Rare events are widely prevalent in various disciplines,including rare adverse reactions to vaccines and drugs,clinical rare diseases,and low-probability clinical outcomes.The reason for research interest on such events is that their occurrence often brings incalculable and serious consequences.In the context of big data,numerous methods have emerged for rare event data analysis,including sampling based,category weighting,ensemble learning,and deep learning.This article systematically summarizes the research progress of current rare event data analysis methods,and introduces their basic principles and applicable scenarios.By analyzing the advantages and disadvantages of existing methods,the challenges of rare event research are sorted out and summarized,and potential research directions in related fields are explored to provide references for researchers.

Objective To observe the value of 18F-D3FSP PET/CT machine learning(ML)models for evaluating subjective cognitive decline(SCD).Methods Thirty-two SCD patients(SCD group)and 16 healthy volunteers(control group)who received 18F-D3FSP PET/CT were selected from Sino Longitudinal Study on Cognitive Decline(SILCODE)and divided into training set(n=34)and test set(n=14)at a ratio of 7∶3.Support vector machine(SVM),random forest(RF)and logistic regression(LR)models were constructed based on Hamilton anxiety scale(HAMA)and standard uptake value ratio(SUVR)of brain regions being significantly different between groups to evaluate SCD.Then PET/CT data were amplified by format conversion and divided into training set(including 8 775 CT images and 1 833 PET images)and test set(including 2 025 CT images and 423 PET images)at the ratio of 8∶2.VGG16 models were constructed based on CT and PET images to evaluate SCD,respectively.Results The area under the receiver operating characteristic curve(AUC)of SVM,RF and LR model for evaluating SCD in training set was all 1.000,while was 0.863,0.872 and 1.000 in test set,respectively.LR model was overfitting,and RF model had better performance.The accuracy of VGG16 model for evaluating SCD based on CT and PET images tended to be stable after the 175th and 150th iterations,with the highest accuracy of 67.11％ and 65.35％ in training set,which tended to be stable after the 165th and 145th iterations,with the highest accuracy of 62.43％ and 59.16％ in test set,respectively.Conclusion 18F-D3FSP PET/CT ML models could be used to evaluate SCD.

Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.

Background: Psoralea corylifolia L. (Buguzhi, BGZ), known for its efficacy in supporting pregnancy and preventing miscarriage, has been used in China for over 1000 years. Recently, BGZ has been identified as a potential cause of drug-induced liver injury. However, its safety during pregnancy remains unclear, which significantly hinders its routine clinical application. Objective: To investigate the effects of BGZ administration during pregnancy on the liver of mouse mothers and their weaned 21-day-old offspring. Methods: Mice were orally administered BGZ at doses of 2.5 and 10 g/kg during pregnancy, with BGZ withdrawal during the lactation period. Liver histopathology (hematoxylin-eosin staining), biochemical analysis, and evaluation of liver bile acid metabolism were performed after the lactation period. Results: BGZ administration at doses of 2.5 and 10 g/kg during pregnancy, followed by withdrawal during the lactation period, caused mild liver damage in both mothers and their 21-day-old offspring. Serum total bile acid (TBA) levels were elevated compared with those in the control group. Additionally, changes were observed in the levels and proportions of various bile acids (BAs) in the liver, suggesting mild effects on BA metabolism. Conclusion: BGZ administration during pregnancy caused mild liver damage and increased serum TBA levels in both mouse mothers and their 21-day-old offspring. This phenomenon may be associated with imbalanced BA metabolism in the liver. Based on the present study and the limited toxicological research on BGZ, pregnant women should avoid prolonged use of BGZ. If BGZ is administered during pregnancy, serum TBA levels should be monitored, and if elevated, BGZ should be discontinued.

