Search Results (686)

Spina bifida (SB) is a congenital malformation of the central nervous system (CNS), resulting from incomplete closure of the neural tube (NT) during early embryogenesis. Myelomeningocele (MMC), the most severe form of SB, leads to progressive neurological, orthopedic, and urological dysfunctions due to both NT developmental failure and secondary intrauterine injury (“two-hit hypothesis”). Prenatal repair of MMC has progressed considerably since the Management of Myelomeningocele Study (MOMS, 2011) trial, which showed that open fetal surgery can decrease the need for shunting and improve motor function, although it carries significant maternal risks. To address these limitations, minimally invasive techniques have been developed, with the goal of achieving similar benefits for the fetus while reducing maternal morbidity. Recent research has shifted toward regenerative strategies, integrating mesenchymal stem cells (MSCs), bioengineered scaffolds, and cell-derived products to move beyond mere mechanical protection toward true NT repair. Preclinical studies in rodent and ovine models have shown that amniotic- and placenta-derived MSCs exert neuroprotective and immunomodulatory paracrine effects, promoting angiogenesis, modulating inflammation, and supporting tissue regeneration. Minimally invasive, cell-based interventions such as Transamniotic Stem Cell Therapy (TRASCET), in preclinical rodent models, offer the possibility of very early treatment without hysterotomy, although translation remains limited by the lack of large-animal validation and long-term safety data. In parallel, advances in biomaterials, nanostructured scaffolds, and exosome-based therapies reinforce a regenerative paradigm that may improve neurological outcomes and quality of life in affected children. Ongoing translational studies are essential to optimize these approaches and define their safety and efficacy in clinical settings. This review provides an integrated overview of embryological mechanisms, diagnostic strategies, and prenatal therapeutic advances in SB treatment, with emphasis on prenatal repair, fetal surgery and emerging regenerative approaches. Full article

Background/Objectives: Chagas disease remains a major unmet medical need due to the limited efficacy and safety of current therapies. Here, we investigated sixteen thiosemicarbazone (TSC) derivatives as cruzain inhibitors using an integrated in silico/in vitro workflow. Methods: Docking against cruzain (PDB 3KKU) guided hit prioritization and correlated with enzyme inhibition; validation by redocking supported the protocol’s reliability. Results: The top compounds—H7, H10 and H11—showed potent cruzain inhibition (IC₅₀ = 0.306, 0.512 and 0.412 µM, respectively) and low-micromolar trypanocidal activity, with negligible cytotoxicity in human fibroblasts (CC₅₀ > 64 µM) and favorable selectivity. Structure–activity insights highlighted the role of expanded aromatic systems and electron-donating groups in enhancing binding within S2/S1′ subsites, while nitro substituents were associated with higher cytotoxicity. In silico ADMET parameters supported oral drug-likeness and acceptable metabolic liabilities. Conclusions: Overall, these data position TSCs as promising anti-T. cruzi leads and underscore the value of rational design against cruzain. Full article

Background/Objectives: The ability to maintain movement quality despite muscle fatigue is essential for daily activities and preserving independence after motor impairments. Many real-life situations involve asymmetrical muscle activation, leading to unilateral muscle fatigue. Repeated unilateral handgrip contractions at submaximal force have been linked to neural changes in both contralateral and ipsilateral motor areas, as well as improved contralateral response times in a button-press task. However, it remains unclear whether these improvements in response latency extend to higher-level benefits in overall arm movement quality. Methods: Thirty healthy participants performed unilateral handgrip fatiguing tasks at 5%, 50%, and 75% of maximum voluntary contraction (MVC) force. Subsequently, bilateral upper-limb movement quality was assessed in an object-hit task using a Kinarm robot. Results: The 50% and 75% MVC protocols elicited muscle fatigue as evidenced by declines in force output, post-exercise MVC, electromyography magnitude changes, and increased perceived exertion compared to the 5% MVC control condition. However, no significant changes in kinematic measures of the object-hit task were observed for either the fatigued (ipsilateral) or non-fatigued (contralateral) arm, indicating that unilateral handgrip fatigue did not affect higher-level movement quality. Conclusions: Previously reported improvements on contralateral response latency in a button-press task were not found to translate into advanced arm movement quality benefits. Full article

