Diabetic nephropathy （DN） is one of the most common and severe microvascular complications of diabetes， with a complex pathogenesis involving immune inflammatory responses， oxidative stress， apoptosis， glomerulosclerosis， renal interstitial fibrosis， and other pathological processes. In recent years， numerous animal or cell model experiments have revealed that the transforming growth factor-β （TGF-β）/mothers against decapentaplegic homolog （Smad）， phosphoinositide 3-kinase （PI3K）/ protein kinase B （Akt）/mammalian target of rapamycin （mTOR）， mitogen-activated protein kinase （MAPK）， AMP-activated protein kinase （AMPK）， nuclear factor-κB （NF-κB）， Janus kinase （JAK）/signal transducer and activator of transcription （STAT）， neurogenic locus notch homolog protein （Notch）， nuclear factor E₂-related factor 2 （Nrf2）， secretory glycoprotein （Wnt）/β-catenin， and other classical signaling pathways play important roles in the occurrence and development of DN. Traditional Chinese medicines， as natural drugs， possess characteristics such as multiple components， multiple targets， and few adverse reactions， demonstrating unique advantages in regulating the aforementioned signaling pathways and improving renal pathological changes. This review summarized recent research progress on the intervention of DN through the regulation of the aforementioned signaling pathways by single compounds and formulas of traditional Chinese medicine， focusing on their mechanisms of action in regulating immune inflammatory responses， inhibiting renal fibrosis， oxidative stress， improving metabolic disorders， and other aspects. The aim is to provide theoretical references for a deeper understanding of the modern pharmacological basis and clinical application of traditional Chinese medicine in the treatment of DN.

Objective: To investigate the protective effects and underlying mechanisms of sodium pyruvate (SP) on RBC storage lesions using an oxidative damage model. Methods: Six units of leukocyte-depleted suspended RBCs (discarded for non-infectious reasons within three days post-collection) were randomly assigned to four groups: negative control (NS), positive control (PS), experimental group 1 (SP1), and experimental group 2 (SP2). Oxidative stress was induced in the PS group by the addition of hydrogen peroxide (H O ), while SP1 and SP2 received SP supplementation at different concentrations (25 mM and 50 mM, respectively) in the presence of H O . After 1 hour of incubation, RBC morphology was assessed microscopically, and biochemical indicators including glutathione (GSH), malondialdehyde (MDA), methemoglobin (MetHb), adenosine triphosphate (ATP), and Na /K -ATPase activity were measured. Results: RBCs in the PS group exhibited pronounced morphological damage, including cell shrinkage and echinocyte formation, whereas both SP-treated groups showed significantly reduced structural injury. SP treatment led to elevated GSH levels and decreased concentrations of MDA and MetHb, suggesting attenuation of oxidative stress. Additionally, SP enhanced intracellular ATP levels and Na /K -ATPase activity, thereby contributing to membrane stability. Notably, the SP2 group (50 mM) demonstrated superior protective effects compared to SP1 (25 mM). Conclusion: Sodium pyruvate effectively attenuates oxidative storage lesions in RBCs, primarily through its antioxidant properties, energy metabolism supporting ability, and celluar membrane stabilizing function. These findings suggest SP as a promising additive for enhancing the quality and safety of stored RBCs.

