Biomarkers Of Disease Progression Research Articles

Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.

Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS. In this retrospective study, we collected 3D T1-weighted brain MRI scans of PwMS to build (1) a cross-sectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal single-center cohort of patients with early MS as a clinical use case. We trained and evaluated a 3D DenseNet architecture to predict DD from minimally preprocessed images while age predictions were obtained with the DeepBrainNet model. The brain-predicted DD gap (the difference between predicted and actual duration) was proposed as a DD-adjusted global measure of MS-specific brain damage. Model predictions were scrutinized to assess the influence of lesions and brain volumes while the DD gap was biologically and clinically validated within a linear model framework assessing its relationship with BAG and physical disability measured with the Expanded Disability Status Scale (EDSS). We gathered MRI scans of 4,392 PwMS (69.7% female, age: 42.8 ± 10.6 years, DD: 11.4 ± 9.3 years) from 15 centers while the early MS cohort included 749 sessions from 252 patients (64.7% female, age: 34.5 ± 8.3 years, DD: 0.7 ± 1.2 years). Our model predicted DD better than chance (mean absolute error = 5.63 years, R2 = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: r = 0.06 [0.00-0.13], p = 0.07). Predictions were influenced by distributed variations in brain volume and, unlike brain-predicted age, were sensitive to MS lesions (difference between unfilled and filled scans: 0.55 years [0.51-0.59], p < 0.001). DD gap significantly explained EDSS changes (B = 0.060 [0.038-0.082], p < 0.001), adding to BAG (ΔR2 = 0.012, p < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change (r = 0.50 [0.39-0.60], p < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (ΔR2 = 0.064, p < 0.001). The brain-predicted DD gap is sensitive to MS-related lesions and brain atrophy, adds to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally, and may be used as an MS-specific biomarker of disease severity and progression.

Choroidal vascularity index in health and systemic diseases: a systematic review

Abstract Background The choroid, a highly vascular structure within the eye, is significantly influenced by various systemic conditions. The advent of enhanced depth optical coherence tomography has improved our ability to evaluate choroidal pathophysiology. The choroidal vascularity index (CVI), a noninvasive and reliable tool, serves as an effective means of assessing the choroidal vascular structure. Recent studies have increasingly focused on exploring CVI alterations under different systemic conditions. This study aims to provide a comprehensive summary of the latest research findings in this area. Methods A systematic literature review was conducted on October 1, 2023, using two databases, MEDLINE (via PubMed) and Scopus. Search terms were tailored specifically for each database to ensure a thorough exploration of relevant literature. The studies identified were qualitatively assessed, with particular emphasis on outcomes related to CVI and choroidal thickness. Results A total of 48 studies were included in the review, encompassing a diverse range of systemic conditions such as diabetes, central nervous system disorders, cardiovascular diseases, autoimmune disorders, and infectious diseases. Notable reductions in CVI were observed in diabetic retinopathy, autoimmune diseases, and neurodegenerative disorders. Additionally, the review highlighted variations in CVI values related to the severity of systemic diseases, indicating its potential use as a biomarker for disease progression. Conclusion This review highlights the significant correlation between variations in the choroidal vascularity index and diverse systemic conditions affecting hemodynamics. An enhanced understanding of CVI provides deeper insights into the pathophysiological mechanisms underlying these disorders and positions CVI as a promising biomarker for early detection and monitoring. Nevertheless, its clinical utility warrants careful assessment. Future research should address the potential limitations of CVI to fully capitalize on its diagnostic and prognostic potential.

