Early Prediction Research Articles

Early prediction of COVID-19 infection using data mining and multi machine learning algorithms

The fields of artificial intelligence (AI) and machine learning (ML) have attracted significant interest and investment from a diverse range of industries, especially during the last several years. Despite the fact that AI methods have been used extensively and put through extensive testing in the healthcare industry, the recently discovered coronavirus disease (COVID-19) necessitates the use of these methods in order to prevent the emergence of the disease. The proposed system is based on six ML algorithms to predict COVID-19 infection as random forest (RF) algorithm, naive bayes (NB) algorithm, support vector machine (SVM) algorithm, decision tree (DT) algorithm, multi-layer perceptron (MLP), and k-nearest neighbor (KNN). It is based on two steps: first, we uploaded the dataset to train the model. Then, we test our model on those cases to work directly after making a trained classifier so it can directly discover with automatic COVID-19 prediction state of a patient suspected or not. The proposed system results showed the high accuracy of NB, DT, and SVM as 98.646%. Besides the better time to build the model and early predict the state of patients is 31 ms of the NB algorithm.

Early prediction of long-term survival of patients with nasopharyngeal carcinoma by multi-parameter MRI radiomics

The objective is to create a comprehensive model that integrates clinical, semantic, and radiomics features to forecast the 5-year progression-free survival (PFS) of individuals diagnosed with non-distant metastatic Nasopharyngeal Carcinoma (NPC). In a retrospective analysis, we included clinical and MRI data from 313 patients diagnosed with primary NPC. Patient classification into progressive and non-progressive categories relied on the occurrence of recurrence or distant metastasis within a 5-year timeframe. Initial screening comprised clinical features and statistically significant image semantic features. Subsequently, MRI radiomics features were extracted from all patients, and optimal features were selected to formulate the Rad-Score.Combining Rad-Score, image semantic features, and clinical features to establish a combined model Evaluation of predictive efficacy was conducted using ROC curves and nomogram specific to NPC progression. Lastly, employing the optimal ROC cutoff value from the combined model, patients were dichotomized into high-risk and low-risk groups, facilitating a comparison of 10-year overall survival (OS) between the groups. The combined model showcased superior predictive performance for NPC progression, reflected by AUC values of 0.84, an accuracy rate of 81.60%, sensitivity at 0.77, and specificity at 0.81 within the training group. In the test set, the AUC value reached 0.81, with an accuracy of 74.6%, sensitivity at 0.82, and specificity at 0.66. The amalgamation of Rad-Score, clinical, and imaging semantic features from multi-parameter MRI exhibited significant promise in prognosticating 5-year PFS for non-distant metastatic NPC patients. The combined model provided quantifiable data for informed and personalized diagnosis and treatment planning.

Towards a disease prediction system: BioBERT-based medical profile representation

<p>Healthcare professionals are increasingly interested in predicting diseases before they manifest, as this can prevent more serious health conditions and even save lives. Machine learning techniques are now playing an important role in healthcare, including in the early prediction of diseases based on prior medical knowledge. However, one of the biggest challenges is how to represent medical information in a way that can be processed by machine learning algorithms. Medical histories are often in a format that computers cannot read, so filtering and converting this information into numerical representations is a crucial step. This process has become easier with the advancement of natural language processing techniques. In this paper, we propose three representations of medical information, two of which are based on BioBERT, the latest text representation techniques for the biomedical sector. The efficiency of these representations is tested on the MIMIC-III database, which contains information on 46,520 patients. The focus of the study is on predicting Coronary Artery Disease, and the results demonstrate the effectiveness of the proposed approach. The study highlights the importance of medical history in disease prediction and demonstrates the potential of machine learning techniques to advance healthcare.</p>