There has been significant interest in solving robot path planning problems using fuzzy logic-based methods. Recently, the Genetic Algorithm-based Hierarchical Interval Type-2 Fuzzy (GA-HIT2F) system has been introduced as a novel planner in this domain. However, this method lacks adaptability to changes in target points, and insufficient flexibility can lead to planning failures in local minimum traps, making it difficult to apply to complex scenarios. In this paper, we identify the limitations of the original GA-HIT2F approach and propose an enhanced Q-Learning-aided Adaptive Hierarchical Interval Type-2 Fuzzy (QL-HIT2F) algorithm for path planning. The proposed planner incorporates reinforcement learning to improve a robot’s capability to avoid collisions with special obstacles. Additionally, the average obstacle orientation (AOO) is introduced to optimize the robot’s angular adjustments. Two supplementary robot parameters are integrated into the reinforcement learning action space, along with fuzzy membership parameters. The training process also introduces the concepts of meta-map and sub-training. Simulation results from a series of path planning experiments validate the feasibility and effectiveness of the proposed QL-HIT2F approach. Full article

On 19 December 2015 and 21 February 2017, Longyearbyen was hit by major avalanches from the steep hillside of the mountain Sukkertoppen. In this article, we specifically consider the 2015 avalanche that destroyed eleven houses and buried nine people; seven were located and rescued, while two died. We describe the meteorological conditions leading up to the avalanche, the rescue operation, the media coverage, and the immediate aftermath of the catastrophe. Both events came as a result of warming, strong easterly winds, and drifting snow, with the December 2015 event being the most extreme. The 2017 avalanche damaged two houses, but no people were hurt. We analyse the catastrophes in relation to the knowledge of the risks and impacts of avalanches in Longyearbyen, as provided through field-based student courses at the University Centre of Svalbard (UNIS). To protect against further avalanche accidents, parts of Longyearbyen have been restructured, and physical barriers against avalanches have been installed on the hillside of Sukkertoppen. Now there are snow drift fences to reduce snow accumulation in the release areas, avalanche protection fences mounted in the hillside, and a large wall at the foot of the mountain to catch avalanche debris in the future. In hindsight, the accidents have contributed to an increased national awareness of the danger of severe weather events. Full article

Background/Objectives: Parkinson’s disease (PD) and Dementia with Lewy Bodies (DLB) are neurodegenerative disorders characterized by the accumulation of misfolded alpha-synuclein protein in the brain. Mutations in the glucocerebrosidase 1 (GBA1) gene have been identified as a significant genetic risk factor for both PD and DLB. GBA1 encodes for the lysosomal enzyme glucocerebrosidase, which is responsible for the breakdown of glucosylceramide (GC). Deficiencies in glucocerebrosidase activity lead to the accumulation of glucosylceramide within lysosomes, contributing to lysosomal dysfunction and impaired protein degradation. The aim of this narrative review is to update the underlying mechanisms by which GBA1 mutations contribute to the pathogenesis of PD and DLB. Methods: A comprehensive literature search was conducted across four major electronic databases (PubMed, Web of Science (Core Collection), Scopus, and Embase) from inception to 8 November 2025. The initial search identified approximately 1650 articles in total, with the number of hits from each database being as follows: PubMed (~450), Web of Science (~380), Scopus (~520), and Embase (~300). Results: The mechanism by which mutations in the GBA1 gene contribute to PD involves both loss-of- function and gain-of-function pathways, which are not mutually exclusive. Typically, GBA1 mutations lead to a loss of function by reducing the activity of the GCase enzyme, impairing the autophagy- lysosomal pathway and leading to α-synuclein accumulation. However, some mutant forms (GBA1L444P) of the GCase enzyme can also acquire a toxic gain of function, contributing to α-synuclein aggregation through mechanisms like endoplasmic reticulum stress and misfolding. While Venglustat effectively reduced GC levels, a key marker associated with GBA1-PD, the lack of clinical improvement led to the discontinuation of its development for this indication. Conclusions: GBA1-mediated lysosomal and lipid dysregulation represents a key pathogenic axis in PD and DLB. Understanding these mechanisms provides crucial insight into disease progression and highlights emerging therapeutic strategies—such as pharmacological chaperones, substrate reduction therapies, and gene-targeted approaches—aimed at restoring GCase function and lysosomal homeostasis to slow or prevent neurodegeneration. Full article

