Mahmood Jasem Jawad

Alzheimer’s disease (AD) is a devastating neurodegenerative disorder defined by progressive memory loss and synaptic failure. For decades, therapeutic development has focused on clearing amyloid-beta plaques, yet the repeated clinical failures of this approach necessitate a fundamental paradigm shift toward the brain’s immunometabolic landscape. The “Viral Mimicry” hypothesis posits that AD represents a state of sterile autoimmunity where the innate immune system mistakenly identifies self-nucleic acids as viral pathogens. This “ghost war” is ignited by the convergence of metabolic dysfunction and genomic instability: specifically, the leakage of mitochondrial DNA into the cytosol and the epigenetic derepression of ancient retrotransposons (LINE-1, HERVs). These endogenous ligands activate the cGAS-STING cytosolic sensing axis, a pathway that drives a chronic interferon response. Consequently, microglia and astrocytes are transformed into senescent, pro-inflammatory phenotypes that release a toxic Senescence-Associated Secretory Phenotype (SASP), directly fueling synaptic elimination. Crucially, major genetic risk factors, including APOE4 and TREM2 variants, exacerbate this cascade by compromising mitochondrial integrity and lipid metabolism, thereby sensitizing the brain to innate surveillance failure. By reconceptualizing AD as an acquired interferopathy driven by the “enemy within,” this framework highlights novel therapeutic targets. Specifically, repurposing Nucleoside Reverse Transcriptase Inhibitors (NRTIs) to block retrotransposition and deploying senolytics to clear dysfunctional glia offer promising strategies to arrest the progression from healthy aging to cognitive decline. This review synthesizes current research on the molecular mechanisms of viral mimicry, detailing the impact of genetic risk factors and evaluating emerging therapeutic interventions targeting this innate immune axis. Graphical Abstract The alternative text for this image may have been generated using AI. The Shift from Molecular Containment to Viral Mimicry in Alzheimer’s Disease. The figure contrasts the physiological state of healthy aging with the pathological state of Alzheimer’s Disease. (Left) In the healthy brain, "Molecular Containment" is maintained: immunogenic mitochondrial DNA is sequestered within the organelle, and ancient retrotransposons are epigenetically silenced within the nucleus. (Right) In Alzheimer’s Disease, the convergence of genomic instability and metabolic failure leads to a "loss of inhibition". The derepression of retroelements (LINE-1 (long interspersed nuclear element-1) dsDNA, HERV dsRNA) and the leakage of mitochondrial DNA into the cytosol activate the cGAS-STING innate immune axis. This viral mimicry transforms microglia into a senescent, Lipid-Droplet Accumulating phenotype, driving a chronic Type I Interferon response that fuels neurodegeneration

Drilling fluids used in high‐performance well operations often struggle to maintain rheological stability, colloidal dispersion, and filtration control under harsh downhole conditions. This study engineered a multifunctional Fe ₃ O ₄ @Saponin/Cu(II) nanocomposite to address these challenges. The combination was selected due to its synergistic magnetic, steric, and interfacial stabilization capabilities that enhance compatibility with polymer–clay systems. Magnetite nanoparticles were synthesized by alkaline coprecipitation, coated with saponin for steric protection, and coordinated with Cu ²⁺ to reinforce interfacial bonding. Characterization by FT‐IR, TGA, and SEM confirmed successful sequential modification, improved dispersion, and increased thermal stability. Dynamic light scattering established 500 ppm as the optimum concentration retaining sub‐100‐nm particle size after several days, while rheological tests showed higher plastic viscosity and yield point without disturbing the Bingham plastic flow behavior. Filtration measurements demonstrated approximately 50% reduction in fluid loss and 44% thinner, denser filter cakes at this dosage. These enhancements arise from nanoscale pore plugging, microstructural reinforcement, and amphiphilic interactions among Fe ₃ O ₄ , saponin, and Cu(II). Overall, the developed nanocomposite offers a stable, sustainable additive that strengthens drilling fluid performance and holds potential for field‐scale deployment.