Deep Learning Model for Estimation of LV Ejection Fraction from Echocardiogram

Heart failure, a leading global cause of death, poses challenges for early prediction of cardiac dysfunction, especially ejection fraction (EF). This study employs Convolutional Neural Networks (CNNs), utilizing ResNet and MobileNet architectures, on the CAMUS dataset with 500 patient records (2CH and 4CH). The goal is to aid healthcare professionals in accurately measuring EF. The CAMUS dataset, comprising multi-modality cardiac imaging and segmentation data, serves as the foundation. The CNN, ResNet, and MobileNet models are fine-tuned through transfer learning and their performance is evaluated based on accuracy. This comparative analysis identifies the model with the best predictive capabilities for EF, showcasing their potential for earlier diagnosis and intervention. Deep learning techniques enhance cardiac healthcare by providing reliable, noninvasive means of predicting heart failure, reducing its impact on patients and healthcare systems.

A Comprehensive Analysis of various Covid-19 Detection and Pandemic Prevention Systems and Methods Using Data Science

Abstract: The paper proposes a technique for early Covid-19 test prediction using AI and data science. The study analyzes various data sources to understand the patterns and trends of the pandemic and the impact of various interventions on the spread of the virus. The proposed system integrates data from multiple sources to identify potential cases and can also track the spread of the virus in real-time. The system has high accuracy in predicting positive cases and has the potential to improve our ability to detect and predict Covid-19 cases, ultimately saving lives and reducing the economic impact of the pandemic. The proposed technique for early Covid-19 test prediction using AI and data science can be a game- changer in our fight against the pandemic. With the integration of multiple data sources, including individual health records, social media, and geographic data, the system can identify potential cases even before symptoms appear. This early detection can help individuals get timely medical attention and prevent the spread of the virus to others.

Heart Disease Prediction using Machine Learning

Abstract: Heart disease remains a leading cause of morbidity and mortality worldwide. Early detection and accurate prediction of heart disease risk factors are crucial for effective prevention and timely intervention. In recent years, machine learning techniques have emerged as powerful tools for predictive analytics in healthcare. This paper presents a comprehensive review of recent advances in the application of machine learning algorithms for heart disease prediction. The review begins by discussing the significance of heart disease prediction and the limitations of traditional risk assessment methods.

Early Prediction of Liver Disease

Abstract: Chronic liver disease (CLD) poses a substantial global health challenge, leading to considerable morbidity and mortality. The timely identification of CLD is imperative to enhance patient outcomes and ensure effective disease management. This research introduces an innovative machine learning framework designed to predict liver disease at an early stage, utilizing a wide range of clinical data resources. By integrating demographic information, laboratory test results, imaging findings, and patient medical history, our model aims to accurately forecast the onset and progression of CLD. Advanced classification algorithms, including Random Forest and Gradient Boosting, are employed for feature selection and model development. Performance evaluation is conducted on a comprehensive dataset comprising longitudinal patient records. The results demonstrate promising accuracy, sensitivity, and specificity, highlighting the potential of machine learning in enhancing CLD risk assessment and enabling timely interventions.

Artificial intelligence and machine learning for early cancer prediction and response

Worldwide, cancer claims more lives than any other disease. Although cancer detection, prognosis, and treatment have all advanced, one major obstacle is the lack of personalized, data-driven care. Artificial intelligence (AI), used to forecast and automate numerous malignancies, has emerged as a viable solution for increasing healthcare accuracy and patient outcomes. Artificial intelligence applications in cancer include risk evaluation, early detection, patient prognosis prediction, and treatment selection based on comprehensive data. Machine learning (ML), a form of artificial intelligence that allows computers to learn from training data, is very successful in predicting breast, brain, lung, liver, and prostate cancers. Indeed, AI and machine learning have outperformed physicians in predicting cancer. These technologies can potentially enhance the diagnosis, prediction, and quality of life of patients with a wide range of disorders, not just cancer. As a result, it is critical to enhance existing AI and ML technologies and create new programs to help patients. This article discusses the use of AI and machine learning algorithms in cancer prediction, including its present uses, future possibilities, and limitations.

