Potential Biomarkers Research Articles

A prognostic matrix gene expression signature defines functional glioblastoma phenotypes and niches.

Interactions among tumor, immune, and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, through computational genomics and proteomics approaches, we analyzed the functional and clinical relevance of CMP expression in GBM at bulk, single cell, and spatial anatomical resolution. We identified genes encoding CMPs whose expression levels categorize GBM tumors into CMP expression-high (M-H) and CMP expression-low (M-L) groups. CMP enrichment is associated with worse patient survival, specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells, and immune checkpoint gene expression. Anatomical and single-cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative niches that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene CMP expression signature, termed Matrisome 17 (M17) signature that further refines the prognostic value of CMP genes. The M17 signature is a significantly stronger prognostic factor compared to MGMT promoter methylation status as well as canonical subtypes, and importantly, potentially predicts responses to PD1 blockade. The matrisome gene expression signature provides a robust stratification of GBM patients by survival and potential biomarkers of functionally relevant GBM niches that can mediate mesenchymal-immune cross talk. Patient stratification based on matrisome profiles can contribute to selection and optimization of treatment strategies.

Bariatric surgery modulates plasma levels of antibodies against angiotensin II type 1 and endothelin 1 type A receptor in severe obesity.

The contribution of endothelial-targeted autoantibodies against the angiotensin II type 1 receptor (anti-AT1R) and the anti-endothelin 1 type A receptor (anti-ETAR1) has been proposed in the development of cardiovascular diseases. However, no data have been reported yet in obesity. In this observational study we evaluated the relationship between anthropometric and metabolic parameters and anti-AT1R and anti-ETAR1 concentrations in a cohort of patients with severe obesity and associated comorbidities undergoing bariatric surgery. Clinical evaluation and metabolic assessment were performed in 36 subjects referring to our Center for the Study and Integrated Treatment of Obesity at the University Hospital of Padova. Circulating inflammatory adipocytokines and the endothelial dysfunction marker asymmetric dimethylarginine (ADMA) were evaluated; plasma levels of anti-AT1R and anti-ETAR1 were also determined. 10 normal-weight subjects were considered as a control group. 29 patients out of 36 were re-evaluated after surgery. With respect to normal-weight controls patients showed significantly higher plasma levels of anti-AT1R (28 ± 20.4 vs 13.5 ± 2.8 U/mL, p < 0.005) and ADMA (0.8 ± 0.1 vs 0.54 ± 0.08 uM/L, p < 0.0001) but not anti-ETAR1 (14.2 ± 1.3 vs 13.3 ± 2 U/mL, p = 0.1). Anti-AT1R concentration showed an increasing trend with the worsening of glycemic status, while the presence of arterial hypertension among the patients did not affect autoantibodies levels. One year after surgery, a significant improvement in body weight and metabolic and inflammatory parameters was observed, along with a significant reduction of anti-AT1R (28.1 ± 20.4 U/mL vs 22.6 ± 16 U/mL, p < 0.05) and anti-ETAR1 (14.2 ± 1.3 U/L vs 13 ± 1.6 U/L, p < 0.01). Subjects with obesity present higher plasma levels of anti-AT1R which are more related to glycemic profile than blood pressure levels, and are reduced by bariatric surgery. Considering the detrimental effects of these autoantibodies on vascular health, they should be assessed as potential biomarkers in obesity and metabolic diseases.

Exploring the Role of Gut Microbiome in Chronic Inflammatory Diseases: A Comparative Study