The emergence of multidrug-resistant Staphylococcus aureus underscores the urgent need for novel therapeutic agents targeting essential bacterial pathways. The lipoteichoic acid synthase (LtaS) is crucial for the synthesis of lipoteichoic acid in the cell wall of Gram-positive bacteria and represents a promising and vulnerable target for antimicrobial drug development. This study employed a comprehensive computational pipeline to identify potent inhibitors of the LtaS enzyme. A library of natural compounds was retrieved from the COCONUT database and screened against the crystal structure of the extracellular domain of LtaS (eLtaS) (PDB ID: 2W5R, obtained from the Protein Data Bank) through a multi-stage molecular docking strategy. This process started with High-Throughput Virtual Screening (HTVS), followed by Standard Precision (SP) docking, and culminated in Extra Precision (XP) docking to refine the selection of hits. The top-ranking compounds from XP docking were subsequently subjected to MM-GBSA binding free energy calculations for further filtration. The stability and dynamic behavior of the resulting candidate complexes were then evaluated using 100 ns molecular dynamics (MD) simulations, which confirmed the structural integrity and binding stability of the ligands. Density Functional Theory calculations revealed that screened ligands exhibit improved electronic stabilization and charge-transfer characteristics compared to a reference compound, suggesting enhanced reactivity and stability relevant for hit identification. Finally, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling was conducted to assess the drug-likeness and pharmacokinetic safety of the lead compounds. These findings support them as promising orally active leads for further optimization. Our integrated approach shortlisted eight initial hits (A–H) that showed interesting scaffold diversity and finally identified two compounds, herein referred to as Compound A and Compound B, which demonstrated stable binding, favorable free energy, and an acceptable Absorption, Distribution, Metabolism, and Excretion, and Toxicity (ADMET) profile. These candidates emerge as promising starting points for developing novel anti-staphylococcal agents targeting the LtaS enzyme that cand be further proved by experimental validation. Full article

Antimicrobial resistance (AMR) has become a major health crisis worldwide, and it is expected to surpass cancer as one of the leading causes of death by 2050. Conventional antibiotics are struggling to keep pace with the rapidly evolving resistance trends, underscoring the urgent need for novel antimicrobial therapeutic strategies. Antimicrobial peptides (AMPs) function through diverse, often membrane-disrupting mechanisms that can address the latest challenges to resistance. However, the identification, prediction, and optimization of novel AMPs can be impeded by several issues, including extensive sequence spaces, context-dependent activity, and the higher costs associated with wet laboratory screenings. Recent developments in artificial intelligence (AI) have enabled large-scale mining of genomes, metagenomes, and quantitative species-resolved activity prediction, i.e., MIC, and de novo AMPs designed with integrated stability and toxicity filters. The current review has synthesized and highlighted progress across different discriminative models, such as classical machine learning and deep learning models and transformer embeddings, alongside graphs and geometric encoders, structure-guided and multi-modal hybrid learning approaches, closed-loop generative methods, and large language models (LLMs) predicted frameworks. This review compares models’ benchmark performances, highlighting AI-predicted novel hybrid approaches for designing AMPs, validated by in vitro and in vivo methods against clinical and resistant pathogens to increase overall experimental hit rates. Based on observations, multimodal paradigm strategies are proposed, focusing on identification, prediction, and characterization, followed by design frameworks, linking active-learning lab cycles, mechanistic interpretability, curated data resources, and uncertainty estimation. Therefore, for reproducible benchmarks and interoperable data, collaborative computational and wet lab experimental validations must be required to accelerate AI-driven novel AMP discovery to combat multidrug-resistant Gram-negative pathogens. Full article

A new series of angular tricyclic pyrrolo[3,2-c]quinoline derivatives (PQs) was designed and synthesized to further explore the previously promising antiproliferative activity exhibited by the 4-benzodioxole-substituted hit 7d. Accordingly, several structural modifications mainly focused on the benzodioxole moiety were introduced, allowing us to gain new insights into the activity and biological profile. NCI antiproliferative screening (SRB colorimetric assay), together with MTS-based assay against six other tumour cell lines, enabled us a deeper understanding of the selectivity and potency patterns. This led to the identification of a new promising hit, compound 7p, which exhibited cytotoxic activity in the low micromolar range against MCF-7 and HeLa cells. Further biological evaluations, including apoptosis induction, clonogenic, and scratch tests, provided additional biological insights into the anticancer potential of these compounds, supporting the subsequent lead optimization process for more potent anticancer activity. The integrated in silico docking results evidenced a clear multi-target profile, as testified by the broad anticancer activity, and suggest a good potential for rational polypharmacology. Full article