Parkinson’s Disease: Biomarkers for Diagnosis and Disease Progression

Parkinson’s disease (PD) is a progressive neurological disease that causes both motor and nonmotor symptoms. While our understanding of putative mechanisms has advanced significantly, it remains challenging to verify biomarkers with sufficient evidence for regular clinical use. Clinical symptoms are the primary basis for diagnosing the disease, which can be mild in the early stages and overlap with other neurological disorders. As a result, clinical testing and medical records are mostly relied upon for diagnosis, posing substantial challenges during both the initial diagnosis and the continuous disease monitoring. Recent biochemical, neuroimaging, and genetic biomarkers have helped us understand the pathophysiology of Parkinson’s disease. This comprehensive study focuses on these biomarkers, which were chosen based on their relevance, methodological excellence, and contribution to the field. Biochemical biomarkers, including α-synuclein and glial fibrillary acidic protein (GFAP), can predict disease severity and progression. The dopaminergic system is widely used as a neuroimaging biomarker to diagnose PD. Numerous genes and genome wide association study (GWAS) sites have been related to the development of PD. Recent research on the SNCA gene and leucine-rich repeat protein kinase 2 (LRRK2) has shown promising results. By evaluating current studies, this review intends to uncover gaps in biomarker validation and use, while also highlighting promising improvements. It emphasizes the need for dependable and reproducible indicators in improving PD diagnosis and prognosis. These biomarkers may open up new avenues for early diagnosis, disease progression tracking, and the development of personalized treatment programs.

Abstract IA020: Deciphering the mechanisms of neutrophil immune response in bone metastatic prostate cancer

Abstract Metastatic disease of prostate cancer (PCa) is currently incurable. Bone is the most common tissue site for prostate cancer metastasis and progression in bone is largely dictated by tumor-stromal interactions in the bone microenvironment. We showed previously that bone marrow neutrophils inhibit bone metastatic PCa growth, however metastatic PCa becomes resistant to neutrophil response and progresses within the bone compartment. It is unclear how these findings translate to a clinical setting. Further, the mechanisms associated with tumor resistance to neutrophil immune response remain unknown and presents a new avenue for therapeutic intervention. Thus, the goals of this study were: 1) to define the impact of metastatic PCa on neutrophil function throughout prostate cancer progression and 2) to determine the potential of neutrophils as predictive biomarkers of PCa disease progression. To do this, peripheral blood polymorphonuclear neutrophils (PMNs) were collected from 4 patient cohorts (localized PCa, metastatic hormone-sensitive PCa (mHSPC), metastatic castration-resistant (mCRPC) and healthy men) and PMN molecular and functional properties assessed, including: cancer cell cytotoxicity and cell surface markers. There was a significant decrease with disease progression in PMN expression of CD10, a marker for differentiation, while in tandem, there was an increase in pro-inflammatory marker, CD88 (complement receptor) in mCRPC men compared to mHSPC. PMNs from all disease stages were equally cytotoxic to human PCa cell lines (LNCaP, C42B, and PC3). However, second generation androgen deprivation therapy (ADT) suppressed patient PMN cytotoxicity against mCRPC cells (C42B, PC3). This suppression was due to androgen receptor (AR)-mediated regulation of the pleotropic and immune suppressant cytokine, transforming growth factor beta receptor I (TβRI). PMNs from mCRPC patients who received 2nd generation ADT, expressed significantly more TβRI than patients who only received 1st line ADT. Further, we found that PMNs in CRPC patient bone metastases express more TβRI than PMNs in matched liver metastases, which express little to no TβRI. Treatment of mouse bone marrow neutrophils with enzalutamide, increased neutrophil TβRI expression in a dose dependent manner and blocked cytotoxicity against PCa cells. Exogenous testosterone treatment in vivo, pharmacologic inhibition (using Repsox, a small molecule kinase inhibitor of TβRI) or genetic deletion (using conditional TβRI knockout mice) rescued enzalutamide-mediated neutrophil suppression and restored neutrophil cytotoxicity. These data collectively suggest that AR inhibition suppresses anti-tumor neutrophil responses via TβRI however, this study highlights the ability to leverage standard-care ADT to generate neutrophil anti-tumor responses against bone metastatic PCa. Citation Format: Leah M Cook. Deciphering the mechanisms of neutrophil immune response in bone metastatic prostate cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr IA020.

