Search Results (146)

Oxidative stress is a defining and pervasive driver of neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). As a molecular accelerant, reactive oxygen species (ROS) and reactive nitrogen species (RNS) compromise mitochondrial function, amplify lipid peroxidation, induce protein misfolding, and promote chronic neuroinflammation, creating a positive feedback loop of neuronal damage and cognitive decline. Despite its centrality in promoting disease progression, attempts to neutralize oxidative stress with monotherapeutic antioxidants have largely failed owing to the multifactorial redox imbalance affecting each patient and their corresponding variation. We are now at the threshold of precision redox medicine, driven by advances in syndromic multi-omics integration, Artificial Intelligence biomarker identification, and the precision of patient-specific therapeutic interventions. This paper will aim to reveal a mechanistically deep assessment of oxidative stress and its contribution to diseases of neurodegeneration, with an emphasis on oxidatively modified proteins (e.g., carbonylated tau, nitrated α-synuclein), lipid peroxidation biomarkers (F2-isoprostanes, 4-HNE), and DNA damage (8-OHdG) as significant biomarkers of disease progression. We will critically examine the majority of clinical trial studies investigating mitochondria-targeted antioxidants (e.g., MitoQ, SS-31), Nrf2 activators (e.g., dimethyl fumarate, sulforaphane), and epigenetic reprogramming schemes aiming to re-establish antioxidant defenses and repair redox damage at the molecular level of biology. Emerging solutions that involve nanoparticles (e.g., antioxidant delivery systems) and CRISPR (e.g., correction of mutations in SOD1 and GPx1) have the potential to transform therapeutic approaches to treatment for these diseases by cutting the time required to realize meaningful impacts and meaningful treatment. This paper will argue that with the connection between molecular biology and progress in clinical hyperbole, dynamic multi-targeted interventions will define the treatment of neurodegenerative diseases in the transition from disease amelioration to disease modification or perhaps reversal. With these innovations at our doorstep, the future offers remarkable possibilities in translating network-based biomarker discovery, AI-powered patient stratification, and adaptive combination therapies into individualized/long-lasting neuroprotection. The question is no longer if we will neutralize oxidative stress; it is how likely we will achieve success in the new frontier of neurodegenerative disease therapies. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Modeling and simulation of aneurysm formation, growth, and rupture plays an essential role in a wide spectrum of application scenarios, ranging from risk stratification to stability prediction, and from clinical decision-making to treatment innovation. Unfortunately, it remains a non-trivial task due to the difficulties imposed by the complex and under-researched pathophysiological mechanisms behind the different development stages of various aneurysms. In this paper, we present a novel computational method for aneurysm simulation using smoothed particle hydrodynamics (SPH). Firstly, we consider blood in a vessel as a kind of incompressible fluid and model its flow dynamics using the SPH method; and then, to simulate aneurysm growth and rupture, the relationship between the aneurysm development and the properties of fluid particles is established by solving the motion control equation. In view of the prevalence of aneurysms in bifurcation vessels, we further enhance the capability of the model by introducing a solution for bifurcation aneurysms simulation according to Murray’s law. In addition, a CUDA parallel computing scheme is also designed to speed up the simulation process. To evaluate the performance of the proposed method, we conduct extensive experiments with different physical parameters associated with morphological characteristics of an aneurysm. The experimental results demonstrate the effectiveness and efficiency of proposed method in modeling and simulating aneurysm formation, growth, and rupture. Full article

