Development Of Biomarkers Research Articles

Identification of extracellular matrix proteins in plasma as a potential biomarker for intervertebral disc degeneration.

Recently, there has been significant focus on extracellular matrix proteolysis due to its importance in the pathological progression of intervertebral disc degeneration (IVDD). The present study investigates the circulating levels of extracellular matrix proteins in the plasma of IVDD and determines their potential relevance as biomarkers in disc degeneration. Global proteomic analysis was performed in the plasma samples of 10 healthy volunteers (HV) and 10 diseased subjects (DS) after depletion of highly abundant proteins such as albumin and IgG. We identified 144 and 135 matrix-associated proteins in plasma samples from healthy volunteers (HV) and patients with disc degeneration (DS), respectively. Among these, 49 of the matrix-associated proteins were identical to the proteins found in intervertebral disc (IVD) tissues retrieved from the in-house library. Applying stringent parameters, we selected 28 proteins, with 26 present in DS and 21 in HV. 19 proteins were found common between the groups, two of which-aggrecan (ACAN) and fibulin 1 (FBLN1) - showed statistically significant differences. Specifically, ACAN was up-regulated and FBLN1 was down-regulated in the DS-plasma. In particular, DS-plasma exhibited specific expression of collagen type 2a1 (COL2A1), native to the nucleus pulposus. The distinct presence of collagen type 2a1 and the elevated expression of aggrecan in IVDD plasma may serve as the basis for the development of a potential biomarker for monitoring the progression of disc degeneration.

Quantification of Replacement Fibrosis in Aortic Stenosis: A Narrative Review on the Utility of Cardiovascular Magnetic Resonance Imaging

Aortic stenosis (AS) is associated with the development of replacement myocardial fibrosis/scar. Given the dose-dependent relationship between scar and clinical outcomes after aortic valve replacement (AVR) surgery, scar quantity may serve as an important risk-stratification tool to aid decision-making on the optimal timing of AVR. Scar is non-invasively assessed and quantified by cardiovascular magnetic resonance (CMR) imaging. Several quantification techniques exist, and consensus on the optimal technique is lacking. These techniques range from a visual manual method to fully automated ones. This review describes the different scar quantification techniques used and highlights their strengths and shortfalls within the context of AS. The two most commonly used techniques in AS include the semi-automated signal threshold versus reference mean (STRM) and full-width half-maximum (FWHM) techniques. The accuracy and reproducibility of these techniques may be hindered in AS by the coexistence of diffuse interstitial fibrosis and the presence of relatively small, non-bright scars. The validation of these techniques against histology, which is the current gold standard for scar quantification in AS, is limited. Based on the best current evidence, the STRM method using a threshold of three standard deviations above the mean signal intensity of remote myocardium is recommended. The high reproducibility of the FWHM technique in non-AS cohorts has been shown and merits further evaluation within the context of AS. Future directions include the use of quantitative T1 mapping for the detection and quantification of scar, as well as the development of serum biomarkers that reflect the fibrotic status of the myocardium in AS.

AI-Enabled Ultra-large Virtual Screening Identifies Potential Inhibitors of Choline Acetyltransferase for Theranostic Purposes.

Alzheimer's disease (AD) and related dementias are among the primary neurological disorders and call for the urgent need for early-stage diagnosis to gain an upper edge in therapeutic intervention and increase the overall success rate. Choline acetyltransferase (ChAT) is the key acetylcholine (ACh) biosynthesizing enzyme and a legitimate target for the development of biomarkers for early-stage diagnosis and monitoring of therapeutic responses. It is also a theranostic target for tackling colon and lung cancers, where overexpression of non-neuronal ChAT leads to the production of acetylcholine, which acts as an autocrine growth factor for cancer cells. Theranostics is a hybrid of diagnostics and therapeutics that can be used to locate cancer cells using radiotracers and kill them without affecting other healthy tissues. Traditional virtual screening protocols have a lot of limitations; given the current rate of chemical database expansion exceeding billions, much faster screening protocols are required. Deep docking (DD) is one such platform that leverages the power of deep neural network (DNN)-based virtual screening, empowering researchers to dock billions of molecules in a speedy, yet explicit manner. Here, we have screened 1.3 billion compounds library from the ZINC20 database, identifying the best-performing hits. With each iteration run where the first iteration gave ∼116 million hits, the second iteration gave ∼3.7 million hits, and the final third iteration gave 168,447 hits from which further refinement gave us the top 5 compounds as potential ChAT inhibitors. The discovery of novel ChAT inhibitors will enable researchers to develop new probes that can be used as novel theranostic agents against cancer and as early-stage diagnostics for the onset of AD, for timely therapeutic intervention to halt the further progression of AD.