Objective:To analyze the preferences of outpatients in a tertiary public specialized hospital, for references for optimizing the allocation of outpatient medical resources, and enhancing the medical experience.Methods:This study used convenience sampling method to select outpatients from a tertiary public stomatological hospital from January to September 2022 as the survey subjects, and conducted a questionnaire survey. The questionnaire mainly included gender, age, place of residence, education level, and medical needs, etc. Logistic regression model and Boruta algorithm were used to analyze the factors influencing patients′ preferred tertiary public specialized hospitals for treatment.Results:A total of 19 255 patients were included in this study. 13 558 patients (70.41%) preferred tertiary public specialized hospital for treatment, including 9 715 patients (71.65%) aged 21 to 45, 1 015 local patients (73.87%), 8 278 patients (61.05%) who chose treatment options, and 6 442 patients (47.51%) who came to the hospital for medical insurance reimbursement. By logistic regression analysis, age, residence, education level, medical needs, oral health habits, access to oral health knowledge, pre interview experience of Internet hospitals, monthly income, number of family members and reasons for seeking medical treatment were the influencing factors of patients′ preference for tertiary public specialized hospitals. According to the Boruta algorithm analysis, the top 6 important factors were the reasons for seeking medical treatment (Z=126.66), oral health habits (Z=96.44), access to oral health knowledge (Z=66.91), medical needs (Z=62.21), age (Z=57.54), and residence (Z=55.21).Conclusions:Local patients and young and middle-aged patients tended to choose tertiary public specialized hospitals for treatment, and treatment projects were the main business types that attract patients to visit tertiary public specialized hospitals. There were many important influencing factors for patients choosing tertiary public specialized hospitals for treatment, including the reasons for seeking medical treatment, oral health habits, access to oral health knowledge, and medical needs.

Objective:To establish a model for predicting the risk of hyperuricemia in helicopter pilots stationed in plateau and to verify the application effect of the model.Methods:By using a convenience sampling method, 409 pilots who recuperated in Air Force Healthcare Center for Special Services Hangzhou from August of 2017 to March of 2023 were selected as the subjects, and a retrospective analysis was conducted on the relevant data. They were randomly divided into modeling group (286 cases) and validation group (123 cases) with the ratio of 7∶3. Lasso regression and Logistic regression analysis were used in screening out the influencing factors of hyperuricemia for the helicopter pilots stationed in plateau. R software was used to construct risk prediction model and column chart model. The prediction effect of the model was tested using the area under the operating characteristic curve of the subjects.Results:TG, ALT, exercise intensity were the influencing factors of hyperuricemia in altitude helicopter pilots ( Z=2.62, 2.68, -3.02, P=0.009, 0.007, 0.003). Based on the above 3 influencing factors, a risk prediction model and a column chart model were constructed, and the receiver operating characteristic curve was drawn. The results showed that the area under the curve (AUC) of the modeling group was 0.758,95% CI was 0.696-0.820. The Yoden index was 0.331, the sensitivity was 76.5%, and the specificity was 56.6%. The AUC of the validation group was 0.747, 95% CI was 0.655-0.840. The Yoden index was 0.642, the sensitivity was 68.6%, and the specificity was 85.2%,respectively, those suggested good predictive result. The calibration curve showed that the model performed well in predicting compliance, and the clinical decision curve analysis showed that the model performed well in clinical efficacy. Conclusions:The prediction model constructed in this study has good discrimination and calibration, and can help medical staff to screen out high-risk populations of the helicopter pilots with hyperuricemia, and take targeted intervention measures.

Objective:To establish a model for predicting the risk of hyperuricemia in helicopter pilots stationed in plateau and to verify the application effect of the model.Methods:By using a convenience sampling method, 409 pilots who recuperated in Air Force Healthcare Center for Special Services Hangzhou from August of 2017 to March of 2023 were selected as the subjects, and a retrospective analysis was conducted on the relevant data. They were randomly divided into modeling group (286 cases) and validation group (123 cases) with the ratio of 7∶3. Lasso regression and Logistic regression analysis were used in screening out the influencing factors of hyperuricemia for the helicopter pilots stationed in plateau. R software was used to construct risk prediction model and column chart model. The prediction effect of the model was tested using the area under the operating characteristic curve of the subjects.Results:TG, ALT, exercise intensity were the influencing factors of hyperuricemia in altitude helicopter pilots ( Z=2.62, 2.68, -3.02, P=0.009, 0.007, 0.003). Based on the above 3 influencing factors, a risk prediction model and a column chart model were constructed, and the receiver operating characteristic curve was drawn. The results showed that the area under the curve (AUC) of the modeling group was 0.758,95% CI was 0.696-0.820. The Yoden index was 0.331, the sensitivity was 76.5%, and the specificity was 56.6%. The AUC of the validation group was 0.747, 95% CI was 0.655-0.840. The Yoden index was 0.642, the sensitivity was 68.6%, and the specificity was 85.2%,respectively, those suggested good predictive result. The calibration curve showed that the model performed well in predicting compliance, and the clinical decision curve analysis showed that the model performed well in clinical efficacy. Conclusions:The prediction model constructed in this study has good discrimination and calibration, and can help medical staff to screen out high-risk populations of the helicopter pilots with hyperuricemia, and take targeted intervention measures.

