Tailored Zwitterionic Hemicyanine Reporters for Early Diagnosis and Prognostic Assessment of Acute Renal Failure.

Drug-induced renal failure (DIRF) poses a serious medical complication with high mortality risk. However, early diagnosis or prognosis of DIRF remain challenging, as current methods rely on detecting late-stage biomarkers. Herein we present a library of zwitterionic unimolecular hemicyanines (ZCs) available for constructing activatable reporters to detect DIRF since its initial stage. Zwitterionic properties of these probes are achieved through interspersedly integrating alkyl sulfonates and quaternary ammonium cations onto hemicyanine skeleton, which result in record low plasma protein binding (<5 %) and remarkable renal clearance efficiencies (≈96 %). An activatable reporter ZCRR is further developed by masking the optimal candidate ZC6 with a tetrapeptide specifically cleavable by caspase-8, an initiating indicator of apoptosis. In living mice with cisplatin-induced DIRF, systematically administered ZCRR efficiently accumulates in kidneys and responds to elevated caspase-8 for near-infrared fluorescence signals 'turn-on', enabling sensitive detection of intrarenal apoptosis 60 h earlier than clinical methods, and precise evaluation of apoptosis remediation effects by different medications on DIRF mice. As it's urinary excretable, ZCRR also allows for remote detection of DIRF and predicting renoprotective efficacy through in vitro optical urinalysis. This study thus presents unimolecular renal clearable scaffolds that are applicable to developing versatile activatable reporters for renal diseases management.

Long-Term and Short-Term Prognostic Value of Circulating Soluble Suppression of Tumorigenicity-2 Concentration in Chronic Heart Failure: A Systematic Review and Meta-Analysis.

INTRODUCTION: Soluble suppression of tumorigenicity-2 (sST2) has been considered as a prognostic factor of cardiovascular disease. However, the prognostic value of sST2 concentration in chronic heart failure remains to be summarized. METHODS: We searched PubMed, Embase, and Web of Science for eligible studies up to January 1, 2020. Data extracted from articles and provided by authors were used in agreement with the PRISMA statement. The endpoints were all-cause mortality (ACM), cardiovascular mortality (CVM)/heart failure-related hospitalization (HFH), and all-cause mortality (ACM)/heart failure-related readmission (HFR). RESULTS: A total of 11 studies with 5,121 participants were included in this analysis. Higher concentration of sST2 predicted the incidence of long-term ACM (hazard ratio [HR]: 1.03, 95% confidence interval [CI]: 1.02-1.04), long-term ACM/HFR (HR: 1.42, CI: 1.27-1.59), and long-term CVM/HFH (HR: 2.25, CI: 1.82-2.79), regardless of short-term ACM/HFR (HR: 2.31, CI: 0.71-7.49). CONCLUSION: Higher sST2 concentration at baseline is associated with increasing risk of long-term ACM, ACM/HFR, and CVM/HFH and can be a tool for the prognosis of chronic heart failure.

Identification and validation of aging-related genes in neuropathic pain using bioinformatics.

Background: Neuropathic pain (NP) is a debilitating and refractory chronic pain with a higher prevalence especially in elderly patients. Cell senescence considered a key pathogenic factor in NP. The objective of this research is to discover genes associated with aging in peripheral blood of individuals with NP using bioinformatics techniques. Methods: Two cohorts (GSE124272 and GSE150408) containing peripheral blood samples of NP were downloaded from the GEO database. By merging the two cohorts, differentially expressed aging-related genes (DE-ARGs) were obtained by intersection with aging-related genes. The potential biological mechanisms of DE-ARGs were further analyzed through GO and KEGG. Three machine learning methods, namely, LASSO, SVM-RFE, and Random Forest, were utilized to identify diagnostic biomarkers. A Nomogram model was developed to assess their diagnostic accuracy. The validation of biomarker expression and diagnostic effectiveness was conducted in three distinct pain cohorts. The CIBERSORT algorithm was employed to evaluate the immune cell composition in the peripheral blood of patients with NP and investigate its association with the expression of diagnostic biomarkers. Results: This study identified a total of 24 DE-ARGs, mainly enriched in "Chemokine signaling pathway," "Inflammatory mediator regulation of TRP channels," "HIF-1 signaling pathway" and "FOXO signaling pathway". Three machine learning algorithms identified a total of four diagnostic biomarkers (CEBPA, CEACAM1, BTG3 and IL-1R1) with good diagnostic performance and the similar expression difference trend in different types of pain cohorts. The expression levels of CEACAM1 and IL-1R1 exhibit a positive correlation with the percentage of neutrophils. Conclusion: Using machine learning techniques, our research identified four diagnostic biomarkers related to aging in peripheral blood, providing innovative approaches for the diagnosis and treatment of NP.

Molecular Engineering of Activatable NIR-II Hemicyanine Reporters for Early Diagnosis and Prognostic Assessment of Inflammatory Bowel Disease.

