BACKGROUND:Sarcopenia is an age-related systemic skeletal muscle disease,which is associated with a variety of adverse outcomes such as falls,functional decline,frailty,and death.Postmenopausal women are one of the high-risk groups for sarcopenia.OBJECTIVE:To develop a predictive model for assessing the risk of sarcopenia in Chinese postmenopausal women based on high-quality database.METHODS:Data for this study were derived from 2 370 postmenopausal women from the China Health and Retirement Longitudinal Study(CHARLS),and sarcopenia was assessed using the Asian Working Group on Sarcopenia 2019(AWGS2019)recommended metrics.The study cohort was randomized into a training set(70％)and a validation set(30％).Risk factors for sarcopenia in postmenopausal women were screened using the least absolute shrinkage and selection operator,ten-fold cross-validation,and logistic regression.Nomogram predicting the risk of sarcopenia in postmenopausal women was constructed based on the risk factors,and the model efficacy was evaluated by the receiver operating characteristic curve and area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS AND CONCLUSION:The prevalence of sarcopenia in this study was 23.50％and age,place of residence,sleep quality,cognitive function,depression,and the number of chronic diseases were selected as predictors of sarcopenia in postmenopausal women.The nomogram model showed good discrimination between the training and validation sets,with an AUC value of 0.751(95％confidence interval=0.724-0.778,P＜0.001),a specificity of 72.2％,and a sensitivity of 63.2％in the training set,and an AUC value of 0.763(95％confidence interval=0.721-0.805,P＜0.001),with a specificity of 69.6％and a sensitivity of 70.8％.The calibration curve showed a relatively significant agreement between the nomogram model and the actual observations,and the decision curve analysis demonstrated broad and good clinical utility.To conclude,the nomogram to assess the risk of sarcopenia constructed based on age,place of residence,sleep quality,cognitive function,depression,and number of chronic diseases,provides an effective tool for identifying and eliminating risk factors for sarcopenia in Chinese postmenopausal women,and helps to reduce the incidence of sarcopenia.

BACKGROUND:Sarcopenia is an age-related systemic skeletal muscle disease,which is associated with a variety of adverse outcomes such as falls,functional decline,frailty,and death.Postmenopausal women are one of the high-risk groups for sarcopenia.OBJECTIVE:To develop a predictive model for assessing the risk of sarcopenia in Chinese postmenopausal women based on high-quality database.METHODS:Data for this study were derived from 2 370 postmenopausal women from the China Health and Retirement Longitudinal Study(CHARLS),and sarcopenia was assessed using the Asian Working Group on Sarcopenia 2019(AWGS2019)recommended metrics.The study cohort was randomized into a training set(70％)and a validation set(30％).Risk factors for sarcopenia in postmenopausal women were screened using the least absolute shrinkage and selection operator,ten-fold cross-validation,and logistic regression.Nomogram predicting the risk of sarcopenia in postmenopausal women was constructed based on the risk factors,and the model efficacy was evaluated by the receiver operating characteristic curve and area under the curve(AUC),calibration curve,and decision curve analysis.RESULTS AND CONCLUSION:The prevalence of sarcopenia in this study was 23.50％and age,place of residence,sleep quality,cognitive function,depression,and the number of chronic diseases were selected as predictors of sarcopenia in postmenopausal women.The nomogram model showed good discrimination between the training and validation sets,with an AUC value of 0.751(95％confidence interval=0.724-0.778,P＜0.001),a specificity of 72.2％,and a sensitivity of 63.2％in the training set,and an AUC value of 0.763(95％confidence interval=0.721-0.805,P＜0.001),with a specificity of 69.6％and a sensitivity of 70.8％.The calibration curve showed a relatively significant agreement between the nomogram model and the actual observations,and the decision curve analysis demonstrated broad and good clinical utility.To conclude,the nomogram to assess the risk of sarcopenia constructed based on age,place of residence,sleep quality,cognitive function,depression,and number of chronic diseases,provides an effective tool for identifying and eliminating risk factors for sarcopenia in Chinese postmenopausal women,and helps to reduce the incidence of sarcopenia.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