Association Between Retinol-Binding Protein 4 Levels and Hepatitis C Virus Infection: A Meta-Analysis

Background and Objectives: The relationship between circulating retinol-binding protein 4 (RBP4) levels and hepatitis C virus (HCV) infection remains unclear. This study aims to systematically assess RBP4 expression in patients with HCV and its correlation with disease severity. Materials and Methods: We searched the Embase, PubMed, and Cochrane databases for relevant studies up to 1 January 2024. This study was registered on PROSPERO (CRD42023489051). Results: Our analysis included eight studies with 2612 participants (1152 controls and 1282 patients with HCV). Overall, RBP4 levels did not significantly differ between patients with HCV and controls (SMD: −0.36; 95% CI: −0.94, 0.23; p = 0.23). However, in a subgroup of Asian subjects, patients with HCV showed significantly lower RBP4 levels (SMD: −0.40; 95% CI: −0.49, −0.31; p = 0.10). Additionally, a negative correlation between RBP4 levels and disease severity was observed across all studied populations. Conclusions: RBP4 levels may vary due to HCV genotype, ethnicity, and environmental factors. In the context of HCV infection, RBP4 levels appear to reflect the severity of disease progression. Our findings indicate that RBP4 could serve as a biomarker for HCV disease progression. Further research is needed to elucidate the complex mechanisms of RBP4 in HCV infection.

Identification of Genomic Biomarkers of Disease Progression and Survival in Primary CNS Lymphoma.

The determination of the genetic subtypes of primary CNS lymphoma (PCNSL) and their relationship to differential chemo-immunotherapeutic response has not been established. There is a particular need for genomic biomarkers that identify patients with newly-diagnosed PCNSL at high risk of early progression and death. We applied targeted next-generation sequencing for detection of recurrent single nucleotide variants, copy number alterations and zygosity abnormalities in diagnostic specimens from 78 PCNSL patients treated with a standard methotrexate-based regimen, to identify prognostically significant molecular subgroups. All patients received induction immunochemotherapy and 44 proceeded to dose-intensive consolidation. Genomic aberrations at four loci were associated with 91% of lymphoma progression events and all 15 deaths: (1) chromosome 6p copy-neutral loss of heterozygosity (CN-LOH) or focal homozygous deletion (HD) at 6p21.3, and mutations of tumor suppressor genes (2) BTG1, (3) ETV6 and (4) TP53. Cox regression multivariate analysis demonstrated a high risk of progression in patients with aberrations at these loci. Genomic aberrations at these loci were also associated with significantly shorter survival. Lower expression of HLA-DR was associated with 6p CN-LOH/6p21.3 HD and inferior prognosis. These genomic aberrations identify a high-risk molecular subgroup that may inform risk stratification in PCNSL. Further elucidation of the mechanisms of therapeutic resistance associated with the high-risk genetic phenotype is requisite to facilitate precision medicine and progress in therapy.

Unravelling T-cell dynamics and immune responses in initial and recurrent uveitis.

This study aimed to identify novel serological targets and investigate immune responses in patients with non-infectious uveitis, focusing on differences between initial onset and recurrent episodes. Differential gene expression analysis, immunocyte typing and T-cell receptor (TCR) gene analysis were conducted on RNA-sequenced peripheral blood samples from healthy individuals (n = 6) and non-infectious uveitis patients (n = 12), divided into 6 patients each at initial onset and recurrent stages. Peripheral blood T-cell types were analysed using flow cytometry. Bioinformatics methods included tools for RNA sequencing data processing, CIBERSORT for immune cell type prediction and specialized software for TCR repertoire analysis. Findings indicated that individuals with recurrent uveitis demonstrated a stronger adaptive immune response and a more pronounced immune imbalance compared to those with initial onset. Memory T cells were predominant in recurrent episodes, suggesting their potential role as biomarkers for disease progression. Significant differences in TCR diversity and V(D)J gene usage were observed between the various uveitis groups and healthy controls. Importantly, 38 uveitis-specific TCR sequences showed substantial expansion in the uveitis patients compared to controls. An elevated expansion of these specific TCR sequences was associated with an increased risk of uveitis development. The study highlights the critical role of adaptive immune responses and specific immune cell types in the pathogenesis of recurrent uveitis. Identification of the uveitis-specific TCR repertoire set could provide deeper insights into the disease and facilitate the development of targeted therapies for uveitis patients.

Enhanced Delta-gamma Phase Amplitude Coupling during Phasic REM Sleep in isolated REM Sleep Behavior Disorder.

