OBJECTIVE To investigate the effects and mechanisms of Lycium ruthenicum Murr. in improving sleep. METHODS Network pharmacology was employed to identify the active components of L. ruthenicum and their associated disease targets， followed by enrichment analysis. A caffeine‑induced zebrafish model of sleep deprivation was established ， and the zebrafish were treated with L. ruthenicum Murr. extract （LRME） at concentrations of 0.1， 0.2 and 0.4 mg/mL， respectively； 24 h later， behavioral changes of zebrafish and pathological alterations in brain neurons were subsequently observed. The levels of inflammatory factors [interleukin-6 （IL-6）， IL-1β， IL-10， tumor necrosis factor-α （TNF-α）]， oxidative stress markers [superoxide dismutase （SOD）， malondialdehyde （MDA）， glutathione peroxidase （GSH-Px）， catalase （CAT）]， and neurotransmitters [5- hydroxytryptamine （5-HT）， γ-aminobutyric acid （GABA）， glutamic acid （Glu）， dopamine （DA）， and norepinephrine （NE）] were measured. The protein expression levels of protein kinase B1 （AKT1）， phosphorylated AKT1 （p-AKT1）， epidermal growth factor receptor （EGFR）， B-cell lymphoma 2 （Bcl-2）， sarcoma proto-oncogene，non-receptor tyrosine kinase （SRC）， and heat shock protein 90α family class A member 1 （HSP90AA1） in the zebrafish were also determined. RESULTS A total of 12 active components and 176 intersecting disease targets were identified through network pharmacology analysis. Among these， apigenin， naringenin and others were recognized as core active compounds， while AKT1， EGFR and others served as key targets； EGFR tyrosine kinase inhibitor resistance signaling pathway was identified as the critical pathway. The sleep improvement rates in zebrafish of LRME low-， medium-， and high-dose groups were 54.60%， 69.03% and 77.97%， 开发。E-mail：hjp_yft@163.com respectively， while the inhibition ratios of locomotor distance were 0.57， 0.83 and 0.95， respectively. Compared with the model group， the number of resting counts， resting time and resting distance were significantly increased/extended in LRME medium- and high-dose groups （P＜0.05）. Neuronal damage in the brain was alleviated. Additionally， the levels of IL-6， IL-1β， TNF-α， MDA， Glu， DA and NE， as well as the protein expression levels of AKT1， p-AKT1， EGFR， SRC and HSP90AA1， were markedly reduced （P＜0.05）， while the levels of IL-10， SOD， GSH-Px， CAT， 5-HT and GABA， as well as Bcl-2 protein expression， were significantly elevated （P＜0.05）. CONCLUSIONS L. ruthenicum Murr. demonstrates sleep-improving effects， and its specific mechanism may be related to the regulation of inflammatory responses， oxidative stress， neurotransmitter balance， and the EGFR tyrosine kinase inhibitor resistance signaling pathway.

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.

Patients with lung cancer experience a heavy symptom burden during the perioperative period, which seriously affects their recovery and quality of life. Traditional symptom management models rely mainly on scheduled ward rounds during hospitalization and outpatient follow-up after discharge. However, they have limitations such as delayed symptom recognition, lack of post-discharge monitoring, and non-quantitative symptom assessment, which often lead to delayed interventions and low patient satisfaction. In recent years, the symptom management model based on electronic patient-reported outcomes (ePRO) has been increasingly valued in clinical practice. Existing high-level evidence from both domestic and international studies indicates that, through proactive monitoring, real-time alerts, and remote interventions, this model enables dynamic and continuous symptom management and helps improve patient recovery and healthcare experience. As a supplement to routine medical care, the ePRO-based symptom management model aims to enhance the quality of care rather than replace existing medical processes. To promote the standardized application of this model in perioperative lung cancer care, this consensus integrates domestic and international evidence. After multiple rounds of voting by more than 50 experts, it formulates 12 consensus statements covering the three core components, symptom monitoring, alerting, and intervention, to provide scientific and practical recommendations for clinical practice.

