Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

Efficiency of electrochemical immuno- vs. apta(geno)sensors for multiple cancer biomarkers detection

The interest in biosensors technology has been constantly growing over the last few years. It is still the biggest challenge to design biosensors able to detect two or more analytes in a single measurement. Electrochemical methods are frequently used for this purpose, mainly due to the possibility of applying two or more different redox labels characterized by independent and distinguished electrochemical signals. In addition to antibodies, nucleic acids (aptamers) have been increasingly used as bioreceptors in the construction of such sensors. Within this review paper, we have collected the examples of electrochemical immuno- and geno(apta)sensors for simultaneous detection of multiple analytes. Based on many published literature examples, we have emphasized the recent application of multiplexed platforms for detection of cancer biomarkers. It has allowed us to compare the progress in design strategies, including novel nanomaterials and amplification of signals, to get as low as possible limits of detection. We have focused on multi-electrode and multi-label strategies based on redox-active labels, such as ferrocene, anthraquinone, methylene blue, thionine, hemin and quantum dots, or metal ions such as Ag+, Pb2+, Cd2+, Zn2+, Cu2+ and others. We have finally discussed the possible way of development, challenges and prospects in the area of multianalyte electrochemical immuno- and geno(apta)sensors.

Conceptual basis for the development of guidance for the use of biomarkers of effect in regulatory risk assessment of chemicals

This Scientific Report was carried out in the context of the self-task mandate (M-2023-00097) of the EFSA's Scientific Committee on 'Guidance on the use of biomarkers of effect in regulatory risk assessment of chemicals'. In the first phase, the project on biomarkers of effect started with a feasibility study (EFSA-Q-2024-00128), with the intention to look closer at definitions and descriptions of biomarkers of effect, as well as to explore several concepts related to the context of application and other scientific principles to be further considered for its development. In addition, relevant activities, initiatives and knowledge in this area were collected and analysed within a complementary mapping study. The outcome of this phase aimed to create a structured basis for future guidance, to identify challenges and to recommend a way forward for its development. The recommendations refer especially to terminologies, the scope of the guidance and several scientific and technical aspects of the selection and interpretation of biomarkers of effect that need to be addressed in future guidance. Moreover, further recommendation refers to the collaborative process to be established with other regulatory organisations that should support the harmonisation and reduce divergencies in the application of methodologies across organisations or sectors.

Deciphering the mechanisms and effects of hyperglycemia on skeletal muscle atrophy

Hyperglycemia, a hallmark of diabetes mellitus, significantly contributes to skeletal muscle atrophy, characterized by progressive muscle mass and strength loss. This review summarizes the mechanisms of hyperglycemia-induced muscle atrophy, examines clinical evidence, and discusses preventive and therapeutic strategies. A systematic search of electronic databases, including PubMed, Scopus, and Web of Science, was conducted to identify relevant papers on hyperglycemic skeletal muscle atrophy. Key mechanisms include insulin resistance, chronic inflammation, oxidative stress, and mitochondrial dysfunction. Crucial molecular pathways involved are Phosphoinositide 3-kinase/Protein kinase B signaling, Forkhead box O transcription factors, the ubiquitin-proteasome system, and myostatin-mediated degradation. Hyperglycemia disrupts normal glucose and lipid metabolism, exacerbating muscle protein degradation and impairing synthesis. Clinical studies support the association between hyperglycemia and muscle atrophy, emphasizing the need for early diagnosis and intervention. Biomarkers, imaging techniques, and functional tests are vital for detecting and monitoring muscle atrophy in hyperglycemic patients. Management strategies focus on glycemic control, pharmacological interventions targeting specific molecular pathways, nutritional support, and tailored exercise regimens. Despite these advances, research gaps remain in understanding the long-term impact of hyperglycemia on muscle health and identifying novel therapeutic targets. The review aims to provide a comprehensive understanding of the mechanisms, clinical implications, and potential therapeutic strategies for addressing hyperglycemia-induced skeletal muscle atrophy.

Role of Proteins in Oncology: Advances in Cancer Diagnosis, Prognosis, and Targeted Therapy-A Narrative Review

Modern oncology increasingly relies on the role of proteins as key components in cancer diagnosis, prognosis, and targeted therapy. This review examines advancements in protein biomarkers across several cancer types, including breast cancer, lung cancer, ovarian cancer, and hepatocellular carcinoma. These biomarkers have proven critical for early detection, treatment response monitoring, and tailoring personalized therapeutic strategies. The article highlights the utility of targeted therapies, such as tyrosine kinase inhibitors and monoclonal antibodies, in improving treatment efficacy while minimizing systemic toxicity. Despite these advancements, challenges like tumor resistance, variability in protein expression, and diagnostic heterogeneity persist, complicating universal application. The review underscores future directions, including the integration of artificial intelligence, advanced protein analysis technologies, and the development of combination therapies to overcome these barriers and refine personalized cancer treatment.

Targeted Chiral Metabolomics of D-Amino Acids: Their Emerging Role as Potential Biomarkers in Neurological Diseases with a Focus on Their Liquid Chromatography-Mass Spectrometry Analysis upon Chiral Derivatization

In neuroscience research, chiral metabolomics is an emerging field, in which D-amino acids play an important role as potential biomarkers for neurological diseases. The targeted chiral analysis of the brain metabolome, employing liquid chromatography (LC) coupled to mass spectrometry (MS), is a pivotal approach for the identification of biomarkers for neurological diseases. This review provides an overview of D-amino acids in neurological diseases and of the state-of-the-art strategies for the enantioselective analysis of chiral amino acids (AAs) in biological samples to investigate their putative role as biomarkers for neurological diseases. Fluctuations in D-amino acids (D-AAs) levels can be related to the pathology of neurological diseases, for example, through their role in the modulation of N-methyl-D-aspartate receptors and neurotransmission. Because of the trace presence of these biomolecules in mammals and the complex nature of biological matrices, highly sensitive and selective analytical methods are essential. Derivatization strategies with chiral reagents are highlighted as critical tools for enhancing detection capabilities. The latest advances in chiral derivatization reactions, coupled to LC-MS/MS analysis, have improved the enantioselective quantification of these AAs and allow the separation of several chiral metabolites in a single analytical run. The enhanced performances of these methods can provide an accurate correlation between specific D-AA profiles and disease states, allowing for a better understanding of neurological diseases and drug effects on the brain.

A new perspective on diagnostic strategies concerning the potential of saliva-based miRNA signatures in oral cancer

Oral cancer, the most prevalent cancer worldwide, is far more likely to occur after the age of forty-five, according to the World Health Organization. Although many biomarkers have been discovered over the years using non-invasive saliva samples, biopsies, and human blood, these biomarkers have not been incorporated into standard clinical practice. Investigating the function of microRNAs (miRNAs) in the diagnosis, aetiology, prognosis, and treatment of oral cancer has drawn more attention in recent years. Though salivary microRNA can act as a window into the molecular environment of the tumour, there are challenges due to the heterogeneity of oral squamous cell carcinoma (OSCC), diversity in sample collection, processing techniques, and storage conditions. The up and downregulation of miRNAs has been found to have a profound role in OSCC as it regulates tumour stages by targeting many genes. As a result, the regulatory functions of miRNAs in OSCC underscore their significance in the field of cancer biology. Salivary miRNAs are useful diagnostic and prognostic indicators because their abnormal expression profiles shed light on tumour behaviour and patient prognosis. In addition to their diagnostic and prognostic value, miRNAs hold promise as therapeutic targets for oral cancer intervention. The current review sheds light on the challenges and potentials of microRNA studies that could lead to a better understanding of oral cancer prognosis, diagnosis, and therapeutic intervention. Furthermore, the clinical translation of OSCC biomarkers requires cooperation between investigators, physicians, regulatory bodies, and business partners. There is much potential for improving early identification, tracking therapy response, and forecasting outcomes in OSCC patients by including saliva-based miRNAs as biomarkers.

