Endoscopic transforaminal lumbar interbody fusion (TLIF) offers substantial advantages in the management of degenerative spinal diseases, including accelerated postoperative recovery. However, its technical complexity and steep learning curve pose risks for nerve root injury. Optimizing nerve root protection in full-endoscopic facet-sparing TLIF (FE fs-TLIF) and full-endoscopic facet-resecting TLIF (FE fr-TLIF) is essential for enhancing surgical safety. This study aimed to improve the nerve root protection in FE fs-TLIF and FE fr-TLIF by optimizing cage glider selection and insertion techniques based on the specific cage shape—banana-shaped or bullet-shaped. The goal was to ensure safe cage positioning and mitigate nerve root injury during discectomy, endplate preparation, and cage insertion. These strategies were validated through cadaveric simulations and clinical implementation. In FE fr-TLIF utilizing bullet-shaped (straight) cages, one-tip and two-tip cage gliders effectively protected the traversing nerve root by facilitating medial cage entry, thereby minimizing irritation of the exiting nerve root. Conversely, in FE fr-TLIF with banana-shaped cages, the lateral tilt of the cage holder during implantation required the use of a two-tip cage glider to protect the traversing and exiting nerve roots, thereby mitigating the potential risk of nerve irritation. In FE fs-TLIF, a one-tip cage glider is preferred for safeguarding the exiting nerve root, while the traversing root is inherently protected by the medial wall of the facet joint. The use of a two-tip cage glider in FE fs-TLIF can cause injury to the nerve root during glider insertion. In addition to the selection of cage gliders, improper cage insertion steps can also contribute to postoperative neurapraxia. The appropriate selection of cage gliders with corresponding insertion techniques is critical for nerve root protection in endoscopic TLIF. Tailoring these choices to the specific approach (FE fs-TLIF or FE fr-TLIF) and cage type (banana or bullet) enhances surgical safety and clinical outcomes.

Licochalcone E is a chalcone isolated from Glycyrrhiza uralensis and G. inflata Batal. This study explored the effect of licochalcone E on improving hepatic steatosis in obese mice and evaluated the role of licochalcone E in regulating lipid accumulation in hepatocytes. In vitro, oleic acid–induced hepatocytes were treated with licochalcone E to investigate its effect on lipid metabolic pathways. In animal experiments, male C57BL/6 mice were fed with a high-fat diet (HFD) and treated with licochalcone E by intraperitoneal injection for 12 weeks to assess its effects on biochemical indexes and hepatic steatosis. Furthermore, mice were fed a methionine/choline-deficient (MCD) diet and administered licochalcone E, followed by evaluation of liver fibrosis. Licochalcone E effectively reduced body weight, epididymal and inguinal fat weight, and adipocyte size in HFD-induced obese mice. Licochalcone E treatment of obese mice also reduced hepatic lipid accumulation and improved hepatocyte steatosis. Licochalcone E regulated the expression of lipogenesis- and lipolysis-related genes in the livers of obese mice and increased AMPK phosphorylation and Sirt1 expression in the liver. Licochalcone E also attenuated hepatic inflammation and oxidative stress in obese mice. Furthermore, treatment of MCD-induced mice with licochalcone E reduced the number of lipid vacuoles and the extent of fibrosis and inhibited liver inflammation. In FL83B hepatocytes, licochalcone E could regulate lipogenesis and lipolysis, and increase the phosphorylation of AMPK and ACC. These findings provide new insights into the role of licochalcone E in regulating lipid metabolism and preventing hepatic steatosis.

Objectives: . The prognostic value of the pretreatment albumin-to-alkaline phosphatase ratio (AAPR) in head and neck cancer (HNC) remains uncertain. This meta-analysis aimed to evaluate the predictive role of AAPR for survival outcomes in patients with HNC. Methods: . A comprehensive search of the Cochrane Library, PubMed, and Embase databases was conducted to identify relevant studies published up to July 30, 2024. We included studies on AAPR and survival outcomes in HNC patients. Results: . Eight studies comprising 1,737 HNC patients were analyzed using random-effects models. Lower AAPR values were significantly correlated with worse overall survival (hazard ratio [HR], 2.08), progression-free survival (HR, 2.00), and disease-free survival (HR, 2.18). Sensitivity analyses confirmed the robustness of these results, with no significant publication bias detected. Conclusion . Our findings suggest that pretreatment AAPR could serve as a valuable and cost-effective prognostic indicator in HNC, potentially aiding clinicians in risk stratification and treatment decision-making. However, additional validation studies are warranted to confirm its clinical applicability.

