Neutrophil-to-Lymphocyte Ratio as a Potential Biomarker to Managing Type 2 Diabetes Mellitus and Predicting Disease Progression.

Introduction Diabetes is a chronic disease that causes dysregulation of blood glucose. Type 2 diabetes mellitus (T2DM) could result in long-term inflammatory conditions that affect different organs of the body. Despite the availability of diagnostic markers like glycated hemoglobin (HbA1c) for T2DM, it is essential to find an appropriate marker that could predict long-term complications. This study evaluates the potential role of neutrophil-to-lymphocyte ratio (NLR) in predicting disease progression and treatment responses.  Methods This case-control study was carried out among 160 T2DM patients and 132 non-diabetic persons. Blood samples were collected from each participant and were processed for hemoglobin, HbA1c, iron, ferritin, and complete blood picture (NLR).  Results The study showed that there was a significant variation in the serum levels of ferritin (264.8±611.6 ng/ml versus 168.3±364.7 ng/ml, p=0.392), iron (4.095±8.851 mcg/dl versus 55.20±37.62 mcg/dl, p=0.0111), and HbA1c (8.169±1.635% versus 5.668±0.5260% p<0.0001) among T2DM patients compared to non-diabetic persons. The NLR values (4.189±4.154 versus 4.095±8.851, p=0.009) among patients with T2DM significantly varied with that of non-diabetic persons. A significant negative correlation was noticed between the serum levels of iron and NLR (r=-0.17, p=0.014) and a positive correlation was noticed between HbA1c and NLR (r=0.19, p=0.014). The serum levels of iron revealed a significant positive correlation with the serum levels of ferritin (r=0.24, p=0.002) and hemoglobin percentage (r=0.41, p=0.008). HbA1c revealed a significant positive correlation with NLR (r=0.19, p=0.014). Additionally, a significant negative correlation was observed between iron with NLR (r=-0.17, p=0.029) and hemoglobin percentage with NLR (r=-0.30, p=0.005). However, no such correlation was demonstrated among non-diabetic persons. With an accuracy of 89.85% and high sensitivity and specificity, NLR showed diagnostic accuracy like HbA1c.  Conclusions NLR demonstrated equivalent efficacy to HbA1c in predicting glycemic control. Since diabetes affects different organs of the body, evaluating NLR probably predicts inflammation. Therefore, NLR could be useful in the management of T2DM and in predicting long-term complications.

Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence.

Introduction: Automated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assisting in epilepsy diagnosis and treatment adjustment. Methods: In this phase 2 exploratory study, the performance of a contactless, marker-free, video-based motor seizure detection system is assessed, considering video recordings of patients (age 0-80 years), in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, with respect to video-electroencephalographic monitoring (VEM) as the medical gold standard. Detection performances of five categories of motor epileptic seizures (tonic-clonic, hyperkinetic, tonic, unclassified motor, automatisms) and psychogenic non-epileptic seizures (PNES) with a motor behavioral component lasting for >10 s were assessed independently at different detection thresholds (rather than as a categorical classification problem). A total of 230 patients were recruited in the study, of which 334 in-scope (>10 s) motor seizures (out of 1,114 total seizures) were identified by VEM reported from 81 patients. We analyzed both daytime and nocturnal recordings. The control threshold was evaluated at a range of values to compare the sensitivity (n = 81 subjects with seizures) and false detection rate (FDR) (n = all 230 subjects). Results: At optimal thresholds, the performance of seizure groups in terms of sensitivity (CI) and FDR/h (CI): tonic-clonic- 95.2% (82.4, 100%); 0.09 (0.077, 0.103), hyperkinetic- 92.9% (68.5, 98.7%); 0.64 (0.59, 0.69), tonic- 78.3% (64.4, 87.7%); 5.87 (5.51, 6.23), automatism- 86.7% (73.5, 97.7%); 3.34 (3.12, 3.58), unclassified motor seizures- 78% (65.4, 90.4%); 4.81 (4.50, 5.14), and PNES- 97.7% (97.7, 100%); 1.73 (1.61, 1.86). A generic threshold recommended for all motor seizures under study asserted 88% sensitivity and 6.48 FDR/h. Discussion: These results indicate an achievable performance for major motor seizure detection that is clinically applicable for use as a seizure screening solution in diagnostic workflows.

