Search Results (12,150)

Accurate early screening of Alzheimer’s disease (AD) is crucial, yet traditional diagnostic methods are often limited by invasiveness or high costs. Therefore, there is a critical need for non-invasive biomarkers that enable precise and accessible screening. In this study, we propose a multi-modal digital biomarker framework designed to accurately detect AD by evaluating impairments across multiple cognitive domains, such as language, working memory, and visuospatial attention. By leveraging voice and video data, our approach significantly enhances user accessibility and real-world applicability. We validated the proposed framework using a dataset of 128 participants, comprising 77 healthy controls (HCs) and 51 patients with AD. While individual cognitive tasks yielded F1-scores ranging from 69.23% to 77.78% and sensitivities from 69.23% to 80.77%, our ensemble strategy significantly enhanced detection performance, achieving an F1-score of 83.64% and a sensitivity of 88.46%. These findings confirm that the proposed multi-modal digital biomarker framework, enhanced via ensembling, provides a highly accurate, scalable, and practical solution for the non-invasive screening and detection of AD. Full article

Background/Objectives: The microbiota–gut–brain axis (MGBA) plays a pivotal role in cognitive function, making psychobiotics a promising strategy for managing neurodegenerative diseases. Lactic acid bacteria (LAB) from traditional fermented foods represent a valuable source of candidate strains, and multi-strain consortia may offer enhanced therapeutic efficacy through synergistic effects. This study evaluated the functional and psychobiotic potential of three lactic acid bacteria (LAB) strains isolated from fermented foods, assessed as monocultures and a multi-strain consortium (MIX). Methods: The research encompassed an initial screening of the individual strains and the MIX, assessing their adhesion to mucin, stability in a static in vitro digestion model, and amino acid profiling. Subsequently, the LAB MIX underwent long-term evaluation in a dynamic gastrointestinal model (SHIME^®) inoculated with microbiota from a patient with Alzheimer’s disease, during which alterations in gut microbiota composition and amino acid metabolism were analyzed. Results: The LAB MIX demonstrated high stability under digestive stress and effective mucoadhesive properties. Furthermore, the consortium demonstrated a distinct metabolic signature, driving enhanced functional effects that complemented or exceeded those observed in individual monocultures. In the SHIME^® model, the MIX induced significant, site-specific shifts in microbial composition, notably increasing lactobacilli abundance. These taxonomic changes correlated with an enriched metabolic profile, including elevated levels of GABA precursors and amino acids with antioxidant potential, which are crucial for MGBA modulation. Conclusions: These results identify the LAB consortium as a compelling psychobiotic candidate. Further in-depth in vivo and clinical studies are required to validate its therapeutic potential for MGBA modulation. Full article

Early-life factors influence adult-brain vulnerability to sporadic Alzheimer’s disease (AD), but the underlying molecular mechanisms are unknown. In this study, we performed an integrated analysis of mitogen-activated protein kinases (MAPK) pathways’ (ERK1/2, JNK, and p38 MAPK) activity in the hippocampus and prefrontal cortex of OXYS rats (a model of sporadic AD) on postnatal days 3 and 10 (P3 and P10): critical periods of brain maturation. Wistar rats (healthy controls) showed extensive developmental transcriptional remodeling of all MAPK pathways. OXYS rats exhibited alterations, most pronounced in the prefrontal cortex at P3, with the JNK pathway showing the greatest divergence. At the protein level, OXYS rats failed to show the normal age-related increase in hippocampal ERK1/2 phosphorylation and in JNK1/2 levels in both regions, indicating developmental signaling deficits. p38 MAPK remained stable among Wistar and OXYS rats. Thus, delayed brain maturation, which contributes to accelerated brain aging and neurodegeneration in OXYS rats, occurs simultaneously with alterations in MAPK signaling. These aberrations potentially are able to increase brain susceptibility to age-related pathologies later in life. Full article