BACKGROUND:With the continuous exploration of the pathogenesis of osteonecrosis,more and more research evidence shows that neuroimaging change is closely related to the onset of osteonecrosis.However,the specific causal relationship between neuroimaging change and osteonecrosis is still unclear.OBJECTIVE:To evaluate the causal relationship between neuroimaging indices and osteonecrosis using Mendelian Randomization analysis.METHODS:Neuroimaging data were obtained from the UK Biobank database in the UK,which included a total of 36 778 individuals.Osteonecrosis data were obtained from the FinnGen database in Finland,including 1 543 cases and 391 037 controls.Instrumental variables were extracted and screened from outcome factors,and two-sample Mendelian randomization analysis was performed.The data were analyzed by inverse variance weighted method,MR-Egger,weighted median method,simple model method,and weighted model method.The inverse variance weighted method was used as the main analysis method,and the other four methods were used as supplements.To verify the feasibility and stability of the data,sensitivity analysis of the results was performed.Based on the complexity of causal inference,a reverse Mendelian randomization analysis was further performed to evaluate the potential reverse causal relationship.RESULTS AND CONCLUSION:(1)The results of inverse variance weighted analysis showed that 97 neuroimaging data were positively correlated with osteonecrosis(P＜0.05,OR＞1);2 data were heterogeneous and 6 data had horizontal pleiotropy.95 neuroimaging phenotypes were negatively correlated with osteonecrosis(P＜0.05,OR＜1);5 data were heterogeneous,and 9 data had horizontal pleiotropy;2 groups of data had reverse causal relationships.(2)The two-sample Mendelian randomization analysis established the causal relationship between neuroimaging indicators and osteonecrosis in the academic community.These large sample numbers from the UK and Finland provide a new theoretical basis for the pathophysiology of osteonecrosis,and also provide ideas and methods for the prediction,screening,early diagnosis and prognosis of osteonecrosis in China,which is conducive to improving the accuracy of clinical diagnosis and the effectiveness of treatment of osteonecrosis.

BACKGROUND:With the continuous exploration of the pathogenesis of osteonecrosis,more and more research evidence shows that neuroimaging change is closely related to the onset of osteonecrosis.However,the specific causal relationship between neuroimaging change and osteonecrosis is still unclear.OBJECTIVE:To evaluate the causal relationship between neuroimaging indices and osteonecrosis using Mendelian Randomization analysis.METHODS:Neuroimaging data were obtained from the UK Biobank database in the UK,which included a total of 36 778 individuals.Osteonecrosis data were obtained from the FinnGen database in Finland,including 1 543 cases and 391 037 controls.Instrumental variables were extracted and screened from outcome factors,and two-sample Mendelian randomization analysis was performed.The data were analyzed by inverse variance weighted method,MR-Egger,weighted median method,simple model method,and weighted model method.The inverse variance weighted method was used as the main analysis method,and the other four methods were used as supplements.To verify the feasibility and stability of the data,sensitivity analysis of the results was performed.Based on the complexity of causal inference,a reverse Mendelian randomization analysis was further performed to evaluate the potential reverse causal relationship.RESULTS AND CONCLUSION:(1)The results of inverse variance weighted analysis showed that 97 neuroimaging data were positively correlated with osteonecrosis(P＜0.05,OR＞1);2 data were heterogeneous and 6 data had horizontal pleiotropy.95 neuroimaging phenotypes were negatively correlated with osteonecrosis(P＜0.05,OR＜1);5 data were heterogeneous,and 9 data had horizontal pleiotropy;2 groups of data had reverse causal relationships.