To standardize the clinical application of oral Chinese patent medicines （CPMs）， and address the safety issues arising from their dosage form characteristics， irrational clinical use， and the lack of targeted pharmacovigilance systems， the China Association of Chinese Medicine organized the formulation and release of Pharmacovigilance Guidelines for Clinical Application of Oral Chinese Patent Medicines， aiming to inform the safe clinical use of oral CPMs and related pharmacovigilance work. According to the principles of GB/T1.1—2020 and the Drug Administration Law of the People's Republic of China （2019 revision）， the Institute of Basic Research in Clinical Medicine， China Academy of Chinese Medical Sciences， led a drafting group comprising 18 institutions. After multiple rounds of expert interviews， literature retrieval， evidence screening， and extensive solicitation of opinions， the Guidelines were registered internationally. Systematic standardization focused on safety monitoring， risk identification， assessment， control， and other aspects. The Guidelines clarified the characteristics of oral CPMs in terms of safety monitoring， known risks， and potential risks， compared to non-oral CPMs. Then， risk control measures were proposed， including medication in special populations and irrational medication. As a special guideline for pharmacovigilance in the clinical application of oral CPMs， the Guidelines systematically construct a technical system in line with the characteristics of traditional Chinese medicine （TCM）， which is essential for improving the clinical safety management of oral CPMs and provides an important reference for medical institutions， pharmaceutical manufacturers， and regulatory authorities.

To standardize the clinical application of oral Chinese patent medicines （CPMs）， and address the safety issues arising from their dosage form characteristics， irrational clinical use， and the lack of targeted pharmacovigilance systems， the China Association of Chinese Medicine organized the formulation and release of Pharmacovigilance Guidelines for Clinical Application of Oral Chinese Patent Medicines， aiming to inform the safe clinical use of oral CPMs and related pharmacovigilance work. According to the principles of GB/T1.1—2020 and the Drug Administration Law of the People's Republic of China （2019 revision）， the Institute of Basic Research in Clinical Medicine， China Academy of Chinese Medical Sciences， led a drafting group comprising 18 institutions. After multiple rounds of expert interviews， literature retrieval， evidence screening， and extensive solicitation of opinions， the Guidelines were registered internationally. Systematic standardization focused on safety monitoring， risk identification， assessment， control， and other aspects. The Guidelines clarified the characteristics of oral CPMs in terms of safety monitoring， known risks， and potential risks， compared to non-oral CPMs. Then， risk control measures were proposed， including medication in special populations and irrational medication. As a special guideline for pharmacovigilance in the clinical application of oral CPMs， the Guidelines systematically construct a technical system in line with the characteristics of traditional Chinese medicine （TCM）， which is essential for improving the clinical safety management of oral CPMs and provides an important reference for medical institutions， pharmaceutical manufacturers， and regulatory authorities.

Qingfeitang， specialized in resolving phlegm to stop cough and producing fluid to moisten dryness， is a classic prescription inherited and developed by physicians of successive generations and has been included in the Catalogue of Ancient Classic Prescriptions （First Batch） published by the National Administration of Traditional Chinese Medicine （TCM） in 2018. Relevant ancient books data and modern literature were collected by bibliometrics to analyze the historic origin， formula composition， herb origin， preparation methods， processing methods， clinical effect， and indications of Qingfeitang. The key information of Qingfeitang was summarized to provide reference for the clinical application of the decoction. In this study， a total of 43 pieces of effective data on relevant ancient literature， including 35 ancient TCM books， were collected based on a systematic collation of relevant historic and modern literature. Results showed that "Qingfeitang" was originated from the "Renshen Qingfeitang" recorded in the Taiping Holy Prescriptions for Universal Relief from the Qing dynasty. The name of "Qinfeitang" was first recorded in the Yeshi Luyanfang written by YE Dalian in the Song dynasty. We suggested the modern dosage and usage of Qingfeitang as follows： "Scutellariae Radix of 5.60 g， Platycodon grandiflora， Poria， Tangerine， Fritillaria， and Cortex Mori of 3.73 g respectively， Angelicae Sinensis Radix， Asparagi Radix， Gardeniae Fructus， Armeniacae Semen Amarum， and Ophiopogonis Radix of 2.61 g respectively， Schisandra of 1 g， and Glycyrrhizae Radix et Rhizoma of 1.12 g， and they were taken 3 times daily. The above formula is recommended to be decocted with 400 mL of water， with 3.37 g ginger and 6 g jujubae fructus， to 320 mL， and taken after a meal， three times per day". Qingfeitang has the effect of resolving phlegm to stop cough and producing fluid to moisten dryness， specialized in treating cough， asthma， rash， and other symptoms in ancient times. Modern applications are mainly focused on the respiratory system， used for treating diseases such as bronchopneumonia and cough. The above research results provide a reference basis for the later development and research of Qingfeitang.

Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F ₁-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.
Humans ; Arrhythmias, Cardiac/diagnosis* ; Algorithms ; Electrocardiography/methods* ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Deep Learning ; Classification Algorithms

Poly(ADP-ribosyl)ation (PARylation) is a specific form of post-translational modification (PTM) predominantly triggered by the activation of poly-ADP-ribose polymerase 1 (PARP1). However, the role and mechanism of PARylation in the advancement of acute kidney injury (AKI) remain undetermined. Here, we demonstrated the significant upregulation of PARP1 and its associated PARylation in murine models of AKI, consistent with renal biopsy findings in patients with AKI. This elevation in PARP1 expression might be attributed to trimethylation of histone H3 lysine 4 (H3K4me3). Furthermore, a reduction in PARylation levels mitigated renal dysfunction in the AKI mouse models. Mechanistically, liquid chromatography-mass spectrometry indicated that PARylation mainly occurred in receptor for activated C kinase 1 (RACK1), thereby facilitating its subsequent phosphorylation. Moreover, the phosphorylation of RACK1 enhanced its dimerization and accelerated the ubiquitination-mediated hypoxia inducible factor-1α (HIF-1α) degradation, thereby exacerbating kidney injury. Additionally, we identified a PARP1 proteolysis-targeting chimera (PROTAC), A19, as a PARP1 degrader that demonstrated superior protective effects against renal injury compared with PJ34, a previously identified PARP1 inhibitor. Collectively, both genetic and drug-based inhibition of PARylation mitigated kidney injury, indicating that the PARylated RACK1/HIF-1α axis could be a promising therapeutic target for AKI treatment.

Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

ObjectiveTo explore the clinical safety of Feilike Mixture （FLK） in the real world. MethodsThe safety of all children who received FLK from 29 institutions in 12 provinces between January 21，2021 and December 25，2021 was evaluated through prospective centralized surveillance and a nested case control study. ResultsA total of 3 035 juveniles were included. There were 29 research centers involved，which are distributed across 12 provinces，including one traditional Chinese medicine （TCM） hospital and 28 general hospitals. The average age among the juveniles was （4.77±3.56） years old，and the average weight was （21.81±12.97） kg. Among them，119 cases （3.92%） of juveniles had a history of allergies. Acute bronchitis was the main diagnosis for juveniles，with 1 656 cases （54.46%）. FLK was first used in 2 016 cases （66.43%），and 142 juvenile patients had special dosages，accounting for 4.68%. Among them，92 adverse drug reactions （ADRs） occurred，including 73 cases of gastrointestinal system disorders，10 cases of metabolic and nutritional disorders，eight cases of skin and subcutaneous tissue diseases，two cases of vascular and lymphatic disorders，and one case of systemic diseases and various reactions at the administration site. The manifestations of ADRs were mainly diarrhea，stool discoloration，and vomiting，and no serious ADRs occurred. The results of multi-factor analysis indicated that special dosages （the use of FLK）［odds ratio （OR） of 2.642， 95% confidence interval （CI） of 1.105-6.323］，combined administration： spleen aminopeptide （OR of 4.978， 95%CI of 1.200-20.655），and reason for combined administration： anti-infection （OR of 1.814， 95%CI of 1.071-3.075） were the risk factors for ADRs caused by FLK. Conclusion92 ADRs occurred among 3 035 juveniles using FLK. The incidence of ADRs caused by FLK was 3.03%，and the severity was mainly mild or moderate. Generally，the prognosis was favorable after symptomatic treatment such as drug withdrawal or dosage reduction，suggesting that FLK has good clinical safety.