Chronic inflammatory diseases, such as inflammatory bowel disease, rheumatoid arthritis, and multiple sclerosis, have been increasingly linked to alterations in the gut microbiome. This study aims to explore the role of the gut microbiome in the pathogenesis and progression of chronic inflammatory diseases through a comparative analysis. The research employed a multi-omics approach, integrating metagenomics, metabolomics, and immunological techniques to characterize the gut microbial composition, metabolic profiles, and host-microbiome interactions across different chronic inflammatory conditions. Fecal samples, blood samples, and clinical data were collected from patients diagnosed with inflammatory bowel disease, rheumatoid arthritis, multiple sclerosis, and healthy controls. Bioinformatics and statistical analyses were performed to identify distinct microbial signatures, metabolic pathways, and immune responses associated with each disease state. The findings revealed significant dysbiosis in the gut microbiome of patients with chronic inflammatory diseases, characterized by reduced diversity, altered microbial composition, and disrupted metabolic pathways. Specific bacterial taxa and metabolites were identified as potential biomarkers for disease severity and progression. Additionally, the study highlighted the intricate interplay between the gut microbiome, immune system, and inflammatory processes, providing insights into potential therapeutic targets and personalized interventions. The research concludes that the gut microbiome plays a crucial role in the development and perpetuation of chronic inflammatory diseases, and its modulation through dietary interventions, probiotics, or targeted therapies holds promise for improving disease management and patient outcomes.

Identification of potential biomarkers and immunoregulatory mechanisms of rheumatoid arthritis based on multichip co-analysis of GEO database

To identify the biomarkers for early rheumatoid arthritis (RA) diagnosis and explore the possible immune regulatory mechanisms. The differentially expressed genesin RA were screened and functionally annotated using the limma, RRA, batch correction, and clusterProfiler. The protein-protein interaction network was retrieved from the STRING database, and Cytoscape 3.8.0 and GeneMANIA were used to select the key genes and predicting their interaction mechanisms. ROC curves was used to validate the accuracy of diagnostic models based on the key genes. The disease-specific immune cells were selected via machine learning, and their correlation with the key genes were analyzed using Corrplot package. Biological functions of the key genes were explored using GSEA method. The expression of STAT1 was investigated in the synovial tissue of rats with collagen-induced arthritis (CIA). We identified 9 core key genes in RA (CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL), which regulate synovial inflammation primarily through cytokines-related pathways. ROC curve analysis showed a high predictive accuracy of the 9 core genes, among which STAT1 had the highest AUC (0.909). Correlation analysis revealed strong correlations of CD3G, ITGAL, LCK, CD8A, and STAT1 with disease-specific immune cells, and STAT1 showed the strongest correlation with M1-type macrophages (R=0.68, P=2.9e-08). The synovial tissues of the ankle joints of CIA rats showed high expressions of STAT1 and p-STAT1 with significant differential expression of STAT1 between the nucleus and the cytoplasm of the synovial fibroblasts. The protein expressions of p-STAT1 and STAT1 in the cell nuclei were significantly reduced after treatment. CD3G, CD8A, SYK, LCK, IL2RG, STAT1, CCR5, ITGB2, and ITGAL may serve as biomarkers for early diagnosis of RA. Gene-immune cell pathways such as CD3G/CD8A/LCK-γδ T cells, ITGAL-Tfh cells, and STAT1-M1-type macrophages may be closely related with the development of RA.

Role of FeNO in predicting the responsiveness of inhaled corticosteroids in COPD: a systematic review.

Fractional exhaled nitric oxide (FeNO) as a predictor of inhaled corticosteroid (ICS) response in asthma has been established. However, the same has not been established in chronic obstructive pulmonary disease (COPD). An optimal value of FeNO for prescribing and monitoring ICS response has not been quantified. To examine the evidence for this association. A systematic review was conducted of randomised controlled trials and observational studies examining the association between FeNO level and response to ICS in COPD patients. All studies examining this association were included. Five databases were searched thoroughly. Systematic screening, full-text reviews, and data extraction were carried out based on eligibility criteria. A total of 8690 studies were identified, 342 texts were screened fully, and six studies were included for the final review. One was a randomised controlled trial and the other five were non-randomised interventional trials. One study was conducted in asthma-COPD overlap (ACO patients). After ICS use, three studies found statistically significant correlations between FeNO and lung function improvement (FEV1), and three studies also found significant correlations between FeNO and COPD quality-of-life scores. Measurement of FeNO is non-invasive and standardised, with results available at the point of testing. Because of the small sample size and short duration of studies, exacerbation frequencies were not measured. Despite this, the review suggests that FeNO may be a potential biomarker for assessing ICS response in COPD. Further research that stratifies patients by FeNO levels and assesses the impact on acute exacerbations is needed to understand its potential value in routine clinical practice.

