Bioscience trends最新文献

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 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.

Water-based therapy has been gaining attention in recent years and is being widely used in clinical settings. Hydrotherapy is the most important area of water-based therapy, and it has distinct advantages and characteristics compared to conventional land-based exercises. Several new techniques and pieces of equipment are currently emerging with advances in computer technologies. However, comprehensive reviews of hydrotherapy are insufficient. Hence, this study reviewed the status quo, mechanisms, adverse events and contraindications, and future prospects of the use of hydrotherapy. This study aims to comprehensively review the latest information regarding the application of hydrotherapy to musculoskeletal diseases, neurological diseases, and COVID-19. We have attempted to provide a "take-home message" regarding the clinical applications and mechanisms of hydrotherapy based on the latest evidence available.

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.

Swallowing is a complex process requiring precise coordination of numerous muscles in the head and neck to smoothly guide ingested material from the mouth to the stomach. Animal and human studies have revealed a complex network of neurons in the brainstem, cortex, and cerebellum that coordinate normal swallowing. The interactions between these regions ensure smooth and efficient swallowing. However, the current understanding of the neurophysiological mechanisms involved in post-stroke dysphagia (PSD) is incomplete, and complete functional connectivity for swallowing recovery remains understudied and requires further exploration. In this review, we discussed the neuroanatomy of swallowing and the pathogenesis of PSD and summarized the factors affecting PSD recovery. We also described the plasticity of neural networks affecting PSD, including enhancing activation of neural pathways, cortical reorganization, regulation of extracellular matrix dynamics and its components, modulation of neurotransmitter delivery, and identification of potential therapeutic targets for functional recovery in PSD. Finally, we discussed the therapeutic strategies based on functional compensation and motor learning. This review aimed to provide a reference for clinicians and researchers to promote the optimization of PSD treatments and explore future research directions.

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.

The prognosis for patients with hepatocellular carcinoma (HCC) depends on tumor stage and remnant liver function. However, it often includes tumor morphology, which is usually assessed with imaging studies or pathologic analysis, leading to limited predictive performance. Therefore, the aim of this study was to develop a simple and low-cost prognostic score for HCC based on serum biomarkers in routine clinical practice. A total of 3,100 patients were recruited. The least absolute shrinkage and selector operation (LASSO) algorithm was used to select the significant factors for overall survival. The prognostic score was devised based on multivariate Cox regression of the training cohort. Model performance was assessed by discrimination and calibration. Albumin (ALB), alkaline phosphatase (ALP), and alpha-fetoprotein (AFP) were selected by the LASSO algorithm. The three variables were incorporated into multivariate Cox regression to create the risk score (APP score = 0.390* ln (ALP) + 0.063* ln(AFP) - 0.033*ALB). The C-index, K-index, and time-dependent AUC of the score displayed significantly better predictive performance than 5 other models and 5 other staging systems. The model was able to stratify patients into three different risk groups. In conclusion, the APP score was developed to estimate survival probability and was used to stratify three strata with significantly different outcomes, outperforming other models in training and validation cohorts as well as different subgroups. This simple and low-cost model could help guide individualized follow-up.