Biomarkers For Diagnosis Research Articles

Glymphatic system function in diverse glucose metabolism states

The rising prevalence of diabetes and prediabetes globally necessitates a deeper understanding of associated complications, including glymphatic system dysfunction. The glymphatic system, crucial for brain waste clearance, is imp-licated in cognitive decline and neurodegenerative diseases like Alzheimer’s disease. This letter explores recent research on glymphatic function across different glucose metabolism states. Tian et al ’s study reveals significant glym-phatic dysfunction in type 2 diabetes mellitus patients, evidenced by lower diffusion tensor imaging analysis along perivascular space indices compared to those with normal glucose metabolism and prediabetes. The research also reveals a link between glymphatic dysfunction and cognitive impairment. Additional research underscores the role of glymphatic impairment in neurodegenerative diseases. These findings highlight the importance of integrating glymphatic health into diabetes management and suggest potential biomarkers for early diagnosis and targeted therapeutic interventions.

Transportery kwasów tłuszczowych jako potencjalne markery diagnostyczne raka jelita grubego

Colorectal cancer (RJG) is one of the most frequently diagnosed malignant neoplasms: approximately 1.9 million new cases are reported annually. Notwithstanding the advent of techniques for the early detection of RJG and the introduction of novel therapeutic modalities, this disease remains the second leading cause of cancer-related mortality. The results of recent studies highlight the role of fatty acid transporters, including fatty acid translocase/cluster of differentiation 36 (FAT/CD36), fatty acid transport proteins (FATPs), and fatty acid-binding proteins (FABPs), in the pathogenesis of RJG. Changes in serum concentrations and in expression levels in tumor tissue may serve as promising biomarkers for the early diagnosis of the disease and/or the monitoring of its progression and the efficacy of its treatment. Moreover, the fatty acid carriers present a promising avenue for the development of efficacious therapies against RJG.

Abstract A058: Tumour-associated myeloid cells expressing IL-10R2/IL-22R1 as a potential biomarker for diagnosis and recurrence of pancreatic ductal adenocarcinoma

Abstract Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with a poor prognosis, largely due to late diagnosis and limited treatment options. The immunosuppressive tumor microenvironment (TME) further exacerbates the situation, hindering the effectiveness of traditional therapies. This study investigates the potential of IL-10R2/IL-22R1 expressing immune cells as novel biomarkers for early PDAC detection and recurrence monitoring, offering a potential avenue for improved patient outcomes. The research revealed a significant increase in the number of IL-10R2/IL-22R1 expressing immune cells in the peripheral blood of PDAC patients compared to healthy controls. These cells were found to infiltrate PDAC tissue and exhibit immunosuppressive functions, contributing to the TME's suppression of the immune response. Further investigation using a mouse model of PDAC demonstrated that early tumor progression was accompanied by a rise in IL-10R2/IL-22R1 expressing immune cells in the blood, highlighting their potential role in tumor development. In post-pancreatectomy patients, the study observed that elevated levels of IL-10R2+ immune cells were associated with earlier recurrence compared to the currently employed biomarker, CA19-9. This finding suggests that IL-10R2/IL-22R1 expressing immune cells could serve as valuable tools for predicting PDAC recurrence and potentially improving treatment outcomes. This research provides compelling evidence for the potential of IL-10R2/IL-22R1 expressing immune cells as promising biomarkers for early PDAC diagnosis and recurrence monitoring. However, further research is crucial to comprehensively understand their generation, function, and clinical utility. Larger-scale studies are needed to validate these findings and translate them into practical clinical applications for improved patient care. Citation Format: Hyung Keun Lee, So Young Kim, Soo-Hyun Chung, Sung Ill Jang, Bongkun Choi, Ji-Eun Kim, Dohee Yoon, Areum Yeo, Hyun Goo Kang, Jusung Lee, Yoon Ha Choi, Joon Seong Park, Yoolim Sung, Jong Kyoung Kim, Eun-Ju Chang, Dong Ki Lee. Tumour-associated myeloid cells expressing IL-10R2/IL-22R1 as a potential biomarker for diagnosis and recurrence of pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A058.