Targeted serum proteome profiling reveals nicotinamide adenine dinucleotide phosphate (NADPH)-related biomarkers to discriminate linear IgA bullous disorder from dermatitis herpetiformis

Linear IgA bullous dermatosis (LABD) and dermatitis herpetiformis (DH) represent the major subtypes of IgA mediated autoimmune bullous disorders. We sought to understand the disease etiology by using serum proteomics. We assessed 92 organ damage biomarkers in LABD, DH, and healthy controls using the Olink high-throughput proteomics. The positive proteomic serum biomarkers were used to correlate with clinical features and HLA type. Targeted proteomic analysis of IgA deposition bullous disorders vs. controls showed elevated biomarkers. Further clustering and enrichment analyses identified distinct clusters between LABD and DH, highlighting the involvement of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase. Comparative analysis revealed biomarkers with distinction between LABD and DH and validated in the skin lesion. Finally, qualitative correlation analysis with DEPs suggested six biomarkers (NBN, NCF2, CAPG, FES, BID, and PXN) have better prognosis in DH patients. These findings provide potential biomarkers to differentiate the disease subtype of IgA deposition bullous disease.

Metabolic signatures and risk of sarcopenia in suburb-dwelling older individuals by LC-MS-based untargeted metabonomics.

Untargeted metabonomics has provided new insight into the pathogenesis of sarcopenia. In this study, we explored plasma metabolic signatures linked to a heightened risk of sarcopenia in a cohort study by LC-MS-based untargeted metabonomics. In this nested case-control study from the Adult Physical Fitness and Health Cohort Study (APFHCS), we collected blood plasma samples from 30 new-onset sarcopenia subjects (mean age 73.2 ± 5.6 years) and 30 healthy controls (mean age 74.2 ± 4.6 years) matched by age, sex, BMI, lifestyle, and comorbidities. An untargeted metabolomics methodology was employed to discern the metabolomic profile alterations present in individuals exhibiting newly diagnosed sarcopenia. In comparing individuals with new-onset sarcopenia to normal controls, a comprehensive analysis using liquid chromatography-mass spectrometry (LC-MS) identified a total of 62 metabolites, predominantly comprising lipids, lipid-like molecules, organic acids, and derivatives. Receiver operating characteristic (ROC) curve analysis indicated that the three metabolites hypoxanthine (AUC=0.819, 95% CI=0.711-0.927), L-2-amino-3-oxobutanoic acid (AUC=0.733, 95% CI=0.598-0.868) and PC(14:0/20:2(11Z,14Z)) (AUC= 0.717, 95% CI=0.587-0.846) had the highest areas under the curve. Then, these significant metabolites were observed to be notably enriched in four distinct metabolic pathways, namely, "purine metabolism"; "parathyroid hormone synthesis, secretion and action"; "choline metabolism in cancer"; and "tuberculosis". The current investigation elucidates the metabolic perturbations observed in individuals diagnosed with sarcopenia. The identified metabolites hold promise as potential biomarkers, offering avenues for exploring the underlying pathological mechanisms associated with sarcopenia.

Insights into the role of mitophagy in lung cancer: current evidence and perspectives.