Molecular imaging in the second near-infrared window (NIR-II) provides high-fidelity visualization of biopathological events in deep tissue. However, most NIR-II probes produce "always-on" output and demonstrate poor signal specificity toward biomarkers. Herein, we report a series of hemicyanine reporters (HBCs) with tunable emission to NIR-II window (715-1188 nm) and structurally amenable to constructing activatable probes. Such manipulation of emission wavelengths relies on rational molecular engineering by integrating benz[c,d]indolium, benzo[b]xanthonium, and thiophene moieties to a conventional hemicyanine skeleton. In particular, HBC4 and HBC5 possess bright and record long emission over 1050 nm, enabling improved tissue penetration depth and superior signal to background ratio for intestinal tract mapping than NIR-I fluorophore HC1. An activatable inflammatory reporter (AIR-PE) is further constructed for pH-triggered site-specific release in colon. Due to minimized background interference, oral gavage of AIR-PE allows clear delineation of irritated intestines and assessment of therapeutic responses in a mouse model of inflammatory bowel disease (IBD) through real-time NIRF-II imaging. Benefiting from its high fecal clearance efficiency (>90%), AIR-PE can also detect IBD and evaluate the effectiveness of colitis treatments via in vitro optical fecalysis, which outperforms typical clinical assays including fecal occult blood testing and histological examination. This study thus presents NIR-II molecular scaffolds that are not only applicable to developing versatile activatable probes for early diagnosis and prognostic monitoring of deeply seated diseases but also hold promise for future clinical translations.

Machine learning and bioinformatics analysis to identify autophagy-related biomarkers in peripheral blood for rheumatoid arthritis.

Background: Although rheumatoid arthritis (RA) is a common autoimmune disease, the precise pathogenesis of the disease remains unclear. Recent research has unraveled the role of autophagy in the development of RA. This research aims to explore autophagy-related diagnostic biomarkers in the peripheral blood of RA patients. Methods: The gene expression profiles of GSE17755 were retrieved from the gene expression ontology (GEO) database. Differentially expressed autophagy-related genes (DE-ARGs) were identified for the subsequent research by inserting autophagy-related genes and differentially expressed genes (DEGs). Three machine learning algorithms, including random forest, support vector machine recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO), were employed to identify diagnostic biomarkers. A nomogram model was constructed to assess the diagnostic value of the biomarkers. The CIBERSORT algorithm was performed to investigate the correlation of the diagnostic biomarkers with immune cells and immune factors. Finally, the diagnostic efficacy and differential expression trend of diagnostic biomarkers were validated in multiple cohorts containing different tissues and diseases. Results: In this study, 25 DE-ARGs were identified between RA and healthy individuals. In addition to "macroautophagy" and "autophagy-animal," DE-ARGs were also associated with several types of programmed cell death and immune-related pathways according to GO and KEGG analysis. Three diagnostic biomarkers, EEF2, HSP90AB1 and TNFSF10, were identified by the random forest, SVM-RFE, and LASSO. The nomogram model demonstrated excellent diagnostic value in GSE17755 (AUC = 0.995, 95% CI: 0.988-0.999). Furthermore, immune infiltration analysis showed a remarkable association between EEF2, HSP90AB1, and TNFSF10 expression with various immune cells and immune factors. The three diagnostic biomarkers also exhibited good diagnostic efficacy and demonstrated the same trend of differential expression in multiple validation cohorts. Conclusion: This study identified autophagy-related diagnostic biomarkers based on three machine learning algorithms, providing promising targets for the diagnosis and treatment of RA.

Global Trends and Hotspots on Microglia Associated with Pain from 2002 to 2022: A Bibliometric Analysis.

Background: Researchers have made significant progress in microglia associated with pain in recent years. However, more relevant bibliometric analyses are still needed on trends and directions in this field. The aim of this study is to provide a comprehensive perspective and to predict future directions of pain-related microglia research via bibliometric tools. Methods: English articles and reviews related with pain and microglia were extracted from the Web of Science core collection (WosCC) database between 2002 to 2022. Bibliometric tools such as VOSviewer, CiteSpace, and Bibliometrix R package were used to analyze publication characteristics, countries, authors, institutions, journals, research hotspots, and trend topics. Results: A total of 2761 articles were included in this analysis. Research on microglia associated with pain has increased significantly over the last two decades. China (n = 1020, 36.94%) and the United States (n = 751, 27.20%) contributed the most in terms of publications and citations, respectively. Kyushu University published the most articles in this field compared to other institutions, and Professor Inoue Kazuhide (n = 54) at this university made outstanding contributions in this field. Molecular Pain (n = 113) was the journal with the most publication, while Journal of Neuroscience had the highest number of citations. According to the authors keywords analysis, the research in this area can be summarized into 7 clusters such as "microglia activation pathways", "pain treatment research", "mental symptoms of chronic pain", and so on. Conclusion: This study provides a comprehensive analysis of pain-related microglia research in the past two decades. We identified the countries, institutions, scholars, and journals with the highest number of publications and the most influence in the field, and the research trends identified in this paper may provide new insights for future research.