The incidence and mortality rates of lung cancer have been continuously rising. Smoking is a crucial modifiable factor contributing to the high incidence of lung cancer, and quitting smoking is of great significance for the treatment and prognosis of lung cancer patients. This article systematically reviews the harms of smoking to lung cancer patients, such as carcinogenic substances triggering lung cancer, affecting the course of the disease, and the improvement of prognosis after quitting smoking. It also analyzes the current situation of smoking cessation among lung cancer patients, who face numerous difficulties and have a relatively small number of successful quitters. Meanwhile, this article provides a detailed introduction to the clinical diagnosis and treatment methods for smoking cessation interventions. This includes the explanation of the pathophysiology of smoking cessation, psychological supportive therapies [brief psychological intervention and 5A’s model (Ask, Advise, Assess, Assist, Arrange) psychological counseling], and pharmacotherapies (nicotine-based and non-nicotine-based smoking cessation medications). In addition, it covers the behavioral intervention therapies for smoking cessation, including the PRECEDE-PROCEED model, cognitive behavior theory model, capacity opportunity motivation-behavior (COM-B) theoretical model, information-motivation-behavioral skills (IMB) model, timing is right (TIR) theoretical model, and the economic incentive intervention model. Although some of the current intervention methods lack the support of clinical randomized controlled studies, existing research and practice have confirmed their positive effects on smoking cessation among lung cancer patients. It is hoped that relevant intervention methods can be further improved in the future to help lung cancer patients improve their quality of life.

ObjectiveTo analyze the research hotspots and development trends of brain-computer interface (BCI) in the treatment for spinal cord injury (SCI). MethodsRelevant literatures on BCI applied in SCI treatment, published from the inception of the Web of Science Core Collection to July, 2025, were retrieved. Visualization analysis was performed using CiteSpace, VOSviewer and Tableau Desktop. ResultsA total of 437 literatures were included, and the annual number of publications showed an overall increasing trend. The United States ranked first in the number of publications; Graz University of Technology was the institution with the highest number of publication; Gernot R Mueller-Putz was the most productive author, while Jonathan R Wolpaw was the most cited author. Brain-computer interface and artificial intelligence were identified as the high-frequency and bursting keywords in this field. The researches were characterized by the cross-integration of five core disciplines: neuroscience and rehabilitation medicine, biomedical engineering, computer science and artificial intelligence, neurophysiology, and materials science. ConclusionResearches on BCI in SCI treatment are accelerating continuously, and technological integration is becoming the core trend.

ObjectiveTo analyze the research hotspots and development trends of brain-computer interface (BCI) in the treatment for spinal cord injury (SCI). MethodsRelevant literatures on BCI applied in SCI treatment, published from the inception of the Web of Science Core Collection to July, 2025, were retrieved. Visualization analysis was performed using CiteSpace, VOSviewer and Tableau Desktop. ResultsA total of 437 literatures were included, and the annual number of publications showed an overall increasing trend. The United States ranked first in the number of publications; Graz University of Technology was the institution with the highest number of publication; Gernot R Mueller-Putz was the most productive author, while Jonathan R Wolpaw was the most cited author. Brain-computer interface and artificial intelligence were identified as the high-frequency and bursting keywords in this field. The researches were characterized by the cross-integration of five core disciplines: neuroscience and rehabilitation medicine, biomedical engineering, computer science and artificial intelligence, neurophysiology, and materials science. ConclusionResearches on BCI in SCI treatment are accelerating continuously, and technological integration is becoming the core trend.

ObjectiveTo analyze the research hotspots and development trends of brain-computer interface (BCI) in the treatment for spinal cord injury (SCI). MethodsRelevant literatures on BCI applied in SCI treatment, published from the inception of the Web of Science Core Collection to July, 2025, were retrieved. Visualization analysis was performed using CiteSpace, VOSviewer and Tableau Desktop. ResultsA total of 437 literatures were included, and the annual number of publications showed an overall increasing trend. The United States ranked first in the number of publications; Graz University of Technology was the institution with the highest number of publication; Gernot R Mueller-Putz was the most productive author, while Jonathan R Wolpaw was the most cited author. Brain-computer interface and artificial intelligence were identified as the high-frequency and bursting keywords in this field. The researches were characterized by the cross-integration of five core disciplines: neuroscience and rehabilitation medicine, biomedical engineering, computer science and artificial intelligence, neurophysiology, and materials science. ConclusionResearches on BCI in SCI treatment are accelerating continuously, and technological integration is becoming the core trend.