The highly directional narrow-beam operation in mmWave networks, while effective at suppressing interference, lacks adaptability to dynamic traffic variations and blockages compared to D-TDD and JT schemes. D-TDD efficiently mitigates DL–UL cross-interference during asymmetric traffic. At the same time, joint transmission coordinates multiple base stations to deliver phase-aligned signals, converting interference into useful combined power and ensuring stable links under dynamic slot changes. However, these adaptive regimes are often overlooked in recent mmWave designs, leading to degraded communication performance. This work proposes D-TDD-based cooperative caching (DTCC) mmWave networks, where randomly distributed base stations with local caches enhance reliability and reduce backhaul load. Closed-form expressions for the cache hit probability and the average content success probability (ASP) are derived under the proposed DTCC framework. Popularity-based caching strategies with both equal and variable file sizes are analysed to maximise network-level performance. The simulation results validate that the proposed DTCC framework consistently enhances ASP in dense small-cell deployments, offering notable reliability gains over conventional single-BS (SBS) and static TDD (S-TDD)-based cooperative caching approaches. Full article

Objective: The aim of our study was to compare two questionnaires regarding their ability to globally assess the impact of headaches on daily functioning in children as a primary endpoint and, secondarily, to evaluate their correlation to frequency and headache strength. Background: Headache is a common complaint in children and adolescents, leading to functional impairment. The impact of primary headaches, such as migraine and tension-type headaches, varies according to pain severity and frequency. Although the PedMIDAS questionnaire is a validated tool for assessing headache-related impact in children, it can be difficult for children to complete. The HIT-6 questionnaire is user-friendly but has been validated exclusively for use in adults. Methods: Our method involved a prospective cohort study in children aged 6–18 years who visited the headache clinic at Bnai Zion Medical Center due to primary headaches. All children filled in both the PedMIDAS and HIT-6. Data on headache diagnosis, frequency and intensity along with demographic data were obtained. Results: Of the 100 children participating, 96 completed both questionnaires. The final sample was 66% (63) female, and the average age was 14 years (±3.3). Migraine was reported by 62% (60), followed by tension-type headaches (18%) and mixed headache (15%). A weak positive spearman correlation was observed between PedMIDAS and HIT-6 scores to age (respectively, ρ 0.3 with p value < 0.005, and ρ 0.2 with p value < 0.05), a weak positive spearman correlation as well between the HIT-6 score and both disease duration and headache intensity (respectively, ρ 0.221 with p value < 0.05 and ρ 0.250 with p value < 0.05). PedMIDAS score was weakly positively correlated to headache frequency (ρ 0.27 with p value < 0.05). A moderately positive spearman correlation was found between the PedMIDAS and HIT scores with ρ 0.6 and p value < 0.005. Linear regression analysis revealed a stronger correlation with headache frequency for the HIT-6 than for the PedMIDAS, when adjusted to gender and headache type. Conclusions: The HIT-6 questionnaire correlates with the PedMIDAS questionnaire and can serve as a good alternative for easily evaluating headache burden in children. Full article

Protein–protein interactions (PPIs) take place in many cellular processes, including the activation of cellular cascades, such as the MAPK/ERK (Mitogen-Activated Protein Kinase/Extracellular-Regulated Kinase) pathway. Deregulation of these pathways leads to the development of diseases, such as cancer. DEL-22379 is an ERK2 dimerization inhibitor, which presents anti-tumoral effects, without affecting ERK2 phosphorylation. Our aim was to identify new therapeutic molecules targeting ERK2 dimerization, based on DEL-22379 structure. In this study, we implemented a combination of computational and experimental workflow, which includes in silico techniques, such as scaffold hopping and virtual screening to generate a dataset of candidate compounds, a native PAGE (PolyAcrylamide Gel Electrophoresis) electrophoresis to experimentally screen the potential inhibitors, and a detailed molecular docking and chemical profile prediction to understand the potential mechanism of action of the selected compounds. From an initial dataset of 536 compounds, we obtained two hit molecules that exhibited inhibitory effects on ERK2 dimerization: Drug73 and Drug120. A computational analysis of the mechanism of action, unveiled that Drug73 and Drug120 presented an improved docking score, and better drug-like properties when compared to DEL-22379. This study shows that computational studies, in combination with experimental evaluation, can be useful and efficient to find new therapeutic compounds. Full article