Lung cancer, recognized globally as a leading cause of malignancy-associated morbidity and mortality, is marked by its high prevalence and lethality, garnering extensive attention within the medical community. Mitophagy is a critical cellular process that plays a crucial role in regulating metabolism and ensuring quality control within cells. Its relevance to lung cancer has garnered significant attention among researchers and scientists. Mitophagy's involvement in lung cancer encompasses its initiation, progression, metastatic dissemination and treatment. The regulatory landscape of mitophagy is complex, involving numerous signaling proteins and pathways that may exhibit aberrant alterations or mutations within the tumor environment. In the field of treatment, the regulation of mitophagy is considered key to determining cancer chemotherapy, radiation therapy, other treatment options, and drug resistance. Contemporary investigations are directed towards harnessing mitophagy modulators, both inhibitors and activators, in therapeutic strategies, with an emphasis on achieving specificity to minimize collateral damage to healthy cellular populations. Furthermore, molecular constituents and pathways affiliated with mitophagy, serving as potential biomarkers, offer promising avenues for enhancing diagnostic accuracy, prognostic assessment, and prediction of therapeutic responses in lung cancer. Future endeavors will also involve investigating the impact of mitophagy on the composition and function of immune cells within the tumor microenvironment, aiming to enhance our understanding of how mitophagy modulates the immune response to lung cancer. This review aims to comprehensively overview recent advancements about the role of mitophagy in the tumor genesis, progenesis and metastasis, and the impact of mitophagy on the treatment of lung cancer. We also discussed the future research direction of mitophagy in the field of lung cancer.

Identification of novel genetic susceptibility loci for calcium-containing kidney stone disease by genome-wide association study and polygenic risk score in a Taiwanese population.

Approximately 80% of kidney stone diseases contain calcium. Inherited genetic factors are among the variables that influence the development of calcium-containing kidney stone diseases (CKSD). Previous genome-wide association studies (GWAS) on stone diseases have been reported worldwide; however, these are not focused on calcium-containing stones. We conducted a GWAS to identify germline genetic polymorphisms associated with CKSD in a Medical Center in Taiwan; hence, this study was based primarily on a hospital-based database. CKSD was diagnosed using the chart records. Patients infected with urea-splitting-microorganisms and those with at least two urinary pH value below 5.5 were excluded. None of the patients had cystic stones based on stone analysis. Those over 40 years of age with no history of CKSD and no microscopic hematuria on urinalysis were considered as controls. The DNA isolated from the blood of 14,934 patients (63.7% male and 36.3% female) with CKSD and 29,868 controls (10,830 men and 19,038 women) at a medical center was genotyped for approximately 714,457 single nucleotide polymorphisms (SNPs) with minor allele frequency of ≥ 0.05. We used PLINK 1.9 to calculate the polygenic risk score (PRS) to investigate the association between CKSD and controls. The accuracy of the PRS was verified by dividing it into the training and testing groups. The statistical analyses were calculated with the area under the curve (AUC) using IBM SPSS version 22. We identified 432 susceptibility loci that reached a genome-wide threshold of P < 1.0 × 10- 5. A total of 132 SNPs reached a threshold of P < 5 × 10- 8 using a stricter definition of significance on chromosomes 4, 13, 16, 17, and 18. At the top locus of our study, SNPs in DGKH, PDILT, BCAS3, and ABCG2 have been previously reported. RN7SKP27, HDAC4, PCDH15, AP003068.2, and NFATC1 were novel findings in this study. PRS was adjusted for sex and age, resulting in an AUC of 0.65. The number of patients in the top quartile of PRS was 1.39 folds in the risk of CKSD than patients in the bottom quartile. Our data identified the significance of GWAS for patients with CKSD in a hospital-based study. The PRS also had a high AUC for discriminating patients with CKSD from controls. A total of 132 SNP loci of SNPs significantly associated with the development of CKSD. This first survey, which focused on patients with CKSD, will provide novel insights specific to CKSD and its potential clinical biomarkers.

Research advances in serum chitinase-3-like protein 1 in liver fibrosis.

While liver fibrosis remains a serious, progressive, chronic liver disease, and factors causing damage persist, liver fibrosis may develop into cirrhosis and liver cancer. However, short-term liver fibrosis is reversible. Therefore, an early diagnosis of liver fibrosis in the reversible transition phase is important for effective treatment of liver diseases. Chitinase-3-like protein 1 (CHI3L1), an inflammatory response factor that participates in various biological processes and is abundant in liver tissue, holds promise as a potential biomarker for liver diseases. Here, we aimed to review research developments regarding serum CHI3L1 in relation to the pathophysiology and diagnosis of liver fibrosis of various etiologies, providing a reference for the diagnosis, treatment, and prognosis of liver diseases.

