ObjectiveTo analyze the research hotspots and development trends in the field of spinal cord stimulation (SCS) for spinal cord injury (SCI). MethodsLiterature about SCS for SCI was retrieve from the Web of Science (WOS) Core Collection database, with a time range from January, 1999 to July, 2025. VOSviewer 1.6.20 and CiteSpace 6.4.R2 were used to analyze the annual publication volume, countries, authors, institutions, journals and keywords. ResultsA total of 636 literatures were included. From 1999 to 2025, the overall publication trend in this field showed an upward trajectory, with recent years fluctuating but tending to stabilize. The country with the most publications was the United States (429 papers), followed by Russia (98 papers) and China (70 papers). The institution with the highest number of publications was the University of California, Los Angeles (76 papers), the author with the most publications was V. Reggie Edgerton (70 papers), and the journal with the most publications was Journal of Clinical Medicine (31 papers). The most frequently cited study focused on exploring the combination of epidural spinal cord stimulation with task-specific training to restore motor function in patients with complete SCI. Keyword analysis showed that the research hotspots in this field were mainly focused on neuroregulation mechanisms, recovery of motor and autonomic nervous dysfunction, artificial intelligence, closed-loop stimulation and brain-computer interface technology innovations. In recent years, the research focus gradually shifted from basic mechanisms to personalized and precise multifunctional rehabilitation strategies. ConclusionThe field of SCS for SCI has undergone phases of basic mechanism exploration and clinical application expansion. Current research hotspots and future trends focus primarily on the development of new stimulation paradigms and combined innovative technologies.

Objective To clarify the active ingredients and the potential molecular mechanism of Guben Qushi Huayu Prescription in treating psoriasis recurrence.Methods An ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF/MS)was applied to analyze the whole formula and the constituents absorbed into blood of Guben Qushi Huayu Prescription,and molecular docking technology was used to study the binding affinity of the constituents absorbed into blood with psoriasis-related immunomodulatory proteins such as CD69 and CD103 proteins.Results Mass spectrometry analysis identified 21 active ingredients such as paeoniflorin in Guben Qushi Huayu Prescription,including several known anti-inflammatory and immunomodulatory compounds.Analysis of the constituents absorbed into blood identified 11 ingredients,including paeoniflorin,that may affect the course of psoriasis through blood circulation.Molecular docking studies revealed that the constituents absorbed into blood,including astilbin,isoastilbin,chlorogenic acid,neochlorogenic acid,cryptochlorogenic acid,helicine,paeoniflorin,ononin,all had high binding affinities with CD69 and CD103 proteins.Conclusion This research reveals the main active ingredients of Guben Qushi Huayu Prescription and their potential mechanism for regulating the recurrence of psoriasis by mass spectrometry and molecular docking technology,contributing to providing scientific basis for further pharmacological research and clinical application.

Objective To construct a Gabra3 gene knockout cell model and explore transcriptomic and proteomic alterations in murine neuronal cells,in order to investigate the molecular mechanisms underlying the increased depth of slow-wave sleep observed following Gabra3 deletion.Methods Multiple sgRNA sequences were designed,and the CRISPR/Cas9 system was used to knock out the Gabra3 gene in the murine GT1-7 neuronal cell line.Gene sequencing was performed to assess knockout efficiency,and TA cloning was used to validate the knockout results.Protein immunoblotting experiments were conducted to confirm the knockout,while cell proliferation assays were used to validate the knockout phenotype.Total RNA and protein were extracted for transcriptomic and proteomic sequencing,respectively.A range of bioinformatics analyses was conducted to assess the functional consequences of Gabra3 knockout in GT1-7 neuronal cells.Results Following Gabra3 gene knockout,pathways related to cortisol and aldosterone synthesis and secretion,as well as circadian rhythm,were significantly enriched.Three key genes,BMP2,GLI2,and DLL1,were identified.Proteomic profiling revealed widespread disturbances in protein expression following Gabra3 knockout.Conclusion Gabra3 gene knockout may increase slow-wave sleep depth by modulating the expression of hormone secretion-related genes and altering circadian regulatory pathways.