MarkerDB 2.0: a comprehensive molecular biomarker database for 2025

MarkerDB (https://markerdb.ca) has become a leading resource for comprehensive information on molecular biomarkers. Over the past 3 years, the database has evolved significantly, reflecting the dynamic landscape of biomarker research and increasing demands from its user community. This year's update, which is called MarkerDB 2.0, introduces key improvements to enhance the database's usability, consistency and the range of biomarkers covered. These improvements include (i) the addition of thousands of new biomarkers and associated health conditions, (ii) the inclusion of many new biomarker types and categories, (iii) upgraded searches and data filtering functionalities, (iv) new features for exploring and understanding biomarker panels and (v) significantly expanded and improved descriptions. These upgrades, along with numerous minor improvements in content, interface, layout and overall website performance, have greatly enhanced MarkerDB's usability and capacity to facilitate biomarker interpretation across various research domains. MarkerDB remains committed to providing a free, publicly accessible platform for consolidated information on a wide range of molecular (protein, genetic, chromosomal and chemical/small molecule) biomarkers, covering diagnostic, prognostic, risk, monitoring, safety and response-related biomarkers. We are confident that these upgrades and updates will improve MarkerDB's user friendliness, increase its utility and greatly expand its potential applications to many other areas of clinical medicine and biomedical research.

Genetic Diversity Landscape in African Population: A Review of Implications for Personalized and Precision Medicine

Introduction

Africa, a continent considered to be the cradle of human beings has the largest genetic diversity among its population than other continents. This review discusses the implications of this high African genetic diversity to the development of personalized and precision medicine.

Methodology

A comprehensive search across PubMed, Google Scholar, Science Direct, DOAJ, AJOL, and the Cochrane Library electronic databases and manual Google searches was conducted using key terms "genetics", "genetic diversity", "Africa", "precision medicine", and "personalized medicine". Updated original and review studies focusing on the implications of African high genetic diversity on personalized and precision medicine were included. Included studies were thematically synthesized to elucidate their positive or negative implications for personalized healthcare, aiming to foster informed clinical practice and scientific inquiry.

Results

African populations' high genetic diversity presents opportunities for personalized and precision medicine including improving pharmacogenomics, understanding gene interactions, discovering new variants, mapping disease genes, creating updated genomic reference panels, and validating biomarkers. However, challenges include underrepresentation in studies, scarcity of reference genomes, inaccuracy of genetic testing and interpretation, and ancestry misclassification. Addressing these requires the establishment of genomic research centers, increasing funding, creating biobanks and repositories, education, infrastructure, and international cooperation to enhance healthcare equity and outcomes through personalized and precision medicine.

Conclusion

High African genetic diversity presents both positive and negative implications for personalized and precision medicine. Deep further research is recommended to harness the challenges and use the opportunities to develop customized treatments.

Exposing telomere length's impact on malnutrition risk among older adults residing in the community: Insights from cross-sectional data analysis

BACKGROUND

Successful aging is associated with an increase in life expectancy. For a better understanding of the aging process, recognize the relationship between telomere length and nutritional status is a novel approach in geriatric science. Telomers shortening coincides with a decrease in life expectancy, and an increased risk of malnutrition-related diseases.

GOALS

The goal of this study was to investigate whether a shorter telomere length is associated with a greater likelihood of malnutrition in community-dwelling older adults.

METHODS

A cross-sectional study with a probabilistic sample of 448 older people aged 60 years old or over, and living in the urban area of an inland Brazilian municipality was conducted. The information was gathered in two stages: a) a personal interview was conducted to obtain sociodemographic, cognitive, and functional autonomy data. The Mini Nutritional Assessment was used to assess the risk of malnutrition. b) a blood sample was taken to proceed with the relative quantitative study of telomere length using real-time qPCR method. The differences between the groups were estimated using Pearson's v2 and Fisher's exact tests. In the data analysis, descriptive statistics and multiple logistic regression were applied.

RESULTS

In 34.15% of the total sample, malnutrition was recognized as a risk factor. Older people with the shortest telomere length had more chances of getting malnutrition (OR = 1.63; IC:95% = 1.04-2.55) compared to those with longer telomeres, independent of age groups, family income, multimorbidity, cognitive decline, and depressive symptoms.

CONCLUSION

The creation of clinical trials and the implementation of therapies to reduce the risk of malnutrition will be aided using the telomere length as an aging innovative biomarker, connected with nutritional status.

Integrating routine blood biomarkers and artificial intelligence for supporting diagnosis of silicosis in engineered stone workers

Engineered stone silicosis (ESS), primarily caused by inhaling respirable crystalline silica, poses a significant occupational health risk globally. ESS has no effective treatment and presents a rapid progression from simple silicosis (SS) to progressive massive fibrosis (PMF), with respiratory failure and death. Despite the use of diagnostic methods like chest x-rays and high-resolution computed tomography, early detection of silicosis remains challenging. Since routine blood tests have shown promise in detecting inflammatory markers associated with the disease, this study aims to assess whether routine blood biomarkers, coupled with machine learning techniques, can effectively differentiate between healthy individuals, subjects with SS, and PMF. To this end, 107 men diagnosed with silicosis, ex-workers in the engineered stone (ES) sector, and 22 healthy male volunteers as controls not exposed to ES dust were recruited. Twenty-one primary biochemical markers derived from peripheral blood extraction were obtained retrospectively from clinical hospital records. Relief-F features selection technique was applied, and the resulting subset of 11 biomarkers was used to build five machine learning models, demonstrating high performance with sensitivities and specificities in the best case greater than 82% and 89%, respectively. The percentage of lymphocytes, the angiotensin-converting enzyme, and lactate dehydrogenase indexes were revealed, among others, as blood biomarkers with significant cumulative importance for the machine learning models. Our study reveals that these biomarkers could detect a chronic inflammatory status and potentially serve as a supportive tool for the diagnosis, monitoring, and early detection of the progression of silicosis.

pH-responsive magnetic CuFe2O4-PMAA nanogel conjugated with amino-modified lignin for controlled breast cancer drug delivery

In this study, a novel magnetic and pH-responsive nanocarrier was developed, incorporating both natural and synthetic polymers, for delivering curcumin (CUR) to breast cancer cells. For this purpose, CuFe2O4@poly(methacrylic acid) (CuFe2O4@PMAA) nanogel was developed and conjugated with amino-modified lignin (Lignin-adipic acid dihydrazide conjugate, Lig-ADH) to achieve the CuFe2O4@PMAA@Lig-ADH nanocarrier. The morphology, structure, and physical properties of the synthesized nanomaterials were examined using a range of techniques, including transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), energy dispersive X-ray (EDX), and vibrating sample magnetometer (VSM). The synthesized nanocarrier exhibited a spherical shape, with an average diameter of approximately 15 nm, and demonstrated good magnetic responsiveness. Moreover, the in vitro drug release was found to be pH-dependent, with an increased release rate in acidic conditions. To evaluate cytotoxicity, the survival of MCF-7 cells was measured using the MTT assay for 24 h. Notably, the synthesized CuFe2O4@PMAA@Lig-ADH@CUR and CUR exhibited significant cytotoxic effects, effectively eliminating MCF-7 cells with IC50 values of 39.80 µg/mL and 4.27 µg/mL, respectively. Also, the significant intracellular uptake of NPs was confirmed by FITC and DAPI staining after 4 h. This research highlighted the potential of CuFe2O4@PMAA@Lig-ADH@CUR as a highly effective nano-delivery system and demonstrated a straightforward method for utilizing renewable lignin.

Machine Learning Models for Tracking Blood Loss and Resuscitation in a Hemorrhagic Shock Swine Injury Model

Hemorrhage leading to life-threatening shock is a common and critical problem in both civilian and military medicine. Due to complex physiological compensatory mechanisms, traditional vital signs may fail to detect patients' impending hemorrhagic shock in a timely manner when life-saving interventions are still viable. To address this shortcoming of traditional vital signs in detecting hemorrhagic shock, we have attempted to identify metrics that can predict blood loss. We have previously combined feature extraction and machine learning methodologies applied to arterial waveform analysis to develop advanced metrics that have enabled the early and accurate detection of impending shock in a canine model of hemorrhage, including metrics that estimate blood loss such as the Blood Loss Volume Metric, the Percent Estimated Blood Loss metric, and the Hemorrhage Area metric. Importantly, these metrics were able to identify impending shock well before traditional vital signs, such as blood pressure, were altered enough to identify shock. Here, we apply these advanced metrics developed using data from a canine model to data collected from a swine model of controlled hemorrhage as an interim step towards showing their relevance to human medicine. Based on the performance of these advanced metrics, we conclude that the framework for developing these metrics in the previous canine model remains applicable when applied to a swine model and results in accurate performance in these advanced metrics. The success of these advanced metrics in swine, which share physiological similarities to humans, shows promise in developing advanced blood loss metrics for humans, which would result in increased positive casualty outcomes due to hemorrhage in civilian and military medicine.

