Identification and validation of oxidative stress-related genes in sepsis-induced myopathy.

BACKGROUND: Sepsis-induced myopathy (SIM) a complication of sepsis that results in prolonged mechanical ventilation, long-term functional disability, and increased patient mortality. This study was performed to identify potential key oxidative stress-related genes (OS-genes) as biomarkers for the diagnosis of SIM using bioinformatics. METHODS: The GSE13205 was obtained from the Gene Expression Omnibus (GEO) database, including 13 SIM samples and 8 healthy samples, and the differentially expressed genes (DEGs) were identified by limma package in R language. Simultaneously, we searched for the genes related to oxidative stress in the Gene Ontology (GO) database. The intersection of the genes selected from the GO database and the genes from the GSE13205 was considered as OS-genes of SIM, where the differential genes were regarded as OS-DEGs. OS-DEGs were analyzed using GO enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Hub genes in OS-DEGs were selected based on degree, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed. RESULTS: A total of 1089 DEGs were screened from the GSE13205, and 453 OS-genes were identified from the GO database. The overlapping DEGs and OS-genes constituted 25 OS-DEGs, including 15 significantly upregulated and 10 significantly downregulated genes. The top 10 hub genes, including CD36, GPX3, NQO1, GSR, TP53, IDH1, BCL2, HMOX1, JAK2, and FOXO1, were screened. Furthermore, 5 diagnostic genes were identified: CD36, GPX3, NQO1, GSR, and TP53. The ROC analysis showed that the respective area under the curves (AUCs) of CD36, GPX3, NQO1, GSR, and TP53 were 0.990, 0.981, 0.971, 0.971, and 0.971, which meant these genes had very high diagnostic values of SIM. Finally, based on these 5 diagnostic genes, we found that miR-124-3p and miR-16-5p may be potential targets for the treatment of SIM. CONCLUSIONS: The results of this study suggest that OS-genes might play an important role in SIM. CD36, GPX3, NQO1, GSR, and TP53 have potential as specific biomarkers for the diagnosis of SIM.

The association between the triglyceride-glucose index and in-stent restenosis in patients undergoing percutaneous coronary intervention: a systematic review and meta-analysis.

BACKGROUND: Insulin resistance (IR) can lead to cellular metabolic disorders, activation of oxidative stress, and endothelial dysfunction, contributing to in-stent restenosis (ISR). The triglyceride-glucose index (TyG index), a new indicator reflecting IR, is extensively researched in the cardiovascular field. This study, through a meta-analysis, aimed to utilize a larger combined sample size and thereby enhance the overall test efficacy to explore the TyG index-ISR relationship. METHODS: A thorough search was conducted in the PubMed, EMBASE, Web of Science, and Cochrane Library databases to find original papers and their references published between 1990 and January 2024. This search included both prospective and retrospective studies detailing the correlation between the TyG index and ISR in individuals with coronary heart disease (CHD). OUTCOMES: The five included articles comprised 3,912 participants, and the odds ratio (OR) extracted from each study was combined using the Inverse Variance method. Results showed that, in the context of CHD patients, each incremental unit in the TyG index, when treated as a continuous variable, corresponded to a 42% elevation in ISR risk (95% CI 1.26-1.59, I²=13%, p < 0.005). When analyzing the TyG index categorically, the results revealed a higher ISR risk in the highest TyG index group compared to the lowest group (OR: 1.69, 95% CI 1.32-2.17, I²=0). Additionally, in patients with chronic coronary syndrome (CCS), each unit increase in the TyG index, the risk of ISR in patients increased by 37% (95% CI 1.19-1.57, I²=0%, p < 0.005). This correlation was also observable in acute coronary syndrome (ACS) patients (OR:1.48, 95% CI 1.19-1.85, I²=0, p < 0.005). CONCLUSIONS: The TyG index, an economical and precise surrogate for IR, is significantly linked with ISR. Furthermore, this correlation is unaffected by the type of coronary heart disease.

Insulin sensitivity estimates and their longitudinal association with coronary artery disease in type 1 diabetes. Does it matter?