Using Apple Watches to Monitor Health and Behaviors of Individuals with Cognitive Impairment: A Case Series Study.

This study explores the potential of developing digital biomarkers from wearables for monitoring individuals with Alzheimer's Disease and Related Dementias, focusing on the feasibility of using Apple Watches for tracking health and behaviors in older adults with cognitive impairment. Data collection used the Amissa Health technology stack, which passively collects time-series data from smartwatches and provides a high-frequency cloud database for secure data storage, query, and visualization by clinicians and researchers. The platform consists of i) AmissaWear, a software app that runs on smartwatches and sends information to a cloud database using a secure API; and ii) AmissaOrbis, a centralized cloud portal for the collected data. Each participant was provided an Apple Watch configured to collect steps, calories burned, accelerometer and gyroscope readings, heart rate, and sleep information. Seven participants, with cognitive impairment diagnosed by a neurologist, were enrolled in the study from December 2023 through June 2024. The watches successfully collected more than 700,000 observations during the study. Each observation contains data recorded from over a dozen sensors (e.g., heart rate, pedometer, gyroscope, accelerometer). The participants wore Apple Watches for an average of 11.48 hours/day for 84.91% of days during a 6-month period without a decrease in usage over time. Overall, the technology yielded high wear adherence and participation within this pilot. This study demonstrates the feasibility of using widely available Apple Watches for continuous monitoring of individuals with cognitive impairment and provides insights into their daily health and activity patterns, which could aid in future development of digital biomarkers.

MiR-3065-5p and miR-26a-5p as Clinical Biomarkers in Colorectal Cancer: A Translational Study

Background/Objectives: The prognosis of colorectal cancer (CRC) is mainly based on the clinical stage; however, CRC is considered a complex disease due to its molecular heterogeneity. The development of novel biomarkers to improve patients’ diagnosis and prognosis remains fundamental. Methods: A cohort of forty-nine CRC patients from the National Cancer Institute of Mexico was included to collect clinical and miRNA expression data. The expression of a group of miRNAs was compared between CRC and non-tumoral adjacent tissues. Prognosis assessment considering each miRNA expression was tested using Kaplan–Meier survival curves and Cox regressions. Statistical significance was defined as p ≤ 0.05. Trial registration: Retrospective study No.2021/046. Results: miR-3065-5p and miR-26a-5p expression differed between non-tumoral adjacent and tumoral tissues (p = 0.02). In terms of overall survival (OS), patients with low expression of miR-3065-5p had a median OS of 70 months, while patients with high levels did not reach the median OS (p = 0.041). Male patients with low expression of this miRNA had an OS of 70 months, whereas patients with high levels did not reach the median OS (p = 0.050). Under uni-multivariate analysis, clinical stage (HR: 1.30, CI 1.23–2.30; p: 0.001) and low levels of miR-3065-5p (HR: 1.30, CI 1.23–2.30; p: 0.001) were determined as predictor factors of OS. To this end, we designed the “Prognosis miRNAs assessment in cancer” (PROMIR-C) algorithm, which integrated clinical features with miR-3065-5p expression levels. Conclusions: These findings support the clinical utility of miR-26a-5p and miR-3065-5p in the diagnosis and prognosis of CRC. PROMIR-C is a fundamental tool for clinicians in treatment decision-making, prognosis assessment, and outcome of CRC.

Exploring the diagnostic potential of miRNA signatures in the Fabry disease serum: A comparative study of automated and manual sample isolations.

