ObjectiveTo investigate the mechanisms by which Bushen Tongluo Jiangzhuo prescription improves renal fibrosis in rats with chronic kidney disease （CKD） through the phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt）/mammalian target of rapamycin （mTOR） signaling pathway. MethodsSeventy specific pathogen-free （SPF） Sprague-Dawley （SD） rats were randomly divided into a control group （n=15） and a modeling group （n=55）. Rats in the modeling group were administered a 2.5% adenine suspension at a dose of 200 mg·kg^-1·d^-1 by gavage for 4 weeks to establish a CKD model. Successfully modeled rats were randomly divided into a model group， an irbesartan group （20.25 mg·kg^-1·d^-1）， and Bushen Tongluo Jiangzhuo prescription low-， medium-， and high-dose groups （5.82， 11.64， and 23.28 g·kg^-1·d^-1， respectively）， with 10 rats in each group. Each group was administered an equal volume of physiological saline， the corresponding concentration of irbesartan， or Bushen Tongluo Jiangzhuo prescription by gavage for 12 weeks. Body weight and renal function indices were dynamically monitored. Serum creatinine （SCr）， blood urea nitrogen （BUN）， urine albumin-to-creatinine ratio （ACR）， 24-hour urinary total protein （24 hUTP）， aspartate aminotransferase （AST）， alanine aminotransferase （ALT）， interleukin-1β （IL-1β）， interleukin-6 （IL-6）， and tumor necrosis factor-α （TNF-α） levels were measured using an automatic biochemical analyzer. Renal histopathological changes were observed by hematoxylin-eosin （HE） and Masson staining. Immunohistochemistry （IHC） was used to detect the expression of PI3K， Akt， phosphorylated Akt （p-Akt）， and mTOR in renal tissues. Western blot was performed to assess the protein expression of PI3K， p-Akt， Akt， phosphorylated mTOR （p-mTOR）， and mTOR in renal tissues. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to determine the mRNA expression levels of PI3K， Akt， and mTOR in renal tissues. ResultsCompared with the model group， rats in the irbesartan group and the low-， medium-， and high-dose Bushen Tongluo Jiangzhuo prescription groups showed significantly decreased levels of SCr， BUN， ACR， 24 hUTP， IL-1β， IL-6， and TNF-α （P<0.01）. AST levels were significantly increased （P<0.01）， while no significant difference was observed in ALT levels. Histopathological examination revealed that， compared with the model group， renal tubular epithelial cell edema and necrosis and Bowman's capsule dilation were alleviated， inflammatory cell infiltration was reduced， and interstitial and glomerular fibrosis was markedly improved in all treatment groups， with the most pronounced effect observed in the high-dose Bushen Tongluo Jiangzhuo prescription group. Real-time PCR results showed that mRNA expression levels of PI3K， Akt， and mTOR were significantly downregulated in the high-dose group （P<0.01）. IHC results demonstrated that PI3K and p-Akt expression levels in renal tissues were significantly decreased in the high-dose group （P<0.01）. Western blot analysis further confirmed that the expression levels of PI3K， p-Akt/Akt， and p-mTOR/mTOR were significantly reduced in the high-dose group （P<0.01）. ConclusionBushen Tongluo Jiangzhuo prescription improves renal function indices in CKD rats， reduces collagen deposition in renal tissues， and decreases serum inflammatory factor levels. Its protective effect on renal function may be achieved by activating autophagy through downregulation of the PI3K/Akt/mTOR signaling pathway， thereby alleviating renal fibrosis.