Ferroptosis-related biomarkers for adamantinomatous craniopharyngioma treatment: conclusions from machine learning techniques

IntroductionAdamantinomatous craniopharyngioma (ACP) is difficult to cure completely and prone to recurrence after surgery. Ferroptosis as an iron-dependent programmed cell death, may be a critical process in ACP. The study aimed to screen diagnostic markers related to ferroptosis in ACP to improve diagnostic accuracy.MethodsGene expression profiles of ACP were obtained from the gene expression omnibus (GEO) database. Limma package was used to analyze the differently expressed genes (DEGs). The intersection of DEGs and ferroptosis-related factors was obtained as differently expressed ferroptosis-related genes (DEFRGs). Enrichment analysis was processed, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), disease ontology (DO), gene set enrichment analysis (GSEA), and Gene Set Variation Analysis (GSVA) analysis. Machine learning algorithms were undertaken for screening diagnostic markers associated with ferroptosis in ACP. The levels of DEFRGs were verified in ACP patients. A nomogram was drawn to predict the relationship between key DEFRG expression and risk of disease. The disease groups were then clustered by consensus clustering analysis.ResultsDEGs were screened between ACP and normal samples. Ferroptosis-related factors were obtained from the FerrDb V2 and GeneCard databases. The correlation between DEFRGs and ferroptosis markers was also confirmed. A total of 6 overlapped DEFRGs were obtained. Based on the results of the nomogram, CASP8, KRT16, KRT19, and TP63 were the protective factors of the risk of disease, while GOT1 and TFAP2C were the risk factors. According to screened DEFRGs, the consensus clustering matrix was differentiated, and the number of clusters was stable. CASP8, KRT16, KRT19, and TP63, were upregulated in ACP patients, while GOT1 was downregulated. CASP8, KRT16, KRT19, TP63, CASP8, and GOT1 affect multiple ferroptosis marker genes. The combination of these genes might be the biomarker for ACP diagnosis via participating ferroptosis process.DiscussionFerroptosis-related genes, including CASP8, KRT16, KRT19, TP63, and GOT1 were the potential markers for ACP, which lays the theoretical foundation for ACP diagnosis.

Is soluble ST2 an useful biomarker for early diagnosis of coronary atherosclerosis?

Is soluble ST2 an useful biomarker for early diagnosis of coronary atherosclerosis?

Interplay of circular RNAs in gastric cancer - a systematic review

Circular RNAs (circRNAs) have gained prominence as important players in various biological processes such as gastric cancer (GC). Identification of several dysregulated circRNAs may serve as biomarkers for early diagnosis or as novel therapeutic targets. Predictive models can suggest potential new interactions and regulatory roles of circRNAs in GCs. Experimental validations of key interactions are being performed using in vitro models, confirming the significance of identified circRNA networks. The aim of this review is to highlight the important circRNAs associated with GC. On top of that an overview of the mechanistic details of the biogenesis and functionalities of the circRNAs are also presented. Furthermore, the potentialities of the circRNAs in the field of new drug discovery are deciphered.

Autoimmune disease: a view of epigenetics and therapeutic targeting

Autoimmune diseases comprise a large group of conditions characterized by a complex pathogenesis and significant heterogeneity in their clinical manifestations. Advances in sequencing technology have revealed that in addition to genetic susceptibility, various epigenetic mechanisms including DNA methylation and histone modification play critical roles in disease development. The emerging field of epigenetics has provided new perspectives on the pathogenesis and development of autoimmune diseases. Aberrant epigenetic modifications can be used as biomarkers for disease diagnosis and prognosis. Exploration of human epigenetic profiles revealed that patients with autoimmune diseases exhibit markedly altered DNA methylation profiles compared with healthy individuals. Targeted cutting-edge epigenetic therapies are emerging. For example, DNA methylation inhibitors can rectify methylation dysregulation and relieve patients. Histone deacetylase inhibitors such as vorinostat can affect chromatin accessibility and further regulate gene expression, and have been used in treating hematological malignancies. Epigenetic therapies have opened new avenues for the precise treatment of autoimmune diseases and offer new opportunities for improved therapeutic outcomes. Our review can aid in comprehensively elucidation of the mechanisms of autoimmune diseases and development of new targeted therapies that ultimately benefit patients with these conditions.