This study aims to analyze phase-amplitude coupling (PAC) patterns during rapid eye movement (REM) sleep in patients with isolated REM sleep behavior disorder (iRBD), compared with demography-matched healthy control (HC) participants. At baseline, electroencephalogram data from 13 iRBD patients and 10 HCs during REM sleep were analyzed. During follow-up, 4 patients (converters) later converted to alpha-synucleinopathies. Phasic and tonic REM states were determined by eye movement in 3-second epochs. PAC was compared between the groups, and correlations with clinical indicators were investigated. Additionally, the contribution of each electrode to PAC components was assessed. Patients with iRBD exhibited increased delta (1-3 Hz)-gamma (30-50 Hz) PAC only during the phasic REM state, but not during the tonic state, compared to the HCs (p < 0.05). Elevated PAC in patients negatively correlated with the REM atonia index (p = 0.011) and olfactory function (p = 0.038). Increase PACs were predominent in the fronto-temporo-occipital regions (corrected p < 0.05). Furthermore, patients showed reduced gamma-amplitude contributions of the parietal region (corrected p < 0.05). This reduction exhibited a progressively decreasing trend from HC to non-converters, and further to converters (p for trend = 0.044). Our findings suggest PAC patterns during REM sleep could provide pathophysiological insights for iRBD. The widespread increase of PAC and reduced gamma-amplitude contribution in the parietal region suggest PAC during phasic REM sleep as potential biomarkers for disease progression in iRBD.

Top-Down Proteomics Identifies Plasma Proteoform Signatures of Liver Cirrhosis Progression

Cirrhosis, advanced liver disease, affects 2-5 million Americans. While most patients have compensated cirrhosis and may be fairly asymptomatic, many decompensate and experience life-threatening complications such as gastrointestinal bleeding, confusion (hepatic encephalopathy), and ascites, reducing life expectancy from 12 to less than 2 years. Among patients with compensated cirrhosis, identifying patients at high risk of decompensation is critical to optimize care and reduce morbidity and mortality. Therefore, it is important to preferentially direct them towards specialty care which cannot be provided to all patients with cirrhosis. We used discovery Top-down Proteomics (TDP) to identify differentially expressed proteoforms (DEPs) in the plasma of patients with progressive stages of liver cirrhosis with the ultimate goal to identify candidate biomarkers of disease progression. In this pilot study, we identified 209 DEPs across three stages of cirrhosis (compensated, compensated with portal hypertension, and decompensated), of which 115 derived from proteins enriched in the liver at a transcriptional level and discriminated the three stages of cirrhosis. Enrichment analyses demonstrated DEPs are involved in several metabolic and immunological processes known to be impacted by cirrhosis progression. We have preliminarily defined the plasma proteoform signatures of cirrhosis patients, setting the stage for ongoing discovery and validation of biomarkers for early diagnosis, risk stratification, and disease monitoring.

Parkinson’s Disease Interstage Prognostic Biomarkers for Early Detection through Hybrid Machine Learning Model

Background: Parkinson’s Disease (PD) is a neurodegenerative condition that takes 15 to 20 years to exhibit the symptoms. However, during the initial stages of the disease, there are microlevel changes in the body that are typically undetected. The pathogenesis of Parkinson’s disease requires understanding of these microlevel changes. Objectives: The objective of the present study is to develop a hybrid ensembled machine learning pipeline model for identifying inter-stage Parkinson’s Disease (PD) biomarkers for understanding disease progression as well as etiology. Methods: The proposed work was carried out on the dataset GSE202667 from Gene Expression Omnibus, containing time-resolved RNA signatures of CD4+ T cells at various stages. Differentiating genes were identified in different interstage groups. Two types of unsupervised learning methods- distance-based (K-means, Agglomerative, Density-Based Spatial Clustering of Application with Noise) and probability-based (Hidden Markov Model, Gaussian Mixture Model Latent Dirichlet Allocation) were applied. The best algorithms were selected and applied to optimize clusters. Enrichment analysis was conducted on the top 10 PD biomarkers in each category. Findings: The top 10 PD biomarkers in each category are identified and gene set enrichment analysis resulted into their enrichment in three KEGG Pathways and depletion in two GO molecular functions. These biomarkers’ depletion is observed in 215 Reactome pathways and enrichment in 18 Reactome pathways. Twelve GO-Cellular components had an enrichment whereas 111 GO-Cellular components had a depletion in the gene set. A total of 25 GO-Biological process components were enriched, while 339 GO-Biological process components were depleted. Novelty: The proposed hybrid ensembled machine learning pipeline model works as a tool to identify Parkinson’s Disease biomarkers from omics data. The model contributes to identify implicit patterns in omics data in order to unveil Parkinson’s disease progress mechanism through biomarkers discovery. Keywords: Gaussian Mixture Model clustering, Early Detection, k-means clustering, Machine learning, Parkinson’s Disease Interstage Biomarkers