ObjectiveTo investigate the molecular mechanism by which the Bushen Kaixuan Tongluo prescription exerts a renal protective effect in mice with diabetic kidney disease （DKD） by regulating the cyclic adenosine monophosphate （cAMP） signaling pathway. MethodsThirty specific pathogen-free （SPF） male db/db mice were adaptively fed for three weeks. Mice with a random tail vein blood glucose level ≥ 11.1 mmol·L^-1 and urinary albumin-creatinine ratio （ACR） ≥ 30 mg·g^-1 were considered successfully modeled. The successfully modeled mice were randomly divided into five groups with six mice in each group： the model group， the low-， medium-， and high-dose Bushen Kaixuan Tongluo prescription groups （administered at doses of 7， 14， 28 g·kg^-1·d^-1 respectively）， and the positive drug irbesartan group （administered at a dose of 20 mg·kg^-1·d^-1）. Additionally， six db/m mice were selected as the blank group. Mice in each group were given intragastric administration of the Bushen Kaixuan Tongluo prescription at the corresponding concentrations， irbesartan， or an equal volume of pure water， and the intervention lasted for 12 weeks. During the experiment， the general conditions， body weight changes， and renal function indicators of the mice were dynamically monitored. After the intervention， a blood glucose meter was used to measure the fasting blood glucose （FBG） of the mice. An automatic biochemical analyzer was employed to detect the levels of serum creatinine （SCr）， blood urea nitrogen （BUN）， urinary microalbumin （uALB）， ACR， aspartate aminotransferase （AST）， alanine aminotransferase （ALT）， total cholesterol （TC）， triglycerides （TG）， leptin （LEP）， glycosylated serum protein （GSP）， and insulin （INS） in the mice. Renal tissues were collected for hematoxylin-eosin （HE） staining， periodic acid-Schiff （PAS） staining， and Masson's trichrome staining to observe the histopathological changes. Immunohistochemistry （IHC） was used to detect the expressions of protein kinase A （PKA） and cAMP response element-binding protein （CREB） in the mice. Western blot analysis was performed to determine the expression levels of PKA， phosphorylated protein kinase A （p-PKA）， CREB， phosphorylated cAMP response element-binding protein （p-CREB）， and B-cell lymphoma-2 （Bcl-2） proteins in the renal tissues of the mice. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect the mRNA expression levels of PKA， CREB， and Bcl-2 in the renal tissues of the mice. ResultsCompared with the blank group， the mice in the model group showed listlessness， decreased activity， and a significant increase in body weight （P<0.01）. Biochemical indicators revealed that the levels of BUN， uALB， ACR， AST， ALT， TC， TG， FBG， LEP， GSP， and INS were significantly increased （P<0.01）， while SCr showed an increasing trend with no statistically significant difference. Compared with the model group， the mice in the Bushen Kaixuan Tongluo prescription intervention groups had improved general conditions and a decreasing trend in body weight. Biochemical indicators showed that the levels of BUN， uALB， ACR， TC， GSP， and INS were significantly decreased （P<0.05）， while SCr， AST， ALT， TG， and LEP showed a decreasing trend with no statistically significant difference. Renal histopathological analysis showed that the model group exhibited typical DKD pathological features such as thickening of the glomerular basement membrane， expansion of the mesangial matrix， and deposition of collagen fibers in the renal tubulointerstitium， and all treatment groups could alleviate the above pathological damages. The IHC results showed that compared with the blank group， the expression levels of p-PKA and p-CREB in the renal tissues of the model group were significantly decreased （P<0.01）. Compared with the model group， the expression level of p-PKA in the medium-dose Bushen Kaixuan Tongluo prescription group was significantly increased （P<0.01）， while the expression level of p-CREB showed an increasing trend with no statistically significant difference. Western blot results showed that compared with the blank group， the expression levels of p-PKA/PKA， p-CREB/CREB， and Bcl-2 in the model group were significantly decreased （P<0.05）. Compared with the model group， the expression levels of these proteins in the medium-dose Bushen Kaixuan Tongluo prescription group were significantly increased （P<0.01）. Real-time PCR results showed that compared with the blank group， the mRNA expressions of PKA， CREB， and Bcl-2 in the model group were significantly down-regulated （P<0.05）. Compared with the model group， the mRNA expressions of these genes in the medium-dose Bushen Kaixuan Tongluo prescription group were significantly up-regulated （P<0.05）. ConclusionThe Bushen Kaixuan Tongluo prescription can improve the liver and kidney functions of db/db mice， correct lipid metabolism disorders and glucose metabolism imbalance. Its renal protective effect is associated with up-regulating the cAMP signaling pathway to improve renal fibrosis and reduce the level of oxidative stress， thereby protecting renal function.