Adrenomedullin as a New Prosperous Biomarker in Infections: Current and Future Perspectives

Adrenomedullin has emerged as a promising biomarker in the field of viral diseases. Numerous studies have demonstrated its potential in assessing disease severity, predicting clinical outcomes, and monitoring treatment response. Adrenomedullin (AM) is a multifaceted peptide implicated in vasodilation, hormone secretion, antimicrobial defense, cellular growth, angiogenesis, and, importantly, chronic pain. AM and related peptides interface with cytoskeletal proteins within neuronal contexts, influencing microtubule dynamics. AM has primarily been utilized in diagnosing diseases of bacterial origin, including sepsis. Nevertheless, there are reports suggesting its utility in diseases of viral origin, and this is the focus of the present study. Furthermore, adrenomedullin has been shown to be elevated in various viral infections, suggesting its role in immune response modulation. Furthermore, AM may contribute to neuronal dysfunction through mechanisms involving immune and inflammatory responses, apoptosis, and disruptions in calcium homeostasis. This review aims to consolidate current knowledge regarding AM and its potential implications in viral diseases, elucidating its diverse roles in neurological pathophysiology. This review highlights the growing importance of adrenomedullin as a biomarker in viral diseases and the need for further functional studies to understand the underlying mechanisms involved.

Exploring the connection between EU-funded research and methodological approaches: insights from a retrospective analysis

BACKGROUND

Over the last two decades, substantial investments have been directed towards supporting fundamental and applied research in Alzheimer's disease (AD), breast cancer (BC), and prostate cancer (PC), which continue to pose significant health challenges. Recently, the Joint Research Centre (JRC) of the European Commission (EC) conducted a retrospective analysis to examine the major scientific advancements resulting from EU-funded research in these disease areas and their impact on society.

METHODS

Building upon this analysis, our subsequent investigation delves into the methodological approaches-both animal and non-animal models and methods-employed in AD, BC, and PC research funded under past EU framework programs (FP5, FP6, FP7, and H2020), and explored the notable research outputs associated with these approaches.

RESULTS

Our findings indicate a prevalent use of animal-based methodologies in AD research, particularly evident in projects funded under H2020. Notably, projects focused on drug development, testing, or repurposing heavily relied on animal models. Conversely, research aimed at clinical trial design, patient stratification, diagnosis and diagnostic tool development, lifestyle interventions, and prevention-outputs with potential societal impact-more frequently utilised non-animal methods. Advanced investigations leveraging imaging, computational tools, biomarker discovery and organ/tissue chip technologies predominantly favoured non-animal strategies.

CONCLUSIONS

These insights highlight a correlation between methodological choices and the translational potential of research outcomes, suggesting the need for a reconsideration of research strategy planning in future framework programs.

Biomarkers From Discovery to Clinical Application: In Silico Pre-Clinical Validation Approach in the Face of Lung Cancer

Background

Clinical biomarkers, allow better classification of patients according to their disease risk, prognosis, and/or response to treatment. Although affordable omics-based approaches have paved the way for quicker identification of putative biomarkers, validation of biomarkers is necessary for translation of discoveries into clinical application.

Objective

Accordingly, in this study, we emphasize the potential of in silico approaches and have proposed and applied 3 novel sequential in silico pre-clinical validation steps to better identify the biomarkers that are truly desirable for clinical investment.

Design

As protein biomarkers are becoming increasingly important in the clinic alongside other molecular biomarkers and lung cancer is the most common cause of cancer-related deaths, we used protein biomarkers for lung cancer as an illustrative example to apply our in silico pre-clinical validation approach.

Methods

We collected the reported protein biomarkers for 3 cases (lung adenocarcinoma-LUAD, squamous cell carcinoma-LUSC, and unspecified lung cancer) and evaluated whether the protein biomarkers have cancer altering properties (i.e., act as tumor suppressors or oncoproteins and represent cancer hallmarks), are expressed in body fluids, and can be targeted by FDA-approved drugs.

Results

We collected 3008 protein biomarkers for lung cancer, 1189 for LUAD, and 182 for LUSC. Of these protein biomarkers for lung cancer, LUAD, and LUSC, only 28, 25, and 6 protein biomarkers passed the 3 in silico pre-clinical validation steps examined, and of these, only 5 and 2 biomarkers were specific for lung cancer and LUAD, respectively.

Conclusion

In this study, we applied our in silico pre-clinical validation approach the protein biomarkers for lung cancer cases. However, this approach can be applied and adapted to all cancer biomarkers. We believe that this approach will greatly facilitate the transition of cancer biomarkers into the clinical phase and offers great potential for future biomarker research.

Measurement of Human and Bovine Exhaled Breath Condensate pH Using Polyaniline-Modified Flexible Inkjet-Printed Nanocarbon Electrodes

The collection, processing, and electrochemical analysis of exhaled breath condensate (EBC) from healthy human and animal subjects is reported on. EBC is a biospecimen potentially rich in biomarkers of respiratory disease. The EBC pH was analyzed potentiometrically using a disposable polyaniline (PANI)-modified inkjet-printed (IJP) carbon electrode. Comparison measurements were performed using a commercial screen-printed carbon (SPC) electrode. The PANI-modified electrodes exhibited reproducible and near-Nernstian responses for pH values between 2 and 9 with slopes from -50 to -60 mV/dec. The PANI-modified IJP carbon electrode exhibited a faster response time and superior reproducibility to the modified SPC electrode. In proof-of-concept studies, the healthy human EBC pH was found to be 6.57 ± 0.09 and the healthy bovine EBC pH was 5.9 ± 0.2. All pH determined using the PANI-modified electrodes were in good agreement with the pH determined using a micro glass pH electrode. An RTube device was used to collect EBC from humans while a modified device was used to collect EBC from calves in the field. EBC volumes of 0.5-2 mL for 5-6 min of tidal breathing were collected from healthy animals. The pH of EBC from healthy calves (17 animals) depends on their age from 1 to 9 weeks with values ranging from 5.3 to 7.2. A distinct alkaline shift was observed for many animals around 20 days of age. The bovine EBC pH also depends on the ambient temperature and humidity at the time of collection. The results indicate that the PANI-modified IJP carbon electrodes outperform commercial SPC and provide reproducible and accurate measurement of pH across various biospecimen types.

Emerging methods and techniques for cancer biomarker discovery

Modern cancer research depends heavily on the identification and validation of biomarkers because they provide important information about the diagnosis, prognosis, and response to treatment of the cancer. This review will provide a comprehensive overview of cancer biomarkers, including their development phases and recent breakthroughs in transcriptomics and computational techniques for detecting these biomarkers. Blood-based biomarkers have great potential for non-invasive tumor dynamics and treatment response monitoring. These include circulating tumor DNA, exosomes, and microRNAs. Comprehensive molecular profiles are provided by multi-omic technologies, which combine proteomics, metabolomics, and genomes to support the identification of biomarkers and the targeting of therapeutic interventions. Genetic changes are detected by next-generation sequencing, and patterns of protein expression are found by protein arrays and mass spectrometry. Tumor heterogeneity and clonal evolution can be understood using metabolic profiling and single-cell studies. It is projected that the use of several biomarkers-genetic, protein, mRNA, microRNA, and DNA profiles, among others-will rise, enabling multi-biomarker analysis and improving individualised treatment plans. Biomarker identification and patient outcome prediction are further improved by developments in AI algorithms and imaging techniques. Robust biomarker validation and reproducibility require cooperation between industry, academia, and doctors. Biomarkers can provide individualized care, meet unmet clinical needs, and enhance patient outcomes despite some obstacles. Precision medicine will continue to take shape as scientific research advances and the integration of biomarkers with cutting-edge technologies continues to offer a more promising future for personalized cancer care.