Research progress on astrocyte-derived extracellular vesicles in the pathogenesis and treatment of neurodegenerative diseases.

Neurodegenerative disorders, including Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington's disease (HD), pose significant global health risks and represent a substantial public health concern in the contemporary era. A primary factor in the pathophysiology of these disorders is aberrant accumulation and aggregation of pathogenic proteins within the brain and spinal cord. Recent investigations have identified extracellular vesicles (EVs) in the central nervous system (CNS) as potential carriers for intercellular transport of misfolded proteins associated with neurodegenerative diseases. EVs are involved in pathological processes that contribute to various brain disorders including neurodegenerative disorders. Proteins linked to neurodegenerative disorders are secreted and distributed from cell to cell via EVs, serving as a mechanism for direct intercellular communication through the transfer of biomolecules. Astrocytes, as active participants in CNS intercellular communication, release astrocyte-derived extracellular vesicles (ADEVs) that are capable of interacting with diverse target cells. This review primarily focuses on the involvement of ADEVs in the development of neurological disorders and explores their potential dual roles- both advantageous and disadvantageousin the context of neurological disorders. Furthermore, this review examines the current studies investigating ADEVs as potential biomarkers for the diagnosis and treatment of neurodegenerative diseases. The prospects and challenges associated with the application of ADEVs in clinical settings were also comprehensively reviewed.

Specific microbiome patterns and their association with breast cancer: the intestinal microbiota as a potential biomarker and therapeutic strategy.

Breast cancer (BC) is one of the most diagnosed cancers in women. Based on histological characteristics, they are classified as non-invasive, or in situ (tumors located within the milk ducts or milk lobules) and invasive. BC may develop from in situ carcinomas over time. Determining prognosis and predicting response to treatment are essential tools to manage this disease and reduce its incidence and mortality, as well as to promote personalized therapy for patients. However, over half of the cases are not associated with known risk factors. In addition, some patients develop resistance to treatment and relapse. Therefore, it is necessary to identify new biomarkers and treatment strategies that improve existing therapies. In this regard, the role of the microbiome is being researched as it could play a role in carcinogenesis and the efficacy of BC therapies. This review aims to describe specific microbiome patterns associated with BC. For this, a literature search was carried out in PubMed database using the MeSH terms "Breast Neoplasms" and "Gastrointestinal Microbiome", including 29 publications. Most of the studies have focused on characterizing the gut or breast tissue microbiome of the patients. Likewise, studies in animal models and in vitro that investigated the impact of gut microbiota (GM) on BC treatments and the effects of the microbiome on tumor cells were included. Based on the results of the included articles, BC could be associated with an imbalance in the GM. This imbalance varied depending on molecular type, stage and grade of cancer, menopause, menarche, body mass index, and physical activity. However, a specific microbial profile could not be identified as a biomarker. On the other hand, some studies suggest that the GM may influence the efficacy of BC therapies. In addition, some microorganisms and bacterial metabolites could improve the effects of therapies or influence tumor development.

Integrated Analysis of Microbiome and Metabolome Reveals Disease-Specific Profiles in Inflammatory Bowel Diseases and Intestinal Behçet's Disease.

The gut microbial and metabolic characteristics of intestinal Behçet's disease (BD), a condition sharing many clinical similarities with ulcerative colitis (UC) and Crohn's disease (CD), are largely unexplored. This study investigated the gut microbial and metabolic characteristics of intestinal BD as well as potential biomarkers, comparing them with those in UC, CD, and healthy controls. Colon tissue and stool samples from 100 patients (35 UC, 30 CD, and 35 intestinal BD) and 41 healthy volunteers were analyzed using 16S ribosomal RNA sequencing to assess microbial diversity, taxonomic composition, and functional profiling. Plasma metabolomic analyses were performed using gas chromatography and ultra-performance liquid chromatography-mass spectrometry. Results indicated reduced microbial diversity in CD but not in intestinal BD, with intestinal BD showing fewer changes compared to controls yet distinct taxonomic features from UC, CD, and controls. Common alterations across all diseases included a reduction in beneficial bacteria producing short-chain fatty acids. Intestinal BD-specific changes featured a decreased abundance of Bacteroides fragilis. Metabolomic profiles in intestinal BD were similar to those in CD but distinct from those in UC, displaying significant changes in energy metabolism and genetic information processing. This integrative analysis revealed both shared and unique profiles in intestinal BD compared with UC, CD, and controls, advancing our understanding of the distinctive features of these diseases.