BACKGROUND: Insulin resistance and chronic kidney disease are both associated with increased coronary artery disease risk. Many formulae estimating glucose disposal rate in type 1 diabetes infer insulin sensitivity from clinical data. We compare associations and performance relative to traditional risk factors and kidney disease severity between three formulae estimating the glucose disposal rate and coronary artery disease in people with type 1 diabetes. METHODS: The baseline glucose disposal rate was estimated by three (Williams, Duca, and Januszewski) formulae in FinnDiane Study participants and related to subsequent incidence of coronary artery disease, by baseline kidney status. RESULTS: In 3517 adults with type 1 diabetes, during median (IQR) 19.3 (14.6, 21.4) years, 539 (15.3%) experienced a coronary artery disease event, with higher rates with worsening baseline kidney status. Correlations between the three formulae estimating the glucose disposal rate were weak, but the lowest quartile of each formula was associated with higher incidence of coronary artery disease. Importantly, only the glucose disposal rate estimation by Williams showed a linear association with coronary artery disease risk in all analyses. Of the three formulae, Williams was the strongest predictor of coronary artery disease. Only age and diabetes duration were stronger predictors. The strength of associations between estimated glucose disposal rate and CAD incidence varied by formula and kidney status. CONCLUSIONS: In type 1 diabetes, estimated glucose disposal rates are associated with subsequent coronary artery disease, modulated by kidney disease severity. Future research is merited regarding the clinical usefulness of estimating the glucose disposal rate as a coronary artery disease risk factor and potential therapeutic target.

The association between triglyceride-glucose index and the likelihood of cardiovascular disease in the U.S. population of older adults aged ≥ 60 years: a population-based study.

BACKGROUND: The association between the triglyceride-glucose (TyG) index and the likelihood of developing cardiovascular disease (CVD) in the general elderly population in the United States aged 60 and above is not well understood. The objective of our study was to examine the relationship between the TyG index and CVD likelihood in the general elderly population over 60 years of age in the United States. METHODS: Data for this cross-sectional study were sourced from the 2003-2018 National Health and Nutrition Examination Survey. Weighted multivariable regression analysis and subgroup analysis were conducted to estimate the independent relationship between the TyG index and the likelihood of CVD. Non-linear correlations were explored using restricted cubic splines. RESULTS: A total of 6502 participants were included, with a mean TyG index of 8.75 ± 0.01. The average prevalence of CVD was 24.31% overall. Participants in the higher TyG quartiles showed high rates of CVD (Quartile 1: 19.91%; Quartile 2: 21.65%; Quartile 3: 23.82%; Quartile 4: 32.43%). For CVD, a possible association between the TyG index and the odds of CVD was observed. Our findings suggest a nonlinear association between the TyG index and the odds of CVD. The threshold of 8.73 for the likelihood of CVD. Interaction terms were employed to assess heterogeneities among each subgroup, revealing a significant difference specifically in alcohol consumption. This suggests that the positive association between the TyG index and the likelihood of CVD is dependent on the drinking status of the participants. CONCLUSION: A higher TyG index is linked to an increased likelihood of CVD in US adults aged ≥ 60 years. TyG index is anticipated to emerge as a more effective metric for identifying populations at early likelihood of CVD.

Identification of sepsis-associated mitochondrial genes through RNA and single-cell sequencing approaches.