Neuronal senescence-like states are increasingly implicated in age-related neurodegeneration, yet the neuron-intrinsic regulators that drive these phenotypes remain poorly defined. Guided by prior transcriptomic analysis of aged basal forebrain cholinergic neurons, we investigated Srebf2 and Zmiz1 using primary basal forebrain neuronal cultures with adeno-associated virus-mediated gain- and loss-of-function, quantitative immunocytochemistry, and low-input transcriptomic profiling of fluorescence-activated cell sorting-isolated neurons. Both perturbation strategies produced the expected directional changes in target transcripts. Overexpression of either gene increased the other, whereas knockdown did not elicit reciprocal suppression, indicating asymmetric regulatory coupling. Phenotypically, Srebf2 showed a bidirectional association with senescence-like changes, as both overexpression and knockdown increased p16 and p21, whereas Zmiz1 acted more directionally, with overexpression increasing and knockdown reducing these markers. Transcriptomic profiling revealed broad direction-dependent remodeling, including a set of 55 genes that changed concordantly across perturbation directions. Pathway analysis further showed specialization, with Zmiz1 preferentially associated with an Alzheimer’s disease-related signature and Srebf2 more strongly linked to cholinergic synapse programs. Together, these findings identify Srebf2 and Zmiz1 as coupled but non-equivalent regulators of a neuronal senescence-like program. Full article

The axis linking the gut to the brain to the immune system connects all tissues involved—bacteria, immune cells, metabolism and the CNS—through a multidirectional communication network. Several studies have confirmed that when this axis is disrupted, it can be responsible for Alzheimer’s disease, Parkinson’s disease, obesity, type 2 diabetes, and NAFLD, and the main consequences come from increased systemic inflammation, altered regulation of immune cells, the production of microbial metabolites that alter signals to the immune cells and nervous system, increase in oxidative stress, breakdown of the gut barrier, and more. In recent years, advanced multi-omics technologies, such as metagenomics, transcriptomics, metabolomics, proteomics, and single-cell sequencing, have provided significant advancement in our understanding of all of the interacting nodes involved in the gut–brain–immune axis. These advanced sequencing technologies can characterize the microbial communities, host immune cells, metabolic profiles, and the degree of cell heterogeneity during a specific disease. Combining multi-omics information can reveal a few shared pathways between neurodegenerative and metabolic disorders, such as NF-κB, NLRP3 inflammasome activation, mitochondrial dysfunction, changes in SCFA metabolism, and the alteration of microbial populations in Alzheimer’s and Parkinson’s disease; metabolic dysbiosis and increased risk for Parkinson’s disease; or changes in gut-to-brain-to-immune signaling contributing to diabetes complications and NAFLD. Artificial intelligence (AI) and machine learning are becoming promising tools for detecting biomarkers from these datasets, extracting knowledge, interpreting systems biology, and helping with developing precision medicine. In this review, we summarize current evidence that supports the role of the gut–brain–immune axis in neurodegenerative and metabolic diseases, highlighting results gained with the utilization of multi-omics approaches. We will describe the key microbial, immune, and metabolic pathways involved in pathogenesis and therapeutic approaches including psychobiotics, tailored nutrition, modulation of the microbiome, and metabolite interventions, discussing future perspectives of the translation of the gut–brain–immune axis knowledge into clinical practice. Full article

Objectives: Alzheimer’s disease (AD) remains one of the most prevalent neurodegenerative conditions among older adults, underscoring the urgent need for accurate and ethically grounded early detection methods. Artificial intelligence (AI) techniques, particularly machine learning and deep learning models, show promise in leveraging neuroimaging biomarkers to support early diagnosis. However, significant challenges persist regarding model explainability, accountability, and responsible implementation in real-world healthcare settings. This study presents a generalized Responsible AI (RAI) framework composed of four core components—explainability, fairness, predictive performance, and uncertainty quantification—to address these challenges. Method: Using the TADPOLE neuroimaging dataset, we implemented a Feedforward Neural Network (FNN) within a unified Responsible AI (RAI) framework integrating explainability, fairness, predictive performance, and uncertainty quantification. Although Random Forest achieved slightly higher predictive accuracy (95%), the FNN was selected as the primary model because it better supports end-to-end uncertainty estimation through Monte Carlo Dropout, enabling more reliable clinical decision support. Results: The proposed framework demonstrated strong predictive performance (92% accuracy), improved fairness reflected by an equalized odds difference of 0.124, and progressively lower predictive entropy across training iterations, indicating enhanced confidence in predictions. The framework further enabled model transparency through explainability analyses and supported the identification of low-confidence predictions for potential clinical review. Conclusions: Our findings highlight not only the feasibility of integrating RAI principles into AD prediction pipelines but also the persistent challenges of applying such frameworks to real-world clinical data. This work contributes practical insights toward operationalizing Responsible AI in healthcare contexts. Full article