Therapeutic approaches targeting aging and cellular senescence in Huntington's disease

Huntington's disease (HD) is a devastating neurodegenerative disease that is manifested by a gradual loss of physical, cognitive, and mental abilities. As the disease advances, age has a major impact on the pathogenic signature of mutant huntingtin (mHTT) protein aggregation. This review aims to explore the intricate relationship between aging, mHTT toxicity, and cellular senescence in HD. Scientific data on the interplay between aging, mHTT, and cellular senescence in HD were collected from several academic databases, including PubMed, Google Scholar, Google, and ScienceDirect. The search terms employed were "AGING," "HUNTINGTON'S DISEASE," "MUTANT HUNTINGTIN," and "CELLULAR SENESCENCE." Additionally, to gather information on the molecular mechanisms and potential therapeutic targets, the search was extended to include relevant terms such as "DNA DAMAGE," "OXIDATIVE STRESS," and "AUTOPHAGY." According to research, aging leads to worsening HD pathophysiology through some processes. As a result of the mHTT accumulation, cellular senescence is promoted, which causes DNA damage, oxidative stress, decreased autophagy, and increased inflammatory responses. Pro-inflammatory cytokines and other substances are released by senescent cells, which may worsen the neuronal damage and the course of the disease. It has been shown that treatments directed at these pathways reduce some of the HD symptoms and enhance longevity in experimental animals, pointing to a new possibility of treating the condition. Through their amplification of the harmful effects of mHTT, aging and cellular senescence play crucial roles in the development of HD. Comprehending these interplays creates novel opportunities for therapeutic measures targeted at alleviating cellular aging and enhancing HD patients' quality of life.

Oral and Gingival Crevicular Fluid Biomarkers for Jawbone Turnover Diseases: A Scoping Review

Gingival crevicular fluid (GCF) and oral fluid have emerged as promising diagnostic tools for detecting biomarkers. This review aimed to evaluate the existing literature on using oral fluids as a source of biomarkers for bone turnover diseases affecting the jawbone. A comprehensive search strategy was executed between August 2014 and August 2024 across five major databases (Web of Science, EBSCOhost Dentistry & Oral Sciences Source, Cochrane Library, Scopus, and PubMed) and grey literature sources. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was applied. The screening was facilitated using Rayyan at rayyan.ai and Endnote X20 software tools, culminating in the evaluation of 14,965 citations from databases and 34 from grey literature. Following rigorous scrutiny, 37 articles were selected for inclusion in this review, encompassing diseases such as periodontitis, medication-related osteonecrosis of the jaw (MRONJ), and osteoporosis. The quality of the included observational studies was assessed using the Revised Risk of Bias Assessment Tool for Non-Randomized Studies (RoBANS 2). Interleukin-1 beta (IL-1β), sclerostin, osteoprotegerin (OPG), and interleukin-34 (IL-34) emerged as significant biomarkers in GCF, and they were mainly from periodontitis and osteoporosis. Osteocalcin (OC), IL-1β, tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), OPG, and matrix metalloproteinase-9 (MMP-9) were significant in oral fluid or saliva, and they were from periodontitis, MRONJ, and osteoporosis. These findings underscore the potential use of oral fluids, which are regarded as non-invasive tools for biomarker identification in bone turnover. Many biomarkers overlap, and it is important to identify other specific biomarkers to enable accurate diagnosis of these conditions.

Multiomic biomarkers after cardiac arrest

Cardiac arrest is a sudden cessation of heart function, leading to an abrupt loss of blood flow and oxygen to vital organs. This life-threatening emergency requires immediate medical intervention and can lead to severe neurological injury or death. Methods and biomarkers to predict neurological outcome are available but lack accuracy. Such methods would allow personalizing healthcare and help clinical decisions. Extensive research has been conducted to identify prognostic omic biomarkers of cardiac arrest. With the emergence of technologies allowing to combine different levels of omics data, and with the help of artificial intelligence and machine learning, there is a potential to use multiomic signatures as prognostic biomarkers after cardiac arrest. This review article delves into the current knowledge of cardiac arrest biomarkers across various omic fields and suggests directions for future research aiming to integrate multiple omics data layers to improve outcome prediction and cardiac arrest patient's care.

"My Biomarkers Are Fine, Thank You": On the Biomarkerization of Modern Medicine

Biomarkers are becoming crucial in ever more medical tasks and are proposed to change medicine in profound ways. By biomarking ever more attributes of human life, they tend to blur the distinction between health and disease and come to characterize life as such. Not only do biomarkers strongly influence the professional conception of disease by pervading ever more diagnoses, but they also impact patients' experience of illness. To manage how biomarkers influence patients, professionals, and societies, we urgently need to move from identifying potentially relevant biomarkers to determine their meaning and value to individuals, professionals, and public health.

Accelerating the Diagnosis of Pandemic Infection Based on Rapid Sampling Algorithm for Fast-Response Breath Gas Analyzers

This paper presents a novel technique for extracting the alveolar part of human breath. Gas exchange occurs between blood and inhaled air in the alveoli, which is helpful in medical diagnostics based on breath analysis. Consequently, the alveolar portion of the exhaled air contains specific concentrations of endogenous EVOC (exogenous volatile organic compound), which, among other factors, depend on the person's health condition. As this part of the breath enables the screening for diseases, accurate sample collection for testing is crucial. Inaccurate sampling can significantly alter the composition of the specimen, alter the concentration of EVOC (biomarkers) and adversely affect the diagnosis. Furthermore, the volume of alveolar air is minimal (usually <350 mL), especially in the case of people affected by respiratory system problems. For these reasons, precise sampling is a key factor in the effectiveness of medical diagnostic systems. A new technique ensuring high accuracy and repeatability is presented in the article. It is based on analyzing the changes in carbon dioxide concentration in human breath using a fast and compensated non-dispersive infrared (NDIR) sensor and the simple moving adjacent average (SMAA) algorithm. Research has shown that this method accurately identifies exhalation phases with an uncertainty as low as 20 ms. This provides around 350 ms of breath duration for carrying out additional stages of the diagnostic process using various types of analyzers.

Serum Biomarkers in Bullous Pemphigoid: A Systematic Review

INTRODUCTION

Bullous pemphigoid (BP) is the most common type of subepidermal blistering disease, usually observed in the elderly population, with a mean age of presentation between 66 and 83 years. BP is a psychosocially ladened disease, with many patients experiencing negative body image, social isolation, and depression. The identification and validation of biomarkers in BP may further the understanding of disease pathogenesis, provide objective measures in assessing efficacy in clinical trials, and identify new targets for targeted therapy.

METHODS/RESULTS

Two databases (Medline and Embase) were searched from database inception to September 2023. All published articles reporting on biomarker levels of BP patients in serum compared to healthy controls were included. A total of 877 unique articles were identified, resulting in the inclusion of 62 case-control studies reporting on a total of 1837 patients and 140 unique biomarkers. Biomarkers were categorized into T-cell mediated, B-cell mediated, innate immune system, and coagulation cascade pathway. The most notable biomarkers identified include increases in anti-BP180/230 immunoglobulin (Ig)G/E, total IgE, TNF-α, B-cell activating factor, interleukin-31, eosinophil cationic protein, MMP-9, and coagulation cascade biomarker levels. The results of this review provide the greatest support for a role of anti-BP180/230 autoantibodies, Th2 cells, eosinophils, and the coagulation cascade in the pathogenesis of BP.

CONCLUSIONS

The pathogenesis of BP has an underlying autoimmune etiology centred around the production of autoantibodies against BP180/230, but increased Th2, eosinophil and coagulation cascade activity may be contributory.

Effects of fermented Crescentia cujete L. on the profile of hematology, clinical chemistry, and circulatory CD4+/CD8+ in Sprague Dawley rats

Background

The calabash (Crescentia cujete L.) is a tropical fruit that offers numerous health benefits. Its fermented calabash (FC) has been found to affect the neurological system positively. However, its impact on hematology, clinical chemistry, and CD4+ and CD8+ levels has yet to be documented.

Aim

Therefore, this study aims to examine the effects of FC on hematology, clinical chemistry, and the levels of CD4+ and CD8+ in the circulatory system using rat models.

Methods

This study used twenty male Sprague Dawley rats. The rats were divided into group 1 (0 mg/kg BW FC), group 2 (50 mg/kg BW FC), group 3 (500 mg/kg BW FC), and group 4 (2,000 mg/kg BW FC). The treatment was administered using a gastric probe once daily for 14 days. On day 15, the blood samples were collected and tested against hematology, clinical chemistry, quality of the erythrocytes, and CD4+/CD8+. The data were then analysed using SPSS with p-value at 0.05.