The role of fungi in the diagnosis of colorectal cancer.

Colorectal cancer (CRC) is a prevalent tumour with high morbidity rates worldwide, and its incidence among younger populations is rising. Early diagnosis of CRC can help control the associated mortality. Fungi are common microorganisms in nature. Recent studies have shown that fungi may have a similar association with tumours as bacteria do. As an increasing number of tumour-associated fungi are discovered, this provides new ideas for the diagnosis and prognosis of tumours. The relationship between fungi and colorectal tumours has also been recently identified by scientists. Therefore, this paper describes the limitations and prospects of the application of fungi in diagnosing CRC and predicting CRC prognosis.

The roles of lncRNAs and miRNAs in pancreatic cancer: a focus on cancer development and progression and their roles as potential biomarkers.

Pancreatic ductal adenocarcinoma (PDAC) is among the most penetrative malignancies affecting humans, with mounting incidence prevalence worldwide. This cancer is usually not diagnosed in the early stages. There is also no effective therapy against PDAC, and most patients have chemo-resistance. The combination of these factors causes PDAC to have a poor prognosis, and often patients do not live longer than six months. Because of the failure of conventional therapies, the identification of key biomarkers is crucial in the early diagnosis, treatment, and prognosis of pancreatic cancer. 65% of the human genome encodes ncRNAs. There are different types of ncRNAs that are classified based on their sequence lengths and functions. They play a vital role in replication, transcription, translation, and epigenetic regulation. They also participate in some cellular processes, such as proliferation, differentiation, metabolism, and apoptosis. The roles of ncRNAs as tumor suppressors or oncogenes in the growth of tumors in a variety of tissues, including the pancreas, have been demonstrated in several studies. This study discusses the key roles of some lncRNAs and miRNAs in the growth and advancement of pancreatic carcinoma. Because they are involved not only in the premature identification, chemo-resistance and prognostication, also their roles as potential biomarkers for better management of PDAC patients.

Integrating machine learning and nontargeted plasma lipidomics to explore lipid characteristics of premetabolic syndrome and metabolic syndrome.

Objective: To identify plasma lipid characteristics associated with premetabolic syndrome (pre-MetS) and metabolic syndrome (MetS) and provide biomarkers through machine learning methods. Methods: Plasma lipidomics profiling was conducted using samples from healthy individuals, pre-MetS patients, and MetS patients. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were employed to identify dysregulated lipids in the comparative groups. Biomarkers were selected using support vector machine recursive feature elimination (SVM-RFE), random forest (rf), and least absolute shrinkage and selection operator (LASSO) regression, and the performance of two biomarker panels was compared across five machine learning models. Results: In the OPLS-DA models, 50 and 89 lipid metabolites were associated with pre-MetS and MetS patients, respectively. Further machine learning identified two sets of plasma metabolites composed of PS(38:3), DG(16:0/18:1), and TG(16:0/14:1/22:6), TG(16:0/18:2/20:4), and TG(14:0/18:2/18:3), which were used as biomarkers for the pre-MetS and MetS discrimination models in this study. Conclusion: In the initial lipidomics analysis of pre-MetS and MetS, we identified relevant lipid features primarily linked to insulin resistance in key biochemical pathways. Biomarker panels composed of lipidomics components can reflect metabolic changes across different stages of MetS, offering valuable insights for the differential diagnosis of pre-MetS and MetS.

Advances and challenges of exosome-derived noncoding RNAs for hepatocellular carcinoma diagnosis and treatment.

