Bioscience trends最新文献

Parkinson's disease (PD) is a progressive neurodegenerative disorder for which reliable blood biomarkers to predict disease progression remain elusive. Plasma extracellular vesicles (EVs) have gained attention as a promising biomarker platform due to their stability and ability to cross the blood-brain barrier. This study explored the potential of EV-cargo proteins, specifically α-synuclein, tau, and β-amyloid, as biomarkers of PD progression. A cohort of 55 people with PD (PwP) and 58 healthy controls (HCs) underwent annual assessments of plasma EV proteins, cognition, and motor symptoms. EVs were isolated and validated using standardized methods, with pathognomonic proteins quantified via immunomagnetic reduction assays. Associations between biomarker changes and clinical symptom progression were analyzed. Over an average of 3.96 visits for PwP and 2.25 visits for HCs, PwP exhibited a distinct pattern of plasma EV protein changes linked to motor symptom progression, particularly in the Unified PD Rating Scale (UPDRS) part II score. Notably, changes in plasma EV α-synuclein levels were significantly correlated with changes in motor and cognitive symptoms, suggesting its central role in disease progression. These findings highlight the potential of plasma EV biomarkers, especially α-synuclein, as indicators of ongoing pathogenesis and as candidates for evaluating α-synuclein-targeted therapies in PD.

Immune checkpoint inhibitors (ICIs) have been widely used in various types of cancer, but they have also led to a significant number of adverse events, including ICI-induced immune-mediated hepatitis (IMH). This study aimed to explore the risk factors for IMH in patients treated with ICIs and to develop and validate a new nomogram model to predict the risk of IMH. Detailed information was collected between January 1, 2020, and December 31, 2023. Univariate logistic regression analysis was used to assess the impact of each clinical variable on the occurrence of IMH, followed by stepwise multivariate logistic regression analysis to determine independent risk factors for IMH. A nomogram model was constructed based on the results of the multivariate analysis. The performance of the nomogram model was evaluated via the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC) analysis. A total of 216 (8.82%) patients developed IMH. According to stepwise multivariate logistic analysis, hepatic metastasis, the TNM stage, the WBC count, LYM, ALT, TBIL, ALB, GLB, and ADA were identified as risk factors for IMH. The AUC for the nomogram model was 0.817 in the training set and 0.737 in the validation set. The calibration curves, DCA results, and CIC results indicated that the nomogram model had good predictive accuracy and clinical utility. The nomogram model is intuitive and straightforward, making it highly suitable for rapid assessment of the risk of IMH in patients receiving ICI therapy in clinical practice. Implementing this model enables early adoption of preventive and therapeutic strategies, ultimately reducing the likelihood of immune-related adverse events (IRAEs), and especially IMH.

Syphilis, a chronic infection caused by Treponema pallidum, is experiencing a global resurgence, posing significant public health challenges. This study examined the escalating trends of syphilis in the United States, China, and some other countries highlighting the impact of the COVID-19 pandemic, changes in sexual behavior, coinfection with the other infectious diseases such as AIDs, and the role of public health funding. The analysis revealed a stark increase in syphilis cases, particularly among high-risk groups such as men who have sex with men (MSM). China's National Syphilis Control Program (NSCP), initiated in 2010, is a comprehensive approach to syphilis management that incorporates health education, access to testing and treatment, partner notification, safe sex promotion, community interventions, and stigma reduction. The success of the NSCP in reducing early syphilis incidence rates and congenital syphilis in Guangdong Province, that may be served as a model for international syphilis control efforts. This study highlights the necessity for targeted public health interventions and the importance of robust healthcare infrastructure in combating the syphilis epidemic.