Livestock is the backbone of the economy in an agricultural country like Pakistan, with cattle serving as a milk and protein source. In 2021/22, Pakistan was hit by the first major outbreak of lumpy skin disease (LSD) in cattle, in all four provinces. LSD is characterized by the development of skin nodules, leading to severe illness, decreased milk production, and mortality, causing huge economic losses. This study aimed to analyze and classify the lumpy skin disease virus (LSDV) strains involved in the outbreak in the Punjab province at the molecular and phylogenetic levels to develop effective control strategies. A combination of different real-time PCRs was used for the identification and differentiation between vaccine, wild-type, and recombinant LSDV strains. This was mented with the sequence determination and phylogenetic analysis of ten genomic loci from two selected isolates from the 2021/22 Pakistan outbreak. The combined data showed that these isolates belonged to LSDV clade 1.2 and were clearly different from the vaccine clade 1.1 (Neethling-like), as well as from the recombinant clade 2 strains. In addition, using a fit-for-purpose gel-based PCR, the isolates from the outbreak were also shown to be different from KSGP0240-based vaccines. Full article

Acne vulgaris is a prevalent inflammatory disease of the pilosebaceous unit in which Cutibacterium acnes (C. acnes) contributes to lesion initiation and persistence, supporting antibacterial interventions as a component of clinical management. Given the essential role of the 50S large ribosomal subunit—particularly 23S rRNA sites in the peptidyl transferase center and nascent peptide exit tunnel—in C. acnes protein synthesis and viability, targeting the 50S offers an effective path to lead discovery for acne treatment. Here, we present an integrated computational–experimental workflow to identify anti-C. acnes candidates from a 186,659-compound natural product library. Curated 50S/23S ligands trained and validated two ML-QSAR regression models built on different molecular fingerprints (MACCS keys and PubChem 2D) to predict anti-C. acnes activity and rapidly triage the library. Compounds were further screened by ADMET filtering and structure-based docking to 23S rRNA pockets, followed by cluster and interaction analysis. Among six experimental hits, three compounds exhibited MICs against C. acnes of ≤8 μg/mL, with tripterin, a pentacyclic triterpenoid, being the most potent (0.5–2 μg/mL across two acne-relevant strains). Collectively, these results indicate that a 50S ribosomal-focused, multistage computational screening workflow, integrated with in vitro assays, efficiently prioritizes compounds with quantifiable anti-C. acnes activity across a broad range of natural products. Full article

Background: Disability-linked neurodegeneration involves cholinergic dysfunction, amyloidogenesis, glutamatergic excitotoxicity, and dopaminergic imbalance, highlighting the need for multi-target modulation. Catharanthus roseus contains a diverse array of metabolites with potential polypharmacological properties. Methods: We curated 318 Catharanthus roseus metabolites and performed structure-based virtual screening against five CNS targets, namely BACE1, AChE, MAO-B, NMDAR, and D1, using target-specific positive controls. Cross-target intersection ranking nominated three hits. We assessed dynamic stability by 200 ns all-atom molecular dynamics simulations (MDS) and MM/PBSA; ADMET-AI profiled CNS-relevant properties. Results: The three metabolites (PubChem CIDs 485711, 56964592, and 162963996) repeatedly ranked among top binders across targets. All five protein–ligand complexes reached stable MD plateaus (RMSD < ~0.30 nm) with sustained key interactions; BACE1 and AChE showed the highest contact persistence and most favorable ΔG_total/ligand-efficiency. Conclusions: Convergent docking, MDS, and MM/PBSA support these metabolites as tractable multi-target leads, with BACE1/AChE prioritized for enzyme-level validation and the remaining targets for follow-up studies. Full article