Evaluation of aortic inflammation in marfan syndrome patients with 18-fluorodeoxyglucose positron emission and computed tomography

Abstract Background The role of inflammation in Marfan Syndrome (MS) has been well studied in current research. These studies have investigated the role of serum Interleukin 6 (IL-6), Transforming Growth Factor-b (TGF-b) and Macrophage Colony Stimulating Factor (MCSF) as biomarkers of disease severity and progression. Surgical repair is the recommended treatment of aortic aneurysms when aortic diameter reaches the indication threshold. It has been observed however that dissection may occur even before this threshold is reached. 18-fluorodeoxyglucose (18 F-FDG) positron emission tomography / computed tomography (PET/CT) is the gold standard imaging modality to noninvasively assess vascular inflammation. Purpose Our aim was to investigate whether MS patients have increased aortic wall inflammation as assessed by 18F-FDG PET/CT, and the relationship between aortic wall inflammation with serum levels of IL-6, TGF-b and MCSF. Methods The study population consisted of fifteen MS patients and seventeen sex and age matched controls. In all subjects, serum levels of circulating IL-6, TGF-b and MCSF were measured. In PET/CT, the arterial target-to-background ratio (TBR) was calculated for aortic root (AR), ascending aorta(As), aortic arch (AA) and descending aorta (DA). Also, all patients were assessed with transthoracic echocardiography where the diameters of AR, As, AA, and DA were estimated at baseline and were reexamined at the end of the follow up period. Results TBR for MS vs. controls was 2.17±0.75 vs 2.01±0.36, p=0.75 at AR; 2.15±0.67 vs 1.84±0.35, p=0.040 at As; 2.16±0.64 vs 1.70±0.25, p=0.03 at AA and 1.97±0.52 vs 1.83±0.42, p=0.23 at DA respectively. Additionally, Marfan patients had increased levels of serum biomarkers. However, this difference was statistically significant (SS) only for IL-6 serum levels; 2.17±0.46 vs 1.08±0.12 p=0.011 and IL-6 was SS associated with AR TBR (rho=0.572, p=0.033), As TBR (rho=0.604, p=0.022) and DA TBR (rho=0.714, p=0.004). Two of our patients were operated due to severe dilatation of the aortic root and the surgical specimens showed aortic wall inflammation of moderate severity along with fibrosis. Conclusion MS patients have increased of aortic wall inflammation as assessed by 18F-FDG PET/CT, a finding that highlights the role of inflammation in the pathway of disturbed TGF signaling. Further research is needed to determine its prognostic role and value in identifying patients with an indication for early intervention.

Role of baseline soluble tumor necrosis factor receptor 2 as a biomarker in primary podocytopathy: Implications for renal impairment and disease progression