Intestinal fibrosis is a significant clinical challenge in inflammatory bowel diseases, but no effective anti-fibrotic therapy is currently available. Glucagon receptor (GCGR) and glucagon-like peptide 1 receptor (GLP1R) are both peptide hormone receptors involved in energy metabolism of epithelial cells. However, their role in intestinal fibrosis and the underlying mechanisms remain largely unexplored. Herein GCGR and GLP1R were found to be reduced in the stenotic ileum of patients with Crohn's disease as well as in the fibrotic colon of mice with chronic colitis. The downregulation of GCGR and GLP1R led to the accumulation of the metabolic byproduct lactate, resulting in histone H3K9 lactylation and exacerbated intestinal fibrosis through epithelial-to-mesenchymal transition (EMT). Dual activating GCGR and GLP1R by peptide 1907B reduced the H3K9 lactylation in epithelial cells and ameliorated intestinal fibrosis in vivo. We uncovered the role of GCGR/GLP1R in regulating EMT involved in intestinal fibrosis via histone lactylation. Simultaneously activating GCGR/GLP1R with the novel dual agonist peptide 1907B holds promise as a treatment strategy for alleviating intestinal fibrosis.

Acute respiratory distress syndrome (ARDS) is a common respiratory emergency, but current clinical treatment remains at the level of symptomatic support and there is a lack of effective targeted treatment measures. Our previous study confirmed that inhalation of hydrogen gas can reduce the acute lung injury of ARDS, but the application of hydrogen has flammable and explosive safety concerns. Drinking hydrogen-rich liquid or inhaling hydrogen gas has been shown to play an important role in scavenging reactive oxygen species and maintaining mitochondrial quality control balance, thus improving ARDS in patients and animal models. Coral calcium hydrogenation (CCH) is a new solid molecular hydrogen carrier prepared from coral calcium (CC). Whether and how CCH affects acute lung injury in ARDS remains unstudied. In this study, we observed the therapeutic effect of CCH on lipopolysaccharide (LPS) induced acute lung injury in ARDS mice. The survival rate of mice treated with CCH and hydrogen inhalation was found to be comparable, demonstrating a significant improvement compared to the untreated ARDS model group. CCH treatment significantly reduced pulmonary hemorrhage and edema, and improved pulmonary function and local microcirculation in ARDS mice. CCH promoted mitochondrial peripheral division in the early course of ARDS by activating mitochondrial thioredoxin 2 (Trx2), improved lung mitochondrial dysfunction induced by LPS, and reduced oxidative stress damage. The results indicate that CCH is a highly efficient hydrogen-rich agent that can attenuate acute lung injury of ARDS by improving the mitochondrial function through Trx2 activation.

Liposomes serve as critical carriers for drugs and vaccines, with their biological effects influenced by their size. The microfluidic method, renowned for its precise control, reproducibility, and scalability, has been widely employed for liposome preparation. Although some studies have explored factors affecting liposomal size in microfluidic processes, most focus on small-sized liposomes, predominantly through experimental data analysis. However, the production of larger liposomes, which are equally significant, remains underexplored. In this work, we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning (ML) model capable of accurately predicting liposomal size. Experimental validation was conducted using a staggered herringbone micromixer (SHM) chip. Our findings reveal that most investigated variables significantly influence liposomal size, often interrelating in complex ways. We evaluated the predictive performance of several widely-used ML algorithms, including ensemble methods, through cross-validation (CV) for both liposome size and polydispersity index (PDI). A standalone dataset was experimentally validated to assess the accuracy of the ML predictions, with results indicating that ensemble algorithms provided the most reliable predictions. Specifically, gradient boosting was selected for size prediction, while random forest was employed for PDI prediction. We successfully produced uniform large (600 nm) and small (100 nm) liposomes using the optimised experimental conditions derived from the ML models. In conclusion, this study presents a robust methodology that enables precise control over liposome size distribution, offering valuable insights for medicinal research applications.

Acute respiratory distress syndrome(ARDS)is a common respiratory emergency,but current clinical treatment remains at the level of symptomatic support and there is a lack of effective targeted treatment measures.Our previous study confirmed that inhalation of hydrogen gas can reduce the acute lung injury of ARDS,but the application of hydrogen has flammable and explosive safety concerns.Drinking hydrogen-rich liquid or inhaling hydrogen gas has been shown to play an important role in scavenging reactive oxygen species and maintaining mitochondrial quality control balance,thus improving ARDS in patients and animal models.Coral calcium hydrogenation(CCH)is a new solid molecular hydrogen carrier prepared from coral calcium(CC).Whether and how CCH affects acute lung injury in ARDS re-mains unstudied.In this study,we observed the therapeutic effect of CCH on lipopolysaccharide(LPS)induced acute lung injury in ARDS mice.The survival rate of mice treated with CCH and hydrogen inhalation was found to be comparable,demonstrating a significant improvement compared to the untreated ARDS model group.CCH treatment significantly reduced pulmonary hemorrhage and edema,and improved pulmonary function and local microcirculation in ARDS mice.CCH promoted mitochon-drial peripheral division in the early course of ARDS by activating mitochondrial thioredoxin 2(Trx2),improved lung mitochondrial dysfunction induced by LPS,and reduced oxidative stress damage.The results indicate that CCH is a highly efficient hydrogen-rich agent that can attenuate acute lung injury of ARDS by improving the mitochondrial function through Trx2 activation.

