Glucocorticoid-induced osteoporosis(GIOP) is a serious metabolic bone disease caused by long-term application of glucocorticoids(GCs). Traditional Chinese medicine(TCM) has unique advantages in improving bone microstructure and antagonizing hormone toxicity. This paper systematically reviews the theoretical research, clinical application, and basic research progress of TCM intervention in GIOP. In terms of theoretical research, the theory of "kidney governing bone and generating marrow" indicates that the kidney is closely related to bone development, revealing that core pathogenesis of GIOP is Yin-Yang disharmony, which can be discussed using the theories of "Yin fire", "ministerial fire", and "Yang pathogen damaging Yin". Thus, regulating Yin and Yang is the basic principle to treat GIOP. In terms of clinical application, effective empirical prescriptions(such as Bushen Zhuanggu Decoction, Bushen Jiangu Decoction, and Zibu Ganshen Formula) and Chinese patent medicines(Gushukang Capsules, Hugu Capsules, Xianling Gubao Capsules, etc.) can effectively increase bone mineral density(BMD) and improve calcium and phosphorus metabolism. The combination of traditional Chinese and western medicine can reduce the risk of fracture and play an anti-GIOP role. In terms of basic research, it has been clarified that active ingredients of TCM(such as fraxetin, ginsenoside Rg_1, and salidroside) reduce bone loss and promote bone formation by inhibiting oxidative stress, ferroptosis, and other pathways, effectively improving bone homeostasis. Additionally, classical prescriptions(Modified Yiguan Decoction, Modified Qing'e Pills, Zuogui Pills, etc.) and Chinese patent medicines(Gushukang Granules, Lurong Jiangu Dropping Pills, Gubao Capsules, etc.) can improve bone marrow microcirculation, promote osteoblast differentiation, and inhibit bone cell apoptosis through multiple pathways, multiple targets, and multiple mechanisms. Through the above three aspects, the TCM research status on GIOP is elucidated in the expectation of providing reference for its diagnosis and treatment using traditional Chinese and western medicine treatment programs.
Osteoporosis/physiopathology* ; Humans ; Glucocorticoids/adverse effects* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Medicine, Chinese Traditional ; Bone Density/drug effects*

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.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

