BACKGROUND:High-frequency repetitive transcranial magnetic stimulation has garnered significant attention due to its potential non-invasive benefits in modulating brain function.However,no studies have comprehensively analyzed the current research landscape and development trends of this field from a macroscopic perspective.OBJECTIVE:To explore research hotspots,current trends,and emerging frontiers in the field of high-frequency repetitive transcranial magnetic stimulation through visualized analysis.METHODS:Data were collected from the Web of Science Core Collection database from January 1,2014 to November 15,2024.CiteSpace was used for analyzing publication volume,collaborations among countries/regions,institutions and authors,citation analysis of journals and co-cited literature,as well as disciplinary distribution.Additionally,keyword co-occurrence,clustering,and burst analyses were conducted,and visualized knowledge maps were generated.RESULTS AND CONCLUSION:A total of 860 articles were included.The publication volume of high-frequency repetitive transcranial magnetic stimulation showed an overall upward trend from 2014 to 2022,followed by a decline from 2022 to 2024.China had the highest publication volume,while Ghent University ranked as the most productive institution.Universities acted as the most high-output institutions.Chris Baeken from Ghent University was identified as the most prolific author.Collaboration among leading authors and institutions worldwide remained limited.The main research hotspots in this field were associated with keywords such as depression,stroke,neuropathic pain,and Parkinson's disease.Burst keywords focused on mild cognitive impairment,reflecting a diversification in research directions.The overall research activity in high-frequency repetitive transcranial magnetic stimulation continues to rise,with primary focuses on its clinical applications for psychiatric and neurological disorders,as well as explorations of its underlying mechanisms.Future research may focus on optimizing treatment parameters for targeting different brain regions in clinical applications and expanding its applications and mechanisms across various domains.

BACKGROUND:High-frequency repetitive transcranial magnetic stimulation has garnered significant attention due to its potential non-invasive benefits in modulating brain function.However,no studies have comprehensively analyzed the current research landscape and development trends of this field from a macroscopic perspective.OBJECTIVE:To explore research hotspots,current trends,and emerging frontiers in the field of high-frequency repetitive transcranial magnetic stimulation through visualized analysis.METHODS:Data were collected from the Web of Science Core Collection database from January 1,2014 to November 15,2024.CiteSpace was used for analyzing publication volume,collaborations among countries/regions,institutions and authors,citation analysis of journals and co-cited literature,as well as disciplinary distribution.Additionally,keyword co-occurrence,clustering,and burst analyses were conducted,and visualized knowledge maps were generated.RESULTS AND CONCLUSION:A total of 860 articles were included.The publication volume of high-frequency repetitive transcranial magnetic stimulation showed an overall upward trend from 2014 to 2022,followed by a decline from 2022 to 2024.China had the highest publication volume,while Ghent University ranked as the most productive institution.Universities acted as the most high-output institutions.Chris Baeken from Ghent University was identified as the most prolific author.Collaboration among leading authors and institutions worldwide remained limited.The main research hotspots in this field were associated with keywords such as depression,stroke,neuropathic pain,and Parkinson's disease.Burst keywords focused on mild cognitive impairment,reflecting a diversification in research directions.The overall research activity in high-frequency repetitive transcranial magnetic stimulation continues to rise,with primary focuses on its clinical applications for psychiatric and neurological disorders,as well as explorations of its underlying mechanisms.Future research may focus on optimizing treatment parameters for targeting different brain regions in clinical applications and expanding its applications and mechanisms across various domains.