BackgroundPodocytopathies, including minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), and collapsing glomerulopathy (CG), are kidney diseases that damage glomerular podocytes, leading to heavy proteinuria and nephrotic syndrome (NS). Inflammation plays a critical role in the progression of chronic kidney disease (CKD), with recent studies linking inflammatory biomarkers to declining kidney function. Tumor necrosis factor-alpha (TNF-α), an essential inflammatory cytokine, interacts with its circulating receptors, TNFR1 and TNFR2. The TNF-α pathway has been implicated in the pathogenesis of FSGS and MCD. Increased circulating TNFR2 levels have been associated with worsening renal function in podocytopathies, suggesting that the TNF-α inflammatory pathway significantly contributes to disease progression.MethodsWe conducted a study involving 53 patients with biopsy-proven MCD or FSGS and 53 healthy, age- and gender-matched controls. All patients were followed for 18 months. We analyzed serum and urine TNFR2 levels and gene expression at baseline and after three months. To assess the ability of TNFR2 to predict persistent decline in estimated glomerular filtration rate (eGFR < 30 mL/min/1.73m2), remission, and relapse, we employed Cox regression analysis. Additionally, we evaluated its prognostic utility for predicting progression to stage 4 CKD using ROC curve analysis.ResultsSerum and urine TNFR2 levels were significantly elevated in patients compared to controls. Serum TNFR2 was a significant predictor in univariate Cox regression analysis for persistent eGFR decline (HR 1.017, 95% CI: 1.003 to 1.032, p = 0.018), remission (HR 0.995, 95% CI: 0.992 to 0.999, p = 0.006), and relapse (HR 1.005, 95% CI: 1.001 to 1.010, p = 0.029). The ROC curve analysis demonstrated that serum TNFR2 levels had a strong prognostic ability for predicting progression to stage 4 CKD, with an AUC of 0.848 (95% CI: 0.737—0.960), sensitivity of 81%, and specificity of 71%.ConclusionThis study underscores the critical role of circulating TNFR2 in kidney injury among patients with primary podocytopathy. Elevated TNFR2 levels are significant predictors of persistent eGFR decline and disease relapse, highlighting their potential as biomarkers for disease progression and prognosis.

Identification and Characterization of Candidate Platelet Proteins as Potential Biomarkers for Alcoholism Patients

ABSTRACT Chronic alcoholism is a significant health issue associated with severe liver complications, including alcohol-related liver disease (ARLD), progresses from fatty liver to cirrhosis and hepatocellular carcinoma. ARLD affects liver function and hemostasis, leading to increased mortality. This study intended to investigate the platelet proteome and detect platelet cell apoptosis via Annexin V in ARLD patients in order to identify potential biomarkers for disease progression. Twenty-three patients with ARLD were recruited, and their platelet proteins were analyzed via mass spectrometry. Approximately 441 proteins were identified, with 73 candidate biomarkers selected on the basis of clinical importance. Key proteins involved in coagulation, inflammation, and cellular interactions are highlighted. Gene ontology and STRING analyses revealed functional clusters and pathways associated with these proteins. This study identified critical platelet proteins, such as von Willebrand factor, vitronectin, and serpin family members, which play essential roles in hemostasis and liver disease pathogenesis. These findings suggest that changes in platelet proteomes could serve as noninvasive biomarkers for diagnosing, monitoring, and treating ARLD.

Enzyme Activity-Based Optical Probe: Imaging Aminopeptidases N in Vulnerable Plaque and Serum.

Cardiovascular disease, a chronic and progressive arterial wall disease, is increasingly recognized for its clinical significance. Aminopeptidases N (APN), crucial in the pathophysiological processes of vulnerable plaque, have been linked to endothelial dysfunction, oxidative stress, and plaque formation, thus highlighting their potential as biomarkers for disease progression. However, current detection methods for APN in body fluids and in vivo have limitations, including insufficient sensitivity and specificity, time delays, and the inability to directly reflect enzyme activity in plaques. To address these challenges, we developed an optical probe, HD-APN, for in vivo imaging of aminopeptidases, providing a potential implementation in cardiovascular disease. Our work demonstrated the applicability of HD-APN for specific monitoring of aminopeptidase levels in plaques and serum, shedding light on its potential for further research in cardiovascular disease.

Oxidative Stress-mediated Lipid Peroxidation-derived Lipid Aldehydes in the Pathophysiology of Neurodegenerative Diseases.

