Soluble ST2:A Novel Biomarker for Diagnosis and Prognosis of Cardiovascular Disease

The increasing incidence of cardiovascular disease(CVD)is a significant global health concern,affecting millions of individuals each year.Accurate diagnosis of acute CVD poses a formidable challenge,as misdiagnosis can significantly decrease patient survival rates.Traditional biomarkers have played a vital role in the diagnosis and prognosis of CVDs,but they can be influenced by various factors,such as age,sex,and renal function.Soluble ST2(sST2)is a novel biomarker that is closely associated with different CVDs.Its low reference change value makes it suitable for continuous measurement,unaffected by age,kidney function,and other confounding factors,facilitating risk stratification of CVDs.Furthermore,the combination of sST2 with other biomarkers can enhance diagnostic accuracy and prognostic value.This review aims to provide a comprehensive overview of sST2,focusing on its diagnostic and prognostic value as a myocardial marker for different types of CVDs and discussing the current limitations of sST2.

Establishment of an optimized CTC detection model consisting of EpCAM,MUC1 and WT1 in epithelial ovarian cancer and its correlation with clinical characteristics

Objective:Emerging studies have demonstrated the promising clinical value of circulating tumor cells(CTCs)for diagnosis,disease assessment,treatment monitoring and prognosis in epithelial ovarian cancer.However,the clinical application of CTC remains restricted due to diverse detection techniques with variable sensitivity and specificity and a lack of common standards.Methods:We enrolled 160 patients with epithelial ovarian cancer as the experimental group,and 90 patients including 50 patients with benign ovarian tumor and 40 healthy females as the control group.We enriched CTCs with immunomagnetic beads targeting two epithelial cell surface antigens(EpCAM and MUC1),and used multiple reverse transcription-polymerase chain reaction(RT-PCR)detecting three markers(EpCAM,MUC1 and WT1)for quantification.And then we used a binary logistic regression analysis and focused on EpCAM,MUC1 and WT1 to establish an optimized CTC detection model.Results:The sensitivity and specificity of the optimized model is 79.4%and 92.2%,respectively.The specificity of the CTC detection model is significantly higher than CA125(92.2%vs.82.2%,P=0.044),and the detection rate of CTCs was higher than the positive rate of CA125(74.5%vs.58.2%,P=0.069)in early-stage patients(stage I and II).The detection rate of CTCs was significantly higher in patients with ascitic volume≥500 mL,suboptimal cytoreductive surgery and elevated serum CA125 level after 2 courses of chemotherapy(P<0.05).The detection rate of CTC;and CTC;was significantly higher in chemo-resistant patients(26.3%vs.11.9%;26.4%vs.13.4%,P<0.05).The median progression-free survival time for CTC;patients trended to be longer than CTC;patients,and overall survival was shorter in CTC;patients(P=0.043).Conclusions:Our study presents an optimized detection model for CTCs,which consists of the expression levels of three markers(EpCAM,MUC1 and WT1).In comparison with CA125,our model has high specificity and demonstrates better diagnostic values,especially for early-stage ovarian cancer.Detection of CTC;and CTC;had predictive value for chemotherapy resistance,and the detection of CTC;suggested poor prognosis.

Identification of NR3C2 as a functional diagnostic and prognostic biomarker and potential therapeutic target in non‐small cell lung cancer

Background:Non‐small cell lung cancer(NSCLC),including the lung squamous cell carcinoma(LUSC)and lung adenocarcinoma(LUAD)subtypes,is a malignant tumor type with a poor 5‐year survival rate.The identification of new powerful diagnostic biomarkers,prognostic biomarkers,and potential therapeutic targets in NSCLC is urgently required.Methods:The UCSC Xena,UALCAN,and GEO databases were used to screen and analyze differentially expressed genes,regulatory modes,and genetic/epigenetic alterations in NSCLC.The UCSC Xena database,GEO database,tissue microarray,and immunohistochemistry staining analyses were used to evaluate the diagnostic and prognostic values.Gain‐of‐function assays were performed to examine the roles.The ESTIMATE,TIMER,Linked Omics,STRING,and DAVID algorithms were used to analyze potential molecular mechanisms.Results:NR3C2 was identified as a potentially important molecule in NSCLC.NR3C2 is expressed at low levels in NSCLC,LUAD,and LUSC tissues,which is significantly related to the clinical indexes of these patients.Receiver operating characteristic curve analysis suggests that the altered NR3C2 expression patterns have diagnostic value in NSCLC,LUAD,and especially LUSC patients.Decreased NR3C2 expression levels can help predict poor prognosis in NSCLC and LUAD patients but not in LUSC patients.These results have been confirmed both with database analysis and real‐world clinical samples on a tissue microarray.Copy number variation contributes to low NR3C2 expression levels in NSCLC and LUAD,while promoter DNA methylation is involved in its downregulation in LUSC.Two NR3C2 promoter methylation sites have high sensitivity and specificity for LUSC diagnosis with clinical application potential.NR3C2 may be a key participant in NSCLC development and progression and is closely associated with the tumor microenvironment and immune cell infiltration.NR3C2 co‐expressed genes are involved in many cancer‐related signaling pathways,further supporting a potentially significant role of NR3C2 in NSCLC.Conclusions:NR3C2 is a novel potential diagnostic and prognostic biomarker and therapeutic target in NSCLC.

Soluble triggering receptor expressed on myeloid cell-1 （sTREM- 1）： a potential biomarker for the diagnosis of infectious diseases

Sensitive and useful biomarkers for the diagnosis and prognosis of infectious diseases have been widely developed. An example of these biomarkers is triggering receptor expressed on myeloid cell-1 （TREM-1）, which is a cell surface receptor expressed on monocytes/macrophages and neutrophils. TREM-1 amplifies inflammation by activating the TREM-1/DAP12 pathway. This pathway is triggered by the interaction of TREM-1 with ligands or stimulation by bacterial lipopolysaccharide. Consequently, pro-inflammatory cytokines and chemokines are secreted. Soluble TREM-1 （sTREM-1） is a special form of TREM-1 that can be directly tested in human body fluids and well-known biomarker for infectious diseases, sTREM-1 level can be potentially used for the early diagnosis and prognosis prediction of some infectious diseases, including infectious pleural effusion, lung infections, sepsis, bacterial meningitis, viral infections （e.g., Crimean Congo hemorrhagic fever and dengue fever）, fungal infections （e.g., Aspergillus infection）, and burn-related infections, sTREM-1 is a more sensitive and specific biomarker than traditional indices, such as C-reactive protein and procalcitonin levels, for these infectious diseases. Therefore, sTREM-1 is a feasible biomarker for the targeted therapy and rapid and early diagnosis of infectious diseases.

SCADA data based condition monitoring of wind turbines

Wind turbines（WTs） are quite expensive pieces of equipment in power industry. Maintenance and repair is a critical activity which also consumes lots of time and effort, hence making it a costly affair. Carefully planning the maintenance based upon condition of the equipment would make the process reasonable. Mostly the WTs are equipped with some kind of condition monitoring device/system, which provides the information about the device to the central data base i.e., supervisory control and data acquisition（SCADA） data base. These devices/systems make use of data processing techniques/methods in order to detect and predict faults. The information provided by condition monitoring equipments keeps on recoding in the SCADA data base. This paper dwells upon the techniques/methods/algorithms developed, to carry out diagnosis and prognosis of the faults, based upon SCADA data.Subsequently data driven approaching for SCADA data interpretation has been reviewed and an artificial intelligence（AI） based framework for fault diagnosis and prognosis of WTs using SCADA data is proposed.