Exosomes, also termed extracellular vesicles (EVs), are an important component of the tumor microenvironment (TME) and exert versatile effects on the molecular communications in the TME of hepatocellular carcinoma (HCC). Exosome-mediated intercellular communication is closely associated with the tumorigenesis and development of HCC. Exosomes can be extracted through ultracentrifugation and size exclusion, followed by molecular analysis through sequencing. Increasing studies have confirmed the important roles of exosome-derived ncRNAs in HCC, including tumorigenesis, progression, immune escape, and treatment resistance. Due to the protective membrane structure of exosomes, the ncRNAs carried by exosomes can evade degradation by enzymes in body fluids and maintain good expression stability. Thus, exosome-derived ncRNAs are highly suitable as biomarkers for the diagnosis and prognostic prediction of HCC, such as exosomal miR-21-5p, miR-221-3p and lncRNA-ATB. In addition, substantial studies revealed that the up-or down-regulation of exosome-derived ncRNAs had an important impact on HCC progression and response to treatment. Exosomal biomarkers, such as miR-23a, lncRNA DLX6-AS1, miR-21-5p, lncRNA TUC339, lncRNA HMMR-AS1 and hsa_circ_0004658, can reshape immune microenvironment by regulating M2-type macrophage polarization and then promote HCC development. Therefore, by controlling exosome biogenesis and modulating exosomal ncRNA levels, HCC may be inhibited or eliminated. In this current review, we summarized the recent findings on the role of exosomes in HCC progression and analyzed the relationship between exosome-derived ncRNAs and HCC diagnosis and treatment.

Bioinformatics analysis and validation of genes related to paclitaxel's anti-breast cancer effect through immunogenic cell death.

Research indicated that Paclitaxel (PTX) can induce immunogenic cell death (ICD) through immunogenic modulation. However, the combination of PTX and ICD has not been extensively studied in breast cancer (BRCA). The TCGA-BRCA and GSE20685 datasets were enrolled in this study. Samples from the TCGA-BRCA dataset were consistently clustered based on selected immunogenic cell death-related genes (ICD-RGs). Next, candidate genes were obtained by overlapping differentially expressed genes (DEGs) between BRCA and normal groups, intersecting genes common to DEGs between cluster1 and cluster2 and hub module genes, and target genes of PTX from five databases. The univariate Cox algorithm and the least absolute shrinkage and selection operator (LASSO) were performed to obtain biomarkers and build a risk model. Following observing the immune microenvironment in differential risk subgroups, single-gene gene set enrichment analysis (GSEA) was carried out in all biomarkers. Finally, the expression of biomarkers was analyzed. Enrichment analysis showed that 626 intersecting genes were linked with inflammatory response. Further five biomarkers (CHI3L1, IL18, PAPLN, SH2D2A, and UBE2L6) were identified and a risk model was built. The model's performance was validated using GSE20685 dataset. Furthermore, the biomarkers were enriched with adaptive immune response. Lastly, the experimental results indicated that the alterations in IL18, SH2D2A, and CHI3L1 expression after treatment matched those in the public database. In this study, Five PTX-ICD-related biomarkers (CHI3L1, IL18, PAPLN, SH2D2A, and UBE2L6) were identified to aid in predicting BRCA treatment outcomes.

Abnormal expression of Krüppel-like transcription factors and their potential values in lung cancer.

Lung cancer still is one of the most common malignancy tumors in the world. However, the mechanisms of its occurrence and development have not been fully elucidated. Zinc finger protein family (ZNFs) is the largest transcription factor family in human genome. Recently, the more and more basic and clinical evidences have confirmed that ZNFs/Krüppel-like factors (KLFs) refer to a group of conserved zinc finger-containing transcription factors that are involved in lung cancer progression, with the functions of promotion, inhibition, dual roles and unknown classifications. Based on the recent literature, some of the oncogenic KLFs are promising molecular biomarkers for diagnosis, prognosis or therapeutic targets of lung cancer. Interestingly, a novel computational approach has been proposed by using machine learning on features calculated from primary sequences, the XGBoost-based model with accuracy of 96.4 % is efficient in identifying KLF proteins. This paper reviews the recent some progresses of the oncogenic KLFs with their potential values for diagnosis, prognosis and molecular target in lung cancer.