Altered Cerebrospinal Fluid Neurofilament Light Chain but Not Neurogranin Levels Are Associated with Response to Ocrelizumab Treatment in Relapsing-Remitting Multiple Sclerosis: A Preliminary Study.

Ocrelizumab is a CD20-targeting monoclonal antibody used for treatment of multiple sclerosis (MS). Serum and cerebrospinal fluid (CSF) neurofilament light (NFL) chain levels are reduced in MS patients under ocrelizumab treatment indicating a preventive action against neuro-axonal degeneration. Our aim, in this preliminary study, was to explore the impact of ocrelizumab treatment on synaptic integrity through assessment of neurogranin levels. Thirteen relapsing-remitting multiple sclerosis (RRMS) patients resistant to first-line immunomodulating agents were enrolled and followed up for 24 months under ocrelizumab treatment. Disease activity was monitored by periodic EDSS, MSSS, and cranial-spinal MRI assessments. No evidence of disease activity (NEDA)-3 was determined, and CSF levels of NFL (marker of neuro-axonal integrity) and neurogranin (marker of synaptic integrity) were measured by ELISA at baseline and 12-month ocrelizumab treatment. Seven RRMS patients, who preserved NEDA-3 status during 24-month follow-up, showed ≥30% NFL level decrease, whereas 6 patients with stable/increased NFL levels displayed relapse, MRI lesion, or disability progression. Although most RRMS patients exhibited increased CSF levels of neurogranin under ocrelizumab treatment, patients with and without neurogranin level increase did not differ in terms of clinical features and NEDA-3 status. Baseline neurogranin levels negatively correlated with baseline EDSS scores. Our results confirm that NFL effectively monitors treatment response of RRMS patients under ocrelizumab treatment. Neurogranin does not appear to exhibit a similar benefit in screening of RRMS disease activity. Nevertheless, lower neurogranin levels are associated with increased disability in RRMS indicating a potential disease activity biomarker function.

Application and progress of artificial intelligence technology in the segmentation of hyperreflective foci in OCT images for ophthalmic disease research.

With the advancement of retinal imaging, hyperreflective foci (HRF) on optical coherence tomography (OCT) images have gained significant attention as potential biological biomarkers for retinal neuroinflammation. However, these biomarkers, represented by HRF, present pose challenges in terms of localization, quantification, and require substantial time and resources. In recent years, the progress and utilization of artificial intelligence (AI) have provided powerful tools for the analysis of biological markers. AI technology enables use machine learning (ML), deep learning (DL) and other technologies to precise characterization of changes in biological biomarkers during disease progression and facilitates quantitative assessments. Based on ophthalmic images, AI has significant implications for early screening, diagnostic grading, treatment efficacy evaluation, treatment recommendations, and prognosis development in common ophthalmic diseases. Moreover, it will help reduce the reliance of the healthcare system on human labor, which has the potential to simplify and expedite clinical trials, enhance the reliability and professionalism of disease management, and improve the prediction of adverse events. This article offers a comprehensive review of the application of AI in combination with HRF on OCT images in ophthalmic diseases including age-related macular degeneration (AMD), diabetic macular edema (DME), retinal vein occlusion (RVO) and other retinal diseases and presents prospects for their utilization.

Application of novel non-invasive ophthalmic imaging to visualize peripapillary wrinkles, retinal folds and peripapillary hyperreflective ovoid mass-like structures associated with elevated intracranial pressure.