Objective: To investigate the association between metabolic dysfunction associated steatotic liver disease (MASLD) and bone mineral density among children and adolescents, so as to provide evidence for the early prevention and intervention of bone health in this population. Methods: In September 2022, a method combining convenience sampling with cluster sampling was used to select 5 089 children and adolescents aged 6-18 years in 9 schools from kindergarten to senior high school in Tongzhou District, Beijing, for physical measurements, ultrasound measurements, blood biochemical index testing, and questionnaire surveys. Participants were categorized into three groups: the normal control group ( n =1 515), the metabolic abnormality group (MA, n = 3 007 ), and the MASLD group ( n =567). Multivariable linear regression model was applied to examine the association between MASLD and bone speed of sound (SOS), while multivariable Logistic regression model was used to assess the association between MASLD and low bone mineral density. Subgroup analysis was conducted by sex and age groups. Results: Compared with the normal control group, the MASLD group showed significantly lower SOS values ( β =-6.31, 95% CI =-9.63 to -2.99), lower SOS Z scores ( β = -0.21, 95% CI =-0.32 to -0.10), and higher susceptibility to low bone mineral density( OR =1.56, 95% CI =1.25-1.96)（all P <0.05）. No significant differences in SOS or odds of low bone density were observed between the MA and normal control groups (all P > 0.05). In sex stratified analyses, males with MASLD exhibited significantly lower SOS Z scores ( β =-0.35, 95% CI =-0.49 to -0.20 , P <0.05), whereas no significant difference was observed in females with MASLD ( β =-0.03, 95% CI =-0.21-0.15; P >0.05). When hepatic steatosis grade (0, 1, 2, and 3) was treated as a continuous variable, each one grade increase was associated with a 31% higher odds of low bone mineral density ( OR =1.31, 95% CI =1.13 to 1.53, P <0.05). Conclusions MASLD is significantly associated with low bone mineral density among children and adolescents, with a stronger association in males. Moreover, children and adolescents with hepatic steatosis have a higher risk of impaired bone health compared with those with metabolic abnormalities alone.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

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.

The importance of consensus research in medical decision-making has become increasinglyprominent. However, this field has long lacked unified terminology definitions and reporting standards, leading to significant heterogeneity in study design, implementation, and result presentation that affects the credibility and reproducibility of outcomes. The ACCurate COnsensus Reporting Document (ACCORD) in the field of biomedical research provides a structured writing framework for various consensus methods such as the Delphi method and nominal group technique, aiming to enhance the completeness and transparency of study reports. Combined with specific cases, this article interprets the core items of ACCORD, offering references for the design, implementation, and reporting of high-quality consensus research in China.