Chronic diabetic wounds, severely complicated by multidrug-resistant (MDR) bacterial infections, represent a profound and escalating global health crisis. The intrinsically hostile microenvironment of diabetic wounds, characterized by localized hypoxia, persistent oxidative stress, and poor vascularization, creates an ideal niche for opportunistic pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa. These bacteria readily construct dense extracellular polymeric substance (EPS) biofilms, which not only physically shield the microbes from host immune responses but also actively trap the wound in a state of chronic, unresolved inflammation. Consequently, conventional systemic and topical antibiotic therapies are becoming increasingly futile, as poor perfusion at the wound site restricts drug bioavailability, while the rapid genetic evolution of bacteria and the impenetrable nature of biofilms lead to catastrophic treatment failures, often culminating in severe tissue necrosis and lower-extremity amputations. To circumvent the limitations of traditional antimicrobials, therapeutic gas delivery has emerged as a highly promising, paradigm-shifting strategy. Gaseous signaling molecules, particularly nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H₂S), and hydrogen (H₂), possess unique physicochemical properties that allow them to seamlessly penetrate dense biofilm matrices and cellular membranes. Once inside, these gases operate via multi-targeted mechanisms that are incredibly difficult for bacteria to develop resistance against; for instance, NO induces severe lipid peroxidation and DNA cleavage in bacteria, CO downregulates pro-inflammatory cytokines, H₂S significantly accelerates endothelial cell migration for neovascularization, and H₂ acts as a powerful selective antioxidant to neutralize tissue-damaging reactive oxygen species (ROS). Together, these therapeutic gases not only exert broad-spectrum bactericidal effects but also actively reprogram the wound bed by promoting the critical M1-to-M2 macrophage polarization and stimulating angiogenesis. Despite their immense biological potential, the direct clinical translation of gas therapies is severely hindered by inherent physicochemical drawbacks, including extreme volatility, short physiological half-lives, poor aqueous solubility, and the high risk of off-target systemic toxicity, if applied indiscriminately. To conquer these immense pharmacokinetic barriers, cutting-edge advancements in materials science have driven the development of gas-releasing micro- and nanoplatforms. Utilizing sophisticated carriers such as metal-organic frameworks (MOFs), mesoporous silica, polymeric nanoparticles, liposomes, and injectable hydrogels, researchers can now encapsulate gas-donor molecules to achieve sustained, localized delivery. More importantly, these advanced nanoplatforms are ingeniously engineered to be stimuli-responsive. By exploiting the pathological hallmarks of the diabetic wound environment, such as elevated glucose concentrations, acidic pH, and overexpressed ROS, or by utilizing external triggers like near-infrared (NIR) light irradiation and ultrasound, these intelligent platforms ensure on-demand, precise spatio-temporal gas release. This often allows for powerful synergistic combinations, such as photothermal or photodynamic therapy coupled with gas release, thereby obliterating biofilms while sparing healthy tissue. While the therapeutic outcomes of these smart delivery systems in eradicating MDR infections and accelerating tissue repair are unprecedented, several critical challenges remain before widespread clinical adoption, as long-term biosafety profiles of the carrier nanomaterials, complexities in large-scale good manufacturing practice (GMP) production, and stringent regulatory hurdles must be rigorously addressed. Looking forward, the next frontier lies in the realm of precision medicine and theranostics, where future research must focus on the seamless integration of these gas-releasing platforms with flexible, wearable biosensors capable of continuously monitoring wound biomarkers (e.g., pH, temperature, uric acid) in real-time. Coupled with artificial intelligence algorithms to govern automated, closed-loop adaptive dosing, these next-generation smart dressings hold the ultimate potential to comprehensively transform the clinical management of complex, infected diabetic wounds.

Chronic diabetic wounds, severely complicated by multidrug-resistant (MDR) bacterial infections, represent a profound and escalating global health crisis. The intrinsically hostile microenvironment of diabetic wounds, characterized by localized hypoxia, persistent oxidative stress, and poor vascularization, creates an ideal niche for opportunistic pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa. These bacteria readily construct dense extracellular polymeric substance (EPS) biofilms, which not only physically shield the microbes from host immune responses but also actively trap the wound in a state of chronic, unresolved inflammation. Consequently, conventional systemic and topical antibiotic therapies are becoming increasingly futile, as poor perfusion at the wound site restricts drug bioavailability, while the rapid genetic evolution of bacteria and the impenetrable nature of biofilms lead to catastrophic treatment failures, often culminating in severe tissue necrosis and lower-extremity amputations. To circumvent the limitations of traditional antimicrobials, therapeutic gas delivery has emerged as a highly promising, paradigm-shifting strategy. Gaseous signaling molecules, particularly nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H₂S), and hydrogen (H₂), possess unique physicochemical properties that allow them to seamlessly penetrate dense biofilm matrices and cellular membranes. Once inside, these gases operate via multi-targeted mechanisms that are incredibly difficult for bacteria to develop resistance against; for instance, NO induces severe lipid peroxidation and DNA cleavage in bacteria, CO downregulates pro-inflammatory cytokines, H₂S significantly accelerates endothelial cell migration for neovascularization, and H₂ acts as a powerful selective antioxidant to neutralize tissue-damaging reactive oxygen species (ROS). Together, these therapeutic gases not only exert broad-spectrum bactericidal effects but also actively reprogram the wound bed by promoting the critical M1-to-M2 macrophage polarization and stimulating angiogenesis. Despite their immense biological potential, the direct clinical translation of gas therapies is severely hindered by inherent physicochemical drawbacks, including extreme volatility, short physiological half-lives, poor aqueous solubility, and the high risk of off-target systemic toxicity, if applied indiscriminately. To conquer these immense pharmacokinetic barriers, cutting-edge advancements in materials science have driven the development of gas-releasing micro- and nanoplatforms. Utilizing sophisticated carriers such as metal-organic frameworks (MOFs), mesoporous silica, polymeric nanoparticles, liposomes, and injectable hydrogels, researchers can now encapsulate gas-donor molecules to achieve sustained, localized delivery. More importantly, these advanced nanoplatforms are ingeniously engineered to be stimuli-responsive. By exploiting the pathological hallmarks of the diabetic wound environment, such as elevated glucose concentrations, acidic pH, and overexpressed ROS, or by utilizing external triggers like near-infrared (NIR) light irradiation and ultrasound, these intelligent platforms ensure on-demand, precise spatio-temporal gas release. This often allows for powerful synergistic combinations, such as photothermal or photodynamic therapy coupled with gas release, thereby obliterating biofilms while sparing healthy tissue. While the therapeutic outcomes of these smart delivery systems in eradicating MDR infections and accelerating tissue repair are unprecedented, several critical challenges remain before widespread clinical adoption, as long-term biosafety profiles of the carrier nanomaterials, complexities in large-scale good manufacturing practice (GMP) production, and stringent regulatory hurdles must be rigorously addressed. Looking forward, the next frontier lies in the realm of precision medicine and theranostics, where future research must focus on the seamless integration of these gas-releasing platforms with flexible, wearable biosensors capable of continuously monitoring wound biomarkers (e.g., pH, temperature, uric acid) in real-time. Coupled with artificial intelligence algorithms to govern automated, closed-loop adaptive dosing, these next-generation smart dressings hold the ultimate potential to comprehensively transform the clinical management of complex, infected diabetic wounds.