Fabry disease, an X-linked lysosomal storage disorder caused by galactosidase α (GLA) gene mutations, exhibits diverse clinical manifestations, and poses significant diagnostic challenges. Early diagnosis and treatment are crucial for improved patient outcomes, pressing the need for reliable biomarkers. In this study, we aimed to identify miRNA candidates as potential biomarkers for Fabry disease using the KingFisher™ automated isolation method and NanoString nCounter® miRNA detection assay. Clinical serum samples were collected from both healthy subjects and Fabry disease patients. RNA extraction from the samples was performed using the KingFisher™ automated isolation method with the MagMAX mirVanaTM kit or manually using the Qiagen miRNeasy kit. The subsequent NanoString nCounter® miRNA detection assay showed consistent performance and no correlation between RNA input concentration and raw count, ensuring reliable and reproducible results. Interestingly, the detection range and highly differential miRNA between the control and disease groups were found to be distinct depending on the isolation method employed. Nevertheless, enrichment analysis of miRNA-targeting genes consistently revealed significant associations with angiogenesis pathways in both isolation methods. Additionally, our investigation into the impact of enzyme replacement therapy on miRNA expression indicated that some differential miRNAs may be sensitive to treatment. Our study provides valuable insights to identify miRNA biomarkers for Fabry disease. While different isolation methods yielded various detection ranges and highly differential miRNAs, the consistent association with angiogenesis pathways suggests their significance in disease progression. These findings lay the groundwork for further investigations and validation studies, ultimately leading to the development of non-invasive and reliable biomarkers to aid in early diagnosis and treatment monitoring for Fabry disease.

Multiomic and clinical analysis of multiply recurrent meningiomas reveals risk factors, underlying biology, and insights into evolution.

An important subset of meningiomas behaves aggressively and is characterized by multiple recurrences. We identify clinical, genetic, and epigenetic predictors of multiply recurrent meningiomas (MRMs) and evaluate the evolution of these meningiomas in patient-matched samples. On multivariable binomial logistic regression, MRMs were significantly associated with male sex (P = 0.012), subtotal resection (P = 0.001), higher number of meningiomas on presentation (P = 0.017), and histopathological sheeting (P = 0.002). Multiomic analysis of primary meningiomas revealed that MRMs have greater copy number losses (P = 0.0313) and increased DNA methylation (P = 0.0155). In meningioma cells with knockdown of EDNRB, a locus with greater promoter methylation and decreased gene expression in MRMs had increased proliferation (P < 0.0001). MRM recurrences were found to be similar to primaries but have a greater burden of copy number gains (P < 0.0001) and increased methylation (P = 0.0045). This clinical and multiomic investigation of MRMs harbors implications for the future development of biomarkers and therapeutic agents for these challenging tumors.

New mechanisms in diisocyanate-mediated allergy/toxicity: are microRNAs in play?

To describe recent findings of diisocyanate-mediated mechanisms in allergy and toxicology by addressing the role of microRNA (miR) in immune responses that may contribute to the development of occupational asthma (OA). Studies of diisocyanate asthma have traditionally focused on the immune and inflammatory patterns associated with diisocyanate exposures; however, recognized knowledge gaps exist regarding the detailed molecular mechanism(s) of pathogenesis. Recent studies demonstrate the critical role endogenous microRNAs play as gene regulators in maintaining homeostasis of the human body, and in the pathophysiology of many diseases including asthma. Given that diisocyanate-OA shares many pathophysiological characteristics with asthma, it is likely that miR-mediated mechanisms are involved in the pathophysiology of diisocyanate-OA. Recent reports have shown that changes in expression of endogenous miRs are associated with exposure to the occupationally relevant diisocyanates, toluene diisocyanate (TDI) and methylene diphenyl diisocyanate (MDI). Continued mechanistic study of these relevant miRs may lead to the development of novel biomarkers of occupational exposure and/or provide efficacious targets for therapeutic strategies in diisocyanate asthma. The molecular mechanisms underlying diisocyanate-OA pathophysiology are heterogeneous and complicated. In this review, we highlight recent research into the roles and potential regulation of miRs in diisocyanate-OA.

Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform.