Objective:To construct the post competency evaluation index system for Da Vinci robot specialist nurses based on onion model, so as to provide references for the training and evaluation of Da Vinci robot specialist nurses in China.Methods:From August to September 2022, using the "onion model" as the theoretical framework, a preliminary draft of the index system was developed using domestic and foreign literature review, semi-structured interviews and group discussions. The Delphi method was applied to 22 nursing experts in the relevant fields to conduct two rounds of expert correspondence, screen and modify the items, and calculate the weight using the analytic hierarchy process. Finally, the post competency index system for Da Vinci robot specialist nurses based on the onion model was formed.Results:Two rounds of expert correspondence were conducted. The positive coefficient of experts was 90.91% (20/22) in the first round and 100.00% (20/20) in the second round, the coefficient of expert authority was 0.840 and 0.873, and the Kendall's coordination coefficient was 0.181 and 0.324 ( P<0.01), respectively. The final post competency evaluation index system for Da Vinci robot specialist nurses consisted of 4 first-level indicators, 14 second-level indicators and 58 third-level indicators. Conclusions:This study constructs a comprehensive post competency evaluation index system for Da Vinci robot specialist nurses based on onion model, with high expert enthusiasm and authority. It can provide references for the training and evaluation of Da Vinci robot specialist nurses in China.

Objective:To compare the clinical and imaging outcomes of fascia lata autograft bridging repair reinforecd with an artificial ligament as the internal brace with the autograft bridging repair for the treatment of irreparable massive rotator cuff tears (IMRCTs).Methods:The data of 26 patients with IMRCT who underwent fascia lata autograft bridging repair augmented with artificial ligament as the internal brace (internal brace group) and of 24 patients with IMRCT who underwent bridging autograft repair alone (control group) were retrospectively evaluated preoperatively and at 2-year follow-up. Clinical outcomes were assessed using shoulder activity, the American Shoulder and Elbow Surgeons (ASES) Score, University of California Los Angeles (UCLA) Score, and visual analogue scale (VAS) for pain. Imaging outcomes were evaluated using acromiohumeral distance (AHD), Goutallier grade, and status of fascia lata grafts according to radiographs or magnetic resonance imaging results.Results:All 50 cases were followed up for 34.2±7.2 months (range 24-45 months). Compared to the control group, the internal brace group showed better ASES score (93.5±5.3 vs. 89.5±5.7, P<0.05), UCLA score (31.7±3.8 vs. 28.5±5.6, P<0.05), improvement in UCLA score (19.6±4.2 vs. 15.9±5.7, P<0.05), active elevation (167.3°±8.4° vs. 159.4°±13.6°, P<0.05), abduction strength (8.9±1.2 vs. 8.2±1.2, P<0.05), improvement in abduction strength (4.1±1.2 vs. 3.3± 1.0, P<0.05), AHD (7.0±1.4 mm vs. 5.9±1.0 mm, P<0.05), improvement in AHD (3.3±1.5 mm vs. 2.0±0.6 mm, P<0.05), and healing rate of fascia lata autografts (92% vs. 54%, P<0.05) at 2-year follow-up. Conclusion:Fascia lata autograft bridging repair reinforced with an artificial ligament as the internal brace improves healing rate of bridging graft and postoperatively short-term clinical outcomes of patients with IMRCT.

