Introduction: Animal-induced models are necessary to bridge the translational gap between transgenic models and clinical trials. Although several models are standard, experimental designs differ, and protocols are not standardized.

Methods: A PRISMA-guided search was performed in PubMed, Embase, and Cochrane, and 60 studies with Aβ1-42 injected into rats were included. Primary outcomes were rat strain, sex, injection site, and dose. Secondary outcomes followed the time between induction and cognitive testing. Data was analyzed using JASP statistics program and applying descriptive statistics, t-tests, chi-square, and correlations.

Results: Bilateral injections showed up in 65% of the studies, most often in Wistar rats (64%), and hit either the intracerebroventricular (i.c.v., 48.7%) or intrahippocampal (i.h.c., 51.3%) regions. Most unilateral models—85.7%—used i.c.v. injections. On average, researchers injected 3.8 ± 2.1 µl in bilateral setups and 4.9 ± 2.6 µl in unilateral ones. They ran behavior tests about 18.4 ± 10.6 days after bilateral and 20.7 ± 12.9 days after unilateral injections. Statistically, those differences were not significant (p > 0.05).

Conclusions: The field still lacks a standard way to run Aβ₁–₄₂-based Alzheimer’s models. Even when timing and doses line up, choices around animal strain, injection site, and laterality vary widely. Establishing standardized reference parameters—dose, site, and timing—appears critical to enhance reproducibility and strengthen the translational validity of preclinical Alzheimer’s research.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder affecting nearly 45 million people worldwide. Current therapeutic options, including cholinesterase inhibitors (rivastigmine, donepezil, and galantamine) and the NMDA receptor antagonist memantine, offer only symptomatic relief without halting disease progression. Thus, there is an urgent need to identify novel therapeutics capable of targeting the molecular drivers of AD pathology.

The hallmark features of AD—amyloid plaques and neurofibrillary tangles (NFTs)—are closely linked to tau hyperphosphorylation mediated by glycogen synthase kinase-3β (GSK-3β) and cyclin-dependent kinases (CDKs). Hyperactive GSK-3β promotes NFT formation, oxidative stress, and neuronal death.

In this study, we evaluated Indirubin-3′-oxime, a potent GSK-3β inhibitor with antioxidant properties, in a streptozotocin-induced rat model of AD. Indirubin-3′-oxime treatment (10 mg/kg, i.p.) administered for 21 days significantly improved cognitive performance in the Morris water maze and novel object recognition tests compared to untreated AD rats (p < 0.01). Biochemical assays revealed a 40–50% reduction in phosphorylated tau levels and marked inhibition of GSK-3β and CDK5 activities. Oxidative stress markers such as MDA and nitrite were significantly decreased, while antioxidant enzymes (SOD, catalase) were restored toward normal values.

Histopathological analysis showed reduced neuronal degeneration in the hippocampal CA1 region, confirming neuroprotection. These findings demonstrate that Indirubin-3′-oxime mitigates tau hyperphosphorylation and oxidative stress, thereby improving cognitive function. The results support its therapeutic potential as a disease-modifying agent in Alzheimer’s disease.

Alzheimer’s disease (AD) is the most common cause of dementia, characterized by amyloid-β plaques, tau neurofibrillary tangles, and progressive neuronal loss. Current diagnostic tools, including cerebrospinal fluid (CSF) analyses and positron emission tomography (PET) imaging, are accurate but invasive, costly, and not widely available. The identification of blood-based biomarkers reflecting AD neuropathology represents a major breakthrough toward accessible and scalable diagnostics.

This systematic review and meta-analysis aimed to evaluate the diagnostic performance of four plasma biomarkers—phosphorylated tau at threonine 181 (p-tau181), phosphorylated tau at threonine 217 (p-tau217), amyloid-beta 42/40 ratio (Aβ42/Aβ40), and neurofilament light chain (NfL)—for early and prodromal stages of AD. Following PRISMA 2020 guidelines, PubMed, Scopus, and Web of Science databases were searched for studies published between 2015 and 2025. Sixty-four studies encompassing 23,456 participants were included and analyzed using random-effects models (DerSimonian–Laird method).

The pooled area under the curve (AUC) was highest for plasma p-tau217 (AUC = 0.91, 95% CI: 0.88–0.94), followed by NfL (AUC = 0.86), p-tau181 (AUC = 0.84), and Aβ42/Aβ40 (AUC = 0.82). Combining p-tau217, NfL, and Aβ42/Aβ40 improved overall diagnostic accuracy (AUC = 0.94) with a sensitivity of 90%. Heterogeneity was moderate (I² = 47%) and mainly related to assay type and cohort variability.

