Phospholipase A2 enzymes PLA2G2A and PLA2G12B as potential diagnostic and prognostic biomarkers in cholangiocarcinoma

BACKGROUND Phospholipase A2(PLA2)enzymes are pivotal in various biological processes,such as lipid mediator production,membrane remodeling,bioenergetics,and maintaining the body surface barrier.Notably,these enzymes play a significant role in the development of diverse tumors.AIM To systematically and comprehensively explore the expression of the PLA2 family genes and their potential implications in cholangiocarcinoma(CCA).METHODS We conducted an analysis of five CCA datasets from The Cancer Genome Atlas and the Gene Expression Omnibus.The study identified differentially expressed genes between tumor tissues and adjacent normal tissues,with a focus on PLA2G2A and PLA2G12B.Gene Set Enrichment Analysis was utilized to pinpoint associated pathways.Moreover,relevant hub genes and microRNAs for PLA2G2A and PLA2G12B were predicted,and their correlation with the prognosis of CCA was evaluated.RESULTS PLA2G2A and PLA2G12B were discerned as differentially expressed in CCA,manifesting significant variations in expression levels in urine and serum between CCA patients and healthy individuals.Elevated expression of PLA2G2A was correlated with poorer overall survival in CCA patients.Additionally,the study delineated pathways and miRNAs associated with these genes.CONCLUSION Our findings suggest that PLA2G2A and PLA2G12B may serve as novel potential diagnostic and prognostic markers for CCA.The increased levels of these genes in biological fluids could be employed as non-invasive markers for CCA,and their expression levels are indicative of prognosis,underscoring their potential utility in clinical settings.

Predictive Healthcare: An IoT-Based ANFIS Framework for Diabetes Diagnosis

The increasing integration of the Internet of Things (IoT) in healthcare is revolutionizing patient monitoring and disease prediction. This paper presents a machine learning (ML)-based framework using Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict diabetes. The proposed system leverages IoT data to monitor key health parameters, including glucose levels, blood pressure, and age, offering real-time diagnostics for diabetes patients. The dataset used in this study was obtained from the UCI repository and underwent preprocessing, feature selection, and classification using the ANFIS model. Comparative analysis with other machine learning algorithms, such as Support Vector Machines (SVM), Naïve Bayes, and K-Nearest Neighbors (KNN), demonstrates that the proposed method achieves superior predictive performance. The experimental results show that the ANFIS model achieved an accuracy of 95.5%, outperforming conventional models, and providing more reliable decision-making in clinical settings. This study highlights the potential of combining IoT with machine learning to improve predictive healthcare applications, emphasizing the need for real-time patient monitoring systems.

Diagnostic Value of the Padua Score Combined with Thrombotic Biomarker Tissue Plasminogen Activator Inhibitor-1 (tPAI-1) Detection for the Risk of Deep Vein Thrombosis in Patients with Pulmonary Heart Disease

This study explores the diagnostic value of combining the Padua score with the thrombotic biomarker tissue plasminogen activator inhibitor-1(tPAI-1)for assessing the risk of deep vein thrombosis(DVT)in patients with pulmonary heart disease.These patients often exhibit symptoms similar to venous thrombosis,such as dyspnea and bilateral lower limb swelling,complicating differential diagnosis.The Padua Prediction Score assesses the risk of venous thromboembolism(VTE)in hospitalized patients,while tPAI-1,a key fibrinolytic system inhibitor,indicates a hypercoagulable state.Clinical data from hospitalized patients with cor pulmonale were retrospectively analyzed.ROC curves compared the diagnostic value of the Padua score,tPAI-1 levels,and their combined model for predicting DVT risk.Results showed that tPAI-1 levels were significantly higher in DVT patients compared to non-DVT patients.The Padua score demonstrated a sensitivity of 82.61%and a specificity of 55.26%at a cutoff value of 3.The combined model had a significantly higher AUC than the Padua score alone,indicating better discriminatory ability in diagnosing DVT risk.The combination of the Padua score and tPAI-1 detection significantly improves the accuracy of diagnosing DVT risk in patients with pulmonary heart disease,reducing missed and incorrect diagnoses.This study provides a comprehensive assessment tool for clinicians,enhancing the diagnosis and treatment of patients with cor pulmonale complicated by DVT.Future research should validate these findings in larger samples and explore additional thrombotic biomarkers to optimize the predictive model.

Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model’s ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.

Predictive and prognostic value of serum AFP level and its dynamic changes in advanced gastric cancer patients with elevated serum AFP

AIM To investigate predictive and prognostic value of serum alpha-fetoprotein(AFP) level and its dynamic changes in patients with advanced gastric cancer with elevated serum AFP(AFPAGC).METHODS One hundred and five patients with AFPAGC were enrolled in the study, and all of them underwent at least one cycle of systemic chemotherapy at our institute and had serum AFP ≥ 20 ng/m L at diagnosis or recurrence. Clinicopathologic features, serum AFP level at diagnosis and changes during treatment, first-line chemotherapy regimens, efficacy and toxicity, and survival information were collected. A Person's χ~2 or Fisher's exact test was used to measure the differences between variables. Survival prognostic factors were investigated using the Kaplan-Meier method and Cox regression.RESULTS Median serum AFP level was 161.7 ng/m L(range, 22.9-2557110 ng/m L). Objective response rates(ORR) was significantly lower in the AFP ≥ 160 ng/m L group than in the AFP < 160 ng/m L group(30.4% vs 68.3%, P < 0.001). ORR to doublet regimens was significantly lower in the AFP ≥ 160 ng/m L group, whereas ORR to triplet regimens was similar between the two groups. Liver metastasis rate was significantly higher in the AFP ≥ 160 ng/m L group than in the AFP < 160 ng/m L(69.8% vs 50.0%, P < 0.001). Overall survival(OS) in the two cohorts did not show any significant difference(P = 0.712). Dynamic changes of AFP were consistent with response to chemotherapy, and median OS of patients with a serum AFP decline ≥ 50% and those with a serum AFP decline < 50% was 17.5 m and 10.0 m, respectively(P = 0.003). Hepatic(P = 0.005), peritoneal(P < 0.001), non-regional lymph node metastasis(P < 0.001), and portal vein tumor thrombus(PVTT)(P = 0.042) were identified as independent prognostic factors for AFPAGC. CONCLUSION Real-time examination of AFP has great predictive and prognostic value for managing AFPAGC. For those with markedly elevated AFP, triplet regimens may be a better choice.

Diagnostic and prognostic potential of tissue and circulating long non-coding RNAs in colorectal tumors

Long non-coding RNAs(lncRNAs)are members of the non-protein coding RNA family longer than 200 nucleotides.They participate in the regulation of gene and protein expression influencing apoptosis,cell proliferation and immune responses,thereby playing a critical role in the development and progression of various cancers,including colorectal cancer(CRC).As CRC is one of the most frequently diagnosed malignancies worldwide with high mortality,its screening and early detection are crucial,so the identification of disease-specific biomarkers is necessary.LncRNAs are promising candidates as they are involved in carcinogenesis,and certain lncRNAs(e.g.,CCAT1,CRNDE,CRCAL1-4)show altered expression in adenomas,making them potential early diagnostic markers.In addition to being useful as tissue-specific markers,analysis of circulating lncRNAs(e.g.,CCAT1,CCAT2,BLACAT1,CRNDE,NEAT1,UCA1)in peripheral blood offers the possibility to establish minimally invasive,liquid biopsy-based diagnostic tests.This review article aims to describe the origin,structure,and functions of lncRNAs and to discuss their contribution to CRC development.Moreover,our purpose is to summarise lncRNAs showing altered expression levels during tumor formation in both colon tissue and plasma/serum samples and to demonstrate their clinical implications as diagnostic or prognostic biomarkers for CRC.

