Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

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.

Aim To investigate the protective effect of Siwu decoction(SWD)on immune injury induced by 60Coγ-rays in mice and the related mechanism.Meth-ods C57BL/6J mice were randomly divided into six groups:Control group(Control),Model group(Mod-el),Siwu decoction low-dose group(SWD-L),Siwu decoction medium-dose group(SWD-M),Siwu decoc-tion high-dose group(SWD-H)and resveratrol positive group(Resveratrol,Res).The drug was continued to be administered 11 days before and after irradiation,with a single whole-body irradiation of 4 Gy,and all in-dexes were detected three days after irradiation.The changes of peripheral blood and organ indexes were de-tected.Serum levels of cytokine interferon gamma(IFN-γ),interleukin-1 β(IL-1 β),IL-2,IL-4,IL-6,IL-10,IL-17,transforming growth factor-β(TGF-β),tumor necrosis factor(TNF)-α,and immunoglobulin A(IgA),IgG,IgM,and complement protein 3(C3),C4 content were detected by enzyme-linked immunosorbent assay(ELISA).B lymphocytes,T lymphocytes,NK cells in spleen and T lymphocytes in thymus were de-tected by flow cytometry.The pathological changes of spleen and thymus were analyzed by hematoxylin-eosin(HE)staining.The expression of Sirt 1 protein in spleen after radiation was detected by immunofluores-cence staining.The protein contents of Sirt1,PI3K,Akt,p-Akt and mTOR in spleen were detected by Western blot assay.Results SWD could significantly increase LYMPH％and reduce pathological injury of spleen and thymus.Flow cytometry showed that SWD could significantly increase the percentage of CD19+B lymphocytes in spleen,decrease the percentage of NK lymphocytes and the ratio of CD4+/CD8+in spleen and thymus.ELISA results showed that SWD signifi-cantly inhibited the expression of IL-1 β,IL-2,IL-6,IL-17,IFN-γ,TGF-β,TNF-α,and increased the content of anti-inflammatory factors IL-4 and IL-10.At the same time,SWD significantly inhibited the increase of IgA,IgG,IgM,C3 and C4 induced by radiation.The results of immunofluorescence staining and Western blot showed that SWD could decrease the expression of PI3K,Akt,p-Akt and mTOR protein,and enhance the expression of Sirt1 protein.Conclusions SWD has obvious preventive effect on immune damage induced by 60Co gamma radiation in mice.The mechanism may be related to Sirt1-PI3K-Akt-mTOR.

Traditional Chinese medicine(TCM) decoction pieces are a core carrier for the inheritance and innovation of TCM, and their quality and safety are critical to public health and the sustainable development of the industry. Conventional quality control models, while having established a well-developed system through long-term practice, still face challenges such as relatively long inspection cycles, insufficient objectivity in characterizing complex traits, and urgent needs for improving the efficiency of integrating multidimensional quality information when confronted with the dual demands of large-scale production and precision quality control. With the rapid development of artificial intelligence, machine learning can deeply analyze multidimensional data of the morphology, spectroscopy, and chemical fingerprints of decoction pieces by constructing high-dimensional feature space analysis models, significantly improving the standardization level and decision-making efficiency of quality evaluation. This article reviews the research progress in the application of machine learning in the processing, production, and rapid quality evaluation of TCM decoction pieces. It further analyzes current challenges in technological implementation and proposes potential solutions, offering theoretical and technical references to advance the digital and intelligent transformation of the industry.
Machine Learning ; Drugs, Chinese Herbal/standards* ; Quality Control ; Medicine, Chinese Traditional/standards* ; Humans

The chemical constituents from Cephalotaxus lanceolata were isolated and purified by using multiple chromatographic techniques, including octadecylsilane(ODS), silica gel, Sephadex LH-20 column chromatography, and semi-preparative high-performance liquid chromatography(HPLC). A total of 17 compounds obtained were identified by using spectroscopic methods such as nuclear magnetic resonance(NMR), mass spectrometry(MS), and ultraviolet(UV) combined with literature data. Compound 1 was a new alkaloid dimer, named cephalancetine E. The known compounds were determined as cephalancetine A(2), 11-hydroxycephalotaxine(3), 4-hydroxycephalotaxine(4), cephalotaxine(5), epicephalotaxine(6), cephalotaxine β-N-oxide(7), acetylcephalotaxine(8), cephalotine A(9), cephalotine B(10), 11-hydroxycephalotaxine hemiketal(11), 3-deoxy-3,11-epoxy-cephalotaxine(12), cephalotaxinone(13), isocephalotaxinone(14), 2,11-epoxy-1,2-dihydro-8-oxo-cephalotaxine(15), cephalotaxamide(16), and drupacine(17), respectively. Compounds 11, 12, and 15 were isolated from the Cephalotaxus genus for the first time. The biological activity was tested for compounds 1-17. The results reveal that compound 17 displays potent inhibitory activities against three human cancer cell lines(HepG-2, MCF-7, and SH-SY5Y).
Cephalotaxus/chemistry* ; Humans ; Cell Line, Tumor ; Drugs, Chinese Herbal/pharmacology* ; Harringtonines/pharmacology* ; Molecular Structure ; Dimerization ; Alkaloids/isolation & purification* ; Magnetic Resonance Spectroscopy