Milk metabolite profiling of dairy cows as influenced by mastitis

Mastitis is a disease with frequent incidence in dairy cows, causing huge financial losses to the dairy industry globally. The identification of certain biomarkers is crucial for the early diagnosis and management of mastitis. Metabolomics technology is a useful tool to accurately and efficiently analyze the changes of metabolites in biofluids in response to internal and external stimulations. Milk is the secreted by udder, and milk metabolites can directly reflect whether the udder are in the healthy or diseased state. The milk metabolomics analysis of mastitis can reveal the physiological and pathological changes of mammary gland and screen the related biomarkers, so as to offer useful reference for the prediction, diagnosis, and management of mastitis. Therefore, the aim of the present study was to comprehensively summarize milk metabolic change caused by naturally occurring or experimentally induced mastitis in dairy cows. In addition, comparative analysis and enrichment analysis were conducted to further discover potential biomarkers of mastitis and to identify the relevant pathways differentiating the healthy and mastitic cows. Multiple milk metabolites were identified to be altered during mastitis based on different metabolomics platforms. It was noteworthy that there were 28 metabolites not only identified by at least two different studies, but also showed consistent change tendency among the different studies. By comparison with literature, we further identified 12 milk metabolites, including acetate, arginine, β-hydroxybutyrate, carnitine, citrate, isoleucine, lactate, leucine, phenylalanine, proline, riboflavin, and valine that were linked with the occurrence of mastitis, which suggested that these 12 milk metabolites could be potential biomarkers of mastitis in dairy cows. Several pathways were revealed to explain the mechanisms of the variation of milk metabolites caused by mastitis, such as phenylalanine, tyrosine and tryptophan biosynthesis, arginine and proline metabolism, riboflavin metabolism, and tricarboxylic acid (TCA) cycle. These results offer a further understanding for the alteration of milk metabolites caused by mastitis, which have a potential significance in the development of more reliable biomarkers for mastitic diagnosis in dairy cows.

Microvesicle-Shuttled microRNA-130b Activates the Hepatic Inflammation by Inhibiting Glucocorticoid-Receptor-Mediated Immunosuppression in High-Fat Diet-Induced Obese Mice

Metabolism-disorder-induced liver diseases have become increasingly prevalent worldwide and are clinically linked to obesity and type 2 diabetes. In addition, a large number of previous literature studies have indicated that plasma miR-130b is a promising biomarker for the early diagnosis and treatment of obesity. However, whether miRNA-130b that was positively correlated with obesity resulted in hepatic inflammation needs to be further studied. Therefore, the study aims to determine the effect of microvesicle-shuttled miRNA-130b (miR-130b-MV) on the hepatic inflammation and its potential mechanism in high-fat diet-induced obese mice. Three-week-old C57BL/6 mice were fed a high-fat diet for eight weeks. Then, the obese mice received tail vein injections of MV-packaged scrambled control microRNA (miR-SC-MV) or miR-130b-MV every other day for 10 days. Compared with the control group, the miR-130b-MV injection significantly reduced the body weight while increasing the ratio of liver wet weight to total body weight. In addition, the miR-130b-MV injection significantly activated the hepatic inflammation by increasing the expression of proinflammatory genes, although the plasma concentrations of IL-6 and TNF-α were only slightly increased. Furthermore, the miR-130b-MV injection significantly increased the hepatic miR-130b expression while significantly suppressing the protein expression and phosphorylation of GR, a potential target of miR-130b. Moreover, the miR-130b overexpression results in a decrease in the expression of endogenous GR protein and a decrease in the activity of the luciferase reporter of GR 3′-UTR. In addition, the miR-130b-MV injection significantly upregulated NF-kB (p50) in both the cytoplasm and nucleus, showing enhanced proinflammation response. The above results demonstrated that miR-130b-MV activated the hepatic inflammation by inhibiting GR-mediated immunosuppression in high-fat diet-induced obese mice, suggesting a novel mechanism underlying the obesity-induced hepatic inflammation, and the inhibition of miR-130b may serve as a new molecular therapeutic target for the prevention and treatment of hepatic inflammation.