ObjectiveTo observe the therapeutic effect of Erchentang on depression in obese mice and explore the therapeutic mechanism. MethodsC57BL/6J mice were randomized into the control， model， metformin （0.65 g·kg^-1）， and low-， medium-， and high-dose （3.35， 6.7， and 13.4 g·kg^-1， respectively） Erchentang groups. Gavage was initiated simultaneously with modeling and continued for 28 days. The therapeutic effects of Erchentang were evaluated through behavioral tests， liver and brain indices， liver function， lipid indicators， and histopathological changes in the liver and brain tissue. RNA-seq technology was used to conduct transcriptomic analysis of mouse liver tissue， and differentially expressed genes were screened and subjected to Kyoto Encyclopedia of Genes and Genomes （KEGG） analysis. Key genes were further verified by Western blot and Real-time polymerase chain reaction（Real-time PCR）. ResultsBehavioral tests， liver function， lipid indicators， and histopathological changes in the liver and brain tissue all demonstrated that Erchentang had therapeutic effects on depression in obese mice. KEGG pathway enrichment analysis of transcriptomic data indicated that Erchentang treated depression in obese mice mainly through the peroxisome proliferator-activated receptor （PPAR） signaling pathway. Western blot and Real-time PCR results showed that compared with the model group， the low-， medium-， and high-dose Erchentang groups exhibited downregulated expression of fatty acid-binding protein （FABP） 1 （P<0.01） and upregulated expression of acyl-CoA oxidase （ACOX） 1， PPARα， cholesterol 7α-hydroxylase （CYP7A） 1， and phosphoenolpyruvate carboxy kinase （PCK） 1 （P<0.05） in the liver tissue. ConclusionErchentang exerts therapeutic effects on obesity-associated depression by regulating the PPAR signaling pathway and the expression of related genes FABP1， ACOX1， PPARα， CYP7A1， and PCK1.

Background: and Purpose Limited evidence exists on the effectiveness of endovascular treatment (EVT) for acute posterior circulation tandem lesion (PCTL). This study aimed to explore the role of extracranial vertebral artery (VA) stenting in patients with PCTL stroke undergoing EVT. Methods: Individual patient data were pooled from the BASILAR (EVT for Acute Basilar Artery Occlusion Study) and PERSIST (Posterior Circulation Ischemic Stroke) registries. Patients with PCTLs who underwent EVT were included in the present cohort and divided into the stenting and nonstenting groups based on the placement of extracranial VA stents. The primary efficacy outcome was the modified Rankin Scale (mRS) scores at 90 days and 1 year. Safety outcomes included 24-hour symptomatic intracranial hemorrhage (sICH) and all-cause mortality at 90 days and 1 year post-surgery. Results: A combined dataset of 1,320 patients with posterior circulation artery occlusion, including 263 (19.9%) with tandem lesions, of whom 217 (median age, 65 years; 82.9% male) met the inclusion criteria for the analysis. The stenting group had 84 (38.7%) patients, while the non-stenting group had 133 (61.3%). After adjustment for the potential confounders, extracranial VA stenting was associated with favorable shifts in mRS scores at both 90 days (adjusted common odds ratio [OR], 2.30; 95% confidence interval [CI], 1.23–4.28; P<0.01) and 1 year (adjusted OR [aOR], 2.04; 95% CI [1.05–3.97]; P=0.04), along with lower rate of mortality at both 90 days (aOR, 0.45; 95% CI [0.21–0.93]; P=0.01) and 1 year (aOR, 0.36; 95% CI [0.16–0.79]; P=0.01), with no significant difference in sICH incidence (aOR, 0.35; 95% CI [0.06–1.98]; P=0.24). Conclusion Extracranial VA stenting during EVT may improve functional outcomes and reduce mortality in patients with PCTL strokes.