Monoclonal Gammopathy of Undetermined Significance (MGUS) is a precursor to hematologic malignancies such as Multiple Myeloma (MM) and Waldenström Macroglobulinemia (WM). Accurate risk stratification of MGUS patients remains a clinical and computational challenge, with existing models often misclassifying both high-risk and low-risk individuals, leading to inefficient healthcare resource allocation. This study presents a machine learning (ML)-based approach for early prediction of MM/WM progression, using routinely collected hematological data, which are selected based on clinical relevance. A retrospective cohort of 292 MGUS patients, including 7 who progressed to malignancy, was analyzed. For each patient, a feature descriptor was constructed incorporating the latest biomarker values, their temporal trends over the previous year, age, and immunoglobulin subtype. To address the inherent class imbalance, data augmentation techniques were applied. Multiple ML classifiers were evaluated, with the Support Vector Machine (SVM) achieving the highest performance (94.3% accuracy and F1-score). The model demonstrates that a compact set of clinically relevant features can yield robust predictive performance. These findings highlight the potential of ML-driven decision-support systems in electronic health applications, offering a scalable solution for improving MGUS risk stratification, optimizing clinical workflows, and enabling earlier interventions. Full article

The increasing interest in tricuspid regurgitation (TR) is due to the deep link between mortality and the severity of TR, as well as the limited application of surgical solutions in a setting marked by high in-hospital mortality, attributed to the late presentation of the disease. This delay in intervention is likely associated with a limited understanding of valvular and ventricular anatomy as well as the pathophysiology of the disease, leading to an underestimation of TR severity. With the rapid development of transcatheter solutions showing early safety and efficacy, there is a growing necessity to accurately understand and diagnose the valvular disease process to determine suitable management strategies. This review will outline the normal and pathological anatomy of the tricuspid valve, classify the anatomical substrates of TR, and present new risk stratification methods to determine the appropriate timing for both medical and surgical treatment. Full article

This study investigates the limitations of localized heating in a single-story dwelling, using a validated computational fluid dynamics (CFD) model to analyze thermal stratification and its impact on occupant comfort. A comparative evaluation of turbulence models (k-ε and k-ω SST) and equations of state (Soave–Redlich–Kwong and Peng–Robinson) identified the k-ω SST model with the Soave–Redlich–Kwong equation as the most accurate and computationally efficient combination for capturing temperature gradients and achieving rapid convergence. Experimental validation demonstrated strong agreement between simulated and measured temperature profiles, confirming the model’s reliability. The results highlight a fundamental trade-off between localized thermal comfort and overall indoor temperature uniformity in conventionally heated spaces. While localized heating enhances comfort near the heat source, it generates vertical temperature disparities exceeding acceptable comfort thresholds at greater distances. Specifically, at 3 m from the heat source, the temperature difference between ankle and head height reached 6 °C, surpassing the 4 °C limit recommended by ASHRAE-55 for standing occupants. These findings underscore the need for alternative heating solutions that prioritize uniform heat distribution, energy efficiency, and optimized ventilation to improve indoor thermal comfort in residential buildings. This study provides critical insights to help develop and implement sustainable heating strategies and the design of energy-efficient dwellings. Full article

Background/Objectives: Current developments in computer-aided systems rely heavily on complex and computationally intensive algorithms. However, even a simple approach can offer a promising solution to reduce the burden on clinicians. Addressing this, we aim to employ unsupervised cluster analysis to identify prognostic subgroups of non-small-cell lung cancer (NSCLC) patients with brain metastasis (BM). Simple-yet-effective algorithms designed to identify similar group characteristics will assist clinicians in categorizing patients effectively. Methods: We retrospectively collected data from 95 NSCLC patients with BM treated at two oncology centers. To identify clinically distinct subgroups, two types of unsupervised clustering methods—two-step clustering (TSC) and hierarchical cluster analysis (HCA)—were applied to the baseline clinical data. Patients were categorized into prognostic classes according to the Diagnosis-Specific Graded Prognostic Assessment (DS-GPA). Survival curves for the clusters and DS-GPA classes were generated using Kaplan–Meier analysis, and the differences were assessed with the log-rank test. The discriminative ability of three categorical variables on survival was compared using the concordance index (C-index). Results: The mean age of the patients was 61.8 ± 0.9 years, and the majority (77.9%) were men. Extracranial metastasis was present in 71.6% of the patients, with most (63.2%) having a single BM. The DS-GPA classification significantly divided the patients into prognostic classes (p < 0.001). Furthermore, statistical significance was observed between clusters created by TSC (p < 0.001) and HCA (p < 0.001). HCA showed the highest discriminatory power (C-index = 0.721), followed by the DS-GPA (C-index = 0.709) and TSC (C-index = 0.650). Conclusions: Our findings demonstrated that the TSC and HCA models were comparable in prognostic performance to the DS-GPA index in NSCLC patients with BM. These results suggest that unsupervised clustering may offer a data-driven perspective on patient stratification, though further validation is needed to clarify its role in prognostic modeling. Full article

