Machado-Joseph disease, or spinocerebellar ataxia type 3 (SCA3), represents the most common autosomal dominant cerebellar ataxia worldwide. Despite its progressive and debilitating nature, disease-modifying therapies remain elusive. Repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising non-invasive intervention; however, its clinical application has been hindered by inconsistent protocols and a lack of mechanistic understanding. A recent landmark study published in Brain Stimulation by Chen et al. addressed these challenges by combining a high-dose intermittent theta-burst stimulation (iTBS) protocol with concurrent transcranial magnetic stimulation-electroencephalography (TMS-EEG). This commentary provides an in-depth analysis of their findings, highlighting the restoration of cerebello-cortical inhibition (CBI) as a key therapeutic mechanism. Furthermore, we discuss the broader implications of this work, proposing that future translational research should integrate accelerated iTBS (aiTBS) paradigms, cortical response measurements (CRM), and individualized neuro-navigation to establish a new era of precision neuromodulation for ataxia.

Machado-Joseph disease, or spinocerebellar ataxia type 3 (SCA3), represents the most common autosomal dominant cerebellar ataxia worldwide. Despite its progressive and debilitating nature, disease-modifying therapies remain elusive. Repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising non-invasive intervention; however, its clinical application has been hindered by inconsistent protocols and a lack of mechanistic understanding. A recent landmark study published in Brain Stimulation by Chen et al. addressed these challenges by combining a high-dose intermittent theta-burst stimulation (iTBS) protocol with concurrent transcranial magnetic stimulation-electroencephalography (TMS-EEG). This commentary provides an in-depth analysis of their findings, highlighting the restoration of cerebello-cortical inhibition (CBI) as a key therapeutic mechanism. Furthermore, we discuss the broader implications of this work, proposing that future translational research should integrate accelerated iTBS (aiTBS) paradigms, cortical response measurements (CRM), and individualized neuro-navigation to establish a new era of precision neuromodulation for ataxia.