(2)The two-sample Mendelian randomization analysis established the causal relationship between neuroimaging indicators and osteonecrosis in the academic community.These large sample numbers from the UK and Finland provide a new theoretical basis for the pathophysiology of osteonecrosis,and also provide ideas and methods for the prediction,screening,early diagnosis and prognosis of osteonecrosis in China,which is conducive to improving the accuracy of clinical diagnosis and the effectiveness of treatment of osteonecrosis.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To investigate the clinical treatment effect and safety of 3D microscopy technology for adjuvant neuraxial tumor resection on neuraxial tumor diseases.Methods A total of 37 patients with neuraxial tumors treated from January 2019 to July 2023,15 patients treated with 3D microscope tumor resection(3D group),and 22 patients treated with general microscope tumor resection(ordinary group)were analyzed.The perioperative indexes,clinical efficacy indexes and safety indexes were compared between the two groups.Results The operation time was(223.78±46.46)min in the ordinary group and(182.93±39.28)min in the 3D group,which was significantly lower than that in the ordinary group(P<0.05),and there was no significant difference in other perioperative indicators between the two groups(P>0.05).All patients had significantly reduced their postoperative pain symptoms and recovered their neurological function to a certain extent.There were statistically significant differences between the two groups(P<0.01),but there was no statistical difference between the two groups(P>0.05),and the McCormick spinal cord function rating was grade I.at one year after surgery.Conclusion The use of 3D microscopy and general microscopy for neuraxial tumor surgery has good clinical efficacy.However,the operation time can be significantly shortened under 3D microscopy,thereby reducing the risk of surgical complications and has better clinical safety.

Aim To investigate the therapeutic effects of a newly developed Tadalafil tablets on pathological myocardial hypertrophy induced by abdominal aortic constriction(AAC)in rats,as well as its influence on the activation of the NF-κB signaling pathway in myo-cardial cells.Methods SD rats were randomly divid-ed into 4 groups:the sham operation group(Sham),the model group(AAC),the tadalafil new tablet treat-ment group(N-Tad,5 mg·kg-1),and the positive control drug treatment group(Cialis,10 mg·kg-1g).The AAC model group and treatment group rats under-went blunt dissection and constrictive ligation of the abdominal aorta at the left renal artery branch point during surgery,while the Sham group rats only had their arteries separated without any constrictive liga-tion.Rats in the treatment groups received either N-Tad or Cialis via gavage three days after modeling,while rats in the sham group and the model group re-ceived physiological saline daily for 8 weeks.Small an-imal ultra-high-resolution echocardiography and hemo-dynamic assessment were applied to evaluate left ven-tricular function in each group of rats,and the calcula-tion of the left ventricular mass index was conducted.By employing Western blot and RT-PCR.we assessed the impact of this treatment on the expression of the hy-pertrophy factor atrial natriuretic peptide(ANP),phosphorylated NF-κB p65 protein(p-NF-κB p65),and phosphorylated IκB-α in the left heart tissue of rats and in H9c2 cardiomyocytes.Results Compared to the Sham group,the AAC rats exhibited a significant decrease in left heart function,an increase in left ven-tricular mass index,and a notable increase in ANP and p-p65 expression in the left heart tissue(P＜0.05).Both N-Tad and Cialis treatments could significantly enhance left ventricular function,decrease left ventric-ular mass index,and inhibit the expression of ANP and phosphorylated NF-κB p65 in rats with myocardial hy-pertrophy(P＜0.05).Notably,the therapeutic effect of low-dose N-Tad was comparable to that of high-dose Cialis.At the cellular level,Tadalafil significantly in-hibited the activation of the NF-κB signaling pathway and reduced the expression of associated proteins in H9c2 cardiomyocytes.Conclusions N-Tad can sig-nificantly inhibit p65 and IκB-α phosphorylation,and the activation of the NF-κB signaling pathway,reduce ANP expression,and improve pathological myocardial hypertrophy,as well as mitigate left heart function damage caused by abdominal aortic constriction.