Elevated intracranial pressure (ICP) is a serious and potentially life-threatening condition, for which clinically useful non-invasive measures have been elusive, in some cases due to their inadequate sensitivity and specificity. Our aim was to evaluate novel non-invasive ophthalmic imaging of selected pathological features seen in elevated ICP, namely peripapillary hyperreflective ovoid mass-like structures (PHOMS), peripapillary wrinkles (PPW) and retinal folds (RF) as potential biomarkers of elevated ICP. This single-center pilot study included subjects with untreated or incompletely treated high ICP. The retinas of these subjects were evaluated with averaged en-face optical coherence tomography (OCT), OCT retinal cross-sections (OCT B-scans), adaptive optics scanning light ophthalmoscopy (AOSLO), and fundus photos. Seven subjects were included in the study. 6 subjects with high ICP (5 idiopathic intracranial hypertension, 1 medication induced, 30.8 ± 8.6 years, 75% female, 5 with papilledema) and 1 control (20-25 years) were included. PHOMS, PPW and RF were present in all subjects with papilledema, but neither in the high ICP subject without papilledema nor in the control subject. Averaged en-face OCT scans and AOSLO were more sensitive for PPW and RF than OCT B-scans and commercial fundus photos. PPW, RF and PHOMS volume have potential as non-invasive biomarkers of ICP. Novel imaging modalities may improve sensitivity. However, lack of automated image acquisition and processing limits current widespread adoption in clinical settings. Further research is needed to validate these structures as biomarkers for elevated ICP and improve clinical utility.

Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning.

Alzheimer's disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21-76 years) and the Open Access Series of Imaging Studies (OASIS, age 64-95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82-92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.

Evaluation of the biomarker potential of miR-650 and miR-663b in tumor tissues and plasma specimens of colon cancer patients living in northwest of Iran

BackgroundColorectal cancer (CRC) is considered as one of the most common malignancy and the fourth leading cause of cancer-related deaths, worldwide. Here, we aimed to investigate the expression of miR–663b and miR–650 in CRC tissue and plasma specimens.MethodsIn this case–control study, tumor specimens, non-tumoral adjacent tissues, and matched-plasma samples were obtained from forty patients with CRC living in the northwestern of Iran. Plasma of healthy patients was also collected as control. Total RNA was extracted from all specimens and studied by real-time PCR. Furthermore, the correlation between the expression of microRNAs and clinico-pathological features were also studied.ResultsOur data illustrated that miR-650 and miR-663b are down-regulated and up-regulated in tumor samples compared to non-tumoral margins, respectively (p < 0.001). However, the results did not show any significant difference in patient’s plasmas compared to controls. Further analysis disclosed that the expression of miR-663b is significantly associated with tumor size, lymph node metastasis, and tumor stage, while miR-650 is remarkably related to TNM stage, lymph node metastasis, distant metastasis, tumor size, and age. (p < 0.05) Furthermore, receiver operating characteristic (ROC) analyses revealed that miR-650 and miR-663b are potential biomarkers in differentiating CRC patients from healthy controls.ConclusionIn conclusion, our data illustrated the potential of miR-650 and miR-663b as biomarkers in colorectal cancer. However, further studies are needed to confirm the employment of these microRNAs in the diagnosis and/or prognosis of colorectal malignancies.

Predicting the potential deterioration of Barrett's esophagus based on gut microbiota: a Mendelian randomization analysis.

