Lung cancer remains one of the leading causes of cancer-related morbidity and mortality worldwide, and its treatment continues to face major challenges such as therapeutic resistance and tumor recurrence. Pyroptosis, a newly characterized form of programmed cell death, induces tumor cell death through gasdermin-mediated membrane pore formation and is accompanied by the release of inflammatory mediators, thereby playing complex roles in lung cancer initiation, progression, and modulation of the tumor microenvironment. Active components and herbal formulas derived from traditional Chinese medicine can modulate pyroptosis-related signaling pathways through multi-target mechanisms, showing potential advantages in inducing lung cancer cell death, inhibiting proliferation and migration, and reversing chemoresistance. This review systematically summarizes relevant studies from domestic and international sources, focusing on the molecular mechanisms of pyroptosis, its roles in lung cancer development and tumor microenvironment remodeling, and the current research progress on traditional Chinese medicine-based interventions targeting pyroptosis, with the aim of providing references for the prevention and treatment of lung cancer using traditional Chinese medicine.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.

AIM To study the chemical constituents from Citri reticulatae Pericarpium Viride and their anti-triple negative breast cancer activities in vitro.METHODS The ethanolic extract of Citri reticulatae Pericarpium Viride was isolated and purified by silica gel,polyamide,MCI,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The anti-triple negative breast cancer activities were screened by SRB assay,and their effects on the proliferation of triple negative breast cancer cell lines HCC1806,HCC1937 and MDA-MB-231 in vitro were evaluated.RESULTS Twenty compounds were isolated and identified as nobiletin(1),tangeritin(2),5,4'-dihydroxy-7,8-dimethoxy flavonoid(3),naringenin(4),artemetin(5),5-demethynobiletin(6),3,5,6,7,8,3',4'-pentamethoxy flavonoid(7),5,4'-dihydroxy-3,6,7,8,3'-pentamethoxyflavone(8),xanthomicrol(9),p-hydroxycinnamic acid(10),5,4'-dihydroxy-6,7,8,3'-tetramethoxyflavone(11),pectolinarigenin(12),4'-dihydroxy-5,6,7-tetramethoxyflavone(13),hispidulin(14),4',5,6,7-tetramethoxy-flavone(15),1-methyl-4-(prop-1-en-2-yl)cyclohexane-1,2-diol(16),umbelliferone(17),5-hydroxymethyl furfural(18),hydroquinone(19),1H-indole-3-carbaldehyde(20).Compound 8 showed a significant inhibitory effect with the IC50 value of(5.36±0.24)μmol/L on HCC1806 cells.CONCLUSION Compound 20 is isolated from genus Citrus for the first time,8,12-13,16-17 are isolated from this plant for the first time.Compound 8 show inhibitory effects on the proliferation of HCC1806,HCC1937 and MDA-MB-231 cells in vitro and have the strongest activities.Compounds 3-4,11-12,15,17 and 19 show strong inhibitory effect on HCC1806 cells.Compounds 15,19 also inhibit the proliferation of HCC1937 cells in vitro.

This study primarily aimed to investigate the prevalence of human papillomavirus (HPV) and other common pathogens of sexually transmitted infections (STIs) in spermatozoa of infertile men and their effects on semen parameters. These pathogens included Ureaplasma urealyticum, Ureaplasma parvum, Chlamydia trachomatis, Mycoplasma genitalium , herpes simplex virus 2, Neisseria gonorrhoeae, Enterococcus faecalis, Streptococcus agalactiae, Pseudomonas aeruginosa , and Staphylococcus aureus . A total of 1951 men of infertile couples were recruited between 23 March 2023, and 17 May 2023, at the Department of Reproductive Medicine of The First People's Hospital of Yunnan Province (Kunming, China). Multiplex polymerase chain reaction and capillary electrophoresis were used for HPV genotyping. Polymerase chain reaction and electrophoresis were also used to detect the presence of other STIs. The overall prevalence of HPV infection was 12.4%. The top five prevalent HPV subtypes were types 56, 52, 43, 16, and 53 among those tested positive for HPV. Other common infections with high prevalence rates were Ureaplasma urealyticum (28.3%), Ureaplasma parvum (20.4%), and Enterococcus faecalis (9.5%). The prevalence rates of HPV coinfection with Ureaplasma urealyticum, Ureaplasma parvum, Chlamydia trachomatis, Mycoplasma genitalium , herpes simplex virus 2, Neisseria gonorrhoeae, Enterococcus faecalis, Streptococcus agalactiae , and Staphylococcus aureus were 24.8%, 25.4%, 10.6%, 6.4%, 2.4%, 7.9%, 5.9%, 0.9%, and 1.3%, respectively. The semen volume and total sperm count were greatly decreased by HPV infection alone. Coinfection with HPV and Ureaplasma urealyticum significantly reduced sperm motility and viability. Our study shows that coinfection with STIs is highly prevalent in the semen of infertile men and that coinfection with pathogens can seriously affect semen parameters, emphasizing the necessity of semen screening for STIs.
Humans ; Male ; Infertility, Male/epidemiology* ; Coinfection/microbiology* ; Papillomavirus Infections/virology* ; Adult ; Sexually Transmitted Diseases/complications* ; China/epidemiology* ; Staphylococcus aureus/isolation & purification* ; Chlamydia trachomatis/isolation & purification* ; Prevalence ; Mycoplasma genitalium/isolation & purification* ; Ureaplasma urealyticum/isolation & purification* ; Neisseria gonorrhoeae/isolation & purification* ; Enterococcus faecalis/isolation & purification* ; Streptococcus agalactiae/isolation & purification* ; Herpesvirus 2, Human/genetics* ; Pseudomonas aeruginosa/isolation & purification* ; Semen/virology* ; Sperm Motility ; Spermatozoa/microbiology* ; Human Papillomavirus Viruses