A nomogram model based on the systemic immune-inflammation index to predict the risk of venous thromboembolism in elderly patients after hip fracture: A retrospective cohort study.

Background and objectives: Venous thromboembolism (VTE), encompassing deep vein thrombosis (DVT) and secondary pulmonary embolism (PE), represents a significant complication post-hip fracture in the elderly. It is a prevalent cause of VTE-related complications, prolonged hospitalization, and mortality. This study aimed to investigate the potential of the systemic immune-inflammation index (SII) as a predictive marker for VTE in older patients following hip fracture. Methods: The study was structured as an observational, analytical, retrospective cohort analysis. A total of 346 elderly patients diagnosed with hip fracture were included. We retrospectively collated clinical and laboratory data for these patients. Using the bootstrap method, the patients were divided in a 7:3 ratio into a training cohort (DVT group = 170 patients; no-DVT group = 72 patients) and an internal validation cohort (DVT group = 81 patients; no-DVT group = 23 patients). In the training cohort, relevant indices were initially identified using univariate analysis. Subsequently, least absolute shrinkage and selection operator logistic analysis was employed to determine significant potential independent risk factors (P < 0.05). A dynamic online diagnostic nomogram was developed, with its discriminative ability assessed using the area under the receiver operating characteristic curve (AUC). The nomogram's accuracy was further appraised using calibration plots. The clinical utility of the nomogram was evaluated through decision curve analysis (DCA) and corroborated by internal validation within the training set. Results: SII emerged as the sole independent risk factor identified from the multivariate logistic analysis of the training cohort and was incorporated into the VTE diagnostic nomogram for older patients' post-hip fracture. The nomogram demonstrated AUC values of 0.648 in the training cohort and 0.545 in the internal testing cohort. Calibration curves corroborated the close alignment of the nomogram's predicted outcomes with the ideal curve, indicating consistency between predicted and actual outcomes. The DCA curve suggested that all patients could derive benefit from this model. These findings were also validated in the validation cohort. Conclusion: The systemic immune-inflammation index is a robust predictor of venous thromboembolism in elderly patients following hip fracture, underscoring its potential as a valuable tool in clinical practice.

Spectral exponent assessment and neurofilament light chain: a comprehensive approach to describe recovery patterns in stroke.

Introduction: Understanding the residual recovery potential in stroke patients is crucial for tailoring effective neurorehabilitation programs. We propose using EEG and plasmatic Neurofilament light chain (NfL) levels as a model to depict longitudinal patterns of stroke recovery. Methods: We enrolled 13 patients (4 female, mean age 74.7 ± 8.8) who underwent stroke in the previous month and were hospitalized for 2-months rehabilitation. Patients underwent blood withdrawal, clinical evaluation and high-definition EEG at T1 (first week of rehabilitation) and at T2 (53 ± 10 days after). We assessed the levels of NfL and we analyzed the EEG signal extracting Spectral Exponent (SE) values. We compared our variables between the two timepoint and between cortical and non-cortical strokes. Results: We found a significant difference in the symmetry of SE values between cortical and non-cortical stroke at both T1 (p = 0.005) and T2 (p = 0.01). SE in the affected hemisphere showed significantly steeper values at T1 when compared with T2 (p = 0.001). EEG measures were consistently related to clinical scores, while NfL at T1 was related to the volume of ischemic lesions (r = 0.75; p = 0.003). Additionally, the combined use of NfL and SE indicated varying trends in longitudinal clinical recovery. Conclusion: We present proof of concept of a promising approach for the characterization of different recovery patterns in stroke patients.