Alzheimer's disease (AD) is the most common type of dementia. Its incidence is rising rapidly as the global population ages, leading to a significant social and economic burden. AD involves complex pathologies, including amyloid plaque accumulation, synaptic dysfunction, and neuroinflammation. This study explores the therapeutic potential of N ⁵ -((perfluorophenyl)amino)glutamine (RA-PF), a derivative of γ-glutamyl-N'-(2-hydroxyphenyl)hydrazide (Ramalin), a compound with antioxidant and anti-inflammatory properties. Administration of RA-PF to 5xFAD mice decreases BACE1, reduces Aβ plaque deposition, inhibits microglial activation, restores synaptic transmission, and improves mitochondrial motility, leading to the recovery of cognitive function. Additionally, RA-PF treatment in 3xTg-AD mice alleviates anxiety-like behaviors, tau phosphorylation via inactivating GSK-3β, and BACE1 expression. Further transcriptomic analysis reveals RA-PF treatment in AD mice models recovers phagosome, inflammation, NOD-like receptor, presynaptic membrane, and postsynaptic membrane related signaling pathways. These findings suggest that RA-PF effectively targets multiple aspects of AD pathology, offering a novel multi-target approach for AD treatment.

Cognitive impairment refers to the impairment of higher brain functions such as perception, thinking or memory that affects the individual's ability to perform daily or social activities. Studies have found that changes in neuronal activity during tasks in patients with cognitive impairment are closely related to changes in cerebral cortical hemodynamics. Functional near-infrared spectroscopy is an indirect method to measure neural activity based on changes in blood oxygen concentration in the cerebral cortex. Due to its strong anti-motion interference, high compatibility, and almost no restriction on participants and environment, it has shown great potential in the research field of cognitive impairment. Recognizing these benefits, this comprehensive review systematically elucidates the rationale, historical development, advantages and disadvantages of functional near-infrared spectroscopy, and also discusses the applications of combining functional near-infrared spectroscopy with other detection techniques. Additionally, this review summarized how functional near-infrared spectroscopy can be applied to cognitive impairment caused by different diseases, ultimately aiding the study of neural mechanisms of cognitive activities, which is crucial for the diagnosis, differentiation and treatment of cognitive impairment.

In human-computer interaction, gesture recognition based on physiological signals offers advantages such as a more natural and fast interaction mode and less constrained by the environment than visual-based. Surface electromyography-based gesture recognition has significantly progressed. However, since individuals have physical differences, researchers must collect data multiple times from each user to train the deep learning model. This data acquisition process can be particularly burdensome for non-healthy users. Researchers are currently exploring transfer learning and data augmentation techniques to enhance the accuracy of small-sample gesture recognition models. However, challenges persist, such as negative transfer and limited diversity in training samples, leading to suboptimal recognition performance. Therefore, We introduce motion information into sEMG-based recognition and propose a multimodal optimal matching and augmentation method for small sample gesture recognition, achieving efficient gesture recognition with only one acquisition per gesture. Firstly, this method utilizes the optimal matching signal selection module to select the most similar signals from the existing data to the new user as the training set, reducing inter-domain differences. Secondly, the similarity calculation augmentation module enhances the diversity of the training set. Finally, the Modal-type embedding enhances the information interaction between each mode signal. We evaluated the effectiveness on Self-collected Stroke Patient, the Ninapro DB1 dataset and the Ninapro DB5 dataset and achieved accuracies of 93.69%, 91.65% and 98.56%, respectively. These results demonstrate that the method achieved performance comparable to traditional recognition models while significantly reducing the collected data.

Growth and differentiation factor 15 (GDF15), a member of the transforming growth factor-βsuperfamily, is considered a stress response factor and has garnered increasing attention in recent years due to its roles in neurological diseases. Although many studies have suggested that GDF15 expression is elevated in patients with neurodegenerative diseases (NDDs), glioma, and ischemic stroke, the effects of increased GDF15 expression and the potential underlying mechanisms remain unclear. Notably, many experimental studies have shown the multidimensional beneficial effects of GDF15 on NDDs, and GDF15 overexpression is able to rescue NDD-associated pathological changes and phenotypes. In glioma, GDF15 exerts opposite effects, it is both protumorigenic and antitumorigenic. The causes of these conflicting findings are not comprehensively clear, but inhibiting GDF15 is helpful for suppressing tumor progression. GDF15 is also regarded as a biomarker of poor clinical outcomes in ischemic stroke patients, and targeting GDF15 may help prevent this disease. Thus, we systematically reviewed the synthesis, transcriptional regulation, and biological functions of GDF15 and its related signaling pathways within the brain. Furthermore, we explored the potential of GDF15 as a therapeutic target and assessed its clinical applicability in interventions for brain diseases. By integrating the latest research findings, this study provides new insights into the future treatment of neurological diseases.