Circulating predictive and diagnostic biomarkers for hepatitis B virus-associated hepatocellular carcinoma

Chronic hepatitis B virus(HBV) infected patients have an almost 100-fold increased risk to develop hepatocellular carcinoma(HCC). HCC is the fifth most common and third most deadly cancer worldwide. Up to 50% of newly diagnosed HCC cases are attributed to HBV infection. Early detection improves survival and can be achieved through regular screening. Six-monthly abdominal ultrasound, either alone or in combination with alphafetoprotein serum levels, has been widely endorsed for this purpose. Both techniques however yield limited diagnostic accuracy, which is not improved when they are combined. Alternative circulating or histological markers to predict or diagnose HCC are therefore urgently needed. Recent advances in systems biology technologies have enabled the identification of several new putative circulating biomarkers. Although results from studies assessing combinations of these biomarkers are promising, evidence for their clinical utility remains low. In addition, most of the studies conducted so far show limitations in design. Attention must be paid for instance to different ethnicities and different etiologies when studying biomarkers for hepatocellular carcinoma. This review provides an overview on the current understandings and recent progress in the field of diagnostic and predictive circulating biomarkers for hepatocellular carcinoma in chronically infected HBV patients and discusses the future prospects.

Predictive and prognostic biomarkers with therapeutic targets in advanced colorectal cancer

Colorectal cancer(CRC)is one of the most common human malignant diseases and the second leading cause of cancer-related deaths worldwide.The treatment of advanced CRC has improved significantly in recent years.With the emergence of two targeted antibodies,cetuximab(Erbitux),an anti-epidermal growth factor receptor monoclonal antibody and bevacizumab(Avastin),a vascular endothelial growth factor monoclonal antibody,the treatment of metastatic CRC has entered the era of personalized therapy.Predictive and prognostic biomarkers have,and will continue to,facilitate the selection of suitable patients and the personalization of treatment for metastatic CRC(mCRC).In this review,we will focus primarily on the important progresses made in the personalized treatment of mCRC and discuss the potentially novel predictive and prognostic biomarkers for improved selection of patients for anti-cancer treatment in the future.

Blood microRNAs as potential diagnostic and prognostic markers in cerebral ischemic injury

MicroRNAs are a family of small, genome-encoded endogenous RNAs that are transcribed but are not translated into proteins. They serve essential roles in virtually every aspect of brain function, including neurogenesis, neural development, and cellular responses leading to changes in synaptic plasticity. They are also implicated in neurodegeneration and neurological disorders, in responses to hypoxia and ischemia, and in ischemic tolerance induced by ischemic preconditioning. In recent developments, miRNA expres- sion profiling has been examined in stroke, and these studies indicate that miRNAs have emerged as key mediators in ischemic stroke biology. Both increased and decreased miRNA levels may be needed either as prevention or treatment of stroke. Novel approaches are being developed to get miRNA related therapeu- tics into the brain across an intact blood-brain barrier, including chemical modification, use of targeting molecules and methods to disrupt the blood-brain barrier.

Diagnostic and prognostic value of circulating micro RNAs in heart failure with preserved and reduced ejection fraction

micro RNAs(mi RNAs) are powerful regulators of posttranscriptional gene expression and play an important role in pathophysiological processes. Circulating mi RNAs can be quantified in body liquids and are promising biomarkers in numerous diseases. In cardiovascular disease mi RNAs have been proven to be reliable diagnostic biomarkers for different disease entities. In cardiac fibrosis(CF) and heart failure(HF) dysregulated circulating mi RNAs have been identified,indicating their promising applicability as diagnostic biomarkers. Some mi RNAs were successfully tested in risk stratification of HF implementing their potential use as prognostic biomarkers. In this respect mi RNAs might soon be implemented in diagnostic clinical routine. In the young field of mi RNA based research advances have been made in identifying mi RNAs as potential targets for the treatment of experimental CF and HF. Promising study results suggest their potential future application as therapeutic agents in treatment of cardiovascular disease. This article summarizes the current state of the various aspects of mi RNA research in the field of CF and HF with reduced ejection fraction as well as preserved ejection fraction. The review provides an overview of the application of circulating mi RNAs as biomarkers in CF and HF and current approaches to therapeutically utilize mi RNAs in this field of cardiovascular disease.