Objective This study aimed to investigate the seasonal variation of mosquitoes in Yantai, Shandong Province, and provide a scientific basis for mosquito and mosquito-borne disease control. Methods From March to November for each of the years between 2020 and 2023, adult mosquitoes were collected from different habitats, including parks, hospitals, urban residential areas, rural households and livestock sheds, in 13 counties of Yantai City using a mosquito trap lamp method, among which, female mosquitoes were separated and identified, and the numbers were counted. We used the circular distribution method to analyze the temporal distribution characteristics of mosquito density. Results The γ values obtained for mosquitoes in Yantai from 2020 to 2023, the distribution of mosquitoes in different habitats, and dominant mosquito species in different months showed strong seasonality, with values ranging from 0.7461 to 0.8531(P ＜ 0.05). The peak period of seasonal variation in mosquitoes occurred from June 28thto September 13th, with August 5thbeing identified as the peak day. The peak periods of seasonal variation in mosquitoes in residential areas, parks, hospitals, peasant households, and animals sheds were observed from June 24thto September 14th, June 25thto September 10th, June 22ndto September 12th, July 1stto September 9th, and July 2ndto September 17th, respectively, with corresponding peak days on August 4th,3rd, 2nd, 5th, and 9th, respectively. The peak periods of Culex pipiens pallens, Aedes albopictus, and Anopheles sinensis were from June 26thto September 11th, June 28thto September 17th, and July 12thto September 17th, respectively, with corresponding peak days on August 3rd,7th, and 14th, respectively. Conclusions Our findings in this study revealed that the peak of mosquito activity in Yantai was concentrated in early August, with a peak period extending from June 28thto September 13th, and there were differences between different habitats(with peak periods for rural households and livestock sheds occurring later than those in residential areas, parks, and hospitals). Moreover, with respect to the mosquito species, populations of Cx. pipiens pallens and Ae. albopictus peaked earlier than those of An. sinensis. Based on these findings, we recommend that city-wide mosquito control work should commence in late June, and include appropriate early prevention and control measures for key habitats, such as livestock sheds, and dominant mosquito species, such as Cx. pipiens pallens, to minimize the density of mosquito vectors and the risk of disease transmission.

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.

Artificial intelligence(AI)has emerged as a cutting-edge technology leading the future and is a key engine for China's development.In the innovation and research of medical devices,AI has provided critical support in the areas of intelligent diagnostic assistance,intelligent therapeutic assis-tance,intelligent monitoring,life support,et al.Ma-chine learning-enabled device software functions(ML-DSFs)have become an essential component of many medical devices.Recently,the United States Food and Drug Administration(FDA)released a draft guidance titled"Marketing Submission Rec-ommendations for a Predetermined Change Con-trol Plan for Artificial Intelligence/Machine Learn-ing(AI/ML)-Enabled Device Software Functions(Draft)."that aimed to provide a forward-looking approach to foster the development of ML medical devices.By supporting iterative updates through modifications,this approach ensures the continu-ous safety and effectiveness of the devices.This guidance represents the latest in regulatory direc-tion and is especially beneficial for enhancing the quality and efficiency of clinical trials for AI prod-ucts.Therefore,we plan to provide a detailed intro-duction and interpretation of the guidance,with the aim of learning from international advanced regulatory concepts and experiences to promote the development of ML-DSFs with more profound international influence.

Artificial intelligence(AI)has emerged as a cutting-edge technology leading the future and is a key engine for China's development.In the innovation and research of medical devices,AI has provided critical support in the areas of intelligent diagnostic assistance,intelligent therapeutic assis-tance,intelligent monitoring,life support,et al.Ma-chine learning-enabled device software functions(ML-DSFs)have become an essential component of many medical devices.Recently,the United States Food and Drug Administration(FDA)released a draft guidance titled"Marketing Submission Rec-ommendations for a Predetermined Change Con-trol Plan for Artificial Intelligence/Machine Learn-ing(AI/ML)-Enabled Device Software Functions(Draft)."that aimed to provide a forward-looking approach to foster the development of ML medical devices.By supporting iterative updates through modifications,this approach ensures the continu-ous safety and effectiveness of the devices.This guidance represents the latest in regulatory direc-tion and is especially beneficial for enhancing the quality and efficiency of clinical trials for AI prod-ucts.Therefore,we plan to provide a detailed intro-duction and interpretation of the guidance,with the aim of learning from international advanced regulatory concepts and experiences to promote the development of ML-DSFs with more profound international influence.

Objective To propose a novel identification algorithm based on ensemble learning for assessing the severity of acute respiratory distress syndrome(ARDS)to achieve continuous monitoring of the disease severity.Methods Firstly,leve-raging the open-source MIMIC-Ⅳ database,a variety of non-invasive physiological parameters of patients were extracted and subjected to preliminary preprocessing.A multivariate feature selection algorithm was employed to rank these parameters and calculate feature importance scores through weighted computation.Secondly,based on the feature importance scores,a subset search algorithm was utilized to identify the subset of features that could yield optimal performance across four machine learning algorithms:neural networks,logistic regression,AdaBoost and XGBoost.Finally,a soft voting ensemble method was designed using a generalized linear regression model to integrate the results of each single machine learning algorithm,and a multivariate ensemble learning algorithm was proposed by combining the optimal feature subsets.The algorithm proposed when used to identify the severity of ADRS was evaluated with MIMIC-Ⅳ database,and compared with the traditional algorithms.Results The sensitivity,specificity,accuracy and AUC of the algorithm were 87.15％,89.23％,88.34％and 0.923 4,respectively,all of which outperformed those of the traditional algorithms.Conclusion The ARDS severity identification algorithm based on ensemble learning is capable of achieving continuous and real-time monitoring of the severity of ARDS,thereby offering robust support for the early identification and warning of ARDS in patients.[Chinese Medical Equipment Journal,2025,46(2):1-9]