Double lateral flow immunosensing of undeclared pork and chicken components of meat products.

Adulteration of meat products is a serious problem in the modern society. Consumption of falsified meat products can be hazardous to health and/or lead to violating religious dietary principles. To identify such products, rapid and simple test systems for point-of-need detection are in demand along with complex laboratory methods. This study presents the first double lateral flow (immunochromatographic) test system, which allows simultaneous revealing two prevalent types of falsifications-undeclared addition of pork and chicken components to meat products. In the proposed test system, porcine myoglobin (MG) and chicken immunoglobulin Y (IgY) were used as specific biomarkers recognizable by antibodies. Within the optimization of the analysis, the concentrations of the immune reagents and regimes of their application on the working membrane were selected, which provided minimal limits of detection (LODs) for both analytes. The developed test system enables the detection of MG and IgY with the LODs of 10 and 12 ng/mL, respectively, which accords to addition of 0.1% of the undeclared meat compounds. The applicability of the test system to control the composition of raw meat mixtures and cooked food products was confirmed. The developed approach can be considered as a promising tool for monitoring composition of meat products. Supplementary Information: The online version contains supplementary material available at 10.1007/s13197-024-05944-y.

Blood sphingolipid as a novel biomarker in patients with neuromyelitis optica spectrum disorder.

BACKGROUND: Sphingolipids are signaling molecules and structural components of the axolemma and myelin sheath. Plasma sphingolipid levels may reflect disease status of neuromyelitis optica spectrum disorder (NMOSD). We aimed to examine plasma sphingolipids as disease severity biomarkers for NMOSD and compare their characteristics with those of serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP). METHODS: We measured plasma sphingolipids, sNfL, and sGFAP levels in NMOSD cases with anti-aquaporin-4-antibody. An unbiased approach, partial least square discriminant analysis (PLS-DA), was utilized to determine whether sphingolipid profiles differ according to the disease state of NMOSD (presence, moderate-to-severe disability [Expanded Disease Severity Scale, (EDSS) > 3.0], and relapses). RESULTS: We investigated 81 patients and 10 controls. PLS-DA models utilizing sphingolipids successfully differentiated patients with EDSS > 3.0, but failed to identify the presence of disease and relapses. Ceramide-C14-a significant contributor to differentiating EDSS > 3.0-positively correlated with EDSS, while its levels were independent of age and the presence of relapses. This characteristic was unique from those of sNfL and sGFAP, which were affected by age and relapses as well as EDSS. CONCLUSION: Plasma sphingolipids may be useful NMOSD biomarkers for disability with distinct characteristics compared to sNfL and sGFAP.

Fast, sensitive LC-MS resolution of α -hydroxy acid biomarkers via SPP-teicoplanin and an alternative UV detection approach.

Enantioseparation of α -hydroxy acids is essential since specific enantiomers of these compounds can be used as disease biomarkers for diagnosis and prognosis of cancer, brain diseases, kidney diseases, diabetes, etc., as well as in the food industry to ensure quality. HPLC methods were developed for the enantioselective separation of 11 α -hydroxy acids using a superficially porous particle-based teicoplanin (TeicoShell) chiral stationary phase. The retention behaviors observed for the hydroxy acids were HILIC, reversed phase, and ion-exclusion. While both mass spectrometry and UV spectroscopy detection methods could be used, specific mobile phases containing ammonium formate and potassium dihydrogen phosphate, respectively, were necessary with each approach. The LC-MS mode was approximately two orders of magnitude more sensitive than UV detection. Mobile phase acidity and ionic strength significantly affected enantioresolution and enantioselectivity. Interestingly, higher ionic strength resulted in increased retention and enantioresolution. It was noticed that for formate-containing mobile phases, using acetonitrile as the organic modifier usually resulted in greater enantioresolution compared to methanol. However, sometimes using acetonitrile with high ammonium formate concentrations led to lengthy retention times which could be avoided by using methanol as the organic modifier. Additionally, the enantiomeric purities of single enantiomer standards were determined and it was shown that almost all standards contained some levels of enantiomeric impurities.