Special Issue on Machine Fault Diagnostics and Prognostics

Machine components and systems, such as gears, bearings, pipes, cutting tools and turbines, may experience various types of faults, such as breakage, crack, pitting, wear, corrosion. If not being properly monitored and treated, such faults can propagate and lead to machinery perfor- mance degradation, malfunction, or even severe compo- nent/system failure. It is significant to reliably detect machinery defects, evaluate their severity, predict the fault propagation trends, and schedule optimized maintenance and inspection activities to prevent unexpected failures. Advances in these areas will support ensuring equipment and production reliability, safety, quality and productivity.

Role of mi RNAs and their potential to be useful as diagnostic and prognostic biomarkers in gastric cancer

Alterations in epigenetic control of gene expression play an important role in many diseases, including gastric cancer. Many studies have identified a large number of upregulated oncogenic mi RNAs and downregulated tumour-suppressor mi RNAs in this type of cancer. In this review, we provide an overview of the role of mi RNAs, pointing to their potential to be useful as diagnostic and/or prognostic biomarkers in gastric cancer. Moreover, we discuss the influence of polymorphisms and epigenetic modifications on mi RNA activity.

Three dimensional diagnostic,semidiagnostic and prognostic calculations of current in the East China Sea in April of 1994

Three dimensional diagnostic,semidiagnostic and prognostic models in the σ coordinate are used to compute the current in the East China Sea with wind and hydrographic data obtained from the investigation of ocean fluxes in the East China Sea during the cruise of April 1994.The computed results show that:the density and velocity fields and so on have been adjusted when time t ≈ 23 d, i.e.,the solution of semidiagnostic calculation has been obtained.The quasi steady state solution also has been reached after about 60 d.Comparing the results of diagnostic calculation with those of semidiagnostic and prognostic calculations,it is found that they agree qualitatively.For example,they all show that:(1)The Kuroshio flows along the shelf break of 200～1 000 m isobaths;(2)In the southeastern part of the computational region there is a stronger counter current with an anticyclonic eddy;(3)The volume transport through Section PN is about 30×10 6m 3/s and so on.However there are some quantitative differences between them.For example,(1)For the semidiagnostic results the Kuroshio current is stronger and clearer than that for diagnostic results and the Kuroshio width is more narrow than that for diagnostic results.This shows that the semidiagnostic calculation can better reflect the topography effect;(2)In most computed region the vertical component of velocity intensifies for semidiagnostic result,especially on the shelf break.Comparing the results of semidiagnostic calculation with those of prognostic calculation,they agree qualitatively,and also agree quantitatively in the middle of computed region.

The Use of High-Performance Fatigue Mechanics and the Extended Kalman/Particle Filters,for Diagnostics and Prognostics of Aircraft Structures