The use of digital biomarkers through remote patient monitoring offers valuable and timely insights into a patient's condition, including aspects such as disease progression and treatment response. This serves as a complementary resource to traditional health care settings leveraging mobile technology to improve scale and lower latency, cost, and burden. Smartphones with embedded and connected sensors have immense potential for improving health care through various apps and mobile health (mHealth) platforms. This capability could enable the development of reliable digital biomarkers from long-term longitudinal data collected remotely from patients. We built an open-source platform, RADAR-base, to support large-scale data collection in remote monitoring studies. RADAR-base is a modern remote data collection platform built around Confluent's Apache Kafka to support scalability, extensibility, security, privacy, and quality of data. It provides support for study design and setup and active (eg, patient-reported outcome measures) and passive (eg, phone sensors, wearable devices, and Internet of Things) remote data collection capabilities with feature generation (eg, behavioral, environmental, and physiological markers). The back end enables secure data transmission and scalable solutions for data storage, management, and data access. The platform has been used to successfully collect longitudinal data for various cohorts in a number of disease areas including multiple sclerosis, depression, epilepsy, attention-deficit/hyperactivity disorder, Alzheimer disease, autism, and lung diseases. Digital biomarkers developed through collected data are providing useful insights into different diseases. RADAR-base offers a contemporary, open-source solution driven by the community for remotely monitoring, collecting data, and digitally characterizing both physical and mental health conditions. Clinicians have the ability to enhance their insight through the use of digital biomarkers, enabling improved prevention, personalization, and early intervention in the context of disease management.

ReMiND: Recovery of missing neuroimaging using diffusion models with application to Alzheimer’s disease

Abstract Missing data is a significant challenge in medical research. In longitudinal studies of Alzheimer’s disease (AD) where structural magnetic resonance imaging (MRI) is collected from individuals at multiple time points, participants may miss a study visit or drop out. Additionally, technical issues such as participant motion in the scanner may result in unusable imaging data at designated visits. Such missing data may hinder the development of high-quality imaging-based biomarkers. To address the problem of missing MRI data in studies of AD, we introduced a novel 3D diffusion model specifically designed for imputing missing structural MRI (Recovery of Missing Neuroimaging using Diffusion models (ReMiND)). The model generates a whole-brain image conditional on a single structural MRI observed at a past visit or conditional on one past and one future observed structural MRI relative to the missing observation. The performance of models was compared with two alternative imputation approaches: forward filling and image generation using variational autoencoders. Experimental results show that our method can generate 3D structural MRI with high similarity to ground-truth images at designated visits. Furthermore, images generated using ReMiND show relatively lower differences in volume estimation between the imputed and observed images compared to images generated by forward filling or autoencoders. Additionally, ReMiND provides more accurate estimated rates of atrophy over time in important anatomical brain regions than the two comparator methods. Our 3D diffusion model can impute missing structural MRI data at a single designated visit and outperforms alternative methods for imputing whole-brain images that are missing from longitudinal trajectories.

Unraveling the potential biomarkers of immune checkpoint inhibitors in advanced ovarian cancer: a comprehensive review.

The ongoing research on the role of immunotherapy in advanced ovarian cancer (OC) and current clinical trials indicate that patients shown limited response to immune checkpoint inhibitor (ICI) monotherapy. When combined with other treatments or drugs, the efficacy of immunotherapy will be significantly improved. Biomarkers can be used to identify patients with better responses, thereby improving the precision and efficacy of immunotherapy. Key biomarkers for advanced OC include homologous repair deficiency, programmed death-ligand (PD-L) 1 expression, chemokines, and tumor infiltrating lymphocytes. These biomarkers could be applied in the future to select the most suitable patient populations. This review comprehensively examines the research and development of biomarkers in OC immunotherapy from three omics perspectives: genomics, transcriptomics, and proteomics, which may provide guidance for the effectiveness of OC immunotherapy strategies.

Identification of a pyroptosis-related long noncoding RNA signature for determining the prognosis and immune status of hepatocellular carcinoma patients.

Despite improvements in cancer screening and diagnosis, hepatocellular carcinoma (HCC) is still diagnosed at an advanced stage, and the prognosis is worse than that of early HCC patients. Therefore, better molecular markers and therapeutic targets in HCC are required. We investigated the predictive value of pyroptosis-related long noncoding RNAs (lncRNAs) in HCC and the effects of these lncRNAs on the immune microenvironment of HCC. RNA sequencing data of HCC patients were extracted from The Cancer Genome Atlas (TCGA) database to identify differentially expressed pyroptosis-related lncRNAs related to overall survival (OS). A model was established to verify the character of pyroptosis-associated lncRNAs in the tumor microenvironment, and their prognostic value was evaluated. A total of 721 PR lncRNAs were identified based on the analysis of the TCGA database. Univariate Cox analysis revealed 37 survival-related PRlncRNAs with prognostic values. As a result of least absolute shrinkage and selection operator (LASSO) regression analysis, 'ELFN-AS1', AC099850.3, AC073389.3, 'HPN-AS1', AC009283.1, and AL139289.1 showed prognostic value. Kaplan-Meier analysis indicated that the OS of the high-risk set was worse than those of the low-risk set in both the training and testing cohorts. Univariate and multivariate analyses revealed that the risk score was a better independent prognostic factor than the stage. The precision of the lncRNA signature was confirmed using receiver operating characteristic curve (ROC) analysis. Immuneand metabolism-related pathways were enriched in both the lowand high-risk groups. Gene set enrichment analysis suggested that the identified lncRNAs regulate HCC tumorigenesis and prognosis by modulating metabolism. Various algorithms were used to confirm the significant differences in immune cells between these 2 groups. These findings could contribute to the development and validation of favorable biomarkers, improve the prognosis and survival of HCC, and help in developed individualized treatment plans for HCC.

