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Microglia activation is observed in various neurodegenerative diseases. Recent advances in single-cell technologies have revealed that these reactive microglia were with high spatial and temporal heterogeneity. Some identified microglia in specific states correlate with pathological hallmarks and are associated with specific functions. Microglia both exert protective function by phagocytosing and clearing pathological protein aggregates and play detrimental roles due to excessive uptake of protein aggregates, which would lead to microglial phagocytic ability impairment, neuroinflammation, and eventually neurodegeneration. In addition, peripheral immune cells infiltration shapes microglia into a pro-inflammatory phenotype and accelerates disease progression. Microglia also act as a mobile vehicle to propagate protein aggregates. Extracellular vesicles released from microglia and autophagy impairment in microglia all contribute to pathological progression and neurodegeneration. Thus, enhancing microglial phagocytosis, reducing microglial-mediated neuroinflammation, inhibiting microglial exosome synthesis and secretion, and promoting microglial conversion into a protective phenotype are considered to be promising strategies for the therapy of neurodegenerative diseases. Here we comprehensively review the biology of microglia and the roles of microglia in neurodegenerative diseases, including Alzheimer’s disease, Parkinson’s disease, multiple system atrophy, amyotrophic lateral sclerosis, frontotemporal dementia, progressive supranuclear palsy, corticobasal degeneration, dementia with Lewy bodies and Huntington’s disease. We also summarize the possible microglia-targeted interventions and treatments against neurodegenerative diseases with preclinical and clinical evidence in cell experiments, animal studies, and clinical trials.

Background Freezing of gait (FOG) is a common, disabling symptom of Parkinson’s disease (PD), but the mechanisms and treatments of FOG remain great challenges for clinicians and researchers. The main focus of this review is to summarize the possible mechanisms underlying FOG, the risk factors for screening and predicting the onset of FOG, and the clinical trials involving various therapeutic strategies. In addition, the limitations and recommendations for future research design are also discussed. Main body In the mechanism section, we briefly introduced the physiological process of gait control and hypotheses about the mechanism of FOG. In the risk factor section, gait disorders, PIGD phenotype, lower striatal DAT uptake were found to be independent risk factors of FOG with consistent evidence. In the treatment section, we summarized the clinical trials of pharmacological and non-pharmacological treatments. Despite the limited effectiveness of current medications for FOG, especially levodopa resistant FOG, there were some drugs that showed promise such as istradefylline and rasagiline. Non-pharmacological treatments encompass invasive brain and spinal cord stimulation, noninvasive repetitive transcranial magnetic stimulation (rTMS) or transcranial direct current stimulation (tDCS) and vagus nerve stimulation (VNS), and physiotherapeutic approaches including cues and other training strategies. Several novel therapeutic strategies seem to be effective, such as rTMS over supplementary motor area (SMA), dual-site DBS, spinal cord stimulation (SCS) and VNS. Of physiotherapy, wearable cueing devices seem to be generally effective and promising. Conclusion FOG model hypotheses are helpful for better understanding and characterizing FOG and they provide clues for further research exploration. Several risk factors of FOG have been identified, but need combinatorial optimization for predicting FOG more precisely. Although firm conclusions cannot be drawn on therapeutic efficacy, the literature suggested that some therapeutic strategies showed promise.

Alzheimer’s disease (AD) is the most common neurodegenerative disease and the most common cause of dementia. Among various pathophysiological aspects, microglia are considered to play important roles in the pathogenesis of AD. Genome wide association studies (GWAS) showed that the majority of AD risk genes are highly or exclusively expressed in microglia, underscoring the critical roles of microglia in AD pathogenesis. Recently, omics technologies have greatly advanced our knowledge of microglia biology in AD. Omics approaches, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics/lipidomics, present remarkable opportunities to delineate the underlying mechanisms, discover novel diagnostic biomarkers, monitor disease progression, and shape therapeutic strategies for diseases. In this review, we summarized research based on microglial “omics” analysis in AD, especially the recent research advances in the identification of AD-associated microglial subsets. This review reinforces the important role of microglia in AD and advances our understanding of the mechanism of microglia in AD pathogenesis. Moreover, we proposed the value of microglia-based omics in the development of therapeutic strategies and biomarkers for AD.

The number and health burden of Parkinson’s disease increase rapidly in China. It is estimated that China will have nearly half of the Parkinson’s disease population in the world in 2030. In this review, we present an overview of epidemiology and health economics status of Parkinson’s disease across China and discuss the risk factors of Parkinson’s disease and related complications. From the view of clinical research, we also discuss the current status of clinical trials, diagnostic biomarkers, treatment of Parkinson’s disease, tertiary network and post-occupation education in Chinese Parkinson’s disease clinics.

