Dongya Huang

Stroke remains a leading cause of morbidity and mortality worldwide, with acute ischemic stroke (AIS) accounting for the majority of cases. Despite advancements in intravenous thrombolytic therapies such as recombinant tissue-type plasminogen activator (rt-PA), limitations including narrow therapeutic windows and variable recovery outcomes underscore the urgent need for adjunctive and alternative treatment options. Human Urinary Kallidinogenase (HUK), a serine protease derived from human urine, has emerged as a promising candidate owing to its multifaceted biological effects. This mini-review synthesizes evidence from preclinical and clinical studies, highlighting HUK’s neuroprotective, anti-inflammatory, anti-apoptotic, anti-oxidative, and angiogenic properties. Mechanistically, HUK modulates several critical signaling pathways, including inhibition of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) axis, activation of the mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK) and phosphoinositide 3-kinase/protein kinase B/Forkhead box O1 (PI3K/AKT/FoxO1) pathways, and regulation of oxidative stress through the nuclear factor erythroid 2-related factor 2/antioxidant response element (Nrf2/ARE) pathway. These mechanisms contribute to enhanced cellular survival and vascular repair. Additionally, HUK demonstrates synergistic effects when combined with agents such as rt-PA and dl-3-n-butylphthalide (NBP), resulting in improved neurological outcomes, reduced stroke recurrence, and enhanced recanalization rates. Beyond summarizing established mechanistic and clinical evidence, this review emphasizes future directions including the development of nanoparticle-based drug delivery systems, personalized treatment approaches guided by genetic and metabolic markers, and the exploration of HUK as a potential biomarker for vascular health and post-stroke complications. The integration of multi-omics technologies—such as transcriptomics, proteomics, and metabolomics—into future clinical trials may further elucidate HUK’s systemic effects and therapeutic interactions. Overall, this review provides a consolidated foundation for advancing research on HUK and advocates for large-scale, multicenter, randomized controlled trials to validate its efficacy and support its integration into comprehensive stroke management.

Background and purpose: Estimating the global burden of ischemic strokes (IS) is crucial for enhancing prevention and control strategies.Methods: We collected four epidemiological indicators—prevalence, incidence, deaths, and disability-adjusted life years (DALYs)—for ischemic stroke (IS) from the Global Burden of Disease (GBD) database, which covers the years 1990 to 2021. Our research analyzed the features of the IS burden and described the trends of these four indicators.Results: The Joinpoint and age-period-cohort models reflected the changing trends in age-standardized indicators. Decomposition analysis examined the factors influencing each epidemiological indicator. The Bayesian Age-Period-Cohort (BAPC) model detailed changes in the number and rate of IS from 1990 to 2021 and projected trends through 2046. The Norpred model was used to verify the stability of the BAPC prediction results. The prevalence, incidence, deaths, and DALYs due to IS generally exhibited a downward trend. However, the predictions indicated that while the age-standardized incidence rate decreased from 1990 to 2015, this trend reversed between 2016 and 2021 and is expected to continue until 2046. This reversal is likely driven by factors such as population aging, given that age is a strongly correlated risk factor for IS. The IS burden was negatively associated with socio-demographic index (SDI) levels, with high systolic blood pressure identified as the largest risk factor for DALYs and deaths. The consistency between the BAPC and Norpred models enhances the reliability of these projections.Conclusion: Over the past two decades, trends in prevalence, incidence, deaths, and DALYs have all declined. However, projections suggest that incidence will show an upward trend over the next two decades.