Hsa_circRNA_000166 accelerates breast cancer progression via the regulation of the miR-326/ELK1 and miR-330-5p/ELK1 axes

Purposes To probe the expression, clinical significance, roles, and molecular mechanisms of circRNA_000166 in breast cancer (BC). Methods Clinical tissue samples were gathered from 84 BC patients who underwent surgery at the Affiliated Hospital of Chengde Medical College. Clinical data were obtained from medical records and postoperative follow-up. Expression levels of circRNA_000166, miR-326, miR-330-5p, and ELK1 mRNA in BC tissues and cells were measured by qRT-PCR, and ELK1 protein levels were assessed by WB. Pearson’s correlation analysis evaluated the interrelationships between these RNAs in clinical samples. Luciferase reporter assays verified the interactions between miR-326/miR-330-5p and circRNA_000166, as well as between miR-326/miR-330-5p and ELK1. Cell proliferation, migration, and apoptosis were examined using CCK-8, colony formation, transwell, and flow cytometry assays, respectively. Results CircRNA_000166 was highly expressed in BC tissues and inversely correlated with miR-326/miR-330-5p levels but positively with ELK1 mRNA levels. ELK1 mRNA also inversely associated with miR-326/miR-330-5p levels in BC tissues. Importantly, our findings demonstrated that circRNA_000166 targets miR-326 and miR-330-5p, while ELK1 is the target of miR-326 and miR-330-5p in BC cells. CircRNA_000166 levels positively correlated with tumour size, TNM stage, histological grade, and lymph node metastasis, and negatively associated with postoperative progression-free survival (PFS) and overall survival (OS) in BC patients. CircRNA_000166 was also highly expressed in BC cells, and knockdown of circRNA_000166 reduced proliferation and migration, and increased apoptosis via miR-326/ELK1 and miR-330-5p/ELK1 pathways in vitro. Conclusion CircRNA_000166 enhances BC progression through miR-326/ELK1 and miR-330-5p/ELK1 pathways and shows potential as a biomarker for BC diagnosis and treatment.

Multi-omics insights implicate the remodeling of the intestinal structure and microbiome in aging

BackgroundAging can impair the ability of elderly individuals to fight infections and trigger persistent systemic inflammation, a condition known as inflammaging. However, the mechanisms underlying the development of inflammaging remain unknown.MethodsWe conducted 16S rRNA sequencing of intestinal contents from young and old C57BL/6J mice to elucidate changes in gut microbiota diversity and microbial community composition after aging. Aging-related differential bacterial taxa were then identified, and their abundance trends were validated in human samples. The variances in intestinal barrier function and circulating endotoxin between groups were also assessed. Furthermore, widely targeted metabolomics was conducted to characterize metabolic profiles after aging and to investigate the key metabolic pathways enriched by the differential metabolites.ResultsOur findings demonstrated an increase in relative proportion of pathogenic bacteria with age, a trend also revealed in healthy populations of different age groups. Additionally, aging individuals exhibited reduced intestinal barrier function and increased circulating endotoxin levels. Widely targeted metabolomics revealed a significant increase in various secondary bile acid metabolites after aging, positively correlated with the relative abundance of several aging-related bacterial taxa. Furthermore, old group had lower levels of various anti-inflammatory or beneficial metabolites. Enrichment analysis identified the starch and sucrose metabolism pathway as potentially the most significantly impacted signaling pathway during aging.ConclusionThis study aimed to provide insights into the complex interactions involved in organismal inflammaging through microbial multi-omics. These findings lay a solid foundation for future research aimed at identifying novel biomarkers for the clinical diagnosis of aging-related diseases or potential therapeutic targets.

Next‐generation biomarkers for alcohol consumption and alcohol use disorder diagnosis, prognosis, and treatment: A critical review

AbstractThis critical review summarizes the current state of omics‐based biomarkers in the alcohol research field. We first provide definitions and background information on alcohol and alcohol use disorder (AUD), biomarkers, and “omic” technologies. We next summarize using (1) genetic information as risk/prognostic biomarkers for the onset of alcohol‐related problems and the progression from regular drinking to problematic drinking (including AUD), (2) epigenetic information as diagnostic biomarkers for AUD and risk biomarkers for alcohol consumption, (3) transcriptomic information as diagnostic biomarkers for AUD, risk biomarkers for alcohol consumption, and (4) metabolomic information as diagnostic biomarkers for AUD, risk biomarkers for alcohol consumption, and predictive biomarkers for response to acamprosate in subjects with AUD. In the final section, the clinical implications of the findings are discussed, and recommendations are made for future research.