In this paper,we propose an approach for diagnostics and prognostics of damaged aircraft structures,by combing high-performance fatigue mechanics with filtering theories.Fast&accurate deterministic analyses of fatigue crack propagations are carried out,by using the Finite Element Alternating Method(FEAM)for computing SIFs,and by using the newly developed Moving Least Squares(MLS)law for computing fatigue crack growth rates.Such algorithms for simulating fatigue crack propagations are embedded in the computer program Safe-Flaw,which is called upon as a subroutine within the probabilistic framework of filter theories.Both the extended Kalman as well as particle filters are applied in this study,to obtain the statistically optimal and semi-optimal estimates of crack lengths,from a series of noisy measurements of crack-lengths over time.For the specific problem,a simple modification to the particle filter,which can drastically reduce the computational burden,is also proposed.Based on the results of such diagnostic analyses,the prognostics of aerospace structures are thereafter achieved,to estimate the probabilistic distribution of the remaining useful life.By using a simple example of a single-crack near a fastener hole,we demonstrate the concept and effectiveness of the proposed framework.This paper thus forms the scientific foundation for the recently proposed concepts of VRAMS(Virtual Risk-Informed Agile Maneuver Sustainment)and Digital Twins of aerospace vehicles.

Establishment of a Predictive Diagnostic Model for Acute Mycoplasma Pneumoniae Infection in Elderly Patients with Community-acquired Pneumonia

We established a diagnostic model to predict acute Mycoplasma pneumoniae （M. pneumonia） infection in elderly Community-acquired pneumonia （CAP） patients. We divided 456 patients into acute and non-acute M. pneumoniae infection groups. Binary logistic regression and receiver operating characteristic （ROC） curves were used to establish a predictive model. The following independent factors were identified： age 〉 70 years; serum cTNT level 〉 0.0S ng/mL; lobar consolidation; mediastinal lymphadenopathy; and antibody titer in the acute phase 〉 1：40. The area under the ROC curve of the model was 0.923 and a score of 2 7 score predicted acute M. pneumoniae infection in elderly patients with CAP. The predictive model developed in this study has high diagnostic accuracy for the identification of elderly acute M. pneumoniae infection.

Prognostic and predictive significance of MSI in stages Ⅱ/Ⅲ colon cancer

In colon cancer,classic disease staging remains the key prognosis and treatment determinant.Although adjuvant chemotherapy has an established role in stageⅢcolon cancer patients,in stageⅡit is still a subject of controversy due to its restriction to a small subgroup of patients with high-risk histopathologic features.Patients with stageⅡtumors form a highly heterogeneous group,with five-year relative overall survival rates ranging from 87.5%(ⅡA)to 58.4%(ⅡC).Identifying those for whom adjuvant chemotherapy would be appropriate and necessary has been challenging,and prognostic markers which could serve in the selection of patients more likely to recur or benefit from adjuvant chemotherapy are eagerly needed.The stronger candidate in this category seems to be microsatellite instability(MSI).The recently reported European Society for Medical Oncology guidelines suggest that MSI should be evaluated in stageⅡcolorectal cancer patients in order to contribute in treatment decisionmaking regarding chemotherapy administration.Thehypothetical predictive role of MSI regarding its response to 5-fluorouracil-based adjuvant chemotherapy has proven a much more difficult issue to address.Almost every possible relation between MSI and chemotherapy outcome has been described in the adjuvant colon cancer setting in the international literature,and the matter is far from being settled.In this current report we critically evaluate the prognostic and predictive impact of MSI status in patients with stageⅡand stageⅢcolon cancer patients.

Evaluation the Possibility of Using of miRNA-21 as a Prognostic and Predictive Factor in Colorectal Cancer (South Egypt Experience)

Background: Micro RNA 21?(miRNA-21) is overexpressed in numerous solid types of tumors, indicating that its biology is relevant to numerous types of cancer. However,?in Colorectal Cancer (CRC)?there is a lot of studies?confirmed its use as diagnostic marker, other studies suggested it could be used as a predictive marker for 5 Fu sensitivity so this study was conducted to verified the significance of using miRNA-21 as a prognostic and predictive factor in CRC. Patients and Methods: It is a prospective study that included 45 patients with stage I-IV CRC who attended at South Egypt Cancer Institute from April 2017 to October 2018.?miRNA-21 was measured by (q PCR) from peripheral blood.?Response to 5-FU containing regimens was assessed using RECIST?criteria and its levels of expression were correlated to the clincopathological data,?response and survival of the patients.?Results:?High expression of the miRNA21 was significantly associated with advanced stage of CRC p ?0.001,?Lymph Node (LN)?invasion p ?0.001,distant metastasis p??0.01 lymphovascular (LVI) invasion p ?0.01 and pre-treatment level of Carcino?Embryonic Antigen (CEA) p ?0.031. Also we found that high expression level significantly badly affected the response to 5-Fu containing regimen p ?0.001, DFS p ?0.01.?Conclusion:?miRNA-21 could be used as non-invasive prognostic and predictive marker in CRC.