BACKGROUND: Sepsis ranks among the most formidable clinical challenges, characterized by exorbitant treatment costs and substantial demands on healthcare resources. Mitochondrial dysfunction emerges as a pivotal risk factor in the pathogenesis of sepsis, underscoring the imperative to identify mitochondrial-related biomarkers. Such biomarkers are crucial for enhancing the accuracy of sepsis diagnostics and prognostication. METHODS: In this study, adhering to the SEPSIS 3.0 criteria, we collected peripheral blood within 24 h of admission from 20 sepsis patients at the ICU of the Southwest Medical University Affiliated Hospital and 10 healthy volunteers as a control group for RNA-seq. The RNA-seq data were utilized to identify differentially expressed RNAs. Concurrently, mitochondrial-associated genes (MiAGs) were retrieved from the MitoCarta3.0 database. The differentially expressed genes were intersected with MiAGs. The intersected genes were then subjected to GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses and core genes were filtered using the PPI (Protein-Protein Interaction) network. Subsequently, relevant sepsis datasets (GSE65682, GSE28750, GSE54514, GSE67652, GSE69528, GSE95233) were downloaded from the GEO (Gene Expression Omnibus) database to perform bioinformatic validation of these core genes. Survival analysis was conducted to assess the prognostic value of the core genes, while ROC (Receiver Operating Characteristic) curves determined their diagnostic value, and a meta-analysis confirmed the accuracy of the RNA-seq data. Finally, we collected 5 blood samples (2 normal controls (NC); 2 sepsis; 1 SIRS (Systemic Inflammatory Response Syndrome), and used single-cell sequencing to assess the expression levels of the core genes in the different blood cell types. RESULTS: Integrating high-throughput sequencing with bioinformatics, this study identified two mitochondrial genes (COX7B, NDUFA4) closely linked with sepsis prognosis. Survival analysis demonstrated that patients with lower expression levels of COX7B and NDUFA4 exhibited a higher day survival rate over 28 days, inversely correlating with sepsis mortality. ROC curves highlighted the significant sensitivity and specificity of both genes, with AUC values of 0.985 for COX7B and 0.988 for NDUFA4, respectively. Meta-analysis indicated significant overexpression of COX7B and NDUFA4 in the sepsis group in contrast to the normal group (P < 0.01). Additionally, single-cell RNA sequencing revealed predominant expression of these core genes in monocytes-macrophages, T cells, and B cells. CONCLUSION: The mitochondrial-associated genes (MiAGs) COX7B and NDUFA4 are intimately linked with the prognosis of sepsis, offering potential guidance for research into the mechanisms underlying sepsis.

Pre-hospital glycemia as a biomarker for in-hospital all-cause mortality in diabetic patients - a pilot study.

BACKGROUND: Type 2 Diabetes Mellitus (T2DM) presents a significant healthcare challenge, with considerable economic ramifications. While blood glucose management and long-term metabolic target setting for home care and outpatient treatment follow established procedures, the approach for short-term targets during hospitalization varies due to a lack of clinical consensus. Our study aims to elucidate the impact of pre-hospitalization and intra-hospitalization glycemic indexes on in-hospital survival rates in individuals with T2DM, addressing this notable gap in the current literature. METHODS: In this pilot study involving 120 hospitalized diabetic patients, we used advanced machine learning and classical statistical methods to identify variables for predicting hospitalization outcomes. We first developed a 30-day mortality risk classifier leveraging AdaBoost-FAS, a state-of-the-art ensemble machine learning method for tabular data. We then analyzed the feature relevance to identify the key predictive variables among the glycemic and routine clinical variables the model bases its predictions on. Next, we conducted detailed statistical analyses to shed light on the relationship between such variables and mortality risk. Finally, based on such analyses, we introduced a novel index, the ratio of intra-hospital glycemic variability to pre-hospitalization glycemic mean, to better characterize and stratify the diabetic population. RESULTS: Our findings underscore the importance of personalized approaches to glycemic management during hospitalization. The introduced index, alongside advanced predictive modeling, provides valuable insights for optimizing patient care. In particular, together with in-hospital glycemic variability, it is able to discriminate between patients with higher and lower mortality rates, highlighting the importance of tightly controlling not only pre-hospital but also in-hospital glycemic levels. CONCLUSIONS: Despite the pilot nature and modest sample size, this study marks the beginning of exploration into personalized glycemic control for hospitalized patients with T2DM. Pre-hospital blood glucose levels and related variables derived from it can serve as biomarkers for all-cause mortality during hospitalization.

Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage-associated biomarkers by bulk and single-cell sequencing.

We investigated subarachnoid haemorrhage (SAH) macrophage subpopulations and identified relevant key genes for improving diagnostic and therapeutic strategies. SAH rat models were established, and brain tissue samples underwent single-cell transcriptome sequencing and bulk RNA-seq. Using single-cell data, distinct macrophage subpopulations, including a unique SAH subset, were identified. The hdWGCNA method revealed 160 key macrophage-related genes. Univariate analysis and lasso regression selected 10 genes for constructing a diagnostic model. Machine learning algorithms facilitated model development. Cellular infiltration was assessed using the MCPcounter algorithm, and a heatmap integrated cell abundance and gene expression. A 3 × 3 convolutional neural network created an additional diagnostic model, while molecular docking identified potential drugs. The diagnostic model based on the 10 selected genes achieved excellent performance, with an AUC of 1 in both training and validation datasets. The heatmap, combining cell abundance and gene expression, provided insights into SAH cellular composition. The convolutional neural network model exhibited a sensitivity and specificity of 1 in both datasets. Additionally, CD14, GPNMB, SPP1 and PRDX5 were specifically expressed in SAH-associated macrophages, highlighting its potential as a therapeutic target. Network pharmacology analysis identified some targeting drugs for SAH treatment. Our study characterised SAH macrophage subpopulations and identified key associated genes. We developed a robust diagnostic model and recognised CD14, GPNMB, SPP1 and PRDX5 as potential therapeutic targets. Further experiments and clinical investigations are needed to validate these findings and explore the clinical implications of targets in SAH treatment.