Objective:To explore the key factors affecting health-related quality of life in children with brain tumors following initial diagnosis and to analyze its impact on survival and prognosis.Methods:Seventy-eight pediatric brain tumor patients who participated in a prospective cohort study between June 1st，2016 and June 30th，2021 were included for health-related quality of life assessment and long-term follow-up（median follow-up duration：52 months）.Results:The male-to-female ratio among the 78 children was 1.1：1，with a median age of 7.0（4.0，10.0）years. The scores of the Pediatric Quality of Life Inventory? 4.0 Generic Core Scales（PedsQL? 4.0）were（67.40±18.26）for parent proxy reports and（67.87±20.40）for child self-reports. Cronbach's α coefficients ranged from 0.790 to 0.927，with the intraclass correlation coefficient（ICC）was 0.673. According to the PedsQL? 4.0，impaired quality of life was observed in 50.0% of children by parent proxy report and 52.8% by child self-report，primarily affecting physical and role functioning. In addition，70% of caregivers reported impaired quality of life，with worry being the most prominent issue. Key factors affecting children's quality of life included radiotherapy，tumor stage，annual family income，and parents' marital status，while caregivers' quality of life was influenced by radiotherapy and the child's IgA levels（all P<0.05）. Children with decreased total scores，impaired physical functioning，or impaired emotional functioning on the PedsQL? 4.0 parent proxy report exhibited an increased risk of mortality（all P<0.05）. In multivariate Cox regression analysis，independent prognostic factors included a decrease in the total score on the PedsQL? 4.0 parent proxy report（ HR=6.702，95% CI：1.442-31.151， P<0.05），presence of hydrocephalus（ HR=33.602，95% CI：4.354-259.333， P<0.05），tumor recurrence（ HR=16.846，95% CI：3.158-89.852， P<0.05），and absence of hydrocephalus shunt surgery（ HR=13.428，95% CI：1.761-102.394， P<0.05）. Conclusion:The quality of life of newly diagnosed children with brain tumors is lower than that of healthy children，and quality of life is an important prognostic factor. Quality of life assessment should be an integral component of a comprehensive management program for children with brain tumors.

Non-alcoholic fatty liver disease （NAFLD） is a chronic liver disease closely related to metabolism， which is mainly characterized by abnormal lipid deposition in hepatocytes. In recent years， with the increasing prevalence of obesity and metabolic syndrome， NAFLD has become one of the most common chronic diseases in the world. The pathogenesis of NAFLD is complex and varied， involving the cross-regulation of multiple signaling pathways such as glucose-lipid metabolism， oxidative stress， and inflammation. The TLR4 signaling pathway plays a key role in the development and progression of NAFLD， and abnormal activation of this pathway accelerates the deterioration of NAFLD by promoting the release of pro-inflammatory cytokines， inducing oxidative stress， and exacerbating insulin resistance. Studies have shown that traditional Chinese medicine （TCM） can regulate the TLR4 signaling pathway to alleviate the symptoms and pathological features of NAFLD. The present review summarizes the experimental research progress in the TCM regulation of the TLR4 signaling pathway in treating NAFLD in the past 5 years， covering a wide range of TCM active ingredients （such as polysaccharides， terpenoids， alkaloids， flavonoids） and compound prescriptions. The active ingredients and compound prescriptions of TCM can effectively ameliorate lipid metabolism disorders， reduce insulin resistance， regulate intestinal flora， and inhibit inflammation and oxidative stress by regulating the TLR4 signaling pathway via multiple targets and pathways， thus slowing down the progression of NAFLD. Through in-depth analysis of the pathological mechanisms of NAFLD and exploration of the potential of TLR4 signaling pathway as a therapeutic target， we can provide theoretical support for the application of TCM in the treatment of NAFLD， as well as new perspectives and directions for future clinical research and new drug development， thereby promoting the innovation and development of therapeutic strategies for NAFLD.