Background: Platinum-based chemotherapy is the frontline treatment for high-grade serous ovarian cancer (HGSOC); however, the development of therapy resistance greatly limits clinical response. Increasing evidence suggests that platinum agent-driven metabolic programming, particularly within redox-associated pathways, may contribute to chemoresistance. Methods: A syngeneic pair of patient-derived HGSOC cell lines representing cisplatin-sensitive (SE) and cisplatin-resistant (CR) states were evaluated using a multi-omics approach. Differential metabolite abundance and gene expression were assessed, followed by gene set and pathway enrichment analyses to identify coordinated metabolic shifts. In silico analysis of an additional sensitive and resistant HGSOC cell line validated the glutathione pathway upregulation seen in the patient-derived model. The functional contribution of the glutathione pathway on cisplatin resistance was evaluated following glutathione inhibition. Results: Chronic cisplatin exposure induced extensive metabolic rewiring in CR cells, characterized by enrichment of glutathione metabolism at both the metabolite and gene levels. Increased reduced glutathione was observed alongside upregulation of key enzymes involved in its de novo biosynthesis, recycling, and utilization, consistent with enhanced detoxification capacity relating to cisplatin-induced oxidative stress. Additionally, taurine was highly enriched, further highlighting a metabolic shift towards enhanced antioxidant mechanisms. CR cells also demonstrated an increase in NADPH-generating pathways, including amino acid metabolism and fatty acid β oxidation, to support redox balance and biosynthetic demands of increased glutathione metabolism. Transcriptional remodeling of the γ-glutamyl cycle further indicated a shift toward increased glutathione turnover, suggesting that the coordinated changes seen may define a metabolic state enhanced in oxidative stress tolerance and therapeutic resistance. These transcriptional changes were also seen in another model of platinum sensitivity/resistance, indicating a conserved response associated with platinum-induced resistance. Finally, concurrent cisplatin treatment and glutathione inhibition significantly increased sensitivity within the CR cells. Conclusions: These findings suggest that cisplatin-resistant cells, previously exposed to a platinum-based agent, may undergo distinct metabolic rewiring towards antioxidant pathways to survive chronic chemotherapeutic stress. Targeting components of these systems may represent a viable strategy to overcome platinum resistance and improve therapeutic outcomes. Full article

Accurately quantifying the expression of individual transcript isoforms remains a formidable challenge, especially in contexts such as neurodegenerative diseases and cancers, which are characterized by high isoform diversity. The present study introduces a junction-based method, named differential splicing frequency analysis (DSFA), which enables more sensitive detection of differential splicing using RNA-seq data. Unlike the existing exon-, isoform-, and event-based methods, DSFA quantifies splice junction usage. We applied DSFA to Alzheimer’s disease (AD)-associated genes through large-scale RNA-seq data mining. The present study is the first to establish that the APP770-, APP751-, APP695-, and APP752-encoding isoforms represent major isoforms of the APP gene. Three important findings are: (1) the APP752-encoding isoform exhibits immune cell specificity; (2) the relative proportion of the APP752-encoding isoform increases during the differentiation of induced pluripotent stem cells (iPSCs) into microglia, akin to the increase in relative proportion of the APP695-encoding isoform during iPSC differentiation into neurons; and (3) the APP751-encoding isoform predominates in both cancer and immune cells. Additionally, we identified APP/58417N and App/52804N as differentially expressed splice junctions in humans and mice, respectively. Through over-expression of U1 snRNA in human embryonic stem cell (hESC)-derived neurons, we found that U1 snRNA over-expression decreases the usage of APP/58417N in neurons, similar to the effects observed in AD samples. Our research highlights that the major isoforms of a gene can differ markedly in their expression across tissue and cell types. Full article

Background: Early endolysosomal and autophagic defects are among the earliest cellular alterations observed in Alzheimer’s disease (AD). However, the molecular mechanisms linking amyloid precursor protein (APP) metabolism to vesicle trafficking dysfunction remain incompletely understood. The APP-derived fragment C99 has emerged as a potential upstream mediator of intracellular toxicity, but its impact on organelle homeostasis and its modulation by metabolic interventions remain unclear. Methods: To investigate these mechanisms, we expressed human C99 in Drosophila neurons and examined intracellular pathology using ultrastructural analysis, fluorescent reporters of autophagy and mitochondrial turnover, and proteomic interactome mapping. The effects of the ketone body β-hydroxybutyrate (BHB) were evaluated to assess the impact of metabolic intervention. Results: Neuronal C99 expression induced pronounced vesicular abnormalities, impaired autophagic turnover, and disrupted mitochondrial quality control. Transmission electron microscopy revealed extensive accumulation of enlarged vesicular compartments, accompanied by reduced mitochondrial turnover and accumulation of aged mitochondria. BHB treatment restored autophagic cargo clearance, improved mitochondrial turnover, and normalized vesicular ultrastructure. These protective effects required neuronal ketone transport, indicating a neuron-intrinsic metabolic mechanism. Proteomic analysis of the C99-associated interactome revealed that ketone treatment remodels networks enriched for vesicle trafficking and proteostasis pathways. Network prioritization identified the retromer component VPS35 as a candidate regulatory hub. Functional analyses demonstrated that depletion of VPS35 abolished the BHB-dependent restoration of autophagy, mitochondrial turnover, and vesicle morphology. Conclusions: Ketone treatment restores mitochondrial quality control and autophagic homeostasis through a VPS35-dependent mechanism in C99-induced neurodegeneration. These findings provide mechanistic insight into how metabolic interventions may restore intracellular homeostasis in Alzheimer’s disease. Full article