Background/Objectives: Cxbladder^® Triage Plus is a multimodal urinary biomarker assay that combines reverse transcription-quantitative analysis of five mRNA targets and droplet-digital polymerase chain reaction (ddPCR) analysis of six DNA single-nucleotide variants (SNVs) from two genes (fibroblast growth factor receptor 3 (FGFR3) and telomerase reverse transcriptase (TERT)) to provide risk stratification for urothelial carcinoma (UC) in patients with hematuria. This study evaluated the analytical validity of Triage Plus. Methods: The development dataset used urine samples from patients with microhematuria or gross hematuria that were previously stabilized with Cxbladder solution. Triage Plus was evaluated for predicted performance, analytical criteria (linearity, sensitivity, specificity, accuracy, and precision), extraction efficiency, and inter-laboratory reproducibility. Results: The development dataset included 987 hematuria samples. Compared with cystoscopy (standard of care), Triage Plus had a predicted sensitivity of 93.6%, specificity of 90.8%, positive predictive value (PPV) of 46.5%, negative predictive value of 99.4%, and test-negative rate of 84.1% (score threshold 0.15); the PPV increased to 74.6% for the 0.54 score threshold. For the individual FGFR3 and TERT SNVs, the limit of detection (analytical sensitivity) was a mutant-to-wild type DNA ratio of 1:440–1:1250 copies/mL. Intra- and inter-assay variance was low, while extraction efficiency was high. All other pre-specified analytical criteria (linearity, specificity, and accuracy) were met. Triage Plus showed good reproducibility (87.9% concordance between laboratories). Conclusions: Cxbladder Triage Plus accurately and reproducibly detected FGFR3 and TERT SNVs and, in combination with mRNA expression, provides a non-invasive, highly sensitive, and reproducible tool that aids in risk stratification of patients with hematuria. Full article

A two-dimensional time-dependent model is presented for upward-propagating internal gravity waves generated by an imposed thermal forcing in a layer of fluid with uniform background velocity and stable stratification under the anelastic approximation. The configuration studied is representative of a situation with deep or shallow latent heating in the lower atmosphere where the amplitude of the waves is small enough to allow linearization of the model equations. Approximate asymptotic time-dependent solutions, valid for late time, are obtained for the linearized equations in the form of an infinite series of terms involving Bessel functions. The asymptotic solution approaches a steady-amplitude state in the limit of infinite time. A weakly nonlinear analysis gives a description of the temporal evolution of the zonal mean flow velocity and temperature resulting from nonlinear interaction with the waves. The linear solutions show that there is a vertical variation of the wave amplitude which depends on the relative depth of the heating to the scale height of the atmosphere. This means that, from a weakly nonlinear perspective, there is a non-zero divergence of vertical momentum flux, and hence, a non-zero drag force, even in the absence of vertical shear in the background flow. Full article

The remote sensing inversion of internal solitary waves (ISWs) enables the retrieval of ISW parameters and facilitates the analysis of their spatial variability. In this study, we utilize continuous optical imagery from the FY-4B satellite to extract real-time ISW propagation speeds throughout their evolution from generation to shoaling. ISW parameters are retrieved in the northern South China Sea based on the quantitative relationship between sea surface current divergence and ISW surface features in optical imagery. The inversion method employs a fully nonlinear equation with continuous stratification to account for the strongly nonlinear nature of ISWs and uses the propagation speed extracted from continuous imagery as a constraint to determine a unique solution. The results show that as ISWs propagate from deep to shallow waters in the northern South China Sea, their statistically averaged amplitude initially increases and then decreases, while their propagation speed continuously decreases with decreasing depth. The inversion results are consistent with previous in situ observations. Furthermore, a three-day consecutive remote sensing tracking analysis of the same ISW revealed that the spatial variation in its parameters aligned well with the abovementioned statistical results. The findings provide an effective inversion approach and supporting datasets for extensive ISW monitoring. Full article