Background: Circadian rhythm (CR) disturbance is intricately associated with Parkinson’s disease (PD). However, the involvement of CR-related mechanisms in the pathogenesis and progression of PD remains elusive. Methods: A total of 141 PD patients and 113 healthy participants completed CR-related clinical examinations in this study. To further investigate the CR-related mechanisms in PD, we obtained datasets (GSE7621, GSE20141, GSE20292) from the Gene Expression Omnibus database to identify differentially expressed genes between PD patients and healthy controls and further selected CR-related genes (CRRGs). Subsequently, the least absolute shrinkage and selection operator (LASSO) followed by logistic algorithms were employed to identify the hub genes and construct a diagnostic model. The predictive performance was evaluated by area under the curve (AUC), calibration curve, and decision curve analyses in the training set and external validation sets. Finally, RT‒qPCR and Western blotting were conducted to verify the expression of these hub genes in blood samples. In addition, Pearson correlation analysis was utilized to validate the association between expression of hub genes and circadian rhythm function. Results: Our clinical observational study revealed that even early-stage PD patients exhibited a higher likelihood of experiencing sleep disturbances, nocturnal hypertension, reverse-dipper blood pressure, and reduced heart rate variability compared to healthy controls. Furthermore, 4 CR-related hub genes (AGTR1, CALR, BRM14, and XPA) were identified and subsequently incorporated as candidate biomarkers to construct a diagnostic model. The model showed satisfactory diagnostic performance in the training set (AUC = 0.941), an external validation set GSE20295 (AUC = 0.842), and our clinical centre set (AUC = 0.805). Additionally, the up-regulation of CALR, BRM14 and the down-regulation of AGTR1, XPA were associated with circadian rhythm disruption. Conclusion: CR disturbance seems to occur in the early stage of PD. The diagnostic model based on CR-related genes demonstrated robust diagnostic efficacy, offering novel insights for future clinical diagnosis of PD and providing a foundation for further exploration into the role of CR-related mechanisms in the progression of PD.

The plaque segmentation of ultrasound images is very important in the disease diagnosis of carotid artery, and it is hard to be realized due to the image noise, low contrast, and plaque shapes, etc. This paper proposed an improved network model called RCS_UNet based on the classical UNet architecture. The residual convolution, CBAM and the multi-scale supervision modules improve the gradient vanishing, the model attention on key features and the performance of decoder part, respectively. RCS_UNet was trained and tested using a dataset containing 443 longitudinal carotid artery ultrasound images. RCS_UNet can accurately segment plaques in carotid artery ultrasound images, with DSC of 0.819±0.091, IoU of 0.703±0.123, recall of 0.845±0.100, and precision of 0.822±0.150, which was better than that of other advanced models. The trained model was used to segment plaques in carotid artery ultrasound images obtained from another ultrasound device, DSC was 0.832±0.075, IoU was 0.719±0.105, recall was 0.792±0.136, precision was 0.900±0.065, which proved that the model had a certain degree of generalization. RCS_UNet possesses excellent encoding and decoding capabilities, it can effectively extract plaque features in carotid ultrasound images for boundary segmentation, which holds certain clinical application value.

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disease, and its pathological mechanisms are thought to be closely linked to apoptosis. Anoikis, a specific type of apoptosis, has recently been suggested to play a role in the progression of Parkinson’s disease; however, the underlying mechanisms are not well understood. To explore the potential mechanisms involved in PD, we selected genes from the GSE28894 dataset and compared their expression in PD patients and healthy controls to identify differentially expressed genes (DEGs), and selected anoikis-related genes (ANRGs) from the DEGs. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression approach and multivariate logistic regression highlighted five key genes—GSK3B, PCNA, CDC42, DAPK2, and SRC—as biomarker candidates. Subsequently, we developed a nomogram model incorporating these 5 genes along with age and sex to predict and diagnose PD. To evaluate the model’s coherence, clinical applicability, and distinguishability, we utilized receiver operating characteristic (ROC) curves, the C-index, and calibration curves and validated it in both the GSE20295 dataset and our center’s external clinical data. In addition, we confirmed the differential expression of the 5 model genes in human blood samples through qRT-PCR and Western blotting. Our constructed anoikis-related PD diagnostic model exhibits satisfactory predictive accuracy and offers novel insights into both diagnosis and treatment strategies for Parkinson’s disease while facilitating its implementation in clinical practice.