Paraplegia caused by spinal cord injury is a serious neurological complication, for which surgery is currently the main treatment method. Due to different surgical approaches, patients are usually expected to maintain a passive prone position for a long time or switch between the supine and prone positions. Affected by multiple factors such as neurogenic sensory disorders, pathological changes in muscle tone and operative duration, the risk of intraoperative acquired pressure injury (IAPI) is significantly increased. Current clinical prevention strategies for IAPI in these patients predominantly focus on localized pressure relief during positioning, lacking systematic, standardized comprehensive prevention protocols or evidence-based guidelines. To address it, Department of Nursing, Orthopedics Branch, China International Exchange and Promotive Association for Medical and Health Care, Spinal Trauma Professional Committee, Orthopedics Branch, Chinese Medical Doctor Association, Nursing Group of Spine and Spinal Cord Professional Committee of Chinese Association of Rehabilitation Medicine organized experts in relevant fields to formulate Guideline for the prevention of intraoperative acquired pressure injury in paraplegic patients with spinal cord injury ( version 2025), based on evidence-based medical evidence and latest research results and clinical practice at home and abroad. Eleven recommendations were put forward from the aspects of preoperative risk assessment, intraoperative prevention strategies, postoperative handover and monitoring, and supportive mechanisms for IAPI prevention, aiming to standardize the prevention measures and management strategies of IAPI in paraplegic patients with spinal cord injury and accelerate the recovery of patients and improve the therapeutic effect.

Objective:To explore the quantitative indexes of dual energy spectrum CT and related clinical data to establish a predictive model for predicting pneumoconiosis.Methods:In April 2024, the information of 203 pneumoconiosis patients diagnosed by the occupational disease appraisal expert group in the Third People's Hospital of Xinjiang Uygur Autonomous Region (Occupational Disease Hospital of Xinjiang Autonomous Region) from January 2022 to December 2023 was retrospectively analyzed. Another 207 non-pneumoconiosis patients with dust exposure history were selected as control group. The measurement data between the two groups were compared using T test or Wilcoxon in dependent quality test, count date asing chi-square or Fishers test, the energy spectrum related indicators and clinical indicators of the patients were compared between groups, and potential factors for diagnosis of pneumoconiosis were screened through univariate analysis, and independent risk factors were further determined by multivariate logistic regression. Based on the results of regression analysis, the machine learning model was constructed, and the reciver operating characteristic curve (ROC) was drawn to evaluate the efficiency of the model, and the Area under cruve (AUC) value, sensitivity and specificity were calculated.Results:Smoking, lung tissue mass, silicon dioxide (SiO 2) equivalent total mass and SiO 2 equivalent concentration were the risk factors for pneumoconiosis ( P<0.05) . Multivariate logistic regression analysis showed that smoking, lung tissue mass, total lung SiO 2 equivalent total volume and total lung SiO 2 equivalent total mass were independent predicators of the diagnosis of pneumoconiosis ( OR=0.53, 0.99, 1.13, 0.85, P<0.05) . Logistic regression machine learning was used to establish a predictive model, and the training set AUC was 0.74, and the verification set AUC was 0.72, indicating that the model had good accuracy and certain ability to diagnose pneumoconiosis. Conclusion:The machine learning prediction model established by the quantitative analysis index of dual energy spectrum CT and clinical related indexes has a good diagnostic performance for the diagnosis of pneumoconiosis.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Abstract In recent years, the prevalence of overweight and obesity among children and adolescents has risen rapidly, posing a serious threat to their physical and mental health. To provide scientific, systematic, and standardized weight management guidance for overweight and obese children and adolescents, the study focuses on the core concept of healthy lifestyle intervention, integrates multidisciplinary expert opinions and research findings,and proposes a comprehensive multidisciplinary intervention framework covering scientific exercise intervention, precise nutrition and diet, optimized sleep management, and standardized psychological support. It calls for the establishment of a multi agent collaborative management mechanism led by the government, implemented by families, fostered by schools, initiated by individuals, optimized by communities, reinforced by healthcare, and coordinated by multiple stakeholders. Emphasizing a child and adolescent centered approach, the consensus advocates for comprehensive, multi level, and personalized guidance strategies to promote the internalization and maintenance of a healthy lifestyle. It serves as a reference and provides recommendations for the effective prevention and control of overweight and obesity, and enhancing the health level of children and adolescents.

Readiness for Hospital Discharge(RHD)refers to a multidimensional assessment of a patient's ability to transition safely from hospital to home,encompassing physiological stability,psychological preparedness,and social support adequacy.For patients requiring Home Nutrition Support(HNS),discharge readiness is particularly critical due to their heightened need for post-discharge specialized care,which significantly influences long-term recovery and quality of life.This paper reviews the concept,influencing factors,and unmet needs of RHD in patients with HNS and proposes targeted strategies to enhance discharge preparedness.By addressing gaps in current practices,we aim to optimize RHD in this vulnerable population and provide clinicians with evidence-based guidance for developing effective discharge plans.