PURPOSE: The retear rate of rotator cuff (RC) after surgery is high, and the rapid and functional enthesis regeneration remains a challenge. Whether acellular amniotic membrane (AAM) helps to promote the healing of tendon to bone and which treatment is better are both unclear. The study aims to investigate the effect of AAM on the healing of RC and the best treatment for RC repair. METHODS: Thirty-three Sprague Dawley rats underwent RC transection and repair using microsurgical techniques and were randomly divided into the suturing repair only (SRO) group (n = 11), the AAM overlaying (AOL) group (n = 11), and the AAM interposition (AIP) group (n = 11), respectively. Rats were sacrificed at 4 weeks, then examined by subsequent micro-CT, and evaluated by histologic and biomechanical tests. The statistical analyses of one-way ANOVA or Kruskal-Wallis test were performed using with SPSS 23.0. A p < 0.05 was considered a significant difference. RESULTS: AAM being intervened between tendon and bone (AIP group) or overlaid over tendon to bone junction (AOL group) in a rat model, promoted enthesis regeneration, increased new bone and cartilage generation, and improved collagen arrangement and biomechanical properties in comparison with suturing repair only (SRO group) (AOL vs. SRO, p < 0.001, p = 0.004, p = 0.003; AIP vs. SRO, p < 0.001, p < 0.001, p < 0.001). Compared with the AOL group, the AIP group had better results in micro-CT evaluation, histological score, and biomechanical testing (p = 0 0.039, p = 0.011, p = 0.003, respectively). CONCLUSION In the RC repair model, AAM enhanced regeneration of the tendon to bone junction. This regeneration was more effective when the AAM was intervened at the tendon to bone interface than overlaid above the tendon to bone junction.
Animals ; Rats, Sprague-Dawley ; Rotator Cuff Injuries/surgery* ; Amnion/transplantation* ; Rats ; Wound Healing ; Rotator Cuff/surgery* ; Male ; X-Ray Microtomography ; Tendons/surgery* ; Biomechanical Phenomena

Testicular histology based on testicular biopsy is an important factor for determining appropriate testicular sperm extraction surgery and predicting sperm retrieval outcomes in patients with azoospermia. Therefore, we developed a deep learning (DL) model to establish the associations between testicular grayscale ultrasound images and testicular histology. We retrospectively included two-dimensional testicular grayscale ultrasound from patients with azoospermia (353 men with 4357 images between July 2017 and December 2021 in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China) to develop a DL model. We obtained testicular histology during conventional testicular sperm extraction. Our DL model was trained based on ultrasound images or fusion data (ultrasound images fused with the corresponding testicular volume) to distinguish spermatozoa presence in pathology (SPP) and spermatozoa absence in pathology (SAP) and to classify maturation arrest (MA) and Sertoli cell-only syndrome (SCOS) in patients with SAP. Areas under the receiver operating characteristic curve (AUCs), accuracy, sensitivity, and specificity were used to analyze model performance. DL based on images achieved an AUC of 0.922 (95% confidence interval [CI]: 0.908-0.935), a sensitivity of 80.9%, a specificity of 84.6%, and an accuracy of 83.5% in predicting SPP (including normal spermatogenesis and hypospermatogenesis) and SAP (including MA and SCOS). In the identification of SCOS and MA, DL on fusion data yielded better diagnostic performance with an AUC of 0.979 (95% CI: 0.969-0.989), a sensitivity of 89.7%, a specificity of 97.1%, and an accuracy of 92.1%. Our study provides a noninvasive method to predict testicular histology for patients with azoospermia, which would avoid unnecessary testicular biopsy.
Humans ; Male ; Azoospermia/diagnostic imaging* ; Deep Learning ; Testis/pathology* ; Retrospective Studies ; Adult ; Ultrasonography/methods* ; Sperm Retrieval ; Sertoli Cell-Only Syndrome/diagnostic imaging*