Results

The conducted study demonstrated that the utilization of FC at varying doses did not have a significant impact on the hematological profile changes (p ≥ 0.05), except for total lymphocytes and a decrease in the neutrophils/lymphocytes (N/L) ratio (p ≤ 0.05). Furthermore, FC did not influence the changes in clinical chemistry, circulatory protein, and electrolyte levels in rat models compared to the control (p ≥ 0.05). The utilization of FC decreased the percentage of hemolysis and elevated the adenosine triphosphate (ATP) concentration (p ≤ 0.05). Additionally, the use of FC led to a significant increase in CD4+ and the ratio of CD4+/CD8+ (p ≤ 0.05), while no significant effect was observed regarding CD8+ (p ≥ 0.05).

Conclusion

The study highlighted FC's beneficial effects on the haemorheology and immune system, specifically on the decrease in the percentage of hemolysis, elevated ATP concentration, number of lymphocytes, ratio N/L, CD4+, and the CD4+/CD8+ ratio, without causing significant changes to the hematological and clinical chemistry profiles in rat models.

Biomarkers in Pemphigus Vulgaris: A Systematic Review

INTRODUCTION

Pemphigus vulgaris (PV) is a rare intraepidermal blistering disease that is potentially life-threatening due to risk of infection and failure of skin barrier function. The identification of biomarkers has the potential to provide diagnostic utility and identify new therapeutic targets. The objective of this systematic review is to identify all potentially relevant PV biomarkers, categorize them, and identify trends to determine the involvement of T-cell-mediated, B-cell-1mediated, and innate immune-mediated pathways in PV pathogenesis.

METHODS/RESULTS

Medline and Embase databases were searched according to Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, resulting in the inclusion of 66 studies that reported on a total of 2463 patients and 146 unique biomarkers. Biomarkers were categorized into T-cell-mediated, B-cell-mediated, and innate immune system pathways. The most notable biomarkers trends include elevations in IL-4, IL-6, IL-17A, anti-Dsg1/3 autoantibodies, and a reduction in Treg cells and FOXP3.

CONCLUSION

The results of this review support current theories of PV pathogenesis, with increased Th2 activity, increased Th17 activity, decreased Treg activity, and production of anti-Dsg1/3 autoantibodies being observed. Targeting of IL-4 and IL-6 may provide therapeutic benefit. However, more research is required to validate biomarkers for clinical utility and assess viability as therapeutic targets.

Current development of Fc gamma receptors (FcγRs) in diagnostics: a review

The ability of the immune system to fight against pathogens relies on the intricate collaboration between antibodies and Fc gamma receptors (FcγRs). These receptors are a group of transmembrane glycoprotein molecules, which can specifically detect and bind to the Fc portion of immunoglobulin G (IgG) molecules. They are distributed on a diverse array of immune cells, forming a strong defence system to eliminate invading threats. FcγRs have gained increasing attention as potential biomarkers for various diseases in recent years due to their ability to reflect immune dysregulation and disease pathogenesis. Increasing lines of evidence have shed new light on the remarkable association of FcγRs polymorphisms with the susceptibility of autoimmune diseases such as systemic lupus erythematosus (SLE) and lupus nephritis. Several studies have also reported the application of FcγR as a novel biomarker for the diagnosis of infection and cancer. Due to the surge in interest and concern regarding the potential of FcγRs as promising diagnostic biomarkers, this review, thereby, serves to provide a comprehensive overview of the structural characteristics, functional roles, and expression patterns of FcγRs, with a particular focus on their evolving role as diagnostic and prognostic biomarkers.

Diagnostic and prognostic value of triglyceride glucose index: a comprehensive evaluation of meta-analysis

OBJECTIVE

The present umbrella review aims to collate and summarize the findings from previous meta-analyses on the Triglyceride and Glucose (TyG) Index, providing insights to clinicians, researchers, and policymakers regarding the usefulness of this biomarker in various clinical settings.

METHODS

A comprehensive search was conducted in PubMed, Scopus, and Web of Science up to April 14, 2024, without language restrictions. The AMSTAR2 checklist assessed the methodological quality of the included meta-analyses. Statistical analyses were performed using Comprehensive Meta-Analysis (CMA) software.

RESULTS

A total of 32 studies were finally included. The results revealed significant associations between the TyG index and various health outcomes. For kidney outcomes, a high TyG index was significantly associated with an increased risk of contrast-induced nephropathy (CIN) (OR = 2.24, 95% CI: 1.82-2.77) and chronic kidney disease (CKD) (RR = 1.46, 95% CI: 1.32-1.63). High TyG index was significantly associated with an increased risk of type 2 diabetes mellitus (T2DM) (RR = 3.53, 95% CI: 2.74-4.54), gestational diabetes mellitus (GDM) (OR = 2.41, 95% CI: 1.48-3.91), and diabetic retinopathy (DR) (OR = 2.34, 95% CI: 1.31-4.19). Regarding metabolic diseases, the TyG index was significantly higher in patients with obstructive sleep apnea (OSA) (SMD = 0.86, 95% CI: 0.57-1.15), metabolic syndrome (MD = 0.83, 95% CI: 0.74-0.93), and non-alcoholic fatty liver disease (NAFLD) (OR = 2.36, 95% CI: 1.88-2.97) compared to those without these conditions. In cerebrovascular diseases, a higher TyG index was significantly associated with an increased risk of dementia (OR = 1.14, 95% CI: 1.12-1.16), cognitive impairment (OR = 2.31, 95% CI: 1.38-3.86), and ischemic stroke (OR = 1.37, 95% CI: 1.22-1.54). For cardiovascular outcomes, the TyG index showed significant associations with an increased risk of heart failure (HF) (HR = 1.21, 95% CI: 1.12-1.30), atrial fibrillation (AF) (SMD = 1.22, 95% CI: 0.57-1.87), and hypertension (HTN) (RR = 1.52, 95% CI: 1.25-1.85).

CONCLUSION

The TyG index is a promising biomarker for screening and predicting various medical conditions, particularly those related to insulin resistance and metabolic disorders. However, the heterogeneity and methodological quality of the included studies suggest the need for further high-quality research to confirm these findings and refine the clinical utility of the TyG index.

Inflammasome Proteins Are Reliable Biomarkers of the Inflammatory Response in Aneurysmal Subarachnoid Hemorrhage

Aneurysmal subarachnoid hemorrhage (aSAH) is caused by abnormal blood vessel dilation and subsequent rupture, resulting in blood pooling in the subarachnoid space. This neurological insult results in the activation of the inflammasome, a multiprotein complex that processes pro-inflammatory interleukin (IL)-1 cytokines leading to morbidity and mortality. Moreover, increases in inflammasome proteins are associated with clinical deterioration in many neurological diseases. Limited studies have investigated inflammasome protein expression following aSAH. Reliable markers of the inflammatory response associated with aSAH may allow for earlier detection of patients at risk for complications and aid in the identification of novel pharmacologic targets. Here, we investigated whether inflammasome signaling proteins may serve as potential biomarkers of the inflammatory response in aSAH. Serum and cerebrospinal fluid (CSF) from fifteen aSAH subjects and healthy age-matched controls and hydrocephalus (CSF) no-aneurysm controls were evaluated for levels of inflammasome signaling proteins and downstream pro-inflammatory cytokines. Protein measurements were carried out using Simple Plex and Single-Molecule Array (Simoa) technology. The area under the curve (AUC) was calculated using receiver operating characteristics (ROCs) to obtain information on biomarker reliability, specificity, sensitivity, cut-off points, and likelihood ratio. In addition, a Spearman r correlation matrix was performed to determine the correlation between inflammasome protein levels and clinical outcome measures. aSAH subjects demonstrated elevated caspase-1, apoptosis-associated speck-like protein with a caspase recruiting domain (ASC), IL-18 and IL-1β levels in serum, and CSF when compared to controls. Each of these proteins was found to be a promising biomarker of inflammation in aSAH in the CSF. In addition, ASC, caspase-1, and IL-1β were found to be promising biomarkers of inflammation in aSAH in serum. Furthermore, we found that elevated levels of inflammasome proteins in serum are useful to predict worse functional outcomes following aSAH. Thus, the determination of inflammasome protein levels in CSF and serum in aSAH may be utilized as reliable biomarkers of inflammation in aSAH and used clinically to monitor patient outcomes.