Evaluation of arteriovenous fistula maturation and early prediction of clinical eligibility, using ultrasound: The Fistula Maturation Evaluation (FAME) Study.

The study of time-related alterations of ultrasound-determined parameters during maturation, and the assessment of time to hemodynamic maturation, enabling early prediction of clinical eligibility, of hemodialysis autologous arteriovenous fistulae (AVF). This is an observational, prospective, study of only AVF-eligible patients referred for access creation, from 02/2019 to 02/2022 (ClinicalTrials.gov identifier: NCT0473687). Brachial artery diameter (dBA), access flow volume (FV), non-augmented efferent vein diameter (dEV), resistivity index (RI), and efferent vein total wall thickness (tEV), were assessed by ultrasound. Measurements were conducted daily in the first week and repeated on days 14, 21, 30, 60, and 90, postoperatively. The primary endpoint included the documentation of serial changes of flow and structural parameters related to AVF maturation in the first 90 days of the post-operative period and maturation early prediction. Secondary endpoints included the determination of factors affecting maturation. One hundred one participants (mean age, 67 ± 6 years; 76 males) were enrolled. Average dBA and FV reached maximum on day 60 (5.64 ± 0.85 mm) and 90 (1.172 ± 617 mL/min), respectively. Day 7 values of dBA (5.48 ± 0.73 mm) and FV (1.039 ± 531 mL/min) did not alter significantly during the follow-up period. Parameters indicative of clinical functionality, dEV (5.82 ± 0.90 mm) and tEV (0.493 ± 0.10 mm), reached approximately 90% of maximum (6.66 ± 1.42 mm and 0.526 ± 0.11 mm), by day 14. RI reached minimum on day 30 (0.46 ± 0.09), without significant changes after day 2 (0.48 ± 0.09, p = 0.284). A significant correlation was identified, between day 7 FV and day 60 dEV (r = 0.40, p = 0.0002). A FV cut-off value ⩾657.51 mL/min, on day 7, predicted successful fistula maturation with 85% sensitivity and 100% specificity. Multivariate analysis identified female gender, age >75, diabetes, and wrist access as independent predictors of decreased values of maturation parameters. Hemodynamic maturation is completed by the first postoperative week, while AVF is clinically functional, by the second. FV can be used for early prediction of maturation.

A novel hybrid deep learning model for early stage diabetes risk prediction

A novel hybrid deep learning model for early stage diabetes risk prediction

Vaginal matrix metalloproteinase-9 (MMP-9) as a potential early predictor of preterm birth.

To evaluate the differences in vaginal matrix metalloproteinases (MMP) and tissue inhibitors of metalloproteinases (TIMPs) in pregnant patients with a history of prior preterm birth compared with controls. A prospective cohort pilot study recruited patients during prenatal care with history of prior spontaneous preterm birth (high-risk group) or no history of preterm birth(low-risk/controls). Inclusion criteria were singleton gestation at 11-16weeks and between 18 and 55 years of age. Exclusion criteria were diabetes mellitus, hypertension, diseases affecting the immune response or acute vaginitis. A vaginal wash was performed at time of enrollment, and patients were followed through delivery. Samples were analyzed using semi-quantitative analysis of MMPS and TIMPS. The study was approved by the IRB and a p value <0.05 was considered significant. A total of 48 pregnant patients were recruited: 16 with a history of preterm birth (high-risk group) and 32 with no history of preterm birth (low-risk group/controls). Groupswere similar in age, race, BMI, and delivery mode. The high-risk group had more multiparous women (100 vs. 68.8 %; p=0.02), a greater preterm birth rate (31.2 vs. 6.3 %; p=0.02), and a lower birth weight (2,885±898 g vs. 3,480±473 g; p=0.02). Levels of vaginal MMP-9 were greater in high-risk patients than low-risk patients (74.9 %±27.0 vs. 49.4 %±31.1; p=0.01). When dividing the cohort into patients that had a spontaneous preterm birth (7/48, 14.6 %) vs. those with a term delivery (41/48, 85.4 %), the vaginal MMP-9 remained elevated in the cohort that experienced a preterm birth (85.46 %+19.79 vs. 53.20 %+31.47; p=0.01). There were no differences in the other MMPS and in TIMPs between highand low-risk groups. There was an increase in vaginal MMP-9 during early pregnancy in those at high risk for preterm birth and in those who delivered preterm, regardless of prior pregnancy outcome. Vaginal MMP-9 may have potential as a marker of increased risk of preterm birth.