Radio frequency interference (RFI) is radio wave interference from natural sources or man-made models. In radio astronomy research, the signals of celestial objects captured by radio telescopes are extremely weak, and the presence of RFI can significantly mask or distort those signals, reducing the accuracy of observational data and seriously affecting the reliability of scientific conclusions. Therefore, accurate RFI detection from radio astronomy data is of great importance. Currently, most RFI detection methods still rely on traditional methods, but due to the limitations of these methods, they are unable to accurately detect RFI in radio telescope observation data. To this end, we propose a novel deep learning–based RFI detection model named ST-U2Net, which combines the Swin Transformer and the Residual U-block (RSU) of U2-Net to form a dual-encoder architecture. The main encoder enhances the feature representation through the efficient multiscale attention (EMA) mechanism, reorganizes the channel information, captures pixel-level relationships, and improves the detection accuracy in complex backgrounds. The auxiliary encoder introduces a spatial interaction module (SIM) and a feature compression module (FCM) to enhance the feature representation and reduce the detail loss of narrowband RFI, respectively. In addition, the multilayer perceptron (MLP) in Swin Transformer is replaced by Kolmogorov–Arnold network (KAN) to enhance the modeling capability of narrowband RFI features. The relational aggregation module (RAM) fuses the characteristics of the two encoders to achieve a more accurate detection of RFI. In this study, the proposed ST-U2Net model is verified using actual observation data collected by the 40-m radio telescope at Yunnan Observatory. The experimental results show that compared to existing deep learning methods, the model proposed in this study achieves a significant improvement in the accuracy of detecting RFI, especially in the detection of narrowband RFI, which exhibits obvious advantages.

Inaugurated in 2011 under the editorship and leadership of Dr. Shengdi Chen, our mission is to promote and foster rapid conversion of basic science research to clinical applications by offering a high-visibility forum for groundbreaking translational research in neurodegenerative diseases and to ultimately benefit not only patients but also societies. According to a report released by the United Nations, 1 in 11 world population was over 65 in 2019 and by 2050 the ratio will be almost doubled to 1 in 6. [...]the mission of our journal in translational neurodegenerative diseases is to, and will continue to, bridge critical basic research with important clinical applications. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative [RAW_REF_TEXT] Editorial [/RAW_REF_TEXT] [RAW_REF_TEXT] Open Access [/RAW_REF_TEXT] [RAW_REF_TEXT] Published:02 January 2022 [/RAW_REF_TEXT] Translational Neurodegeneration in the era of fast growing international brain research [RAW_REF_TEXT] Jialin C. Zheng 1 & [/RAW_REF_TEXT] [RAW_REF_TEXT] Shengdi Chen 2 [/RAW_REF_TEXT] Translational Neurodegeneration volume 11, Article number: 1 (2022) Cite this article [RAW_REF_TEXT] 156 Accesses [/RAW_REF_TEXT] [RAW_REF_TEXT] 7 Altmetric [/RAW_REF_TEXT] [RAW_REF_TEXT] Metrics details [/RAW_REF_TEXT] Another year has flown by and we have entered 2022.

Alzheimer’s disease (AD), the most common cause of dementia, is a neurodegenerative disease that seriously threatens human health and life quality. The main pathological features of AD include the widespread deposition of amyloid-beta and neurofibrillary tangles in the brain. So far, the pathogenesis of AD remains elusive, and no radical treatment has been developed. In recent years, mounting evidence has shown that there is a bidirectional interaction between the gut and brain, known as the brain–gut axis, and that the intestinal microbiota are closely related to the occurrence and development of neurodegenerative diseases. In this review, we will summarize the laboratory and clinical evidence of the correlation between intestinal flora and AD, discuss its possible role in the pathogenesis, and prospect its applications in the diagnosis and treatment of AD.

Type 2 diabetes mellitus (T2DM) is a complex metabolic disorder frequently accompanied by cognitive impairment. Contributing factors such as modern lifestyle, genetic predisposition, and gene environmental interactions have been postulated, but the pathogenesis remains unclear. In this study, we attempt to investigate the potential mechanisms and interventions underlying T2DM-induced cognitive deficits from the brain–gut axis perspective. A combined analysis of the brain transcriptome, plasma metabolome, and gut microbiota in db/db mice with cognitive decline was conducted. Transcriptome analysis identified 222 upregulated gene sets and 85 downregulated gene sets, mainly related to mitochondrial respiratory, glycolytic, and inflammation. In metabolomic analysis, a total of 75 significantly altered metabolites were identified, correlated with disturbances of glucose, lipid, bile acid, and steroid metabolism under disease state. Gut microbiota analysis suggested that the species abundance and diversity of db/db mice were significantly increased, with 23 significantly altered genus detected. Using the multi-omics integration, significant correlations among key genes (n = 33), metabolites (n = 41), and bacterial genera (n = 21) were identified. Our findings suggest that disturbed circulation and brain energy metabolism, especially mitochondrial-related disturbances, may contribute to cognitive impairment in db/db mice. This study provides novel insights into the functional interactions among the brain, circulating metabolites, and gut microbiota.