Monkeypox,caused by the monkeypox virus(MPXV),is a zoonotic infectious disease characterized clinically by fever,rash,and lymphadenopathy.In September 2024,the outbreak of a novel MPXV Clade Ib variant in the Democratic Republic of the Congo was designated by the World Health Organization as a public health emergency of international concern,highlighting deficiencies in current prevention and control systems.Although modified attenuated smallpox vaccines exhibit cross-protective efficacy against MPXV infection,their adverse effects still limit clinical applications.Further-more,the therapeutic efficacy of tecovirimat—the FDA emergency-authorized antiviral drug for Smallpox—is limited.Recent studies have shown that tecovirimat does not significantly improve lesion healing time or reduce overall mortality in patients infected with MPXV CladeⅠ.Monoclonal antibodies,which neutralize viral activity by targeting critical membrane proteins,have emerged as a pivotal strategy to overcome current therapeutic bottlenecks by integrating therapeutic,diagnostic and prophylactic functions.This article reviews the epidemiological and virological characteristics of MPXV,along with functional characteristics of MPXV major membrane proteins,including A29L,A35R,B6R,E8L,M1R and H3L,focusing on the research progress in monoclonal antibodies against these key targets.Their protective effects in vitro/in vivo are explored while such as strategies monoclonal antibody combina-tion therapy are recommended to enhance efficacy and overcome drug resistance.

An automatic epileptic seizure detection model based on multi-channel recurrence plots and SE-VGG16 is proposed to enhance the accuracy of automatic detection of epileptic seizures.Firstly,triaxial accelerometer is used to collect patients'motion signals as a substitute of electroencephalogram signals,and these signals are converted into two-dimensional images through recurrence plots and then subjected to multi-channel fusion for generating multi-channel recurrence plots.Subsequently,squeeze-and-excitation module is employed to improve VGG16,which enhances the network's adaptability to different channels and enables precise classification.Experimental results show that the proposed model achieves the best performance across all indicators,attaining an accuracy of 99.3%,precision of 99.2%,recall rate of 98.9%,and F1-score of 99.0%.This model significantly boosts the accuracy and applicability of epileptic seizure detection,expands the potential of wearable devices for epileptic seizure monitoring in non-clinical settings,and provides a new technical pathway for epilepsy management and research.

Objective:To explore the relationship between insomnia, depression, sense of meaning in life and suicidal behavior among college students.Methods:A cross-sectional survey was conducted among 7 260 college students at a university in Hebei Province from September to November 2024. The insomnia severity index (ISI), patient health questionnaire(PHQ-9), meaning in life questionnaire(MLQ), and suicide behavior questionnaire-revised (SBQ-R) were used for assessment. Structural equation modeling test was conducted by Mplus 8.0 to test the mediating effect of depression and the moderating effect of sense of meaning in life.Results:(1) The scores of insomnia (6.00 (4.00, 8.00)), depression (2.50 (0, 2.00)), and suicidal behavior (4.00 (3.00, 4.00)) were significantly and positively correlated with each other ( r=0.51-0.66, all P<0.01). The sense of meaning in life (47.80 (39.00, 58.00)) was negatively correlated with insomnia, depression, and suicidal behavior ( r=-0.04--0.22, all P<0.01). (2) Insomnia could positively predict suicidal behavior among college students ( β= 0.15, P<0.01). (3) Depression played a partial mediating role between insomnia and suicidal behavior, with a mediating effect value of 0.40 (95% CI= 0.37-0.43), accounting for 72.73%(0.40/0.55) of the total effect. Conclusion:Depression plays a partial mediating role between insomnia and suicidal behavior among college students. The sense of meaning in life can moderate the impact of insomnia on depression and suicidal behavior.