Predicting who has delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage using machine learning approach: a multicenter, retrospective cohort study.

Early prediction of delayed cerebral ischemia (DCI) is critical to improving the prognosis of aneurysmal subarachnoid hemorrhage (aSAH). Machine learning (ML) algorithms can learn from intricate information unbiasedly and facilitate the early identification of clinical outcomes. This study aimed to construct and compare the ability of different ML models to predict DCI after aSAH. Then, we identified and analyzed the essential risk of DCI occurrence by preoperative clinical scores and postoperative laboratory test results. This was a multicenter, retrospective cohort study. A total of 1039 post-operation patients with aSAH were finally included from three hospitals in China. The training group contained 919 patients, and the test group comprised 120 patients. We used five popular machine-learning algorithms to construct the models. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, and f1 score were used to evaluate and compare the five models. Finally, we performed a Shapley Additive exPlanations analysis for the model with the best performance and significance analysis for each feature. A total of 239 patients with aSAH (23.003%) developed DCI after the operation. Our results showed that in the test cohort, Random Forest (RF) had an AUC of 0.79, which was better than other models. The five most important features for predicting DCI in the RF model were the admitted modified Rankin Scale, D-Dimer, intracranial parenchymal hematoma, neutrophil/lymphocyte ratio, and Fisher score. Interestingly, clamping or embolization for the aneurysm treatment was the fourth button-down risk factor in the ML model. In this multicenter study, we compared five ML methods, among which RF performed the best in DCI prediction. In addition, the essential risks were identified to help clinicians monitor the patients at high risk for DCI more precisely and facilitate timely intervention.

A clinical risk score enables early prediction of dissatisfaction 1 year after total knee arthroplasty.

Dissatisfaction after total knee arthroplasty (TKA) is a prevalent and clinically relevant problem that affects approximately 10%-20% of patients. The aim of this study is to identify factors associated with dissatisfaction 1 year after TKA. A total of 236 patients undergoing TKA were included in this prospective cohort study. Demographic data, preoperative clinical parameters (e.g., axial alignment, osteoarthritis severity) and patient-reported outcome measures (PROMs) were collected preoperatively, at 1 month and 1 year after TKA, encompassing the Knee Society Score (KSS) and Knee injury and Osteoarthritis Outcome Score (KOOS). The primary outcome was dissatisfaction 1 year after TKA, defined as ≤20 points on the KSS satisfaction scale. A risk score based on multiple regression and area under the curve (AUC) analyses was calculated to predict dissatisfaction. One year after TKA, 16% of the patients were dissatisfied. Dissatisfied patients were significantly younger (p = 0.023) and had a higher body mass index (BMI) (p = 0.007). No differences were observed in preoperative objective (p = 0.903) and functional KSS (p = 0.346), pain (p = 0.306), osteoarthritis severity (p = 0.358), axial knee alignment (p = 0.984) or psychological distress (p = 0.138). The likelihood of dissatisfaction at 1 year was 3.0, 4.0, 7.4, 4.3 and 2.8 times higher amongst patients aged <63.5 years, with a BMI > 30.1 kg/m2, a KOOSPain < 50%, a KSSFunction < 42 points and a KSSExpectation < 9 points (all at 1 month), respectively. Using these variables, a risk score with a maximum of 7 points was developed, demonstrating a high predictive value for dissatisfaction (AUC: 0.792 [95% confidence interval: 0.700-0.884], p < 0.001). Dissatisfaction 1 year after TKA can be predicted by a weighted risk score that includes patient age, BMI, pain, subjective functionality and unmet expectation 1 month postoperatively. Using the risk score, early detection of dissatisfaction has the potential to enable targeted interventions and improve patients' quality of life. Level II, Prognostic study.