Multi-omic molecular characterization and diagnostic biomarkers for occult hepatitis B infection and HBsAg-positive hepatitis B infection

BackgroundThe pathological and physiological characteristics between HBsAg-positive HBV infection and occult hepatitis B infection (OBI) are currently unclear. This study aimed to explore the immune microenvironment in the peripheral circulation of OBI patients through integration of proteomic and metabolomic sequencing, and to identify molecular biomarkers for clinical diagnosis of HBsAg-positive HBV and OBI.MethodsThis research involved collection of plasma from 20 patients with OBI (negative for HBsAg but positive for HBV DNA, with HBV DNA levels < 200 IU/mL), 20 patients with HBsAg-positive HBV infection, and 10 healthy individuals. Mass spectrometry-based detection was used to analyze the proteome, while nuclear magnetic resonance spectroscopy was employed to study the metabolomic phenotypes. Differential molecule analysis, pathway enrichment and functional annotation, as well as weighted correlation network analysis (WGCNA), were conducted to uncover the characteristics of HBV-related liver disease. Prognostic biomarkers were identified using machine learning algorithms, and their validity was confirmed in a larger cohort using enzyme linked immunosorbent assay (ELISA).ResultsHBsAg-positive HBV individuals showed higher ALT levels (p=0.010) when compared to OBI patients. The influence of HBV infection on metabolic functions and inflammation was evident through the analysis of distinct metabolic pathways in HBsAg-positive HBV and OBI groups. Tissue tracing demonstrated a connection between Kupffer cells and HBsAg-positive HBV infection, as well as between hepatocytes and OBI. Immune profiling revealed the correlation between CD4 Tem cells, memory B cells and OBI, enabling a rapid response to infection reactivation through cytokine secretion and antibody production. A machine learning-constructed and significantly expressed molecule-based diagnostic model effectively differentiated HBsAg-positive and OBI groups (AUC values > 0.8). ELISA assay confirmed the elevation of FGB and FGG in OBI samples, suggesting their potential as biomarkers for distinguishing OBI from HBsAg-positive infection.ConclusionsThe immune microenvironment and metabolic status of HBsAg-positive HBV patients and OBI patients vary significantly. The machine learning-based diagnostic model described herein displayed impressive classification accuracy, presenting a non-invasive means of differentiating between OBI and HBsAg-positive HBV infections.

A three-classification model for identifying migraine with right-to-left shunt using lateralization of functional connectivity and brain network topology: a resting-state fMRI study

IntroductionRight-to-left shunting has been significantly associated with migraine, although the neural mechanisms remain complex and not fully elucidated. The aim of this study was to investigate the variability of brain asymmetry in individuals with migraine with right-to-left shunting, migraine without right-to-left shunting and normal controls using resting-state fMRI technology and to construct a three-classification model.MethodsFirstly, asymmetries in functional connectivity and brain network topology were quantified to laterality indices. Secondly, the laterality indices were employed to construct a three-classification model using decision tree and random forest algorithms. Ultimately, through a feature score analysis, the key brain regions that contributed significantly to the classification were extracted, and the associations between these brain regions and clinical features were investigated.ResultsOur experimental results showed that the initial classification accuracy reached 0.8961. Subsequently, validation using an independent sample set resulted in a classification accuracy of 0.8874. Further, after expanding the samples by the segmentation strategy, the classification accuracies were improved to 0.9103 and 0.9099. Additionally, the third sample set yielded a classification accuracy of 0.8745. Finally, 9 pivotal brain regions were identified and distributed in the default network, the control network, the visual network, the limbic network, the somatomotor network and the salience/ventral attention network.DiscussionThe results revealed distinct lateralization features in the brains of the three groups, which were closely linked to migraine and right-to-left shunting symptoms and could serve as potential imaging biomarkers for clinical diagnosis. Our findings enhanced our understanding of migraine and right-to-left shunting mechanisms and offered insights into assisting clinical diagnosis.