The relation of a cerebrospinal fluid profile associated with Alzheimer's disease with cognitive function and neuropsychiatric symptoms in sporadic cerebral amyloid angiopathy.

BACKGROUND: Patients with sporadic cerebral amyloid angiopathy (sCAA) frequently report cognitive or neuropsychiatric symptoms. The aim of this study is to investigate whether in patients with sCAA, cognitive impairment and neuropsychiatric symptoms are associated with a cerebrospinal fluid (CSF) biomarker profile associated with Alzheimer's disease (AD). METHODS: In this cross-sectional study, we included participants with sCAA and dementia- and stroke-free, age- and sex-matched controls, who underwent a lumbar puncture, brain MRI, cognitive assessments, and self-administered and informant-based-questionnaires on neuropsychiatric symptoms. CSF phosphorylated tau, total tau and Aß42 levels were used to divide sCAA patients in two groups: CAA with (CAA-AD+) or without a CSF biomarker profile associated with AD (CAA-AD-). Performance on global cognition, specific cognitive domains (episodic memory, working memory, processing speed, verbal fluency, visuoconstruction, and executive functioning), presence and severity of neuropsychiatric symptoms, were compared between groups. RESULTS: sCAA-AD+ (n=31; mean age: 72 ± 6; 42%, 61% female) and sCAA-AD- (n=23; 70 ± 5; 42% female) participants did not differ with respect to global cognition or type of affected cognitive domain(s). The number or severity of neuropsychiatric symptoms also did not differ between sCAA-AD+ and sCAA-AD- participants. These results did not change after exclusion of patients without prior ICH. CONCLUSIONS: In participants with sCAA, a CSF biomarker profile associated with AD does not impact global cognition or specific cognitive domains, or the presence of neuropsychiatric symptoms.

TYG Index as a Novel Predictor of Clinical Outcomes in Advanced Chronic Heart Failure with Renal Dysfunction Patients.

Background: The triglyceride-glucose (TYG) index is a novel and reliable marker reflecting insulin resistance. Its predictive ability for cardiovascular disease onset and prognosis has been confirmed. However, for advanced chronic heart failure (acHF) patients, the prognostic value of TYG is challenged due to the often accompanying renal dysfunction (RD). Therefore, this study focuses on patients with aHF accompanied by RD to investigate the predictive value of the TYG index for their prognosis. Methods and Results: 717 acHF with RD patients were included. The acHF diagnosis was based on the 2021 ESC criteria for acHF. RD was defined as the eGFR < 90 mL/(min/1.73 m2). Patients were divided into two groups based on their TYG index values. The primary endpoint was major adverse cardiovascular events (MACEs), and the secondary endpoints is all-cause mortality (ACM). The follow-up duration was 21.58 (17.98-25.39) months. The optimal cutoff values for predicting MACEs and ACM were determined using ROC curves. Hazard factors for MACEs and ACM were revealed through univariate and multivariate COX regression analyses. According to the univariate COX regression analysis, high TyG index was identified as a risk factor for MACEs (hazard ratio = 5.198; 95% confidence interval [CI], 3.702-7.298; P < 0.001) and ACM (hazard ratio = 4.461; 95% CI, 2.962-6.718; P < 0.001). The multivariate COX regression analysis showed that patients in the high TyG group experienced 440.2% MACEs risk increase (95% CI, 3.771-7.739; P < 0.001) and 406.2% ACM risk increase (95% CI, 3.268-7.839; P < 0.001). Kaplan-Meier survival analysis revealed that patients with high TyG index levels had an elevated risk of experiencing MACEs and ACM within 30 months. Conclusion: This study found that patients with high TYG index had an increased risk of MACEs and ACM, and the TYG index can serve as an independent predictor for prognosis.