Progress of circulating tumor DNA methylation for gastric cancer screening and management

Circulating tumor DNA (ctDNA) is cell-free DNA released by tumors or circulating tumor cells, containing abundant tumor-specific information that can serve as biomarkers for cancer early screening, monitoring, prognosis, and prediction of treatment response. This is particularly attractive in the field of gastric cancer, where high-quality screening, monitoring, and prediction methods are currently lacking. Gastric cancer exhibits significant tumor heterogeneity, with large differences in genetic and epigenetic characteristics among different subgroups. Methylated ctDNA has high sensitivity and specificity, which can help clarify tumor genotyping and facilitate the formulation of precise diagnostic and therapeutic strategies. Furthermore, numerous studies have confirmed the unique advantages of methylated DNA in predicting treatment response, adjuvant therapy, and drug resistance assessment, which may be used in the future to enhance the efficacy of chemotherapy regimens and improve patient chemotherapeutic response, and even treat multidrug resistance. However, there are several challenges associated with methylated ctDNA, such as low sensitivity and specificity at single-target sites, limited association between some gastric cancer subtypes and ctDNA, off-target risks, and the lack of large-scale and high-quality clinical research evidence. This review mainly summarizes current research on the methylation status of ctDNA in gastric cancer and connects these findings to early screening, recurrence monitoring, and potential treatment opportunities for gastric cancer. With advances in technology and the deepening of interdisciplinary research, ctDNA detection will reveal more disease information and become an essential foundation for gastric cancer research and precision medicine treatment.

Advancements in Telehealth: Enhancing Breast Cancer Detection and Health Automation through Smart Integration of IoT and CNN Deep Learning in Residential and Healthcare Settings

The rapid evolution of telehealth, or telemedicine, has spurred crucial technological advancements aimed at addressing the early stages of complex cancer conditions, where conventional diagnostic methods face challenges. This research introduces a cancer detection system that utilizes Internet of Things (IoT)-based patient records and machine learning. The primary objective is to automate real-time breast cancer monitoring and detection in residential institutions and smart hospitals, thus enhancing the delivery of quality cancer healthcare. Background: Traditional diagnostic methods, particularly physical inspection, exhibit inherent limitations in identifying breast cancer at early stages. This research responds to this challenge by leveraging innovative technologies, such as IoT and deep learning-based techniques, to overcome the constraints of conventional approaches. Objective: The primary goal of this study is to develop and implement a cancer detection system that integrates IoT-based patient records and machine learning for real-time breast cancer monitoring in residential and healthcare settings. Method: The research employs a synergistic combination of IoT technology for collecting images of residential users and Convolutional Neural Network (CNN), a deep learning technique, for early cancer prediction. The focus lies on contributing to the overall well-being of individuals who may unknowingly be living with cancer. Result: Simulated outcomes after 25 epochs are presented, emphasizing the training accuracy of the model and its validation accuracy using the proposed VGG16 classifier. Graphical representations of the results indicate consistent performance metrics, with both validation and training accuracy exceeding 99%. Specifically, the training accuracy measures at an impressive 99.64%, while the validation accuracy stands at 99.12%. Main Findings: The study demonstrates the effectiveness of the integrated IoT and deep learning techniques in achieving high accuracy rates for early breast cancer prediction. The findings affirm the potential of this approach to assist dermatologists in identifying breast malignancies at treatable stages. Conclusion: This research establishes a foundational framework for the integration of IoT and deep learning techniques, presenting a promising avenue for advancing early cancer detection in smart healthcare systems. The proposed cancer detection system holds significant potential for improving healthcare outcomes and contributing to the overall well-being of individuals at risk of breast cancer.