Esophageal adenocarcinoma (EAC) is one of the most malignant tumors in the digestive system. To make thing worse, the scarcity of treatment options is disheartening. However, if detected early, there is a possibility of reversing the condition. Unfortunately, there is still a lack of relevant early screening methods. Considering that Barrett's esophagus (BE), a precursor lesion of EAC, has been confirmed as the only known precursor of EAC. Analyzing which BE cases will progress to EAC and understanding the processes and mechanisms involved is of great significance for early screening of such patients. Considering the significant alterations in the gut microbiota of patients with BE and its potential role in the progression to EAC, this study aims to analyze the relationship between BE, EAC, and GM to identify potential diagnostic biomarkers and therapeutic targets. This study utilized comprehensive statistical data on gut microbiota from a large-scale genome-wide association meta-analysis conducted by the MiBioGen consortium (n = 18,340). Subsequently, we selected a set of single nucleotide polymorphisms (SNPs) that fell below the genome-wide significance threshold (1 × 10-5) as instrumental variables. To investigate the causal relationship between gut microbiota and BE and EAC, we employed various MR analysis methods, including Inverse Variance Weighting (IVW), MR-Egger regression, weighted median (WM), and weighted mean. Additionally, we assessed the level of pleiotropy, heterogeneity, and stability of genetic variations through MR-Egger intercept test, MR-PRESSO, Cochran's Q test, and "leave-one-out" sensitivity analysis. Furthermore, we conducted reverse MR analysis to identify the causal relationships between gut microbiota and BE and EAC. The results from the Inverse Variance-Weighted (IVW) analysis indicate that Alistipes (P = 4.86 × 10-2), Lactobacillus (P = 2.11 × 10-2), Prevotella 7 (P = 4.28 × 10-2), and RuminococcaceaeUCG004 (P = 4.34 × 10-2) are risk factors for Barrett's esophagus (BE), while Flavonifractor (P = 8.81 × 10-3) and RuminococcaceaeUCG004 (P = 4.99 × 10-2) are risk factors for esophageal adenocarcinoma (EAC). On the other hand, certain gut microbiota genera appear to have a protective effect against both BE and EAC. These include Eubacterium (nodatum group) (P = 4.51 × 10-2), Holdemania (P = 1.22 × 10-2), and Lactococcus (P = 3.39 × 10-2) in the BE cohort, as well as Eubacterium (hallii group) (P = 4.07 × 10-2) and Actinomyces (P = 3.62 × 10-3) in the EAC cohort. According to the results of reverse MR analysis, no significant causal effects of BE and EAC on gut microbiota were observed. Furthermore, no significant heterogeneity or pleiotropy was detected in the instrumental variables. We have established a causal relationship between the gut microbiota and BE and EAC. This study holds profound significance for screening BE patients who may be at risk of deterioration, as it can provide them with timely medical interventions to reverse the condition.

Circulating microRNA as a Potential Biomarker for Skeletal Disease in Primary Hyperparathyroidism: A Case-control Study.

The goal of this study was to characterize the microRNA (miRNA) expression signatures in patients with PHPT and identify miRNA biomarkers of bone homeostasis. Primary hyperparathyroidism (PHPT) is associated with increased bone turnover and decreased bone mass. miRNA are markers of bone remodeling. We performed a prospective case-control study of post-menopausal females with PHPT and control subjects matched for race, age, and BMD. We collected clinical and biochemical data, assessed BMD by dual-energy X-ray absorptiometry, and measured 27 serum miRNAs related to bone remodeling. We used linear regression to assess the correlation between miRNA levels, conventional biochemical markers and BMD. A total of 135 subjects were evaluated, including 49 with PHPT (discovery group), 47 control patients without PHPT, and an independent validation cohort of 39 PHPT patients. Of 27 miRNAs evaluated, nine (miR-335-5p, miR-130b-3p, miR-125b-5p, miR-23a-3p, miR-152-3p, miR-582-5p, miR-144-5p, miR-320a and miR-19b-3p) were differentially expressed in PHPT compared to matched control subjects. All nine differentially expressed miRNAs significantly correlated with levels of serum parathyroid hormone (PTH), and eight of the nine correlated with calcium levels. No differentially expressed miRNAs were consistently correlated with markers of BMD. Subjects with PHPT segregate from controls based on the signature of these nine miRNAs on principle component analysis. These data suggest that PHPT is characterized by a unique miRNA signature that is distinct from postmenopausal and idiopathic osteoporosis. Levels of specific miRNAs significantly correlate with PTH, suggesting that bone remodeling in PHPT may be mediated in part by PTH-induced changes in miRNA.