Lung cancer remains the leading cause of cancer-related mortality globally, largely due to late-stage diagnoses. While low-dose computed tomography (LDCT) has improved early detection and reduced mortality in high-risk populations, traditional screening strategies often adopt a one-size-fits-all approach based primarily on age and smoking history. This can lead to limitations, such as overdiagnosis, false positives, and the underrepresentation of non-smokers, which are especially prevalent in Asian populations. Precision medicine offers a transformative solution by tailoring screening protocols to individual risk profiles through the integration of clinical, genetic, environmental, and radiological data. Emerging tools, such as risk prediction models, radiomics, artificial intelligence (AI), and liquid biopsies, enhance the accuracy of screening, allowing for the identification of high-risk individuals who may not meet conventional criteria. Polygenic risk scores (PRSs) and molecular biomarkers further refine stratification, enabling more personalized and effective screening intervals. Incorporating these innovations into clinical workflows, alongside shared decision-making (SDM) and robust data infrastructure, represents a paradigm shift in lung cancer prevention. However, implementation must also address challenges related to health equity, algorithmic bias, and system integration. As precision medicine continues to evolve, it holds the promise of optimizing early detection, minimizing harm, and extending the benefits of lung cancer screening to broader and more diverse populations. This review explores the current landscape and future directions of precision medicine in lung cancer screening, emphasizing the need for interdisciplinary collaboration and population-specific strategies to realize its full potential in reducing the global burden of lung cancer. Full article

Background/Objectives: Genetic abnormalities play a pivotal role in patient risk stratification, therapeutic decision-making, and elucidating the disease pathogenesis in hematological malignancies. In multiple myeloma (MM) and acute myeloid leukemia (AML)/myelodysplastic syndrome (MDS), numerous recurring genetic aberrations are well documented. Fluorescence in situ hybridization (FISH) is a cornerstone of clinical diagnostics for detecting these abnormalities. Conventionally, FISH assesses up to two biomarkers, with one or two circles per slide, but this approach faces challenges when cancer cell yields are limited, particularly in post-treatment follow-up specimens. Methods: To overcome this limitation, we developed a multi-well method, enabling the simultaneous testing of multiple biomarkers on a single microscopic slide. This study included 53 MM and 129 AML/MDS cases. Results: With a cohort of 182 patients, 1016 FISH assays performed on multi-well slides accurately detected diagnostic genetic aberrations previously identified by karyotyping and/or FISH, achieving a sensitivity and specificity of 100%. The use of multi-well slides achieved up to a 2.5-fold increase in the number of wells per slide while achieving more than a 3-fold reduction in the reagent volume compared to traditional methods. This advancement leverages distinct FISH signal patterns to strategically combine biomarkers within multiple wells, suitable for specimens from diagnosis, follow-ups, and relapses, regardless of the cancer cell quantity. Conclusions: The multi-well approach enhances the accessibility to comprehensive biomarker analysis, reducing both the processing time and costs. Beyond MM and AML/MDS, this technique holds promise for use with other hematological malignancies with limited sample volumes, offering an efficient, cost-effective solution for precision diagnostics. Full article