Integrating Tumor and Nodal Radiomics to Predict the Response to Neoadjuvant Chemotherapy and Recurrence Risk for Locally Advanced Gastric Cancer.

To develop and evaluate machine learning models using tumor and nodal radiomics features for predicting the response to neoadjuvant chemotherapy (NAC) and recurrence risk in locally advanced gastric cancer (LAGC). Early and accurate response prediction is vital to stratify LAGC patients and select proper candidates for NAC. A total of 218 patients with LAGC undergoing NAC followed by gastrectomy were enrolled in our study and were randomly divided into a training cohort (n = 153) and a validation cohort (n = 65). We extracted 1316 radiomics features from the volume of interest of the primary lesion and maximal lymph node on venous phase CT images. We built 3 radiomics signatures for distinguishing good responders and poor responders based on tumor radiomics (TR), nodal radiomics (NR), and a combination of the two (TNR), respectively. A nomogram was then developed by integrating the radiomics signature and clinical factors. Kaplan- Meier survival curves were used to evaluate the prognostic value of the nomogram. The TNR signature achieved improved predictive value, with AUCs of 0.755 and 0.744 in the training and validation cohorts. Our proposed nomogram model (TNRN) showed a good performance for GR prediction in the prediction efficacy, calibration ability, and clinical benefit, with AUCs of 0.779 and 0.732 in the training and validation cohorts, superior to the clinical model. Moreover, the TNRN could accurately classify the patients into high-risk and low-risk groups in both training and validation cohorts with regard to postoperative recurrence and metastasis. The TNRN performed well in identifying good responders and provided valuable information for predicting progression-free survival time (PFS) in patients with LAGC who underwent NAC.

Exploring T-cell exhaustion features in Acute myocardial infarction for a Novel Diagnostic model and new therapeutic targets by bio-informatics and machine learning

BackgroundT-cell exhaustion (TEX), a condition characterized by impaired T-cell function, has been implicated in numerous pathological conditions, but its role in acute myocardial Infarction (AMI) remains largely unexplored. This research aims to identify and characterize all TEX-related genes for AMI diagnosis.MethodsBy integrating gene expression profiles, differential expression analysis, gene set enrichment analysis, protein-protein interaction networks, and machine learning algorithms, we were able to decipher the molecular mechanisms underlying TEX and its significant association with AMI. In addition, we investigated the diagnostic validity of the leading TEX-related genes and their interactions with immune cell profiles. Different types of candidate small molecule compounds were ultimately matched with TEX-featured genes in the “DrugBank” database to serve as potential therapeutic medications for future TEX-AMI basic research.ResultsWe screened 1725 differentially expressed genes (DEGs) from 80 AMI samples and 71 control samples, identifying 39 differential TEX-related transcripts in total. Functional enrichment analysis identified potential biological functions and signaling pathways associated with the aforementioned genes. We constructed a TEX signature containing five hub genes with favorable prognostic performance using machine learning algorithms. In addition, the prognostic performance of the nomogram of these five hub genes was adequate (AUC between 0.815 and 0.995). Several dysregulated immune cells were also observed. Finally, six small molecule compounds which could be the future therapeutic for TEX in AMI were discovered.ConclusionFive TEX diagnostic feature genes, CD48, CD247, FCER1G, TNFAIP3, and FCGRA, were screened in AMI. Combining these genes may aid in the early diagnosis and risk prediction of AMI, as well as the evaluation of immune cell infiltration and the discovery of new therapeutics.

Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites

Online reviews have become an important source of information for users before making an informed purchase decision. Early reviews of a product tend to have a high impact on the subsequent product sales. In this project, we take the initiative to study the behaviour characteristics of early reviewers through their posted reviews on two real-world large e-commerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime into three consecutive stages, namely early, majority and laggards. A user who has posted a review in the early stage is considered as an early reviewer. We quantitatively characterize early reviewers based on their rating behaviours, the helpfulness scores received from others and the correlation of their reviews with product popularity. We have found that (1) an early reviewer tends to assign a higher average rating score; and (2) an early reviewer tends to post more helpful reviews. Our analysis of product reviews also indicates that early reviewers’ ratings and their received helpfulness scores are likely to influence product popularity. By viewing review posting process as a multiplayer competition game, we propose a novel margin-based embedding model for early reviewer prediction. Extensive experiments on two different e-commerce datasets have shown that our proposed approach outperforms a number of competitive baselines. In our project we have used algorithms like Decision Tree (DT) and Multi-Layer Perceptron (MLP). All are measured in terms of accuracy

Early cognitive dysfunction development immunological predictors in the ischemic stroke acute period

The purpose of the study is activity identification CXC family cytokines in patients with cognitive impairments in the acute period of ischemic stroke.Material and methods. 78 patients with diagnosis “Ischemic stroke” were examined. Depending on cognitive impairment (Montreal Cognitive Assessment (MoCA)) patients were divided into two groups: the 1st group — 58 patients with cognitive decline (MoCA ≤ 25 points); the 2nd group — 20 patients without cognitive decline. Neuropsychologic testing was performed on the second day of hospitalisation and included episodic memory, executive function, speech, gnosis, praxis and IQCODE parameters examination. Laboratory diagnosis consisted of level assessing of CXC family chemokines (CXCL10, CXCL11, CXCL9, CXCL1, CXCL8) and TNF-α cytokine in patients’ plasma on the second day of hospitalisation. Statistical analysis was employed using the Python programing language and its libraries Pandas and SciPy.Results. Statistical analysis revealed the highest level of IP-10/CXCL10 chemokines (p = 0.002) and Gro-a/CXCL1 (p = 0.044) in patients of the 1st group, statistically significant correlations of MoCA and IQCODE with IP-10/CXCL10 and Gro-a/CXCL1 concentrations, correlations of IP-10/CXCL10 concentrations with semantic information processing functions (r = –0.512), subject gnosis (r = –0.211), memory (r = 0.275), speech (r = –0.400), and Gro-a/ CXCL1 level with semantic information processing (r = –0.418).Conclusion. The study of chemokines of the CXC cluster represents a relevant and promising direction in the diagnosis and assessment of progression of early post-stroke cognitive impairment of mixed genesis due to minimal invasiveness and high specificity. Further studies are needed to verify CXCL chemokines, particularly IP-10/CXCL10 and Gro-a/CXCL1 as potential molecular markers of neurological damage in neurodegenerative and inflammatory diseases of the central nervous system.

Diagnostic Accuracy of DNA-Methylation in Detection of Cervical Dysplasia: Findings from a Population-Based Screening Program

Background: Epigenetic biomarkers in cancer have emerged as promising tools for early detection, prognosis, and treatment response prediction. In cervical cells, hypermethylation of the host and viral HPV-genome increases with the severity of lesions, providing a useful biomarker in the triage of hr-HPV-positive women and during treatment. The present study focuses on evaluating the clinical performance of the FAM19A4/miR124-2 methylation test in a population-based cervical screening program. Methods: Previously collected cervical samples, after bisulfite-converted DNA, were analyzed by PreCursor-M+ kit (distributed by Fujirebio Europe), for DNA methylation. The sensitivity, specificity, and negative/positive predictive values of DNA methylation were compared to histology, colposcopy, the HPV-DNA test, and cytology results. Results: Among the 61-sample set, the specificity of methylation vs. positive histology (≥CIN2) and colposcopy (≥G2) were 87% and 90%, whereas the sensitivity was 50% and 33.3%, respectively. The combination of methylation analysis with standard methods increases diagnostic accuracy. Conclusions: Overall, we found a good specificity of DNA methylation in comparison to currently used techniques. Further larger studies could support the use of FAM19A4/miR124-2 as reliable biomarkers in the prevention of cervical cancer as triage in the screening protocol.