OBJECTIVE To provide medication reference for duloxetine use in clinical settings， particularly for patients with depression in primary medical institutions in Xinjiang that lack therapeutic drug monitoring conditions. METHODS The medical records of 281 depression inpatients taking duloxetine in the First Affiliated Hospital of Xinjiang Medical University from January 2022 to December 2023 were retrospectively collected. They were divided into training set （196 cases） and test set （85 cases） in the ratio of 7∶3. Feature selection was performed by encapsulating random forests （RF） with recursive feature elimination. Four machine learning algorithms， namely support vector machine， RF， extreme gradient boosting （XGBoost） and artificial neural network， were used to construct duloxetine blood concentration prediction model. The prediction performance of the models was evaluated and compared by coefficient of determination （R2）， mean absolute error （MAE） and root mean squared error （RMSE）. The feature of the selected optimal model was explained by Shapley additive explanation method， and the importance ranking of the features and the influence on the prediction results of duloxetine blood concentration were determined. RESULTS A total of 29 characteristic variables were selected， including age， ethnicity， body mass index（BMI）， etc. XGBoost showed the highest R2 （0.808）， and the lowest MAE （7.644） and RMSE （10.808）. The ranking of feature importance for predicting the blood concentration of duloxetine was as follows： BMI＞age＞other 20 feature sets （including liver and kidney function and biochemical indicators）＞daily dosage＞comorbidities＞combination therapy＞ethnicity＞white blood cell count＞hemoglobin＞height. CONCLUSIONS XGBoost model possesses the best prediction performance of duloxetine blood concentration； BMI and age have a greater impact on the prediction of duloxetine blood concentration.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that severely affects the health of the elderly, marked by its incurability, high prevalence, and extended latency period. The current approach to AD prevention and treatment emphasizes early detection and intervention, particularly during the pre-AD stage of mild cognitive impairment (MCI), which provides an optimal “window of opportunity” for intervention. Clinical detection methods for MCI, such as cerebrospinal fluid monitoring, genetic testing, and imaging diagnostics, are invasive and costly, limiting their broad clinical application. Speech, as a vital cognitive output, offers a new perspective and tool for computer-assisted analysis and screening of cognitive decline. This is because elderly individuals with cognitive decline exhibit distinct characteristics in semantic and audio information, such as reduced lexical richness, decreased speech coherence and conciseness, and declines in speech rate, voice rhythm, and hesitation rates. The objective presence of these semantic and audio characteristics lays the groundwork for computer-based screening of cognitive decline. Speech information is primarily sourced from databases or collected through tasks involving spontaneous speech, semantic fluency, and reading, followed by analysis using computer models. Spontaneous language tasks include dialogues/interviews, event descriptions, narrative recall, and picture descriptions. Semantic fluency tasks assess controlled retrieval of vocabulary items, requiring participants to extract information at the word level during lexical search. Reading tasks involve participants reading a passage aloud. Summarizing past research, the speech characteristics of the elderly can be divided into two major categories: semantic information and audio information. Semantic information focuses on the meaning of speech across different tasks, highlighting differences in vocabulary and text content in cognitive impairment. Overall, discourse pragmatic disorders in AD can be studied along three dimensions: cohesion, coherence, and conciseness. Cohesion mainly examines the use of vocabulary by participants, with a reduction in the use of nouns, pronouns, verbs, and adjectives in AD patients. Coherence assesses the ability of participants to maintain topics, with a decrease in the number of subordinate clauses in AD patients. Conciseness evaluates the information density of participants, with AD patients producing shorter texts with less information compared to normal elderly individuals. Audio information focuses on acoustic features that are difficult for the human ear to detect. There is a significant degradation in temporal parameters in the later stages of cognitive impairment; AD patients require more time to read the same paragraph, have longer vocalization times, and produce more pauses or silent parts in their spontaneous speech signals compared to normal individuals. Researchers have extracted audio and speech features, developing independent systems for each set of features, achieving an accuracy rate of 82% for both, which increases to 86% when both types of features are combined, demonstrating the advantage of integrating audio and speech information. Currently, deep learning and machine learning are the main methods used for information analysis. The overall diagnostic accuracy rate for AD exceeds 80%, and the diagnostic accuracy rate for MCI also exceeds 80%, indicating significant potential. Deep learning techniques require substantial data support, necessitating future expansion of database scale and continuous algorithm upgrades to transition from laboratory research to practical product implementation.