Update of biomarkers to diagnose diabetic foot osteomyelitis: A meta-analysis and systematic review.

The aim of this study was to evaluate the diagnostic characteristics of biomarker for diabetic foot osteomyelitis (DFO). We searched PubMed, Scopus, Embase and Medline for studies who report serological markers and DFO before December 2022. Studies must include at least one of the following diagnostic parameters for biomarkers: area under the curve, sensitivities, specificities, positive predictive value, negative predictive value. Two authors evaluated quality using the Quality Assessment of Diagnostic Accuracy Studies tool. We included 19 papers. In this systematic review, there were 2854 subjects with 2134 (74.8%) of those patients being included in the meta-analysis. The most common biomarkers were erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and procalcitonin (PCT). A meta-analysis was then performed where data were evaluated with Forrest plots and receiver operating characteristic curves. The pooled sensitivity and specificity were 0.72 and 0.75 for PCT, 0.72 and 0.76 for CRP and 0.70 and 0.77 for ESR. Pooled area under the curves for ESR, CRP and PCT were 0.83, 0.77 and 0.71, respectfully. Average diagnostic odds ratios were 16.1 (range 3.6-55.4), 14.3 (range 2.7-48.7) and 6.7 (range 3.6-10.4) for ESR, CRP and PCT, respectfully. None of the biomarkers we evaluated could be rated as 'outstanding' to diagnose osteomyelitis. Based on the areas under the curve, ESR is an 'excellent' biomarker to detect osteomyelitis, and CRP and PCT are 'acceptable' biomarkers to diagnose osteomyelitis. Diagnostic odds ratios indicate that ESR, CRP and PCT are 'good' or 'very good' tools to identify osteomyelitis.

Elucidating the role of FOS in modulating the immune microenvironment through fibroblast and myeloid cell regulation in locoregional recurrent HNSCC.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) presents a significant clinical challenge, particularly due to its high propensity for locoregional recurrence. Current research underscores the need to unravel the complex interactions within the tumor microenvironment. This study addresses the critical gap in understanding how FOS modulates the immune landscape in HNSCC, with a focus on its influence on fibroblast and myeloid cell dynamics. METHODS: Employing a comprehensive approach, we analyzed tissue samples from HNSCC patients and adjacent non-cancerous tissues using bulk RNA sequencing complemented by in-depth bioinformatics analyses, including gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and immune infiltration assessment. A pivotal aspect of our research involved dissecting single-cell RNA-seq data from GSE234933 to elucidate the cell-type-specific expression of FOS. RESULTS: We found that FOS expression varies significantly in different cell populations in the HNSCC tumor microenvironment, especially in fibroblasts and myeloid cells. This expression difference may reflect the different roles of these cells in tumor progression and their impact on the tumor microenvironment. CONCLUSION: Our results uncover a significant correlation between FOS expression and key immune and hypoxia-related pathways, suggesting its integral role in the tumor microenvironment. These findings not only enhance our understanding of HNSCC pathogenesis but also highlight FOS as a potential therapeutic target. This study marks a significant step towards addressing the urgent need for targeted interventions in HNSCC, particularly in the context of locoregional recurrence.

LXRα Promotes Abdominal Aortic Aneurysm Formation Through UHRF1 Epigenetic Modification of miR-26b-3p.