These results demonstrate that plasma p-tau217 and NfL can achieve near-CSF accuracy for detecting AD pathology, providing robust, non-invasive, and cost-effective biomarkers suitable for large-scale screening and early intervention. Integrating plasma biomarkers with machine learning models may further enhance diagnostic precision, supporting a paradigm shift toward accessible and personalized approaches in Alzheimer’s disease detection and monitoring.

Abstract:
Neurodegenerative diseases (NDDs) such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis are among the most debilitating disorders worldwide, characterized by progressive neuronal loss, molecular heterogeneity, and limited therapeutic options. Despite extensive research, the early detection and mechanistic understanding of these diseases remain major clinical challenges.

This study employs a comprehensive multi-omics framework integrating transcriptomic, proteomic, and metabolomic datasets from human post-mortem brain tissues and cerebrospinal fluid. By leveraging artificial intelligence (AI) and machine learning algorithms, including random forest classifiers and deep autoencoders, data from over 2,000 patient samples (ADNI, GEO, and AMP-PD) were analyzed to identify robust biomarkers and molecular subtypes.

Results demonstrate 31 shared molecular networks dysregulated across NDDs, prominently involving mitochondrial impairment, autophagy dysfunction, and neuroinflammatory pathways. The AI-based diagnostic model achieved 92% classification accuracy for distinguishing early Alzheimer’s disease from age-matched controls. Key hub genes—LRRK2, TREM2, and SYNGR3—were identified as central regulatory nodes. In-silico drug repurposing further suggested metformin and rapamycin analogs as potential modulators of these targets.

In conclusion, this research underscores the potential of AI-driven multi-omics integration in unveiling cross-disease biomarkers and accelerating precision diagnostics in neurodegenerative disorders. Future work aims to validate these findings through clinical cohorts and digital neurophenotyping.

In conclusion, this research underscores the potential of AI-driven multi-omics integration in unveiling cross-disease biomarkers and accelerating precision diagnostics in neurodegenerative disorders. Future work aims to validate these findings through clinical cohorts and digital neurophenotyping.

Introduction

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder characterized by progressive memory loss, cognitive decline, and behavioral disturbances. Pathological hallmarks include amyloid-β deposition, tau hyperphosphorylation, and oxidative stress. Rivastigmine tartrate, a cholinesterase inhibitor approved for AD management, is typically administered orally but suffers from poor bioavailability due to extensive first-pass metabolism and is further associated with adverse cardiovascular and gastrointestinal effects. To address these limitations, the present study explored the development of an intranasal nanocarrier-based formulation of rivastigmine tartrate using multi-walled carbon nanotubes (MWCNTs) functionalized with carboxyl and polyethylene glycol (MWCNT-COOH-PEG).

Methods

Intranasal administration was chosen to bypass the blood–brain barrier, enable direct nose-to-brain transport, and reduce systemic side effects. Functionalized MWCNTs were selected as drug carriers for their high surface area, biocompatibility, and reported neuroprotective potential. The formulation was optimized using a 3² factorial design. Preformulation assessments, including Fourier-transform infrared spectroscopy and differential scanning calorimetry, confirmed drug–excipient compatibility. Critical formulation parameters such as particle size, zeta potential, and entrapment efficiency were studied. In vitro release testing and ex vivo permeation studies using goat nasal mucosa were conducted. Stability studies were performed to evaluate formulation robustness, while in vivo pharmacokinetic and biodistribution assessments were carried out using male Wistar rats.

Results

The optimized intranasal formulation exhibited favorable particle size distribution, surface charge stability, and high drug entrapment efficiency. In vitro release studies revealed a sustained drug release pattern. Ex vivo permeation results demonstrated 56% cumulative drug release at 12 hours and 65.55% at 24 hours across goat nasal mucosa, confirming controlled and prolonged diffusion. Stability studies validated the physical and chemical stability of the formulation. Pharmacokinetic and biodistribution studies indicated improved brain uptake of rivastigmine tartrate compared to conventional administration routes.

Conclusion

The study demonstrated that intranasal delivery of rivastigmine tartrate-loaded MWCNT-COOH-PEG nanocarriers is a promising alternative to conventional oral therapy for Alzheimer’s disease.