ObjectivesAlthough observational studies have suggested a link between hypothyroidism and myasthenia gravis (MG), a causal relationship has not been established. We aimed to investigate the causal association using a two-sample Mendelian randomization (MR) study.MethodsUsing summary statistics from genome-wide association studies involving 494,577 and 38,243 individuals, single-nucleotide polymorphisms exhibiting no linkage disequilibrium (r2 & LE; 0.001) and displaying significant differences (p & LE; 5 x 10-8) were selected for hypothyroidism and MG. To assess the potential causality relationship between hypothyroidism and MG, MR analysis was conducted using inverse variance weighted (IVW), weighted median method, and MR-Egger. The MR-Egger regression, heterogeneity test, pleiotropy test, and leave-one-out sensitivity test were employed to examine sensitivity analyses. In addition, validation datasets were used to validate the relevant results.ResultsGenetic liability to hypothyroidism was positively associated with MG (IVW, OR: 1.36, 95% CI: 1.17-1.58, p = 7.53 x 10-05; weighted median, OR: 1.19, 95% CI: 0.70-2.02, p = 0.522; MR-Egger, OR: 1.19, 95% CI: 0.98-1.45, p = 0.080). Among the three MR methods, the correlation between hypothyroidism and MG genetic prediction was consistent. The independent validation set (IVW, OR: 466.47, 95% CI: 4.70 -46,285.95, p = 0.01) further supported this. Additionally, bidirectional studies showed that using IVW, there was no reverse causality (OR: 1.104, 95%CI: 0.96-1.27, p = 0.170).DiscussionThis MR study showed that hypothyroidism can increase the risk of MG. Further investigation into the underlying mechanisms of this potential causality is warranted to offer novel therapeutic options for MG in the future.

Introduction Olfactory dysfunction (OD), one of the most common non-motor symptoms in Parkinson’s disease (PD), is a cardinal prodromal symptom that can appear years before the onset of motor symptoms. Ongoing studies have demonstrated that microRNAs (miRNAs) are suitable biomarkers for PD, while there is a lack of robust miRNAs that can serve as markers for OD in PD. Methods The concordantly differentially expressed miRNAs (DE miRNAs) in the damaged olfactory system were first identified in 2 OD-related Gene Expression Omnibus (GEO) datasets. Then, they were verified in another PD-related GEO dataset and only one miRNA (miR-20a) was found to be significantly altered. Serum levels of miR-20a were further measured by qPCR in 79 PD patients with OD (PD-OD), 52 PD patients without OD (PD-NOD), and 52 healthy controls (HC). Objective measure of OD was defined by 16-item Sniffin’ Sticks odor identification test. All the participants underwent a demographic and comprehensive PD-related clinical assessment. Results Our results proved that miR-20a was significantly downregulated in PD-OD compared with PD-NOD and the area under curve (AUC) for OD detection by miR-20a was 0.803 (95% confidence interval, 0.724–0.883). In addition, PD-OD had higher scores of Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (UPDRS) II, Hoehn and Yahr stage (H–Y), Non-Motor Symptoms Scale (NMSS) 3, NMSS 5, NMSS 9, Hamilton Rating Scale for Depression (HAMD), Hamilton Anxiety Scale (HAMA), Activity of Daily Living (ADL), and lower scores of Mini-Mental State Examination (MMSE) and 39-item PD Quality of Life Questionnaire (PDQ-39) than PD-NOD. Binary regression model further presented that lower expressions of miR-20a and poorer cognitive function acted as promoting factors in the development of OD. Conclusion Our results suggest that miR-20a could be a novel biomarker for OD in PD and PD-OD patients tend to have higher disease stage, poorer motor aspects of experiences of daily living, worse cognitive scores, and inferior quality of life, and were more likely to have mental disorders. Cognitive function, in particular, is strongly associated with OD in PD patients.