Background: and Purpose Vertebrobasilar fusiform aneurysms (VBFAs) carry substantial morbidity and mortality, but optimal management for unruptured VBFAs remains unclear. We compared the safety and efficacy of conservative management (CM), stent-assisted coiling (SAC), and flow diverters (FDs) in patients with unruptured VBFAs, focusing on long-term prognosis. Methods: This study included data from a nationwide Chinese cohort of patients with vertebrobasilar dissecting aneurysms. Inverse probability of treatment weighting (IPTW) balanced confounders across groups. The primary outcome was poor prognosis (modified Rankin Scale score >2). Secondary outcomes included aneurysm rupture, ischemic stroke, compression symptoms, and VBFA-related deaths. Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs). Subgroup and sensitivity analyses were performed. Results: Among 1,115 patients with unruptured VBFAs, 838 (median age, 54 years; 655 men) were included. After IPTW, baseline characteristics were balanced. Median follow-up was 54 months. FD was associated with a lower risk of poor prognosis than CM (OR, 0.48 [95% CI, 0.30 to 0.77]; p=0.002), with no difference between CM and SAC. FD also reduced aneurysm rupture (OR, 0.20 [95% CI, 0.07 to 0.60]; p=0.004) and compression symptoms (OR, 0.30 [95% CI, 0.13 to 0.68]; p=0.004) versus CM. Time-to-event analyses further revealed significant differences in vertebral artery lesions and Type I–II VBFAs, whereas no significant differences were observed in basilar or vertebrobasilar junction lesions or in Type III–IV VBFAs. Conclusions Compared with CM, FD was associated with improved long-term outcomes in unruptured VBFAs, particularly in vertebral artery lesions and Type I–II VBFAs, although residual confounding cannot be excluded.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective This study reports on the obligatory parasitism of water mites Arrenurus sp. on Anopheles messeae at the Manzhouli Port, Inner Mongolia, China. Methods Duing July 2024, a survey on the mosquito diversity was conducted at the Manzhouli Port. Captured mosquitoes and their ectoparasites were identified to species level. Results A total of 1840 adult mosquitoes were collected, representing species from three genera: Culex(Cx. modestus, Cx. pipiens pallens), Aedes(Ae. dorsalis, Ae. flavidorsalis, Ae. flavescens), and Anopheles (An. messeae). Among all the mosqutioes specimens,3 out of 150 captured An. messeae were found to carry ectoparasitic mites, with number of 2,4,27 mites separately. Morphological and molecular identification reached the same result as water mites(Hydrachnidiae, Hydracrina). COI gene sequence showed 94% similarity with the closest species Arrenurus truncatellus. Conlusions Literature review suggests water mites are host-specific parasitism of mosquito species and herein with the first record of Arrenurus sp. parasiting on An. Messeae in the most high-latitude region globally.

Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.

Subarachnoid hemorrhage (SAH) is a serious intracranial hemorrhage characterized by acute bleeding into the subarachnoid space. The effects of shikonin, a natural compound from the roots of Lithospermum erythrorhizon, on oxidative stress and blood–brain barrier (BBB) injury in SAH was evaluated in this study. A rat model of SAH was established by endovascular perforation to mimic the rupture of intracranial aneurysms. Rats were then administered 25 mg/kg of shikonin or dimethylsulfoxide after surgery. Brain edema, SAH grade, and neurobehavioral scores were measured after 24 h of SAH to evaluate neurological impairment. Concentrations of the oxidative stress markers superoxide dismutase (SOD), glutathione (GSH), and malondialdehyde (MDA) in the brain cortex were determined using the corresponding commercially available assay kits. Evans blue staining was used to determine BBB permeability. Western blotting was used to quantify protein levels of tight junction proteins zonula occludens-1, Occludin, and Claudin-5. After modeling, the brain water content increased significantly whereas the neurobehavioral scores of rats with SAH decreased prominently. MDA levels increased and the levels of the antioxidant enzymes GSH and SOD decreased after SAH. These changes were reversed after shikonin administration. Shikonin treatment also inhibited Evans blue extravasation after SAH. Furthermore, reduction in the levels of tight junction proteins after SAH modeling was rescued after shikonin treatment. In conclusion, shikonin exerts a neuroprotective effect after SAH by mitigating BBB injury and inhibiting oxidative stress in the cerebral cortex.

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

Under the influence of long-term space flights and the confined space environment,it is very easy to induce space depression syndrome,mainly manifested as decreased emotional stability,sleep disorders,mental fatigue,etc.,which seriously affect the living conditions and working abilities of astronauts.The current treatment methods mainly focus on psychological support and drug intervention.Acupuncture has a good effect in treating depression.Therefore,starting from the TCM pathogenesis and modern medical pathogenesis of aerospace depression syndrome,we conducted literature retrieval from databases such as the Chinese Medical Classic,China National Knowledge Infrastructure(CNKI),Wanfang,VIP,PubMed,and Web of science.For the included literature,we adopted the stratified evidence scoring method and combined the TCM mechanism and modern medical mechanism of the effect of acupuncture.A prescription for acupuncture points was constructed to provide a basis for selecting acupuncture points for the prevention of aerospace depression syndrome through acupuncture.