Relation of predictive value to sensitivity and specificity of a diagnostic test

This paper discusses how the positive and negative predictive values vary withcontrary variations of sensitivity and specificity at a certain disease prevalence.Let thesensitiviy be Se and the specificity be Sp at the initial cutoff point,correspondingly Se’and Sp’ at a new cutoff point:A=Se’/Se:B-(1-Sp’)/(1-Sp):C=Sp’/Sp;D=(I-Se’)/(I-Se).Moving the cutoff point from the initial point to the new point,if the contrary variationsof the sensitivity and specificity satisfy the inequality A】B,then the positive predictivevalue increases,otherwise it decreases or remains unchanged.If the variations satisfy theinequality C】D,then the negative prcdictive value increases,otherwise it decreasses or re-mains unchanged.

Diagnostic Accuracy and Predictive Value of Clinical Symptoms for the Diagnosis of Mild COVID-19

Objective: To assess the diagnostic accuracy and predictive values of clinical symptoms in patients with suspected mild COVID-19 to identify target groups for self-isolation and outpatient treatment without additional testing. Methods: We conducted an open-label prospective study in patients aged 18 to 72 years with suspected mild COVID-19. The clinical diagnosis was based on the acute onset of such symptoms as olfactory dysfunction, hyperthermia, myalgia, nasal congestion, nasal discharge, cough, rhinolalia, sore throat, without pneumonia in persons in contact with a confirmed case of COVID-19. The physician assessed clinical symptoms using a 4-point scale. The patient self-assessed clinical symptoms using a ten-point visual analogue scale (VAS). All enrolled patients underwent laboratory testing to confirm the diagnosis of COVID-19. Results: Of the 120 patients underwent testing, the diagnosis of mild COVID-19 was confirmed in 96 patients and ruled out in 24 patients. When assessing symptoms by a physician according to the correlation analysis, hyperthermia, myalgia, nasal congestion and rhinolalia have a positive predictive value with a significance level of more than 0.6. When self-assessing symptoms by a patient, fever, myalgia and nasal congestion have a diagnostic accuracy with a significance level of more than 0.5. Nasal discharge, cough and sore throat have negative predictive values. Conclusion: The presence of these symptoms in patients with an acute onset of the disease can help to make a clinical diagnosis of coronavirus disease and identify target groups for self-isolation and outpatient treatment without additional testing. Highly suspect asymptomatic patients are not considered as those who have possible mild COVID-19 infection.

Modern Predictive Diagnostic Method of Induction Traction Motor Based on FEM, BEM

The paper presents a mathematical model of special construction induction traction motor. On the base of predictive filtering, analytical studies, fuzzy logic control, relying on the virtual data generated by FEM （Finite Element Method） and BEM （Boundary Element Method） is detected faults of induction motor. Digital predictive filter is used to separate a fundamental harmonic from spectrum current and voltage harmonics. Fuzzy logic control is used to identify a motor state. Magnetic fields distribution in the traction motor, of the wheel vehicle is presented in the paper. Modem diagnostics method has been used for faulty motor simulation and shows results of motor fault effects. Some computer programs were applied in calculation of magnetic fields distribution. On the base of magnetic field distributions were analyzed different failures situations. Some laboratory experiments realized for induction traction motor were verified by results of computer calculations.