A Proteomics Pipeline for Generating Clinical Grade Biomarker Candidates from Data-Independent Acquisition Mass Spectrometry (DIA-MS) Discovery.

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package that uses data-independent acquisition analysis from a discovery cohort to select precursors, peptides, and proteins that adhere to analytical criteria required for established targeted assays. TEAQ was applied to DIA-MS data from plasma samples acquired on a new high resolution accurate mass (HRAM) mass spectrometry platform where precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation based on 8- or 11-point loading curves at three throughputs. This data can be used as a general resource for developing other targeted assays. TEAQ analysis of data from a case and control cohort for inflammatory bowel disease (n=492) identified 1110 signature peptides for 326 quantifiable proteins from the 1179 identified proteins. Applying TEAQ analysis to discovery data will streamline targeted assay development and the transition to validation and clinical studies.

A Proteomics Pipeline for Generating Clinical Grade Biomarker Candidates from Data‐Independent Acquisition Mass Spectrometry (DIA‐MS) Discovery

Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package that uses data‐independent acquisition analysis from a discovery cohort to select precursors, peptides, and proteins that adhere to analytical criteria required for established targeted assays. TEAQ was applied to DIA‐MS data from plasma samples acquired on a new high resolution accurate mass (HRAM) mass spectrometry platform where precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra‐protein correlation based on 8‐ or 11‐point loading curves at three throughputs. This data can be used as a general resource for developing other targeted assays. TEAQ analysis of data from a case and control cohort for inflammatory bowel disease (n=492) identified 1110 signature peptides for 326 quantifiable proteins from the 1179 identified proteins. Applying TEAQ analysis to discovery data will streamline targeted assay development and the transition to validation and clinical studies.

Targeted Drug Screening Leveraging Senescence-Induced T-Cell Exhaustion Signatures in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the sixth most prevalent cancer and a leading cause of cancer-related mortality globally, with most patients diagnosed at advanced stages and facing limited early treatment options. This study aimed to identify characteristic genes associated with T-cell exhaustion due to senescence in hepatocellular carcinoma patients, elucidating the interplay between senescence and T-cell exhaustion. We constructed prognostic models based on five signature genes (ENO1, STMN1, PRDX1, RAN, and RANBP1) linked to T-cell exhaustion, utilizing elastic net regression. The findings indicate that increased expression of ENO1 in T cells may contribute to T-cell exhaustion and Treg infiltration in hepatocellular carcinoma. Furthermore, molecular docking was employed to screen small molecule compounds that target the anti-tumor effects of these exhaustion-related genes. This study provides crucial insights into the diagnosis and treatment of hepatocellular carcinoma, establishing a strong foundation for the development of predictive biomarkers and therapeutic targets for affected patients.

Neuroimaging-Based Brain Morphometry in Alzheimer’s Disease

Background/Objectives: Alzheimer’s disease (AD) is a leading cause of death worldwide, affecting millions of older Americans and resulting in a substantial economic burden. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) aims to investigate and develop treatments for AD. Methods: This study included 60 participants, divided equally into AD and control cohorts, and utilized magnetic resonance imaging (MRI) scans to detect gray matter volumetric alterations, a key biomarker of AD. The participants’ cortical volume and surface area were quantified using an automated pipeline in MIMICS (Materialise Interactive Medical Imaging Control System). Results: A multivariate regression analysis was conducted to explore the relationship between cortical measurements and potential factors influencing AD susceptibility. The study found that both cortical volume and surface area were statistically significant predictors of AD (p = 0.0004 and p = 0.011, respectively). Age was also a significant factor, with the 65–70 age group showing the strongest association (p &lt; 0.001). The model achieved an accuracy of 0.68 in predicting AD. Conclusions: While voxel-based morphometry (VBM) using MIMICS showed promise, further development of the automated pipeline could enhance accuracy and correlation indices. These findings contribute to our understanding of brain atrophy in AD pathophysiology and highlight the potential of MRI morphometry as a tool for AD biomarker development.