Chronic depression is associated with oxidative stress, neuroinflammation, neurotransmitter imbalance, and Alzheimer’s-like changes. Current monoamine oxidase inhibitors have limited cognitive benefits and disease-modifying properties. A new nanotherapeutic, combining chitosan nanoparticles, propargylamino-1-(4-methylthiophenyl) propane (PAMTP), and L-tyrosine (En@PAMTP_Tyr), was developed. En@PAMTP_Tyr nanoparticles were ~140 nm in diameter, with a zeta potential of +27 mV and entrapment efficiencies of 73.45% for PAMTP and 90.85% for L-tyrosine. Drug release was pH-sensitive, favoring acidity. Intraperitoneal administration of En@PAMTP_Tyr reduced anhedonia, despair, cognitive deficits, and neuromuscular weakness, with efficacy matching or exceeding that of selegiline. In treated rats’ hippocampal tissue, En@PAMTP_Tyr increased superoxide dismutase and glutathione, normalized MAO and acetylcholinesterase activities, and corrected CUSD-induced TNF-α and IL-10 changes, showing antioxidant and anti-inflammatory effects. Histological analyses revealed that En@PAMTP_Tyr preserved CA1 pyramidal neurons, reduced β-amyloid levels, restored tau protein, and improved brain-derived neurotrophic factor levels, indicating reduced neurodegeneration. Molecular docking studies showed that PAMTP had high affinity for monoamine oxidase and acetylcholinesterase, supporting its role as an MAO-B inhibitor and cholinergic modulator. These findings suggest that En@PAMTP_Tyr is a promising nanoplatform for targeting MAO-B in depression, addressing mood, cognitive function, oxidative stress, inflammation, and Alzheimer-like pathology in the hippocampus. Full article

Alzheimer’s disease (AD) is characterized by Tau hyperphosphorylation, β-amyloid (Aβ) accumulation, and progressive neuronal loss. Gastrodia elata (G. elata), a traditional Chinese medicine with well-established neuroprotective properties, was investigated. Two G. elata-derived miRNAs, Gas-miR04-3p and Gas-miR19-5p, were identified as regulators of JNK3. By means of Western blot, RT-qPCR, and assessments of antioxidant indices, it was demonstrated that Gas-miR04-3p and Gas-miR19-5p can suppress JNK3 expression, reduce Tau phosphorylation at Ser202 and Ser396, enhance antioxidant capacity, and attenuate apoptosis in AD-related cellular and molecular pathology models. These miRNAs were also detectable in murine brain tissues following oral administration of total RNA extracted from G. elata. Their administration was associated with decreased JNK3 activation, alleviated Tau hyperphosphorylation, and improved expression of apoptosis-related proteins in AD mouse models. These results suggest that G. elata miRNAs may exert neuroprotective effects through regulation of JNK3 signaling, thereby attenuating Tau-related pathological changes and neuronal injury in AD-related models. Full article

Neuroinflammation plays a central role in the onset and progression of neurodegenerative disorders. Several disease-modifying therapies have been developed to target neuroinflammatory pathways in specific disorders. However, their ability to stop disease progression or restore neuronal and mitochondrial homeostasis remains limited. This is still a major unmet clinical need. In this context, mesenchymal stromal cell (MSC)-derived Extracellular Vesicles (EVs) have emerged as a promising cell-free therapeutic strategy due to their ability to modulate immune responses and promote neuroprotection through the delivery of bioactive cargo. Recent evidence has identified a distinct subset of EVs, known as mitochondrial EVs (mito-EVs), which carry mitochondrial DNA, proteins, and functional components. These vesicles may uniquely influence cellular bioenergetics, redox balance, and neuroinflammatory signaling, offering additional therapeutic potential compared to conventional MSC-EVs. This review summarizes the role of MSC-derived EVs in neuroinflammatory disorders, with a particular focus on mito-EVs. It also discusses preconditioning strategies to enhance EV efficacy, including hypoxic, inflammatory, pharmacological priming and genetic engineering approaches. Finally, we critically evaluate current preclinical evidence regarding the treatment of major neurodegenerative disorders, including Alzheimer’s disease, Parkinson’s disease, Multiple Sclerosis, and Amyotrophic Lateral Sclerosis, as well as Traumatic Injury, highlighting the key challenges for clinical translation. Full article