Background: Aristolochic acid II (AAII), a major nephrotoxic and carcinogenic component of aristolochic acids (AAs), has been less studied compared with its well-characterized analog, aristolochic acid I (AAI). Although AAs are known to induce carcinogenesis via DNA adduct formation, the toxicity mechanisms, environmental prevalence, and long-term health impacts of AAII remain poorly understood. Objective: This study aimed to systematically evaluate AAII’s acute and chronic toxicity, carcinogenic mechanisms, and environmental exposure patterns using integrated murine models and phytochemical analyses to clarify its toxicological profile and associated health risks. Methods: C57BL/6J mice were used in the following experiments: (1) determination of AAII content in 3 commonly used Aristolochia medicinal materials via liquid chromatography-mass spectrometry/mass spectrometry; (2) acute toxicity testing with single doses of 10, 20, or 40 mg/kg; and (3) chronic exposure with 1 or 10 mg/kg administered every other day for 24 weeks, followed by 21 to 40 weeks of postexposure monitoring. Histopathological examination, whole-exome sequencing, biochemical assays, and micronucleus tests were performed to assess multi-organ damage, tumorigenesis, genomic mutation signatures, and direct clastogenicity. Phytochemical analyses were used to evaluate environmental distribution. Results: (1) A single 40 mg/kg dose of AAII induced dose-dependent renal tubular degeneration without hepatotoxicity; (2) the 10 mg/kg group showed significant mortality (20%), tumor incidence (33.3%, primarily forestomach and bladder transitional cell carcinomas), persistent renal interstitial fibrosis, and subclinical hepatic injury. Chronic exposure to 1 mg/kg still induced 13.3% mortality and 15.5% tumor incidence over a 64-week period; (3) whole-exome sequencing revealed a predominance of C>T mutations and pathway enrichment in chemical carcinogenesis and cytochrome P450-mediated metabolism, indicating reactive metabolite-driven mechanisms distinct from classical AA-DNA adducts; and (4) no histopathological changes were observed in nontarget organs (brain, heart, and testes), and micronucleus assays confirmed the absence of direct clastogenicity. Conclusion: This study highlights the delayed carcinogenic risks of low-dose chronic AAII exposure and emphasizes the need to update regulatory frameworks to ensure the safe use of aristolochiaceae-containing herbal products.

OBJECTIVE: The study aim was to investigate the effects of exposure to multiple environmental organic pollutants on cardiopulmonary health with a focus on the potential mediating role of oxidative stress. METHODS: A repeated-measures randomized crossover study involving healthy college students in Beijing was conducted. Biological samples, including morning urine and venous blood, were collected to measure concentrations of 29 typical organic pollutants, including hydroxy polycyclic aromatic hydrocarbons (OH-PAHs), bisphenol A and its substitutes, phthalates and their metabolites, parabens, and five biomarkers of oxidative stress. Health assessments included blood pressure measurements and lung function indicators. RESULTS: Urinary concentrations of 2-hydroxyphenanthrene (2-OH-PHE) ( β = 4.35% [95% confidence interval ( CI): 0.85%, 7.97%]), 3-hydroxyphenanthrene ( β = 3.44% [95% CI: 0.19%, 6.79%]), and 4-hydroxyphenanthrene (4-OH-PHE) ( β = 5.78% [95% CI: 1.27%, 10.5%]) were significantly and positively associated with systolic blood pressure. Exposures to 1-hydroxypyrene (1-OH-PYR) ( β = 3.05% [95% CI: -4.66%, -1.41%]), 2-OH-PHE ( β = 2.68% [95% CI: -4%, -1.34%]), and 4-OH-PHE ( β = 3% [95% CI: -4.68%, -1.29%]) were negatively associated with the ratio of forced expiratory volume in the first second to forced vital capacity. These findings highlight the adverse effects of exposure to multiple pollutants on cardiopulmonary health. Biomarkers of oxidative stress, including 8-hydroxy-2'-deoxyguanosine and extracellular superoxide dismutase, mediated the effects of multiple OH-PAHs on blood pressure and lung function. CONCLUSION Exposure to multiple organic pollutants can adversely affect cardiopulmonary health. Oxidative stress is a key mediator of the effects of OH-PAHs on blood pressure and lung function.
Humans ; Oxidative Stress/drug effects* ; Male ; Cross-Over Studies ; Female ; Young Adult ; Environmental Pollutants/toxicity* ; Environmental Exposure/adverse effects* ; Biomarkers/blood* ; Adult ; Blood Pressure/drug effects* ; Polycyclic Aromatic Hydrocarbons/urine* ; Beijing