SURG-04. DEVELOPMENT OF MICROSURGICAL FORCEPS EQUIPPED WITH HAPTICS TECHNOLOGY FOR IN SITU DIFFERENTIATION OF BRAIN TUMORS DURING MICROSURGERY

Abstract BACKGROUND The stiffness of human cancers is correlated with their pathology, and can be used as a biomarker for diagnosis, malignancy prediction, molecular expression, and postoperative complications. Neurosurgeons perform tumor resection based on tactile sensations. However, it takes years of surgical experience to appropriately distinguish brain tumors from surrounding parenchymal tissue. Haptics is a technology that can amplify, transmit, record, and reproduce real tactile sensation, and the physical properties (e.g., stiffness) of an object can be quantified. METHODS glioblastoma (SF126-FmC, U87-FmC, U251-FmC) and malignant meningioma (IOMM-Lee-FmC, HKBMM-FmC) cell lines were transplanted into nude mice, and the stiffness of tumors and normal brain tissues were measured using our newly developed surgical forceps equipped with haptic technology (haptic forceps). Furthermore, pathological examination was performed to explore the correlation between stiffness and histological characteristics. RESULTS We found that all five brain tumor tissues were stiffer than normal brain tissue (p<0.001), and that brain tumor (three types of glioblastomas and two types of malignant meningioma) was significantly stiffer than normal brain tissue (p<0.001 for all). The stiffness among glioblastomas was not statistically significant (p=0.468) however, among the 2 malignant meningiomas, IOMM-Lee-FmC was significantly stiffer than HKBMM-FmC (p=0.032). Upon pathological examination, no significant differences were observed in the expression of collagen fibers. However, HKBMM-FmC demonstrated a higher prevalence of necrotic changes compared to IOMM-Lee-FmC. CONCLUSIONS Our findings suggest that tissue stiffness could be a marker not only to distinguish brain tumors from surrounding parenchymal tissue but also to distinguish pathology and malignancy of tumors. It is also suggested that the stiffness perceived during actual surgery and the stiffness quantified using haptic forceps may correlate with histological differences. Haptic forceps may help neurosurgeons to sense minute changes in tissue stiffness.

GNG5 is a novel regulator of Aβ42 production in Alzheimer’s disease

AbstractThe therapeutic options for Alzheimer’s disease (AD) are limited, underscoring the critical need for finding an effective regulator of Aβ42 production. In this study, with 489 human postmortem brains, we revealed that homotrimer G protein subunit gamma 5 (GNG5) expression is upregulated in the hippocampal–entorhinal region of pathological AD compared with normal controls, and is positively correlated with Aβ pathology. In vivo and in vitro experiments confirm that increased GNG5 significantly promotes Aβ pathology and Aβ42 production. Mechanically, GNG5 regulates the cleavage preference of γ-secretase towards Aβ42 by directly interacting with the γ-secretase catalytic subunit presenilin 1 (PS1). Moreover, excessive GNG5 increases the protein levels and the activation of Rab5, leading to the increased number of early endosomes, the major cellular organelle for production of Aβ42. Furthermore, immunoprecipitation and immunofluorescence revealed co-interaction of Aβ42 with GPCR family CXCR2, which is known as the receptor for IL-8, thus facilitating the dissociation of G-proteins βγ from α subunits. Treatment of Aβ42 in neurons combined with structure prediction indicated Aβ42 oligomers as a new ligand of CXCR2, upregulating γ subunit GNG5 protein levels. The co-localizations of GNG5 and PS1, CXCR2 and Aβ42 were verified in eight human brain regions. Besides, GNG5 is significantly reduced in extracellular vesicles (EVs) derived from cerebral cortex or serum of AD patients compared with healthy cognition controls. In brief, GNG5 is a novel regulator of Aβ42 production, suggesting its clinical potential as a diagnosis biomarker and the therapeutic target for AD.