BACKGROUND: Abdominal aortic aneurysm (AAA) is a severe aortic disease without effective pharmacological approaches. The nuclear hormone receptor LXRα (liver X receptor α), encoded by the NR1H3 gene, serves as a critical transcriptional mediator linked to several vascular pathologies, but its role in AAA remains elusive. METHODS: Through integrated analyses of human and murine AAA gene expression microarray data sets, we identified NR1H3 as a candidate gene regulating AAA formation. To investigate the role of LXRα in AAA formation, we used global Nr1h3-knockout and vascular smooth muscle cell-specific Nr1h3-knockout mice in 2 AAA mouse models induced with angiotensin II (1000 ng·kg·min; 28 days) or calcium chloride (CaCl2; 0.5 mol/L; 42 days). RESULTS: Upregulated LXRα was observed in the aortas of patients with AAA and in angiotensin II- or CaCl2-treated mice. Global or vascular smooth muscle cell-specific Nr1h3 knockout inhibited AAA formation in 2 mouse models. Loss of LXRα function prevented extracellular matrix degeneration, inflammation, and vascular smooth muscle cell phenotypic switching. Uhrf1, an epigenetic master regulator, was identified as a direct target gene of LXRα by integrated analysis of transcriptome sequencing and chromatin immunoprecipitation sequencing. Susceptibility to AAA development was consistently enhanced by UHRF1 (ubiquitin-like containing PHD and RING finger domains 1) in both angiotensin II- and CaCl2-induced mouse models. We then determined the CpG methylation status and promoter accessibility of UHRF1-mediated genes using CUT&Tag (cleavage under targets and tagmentation), RRBS (reduced representation bisulfite sequencing), and ATAC-seq (assay for transposase-accessible chromatin with sequencing) in vascular smooth muscle cells, which revealed that the recruitment of UHRF1 to the promoter of miR-26b led to DNA hypermethylation accompanied by relatively closed chromatin states, and caused downregulation of miR-26b expression in AAA. Regarding clinical significance, we found that underexpression of miR-26b-3p correlated with high risk in patients with AAA. Maintaining miR-26b-3p expression prevented AAA progression and alleviated the overall pathological process. CONCLUSIONS: Our study reveals a pivotal role of the LXRα/UHRF1/miR-26b-3p axis in AAA and provides potential biomarkers and therapeutic targets for AAA.

Exploratory study of blood biomarkers in patients with post-stroke epilepsy.

INTRODUCTION: In addition to clinical factors, blood-based biomarkers can provide useful information on the risk of developing post-stroke epilepsy (PSE). Our aim was to identify serum biomarkers at stroke onset that could contribute to predicting patients at higher risk of PSE. PATIENTS AND METHODS: From a previous study in which 895 acute stroke patients were followed-up, 51 patients developed PSE. We selected 15 patients with PSE and 15 controls without epilepsy. In a biomarker discovery setting, 5 Olink panels of 96 proteins each, were used to determine protein levels. Biomarkers that were down-regulated and overexpressed in PSE patients, and those that showed the strongest interactions with other proteins were validated using an enzyme-linked immunosorbent assay in samples from 50 PSE patients and 50 controls. A ROC curve analysis was used to evaluate the predictive ability of significant biomarkers to develop PSE. RESULTS: Mean age of the PSE discovery cohort was 68.56 ± 15.1, 40% women and baseline NIHSS 12 [IQR 1-25]. Nine proteins were down-expressed: CASP-8, TNFSF-14, STAMBP, ENRAGE, EDA2R, SIRT2, TGF-alpha, OSM and CLEC1B. VEGFa, CD40 and CCL4 showed greatest interactions with the remaining proteins. In the validation analysis, TNFSF-14 was the single biomarker showing statistically significant downregulated levels in PSE patients (p = 0.006) and it showed a good predictive capability to develop PSE (AUC 0.733, 95% CI 0.601-0.865). DISCUSSION AND CONCLUSION: Protein expression in PSE patients differs from that of non-epileptic stroke patients, suggesting the involvement of several different proteins in post-stroke epileptogenesis. TNFSF-14 emerges as a potential biomarker for predicting PSE.