Forsythia suspensa is a traditional Chinese medicine and a commonly used landscaping plant. Its dried fruit is used in medicine for its functions of clearing heat, removing toxins, reducing swelling, dissipating masses, and dispersing wind and heat. It possesses extremely high medicinal and economic value. However, the genetic differentiation and diversity of its wild populations remain unclear. In this study, chloroplast genome sequences were obtained from 15 wild individuals of F. suspensa using high-throughput sequencing technology. The sequence characteristics and intraspecific variations were analyzed. The results were as follows:(1) The full length of the F. suspensa chloroplast genome ranged from 156 184 to 156 479 bp, comprising a large single-copy region, a small single-copy region, and two inverted repeat regions. The chloroplast genome encoded a total of 132 genes, including 87 protein-coding genes, 37 tRNA genes, and 8 rRNA genes.(2) A total of 166-174 SSR loci, 792 SNV loci, and 63 InDel loci were identified in the F. suspensa chloroplast genome, indicating considerable genetic variation among individuals.(3) Population structure analysis revealed that F. suspensa could be divided into five or six groups. Both the population structure analysis and phylogenetic reconstruction results indicated significant genetic variation within the wild populations of F. suspensa, with no obvious correlation between intraspecific genetic differentiation and geographical distribution. This study provides new insights into the genetic diversity and differentiation within F. suspensa species and offers additional references for the conservation of species diversity and the utilization of germplasm resources in wild F. suspensa.
Genome, Chloroplast ; Forsythia/classification* ; Phylogeny ; Genetic Variation ; Chloroplasts/genetics* ; Microsatellite Repeats

PURPOSE: The study aimed to examine the pattern of motorization and the mortality rate related to road traffic crashes in Zunyi (a city in northern Guizhou province of China) from 2013 to 2022, and to identify the epidemiological characteristics of these crashes with to provide insights that could help improve road safety. METHODS: Data were obtained from the Zunyi traffic management data platform, and the mortality rates were calculated. We deployed various analytical methods, including descriptive analysis, Chi-square test or Fisher's exact test for categorical variables, circular distribution map analysis, and Rayleigh test to characterize the traits of road traffic crashes in the region. RESULTS: During the 10-year study period, 7488 people died due to road traffic accidents, with males accounting for 70.4% and females 29.6% (χ² = 101.97, p < 0.001). The mortality rate increased from 7.80 deaths per 100,000 people in 2013 to 10.70 deaths per 100,000 people in 2016, but then decreased to 9.54 deaths per 100,000 people in 2019. A notable finding was that the death rate per 10,000 vehicles declined from 16.09 deaths per 10,000 vehicles in 2013 to 5.48 deaths per 10,000 vehicles in 2022. The study also found that vulnerable road users represented nearly half (48.76%) of all accident fatalities, and unlicensed or inexperienced driving contributed significantly to the occurrence of road traffic accidents. CONCLUSION Although the number of road traffic accidents in Zunyi has decreased, there are still some critical issues that need to be addressed, particularly for vulnerable road users and unlicensed drivers. Our results highlight the need for targeted interventions to address the specific risk factors of road traffic crashes, particularly those affecting vulnerable road users and drivers without sufficient experience or license.
Humans ; Accidents, Traffic/statistics & numerical data* ; China/epidemiology* ; Male ; Female ; Adult ; Middle Aged ; Aged ; Adolescent ; Young Adult ; Child

Objective To establish a reliable prediction model for radiation pneumonitis(RP)based on multi-regional radiomics analysis of localizable CT images.Methods A retrospective analysis was conducted on 185 patients who received radiotherapy from January 2021 to June 2023 in the Department of Radiotherapy,Xuzhou Cancer Hospital.Patients were classified as having RP or not based on imaging combined with clinical diagnosis.Three regions of interest(ROI)were defined in the localizable CT images:Lung,Lung-PTV and PTV,and their radiomics features were extracted.After feature screening using methods such as Mann-Whitney Utest,recursive feature elimination,and Lasso,a prediction model was established using support vector machine classification algorithm.The model performance was validated using 6 evaluation metrics:the area under the receiver operating characteristic curve(AUC),accuracy,specificity,sensitivity,positive predictive value,and negative predictive value.Results The prediction model consisted of 7 radiomics features.The clinical model of target-to-lung ratio,PTV model,Lung model,and Lung-PTV model achieved AUC values of 0.535,0.801,0.672,and 0.706 in the test set,respectively.The AUC value and accuracy of PTV model reached 0.843 and 0.775 in the training set,while 0.801 and 0.750 in the test set.PTV model was superior to Lung model,Lung-PTV model,and clinical model in predictive performance.The AUC values of the combined PTV+(Lung-PTV)model in the training and test sets were 0.867 and 0.806,respectively,higher than those of PTV model and Lung-PTV model.Conclusion The predictive ability of the prediction models constructed from radiomics features in different ROI for symptomatic RP varies.The radiomics prediction model using PTV as ROI exhibits superior predictive performance,and the combined multi-regional radiomics model can further improve the predictive ability for RP.