Distinct methylome profile of cfDNA in AMI patients reveals significant alteration in cAMP signaling pathway genes regulating cardiac muscle contraction

BackgroundThe role of epigenetics in cardiovascular diseases has paved the way for innovative therapeutic approaches. Investigating epigenetic changes using cell-free DNA (cfDNA) holds substantial promise beyond mere diagnostics, especially for heart-related conditions like acute myocardial infarction (AMI), where obtaining tissue samples is a challenge. This study explores the methylation patterns of cfDNA in AMI patients and compares them with genomic DNA (gDNA) from the same individuals, aiming to evaluate the effectiveness of cfDNA as a valuable resource for studying heart-related diseases.MethodologyWe generated global methylome profiles of cfDNA and gDNA from 25 AMI patients using EM-Seq. Tissue deconvolution analysis was performed to estimate tissue specificity based on the methylation patterns. Differentially methylated loci were identified and explored to understand AMI pathophysiology.ResultsComparative analysis of cfDNA and gDNA methylation patterns in AMI patients reveals cfDNA holds more significance than gDNA. Principal component analysis revealed distinct clusters for cfDNA and gDNA, indicating distinct methylome profiles. cfDNA originated from multiple sources, predominantly from neutrophils (~ 75%) and about 10% from the left atrium, highlighting cardiac-specific changes. In contrast, immune cells are the major source of gDNA, indicative of inflammatory responses. Gene set enrichment analysis (GSEA) associates cfDNA methylation patterns with pathways related to cardiac muscle contraction, inflammation, hypoxia, and lipid metabolism. The affected genes include G protein-coupled receptors (GHSR, FFAR2, HTR1A, and VIPR2) that are part of the cAMP signaling pathway.ConclusionEpigenetic changes in cfDNA are more specific to cardiac tissue compared to those in gDNA, providing better insights into the molecular mechanisms involved in AMI. Genes that are differentially methylated in cfDNA and regulate core pathways, such as cAMP signaling, could be targeted for clinical applications, including the development of effective biomarkers and therapeutic targets.

CURRENT STATUS OF DIAGNOSTIC INFECTIONS CAUSED BY HELICOBACTER PYLORI

The article is devoted to the review of the latest scientific research related to methods of diagnosis of infections caused by Helicobacter pylori (H. pylori) bacteria. Based on the review of research in the field of diagnosis of infections caused by Helicobacter pylori, modern approaches used in global medical practice were analysed. In modern times, diagnosing H. pylori is a significant problem given the variety of methods and their effectiveness, and there is no single universal approach for detecting this pathogenic bacterium. Infection caused by the bacterium Helicobacter pylori is one of the world's most common stomach and intestinal infections. More than half of the world's population is infected with this bacterium, which often leads to severe diseases such as stomach and duodenal ulcers, as well as stomach cancer. Modern medicine develops every day, providing new methods of diagnosis and treatment. Significant scientific and technological changes are taking place in Helicobacter pylori diagnostics, which open up new perspectives and opportunities. One of the leading prospects is the search for more accurate, fast and convenient diagnostic methods that would be less invasive and more accessible to patients. The development of molecular and genetic methods, in particular polymerase chain reaction (PCR), opens up opportunities for accurate and rapid detection of H. pylori and the determination of its virulent properties. Another perspective is the development of accurate biomarkers and prognostic methods that will predict the risk of developing complications due to H. pylori infection. This will help plan and individualise treatment strategies for patients with this infection. The search for new drugs to eradicate H. pylori is also a critical prospect, especially considering the growing problem of antibiotic resistance. The article compares invasive and non-invasive methods of disease research, highlighting their advantages and limitations. Therefore, leading scientists and practitioners in the field of gastroenterology are recommended to use two or more methods, in particular, to combine invasive and non-invasive approaches, to use a serological method for population screening, to control and eradicate H. pylori mainly by non-invasive methods, using PCR as the most accurate method for diagnosing H. pylori, as well as to determine the molecular and genetic characteristics of the microorganism and assess its virulence to form an idea of the further course and prognosis of the disease. The purpose of the work is to systematically review modern methods of diagnosing infections caused by the bacterium Helicobacter pylori (H. pylori) to evaluate their effectiveness, advantages and limitations. An overview of the state of the art in diagnosing Helicobacter pylori infections is presented, focusing on the various detection methods, their advantages and disadvantages, and their role in the course of the disease and the treatment of this infection. A variety of diagnostic methods are reviewed, including both invasive and non-invasive approaches, including endoscopy, histology, urea breath test (UBT), polymerase chain reaction (PCR)-based methods, next-generation sequencing (NGS), biosensors, and MALDI-TOF MS . The advantages and limitations of each of the methods, as well as their effectiveness in the diagnosis of H. pylori infections, are discussed. Studies have shown the importance of developing new approaches and their application in practice to improve the diagnosis and treatment of this infection.