The time-dependent changes in serum immunoglobulin after kidney transplantation and its association with infection.

Introduction: Kidney transplant recipients often experience significant alterations in their immune system, which can lead to increased susceptibility to infections. This study aimed to analyze time-dependent changes in serum immunoglobulin and complement levels and determine the risk factors associated with infection. Methods: A retrospective analysis of serum samples from 192 kidney transplant recipients who received transplantations between August 2016 and December 2019 was conducted. The serum samples were obtained at preoperative baseline (T0), postoperative 2 weeks (T1), 3 months (T2), and 1 year (T3). The levels of serum C3, C4, IgG, IgA, and IgM were measured to evaluate immune status over time. Results: The analysis revealed significant decreases in IgG and IgA levels at T1. This period was associated with the highest occurrence of hypogammaglobulinemia (HGG) and hypocomplementemia (HCC), as well as an increased incidence of severe infection requiring hospitalization and graft-related viral infections. Using a time-dependent Cox proportional hazards model adjusted for time-varying confounders, HGG was significantly associated with an increased risk of infection requiring hospitalization (HR, 1.895; 95% CI: 1.871-1.920, P-value<0.001) and graft-related viral infection (HR, 1.152; 95% CI: 1.144-1.160, P-value<0.001). Discussion: The findings suggest that monitoring serum immunoglobulin levels post-transplant provides valuable insights into the degree of immunosuppression. Hypogammaglobulinemia during the early post-transplant period emerges as a critical risk factor for infection, indicating that serum immunoglobulins could serve as feasible biomarkers for assessing infection risk in kidney transplant recipients.

The immune landscape of sepsis and using immune clusters for identifying sepsis endotypes.

Background: The dysregulated immune response to sepsis still remains unclear. Stratification of sepsis patients into endotypes based on immune indicators is important for the future development of personalized therapies. We aimed to evaluate the immune landscape of sepsis and the use of immune clusters for identifying sepsis endotypes. Methods: The indicators involved in innate, cellular, and humoral immune cells, inhibitory immune cells, and cytokines were simultaneously assessed in 90 sepsis patients and 40 healthy controls. Unsupervised k-means cluster analysis of immune indicator data were used to identify patient clusters, and a random forest approach was used to build a prediction model for classifying sepsis endotypes. Results: We depicted that the impairment of innate and adaptive immunity accompanying increased inflammation was the most prominent feature in patients with sepsis. However, using immune indicators for distinguishing sepsis from bacteremia was difficult, most likely due to the considerable heterogeneity in sepsis patients. Cluster analysis of sepsis patients identified three immune clusters with different survival rates. Cluster 1 (36.7%) could be distinguished from the other clusters as being an "effector-type" cluster, whereas cluster 2 (34.4%) was a "potential-type" cluster, and cluster 3 (28.9%) was a "dysregulation-type" cluster, which showed the lowest survival rate. In addition, we established a prediction model based on immune indicator data, which accurately classified sepsis patients into three immune endotypes. Conclusion: We depicted the immune landscape of patients with sepsis and identified three distinct immune endotypes with different survival rates. Cluster membership could be predicted with a model based on immune data.

Unravelling B cell heterogeneity: insights into flow cytometry-gated B cells from single-cell multi-omics data.

Introduction: B cells play a pivotal role in adaptive immunity which has been extensively characterised primarily via flow cytometry-based gating strategies. This study addresses the discrepancies between flow cytometry-defined B cell subsets and their high-confidence molecular signatures using single-cell multi-omics approaches. Methods: By analysing multi-omics single-cell data from healthy individuals and patients across diseases, we characterised the level and nature of cellular contamination within standard flow cytometric-based gating, resolved some of the ambiguities in the literature surrounding unconventional B cell subsets, and demonstrated the variable effects of flow cytometric-based gating cellular heterogeneity across diseases. Results: We showed that flow cytometric-defined B cell populations are heterogenous, and the composition varies significantly between disease states thus affecting the implications of functional studies performed on these populations. Importantly, this paper draws caution on findings about B cell selection and function of flow cytometric-sorted populations, and their roles in disease. As a solution, we developed a simple tool to identify additional markers that can be used to increase the purity of flow-cytometric gated immune cell populations based on multi-omics data (AlliGateR). Here, we demonstrate that additional non-linear CD20, CD21 and CD24 gating can increase the purity of both naïve and memory populations. Discussion: These findings underscore the need to reconsider B cell subset definitions within the literature and propose leveraging single-cell multi-omics data for refined characterisation. We show that single-cell multi-omics technologies represent a powerful tool to bridge the gap between surface marker-based annotations and the intricate molecular characteristics of B cell subsets.