ObjectiveTo explore the effectiveness and safety of Shenlong Dingji Formula （参龙定悸方） on the quality of life in patients with paroxysmal atrial fibrillation （PAF） of qi-yin deficiency and phlegm-stasis obstructing collaterals syndrome. MethodsA total of 60 patients with PAF of qi-yin deficiency and phlegm-stasis obstructing collaterals syndrome were recruited and randomly divided into a treatment group and a control group， with 30 patients in each group. The control group received standard western medicine treatment， while the treatment group was additionally given Shenlong Dingji Formula orally， one dose per day. Both groups were treated for 4 weeks. The primary outcome measure is the Atrial Fibrillation Effect on Quality of Life （AFEQT） score including scores of four dimensions，i.e. atrial fibrillation-related symptoms， treatment concerns， daily activities， and treatment satisfaction. The secondary outcome measures included the frequency and duration of symptomatic atrial fibrillation episodes and traditional Chinese medicine （TCM） syndrome scores covering symptoms such as palpitations， chest tightness， fatigue， shortness of breath， reluctance to speak， spontaneous sweating， stabbing pain， and insomnia. These indicators were assessed at baseline （before treatment）， after 2-week of treatment， after 4-week of treatment， and 4 weeks after the end of treatment （follow-up）. Additionally， safety indicators before and after treatment and adverse events occurring during the trial were recorded to evaluate safety. ResultsA total of 56 patients completed the study， with 28 in each group. Primary outcome indicators： 1） the treatment group showed significant improvement in the total score of the AFEQT scale， with significantly higher total scores after 2-week treatment， 4-week treatment， and follow-up compared to the previous time point （P＜0.05）. In the control group， the AFEQT score significantly increased only after 4-week treatment compared to baseline （P＜0.05）. In the treatment group， the AFEQT scores after 2-week， 4-week treatment， and during follow-up were all higher than those of the control group at the corresponding time points （P＜0.01）. 2） In the treatment group， there was no statistically significant difference in the AFEQT treatment satisfaction dimension score during follow-up compared to that after 4-week treatment （P＞0.05）. However， the scores for all other dimensions at each time point were higher than those at the previous time point （P＜0.05）. In the control group， the scores for the atrial fibrillation-related symptom dimension were higher after 2-week and 4-week treatment than those of the previous time points （P＜0.05）. For the treatment satisfaction dimension， significant increases were observed only after 2-week and 4-week treatment compared to baseline （P＜0.05）. Secondary outcome indicators： 1） In the treatment group， the frequency and duration of symptomatic atrial fibrillation episodes decreased significantly at each time point compared to the previous time point （P＜0.05）， except for the duration of trial fibrillation at follow-up. In the control group， the frequency of episodes decreased significantly at all time points compared to baseline （P＜0.05）， while the duration of trial fibrillation showed a significant reduction at follow-up compared to those after 2-week treatment （P＜0.05）. 2） In the treatment group， TCM syndrome scores significantly reduced after 2-week treatment， 4-week treatment， and during follow-up compared to the previous time point and baseline （P＜0.05）. In the control group， significant reductions were observed only after 4-week after treatment and during follow-up （P＜0.05）. The TCM syndrome scores in the treatment group were lower than those in the control group at the same time points （P＜0.01）. No adverse events occurred during the trial in either group， and safety indicators showed no significant changes after treatment. ConclusionShenlong Dingji Formula effectively improves the quality of life， alleviates TCM syndromes， and reduces the frequency and duration of symptomatic atrial fibrillation in patients with PAF of qi-yin deficiency and phlegm-stasis obstructing collaterals syndrome， and demonstrates good safety.

AIM: To investigate the genetic association and potential causal relationship between diabetic nephropathy(DN)and diabetic retinopathy(DR), and to elucidate their shared molecular mechanisms through differential gene expression analysis and Mendelian randomization(MR).METHODS: Transcriptomic data of DN and DR were obtained from the Gene Expression Omnibus(GEO)database and analyzed for differentially expressed genes(DEGs). Genes meeting the significance threshold(log2FC>1, P<0.05)were identified, followed by Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis to explore shared biological pathways. Using genome-wide association study(GWAS)summary statistics for DN and DR, two-sample MR analysis was performed, with DN as the exposure and DR as the outcome. The causal effect was primarily estimated with the inverse-variance weighted(IVW)method, and sensitivity analyses were conducted to assess robustness.RESULTS: MR analysis revealed that DN significantly increased the risk of DR. IVW estimates indicated that the odds ratio(OR)for non-proliferative DR(NPDR)was 3.23(95% CI: 2.12-4.95, P<0.001), and the OR for proliferative DR(PDR)was 1.10(95% CI: 1.06-1.15, P<0.001). DEG analysis identified several key genes, including FN1, COL1A2, and THBS2. FN1 and COL1A2 are involved in extracellular matrix remodeling and fibrosis, contributing to vascular permeability alterations and microvascular damage in diabetic complications. THBS2 is closely associated with angiogenesis and vascular homeostasis, suggesting its potential role in DR. KEGG enrichment analysis showed that these DEGs were mainly enriched in advanced glycation end products(AGEs)-RAGE signaling, extracellular matrix degradation, and oxidative stress pathways, all of which are highly relevant to the pathogenesis of DN and DR.CONCLUSION: This study demonstrates the genetic association between DN and DR using MR and DEGs analyses. The shared mechanisms, particularly involving extracellular matrix remodeling, inflammatory response, and angiogenesis, may serve as novel therapeutic targets and provide a theoretical basis for the early diagnosis and targeted treatment of diabetic complications.