Multiple sclerosis (MS) is an autoimmune disease that produces a complex range of symptoms including movement trouble, cognitive impairment, and bowel dysfunction. The disease progresses through the central nervous system (CNS), particularly the cerebrospinal fluid (CSF). Previous research suggests that CSF lymphocytes contribute significantly to disease progression, resulting in the misregulation of many genes. This thorough study examined the bulk tissue expression and single-nucleotide polymorphisms (SNPs) of 16 misregulated CSF genes. The study noted ethnic populations that are more susceptible to genetic variation along with drugs that can be used as MS treatments. The bulk tissue plots revealed a correlation between gene expression changes in the CSF and damage in the gut-associated lymphoid tissue (GALT). The SNP data further affirmed this finding with the tibial nerve—often associated with bowel damage in MS—having the most variants. Analyzing the population genetics of the collected SNPs showed that ethnic groups in Latin America and Africa were most likely to have the highest frequency in the least common alleles. Lastly, the study identified 12 drugs that regulate the dysregulated CSF genes with the vast majority being anti-cancer agents or histone deacetylase (HDAC) inhibitors. In short, discovering the effects and treatments of the misregulated CSF genes produced findings that deviate from prior MS studies. The results unveiled a much greater significance of the peripheral nervous system (PNS) and enteric nervous system (ENS), non-European susceptibility to symptoms, and drugs similar to cancer treatment.

Coordination compounds are promising redox-active systems for applications across multiple fields, including neuroscience; however, their effects on mitochondrial function in neural cells remain poorly investigated. Here, we evaluated the neuroprotective potential of Cu^II, Fe^III, and Mn^II coordination compounds derived from the ligand bis(pyridin-2-ylmethylamine) -3-chloropropan-2-ol (HP), targeting mitochondrial dysfunction and oxidative stress in C6 glial cells. A system with impaired mitochondrial activity was elicited by exposing C6 glial cells to rotenone (50 µM; 1 h). The treatment severely impaired mitochondrial respiration by reducing basal oxygen consumption from 55 ± 13.2 to 14 ± 7.2 pmol O₂ s⁻¹ 1x10⁴ cells and maximal respiration from 123 ± 8.2 to 35 ± 3.5 pmol O₂ s⁻¹ 1x10⁴ cells. CuHP, FeHP, and MnHP treatment (after rotenone) restored basal respiration (56 ± 8.9, 50 ± 7.5, and 57 ± 3.8 pmol O₂ s⁻¹ 1x10⁴ cells, respectively) and maximal respiration (134 ± 6.8, 118 ± 5.2, and 152 ± 3.2 pmol O₂ s⁻¹ 1x10⁴ cells), comparable to the SOD mimetic EUK-8 (148 ± 8.2 pmol O₂ s⁻¹ 1x10⁴ cells). Exposure to the HP series (after rotenone; 1 h) inhibited ROS generation at 3–30 µM in rotenone-induced cytotoxicity, with FeHP showing consistent suppression across all concentrations and superior performance to EUK-8. At 100 µM, CuHP and MnHP reduced ROS generation by approximately 80%, whereas the reference compound EUK-8 achieved only 60% inhibition. HP coordination compounds showed significantly greater efficacy than EUK-8 in suppressing rotenone-induced ROS production. The HP series effectively counteracted rotenone-induced mitochondrial dysfunction and oxidative stress in C6 glial cells. The recovery of mitochondrial respiration and ROS inhibition demonstrated that the neuroprotective effects were dependent on the nature of the central metal ion (MnHP > FeHP > CuHP). Thus, coordination compounds can modulate mitochondrial bioenergetics and redox balance, supporting their potential as mitochondria-targeted neuroprotective agents.

The dynamic between early infant behavior and caregiver sensitivity can be complex. It is also fundamental in shaping early infant development and long‑term psychological well‑being. Several studies confirm a positive association between caregiver sensitivity in the postpartum period and secure parent–infant attachment relationships. Oxytocin has emerged as a key neurohormone influencing this process.

The purpose of this paper is to provide an overview of the neurobiological basis of mother–infant bonding in response to the oxytocin system and propose favorable midwifery practices that enhance its natural secretion.

This study is a narrative review. Studies published between 2015 and 2025 were selected regardless of design, aiming to summarize the current evidence. A comprehensive literature search was conducted to identify articles examining the oxytocin’s role in infant–maternal bonding and favorable midwifery practices that enhance this process. The databases used were PubMed, Scopus, and PsycINFO using keywords such as “oxytocin”, “maternal bonding”, “attachment” and “neurobiology”.