Circulating HMGB1 in acute ischemic stroke and its association with post-stroke cognitive impairment.

Background: Ischemic stroke frequently leads to a condition known as post-stroke cognitive impairment (PSCI). Timely recognition of individuals susceptible to developing PSCI could facilitate the implementation of personalized strategies to mitigate cognitive deterioration. High mobility group box 1 (HMGB1) is a protein released by ischemic neurons and implicated in inflammation after stroke. Circulating levels of HMGB1 could potentially serve as a prognostic indicator for the onset of cognitive impairment following ischemic stroke. Objective: To investigate the predictive value of circulating HMGB1 concentrations in the acute phase of ischemic stroke for the development of cognitive dysfunction at the 3-month follow-up. Methods: A total of 192 individuals experiencing their initial episode of acute cerebral infarction were prospectively recruited for this longitudinal investigation. Concentrations of circulating HMGB1 were quantified using an enzyme-linked immunosorbent assay (ELISA) technique within the first 24 hours following hospital admission. Patients underwent neurological evaluation including NIHSS scoring. Neuropsychological evaluation was conducted at the 3-month follow-up after the cerebrovascular event, employing the Montreal Cognitive Assessment (MoCA) as the primary tool for assessing cognitive performance. Multivariable logistic regression models were employed to investigate the relationship between circulating HMGB1 concentrations and cognitive dysfunction following stroke, which was operationalized as a MoCA score below 26, while controlling for potential confounders including demographic characteristics, stroke severity, vascular risk factors, and laboratory parameters. Results: Of 192 patients, 84 (44%) developed PSCI. Circulating HMGB1 concentrations were significantly elevated in individuals who developed cognitive dysfunction following stroke compared to those who maintained cognitive integrity (8.4 ± 1.2 ng/mL vs 4.6 ± 0.5 ng/mL, respectively; p < 0.001). The prevalence of PSCI showed a dose-dependent increase with higher HMGB1 quartiles. After controlling for potential confounders such as demographic factors (age, gender, and education), stroke severity, vascular risk factors, and laboratory parameters in a multivariable logistic regression model, circulating HMGB1 concentrations emerged as a significant independent predictor of cognitive dysfunction following stroke (regression coefficient = 0.236, p < 0.001). Conclusion: Circulating HMGB1 concentrations quantified within the first 24 hours following acute cerebral infarction are significantly and independently correlated with the likelihood of developing cognitive dysfunction at the 3-month follow-up, even after accounting for potential confounding factors. HMGB1 may be a novel biomarker to identify patients likely to develop post-stroke cognitive impairment for targeted preventive interventions.

Phosphoproteomics analysis of serum from dogs affected with pulmonary hypertension secondary to degenerative mitral valve disease.

Pulmonary hypertension (PH), a common complication in dogs affected by degenerative mitral valve disease (DMVD), is a progressive disorder characterized by increased pulmonary arterial pressure (PAP) and pulmonary vascular remodeling. Phosphorylation of proteins, impacting vascular function and cell proliferation, might play a role in the development and progression of PH. Unlike gene or protein studies, phosphoproteomic focuses on active proteins that function as end-target proteins within signaling cascades. Studying phosphorylated proteins can reveal active contributors to PH development. Early diagnosis of PH is crucial for effective management and improved clinical outcomes. This study aimed to identify potential serum biomarkers for diagnosing PH in dogs affected with DMVD using a phosphoproteomic approach. Serum samples were collected from healthy control dogs (n = 28), dogs with DMVD (n = 24), and dogs with DMVD and PH (n = 29). Phosphoproteins were enriched from the serum samples and analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Data analysis was performed to identify uniquely expressed phosphoproteins in each group and differentially expressed phosphoproteins among groups. Phosphoproteomic analysis revealed nine uniquely expressed phosphoproteins in the serum of dogs in the DMVD+PH group and 15 differentially upregulated phosphoproteins in the DMVD+PH group compared to the DMVD group. The phosphoproteins previously implicated in PH and associated with pulmonary arterial remodeling, including small nuclear ribonucleoprotein G (SNRPG), alpha-2-macroglobulin (A2M), zinc finger and BTB domain containing 42 (ZBTB42), hemopexin (HPX), serotransferrin (TRF) and complement C3 (C3), were focused on. Their unique expression and differential upregulation in the serum of DMVD dogs with PH suggest their potential as biomarkers for PH diagnosis. In conclusion, this phosphoproteomic study identified uniquely expressed and differentially upregulated phosphoproteins in the serum of DMVD dogs with PH. Further studies are warranted to validate the diagnostic utility of these phosphoproteins.

