Acute cerebral infarction is a common neurological emergency associated with high morbidity and mortality. Management becomes particularly challenging in patients with decompensated liver cirrhosis, who frequently present with coagulopathy and thrombocytopenia, thereby carrying simultaneous risks of hemorrhage and thrombosis. These competing clinical risks substantially complicate antiplatelet treatment decisions. We report a case of acute right brainstem infarction in a patient with hepatitis B– related decompensated liver cirrhosis, prior transjugular intrahepatic portosystemic shunt (TIPS) placement, coagulopathy, and progressive thrombocytopenia. Owing to the patient’s markedly elevated bleeding risk, antiplatelet therapy was initially withheld. Conservative management was undertaken, including plasma transfusion to improve coagulation status, acid suppression and gastric mucosal protection, and maintenance of cerebral perfusion, accompanied by close dynamic monitoring of coagulation parameters and platelet counts. This case highlights the complexities of individualized clinical decision-making in patients with acute cerebral infarction complicated by advanced liver disease and severe hematological abnormalities. It further underscores the importance of balancing thrombotic and hemorrhagic risks in developing safe and effective management strategies for this highly vulnerable patient population.

The prediction of three-dimensional protein structures has undergone a paradigm shift, driven primarily by deep learning-based tools. These advances are beginning to permeate neuroscience research, offering new routes for understanding the molecular basis of neurological disease and accelerating early-stage central nervous system (CNS) drug discovery. This article examines the current state of in silico structural modeling as it applies to translational neuroscience, highlighting areas of progress — including G protein-coupled receptor (GPCR) pharmacology, cryo-EM-informed structural neurobiology, protein aggregation in neurodegeneration, and AI-driven small molecule discovery — alongside discussion of limitations. These include the misinterpretation of static computational models, and the continuing gap between structural insight and clinical validation. The argument advanced here is that structural modeling has already meaningfully altered the landscape of early drug discovery, but that its translational promise will only be realised through sustained interdisciplinary integration and rigorous experimental follow-through.

Amyotrophic Lateral Sclerosis (ALS) is strongly associated with TDP43 proteinopathy, where mutations in TARDBP gene alter the protein’s stability, solubility, and RNA related functions. This study examines eight ALS-linked missense mutations located in both the structured RRM domains and the intrinsically disordered C-terminal region, aiming to clarify how single residue substitutions perturb TDP43 structure and behavior. The full length TDP43 model was obtained from the AlphaFold database due to the absence of complete crystallographic structures. Each mutation was manually introduced into the appropriate domain using Molecular Operating Environment (MOE) software. Under appropriate conditions, Molecular Dynamics simulations were performed on wildtype and mutated structures; Wildtype and mutant proteins were then compared with respect to conformational changes, focusing on energetic profiles, surfaces, and electrostatics properties, considering how these variations can be related to effects of mutations found in literature. RRM domain mutations showed localized but mechanistically distinct alterations. D169G increased domain stability through a subtle β-turn rearrangement without affecting nucleic acid binding. K181E and K263E reversed local electrostatics, disrupting the positively charged RNA binding groove. C terminal mutations (Q331K, A315E, M337V, N345K, G298S) produced broader effects, including enhanced aggregation propensity, altered phase behavior, impaired DNA repair interactions, and increased protein half-life. Despite their heterogeneity, all mutations converge toward mechanisms that promote TDP43 misfolding, aggregation, and loss of nuclear function-key drivers of ALS pathology. The conclusions of this study indicate that even minimal local perturbations can result in significant functional consequences, reinforcing the importance of mutation specific structural insights for future therapeutic strategies.

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by tau hyperphosphorylation, which contributes to neurofibrillary tangle formation and neuronal loss. Multiple kinases are involved in this process making them potential therapeutic targets. However, the identification of selective inhibitors with favourable pharmacokinetic properties and adequate brain penetration remains a challenge. In this study, thirteen tau-related kinases were prioritized according to the number of predicted tau serine phosphorylation sites identified using a percentile-based filtering approach. Known inhibitors for each kinase were evaluated using Boltz-2 AI-based docking software and predicted binding affinity likelihood (BAL) scores and IC₅₀-like values were combined to rank inhibitors within each kinase-specific set. The selected compounds were subsequently evaluated using SwissADME pharmacokinetic software, focusing on blood-brain barrier permeability, gastrointestinal absorption, P-glycoprotein interaction, and compliance with Lipinski’s rule of five. Molecular selectivity was estimated using experimentally determined IC₅₀ data retrieved from ChEMBL, while protein brain expression and protein–protein interaction data were obtained from the Human Protein Atlas and STRING databases. The results identified kinase inhibitors with favourable predicted pharmacokinetic properties and varying degrees of target selectivity. Together, these analyses provide a framework for prioritizing kinase inhibitors for further investigation and experimental validation in the context of future AD therapy development.