Objective This study aimed to provide an effective scientific basis for prevention and control of cockroaches on aircrafts by identifying cockroach-carried pathogens,and assess the insecticidal efficacy of gel bait mediated cockroach control on aircrafts,to provide technical guidance for aircraft disinsection.Methods Cassette-trapping was used to trap cockroaches,and the carried pathogens were detected using bacterial cultivation techniques.The gel bait mediated killing rate was calculated after 1,7,and 30 d by field application of gel bait.Results A total of 411 cockroaches were captured,and all were identified as Blattella germanica.26 strains of pathogenic bacteria were isolated from the trapped cockroaches.The killing rates of cockroaches were 58.8％-96.3％with 1-30 day application of gel bait.Statistically significant differences were observed in cockroach killing rates on different days(χ2=58.95,P<0.01).Conclusions B.germanica carry a large variety of pathogenic bacteria and opportunistic pathogens and are thus important infectious disease carriers.Gel bait agents have proven to be very effective against cockroaches on aircrafts.

Objective This research was performed to identify rodent-borne pathogens in Hekou Port,Yunnan Province.Methods Rodents were captured using cages and dissected to collect their lungs,liver,spleen,and other viscera.Eight pathogens,including Yersinia pestis,Leptospira,Bartonella,and Anaplasmataceae,were identified using polymerase chain reaction amplification.Amplified pathogen sequences from positive samples were sequenced,and BLAST homology searches were conducted using GenBank to confirm pathogen identities.A phylogenetic tree of the identified pathogens was constructed using the neighbor joining method.Results The total of 31 rodents,identified as Rattus tanezumi,R.norvegicus,and Mus musculus,were captured.Among these,R.tanezumi was the dominant species,accounting for 64.52％of the total.Two pathogens,Leptospira interrogans and Neoehrlichia mikurensis,were detected,with positivity rates of 9.68％and 29.03％,respectively.No other pathogens were detected.The overall positivity rate for rodent-borne pathogens was 35.48％.Conclusions The single 16S rRNA gene fragment is insufficient for the molecular identification of all Neoehrlichia species.Accurate species identification should be based on a combined analysis of multiple genes.The prevalence of rodent-borne pathogens in Hekou Port indicates the necessity for enhanced surveillance of rodent-borne diseases and implementation of additional prevention and control measures in border ports.

Objective This study aimed to investigate natural infection of rodents with Ehrlichia and Neoehrlichia at major Chinese land-border ports along the"Belt and Road".Methods In 2022,rodents were monitored in 10 ports in northern and southern China and identified based on diagnostic morphological characteristics.The 16S rRNA genes of Ehrlichia and Neoehrlichia were detected by PCR using universal primers from rodent samples and phylogenetic analysis was performed based on the sequences of the detected positive pathogens.Results A total of 356 rodents were sampled,including 2 orders,5 families,15 genera,and 20 species.Predominantly,73,61,56,and 58 were Meriones unguiculatus(20.51%),Rattus norvegicus(17.13%),Apodemus agrarius(15.73%),and Microtus gregalis(16.29%).Only one Microtus fortis from Suifenghe Port was infected with Ehrlichia sp.Moreover,12 rodents were infected with Neoehrlichia spp.(overall positivity rate:3.37%).Conclusions Natural infections with Ehrlichia spp.and Neoehrlichia spp.were demonstrated in rodents at important Chinese land-border ports.The positivity rate of Neoehrlichia spp.was high in some ports,indicating that surveillance for ticks and their prevention and control measures should be intensified in these regions.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