The Association of Micro-RNA 21 and Hypertension: A Meta-Analysis.

Hypertension is a multifactorial, complex disease with high morbidity and mortality rates. Studies have found that micro-RNA 21 (miR-21) levels are significantly increased in patients with hypertension. However, other studies have reported opposite results. Therefore, the relationship between miR-21 expression and hypertension remains controversial. This meta-analysis was conducted to statistically evaluate the miR-21 levels of patients with hypertension. A literature research was conducted using Web of Science, Embase, PubMed, and CNKI. To search for titles or abstracts, 'hypertension' in combination with the terms 'miR-21,' 'microRNA-21,' or 'miRNA-21' were used as keywords. Standardized mean differences (SMD) with corresponding 95% confidence intervals (CIs) were determined from the results of the meta-analysis. In total, 12 articles were included in this meta-analysis, involving 546 cases and 436 controls. The results of the meta-analysis showed that miR-21 levels in patients with hypertension were significantly higher than those in the controls (SMD: 1.22; 95% CI [0.35, 2.09]). This meta-analysis is the first to evaluate miR-21 in patients with hypertension. MiR-21 may be a new target for the prediction and treatment of hypertension. Further high-quality studies are needed to better support the association between miR-21 and hypertension.

Ultraviolet Protection From a Patented Amino Acid Complex Technology.

OBJECTIVE:   This study aimed to investigate the ultraviolet (UV) protection/repair benefits of a patented Amino Acid Complex (AAComplex). METHODS: I) AAComplex was incubated with dermal fibroblasts, with/without UVA, and collagen I was measured with a GlasBoxPlus device. II) A lotion, with/without AAComplex (1%) was applied topically to skin explants, following UVA irradiation, and quantified for health-related biomarkers (TNFalpha, histamine, and MMP-1). III) A broad spectrum sunscreen with SPF 46 and a skincare serum containing AAComplex (2%) were assessed using epidermal equivalents, in the presence of UV irradiation, for effects on IL-1alpha, thymine dimers, Ki-67, filaggrin and Nrf2. RESULTS: I) Collagen I synthesis in dermal fibroblasts was significantly decreased after UVA compared to without UV. The presence of AAComplex prevented this decrease. II) UVA irradiation of skin explants increased histamine, TNFα, and MMP-1. Hydrocortisone aceponate cream significantly decreases all 3 biomarkers. AAComplex contained lotion also significantly decreased all 3 biomarkers, the no AAComplex control lotion only reduced histamine. III) With the regimen of sunscreen + AAComplex contained skincare serum, the significant reduction in IL-1alpha was observed along with a complete recovery of Ki-67 and stimulation of filaggrin and Nrf2T. No thymine dimer positive cell was observed indicating the most positive skin impact from the regiment.  Conclusion: This research using different human skin models demonstrated that AAComplex can provide protection and damage repair caused by UV, at the ingredient level also when formulated in a serum or lotion formula. Skin may be best protected from UV damage when the regimen is used.   J Drugs Dermatol. 2024;23(5):366-375. doi:10.36849/JDD.7916.

Identification of urine biomarkers of endometriosis-Protein mass spectrometry.