Parkinson's disease (PD) is a progressive disease that requires effective staging management. The role of intestinal microbiota in PD has been studied, but its changes at different stages are not clear. In this study, meta- analysis, bioinformatics analysis and in vivo simulation were used to explore the intestinal microbiota distribution of PD patients and models at different stages. Two PD models at different stages were established in rotenone-treated rats and MPTP-induced mice. The differences in the intestinal microbiota among the different stages of PD patients or models were compared and analyzed. There were significant differences between PD patients and controls, including Actinobacteriota, Deltaproteobacteria, Clostridiales, Lachnospiraceae, Parabacteroides, etc. Through bioinformatics analysis, we revealed significant differences between PD patients at different stages and controls, including Actinobacteriota, Methanobacteria, Erysipelotrichales, Prevotellaceae, Parabacteroides, Parabacteroides gordonii, etc. Through meta-analysis, we found that Actinobacteriota and Erysipelotrichaceae had significantly increased in the chronic MPTP model, while Prevotellaceae had significantly decreased. PD rats and mice presented significant damage to motor function, coordination, autonomous activity ability and gastrointestinal function, and the damage in the late group was greater than that in the early group. There were significant differences in intestinal microbiota between PD patients or models at different stages and the control groups. In the early stage, the dominant microbiota are Akkermansia, Alistipes, Anaerotruncus, Bilophila, Rikenellaceae, Verrucomicrobia and Verrucomicrobiae, whereas in the late stage, the dominant microbiota are Actinobacteriota and Erysipelotrichaceae. These differences can lay a foundation for subsequent research on the treatment and mechanism of PD at different stages.

Alzheimer's disease (AD), the leading cause of dementia, significantly impacts global public health, with cases expected to exceed 150 million by 2050. Late-onset Alzheimer's disease (LOAD), predominantly influenced by the APOE-ε4 allele, exhibits complex pathogenesis involving amyloid-β (Aβ) plaques, neurofibrillary tangles (NFTs), neuroinflammation, and blood-brain barrier (BBB) disruption. Proteomics has emerged as a pivotal technology in uncovering molecular mechanisms and identifying biomarkers for early diagnosis and intervention in AD. This paper reviews the genetic and molecular roles of APOE-ε4 in the pathology of AD, including its effects on Aβ aggregation, tau phosphorylation, neuroinflammation, and BBB integrity. Additionally, it highlights recent advances in serum proteomics, revealing APOE-ε4-dependent and independent protein signatures with potential as early biomarkers for AD. Despite technological progress, challenges such as population diversity, standardization, and distinguishing AD-specific biomarkers remain. Directions for future research emphasize multicenter longitudinal studies, multi-omics integration, and the clinical translation of proteomic findings to enable early detection of AD and personalized treatment strategies. Proteomics advances in AD research hold the promise of improving patient outcomes and reducing the global disease burden.

Lymph node metastasis in intrahepatic cholangiocarcinoma significantly impacts overall survival, emphasizing the need for a predictive model. This study involved patients who underwent curative liver resection between different time periods. Three machine learning models were constructed with a training cohort (2010-2016) and validated with a separate cohort (2019-2023). A total of 170 patients were included in the training set and 101 in the validation cohort. The lymph node status of patients not undergoing lymph node dissection was predicted, followed by survival analysis. Among the models, the support vector machine (SVM) had the best discrimination, with an area under the curve (AUC) of 0.705 for the training set and 0.754 for the validation set, compared to the random forest (AUC: 0.780/0.693) and the logistic regression (AUC: 0.703/0.736). Kaplan-Meier analysis indicated that patients in the positive lymph node group or predicted positive group had significantly worse overall survival (OS: p < 0.001 for both) and disease-free survival (DFS: p < 0.001 for both) compared to negative groups. An online user-friendly calculator based on the SVM model has been developed for practical application.