Development of a Panel of Biomarkers for Differential Diagnosis of Multiple Sclerosis

Abstract— Demyelinating diseases are a group of heterogeneous pathologies that affect the nervous system and reduce the quality of life. One of such diseases is multiple sclerosis (MS), an inflammatory autoimmune neurodegenerative disease of the central nervous system (CNS). At the initial stages, MS can mimic some infectious, neoplastic, genetic, metabolic, vascular, and other pathologies. Accurate differential diagnosis of this disease is important to improve the quality of life of patients and reduce possible irreversible damage to the central nervous system. In this work, we confirmed the possibility of using our previously proposed candidate panel of MS biomarkers to distinguish MS from neuromyelitis optica spectrum disorder (NMOSD) and amyotrophic lateral sclerosis (ALS). We have shown that our proposed panel (SPTAN1601-644 + PRX451-494 + PTK6301-344 + LMP1285-330) allows us to distinguish MS from ALS (AUC = 0.796) and NMOSD (AUC = 0.779).

Omics Studies in CKD: Diagnostic Opportunities and Therapeutic Potential

ABSTRACTOmics technologies have significantly advanced the prediction and therapeutic approaches for chronic kidney disease (CKD) by providing comprehensive molecular insights. This is a review of the current state and future prospects of integrating biomarkers into the clinical practice for CKD, aiming to improve patient outcomes by targeted therapeutic interventions. In fact, the integration of genomic, transcriptomic, proteomic, and metabolomic data has enhanced our understanding of CKD pathogenesis and identified novel biomarkers for an early diagnosis and targeted treatment. Advanced computational methods and artificial intelligence (AI) have further refined multi‐omics data analysis, leading to more accurate prediction models for disease progression and therapeutic responses. These developments highlight the potential to improve CKD patient care with a precise and individualized treatment plan .

Meta-Analysis of Novel Biomarkers for Early Diagnosis of Neonatal Sepsis

Neonatal sepsis is a major cause of illness and death in newborns worldwide. Early diagnosis is critical for quick treatment and better outcomes. However, traditional markers like white blood cell count and immature-to-total neutrophil ratio are often not sensitive or specific enough. This meta-analysis reviewed PubMed, Embase, and the Cochrane Library from January 2013 to December 2023. It included studies that assessed the accuracy of procalcitonin (PCT), interleukin-6 (IL-6), and C-reactive protein (CRP) in diagnosing neonatal sepsis. The QUADAS-2 tool was used to check the quality of the studies. Six studies, including 1245 newborns, were analyzed. PCT had the highest pooled sensitivity (0.85, 95% CI 0.80-0.90) and specificity (0.82, 95% CI 0.75-0.89), followed by IL-6 (sensitivity: 0.78, 95% CI 0.70-0.85; specificity: 0.75, 95% CI 0.65-0.83). CRP was less sensitive (0.70, 95% CI 0.60-0.79) and specific (0.68, 95% CI 0.58-0.77). In conclusion, new biomarkers, especially PCT, are promising for early diagnosis of neonatal sepsis. However, more large-scale studies are needed to confirm these findings.

Proteomics analysis of extracellular vesicles for biomarkers of autism spectrum disorder

BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by symptoms that include social interaction deficits, language difficulties and restricted, repetitive behavior. Early intervention through medication and behavioral therapy can eliminate some ASD-related symptoms and significantly improve the life-quality of the affected individuals. Currently, the diagnosis of ASD is highly limited.MethodsTo investigate the feasibility of early diagnosis of ASD, we tested extracellular vesicles (EVs) proteins obtained from ASD cases. First, plasma EVs were isolated from healthy controls (HCs) and ASD individuals and were analyzed using proximity extension assay (PEA) technology to quantify 1,196 protein expression level. Second, machine learning analysis and bioinformatic approaches were applied to explore how a combination of EV proteins could serve as biomarkers for ASD diagnosis.ResultsNo significant differences in the EV morphology and EV size distribution between HCs and ASD were observed, but the EV number was slightly lower in ASD plasma. We identified the top five downregulated proteins in plasma EVs isolated from ASD individuals: WW domain-containing protein 2 (WWP2), Heat shock protein 27 (HSP27), C-type lectin domain family 1 member B (CLEC1B), Cluster of differentiation 40 (CD40), and folate receptor alpha (FRalpha). Machine learning analysis and correlation analysis support the idea that these five EV proteins can be potential biomarkers for ASD.ConclusionWe identified the top five downregulated proteins in ASD EVs and examined that a combination of EV proteins could serve as biomarkers for ASD diagnosis.