Background: and Purpose Limited evidence exists on the effectiveness of endovascular treatment (EVT) for acute posterior circulation tandem lesion (PCTL). This study aimed to explore the role of extracranial vertebral artery (VA) stenting in patients with PCTL stroke undergoing EVT. Methods: Individual patient data were pooled from the BASILAR (EVT for Acute Basilar Artery Occlusion Study) and PERSIST (Posterior Circulation Ischemic Stroke) registries. Patients with PCTLs who underwent EVT were included in the present cohort and divided into the stenting and nonstenting groups based on the placement of extracranial VA stents. The primary efficacy outcome was the modified Rankin Scale (mRS) scores at 90 days and 1 year. Safety outcomes included 24-hour symptomatic intracranial hemorrhage (sICH) and all-cause mortality at 90 days and 1 year post-surgery. Results: A combined dataset of 1,320 patients with posterior circulation artery occlusion, including 263 (19.9%) with tandem lesions, of whom 217 (median age, 65 years; 82.9% male) met the inclusion criteria for the analysis. The stenting group had 84 (38.7%) patients, while the non-stenting group had 133 (61.3%). After adjustment for the potential confounders, extracranial VA stenting was associated with favorable shifts in mRS scores at both 90 days (adjusted common odds ratio [OR], 2.30; 95% confidence interval [CI], 1.23–4.28; P<0.01) and 1 year (adjusted OR [aOR], 2.04; 95% CI [1.05–3.97]; P=0.04), along with lower rate of mortality at both 90 days (aOR, 0.45; 95% CI [0.21–0.93]; P=0.01) and 1 year (aOR, 0.36; 95% CI [0.16–0.79]; P=0.01), with no significant difference in sICH incidence (aOR, 0.35; 95% CI [0.06–1.98]; P=0.24). Conclusion Extracranial VA stenting during EVT may improve functional outcomes and reduce mortality in patients with PCTL strokes.

Background: and Purpose Limited evidence exists on the effectiveness of endovascular treatment (EVT) for acute posterior circulation tandem lesion (PCTL). This study aimed to explore the role of extracranial vertebral artery (VA) stenting in patients with PCTL stroke undergoing EVT. Methods: Individual patient data were pooled from the BASILAR (EVT for Acute Basilar Artery Occlusion Study) and PERSIST (Posterior Circulation Ischemic Stroke) registries. Patients with PCTLs who underwent EVT were included in the present cohort and divided into the stenting and nonstenting groups based on the placement of extracranial VA stents. The primary efficacy outcome was the modified Rankin Scale (mRS) scores at 90 days and 1 year. Safety outcomes included 24-hour symptomatic intracranial hemorrhage (sICH) and all-cause mortality at 90 days and 1 year post-surgery. Results: A combined dataset of 1,320 patients with posterior circulation artery occlusion, including 263 (19.9%) with tandem lesions, of whom 217 (median age, 65 years; 82.9% male) met the inclusion criteria for the analysis. The stenting group had 84 (38.7%) patients, while the non-stenting group had 133 (61.3%). After adjustment for the potential confounders, extracranial VA stenting was associated with favorable shifts in mRS scores at both 90 days (adjusted common odds ratio [OR], 2.30; 95% confidence interval [CI], 1.23–4.28; P<0.01) and 1 year (adjusted OR [aOR], 2.04; 95% CI [1.05–3.97]; P=0.04), along with lower rate of mortality at both 90 days (aOR, 0.45; 95% CI [0.21–0.93]; P=0.01) and 1 year (aOR, 0.36; 95% CI [0.16–0.79]; P=0.01), with no significant difference in sICH incidence (aOR, 0.35; 95% CI [0.06–1.98]; P=0.24). Conclusion Extracranial VA stenting during EVT may improve functional outcomes and reduce mortality in patients with PCTL strokes.