Neurodegenerative diseases such as Alzheimer's, Parkinson's, and amyotrophic lateral sclerosis cause damage and gradual loss of neurons affecting the central nervous system. Neurodegenerative diseases are most commonly seen in the ageing process. Ageing causes increased reactive oxygen species and decreased mitochondrial ATP generation, resulting in redox imbalance and oxidative stress. Oxidative stress-generated free radicals cause damage to membrane lipids containing polyunsaturated fatty acids, leading to the formation of toxic lipid aldehyde products such as 4- hydroxynonenal and malondialdehyde. Several studies have shown that lipid peroxidation-derived aldehyde products form adducts with cellular proteins, altering their structure and function. Thus, these lipid aldehydes could act as secondary signaling intermediates, modifying important metabolic pathways, and contributing to the pathophysiology of several human diseases, including neurodegenerative disorders. Additionally, they could serve as biomarkers for disease progression. This narrative review article discusses the biological and clinical significance of oxidative stress-mediated lipid peroxidation-derived lipid aldehydes in the pathophysiology of various neurodegenerative diseases.

Exploring the Gene Expression and Plasma Protein Levels of HSP90, HSP60, and GDNF in Multiple Sclerosis Patients and Healthy Controls.

Multiple sclerosis (MS) is a chronic neurodegenerative disease characterized by immune-mediated inflammation and neurodegeneration in the central nervous system (CNS). In this study; we aimed to investigate the gene expression and plasma protein levels of three neuroprotective genes-heat shock proteins (HSP90 and HSP60) and glial cell line-derived neurotrophic factor (GDNF)-in MS patients compared to healthy controls. Forty patients with relapsing-remitting MS and 40 healthy volunteers participated in this study. Gene expression was measured using reverse transcription quantitative real-time PCR, and protein levels were assessed via ELISA. The results showed a significant increase in HSP90 (1.7-fold) and HSP60 (2-fold) gene expression in MS patients compared to controls, along with corresponding increases in protein levels (1.5-fold for both HSP90 and HSP60). In contrast, GDNF gene expression and protein levels were significantly reduced in MS patients, with a 7-fold decrease in gene expression and a 1.6-fold reduction in protein levels. Notably, a non-linear relationship between GDNF gene expression and protein concentration was observed in MS patients, suggesting complex regulatory mechanisms influencing GDNF in the disease. The upregulation of HSP90 and HSP60 in MS highlights their roles in immune regulation and stress responses, while the reduction in GDNF indicates impaired neuroprotection. These findings suggest that HSP90, HSP60, and GDNF could serve as biomarkers for disease progression and as potential therapeutic targets in MS, offering promising avenues for future research and treatment development.

A Machine Learning Approach to Gene Expression in Hypertrophic Cardiomyopathy

Background/Objectives: Hypertrophic cardiomyopathy (HCM) is a common heart disorder characterized by the thickening of the heart muscle, particularly in the left ventricle, which increases the risk of cardiac complications. This study aims to analyze the expression of apoptosis-regulating genes (CASP8, CASP9, CASP3, BAX, and BCL2) in blood samples from HCM patients, to better understand their potential as biomarkers for disease progression. Methods: Quantitative real-time PCR (qPCR) was used to evaluate gene expression in blood samples from 93 HCM patients. The correlation between apoptosis-regulating genes was conducted and clinical parameters were integrated for feature importance and clustering analysis. Results: Most patients exhibited significant downregulation of CASP8, CASP9, and CASP3. In contrast, BAX expression was elevated in 71 out of 93 patients, while BCL2 was increased in 55 out of 93 patients. Correlation analysis revealed weak negative correlations between the BAX/BCL2 ratio and CASP gene expression. Conclusions: These findings suggest that reduced expression of apoptotic genes may indicate a protective cellular mechanism, which could serve as a biomarker for disease progression. Further studies are needed to investigate the potential for therapeutic modulation of these pathways to improve patient outcomes.