ObjectiveMigraine is a leading neurological disorder and the fourth most common cause of years lived with disability worldwide, affecting nearly 116 million individuals. Although pharmacological treatments are available, their efficacy is often limited by side effects and variable response rates. Repetitive transcranial magnetic stimulation (rTMS) over the dorsolateral prefrontal cortex (DLPFC) offers a safe, non-invasive alternative for migraine management. However, the neurophysiological mechanisms, particularly how rTMS modulates local cortical excitability and distributed pain-related circuits, remain poorly understood. Elucidating these mechanisms is essential for optimizing treatment protocols and improving clinical outcomes. MethodsThis study employed concurrent transcranial magnetic stimulation and electroencephalography (TMS-EEG) to investigate neuroplastic and neurocircuitry mechanisms of DLPFC-rTMS in migraine. Study 1 compared 30 migraineurs and 28 healthy controls to identify abnormalities in TMS-evoked potentials (TEPs) and significant current density (SCD) within sensory-discriminative regions including the primary somatosensory cortex (S1) and posterior insula (pINS), cognitive-affective regions including the anterior insula (aINS) and midcingulate cortex (MCC), and a descending modulatory region, the periaqueductal gray (PAG). Study 2 used a single-blind, crossover, sham-controlled design in 34 healthy participants. Each participant received both active (10 Hz, 80% RMT, 1 500 pulses) and sham DLPFC-rTMS in counterbalanced order. TMS-EEG and cold pain tolerance were assessed before and after each session. ResultsIn Study 1, migraineurs showed a significantly less negative N120 amplitude compared to healthy controls (P=0.027, Cohen’s d=0.60), indicating local intracortical disinhibition. No group differences were observed for N40, P60, or P180 components. At the source level, migraineurs exhibited significantly higher SCD in the S1, pINS, aINS, and MCC (allQ<0.05), but not in the ventroposterior thalamus (vpTHAL), mediodorsal thalamus (mdTHAL), or PAG. In Study 2, active rTMS significantly reduced SCD from pre- to post-stimulation in the S1, aINS, and MCC (all Q<0.05). Sham stimulation also reduced SCD in the S1 (Q<0.05) but not in the aINS or MCC. Although no significant group-level analgesic effect was observed between active and sham conditions (P=0.107), correlation analyses revealed that greater SCD reductions in the S1 and MCC were significantly associated with higher post-rTMS pain tolerance (R=-0.487 and -0.495, both Q<0.01) and larger improvements in pain tolerance(R=-0.487 and -0.451, both Q<0.05). No such correlations were found following sham stimulation, suggesting that the behavioural relevance of neural changes is specific to active rTMS. ConclusionThis study provides novel evidence that migraineurs exhibit both local neuroplastic abnormalities (reduced N120 amplitude) and hyperactivity in key pain-processing regions (S1, pINS, aINS, MCC). A single session of DLPFC-rTMS reduced hyperactivity in the aINS, MCC, and S1. Notably, greater reductions in the S1 and MCC were associated with improved pain tolerance. These findings identify distinct cortical circuitries, particularly within the cognitive-affective pain network, that may serve as potential biomarkers for optimizing rTMS treatment in migraine and other chronic pain conditions. Future studies should validate these results in patient populations experiencing spontaneous migraine attacks and explore multi-session or accelerated rTMS protocols.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective:To investigate the effect of long non-coding RNA glucuronidase β pseudogene 11(GUSBP11)regulating miR-339-5p/mouse two-minute homolog 2(MDM2)axis on the proliferation,migration,and invasion of gastric cancer AGS cells.Methods:Cancerous and adjacent tissues from 25 gastric cancer patients who underwent surgical treatment at Foshan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Chinese Medicine from December 2023 to June 2024 were collected.Gastric cancer AGS cells and normal gastric mucosal epithelial GES-1 cells were routinely cultured.The control plasmids and knockdown plasmids were transfected into AGS cells using transfection reagents,dividing the cells into Ctrl group,sh-NC group,sh-GUSBP11 group,sh-GUSBP11+anti-NC group,and sh-GUSBP11+anti-miR-339-5p group.The mRNA expression of GUSBP11,miR-339-5p,and MDM2 in gastric cancer tissues and cells of each group was detected by qPCR.A dual-luciferase reporter gene assay was used to detect the targeting relationship between GUSBP11 or MDM2 and miR-339-5p.EdU staining,scratch healing assay,and Transwell chamber assay were adopted to assess the proliferation,migration,and invasion abilities of AGS cells,respectively.WB assay was used to measure the protein expression of CDK1,MMP-2,and MMP-9 in AGC cells.The effects of GUSBP11 knockdown on tumor growth were examined through AGS cell xenograft experiments.Results:The mRNA expression of GUSBP11 and MDM2 were significantly upregulated in gastric cancer tissues and cells(both P<0.05),while miR-339-5p was significantly downregulated(P<0.05).A targeting relationship was found between GUSBP11 and miR-339-5p,as well as between MDM2 and miR-339-5p.Knockdown of GUSBP11 in AGS cells significantly inhibited MDM2 protein expression and promoted miR-339-5p expression,while inhibition of miR-339-5p promoted MDM2 protein expression.GUSBP11 knockdown significantly inhibited the proliferation,migration,and invasion of AGS cells,while inhibition of miR-339-5p reversed this effect.GUSBP11 knockdown significantly inhibited the protein expression of CDK1,MMP-2,and MMP-9,and inhibition of miR-339-5p reversed this effect.Furthermore,GUSBP11 knockdown significantly inhibited the growth of AGS cell xenografts.Conclusion:GUSBP11 is highly expressed in gastric cancer tissues and cells,and knocking down GUSBP11 expression may inhibit malignant biological behaviors of gastric cancer cells through regulating the miR-339-5p/DM2 axis.

AIM To study the chemical constituents from salt-processed Litchi Semen and their antioxidant activities.METHODS The 85％ethanol extract from salt-processed Litchi Semen was isolated and purified by silica gel,Sephadex LH-20,MCI,ODS and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.DPPH and ABTS+free radical scavenging method were used to evaluate their antioxidant activities.RESULTS Fifteen compounds were isolated and identified as dehydrocostuslactone(1),ananosmoside A(2),funingensin A(3),(2S)-pinocembrin-7-O-(6-O-α-L-rhamnopyranosyl-β-D-glucopyranoside)(4),liquiritienin(5),quercetin(6),rutin(7),isorhamnetin-3-O-β-rutinoside(8),procyanidin A2(9),procyanidin A1(10),ethyl protocatechuate(11),5-hydroxymethylfurfural(12),di(2-ethyl-hexyl)phthalate(13),nicotinamide(14),(10E,15Z)-9,12,13-trihydroxyoctadeca-10,15-dienoic acid(15).Compounds 6-7,9-10 exhibited scavenging activities against DPPH radicals with IC50 values of(12.929±1.232),(14.104±0.946),(10.417±1.736),(6.944±0.030)μmol/L,respectively.Compounds 6-10 exhibited scavenging activities against ABTS+radicals with IC50 values of(21.952±0.577),(25.683±0.625),(22.970±1.336),(20.210±1.435),(18.725±0.324)μmol/L,respectively.CONCLUSION Compounds 1,5,14-15 are isolated from Litchi genus for the first time.Compounds 6-7,9-10 have strong in vitro antioxidant activities.