Background: Acute phosphate nephropathy (APN) is an underrecognized cause of acute kidney injury (AKI), typically associated with the use of oral sodium phosphate (OSP)-based bowel preparations. It is characterized by calcium phosphate crystal deposition within the renal tubules and may result in permanent renal impairment. Despite known risks, phosphate-containing solutions are still widely used without sufficient risk stratification. Methods: We retrospectively evaluated 517 native kidney biopsies performed in our nephrology clinic between 2017 and 2022. Among these, 12 patients with unexplained AKI and recent colonoscopy history were identified. In nine cases, non-specific tubular deposits on routine staining prompted further histochemical analysis. All had a history of recent OSP-based bowel cleansing. The use of von Kossa staining confirmed calcium phosphate deposition, consistent with APN. Results: Out of 517 kidney biopsies performed during the study period, 9 patients were diagnosed with APN based on histopathological findings following recent colonoscopy and OSP-based bowel cleansing. The mean age was 58.7 years, and three were female. Hypertension was present in seven patients, diabetes mellitus in three, and epilepsy in two; one patient had no comorbidities. Baseline renal function was normal (mean serum creatinine 0.86 mg/dL) and increased to 1.76 mg/dL at three months post-exposure. All biopsies revealed tubulointerstitial calcium phosphate deposits and interstitial inflammation; mesangial hypercellularity was observed in five cases, tubular atrophy in three, and acute tubular necrosis in one. All samples stained positive with von Kossa staining. Over time, all patients developed chronic kidney disease, and one progressed to end-stage renal disease requiring dialysis. Conclusions: In patients presenting with unexplained AKI and recent OSP-based bowel preparation, APN should be considered in the differential diagnosis. When routine histology is inconclusive, definitive diagnosis may require special histochemical staining. Risk-based restrictions on phosphate-containing agents are warranted to reduce preventable kidney injury. Full article

Large-scale commercial wine fermentation requires the monitoring and control of multiple variables to achieve optimal results. Challenges in measurement arise from turbidity, stratification in large unmixed volumes, the presence of grape skins and solids during red wine fermentations, the small changes in variables that necessitate precise sensors, and the unique composition of each juice, which makes every fermentation distinct. These complications contribute to nonlinear and time-variant characteristics for most control variables. This paper reviews sensors, particularly online ones, utilized in commercial winemaking. It examines the measurement of solution properties (density, weight, volume, osmotic pressure, dielectric constant, and refractive index), sugar consumption, ethanol and glycerol production, redox potential, cell mass, and cell viability during wine fermentation and their relevance as variables that could enhance the estimation of parameters in diagnostic and predictive wine fermentation models. Various methods are compared based on sensitivity, availability of sensor systems, and their appropriateness for measuring properties in large commercial wine fermentations. Additionally, factors influencing the adoption of control strategies are discussed. Finally, potential opportunities for control strategies and challenges for future sensor developments are outlined. Full article

Plane geometry problem solving has been a long-term challenge in mathematical reasoning and symbolic artificial intelligence. With the continued advancement of automated methods, the need for large-scale datasets and rigorous evaluation frameworks has become increasingly critical for benchmarking and guiding system development. However, existing resources often lack sufficient scale, systematic difficulty modeling, and quantifiable, process-based evaluation metrics. To address these limitations, we propose FGeo-Eval, a comprehensive evaluation system for plane geometry problem solving, and introduce the FormalGeo30K dataset, an extended dataset derived from FormalGeo7K. The evaluation system includes a problem completion rate metric PCR to assess partial progress, theorem weight computation to quantify knowledge importance, and a difficulty coefficient based on reasoning complexity. By analyzing problem structures and solution dependencies, this system enables fine-grained difficulty stratification and objective performance measurement. Concurrently, FormalGeo30K expands the dataset to 30,540 formally annotated problems, supporting more robust model training and evaluation. Experimental results demonstrate that the proposed metrics effectively evaluate problem difficulty and assess solver capabilities. With the augmented dataset, the average success rate across all difficulty levels for the FGeo-HyperGNet model increases from 77.43% to 85.01%, while the average PCR increases from 88.57% to 91.79%. These contributions provide essential infrastructure for advancing plane geometry reasoning systems, offering standardized benchmarks for model development and guiding optimization of geometry-solving models. Full article