Molecular and cell phenotype programs in oral epithelial cells directed by co-exposure to arsenic and smokeless tobacco.

Chronic arsenic exposure can lead to various health issues, including cancer. Concerns have been mounting about the enhancement of arsenic toxicity through co-exposure to various prevalent lifestyle habits. Smokeless tobacco products are commonly consumed in South Asian countries, where their use frequently co-occurs with exposure to arsenic from contaminated groundwater. To decipher the in vitro molecular and cellular responses to arsenic and/or smokeless tobacco, we performed temporal multi-omics analysis of the transcriptome and DNA methylome remodelling in exposed hTERT-immortalized human normal oral keratinocytes (NOK), as well as arsenic and/or smokeless tobacco genotoxicity and mutagenicity investigations in NOK cells and in human p53 knock-in murine embryonic fibroblasts (Hupki MEF). RNAseq results from acute exposures to arsenic alone and in combination with smokeless tobacco extract revealed upregulation of genes with roles in cell cycle changes, apoptosis and inflammation responses. This was in keeping with global DNA hypomethylation affecting genes involved in the same processes in response to chronic treatment in NOK cells. At the phenotypic level, we observed a dose-dependent decrease in NOK cell viability, induction of DNA damage, cell cycle changes and increased apoptosis, with the most pronounced effects observed under arsenic and SLT co-exposure conditions. Live-cell imaging experiments indicated that the DNA damage likely resulted from induction of apoptosis, an observation validated by a lack of exome-wide mutagenesis in response to chronic exposure to arsenic and/or smokeless tobacco. In sum, our integrative omics study provides novel insights into the acute and chronic responses to arsenic and smokeless tobacco (co-)exposure, with both types of responses converging on several key mechanisms associated with cancer hallmark processes. The generated rich catalogue of molecular programs in oral cells regulated by arsenic and smokeless tobacco (co-)exposure may provide bases for future development of biomarkers for use in molecular epidemiology studies of exposed populations at risk of developing oral cancer.

Genetic Variation and Metabolic Basis of Kidney Cancer: New Opportunities for Targeted Therapy

Renal cell carcinoma (RCC) has previously been considered as a single disease. However, it is currently characterized as a heterogeneous group of tumors that differ in histological features, genetic abnormalities, and variable clinical course. In normal cells, energy is produced by the cleavage of chemical bonds in nutrients through the oxidation of fats, proteins, or carbohydrates. Mutational alterations in genes associated with RCC, including VHL, FLCN, PTEN and SDH, lead to abnormal cellular adaptation to changes in oxygen status, iron metabolism and nutrients. The present paper reviews the known genetic abnormalities observed in RCC and their impact on metabolic alterations. Understanding the genetic and metabolic mechanisms underlying RCC is crucial for the development of effective therapies. Targeting specific genetic abnormalities or metabolic pathways represents a promising approach to the RCC treatment. In addition, studies into the metabolic basis of RCC contribute to the development of new biomarkers for early diagnosis and monitoring of the disease. Moreover, investigating the role of VHL, FLCN, PTEN, and SDH genes in the development of RCC provides valuable information on the molecular mechanisms behind the disease. As a result, it may lead to the development of new treatment strategies aimed at restoring the normal function of these genes or compensating for their abnormalities. Overall, an integrated approach to the study of RCC that considers genetic, metabolic, and clinical aspects will ensure that more effective treatments are developed and prognosis for patients with this disease are improved.