Background New therapies are urgently needed for Alzheimer’s disease (AD). Sodium oligomannate (GV-971) is a marine-derived oligosaccharide with a novel proposed mechanism of action. The first phase 3 clinical trial of GV-971 has been completed in China. Methods We conducted a phase 3, double-blind, placebo-controlled trial in participants with mild-to-moderate AD to assess GV-971 efficacy and safety. Participants were randomized to placebo or GV-971 (900 mg) for 36 weeks. The primary outcome was the drug-placebo difference in change from baseline on the 12-item cognitive subscale of the Alzheimer’s Disease Assessment Scale (ADAS-cog12). Secondary endpoints were drug-placebo differences on the Clinician’s Interview-Based Impression of Change with caregiver input (CIBIC+), Alzheimer’s Disease Cooperative Study-Activities of Daily Living (ADCS-ADL) scale, and Neuropsychiatric Inventory (NPI). Safety and tolerability were monitored. Results A total of 818 participants were randomized: 408 to GV-971 and 410 to placebo. A significant drug-placebo difference on the ADAS-Cog12 favoring GV-971 was present at each measurement time point, measurable at the week 4 visit and continuing throughout the trial. The difference between the groups in change from baseline was − 2.15 points (95% confidence interval, − 3.07 to − 1.23; p  < 0.0001; effect size 0.531) after 36 weeks of treatment. Treatment-emergent adverse event incidence was comparable between active treatment and placebo (73.9%, 75.4%). Two deaths determined to be unrelated to drug effects occurred in the GV-971 group. Conclusions GV-971 demonstrated significant efficacy in improving cognition with sustained improvement across all observation periods of a 36-week trial. GV-971 was safe and well-tolerated. Trial registration ClinicalTrials.gov, NCT0229391 5. Registered on November 19, 2014

Diagnosing early stage Parkinson's disease (PD) is still a clinical challenge. Previous studies using iron, neuromelanin (NM) or the Nigrosome-1 (N1) sign in the substantia nigra (SN) by themselves have been unable to provide sufficiently high diagnostic performance for these methods to be adopted clinically. Our goal in this study was to extract the NM complex volume, iron content and volume representing the entire SN, and the N1 sign as potential complementary imaging biomarkers using a single 3D magnetization transfer contrast (MTC) gradient echo sequence and to evaluate their diagnostic performance and clinical correlations in early stage PD. A total of 40 early stage idiopathic PD subjects and 40 age- and sex-matched healthy controls (HCs) were imaged at 3T. NM boundaries (representing the SN pars compacta (SNpc) and parabrachial pigmented nucleus) and iron boundaries representing the total SN (SNpc and SN pars reticulata) were determined semi-automatically using a dynamic programming (DP) boundary detection algorithm. Receiver operating characteristic analyses were performed to evaluate the utility of these imaging biomarkers in diagnosing early stage PD. A correlation analysis was used to study the relationship between these imaging measures and the clinical scales. We also introduced the concept of NM and total iron overlap volumes to demonstrate the loss of NM relative to the iron containing SN. Furthermore, all 80 cases were evaluated for the N1 sign independently. The NM and SN volumes were lower while the iron content was higher in the SN for PD subjects compared to HCs. Interestingly, the PD subjects with bilateral loss of the N1 sign had the highest iron content. The area under the curve (AUC) values for the average of both hemispheres for single measures were: .960 for NM complex volume; .788 for total SN volume; .740 for SN iron content and .891 for the N1 sign. Combining NM complex volume with each of the following measures through binary logistic regression led to AUC values for the averaged right and left sides of: .976 for total iron content; .969 for total SN volume, .965 for overlap volume and .983 for the N1 sign. We found a negative correlation between SN volume and UPDRS-III (R2 = .22, p = .002). While the N1 sign performed well, it does not contain any information about iron content or NM quantitatively, therefore, marrying this sign with the NM and iron measures provides a better physiological explanation of what is happening when the N1 sign disappears in PD subjects. In summary, the combination of NM complex volume, SN volume, iron content and the N1 sign as derived from a single MTC sequence provides complementary information for understanding and diagnosing early stage PD.