Objective:To establish a method for determination of fluoride in water using a fully automated biochemical analyzer (abbreviated as this method).Methods:Based on the parameters of the fully automatic biochemical analyzer, appropriate reagent volumes and reaction time were optimized to determine fluoride ions in water within the standard curve range (0.0 - 2.0 mg/L). The method was validated through evaluation of linear range, detection limit, precision, accuracy, and comparative analysis with the manual measurement results of fluoride reagent spectrophotometric method specified in the "Standard Examination Methods for Drinking Water - Part 5: Inorganic Nonmetallic indices" (GB/T 5750.5-2023).Results:Within the range of 0.0 - 2.0 mg/L fluoride mass concentration, the absolute values of the correlation coefficients were > 0.999 0 and the detection limit of this method was 0.082 mg/L. The relative standard deviation for the determination of fluoride level in water samples with low, medium and high fluoride mass concentrations was 4.03%, 2.16% and 1.68%, respectively. The spiked recovery rates were 98.1%, 99.5%, and 100.2%, respectively. There was no statistically significant difference between the results obtained by this method and manual measurement ( t = 1.07, P = 0.295). Conclusion:This method exhibits high sensitivity, accuracy, and efficiency in detecting fluoride, enabling rapid sample detection through instrumental automation instead of manual operation.

Objective : To observe the brain tissue injury during hypoxia-ischemia, as well as the pathological changes and the expression of ferroptosis-related factors after the use of Shenfu injection(SFI), and to explore the protective effect of SFI on hypoxic-ischemic brain injury(HIBD) by inhibiting ferroptosis. Methods : An animal model of HIBD in SD rats was constructed and intervened with SFI. Pathologic changes in brain tissue were observed by HE staining methods. Nissen staining was used to observe neuron survival. Glutathione Peroxidase 4(GPX4) and Divalent Metal Transporter 1(DMT1) expression were detected in brain tissue by Western blot, immunohistochemistry and immunofluorescence. Reduced Glutathione(GSH), Lactate Dehydrogenase(LDH), Malondialdehyde(MDA), Superoxide Dismutase(SOD) and tissue iron content were determined with the kits. BV-2 microglial cell line(BV2) cells were culturedin vitroand divided into control group(Ctrl group), oxygen-glucose deprivation group(OGD group), iron ferroptosis-inducing group(Erastin group), iron ferroptosis-inhibiting group(Fer-1 group), Shenfu injection group(SFI group), and Erastin+Shenfu injection group(Erastin+SFI group). 2′,7′-Dichlorodihydrofluorescein diacetate(DCFH-DA) reactive oxygen species(ROS) fluorescent probe was used to detect the ROS release level; Immunofluorescence was used to observe intracellular GPX4, DMT1 expression. Results : Compared with the Sham group, rats in the HIBD group showed significant neuronal cell damage in brain tissue, decreased GPX4 expression(P<0.01), increased DMT1 expression(P<0.01), decreased GSH and SOD levels(P<0.01), and increased LDH, MDA and tissue iron levels(P<0.05,P<0.05,P<0.01). In contrast, after the intervention of SFI, GPX4 expression was elevated(P<0.01), DMT1 expression decreased(P<0.01), GSH and SOD levels were elevated(P<0.01), and LDH, MDA, and tissue iron levels decreased(P<0.05,P<0.05,P<0.01). The cells experiments showed that compared with the Ctrl group, the OGD group had a significantly higher ROS content and a decrease in the expression of GPX4 fluorescence intensity, and an increase in the fluorescence intensity of DMT1(P<0.01), compared with the OGD group, the ROS content was reduced in the SFI group, while the expression of GPX4 was elevated and the expression of DMT1 was reduced(P<0.01). Conclusion Hippocampal and cortical regions are severely damaged after HIBD in neonatal rats, and their brain tissues show decreased expression of GPX4 and increased expression of DMT1. The above suggests that ferroptosis is involved in HIBD brain injury in neonatal rats. In contrast, Shenfu injection has a protective effect on HIBD experimental animal model and BV2 cell injury model by reducing iron aggregation and ROS production.