Lung Cancer Subtyping: A Short Review

As of 2022, lung cancer is the most commonly diagnosed cancer worldwide, with the highest mortality rate. There are three main histological types of lung cancer, and it is more important than ever to accurately identify the subtypes since the development of personalized, type-specific targeted therapies that have improved mortality rates. Traditionally, the gold standard for the confirmation of histological subtyping is tissue biopsy and histopathology. This, however, comes with its own challenges, which call for newer sampling techniques and adjunctive tools to assist in and improve upon the existing diagnostic workflow. This review aims to list and describe studies from the last decade (n = 47) that investigate three such potential omics techniques-namely (1) transcriptomics, (2) proteomics, and (3) metabolomics, as well as immunohistochemistry, a tool that has already been adopted as a diagnostic adjunct. The novelty of this review compared to similar comprehensive studies lies with its detailed description of each adjunctive technique exclusively in the context of lung cancer subtyping. Similarities between studies evaluating individual techniques and markers are drawn, and any discrepancies are addressed. The findings of this study indicate that there is promising evidence that supports the successful use of omics methods as adjuncts to the subtyping of lung cancer, thereby directing clinician practice in an economical and less invasive manner.

Single cell transcriptomic analysis reveals tumor immune infiltration by NK cells gene signature in lung adenocarcinoma

Background

Natural Killer (NK) cells are vital components of the innate immune system, crucial for combating infections and tumor growth, making them pivotal in cancer prognosis and immunotherapy. We sought to understand the diverse characteristics of NK cells within lung adenocarcinoma (LUAD) by conducting single-cell RNA sequencing analyses.

Methods

Using the scRNA-seq dataset for multiple primary lung cancers (MPLCs), we examined two major NK cell groups, NK1 and NK2, comparing the expression profiles of 422 differentially expressed NK signature genes. We identified eight genes (SPON2, PLEKHG3, CAMK2N1, RAB27B, CTBP2, EFHD2, GOLM1, and PLOD1) that distinguish NK1 from NK2 cells. A prognostic signature, the NK gene signature (NKGS) score, was established through LASSO Cox regression. High NKGS scores were linked to poorer overall survival in TCGA-LUAD patients and consistently validated in other datasets (GSE31210 and GSE14814).

Results

Functional analysis revealed an enrichment of genes related to the TGF-β signaling pathway in the high NKGS score group. Moreover, a high NKGS score correlated with an immunosuppressive tumor microenvironment (TME) driven by immune evasion mechanisms. We also observed reduced T-cell receptor (TCR) repertoire diversity in the high-risk NKGS group, indicating a negative association between inflammation and risk score.

Conclusion

This study introduced the innovative NKGS score, differentiating NK1 from NK2 cells. High NKGS scores were associated with the TGF-β pathway and provided insights into LUAD prognosis and immune activities.

Navigating Disease Management: A Comprehensive Review of the De Ritis Ratio in Clinical Medicine

The De Ritis ratio, defined as the serum aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio, is a widely recognized biochemical marker with significant applications in diagnosing and managing various diseases, particularly liver disorders. This comprehensive review synthesizes current knowledge surrounding the clinical relevance of the De Ritis ratio, examining its historical development, diagnostic utility, and prognostic significance across various medical conditions, including liver diseases, cardiovascular disorders, and muscular pathologies. Through an in-depth analysis of literature spanning several decades, this review highlights the role of the De Ritis ratio not only in differential diagnosis but also as a prognostic indicator for disease progression and patient outcomes. The ratio's ability to distinguish between different types of liver pathology, aid in early disease detection, and its potential use in monitoring treatment response are discussed. Additionally, the review addresses the methodological considerations, such as confounding factors and interpretation challenges, that impact the clinical utility of the De Ritis ratio. Given the evolving landscape of clinical diagnostics and the push toward more personalized medicine, the review concludes with recommendations for further research. These include longitudinal studies to explore the ratio's changes over time, comparative research across diverse populations, and technological integration to enhance diagnostic accuracy and patient care. This review aims to reaffirm the importance of the De Ritis ratio in modern clinical practice and encourages continued exploration into its potential applications and benefits in healthcare.

Immunomodulatory gene polymorphisms in non-small cell lung carcinoma susceptibility and survival

Lung cancer is the leading cause of cancer-associated mortality and non-small cell lung carcinoma (NSCLC) constitutes 85 % of all lung cancer cases. This malignancy is characterized by multifactorial risk factors, poor prognosis, and deplorable clinical outcome. Considerable evidence indicates that there is inter-individual variability in the lung cancer predisposition and survival due to genetic variations introduced by genetic polymorphisms between individuals, indirectly affecting the lung cancer susceptibility and the patient survival. In the past decades, immune landscape in the tumour environment and host immune response are constantly implicated as determining factor in NSCLC development and patients' survival. With the change of paradigm in NSCLC treatment to immunotherapy and increasing recognition of the role of the immune system in cancer development and survival, the inspection of single nucleotide polymorphisms (SNPs) in immunomodulated markers associated with the risk and prognosis for NSCLC is crucial. Despite extensive studies reported the implication of SNPs in predicting the risk and survival of NSCLC. SNPs in the genes that modulate immune response in NSCLC have not been reviewed before. Hence, this review uncovers the evidence on the genetic polymorphisms of immunomodulatory markers which include immune checkpoints, immune checkpoint inhibitors, chemokines, interleukins, human leukocyte antigen and its receptors, and antigen presenting machinery genes, and their significance in the susceptibility, prognosis and survival in NSCLC. The identification of genetic factors associated with NSCLC risk and survival provides invaluable information for a greater comprehension of the pathogenesis and progression of the disease, also to refine prognosis and personalize clinical care in early and advanced-stages disease.

Optimising Extracellular Vesicle Metabolomic Methodology for Prostate Cancer Biomarker Discovery

Conventional diagnostic tools for prostate cancer (PCa), such as prostate-specific antigen (PSA), transrectal ultrasound (TRUS), digital rectal examination (DRE), and tissue biopsy face, limitations in individual risk stratification due to invasiveness or reliability issues. Liquid biopsy is a less invasive and more accurate alternative. Metabolomic analysis of extracellular vesicles (EVs) holds a promise for detecting non-genetic alterations and biomarkers in PCa diagnosis and risk assessment. The current research gap in PCa lies in the lack of accurate biomarkers for early diagnosis and real-time monitoring of cancer progression or metastasis. Establishing a suitable approach for observing dynamic EV metabolic alterations that often occur earlier than being detectable by other omics technologies makes metabolomics valuable for early diagnosis and monitoring of PCa. Using four distinct metabolite extraction approaches, the metabolite cargo of PC3-derived large extracellular vesicles (lEVs) was evaluated using a combination of methanol, cell shearing using microbeads, and size exclusion filtration, as well as two fractionation chemistries (pHILIC and C18 chromatography) that are also examined. The unfiltered methanol-microbeads approach (MB-UF), followed by pHILIC LC-MS/MS for EV metabolite extraction and analysis, is effective. Identified metabolites such as L-glutamic acid, pyruvic acid, lactic acid, and methylmalonic acid have important links to PCa and are discussed. Our study, for the first time, has comprehensively evaluated the extraction and separation methods with a view to downstream sample integrity across omics platforms, and it presents an optimised protocol for EV metabolomics in PCa biomarker discovery.

Implementation of Modern Therapeutic Drug Monitoring and Lipidomics Approaches in Clinical Practice: A Case Study with Colistin Treatment

Nowadays, lipidomics plays a crucial role in the investigation of novel biomarkers of various diseases. Its implementation into the field of clinical analysis led to the identification of specific lipids and/or significant changes in their plasma levels in patients suffering from cancer, Alzheimer's disease, sepsis, and many other diseases and pathological conditions. Profiling of lipids and determination of their plasma concentrations could also be helpful in the case of drug therapy management, especially in combination with therapeutic drug monitoring (TDM). Here, for the first time, a combined approach based on the TDM of colistin, a last-resort antibiotic, and lipidomic profiling is presented in a case study of a critically ill male patient suffering from Pseudomonas aeruginosa-induced pneumonia. Implementation of innovative analytical approaches for TDM (online combination of capillary electrophoresis with tandem mass spectrometry, CZE-MS/MS) and lipidomics (liquid chromatography-tandem mass spectrometry, LC-MS/MS) was demonstrated. The CZE-MS/MS strategy confirmed the chosen colistin drug dosing regimen, leading to stable colistin concentrations in plasma samples. The determined colistin concentrations in plasma samples reached the required minimal inhibitory concentration of 1 μg/mL. The complex lipidomics approach led to monitoring 545 lipids in collected patient plasma samples during and after the therapy. Some changes in specific individual lipids were in good agreement with previous lipidomics studies dealing with sepsis. The presented case study represents a good starting point for identifying particular individual lipids that could correlate with antimicrobial and inflammation therapeutic management.

