Parkinson’s disease (PD), the second most common neurodegenerative disease after Alzheimer’s disease, commonly occurs in the elderly population, causing a significant medical and economic burden to the aging society worldwide. At present, there are few effective methods that achieve satisfactory clinical results in the treatment of PD. Platelet-derived growth factors (PDGFs) and platelet-derived growth factor receptors (PDGFRs) are important neurotrophic factors that are expressed in various cell types. Their unique structures allow for specific binding that can effectively regulate vital functions in the nervous system. In this review, we summarized the possible mechanisms by which PDGFs/PDGFRs regulate the occurrence and development of PD by affecting oxidative stress, mitochondrial function, protein folding and aggregation, Ca2+ homeostasis, and cell neuroinflammation. These modes of action mainly depend on the type and distribution of PDGFs in different nerve cells. We also summarized the possible clinical applications and prospects for PDGF in the treatment of PD, especially in genetic treatment. Recent advances have shown that PDGFs have contradictory roles within the central nervous system (CNS). Although they exert neuroprotective effects through multiple pathways, they are also associated with the disruption of the blood–brain barrier (BBB). Our recommendations based on our findings include further investigation of the contradictory neurotrophic and neurotoxic effects of the PDGFs acting on the CNS.
Objective: To investigate the effects of sport stacking on the overall cognition and brain function in patients with mild Alzheimer's disease (AD) and mild cognitive impairment (MCI).
Methods: A single-blind randomized controlled design was performed using sport stacking for 30 min, 5 days/week for 12 weeks. Forty-eight subjects with mild AD or MCI were randomly divided into the sport stacking group (T-mAD = 12, T-MCI = 12) and the active control group (C-mAD = 11, C-MCI = 13). Auditory Verbal Learning Test (AVLT), Alzheimer's Disease Cooperative Study–Activities of Daily Living scale (ADCS-ADL), Geriatric Depression Scale (GDS-30), and Pittsburgh Sleep Quality Index (PSQI) were performed, the level of amyloid β-protein-40 (Aβ-40), Aβ-42, brain-derived neurotrophic factor (BDNF), insulin-like growth factor-1(IGF-1), tumor necrosis factor-alpha (TNF-α), Interleukin-6 (IL-6), and soluble trigger receptor expressed on myeloid cells 2 (sTREM2) in plasma were tested, and brain functional connectivity in resting state and activation under finger movement task were analyzed by functional near-infrared spectroscopy (fNIRS).
Results: Thirty-nine patients completed the trial. After 4 weeks, we found a significant increase in AVLT score in T-MCI (6.36 ± 5.08 vs. −1.11 ± 4.23, p = 0.004), and T-mAD group (4.60 ± 4.77 vs. −0.11 ± 2.89, p = 0.039). After 12 weeks, there was a significantly improved in AVLT (9.64 ± 4.90 vs. −0.33 ± 6.10, p = 0.002) and ADCS-ADL (3.36 ± 3.59 vs. −1.89 ± 2.71, p = 0.003) in T-MCI. There was a significant improvement in AVLT (5.30 ± 5.42 vs. 0.44 ± 2.40) in T-mAD (p < 0.05). Plasma levels of BDNF were upregulated in both T-MCI and T-mAD, and IGF-1 increased in T-MCI (P < 0.05) compared to the control groups. The functional connectivity in MCI patients between DLPFC.R and SCA.R, SMA.L, and SCA.R was decreased. In contrast, in mAD patients, the brain regional function connection was increased between DLPFC.R and Broca's.L. The activation of channel 36 located in the left primary somatosensory cortex was significantly increased after 12-week training, which was correlated with the improved AVLT and the increase of BDNF.
Conclusion: Our findings suggested that sport stacking is effective for patients with MCI and mild AD, possibly through increasing the expression of neuroprotective growth factors and enhancing neural plasticity to improve neurocognitive performance.
Clinical Trial Registration: https://www.ClinicalTrials.gov, ChiCTR.org.cn, identifier: ChiCTR-2100045980.
Amyotrophic lateral sclerosis (ALS) is a fatal disease characterized by the degeneration and death of motor neurons. Systemic neuroinflammation contributes to the pathogenesis of ALS. The proinflammatory milieu depends on the continuous crosstalk between the peripheral immune system (PIS) and central immune system (CIS). Central nervous system (CNS) resident immune cells interact with the peripheral immune cells via immune substances. Dysfunctional CNS barriers, including the blood–brain barrier, and blood–spinal cord barrier, accelerate the inflammatory process, leading to a systemic self-destructive cycle. This review focuses on the crosstalk between PIS and CIS in ALS. Firstly, we briefly introduce the cellular compartments of CIS and PIS, respectively, and update some new understanding of changes specifically occurring in ALS. Then, we will review previous studies on the alterations of the CNS barriers, and discuss their crucial role in the crosstalk in ALS. Finally, we will review the moveable compartments of the crosstalk, including cytokines, chemokines, and peripheral immune cells which were found to infiltrate the CNS, highlighting the interaction between PIS and CIS. This review aims to provide new insights into pathogenic mechanisms and innovative therapeutic approaches for ALS.
For decades, it has been widely believed that the blood–brain barrier (BBB) provides an immune privileged environment in the central nervous system (CNS) by blocking peripheral immune cells and humoral immune factors. This view has been revised in recent years, with increasing evidence revealing that the peripheral immune system plays a critical role in regulating CNS homeostasis and disease. Neurodegenerative diseases are characterized by progressive dysfunction and the loss of neurons in the CNS. An increasing number of studies have focused on the role of the connection between the peripheral immune system and the CNS in neurodegenerative diseases. On the one hand, peripherally released cytokines can cross the BBB, cause direct neurotoxicity and contribute to the activation of microglia and astrocytes. On the other hand, peripheral immune cells can also infiltrate the brain and participate in the progression of neuroinflammatory and neurodegenerative diseases. Neurodegenerative diseases have a high morbidity and disability rate, yet there are no effective therapies to stop or reverse their progression. In recent years, neuroinflammation has received much attention as a therapeutic target for many neurodegenerative diseases. In this review, we highlight the emerging role of the peripheral and central immune systems in neurodegenerative diseases, as well as their interactions. A better understanding of the emerging role of the immune systems may improve therapeutic strategies for neurodegenerative diseases.
Population aging is an inevitable problem nowadays, and the elderly are going through a lot of geriatric symptoms, especially cognitive impairment. Irisin, an exercise-stimulating cleaved product from transmembrane fibronectin type III domain-containing protein 5 (FNDC5), has been linked with favorable effects on many metabolic diseases. Recently, mounting studies also highlighted the neuroprotective effects of irisin on dementia. The current evidence remains uncertain, and few clinical trials have been undertaken to limit its clinical practice. Therefore, we provided an overview of current scientific knowledge focusing on the preventive mechanisms of irisin on senile cognitive decline and dementia, in terms of the possible connections between irisin and neurogenesis, neuroinflammation, oxidative stress, and dementia-related diseases. This study summarized the recent advances and ongoing studies, aiming to provide a better scope into the effectiveness of irisin on dementia progression, as well as a mediator of muscle brain cross talk to provide theoretical support for exercise therapy for patients with dementia. Whether irisin is a diagnostic or prognostic factor for dementia needs more researches.
Frontiers in Plant Science
Recent Advances in Big Data, Machine, and Deep Learning for Precision Agriculture