OBJECTIVE: Several epidemiological observational studies have related particulate matter (PM) exposure to Inflammatory bowel disease (IBD), but many confounding factors make it difficult to draw causal links from observational studies. The objective of this study was to explore the causal association between PM _2.5 exposure, its absorbance, and IBD. METHODS: We assessed the association of PM _2.5 and PM _2.5 absorbance with the two primary forms of IBD (Crohn's disease [CD] and ulcerative colitis [UC]) using Mendelian randomization (MR) to explore the causal relationship. We conducted two-sample MR analyses with aggregated data from the UK Biobank genome-wide association study. Single-nucleotide polymorphisms linked with PM _2.5 concentrations or their absorbance were used as instrumental variables (IVs). We used inverse variance weighting (IVW) as the primary analytical approach and four other standard methods as supplementary analyses for quality control. RESULTS: The results of MR demonstrated that PM _2.5 had an adverse influence on UC risk (odds ratio [ OR] = 1.010; 95% confidence interval [ CI] = 1.001-1.019, P = 0.020). Meanwhile, the results of IVW showed that PM _2.5 absorbance was also causally associated with UC ( OR = 1.012; 95% CI = 1.004-1.019, P = 0.002). We observed no causal relationship between PM _2.5, PM _2.5 absorbance, and CD. The results of sensitivity analysis indicated the absence of heterogeneity or pleiotropy, ensuring the reliability of MR results. CONCLUSION Based on two-sample MR analyses, there are potential positive causal relationships between PM _2.5, PM _2.5 absorbance, and UC.
Humans ; Mendelian Randomization Analysis ; Particulate Matter/analysis* ; Polymorphism, Single Nucleotide ; Inflammatory Bowel Diseases/genetics* ; Air Pollutants/analysis* ; Crohn Disease/genetics* ; Colitis, Ulcerative/genetics* ; Genome-Wide Association Study ; Risk Factors ; Environmental Exposure

Objective To explore the functional mechanism of spinal cord fragile X mental retardation protein(FMRP)involved in neuropathic pain(NP)by using the sciatic nerve model of chronic compression injury(CCI).Methods First,to investigate the changes of spinal cord FMRP and β-catenin following the development of NP,this study compared the 50%mechanical withdrawal threshold(MWT)and thermal withdrawal latency(TWL)in CCI rats,as well as changes of FMRP and β-catenin in the spinal dorsal horn post-surgery,through random grouping.Immunofluorescence staining was performed on spinal cord tissue sections from CCI rats.Second,to further validate the alterations in pain behavior when the FMRP function was lost,we measured the 50%MWT,TWL,and FMRP and β-catenin in the spinal dorsal horn after FMRP knockdown in CCI rats.Finally,we measured the 50%MWT,TWL,and FMRP and β-catenin in the case of FMRP hyperfunction for validation.Results Compared with the baseline CCI group and the naive and sham groups after modeling,the CCI group after modeling showed decreases in 50%MWT and TWL(all P<0.001).After modeling,compared with the naive group and the sham group,the CCI group presented up-regulated expression of FMRP(P=0.027,P=0.022)and β-catenin(P<0.001,P=0.001)in the spinal dorsal horn.No co-localization of FMRP with astrocytes and microglia was observed in the spinal cord,while the co-localization with neurons was observed.Compared with the baseline,the CCI+FMRP knockdown group showed decreases in 50%MWT(P=0.015)and TWL(P=0.001)after modeling.After intrathecal injection of small interfering RNA(siRNA),the 50%MWT(P=0.020)and TWL(P=0.009)of the CCI+FMRP knockdown group were increased.Moreover,compared with the CCI group and the CCI+solvent group,the CCI+FMRP knockdown group showed increases in 50%MWT(both P<0.001)and TWL(P=0.005,P=0.006).After intrathecal injection of siRNA,the expression levels of FMRP(P=0.012,P=0.007)and β-catenin(both P<0.001)in the spinal dorsal horn of the CCI+FMRP knockdown group were lower than those of the CCI group and the CCI+solvent group.Compared with the baseline FMRP overexpression group and the naive and negative control groups after adeno-associated virus(AAV)injection,the FMRP overexpression group after AAV injection showed decreases in 50%MWT and TWL(all P<0.001).After AAV injection,compared with the naive group and the negative control group,the FMRP overexpression group demonstrated up-regulated expression of FMRP(both P<0.001)and β-catenin(P=0.006,P=0.008)in the spinal cord.Conclusions This study confirms that spinal cord FMRP and β-catenin are involved in NP induced by CCI.Spinal cord FMRP may be one of the potential therapeutic targets for NP.
Animals ; beta Catenin/metabolism* ; Neuralgia/metabolism* ; Fragile X Mental Retardation Protein/physiology* ; Spinal Cord/metabolism* ; Rats ; Rats, Sprague-Dawley ; Male