OBJECTIVE: To propose a non-contrast CT-based algorithm for automated and accurate detection of pancreatic lesions at a low cost. METHODS: With Faster RCNN as the benchmark model, an advanced Faster RCNN (aFaster RCNN) model for pancreatic lesions detection based on plain CT was constructed. The model uses the residual connection network Resnet50 as the feature extraction module to extract the deep image features of pancreatic lesions. According to the morphology of pancreatic lesions, 9 anchor frame sizes were redesigned to construct the RPN module. A new Bounding Box regression loss function was proposed to constrain the training process of RPN module regression subnetwork by comprehensively considering the constraints of the lesion shape and anatomical structure. Finally, a detection frame was generated using the detector in the second stage. The data from a total of 728 cases of pancreatic diseases from 4 clinical centers in China were used for training (518 cases, 71.15%) and testing (210 cases, 28.85%) of the model. The performance of aFaster RCNN was verified through ablation experiments and comparison experiments with 3 classical target detection models SSD, YOLO and CenterNet. RESULTS: The aFaster RCNN model for pancreatic lesion detection achieved recall rates of 73.64% at the image level and 92.38% at the patient level, with an average precision of 45.29% and 53.80% at the image and patient levels, respectively, which were higher than those of the 3 models for comparison. CONCLUSION The proposed method can effectively extract the imaging features of pancreatic lesions from non-contrast CT images to detect the pancreatic lesions.
Humans ; Pancreas/diagnostic imaging* ; Algorithms ; China ; Pancreatic Neoplasms/diagnostic imaging* ; Tomography, X-Ray Computed

Objective: To create a model for early prediction of essential hypertension (EH) based on the TreeNet algorithm, so as to provide a tool for early monitoring of EH. Methods: The health examination data were collected from individuals receiving health examinations in Hangzhou Haiqin Health Examination Center or Shanghai Yibao Health Management Co., Ltd from 2014 to 2016, and a predictive model for EH was created based on the TreeNet algorithm. The effectiveness of the model for early prediction of EH was evaluated using root mean square error (RMSE), mean absolute deviation (MAD), coefficient of determination (R2) and receiver operating characteristic (ROC) curve. Results: A total of 12 variables were included in the model, and the highest contributing variable was body mass index (BMI), followed by BMI difference, two-year BMI difference, two-year triglyceride (TG) difference, two-year total cholesterol (TC) difference, high-density lipoprotein cholesterol (HDL-C) in 2014, TG in 2014, low-density lipoprotein cholesterol (LDL-C) in 2014, body weight in 2015, fasting blood glucose in 2015, TG in 2015, urea nitrogen difference and platelet in 2015. The highest predictive accuracy was 100.00%, and the lowest was 56.89%. The risk of EH significantly increased among individuals with BMI in 2015 of >25 kg/m2, two-year BMI difference of >0.5 kg/m2, two-year TG difference ranging from 1.3 to 3.3 mmol/L, TC in 2015 of 2.0 to 2.4 mmol/L and HDL-C in 2014 of <0.52 mmol/L. The model presented RMSE of 0.082, MAD of 0.064, R2 of 0.811, area under the ROC curve of 0.788 (95%CI: 0.741-0.815), sensitivity of 69.05% and specificity of 66.21% for prediction of EH Conclusion The TreeNet algorithm-based model is effective for early monitoring of high-risk individuals for EH.

The oral specialist diagnosis and treatment has the characteristics of diagnosis and treatment behavior immediacy, phased diagnosis and treatment process, diagnosis and treatment population diversity, diagnosis and treatment effect subjectivity, and patient dominance of outpatient medical record storage. In the process of fulfilling the notification obligation, there may be risks such as the protection of the autonomous rights and interests of minor patients, the limited communication time, the insufficient clarity of the first diagnosis plan, the new requirements for the performance form, and the expansion of the application of the presumption of fault. The oral specialist diagnosis should pay attention to the true expression of the informed object; the diversification of the informative channels to obtain the clear attitude of the patient; the optimization of the informative process to make up for the information imbalance; the strict implementation of the core medical safety system to promote the actual fulfillment of the notification obligation.