Objective To evaluate the influence of the wearing position of dosimeters outside lead aprons on effective dose estimation for interventional radiology workers, analyze the differences between single and double dosimeter methods in effective dose estimation, and provide a reference for the personal dose monitoring of interventional radiology workers. Methods This study employed a combined approach of on-site monitoring and Monte Carlo simulation to evaluate the impact of the wearing position of dosimeters outside lead aprons on effective dose estimation, as well as the differences between effective doses measured using single and double dosimeters. Interventional radiology workers wore dosimeters at three positions: the neck outside the lead collar, the left chest outside the lead apron, and inside the lead apron. Effective doses were estimated using the single and double dosimeter methods specified in GBZ 128-2019 Specifications for individual monitoring of occupational external exposure, and the impact of different wearing positions on the estimation results was compared. Geant4 Monte Carlo simulations were used to model dose distributions at the neck outside the lead collar and at the left chest outside the lead apron for operators performing cardiovascular interventions under tube voltages of 70, 80, 90, and 100 kVp and exposure angles of posteroanterior (PA), anteroposterior (AP), and left anterior oblique 45° (LAO45°) positions. The study assessed the impact of dosimeter wearing position on effective dose estimation. Results Monte Carlo simulations demonstrated that neck doses consistently exceeded left chest doses across different tube voltages and exposure angles, with neck-to-chest dose ratios of 0.80-0.90. Under identical tube voltage conditions, AP showed the highest doses, followed by LAO45°, and PA demonstrated the lowest doses. The single and double dosimeter methods exhibited consistent patterns in effective dose estimation. Single dosimeter method generally yielded higher effective doses with relative deviations of 9.9% to 83%, though these deviations decreased under high tube voltages. Field monitoring data indicated that most interventional radiology workers maintained relative deviations between single and double dosimeter calculations below 6%, with neck-to-chest dose ratios of 0.95-1.1. The estimation patterns remained consistent across both methods, though single dosimeter method showed slightly higher results. Conclusion Under PA, AP, or LAO45°, the doses at the neck consistently exceeded those at the left chest. Therefore, when wearing lead protective equipment, the dosimeter should be properly positioned at the neck outside the lead collar to accurately reflect the radiation doses of surgeons. Some interventional radiology workers improperly positioned the dosimeter (intended at the neck outside the lead collar) at the left chest outside the lead apron, and this may result in an underestimation of the effective dose.

Solid-phase extraction(SPE)combined with surface-enhanced Raman spectroscopy(SERS)has emerged as a promising analytical technique for detection and analysis of trace components in complex sample matrices.SPE enriches analytes through selective adsorption and solvent elution,effectively increasing the concentration and signal intensity.SERS enables ultra-sensitive and highly selective molecular analysis through the use of SERS-active substrates engineered to amplify Raman signals.The integration of these two techniques overcomes the limitations of conventional Raman spectroscopy in low-concentration detection field,while significantly improving sample preparation efficiency and analytical accuracy.This review provided a comprehensive overview of the characteristics of three SPE-SERS coupling modes,including two-step,one-step,and online integration.Special emphasis was placed on recent advancements in one-step SPE-SERS approaches based on novel functional adsorbent materials such as graphene,metal-organic frameworks,covalent organic frameworks,and molecularly imprinted polymers.Furthermore,future directions and development prospects of SPE-SERS technology were discussed.

Abdominal war trauma is a common and high-risk type of injury in the modern battlefield,with rapid changes in condition and a high mortality rate.There is an urgent need for emerging medical technologies to improve the efficiency and success rate of first aid for military casualties.With the development of artificial intelligence(AI),5G,and other emerging technologies,the concept of intelligent medical treatment is gradually forming and can assist in the diagnosis and treatment of abdominal trauma.This paper reviews the characteristics of abdominal war trauma in modern wars,discusses the application of intelligent medical treatment for abdominal war trauma and its drawbacks to be solved,aiming to provide reference for research related to abdominal war trauma.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

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.