This study aims to create a machine-learning framework that integrates histopathological imaging, immune marker data, and clinical features to classify tum

Complex systemic immune dysregulation and chronic inflammation in rheumatological diseases lead to long-term inflammation and morbidity, which are primarily controlled by systemic immunosuppressant agents with diverse effects, underscoring the need for more precise and innovative treatments. The unmatched specificity in targeting disease-driving genes and pathways makes small interfering RNA (siRNA) a transformative treatment approach. This review examines how siRNA can precisely transform the therapy of rheumatological diseases by targeting critical molecular pathways associated with inflammation, immunological dysregulation, and tissue damage. While this study elucidates siRNA therapies targeting inflammatory pathways to treat rheumatological diseases, it moves beyond the conventional therapeutic application of siRNAs targeting pro-inflammatory cytokines; it explicitly focuses on upstream signaling hubs within synovial fibroblasts, T cells, and plasma cells, as well as on non-inflammatory mechanisms that lead to bone damage. The review also integrates in vitro and in vivo, as well as preclinical siRNA studies, to identify the most promising targets and determine whether these siRNA modifications, combined with delivery systems, achieve body-wide and joint-specific immune-stromal network reprogramming through targeted delivery without causing total immune system breakdown.

Accurate prediction of polyethylene glycol (PEG) density is critical for its growing use as an eco‐friendly solvent in chemical separations and industrial processes, yet experimental measurements are time‐intensive and costly. This study addresses the challenge by developing a hybrid gradient boosting decision tree (GBDT) machine learning model to predict PEG density with high precision. The model's hyperparameters were optimized using four evolutionary algorithms including evolutionary strategies (ES), Bayesian probability improvement (BPI), batch Bayesian optimization (BBO), and self‐adaptive differential evolution (SADE) on a dataset of 293 points compiled from existing literature, with K ‐fold cross‐validation applied to prevent overfitting. Model performance was assessed via optimization runtime and metrics including R ‐squared ( R ² ), mean squared error (MSE), and average absolute relative error percentage (AARE%). Key findings indicate that temperature has the strongest influence on PEG density (relevance factor: −0.87), followed by weaker correlations with pressure (0.39) and molecular weight (−0.17). The ES algorithm achieved the highest accuracy ( R ² : 0.996, MSE: 1.149, AARE%: 0.078% on the test dataset), closely followed by BPI ( R ² : 0.994, MSE: 1.663, AARE%: 0.092%), while SADE performed least effectively ( R ² : 0.989, MSE: 1.939, AARE%: 0.091%) with the longest runtime (~2000 s). Sensitivity and SHAP analyses confirmed temperature's dominant role. These hybrid models offer a novel, computationally efficient alternative to experimental methods, advancing predictive modeling for PEG's physicochemical properties by integrating evolutionary optimization with GBDT, achieving superior accuracy compared to prior approaches.

Accurate prediction of the coke reactivity index (CRI) is vital for controlling the performance and efficiency of metallurgical coke production. This study aims to establish an advanced predictive framework based on gradient boosting machine (GBM) algorithm optimized through four metaheuristic strategies. A total of 636 coal samples obtained from an operating coke plant were analyzed, with eight physicochemical predictors measured according to ASTM standards. Data integrity was confirmed using the Isolation Forest technique, and model accuracy was assessed through five‐fold cross‐validation employing R ² , MSE, RE%, and AARE% metrics. The results demonstrated that GBM models optimized via Bayesian probability improvement (BPI) and Gaussian process optimization (GPO) achieved superior prediction accuracy and generalization ( R ² = 0.853 and total MSE ≈ 0.47) compared to other optimization methods. SHAP interpretation revealed volatile matter, sulfur, and ash as dominant features governing coke reactivity, while MMR, moisture, and fluidity exhibited inverse contributions. The developed hybrid optimization‐based gradient boosting decision tree (GBDT) methodology provides a robust and transparent modeling framework for industrial coke quality assessment and can be extended for real‐time evaluation within process control environments.