Biomarkers in rosacea: A systematic review

Rosacea is a chronic and psychologically ladened disease affecting 1%-3% of people worldwide. The identification and validation of biomarkers in rosacea patients has the potential to improve disease progression, support diagnosis, provide objective measures for clinical trials and aid in management. The objective of this review is to systematically identify all rosacea biomarkers, categorize them by type and identify trends to improve disease expression. Eligibility criteria for this review (PROSPERO CRD42023397510) include randomized controlled trials, case-control studies, cohort studies and other observational studies. No restrictions were placed on patient demographics (age, sex, ethnicity) or language of publication until February 2023. Quality of studies was assessed using the National Institute of Health quality assessment tool. The literature search was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. A total of 805 unique articles were screened based on the applied inclusion and exclusion criteria. After the articles were screened based on title/abstract and full-text, a total of 38 studies were included, reporting on a total of 119 unique biomarkers. The results of this review and current rosacea pathogenic mechanisms provide the greatest support for the innate cathelicidin and inflammasome, Th1 and Th17 pathways. The most commonly reported biomarkers include IL-1β, TNF-α, IL-37, IFN-γ and MMP-9. Biomarkers identified in this study support current theories of rosacea pathogenesis and provide direction for research to further our knowledge. However, more research is needed to identify biomarkers panels that can provide diagnostic utility. This may be difficult due to the heterogeneity of the disease and potential differences between rosacea subtypes.

Integrative Assessment of Seminal Plasma Biomarkers: A Narrative Review Bridging the Gap between Infertility Research and Clinical Practice

Infertility represents a significant global health challenge impacting millions of couples worldwide. Approximately half of all infertile couples exhibit compromised semen quality, indicative of diminished male fertility. While the diagnosis of male infertility traditionally relies on semen analysis, its limitations in providing a comprehensive assessment of male reproductive health have spurred efforts to identify novel biomarkers. Seminal plasma, a complex fluid containing proteins, lipids, and metabolites, has emerged as a rich source of such indicators. Reproduction depends heavily on seminal plasma, the primary transporter of chemicals from male reproductive glands. It provides a non-invasive sample for urogenital diagnostics and has demonstrated potential in the identification of biomarkers linked to illnesses of the male reproductive system. The abundance of seminal proteins has enabled a deeper understanding of their biological functions, origins, and differential expression in various conditions associated with male infertility, including azoospermia, asthenozoospermia, oligozoospermia, teratozoospermia, among others. The true prevalence of male infertility is understated due to the limitations of the current diagnostic techniques. This review critically evaluates the current landscape of seminal plasma biomarkers and their utility in assessing male infertility. Βy bridging the gap between research and clinical practice, the integrative assessment of seminal plasma biomarkers offers a multimodal approach to comprehensively evaluate male infertility.

Label-free and label-based electrochemical detection of disease biomarker proteins

Introduction

Biosensors, analytical devices integrating biological sensing elements with physicochemical transducers, have gained prominence as rapid and convenient tools for monitoring human health status using biochemical analytes. Due to its cost-effectiveness, simplicity, portability, and user-friendliness, electrochemical detection has emerged as a widely adopted method in biosensor applications. Crucially, biosensors enable early disease diagnosis by detecting protein biomarkers associated with various conditions. These biomarkers offer an objective indication of medical conditions that can be accurately observed from outside the patient.

Method

This review comprehensively documents both label-free and labelled detection methods in electrochemical biosensor techniques. Label-free detection mechanisms elicit response signals upon analyte molecule binding to the sensor surface, while labelled detection employs molecular labels such as enzymes, nanoparticles, and fluorescent tags.

Conclusion

The selection between label-free and labelled detection methods depends on various factors, including the biomolecular compound used, analyte type and biological binding site, biosensor design, sample volume, operational costs, analysis time, and desired detection limit. Focusing on the past six years, this review highlights the application of label-free and labelled electrochemical biosensors for detecting protein biomarkers of diseases.

A Comprehensive Review of the Role of Biomarkers in the Early Detection of Endocrine Disorders in Critical Illnesses

Endocrine disorders pose significant challenges in the management of critically ill patients, contributing to morbidity and mortality in intensive care settings. Timely detection of these disorders is essential to optimizing patient outcomes. Biomarkers, as measurable indicators of biological processes or disease states, play a crucial role in the early identification and monitoring of endocrine dysfunction. This comprehensive review examines the role of biomarkers in the early detection of endocrine disorders in critical illnesses. We provide an overview of common endocrine disorders encountered in the intensive care unit (ICU) and discuss the impact of endocrine dysregulation on patient outcomes. Additionally, we classify biomarkers and explore their significance in diagnosing and monitoring endocrine disorders, including thyroid dysfunction, adrenal insufficiency, and hypopituitarism. Furthermore, we discuss the clinical applications of biomarkers, including their utility in guiding therapeutic interventions, monitoring disease progression, and predicting outcomes in critical illnesses. Emerging trends and future directions in biomarker research are also highlighted, emphasizing the need for continued investigation into novel biomarkers and technological advancements. Finally, we underscore the potential of biomarkers to revolutionize the early detection and management of endocrine disorders in critical illnesses, ultimately improving patient care and outcomes in the ICU.

Blood-brain barrier biomarkers

The blood-brain barrier (BBB) is a dynamic interface that regulates the exchange of molecules and cells between the brain parenchyma and the peripheral blood. The BBB is mainly composed of endothelial cells, astrocytes and pericytes. The integrity of this structure is essential for maintaining brain and spinal cord homeostasis and protection from injury or disease. However, in various neurological disorders, such as traumatic brain injury, Alzheimer's disease, and multiple sclerosis, the BBB can become compromised thus allowing passage of molecules and cells in and out of the central nervous system parenchyma. These agents, however, can serve as biomarkers of BBB permeability and neuronal damage, and provide valuable information for diagnosis, prognosis and treatment. Herein, we provide an overview of the BBB and changes due to aging, and summarize current knowledge on biomarkers of BBB disruption and neurodegeneration, including permeability, cellular, molecular and imaging biomarkers. We also discuss the challenges and opportunities for developing a biomarker toolkit that can reliably assess the BBB in physiologic and pathophysiologic states.

The Pharmacological Effect of Hemin in Inflammatory-Related Diseases: A Systematic Review

BACKGROUND

Hemin is clinically used in acute attacks of porphyria; however, recent evidence has also highlighted its capability to stimulate the heme oxygenase enzyme, being associated with cytoprotective, antioxidant, and anti-inflammatory effects. Indeed, current preclinical evidence emphasizes the potential anti-inflammatory role of hemin through its use in animal models of disease. Nevertheless, there is no consensus about the underlying mechanism(s) and the most optimal therapeutic regimens. Therefore, this review aims to summarize, analyze, and discuss the current preclinical evidence concerning the pharmacological effect of hemin.

METHODS

Following the application of the search expression and the retrieval of the articles, only nonclinical studies in vivo written in English were considered, where the potential anti-inflammatory effect of hemin was evaluated.

RESULTS

Forty-nine articles were included according to the eligibility criteria established. The results obtained show the preference of using 30 to 50 mg/kg of hemin, administered intraperitoneally, in both acute and chronic contexts. This drug demonstrates significant anti-inflammatory and antioxidant activities considering its capacity for reducing the expression of proinflammatory and oxidative markers.

CONCLUSIONS

This review highlighted the significant anti-inflammatory and antioxidant effects of hemin, providing a clearer vision for the medical community about the use of this drug in several human diseases.