INTRODUCTION: Endometriosis is a chronic inflammatory disease that leads to a series of pathological reactions. The basis is a changed proinflammatory activated immune system, which results in more pronounced oxidative stress, disturbed function of proteolysis and cell apoptosis. These processes are crucial in the development of the disease because their dysfunctional activities cause the progression of the disease. It is believed that the proteins excreted in the urine interact with each other and promote pathological processes in endometriosis. METHODS: We analyzed the urine proteome of patients and aimed to detect a potential protein biomarker for endometriosis in the urine proteome. We collected urine samples from 16 patients with endometriosis and 16 patients in the control group with functional ovarian cysts. The diagnosis for all patients was confirmed through pathohistological analysis. After the preanalytical preparation of the urine, chromatography and mass spectrometry (LC-MS/MS) used the technology of urine proteome analysis. RESULTS: The main finding was a significantly different concentration of 14 proteins in the urine samples. We recorded a considerably higher concentration of proteins that have a significant role in activating the immune system (SELL), iron metabolism (HAMP) and cell apoptosis (CHGA) in endometriosis compared to controls. Proteins having an antioxidant function (SOD1) and a role in proteolysis of the extracellular matrix (MMP-9) were significantly reduced in endometriosis compared to controls. CONCLUSION: Consistent with the known pathogenesis of endometriosis, the study results complement the pathological responses that occur with disease progression.

Evaluating lumbar disc degeneration by MRI quantitative metabolic indicators: the perspective of factor analysis.

PURPOSE: This study aimed to investigate an early diagnostic method for lumbar disc degeneration (LDD) and improve its diagnostic accuracy. METHODS: Quantitative biomarkers of the lumbar body (LB) and lumbar discs (LDs) were obtained using nuclear magnetic resonance (NMR) detection technology. The diagnostic weights of each biological metabolism indicator were screened using the factor analysis method. RESULTS: Through factor analysis, common factors such as the LB fat fraction, fat content, and T2* value of LDs were identified as covariates for the diagnostic model for the evaluation of LDD. This model can optimize the accuracy and reliability of LDD diagnosis. CONCLUSION: The application of biomarker quantification methods based on NMR detection technology combined with factor analysis provides an effective means for the early diagnosis of LDD, thereby improving diagnostic accuracy and reliability.

The Stress Phenotyping Framework: A multidisciplinary biobehavioral approach for assessing and therapeutically targeting maladaptive stress physiology.

Although dysregulated stress biology is becoming increasingly recognized as a key driver of lifelong disparities in chronic disease, we presently have no validated biomarkers of toxic stress physiology; no biological, behavioral, or cognitive treatments specifically focused on normalizing toxic stress processes; and no agreed-upon guidelines for treating stress in the clinic or evaluating the efficacy of interventions that seek to reduce toxic stress and improve human functioning. We address these critical issues by (a) systematically describing key systems and mechanisms that are dysregulated by stress; (b) summarizing indicators, biomarkers, and instruments for assessing stress response systems; and (c) highlighting therapeutic approaches that can be used to normalize stress-related biopsychosocial functioning. We also present a novel multidisciplinary Stress Phenotyping Framework that can bring stress researchers and clinicians one step closer to realizing the goal of using precision medicine-based approaches to prevent and treat stress-associated health problems.

The prognostic value of platelet aggregation in patients with sepsis.

BACKGROUND: This study aims to investigate the relevance of platelet aggregation markers, specifically arachidonic acid (AA) and adenosine diphosphate (ADP), in relation to the prognosis of sepsis patients. METHODS: A cohort of 40 sepsis patients was included and stratified, based on their 28-day post-treatment prognosis, into two groups: a survival group (n = 31) and a severe sepsis group (n = 9. Then, their various clinical parameters, including patient demographics, platelet counts (PLT), inflammatory markers, and platelet aggregation rates (PAR) induced by AA and ADP between the two groups, were compared. Long-term health implications of sepsis were assessed using the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, and logistic regression analysis was conducted to evaluate the prognostic significance of PAR in sepsis patients. RESULTS: Patients with severe sepsis exhibited significantly elevated levels of procalcitonin (PCT), platelet adhesion rates, and PAR induced by ADP (P < 0.05), but having lower PLT (P < 0.05), compared to those in the survival group. Logistic regression analysis demonstrated that PAR induced by ADP was a protective factor in predicting prognosis in sepsis patients (P < 0.01). CONCLUSIONS: Activation of platelets in sepsis intensifies inflammatory response. Patients with sepsis whose ADP-induced PAR was < 60% displayed significant impairment in platelet aggregation function, and had higher mortality rate. Monitoring ADP-induced PAR is crucial for management of sepsis.