were 0.95 and 0.98 respectively, both judgment basis coefficients were 0.98, and the Kendall′s coefficient of concordance were 0.187 and 0.451 respectively. The mean values of each indicator in semantic analysis ranged from 4.30 to 5.00, and the standard deviation was 0-1.06. The final constructed quality indicator system for the pain nursing process in adult inpatients included 5 primary-level indicators (pain assessment, pain nursing diagnosis, pain nursing plan, pain nursing measures, and pain nursing evaluation) and 28 secondary-level indicators.Conclusions:The quality indicator system for the pain nursing process is successfully constructed in adult inpatients, the method is scientific and reasonable, and the content is practical and reliable. It has guiding significance for evaluating the quality of pain nursing process for inpatients.

Objective:To identify the optimal machine learning model for predicting cholecystocardiac syndrome (CCS) in elderly patients and determine key predictive factors.Methods:A total of 150 elderly patients diagnosed as having CCS who presented with chest discomfort as the initial symptom were retrospectively included at Hangzhou First People's Hospital from July 2021 to December 2023, with 150 propensity score-matched controls (1∶1 matching for age, gender, smoking, and drinking history) with chest discomfort but without CCS during the same period. Demographic characteristics, vital signs at admission, laboratory test results, and electrocardiogram (ECG) findings of all patients were collected. The predictive performance of six machine learning models—gradient boosting, logistic regression, random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM) for early CCS recognition was compared. The model with the best overall performance was selected according to area under the curve (AUC), and its key predictive factors were identified based on feature importance ranking.Results:Among the six machine learning models, the RF model demonstrated the highest AUC (0.84) and lowest Brier score (0.165) and was therefore identified as the optimal model. In the independent test set, the RF model achieved an AUC of 0.87 (95% CI: 0.735-0.908), with a sensitivity of 86.7% (39/45), specificity of 80.0% (36/45), positive predictive value of 81.3% (39/48) and negative predictive value of 85.7% (36/42). The six most important predictive variables were procalcitonin (PCT), serum amyloid A (SAA), heart rate variability low-frequency to high-frequency ratio (LF/HF), percentage of successive normal RR intervals differing by >50 ms (PMN50), γ-glutamyltransferase/aspartate aminotransferase ratio (GGT/AST), and direct bilirubin (DBIL). Conclusion:The RF model can predict early risk of CCS in elderly patients, with the key predictive factors including PCT, SAA, LF/HF, PMN50, GGT/AST, and DBIL.

Objective:To evaluate the clinical application of urine sediment score (USS) in early diagnosis, etiological differentiation, staging and prognosis of acute kidney injury (AKI), and to investigate the diagnostic efficacy of independent USS and its combination with blood urea nitrogen(Bun) serum creatinine(sCr) and uric acid(UA) in AKI.Methods:From August 23 to September 28, 2023, 9 020 morning urine samples of hospitalized patients in the First Hospital of Jilin University were detected by Sysmex UF5000.A total of 3 226 ssamples with small and round cell (SRC) > 1/μl and/or CAST>1/μl were screened for microscopic examination, and 404 cases with positive renal tubular epithelial cells and/or cast were enrolled in this study. There were 218 males and 186 females, aged 59.5 (49.0, 71.0) years. The 404 cases were divided into the USS AKI group (345 cases) and the USS non-AKI group (59 cases) according to the USS results based on the microscopic findings. According to Kidney Disease: Improving Global Outcomes (KDIGO) criteria, they were divided into KDIGO criteria AKI group (63 cases) and KDIGO criteria non-AKI group (341 cases), and the AKI group was divided into renal AKI group (33 cases) and non-renal AKI group (30 cases). According to the clinical diagnosis recorded in the medical records, they were divided into clinically diagnosed AKI group (29 cases) and clinically diagnosed non-AKI group (375 cases).The χ 2 test or Fisher exact test was used to compare USS in different AKI causes and stages. Logistic regression was used to calculate the odds ratio of renal AKI and stage 3 AKI. The area under the receiver operating characteristic curve was used to evaluate the sensitivity and specificity of USS, sCr, UA and Bun alone and in combination in the diagnosis of AKI, and the best cut-off value, sensitivity and specificity in the diagnosis of AKI were calculated. P < 0.05 was considered statistically significant. Results:The USS was used to identify the etiology of KDIGO standard AKI group,and there were significant differences in USS between renal AKI group and non-renal AKI group (χ 2=11.070, P<0.001). Compared to USS=1, the odds ratio of renal AKI was 8.125 when USS≥2 (95% CI 2.208—29.901). There was a statistically significant difference in the comparison of USS between groups in each stage of the AKI staging study based on USS (χ 2=15.724, P<0.05). Compared to USS=1, the odds ratio of stage 3 AKI was 9.714 when USS≥2 (95% CI 1.145-82.390). The AUC of independent USS in the diagnosis of AKI was 0.687 (95% CI 0.618-0.757, P<0.001), the specificity was 65.7% and the sensitivity was 61.9%. The AUC of USS combined with Bun, sCr, UA in the diagnosis of AKI was 0.794 (95% CI 0.608-0.980, P<0.05), the specificity was 82.4%, and the sensitivity was 88.9%. Conclusions:There wasan increased likelihood of renal AKI or stage 3 AKI while USS≥2,and whose combination with Bun, sCr and UA will improve the diagnostic efficiency of AKI.

Odontogenic maxillary sinusitis(OMS)is a subtype of maxillary sinusitis(MS).It is actually inflammation of the maxillary sinus that secondary to adjacent infectious maxillary dental lesion.Due to the lack of unique clinical features,OMS is difficult to distinguish from other types of rhinosinusitis.Besides,the characteristic infectious pathogeny of OMS makes it is resistant to conventional therapies of rhinosinusitis.Its current diagnosis and treatment are thus facing great difficulties.The multi-disciplinary cooperation between otolaryngologists and dentists is absolutely urgent to settle these questions and to acquire standardized diagnostic and treatment regimen for OMS.However,this disease has actually received little attention and has been underrepresented by relatively low publication volume and quality.Based on systematically reviewed literature and practical experiences of expert members,our consensus focuses on characteristics,symptoms,classification and diagnosis of OMS,and further put forward multi-disciplinary treatment decisions for OMS,as well as the common treatment complications and relative managements.This consensus aims to increase attention to OMS,and optimize the clinical diagnosis and decision-making of OMS,which finally provides evidence-based options for OMS clinical management.

Bladder cancer is one of the most common malignancy in the urinary system over the world. Urine cytology and cystoscopy are important tools for bladder cancer diagnosis and recurrence monitoring. However, due to the limited sensitivity and invasive procedure, there is an urgent need to develop new non-invasive and highly sensitive liquid biopsy approaches. Urine biopsy is a research focus in the field and has great potential. This review focused on protein-based urine markers (including NMP22, BTA and UroVysion etc.) and DNA or RNA-based urine markers (including cfDNA, AssureMDx and Xpert BC Monitor etc.), which were used for bladder cancer diagnosis and recurrence monitoring, and summarized the sensitivity and specificity of each biomarker as well as their characteristics in the diagnosis and recurrence surveillance of bladder cancer. This study provides theoretical and empirical support for further optimization and application of these biomarkers in clinical practice.