Human activity recognition—HAR—plays a crucial role in the lives of patients battling neurodegenerative diseases. These debilitating conditions, such as Alzheimer’s or Parkinson’s, affect individuals’ ability to perform daily tasks autonomously and safely. HAR technology offers an invaluable solution by enabling real-time monitoring and assistance, helping to maintain independence and quality of life for patients. Additionally, this technology provides a valuable data source for doctors and caregivers, allowing for more precise and personalized care, which can make a difference in managing and treating these neurodegenerative diseases. The objective of this review is to identify the contribution of Transfer Learning and Reinforcement Learning in supporting the processes of daily activity recognition, thus enhancing the quality of life for patients. As this is a trending topic, the literature surrounding it is quite dispersed, which is why this review aims to present the current line of research in this field. To carry out this analysis, the science tree paradigm was used, which establishes two fundamental stages of analysis. The first is delimited by scientometrics, where the leading countries in the application of such technologies can be identified. This review highlights the evolution in the use of transfer learning and reinforcement learning in HAR in the healthcare field, where these techniques have significantly improved the accuracy and adaptability of real-time monitoring systems. The studies reviewed indicate that transfer learning has allowed models to adapt to data variations without requiring large volumes of manual labeling, which is essential in clinical and patient monitoring contexts. Additionally, reinforcement learning has optimized decision-making in complex scenarios, enabling activity recognition systems to dynamically adjust monitoring parameters, enhancing detection and response to critical or unusual activities in multi-user environments. These advances demonstrate that, by integrating these approaches, greater personalization and robustness can be achieved in human activity recognition, thereby improving the quality of life for patients in clinical settings. Full article

Alzheimer’s disease (AD) and metabolic syndrome often occur together, sharing characteristics such as insulin resistance, dyslipidemia, and chronic inflammation. Metabolic dysfunction frequently precedes cognitive decline, indicating that early intervention might alter the disease’s progression. We investigated whether the GLP-1 receptor agonist semaglutide (SMGL) influences metabolic impairment and AD pathology in an AD mouse model. Male and female 5xFAD and wild-type (WT) mice on regular (RD) or high-fat diets (HFD) were administered SMGL for 13 weeks. SMGL-treated groups exhibited significant, context-dependent effects. In metabolically challenged 5xFAD HFD mice, treatment led to reduced body weight, improved glucose tolerance, normalized cholesterol levels, and a restored balance of adiponectin and leptin. These improvements were associated with reduced Aβ40 and Aβ42 levels, restored GLP-1 receptor expression, increased synaptophysin and βIII-tubulin levels, and enhanced spatial memory. SMGL also decreased Iba1 and CD68 immunoreactivity in the hippocampus and cortex, reduced macrophage infiltration, and lowered CD36 expression in visceral adipose tissue (VAT), indicating coordinated anti-inflammatory effects. WT RD mice showed minimal metabolic responses and a modest decline in Y-maze performance, suggesting that excessive GLP-1 receptor activation may disrupt neuronal homeostasis when metabolic status is normal. SMGL acts as a context-specific metabolic and neuroprotective agent, offering the greatest benefits under conditions of metabolic dysfunction. These findings in a preclinical model suggest that targeting early metabolic disturbances provides a testable hypothesis for attenuating AD-related neurodegeneration, though further translational studies are required. Full article

Central nervous system (CNS) diseases, including ischemic stroke, Alzheimer’s disease, and Parkinson’s disease, impose a heavy socioeconomic burden worldwide. Current therapeutic strategies for CNS diseases primarily include modulating ion channels, inhibiting excitotoxicity, and applying anti-inflammatory, antioxidant, and anti-apoptotic approaches. However, existing drugs have not yet met the growing clinical demands. This paper summarizes novel lead compounds recently reported for CNS diseases and discusses the current challenges and emerging strategies in CNS drug development, aiming to provide a reference and scientific basis for future drug discovery and research. Full article