Emodin is a hydroxyanthraquinone compound that is widely distributed and has multiple pharmacological activities, including anti-diarrheal, anti-inflammatory, and liver-protective effects. Research indicates that emodin may be one of the main components responsible for inducing hepatotoxicity. However, studies on the mechanisms of liver injury are relatively limited, particularly those related to bile acids(BAs) metabolism. This study aims to systematically investigate the effects of different dosages of emodin on BAs metabolism, providing a basis for the safe clinical use of traditional Chinese medicine(TCM)containing emodin. First, this study evaluated the safety of repeated administration of different dosages of emodin over a 5-week period, with a particular focus on its impact on the liver. Next, the composition and content of BAs in serum and liver were analyzed. Subsequently, qRT-PCR was used to detect the mRNA expression of nuclear receptors and transporters related to BAs metabolism. The results showed that 1 g·kg~(-1) emodin induced hepatic damage, with bile duct hyperplasia as the primary pathological manifestation. It significantly increased the levels of various BAs in the serum and primary BAs(including taurine-conjugated and free BAs) in the liver. Additionally, it downregulated the mRNA expression of farnesoid X receptor(FXR), retinoid X receptor(RXR), and sodium taurocholate cotransporting polypeptide(NTCP), and upregulated the mRNA expression of cholesterol 7α-hydroxylase(CYP7A1) in the liver. Although 0.01 g·kg~(-1) and 0.03 g·kg~(-1) emodin did not induce obvious liver injury, they significantly increased the level of taurine-conjugated BAs in the liver, suggesting a potential interference with BAs homeostasis. In conclusion, 1 g·kg~(-1) emodin may promote the production of primary BAs in the liver by affecting the FXR-RXR-CYP7A1 pathway, inhibit NTCP expression, and reduce BA reabsorption in the liver, resulting in BA accumulation in the peripheral blood. This disruption of BA homeostasis leads to liver injury. Even doses of emodin close to the clinical dose can also have a certain effect on the homeostasis of BAs. Therefore, when using traditional Chinese medicine or formulas containing emodin in clinical practice, it is necessary to regularly monitor liver function indicators and closely monitor the risk of drug-induced liver injury.
Emodin/administration & dosage* ; Bile Acids and Salts/metabolism* ; Animals ; Male ; Liver/injuries* ; Chemical and Drug Induced Liver Injury/genetics* ; Drugs, Chinese Herbal/adverse effects* ; Humans ; Rats, Sprague-Dawley ; Mice ; Rats

Traditional Chinese medicine (TCM) serves as a treasure trove of ancient knowledge, holding a crucial position in the medical field. However, the exploration of TCM's extensive information has been hindered by challenges related to data standardization, completeness, and accuracy, primarily due to the decentralized distribution of TCM resources. To address these issues, we developed a platform for TCM knowledge discovery (TCMKD, https://cbcb.cdutcm.edu.cn/TCMKD/). Seven types of data, including syndromes, formulas, Chinese patent drugs (CPDs), Chinese medicinal materials (CMMs), ingredients, targets, and diseases, were manually proofread and consolidated within TCMKD. To strengthen the integration of TCM with modern medicine, TCMKD employs analytical methods such as TCM data mining, enrichment analysis, and network localization and separation. These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights. In addition to its analytical capabilities, a quick question and answer (Q&A) system is also embedded within TCMKD to query the database efficiently, thereby improving the interactivity of the platform. The platform also provides a TCM text annotation tool, offering a simple and efficient method for TCM text mining. Overall, TCMKD not only has the potential to become a pivotal repository for TCM, delving into the pharmacological foundations of TCM treatments, but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems, extending beyond just TCM.

Objective To observe the value of 18F-D3FSP PET/CT machine learning(ML)models for evaluating subjective cognitive decline(SCD).Methods Thirty-two SCD patients(SCD group)and 16 healthy volunteers(control group)who received 18F-D3FSP PET/CT were selected from Sino Longitudinal Study on Cognitive Decline(SILCODE)and divided into training set(n=34)and test set(n=14)at a ratio of 7∶3.Support vector machine(SVM),random forest(RF)and logistic regression(LR)models were constructed based on Hamilton anxiety scale(HAMA)and standard uptake value ratio(SUVR)of brain regions being significantly different between groups to evaluate SCD.Then PET/CT data were amplified by format conversion and divided into training set(including 8 775 CT images and 1 833 PET images)and test set(including 2 025 CT images and 423 PET images)at the ratio of 8∶2.VGG16 models were constructed based on CT and PET images to evaluate SCD,respectively.Results The area under the receiver operating characteristic curve(AUC)of SVM,RF and LR model for evaluating SCD in training set was all 1.000,while was 0.863,0.872 and 1.000 in test set,respectively.LR model was overfitting,and RF model had better performance.The accuracy of VGG16 model for evaluating SCD based on CT and PET images tended to be stable after the 175th and 150th iterations,with the highest accuracy of 67.11％ and 65.35％ in training set,which tended to be stable after the 165th and 145th iterations,with the highest accuracy of 62.43％ and 59.16％ in test set,respectively.Conclusion 18F-D3FSP PET/CT ML models could be used to evaluate SCD.