Objective To evaluate the effects of transcatheter aortic valve replacement(TAVR)on left ventricular reverse remodeling(LVRR)and outcomes in patients with mixed aortic valve disease(MAVD)and predominant aortic stenosis(AS).Methods Patients undergoing TAVR at our center between January 2020 and December 2022 were enrolled consecutively.Propensity score matching(PSM)(1∶1 ratio)was used to reduce selection bias.Transthoracic echocardiography(TTE)was used to monitor left ventricular ejection fraction(LVEF)and other structural parameters over time.The study outcome was a composite of cardiovascular death and rehospitalization due to cardiovascular causes.Linear mixed-effects models and logistic regression were utilized for comparing echocardiographic changes across groups and identifying independent risk factors for no-LVRR,respectively.Results After PSM,126 patients were included.MAVD group exhibited larger structural parameters(left ventricular end-systolic/end-diastolic diameter and volume,left ventricular mass index)and a lower left ventricular ejection fraction(LVEF)(all P＜0.05).However,more pronounced improvements in left ventricular structure and hemodynamics were observed during follow-up.Multivariate logistic regression analysis indicated that the left ventricular mass index(LVMI)was an independent predictor of left ventricular reverse remodeling(LVRR)after TAVR,whereas persistent moderate or greater mitral regurgitation(MR)and paravalvular leak(PVL)significantly reduced the incidence of LVRR.During a median follow-up period of 23 months,a total of 31 endpoint events occurred,and there was no statistically significant difference in long-term prognosis between the two groups(Log-rank P=0.330).Conclusions Compared to patients in the AS group,those in the MAVD group exhibited more severe left ventricular remodeling before TAVR.However,more significant LVRR was observed during postoperative follow-up.Additionally,the long-term prognosis was comparable between the two groups.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To evaluate the effects of transcatheter aortic valve replacement(TAVR)on left ventricular reverse remodeling(LVRR)and outcomes in patients with mixed aortic valve disease(MAVD)and predominant aortic stenosis(AS).Methods Patients undergoing TAVR at our center between January 2020 and December 2022 were enrolled consecutively.Propensity score matching(PSM)(1∶1 ratio)was used to reduce selection bias.Transthoracic echocardiography(TTE)was used to monitor left ventricular ejection fraction(LVEF)and other structural parameters over time.The study outcome was a composite of cardiovascular death and rehospitalization due to cardiovascular causes.Linear mixed-effects models and logistic regression were utilized for comparing echocardiographic changes across groups and identifying independent risk factors for no-LVRR,respectively.Results After PSM,126 patients were included.MAVD group exhibited larger structural parameters(left ventricular end-systolic/end-diastolic diameter and volume,left ventricular mass index)and a lower left ventricular ejection fraction(LVEF)(all P＜0.05).However,more pronounced improvements in left ventricular structure and hemodynamics were observed during follow-up.Multivariate logistic regression analysis indicated that the left ventricular mass index(LVMI)was an independent predictor of left ventricular reverse remodeling(LVRR)after TAVR,whereas persistent moderate or greater mitral regurgitation(MR)and paravalvular leak(PVL)significantly reduced the incidence of LVRR.During a median follow-up period of 23 months,a total of 31 endpoint events occurred,and there was no statistically significant difference in long-term prognosis between the two groups(Log-rank P=0.330).Conclusions Compared to patients in the AS group,those in the MAVD group exhibited more severe left ventricular remodeling before TAVR.However,more significant LVRR was observed during postoperative follow-up.Additionally,the long-term prognosis was comparable between the two groups.

Aim To investigate the anti-acute lung injury(ALI)effect of Sterculiae Lychnophorae Semen(SLS)and its mechanism.Methods The main ac-tive components of SLS and their core targets and path-ways of action against ALI were obtained by network pharmacology methods.Subsequently,molecular doc-king technology and in vitro cellular experiments were applied for validation.Results A total of 19 core tar-gets were obtained,including HSP90AA1,CASP3,TNF,MAPK8 and MAPK14.The mechanisms may in-volve signaling pathways such as cancer,PI3K/Akt and MAPK.Molecular docking confirmed that the key targets of SLS formed a better binding activity with the relevant active ingredients.The in vitro results showed that SLS was able to protect the PM2.5-contaminated BEAS-2B cells,inhibit their NO,IL-1β and TNF-αlevels,and reduce the expression of p-p38 MAPK and p-JNK proteins.Conclusions The study successfully predicts the active ingredients,targets and signaling pathways of SLS against ALI,and in vitro experiments demonstrate that SLS might protect BEAS-2B cells from PM2.5 stimulus-induced inflammation and apoptosis by inhibiting the over-activation of p38 MAPK and JNK signaling pathways.