Objective To analyze the epidemiological characteristics of acute respiratory infections(ARIs)during the autumn-winter season in Beijing,providing evidence for the prevention,control,diagnosis,and treatment of ARIs.Methods A convenience sampling method was employed,enrolling patients who visited Peking Union Medical College Hospital(PUMCH)between September 2023 and February 2024 due to ARIs.Na-sopharyngeal swabs were collected,and real-time fluorescence quantitative PCR was used to detect six common respiratory pathogens[influenza A virus(FluA),influenza B virus(FluB),human rhinovirus(HRV),Myco-plasma pneumoniae(MP),respiratory syncytial virus(RSV),and adenovirus(ADV)],as well as SARS-CoV-2 infection.The distribution patterns of pathogen infections were analyzed.Results A total of 5556 eligible patients were included.The overall positivity rate for the six common respiratory pathogens was 63.7％,with sin-gle-pathogen positivity at 54.0％,dual-pathogen positivity at 8.9％,and triple or more pathogen positivity at 0.7％.The predominant pathogens detected were FluA(16.1％)and RSV(15.7％),followed by ADV(11.1％),MP(11.1％),HRV(10.0％),and FluB(10.0％).No significant difference in overall pathogen positivity was observed between genders.However,significant differences were found between autumn and winter(x2=34.617,P＜0.001)and among pediatric,young/middle-aged,and elderly patients(x2=422.38,P＜0.001).Specifically,MP(x2=8.647,P=0.003),FluA(x2=131.932,P＜0.001),and HRV(x2=174.199,P＜0.001)exhibited significantly higher positivity rates in autumn than in winter,whereas FluB was more prevalent in winter(x2=287.894,P＜0.001).In pediatric patients,MP,RSV,HRV,and ADV positivity rates were significantly higher than in young/middle-aged and elderly patients(all P＜0.001),whereas FluB was more common in young/middle-aged patients(both P＜0.001).The positivity rates of the six common respiratory pathogens significantly declined during the SARS-CoV-2 epidemic period,exhibiting an asynchronous seasonal pattern.Conclusions The prevalence of respiratory pathogens in Beijing is associated with age and season.Tar-geted preventive measures should be implemented in different seasons and for key populations.

Liposomes serve as critical carriers for drugs and vaccines,with their biological effects influenced by their size.The microfluidic method,renowned for its precise control,reproducibility,and scalability,has been widely employed for liposome preparation.Although some studies have explored factors affecting liposomal size in microfluidic processes,most focus on small-sized liposomes,predominantly through experimental data analysis.However,the production of larger liposomes,which are equally significant,remains underexplored.In this work,we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning(ML)model capable of accurately predicting liposomal size.Experimental validation was conducted using a staggered herringbone micromixer(SHM)chip.Our findings reveal that most investigated variables significantly influence liposomal size,often interrelating in complex ways.We evaluated the predictive performance of several widely-used ML algorithms,including ensemble methods,through cross-validation(CV)for both lipo-some size and polydispersity index(PDI).A standalone dataset was experimentally validated to assess the accuracy of the ML predictions,with results indicating that ensemble algorithms provided the most reliable predictions.Specifically,gradient boosting was selected for size prediction,while random forest was employed for PDI prediction.We successfully produced uniform large(600 nm)and small(100 nm)liposomes using the optimised experimental conditions derived from the ML models.In conclusion,this study presents a robust methodology that enables precise control over liposome size distribution,of-fering valuable insights for medicinal research applications.

With the rapid advancement of artificial intelligence technology, chatbots have shown great potential in the healthcare sector. From personalized health advice to chronic disease management and psychological support, chatbots have demonstrated significant advantages in improving the efficiency and quality of healthcare services. As the scope of their applications expands, the relationship between technological complexity and practical application scenarios has become increasingly intertwined, necessitating a more comprehensive evaluation of both aspects. This paper, from the perspective of he althcare applications, systematically reviews the technological pathways and development of chatbots in the medical field, providing an in-depth analysis of their performance across various medical scenarios. It thoroughly examines the advantages and limitations of chatbots, aiming to offer theoretical support for future research and propose feasible recommendations for the broader adoption of chatbot technologies in healthcare.