Findings indicate that oxytocin facilitates bonding through modulation of several pathways. Oxytocin, is a neuropeptide synthesized in the hypothalamus which enhances activity in the nucleus accumbens, reinforcing maternal caregiving, modulates amygdala responses, reducing fear and stress and promotes recognition of social cues and attentional focus on infant signals. Such findings may support evidence-based midwifery training and perinatal care strategies fostering maternal–infant well-being. Emerging evidence also indicates oxytocin’s role in paternal bonding and co-parental synchronization.

Midwifery‑led care practices such as skin‑to‑skin contact, breastfeeding, physical touch, exposure to maternal live voice (speaking or singing), and supportive birth environments further enhance natural oxytocin release in infants, mothers and fathers. Limitations include variability in measurement methods and inconsistent correlations between peripheral and central oxytocin activity. Overall, this review highlights oxytocin’s multidimensional role in establishing bonding and underscores the importance of the midwifery-led care model as a neurobiologically attuned model for optimizing early attachment outcomes.

The application of machine learning within psychiatric research has proliferated in recent years, driven by the growing availability of large-scale neurobiological, behavioral, and clinical datasets. This development has enabled novel approaches to modeling the complex and multifactorial processes underlying mental disorders. Yet, a persistent tension remains in the field between predictive accuracy and mechanistic interpretability, which constrains the translational potential of ML-derived insights for both neuroscientific theory and clinical application. This systematic review critically synthesizes contemporary ML-based methodologies in psychiatric research, with particular attention to the distinction between predictive models, designed to maximize classification performance or prognostic precision, and mechanistic models, which seek to elucidate the latent cognitive and neural processes implicated in psychopathology. This review synthesizes peer-reviewed machine learning studies in psychiatric research spanning neurobiological, behavioral, and clinical domains, with emphasis on predictive and mechanistic models. The extant literature reveals a pronounced predominance of predictive modeling approaches, typically employing supervised learning algorithms such as support vector machines and deep learning architectures to address tasks ranging from diagnostic categorization and symptom severity estimation to treatment response prediction in conditions such as schizophrenia, mood disorders, and anxiety disorders. In contrast, mechanistic machine learning frameworks, often situated within computational psychiatry paradigms such as reinforcement learning and Bayesian modeling, are comparatively underutilized, though they offer critical explanatory power by linking model parameters to underlying cognitive and neural mechanisms. A subset of emerging hybrid approaches seeks to reconcile predictive utility with mechanistic clarity, though they remain methodologically heterogeneous and insufficiently validated. The findings highlight enduring methodological challenges in the field, including limited generalizability across cohorts, inconsistent validation strategies, and difficulty aligning learned representations with underlying neurocognitive processes. These limitations underscore the imperative for computational models that are not only predictive but also mechanistically interpretable, thereby advancing both theoretical insight and translational relevance.

Alzheimer’s disease (AD) represents the leading cause of dementia globally, characterized by progressive cognitive decline, β-amyloid (Aβ) aggregation, tau hyperphosphorylation, neuroinflammation, and synaptic dysfunction. Due to its complex multifactorial nature, the development of appropriate animal models has become indispensable for understanding the pathophysiology of AD and identifying potential therapeutic targets.

This paper provides a comparative analysis of the principal preclinical models used in AD research, encompassing transgenic, pharmacological, and toxin-induced paradigms. Transgenic models, such as 5×FAD, APP/PS1, and 3×Tg-AD, replicate essential molecular and behavioral features of familial AD. The 5×FAD line, carrying five human APP and PSEN1 mutations, develops early and aggressive amyloid pathology with profound cognitive deficits, while the 3×Tg-AD model integrates both amyloid and tau lesions, offering broader pathological relevance. Pharmacological models, including scopolamine- and streptozotocin-induced neurotoxicity, mimic sporadic AD through cholinergic hypofunction and impaired insulin signaling. Toxicological paradigms employing β-amyloid peptides or aluminum compounds reproduce oxidative stress and neuronal loss typical of late-stage pathology.

A critical comparison of these models in terms of construct, face, and predictive validity highlights their differential translational potential and limitations. While transgenic models provide genetic precision, pharmacological paradigms allow reversible and cost-effective testing of neuroprotective agents. Integrative approaches combining genetic, metabolic, and environmental factors promise more faithful recapitulation of human AD. This synthesis underscores the necessity of methodological pluralism, ethical refinement, and data reproducibility to advance preclinical neuroscience and accelerate therapeutic discovery in neurodegenerative research.