TRANSFORMING HEALTHCARE DELIVERY THROUGH BIG DATA IN HOSPITAL MANAGEMENT SYSTEMS: A REVIEW OF RECENT LITERATURE TRENDS

This systematic review investigates the transformative role of big data technologies in biomedical research, analyzing 40 peer-reviewed articles published between 2010 and 2023. The review specifically explores advancements in next-generation sequencing (NGS), multi-omics approaches, machine learning, and artificial intelligence (AI), all of which have significantly enhanced the understanding of complex biological systems and diseases. NGS has emerged as a key tool in personalized medicine, enabling rapid and cost-effective genome sequencing that has facilitated the identification of genetic mutations and biomarkers associated with various diseases, particularly in oncology. Of the 40 studies reviewed, 12 focused on the integration of multi-omics data—genomics, transcriptomics, proteomics, and metabolomics—to provide a comprehensive view of biological processes. These multi-omics approaches have been instrumental in identifying biomarkers for disease progression and response to treatments, offering new avenues for drug development and precision medicine. Additionally, 15 studies highlighted the growing application of machine learning and AI algorithms in managing and analyzing vast biomedical datasets. These tools are now critical in uncovering hidden patterns within large datasets, predicting disease outcomes, and improving the accuracy of clinical decision-making. However, 10 studies emphasized ongoing challenges related to data storage, privacy concerns, and the lack of standardized data formats, which hinder effective data sharing across institutions. Despite these challenges, the integration of AI, IoT devices, and big data analytics is paving the way for more personalized, real-time healthcare monitoring and treatment solutions. This review concludes that while significant advancements have been made, further efforts are required to address the ethical and technical barriers that limit the full potential of big data technologies in biomedical research.

Determination of digital biomarkers of disease progression for digital phenotyping of patients with arterial hypertension.

Background: To compare the effectiveness of digital phenotyping of patients diagnosed with arterial hypertension with traditional monitoring methods over a three-year period. Patients and methods: The study was conducted from January 2021 to January 2024 among 800 patients diagnosed with arterial hypertension at 6 clinics in Moscow, Russia, evenly divided into experimental (identification of digital biomarkers of disease progression for digital phenotyping) and control (standard monitoring methods) groups. The intervention included lifestyle changes focused on increasing physical activity, improving sleep quality, reducing stress, and modifying diet. Significant improvements were observed in the experimental group compared to the control group. Systolic blood pressure decreased by 10 mmHg (p<0.001), pulse by 5 beats per minute (p<0.001), and stress level by 2 points (p<0.001) in the experimental group. Additionally, physical activity increased by 15 minutes per day (p<0.001), and sleep quality improved by 2 points on a scale from 1 to 10 (p<0.001). Results: Multiple regression analysis showed a decrease in the significance of digital biomarkers over the study period, indicating a positive response to the intervention. Conclusions: The obtained results emphasize the importance of comprehensive interventions in managing arterial hypertension and its related conditions. Implementing comprehensive lifestyle changes can lead to significant health improvements and serve as an effective preventive strategy. Further research is needed to explore optimal intervention strategies for promoting societal health.

The contribution of the sphingosine 1-phosphate signaling pathway to chronic kidney diseases: recent findings and new perspectives.

Chronic kidney disease (CKD) is a multifactorial condition with diverse etiologies, such as diabetes mellitus, hypertension, and genetic disorders, often culminating in end-stage renal disease (ESRD). A hallmark of CKD progression is kidney fibrosis, characterized by the excessive accumulation of extracellular matrix components, for which there is currently no effective anti-fibrotic therapy. Recent literature highlights the critical role of sphingosine 1-phosphate (S1P) signaling in CKD pathogenesis and renal fibrosis. This review provides an in-depth analysis of the latest findings on S1P metabolism and signaling in renal fibrosis and in specific CKDs, including diabetic nephropathy (DN), lupus nephritis (LN), focal segmental glomerulosclerosis (FSGS), Fabry disease (FD), and IgA nephropathy (IgAN). Emerging studies underscore the therapeutic potential of modulating S1P signaling with receptor modulators and inhibitors, such as fingolimod (FTY720) and more selective agents like ozanimod and cenerimod. Additionally, the current knowledge about the effects of established kidney protective therapies such as glucocorticoids and SGLT2 and ACE inhibitors on S1P signaling will be summarized. Furthermore, the review highlights the potential role of S1P as a biomarker for disease progression in CKD models, particularly in Fabry disease and diabetic nephropathy. Advanced technologies, including spatial transcriptomics, are further refining our understanding of S1P's role within specific kidney compartments. Collectively, these insights emphasize the need for continued research into S1P signaling pathways as promising targets for CKD treatment strategies.