OBJECTIVE To conduct a comprehensive clinical evaluation of four nucleoside （acid） analogues that have been approved and marketed in China， such as entecavir， tenofovir disoproxil fumarate， tenofovir alafenamide fumarate， and tenofovir amibufenamide. METHODS According to the Guideline for the Administration of Clinical Comprehensive Evaluation of Drugs （2021 edition， trial implementation）， a comprehensive search was conducted across databases including CNKI， Wanfang Data， VIP， PubMed， the Cochrane Library， Embase， as well as relevant official websites. Drug package inserts， guidelines， consensus statements， and relevant literature for the four drugs were collected and subjected to a comprehensive evaluation across six dimensions： safety， efficacy， cost-effectiveness， innovativeness， suitability， and accessibility. RESULTS The scores for entecavir in terms of safety， efficacy， cost-effectiveness， innovativeness， suitability， and accessibility-along with its comprehensive score-were 13， 14， 13， 10， 18， and 6， totaling 74 points. For tenofovir disoproxil fumarate， the respective scores were 13， 17， 18， 8， 18， and 7， totaling 81 points. For tenofovir alafenamide fumarate， the scores were 14， 20， 12， 8， 18， and 5， totaling 77 points. Finally， for tenofovir amibufenamide， the scores were 10.5， 17， 10， 6， 15， and 4， totaling 62.5 points. CONCLUSIONS Tenofovir disoproxil fumarate， with the highest score， is recommended as the first-line option， suitable for adults， children， and pregnant women. However， caution is warranted for potential renal impairment. Tenofovir alafenamide fumarate is recommended as a second-line alternative， particularly for individuals at high risk for bone and renal damage. Entecavir has a score similar to tenofovir alafenamide fumarate but requires dosing on an empty stomach and dose adjustment based on renal function of patients. Tenofovir amibufenamide received the lowest score and is considered a weak recommendation. The clinical application of these nucleoside （acid） analogues should be individualized based on the patient’s age， physiological status， and risk factors.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Objective To investigatethe relationship between gastroesophageal reflux disease (GERD) symptoms and clinicopathological characteristics, p53 expression, and survival of Chinese patients with esophageal adenocarcinoma. Methods A total of 3882 patients with esophageal adenocarcinoma, 970 matched patients with esophageal squamous cell carcinoma, and 970 matched patients with gastric cardia adenocarcinoma were included to compare the incidence of GERD symptoms. Patients with esophageal adenocarcinoma were divided into two groups according to the presence and absence of GERD symptoms. The differences in clinicopathological characteristics and p53 expression between the two groups were compared by chi-square test, and the differences in survival between the two groups were compared by landmark analysis. Results The incidence of GERD symptoms increased successively in esophageal squamous cell carcinoma, esophageal adenocarcinoma, and gastric cardia adenocarcinoma. In patients with esophageal adenocarcinoma, the proportions of male, less than 65 years old, lower thoracic tumors, tumors without squamous cell carcinoma, poorly differentiation, early tumors (stage 0-I), and p53-positive tumors in the group with GERD symptoms were higher than those in the group without GERD symptoms; moreover, the long-term survival (≥15 years) was better. Conclusion GERD symptom is an important clinical sign of esophageal adenocarcinoma. It is closely related to specific clinicopathological characteristics, p53 overexpression, and good long-term prognosis.

ObjectiveTo develop a nomogram-based risk prediction model for chronic obstructive pulmonary disease (COPD) incidence among the community-dwelling population aged 40 years old and above, so as to provide targeted references for the screening and prevention of COPD. MethodsBased on a natural population cohort in suburban Shanghai, a total of 3 381 randomly selected participants aged ≥40 years underwent pulmonary function tests between July and October 2021. Cox stepwise regression analysis was used to develop overall and gender-specific risk prediction models, along with the construction of corresponding risk nomograms. Model predictive performance was evaluated using the C-indice, area under the curve (AUC) values, and Brier score. Stability was assessed through 10-fold cross-validation and sensitivity analysis. ResultsA total of 3 019 participants were included, with a median follow-up duration of 4.6 years. The COPD incidence density was 17.22 per 1 000 person-years, significantly higher in males (32.04/1 000 person-years) than that in females (7.38/1 000 person-years) (P<0.001). The overall risk prediction model included the variables such as gender, age, education level, BMI, smoking, passive smoking, and respiratory comorbidities. The male-specific model incorporated the variables such as age, BMI, respiratory comorbidities, and smoking, while the female-specific model included age, marital status, respiratory comorbidities, and pulmonary tuberculosis history. The C-indices for the overall, male-specific, and female-specific models were 0.829, 0.749, and 0.807, respectively. The 5-year AUC values were 0.785, 0.658, and 0.811, with Brier scores of 0.103, 0.176, and 0.059, respectively. Both 10-fold cross-validated C-indices and sensitivity analysis (excluding participants with a follow-up duration of <6 months) yielded C-indices were above 0.740. ConclusionThis study developed concise and practical overall and gender-specific COPD risk prediction models and corresponding nomograms. The models demonstrated robust performance in predicting COPD incidence, providing a valuable reference for identifying high-risk populations and formulating targeted screening and personalized management strategies.