Objective: To develop a quality appraisal tool for case reports in traditional Chinese medicine (TCM) based on their characteristics. [Methods]: An extensive literature search was conducted in Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI), and China Science and Technology Journal Database (CSTJ), focusing on expert consensus statements and checklists for TCM case reports. Relevant items were extracted, and a Delphi method involving 34 experts was used in two rounds to rate each item on a 5-point Likert scale. Items were screened based on measures of central tendency and coordination (including total score, mean score, percentage of items rated as unimportant, and coefficient of variation). The weighted average method was used to determine item weights and construct the appraisal tool. Internal consistency was assessed using Cronbach’s α coefficient. The finalized tool was pilot-tested by two reviewers independently appraising 20 case reports, with an additional four reviewers evaluating 5 of these cases to compare inter-rater consistency. Results: A total of 9 513 articles were retrieved, and 96 items from 25 articles were extracted. After two rounds of the Delphi method, 27 items across 10 domains were retained. The Cronbach’s α coefficient was 0.72 in the first round (acceptable range), and 0.96 in the second round, indicating strong internal consistency. The tool was piloted by six reviewers, achieving a kappa value of 0.663 and a Kendall’s coefficient of concordance of 0.845, demonstrating high consistency among reviewers. Conclusion The developed TCM case report quality appraisal tool, consisting of 27 items in 10 domains, offers a scientific and reliable means of assessing the quality of TCM case reports. The tool showed high consistency and practical utility, and its application is expected to enhance the standardization, scientific rigor, and evidence quality of TCM case reports, facilitating the integration of traditional medical knowledge with modern evidence-based standards.

This paper summarizes Professor WANG Xiuxia's clinical experience in treating hyperprolactinemia using the liver soothing therapy. Professor WANG identifies liver qi stagnation and rebellious chong qi （冲气） as the core pathomechanisms of hyperprolactinemia. Furthermore， liver qi stagnation may transform into fire or lead to pathological changes such as spleen deficiency with phlegm obstruction or kidney deficiency with essence depletion. The treatment strategy centers on soothing the liver， with a modified version of Qinggan Jieyu Decoction （清肝解郁汤） as the base formula. Depending on different syndrome patterns such as liver stagnation transforming into fire， liver stagnation with spleen deficiency， or liver stagnation with kidney deficiency， heat clearing， spleen strengthening， or kidney tonifying herbs are added accordingly. In addition， three paired herb combinations are commonly used for symptom specific treatment， Danggui （Angelica sinensis） with Chuanxiong （Ligusticum chuanxiong）， Zelan （Lycopus lucidus） with Yimucao （Leonurus japonicus） ， and Jiegeng （Platycodon grandiflorus） with Zisu （Perilla frutescens）.

Objective:s To investigate the risk factors for delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) in patients with acute carbon monoxide poisoning (ACOP) and to develop predictive models based on machine learning algorithms.Methods:Patients with ACOP hospitalized at the First Affiliated Hospital of Zhengzhou University from August 2019 to October 2024 were included, with the occurrence of DEACMP as the outcome measure. The dataset was randomly divided into training and validation sets at a ratio of 7:3. Lasso regression was used to select features influencing the outcome in training sets. Nine machine learning models—including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—were constructed. Receiver operating characteristic (ROC) curves were plotted and the area under the curve (AUC) calculated for each model. Calibration curves were used to assess accuracy, and decision curve analysis (DCA) was applied to evaluate clinical utility. The SHapley Additive exPlanations (SHAP) method was employed to visualize and interpret the best-performing model.Results:A total of 264 ACOP patients were included, of whom 54 (20.5%) developed DEACMP. Lasso regression identified eight key feature variables. Based on these factors, predictive models were constructed, showing good AUC stability across the nine machine learning models in both training (0.92–0.99) and validation sets (0.85–0.91). The RF model performed best, with an AUC of 0.99 in the training set and 0.90 in the validation set; its calibration curve and DCA curve also demonstrated excellent performance. SHAP analysis of the RF model revealed the importance ranking of factors from highest to lowest as follows: Glasgow Coma Scale (GCS) score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, diastolic blood pressure (DBP), and drinking history.Conclusions:The RF model exhibited the highest predictive performance for DEACMP occurrence in ACOP patients. The influencing factors, ranked in order of importance from highest to lowest, are as follows: GCS score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, DBP, and drinking history.