Impact of diabetes mellitus on osteoarthritis: a scoping review on biomarkers

Osteoarthritis (OA) commonly affects the knee and hip joints and accounts for 19.3% of disability-adjusted life years and years lived with disability worldwide (Refs , ). Early management is important in order to avoid disability uphold quality of life (Ref. ). However, a lack of awareness of subclinical and early symptomatic stages of OA often hampers early management (Ref. ). Moreover, late diagnosis of OA among those with severe disease, at a stage when OA management becomes more complicated is common (Refs , , , ). Established risk factors for the development and progression of OA include increasing age, female, history of trauma and obesity (Ref. ). Recent studies have also drawn a link between OA and metabolic syndrome, which is characterized by insulin resistance, dyslipidaemia and hypertension (Refs , ).

Exploring ncRNA-mediated pathways in sepsis-induced pyroptosis

Sepsis, a potentially fatal illness caused by an improper host response to infection, remains a serious problem in the world of healthcare. In recent years, the role of ncRNA has emerged as a pivotal aspect in the intricate landscape of cellular regulation. The exploration of ncRNA-mediated regulatory networks reveals their profound influence on key molecular pathways orchestrating pyroptotic responses during septic conditions. Through a comprehensive analysis of current literature, we navigate the diverse classes of ncRNAs, including miRNAs, lncRNAs, and circRNAs, elucidating their roles as both facilitators and inhibitors in the modulation of pyroptotic processes. Furthermore, we highlight the potential diagnostic and therapeutic implications of targeting these ncRNAs in the context of sepsis, aiming to cover the method for novel and effective strategies to mitigate the devastating consequences of septic pathogenesis. As we unravel the complexities of this regulatory axis, a deeper understanding of the intricate crosstalk between ncRNAs and pyroptosis emerges, offering promising avenues for advancing our approach to sepsis intervention. The intricate pathophysiology of sepsis is examined in this review, which explores the dynamic interaction between ncRNAs and pyroptosis, a highly regulated kind of programmed cell death.

Salivary High-Sensitivity C-Reactive Protein and Its Clinical Relevance in Modern Medicine: A Comprehensive Review

High-sensitivity C-reactive protein (hsCRP) has emerged as a critical biomarker in inflammation, offering insights into various chronic diseases. However, traditional blood-based assays for hsCRP measurement pose limitations regarding invasiveness and cost. In recent years, saliva has garnered attention as an alternative diagnostic medium, presenting a noninvasive and easily accessible option for biomarker analysis. Salivary hsCRP has thus emerged as a promising avenue for research and clinical application, offering potential advantages over blood-based assays. This comprehensive review aims to elucidate the biological basis of salivary hsCRP, its clinical applications, and methodologies for measurement. By exploring its diagnostic potential, prognostic value, and implications for treatment monitoring, this review highlights the potential impact of salivary hsCRP in modern medicine. Moreover, it emphasizes the need for continued exploration, validation, and integration of salivary hsCRP into routine clinical practice to realize its full potential for enhancing patient care and advancing personalized medicine approaches.

Comparative study on gene expression in psoriatic lesions versus chronic wound healing processes

Psoriasis and chronic ulcers not only significantly impair quality of life but also pose a challenge in dermatological treatment. This study aimed to identify new therapeutic targets and biomarkers for psoriasis and chronic ulcers by comparing their gene expression profiles. The gene expression profiles of psoriatic, wound and chronic ulcer patients, as well as healthy controls, were determined via RNA extraction and next-generation sequencing of biopsies. In order to identify biomarkers, functional enrichment, differential expression analysis and machine learning algorithms were implemented. It is worth mentioning that the genes IL17A, TNF, KRT16, MMP9, and CD44 exhibited substantial correlations with the pathogenesis of the conditions being studied. As evidenced by their AUC-ROC values approaching 0.90, machine learning models accurately identified these biomarkers. The differential gene expression was consequently validated via qRT-PCR, which highlighted the increased expression of matrix remodelling enzymes and inflammatory cytokines. Additionally, genes essential for maintaining epidermis integrity and facilitating wound healing exhibited downregulation. These insights into the molecular mechanisms of psoriasis and chronic ulcers pave the way for the development of targeted therapies, offering hope for improved treatment strategies.

Exploring the Potential of Exosomes as Biomarkers in Tuberculosis and Other Diseases

Tuberculosis (TB) is a major cause of morbidity and mortality and remains an important public health issue in developing countries worldwide. The existing methods and techniques available for the diagnosis of TB are based on combinations of laboratory (chemical and biological), radiological, and clinical tests. These methods are sophisticated and laborious and have limitations in terms of sensitivity, specificity, and accuracy. Clinical settings need improved diagnostic biomarkers to accurately detect biological changes due to pathogen invasion and pharmacological responses. Exosomes are membrane-bound vesicles and mediators of intercellular signaling processes that play a significant role in the pathogenesis of various diseases, such as tuberculosis, and can act as promising biomarkers for the monitoring of TB infection. Compared to conventional biomarkers, exosome-derived biomarkers are advantageous because they are easier to detect in different biofluids, are more sensitive and specific, and may be useful in tracking patients' reactions to therapy. This review provides insights into the types of biomarkers, methods of exosome isolation, and roles of the cargo (proteins) present in exosomes isolated from patients through omics studies, such as proteomics. These findings will aid in developing new prognostic and diagnostic biomarkers and could lead to the identification of new therapeutic targets in the clinical setting.

Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review

The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control studies or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation, are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumors, and addressing cancer-related information, such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application. This review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI.

Plant biomarkers as early detection tools in stress management in food crops: a review

MAIN CONCLUSION

Plant Biomarkers are objective indicators of a plant's cellular state in response to abiotic and biotic stress factors. They can be explored in crop breeding and engineering to produce stress-tolerant crop species. Global food production safely and sustainably remains a top priority to feed the ever-growing human population, expected to reach 10 billion by 2050. However, abiotic and biotic stress factors negatively impact food production systems, causing between 70 and 100% reduction in crop yield. Understanding the plant stress responses is critical for developing novel crops that can adapt better to various adverse environmental conditions. Using plant biomarkers as measurable indicators of a plant's cellular response to external stimuli could serve as early warning signals to detect stresses before severe damage occurs. Plant biomarkers have received considerable attention in the last decade as pre-stress indicators for various economically important food crops. This review discusses some biomarkers associated with abiotic and biotic stress conditions and highlights their importance in developing stress-resilient crops. In addition, we highlighted some factors influencing the expression of biomarkers in crop plants under stress. The information presented in this review would educate plant researchers, breeders, and agronomists on the significance of plant biomarkers in stress biology research, which is essential for improving plant growth and yield toward sustainable food production.

Sickle Cell Disease Update: New Treatments and Challenging Nutritional Interventions

Sickle cell disease (SCD), a distinctive and often overlooked illness in the 21st century, is a congenital blood disorder characterized by considerable phenotypic diversity. It comprises a group of disorders, with sickle cell anemia (SCA) being the most prevalent and serious genotype. Although there have been some systematic reviews of global data, worldwide statistics regarding SCD prevalence, morbidity, and mortality remain scarce. In developed countries with a lower number of sickle cell patients, cutting-edge technologies have led to the development of new treatments. However, in developing settings where sickle cell disease (SCD) is more prevalent, medical management, rather than a cure, still relies on the use of hydroxyurea, blood transfusions, and analgesics. This is a disease that affects red blood cells, consequently affecting most organs in diverse manners. We discuss its etiology and the advent of new technologies, but the aim of this study is to understand the various types of nutrition-related studies involving individuals suffering from SCD, particularly in Africa. The interplay of the environment, food, gut microbiota, along with their respective genomes collectively known as the gut microbiome, and host metabolism is responsible for mediating host metabolic phenotypes and modulating gut microbiota. In addition, it serves the purpose of providing essential nutrients. Moreover, it engages in direct interactions with host homeostasis and the immune system, as well as indirect interactions via metabolites. Nutrition interventions and nutritional care are mechanisms for addressing increased nutrient expenditures and are important aspects of supportive management for patients with SCD. Underprivileged areas in Sub-Saharan Africa should be accompanied by efforts to define and promote of the nutritional aspects of SCD. Their importance is key to maintaining well-being and quality of life, especially because new technologies and products remain limited, while the use of native medicinal plant resources is acknowledged.

HFpEF: New biomarkers and their diagnostic and prognostic value

Heart failure characterized by preserved ejection fraction (HFpEF) poses a substantial challenge to healthcare systems worldwide and the diagnostic algorithms used currently mirror those utilized for reduced Ejection Fraction (HFrEF). This literature review aims to explore the diagnostic and prognostic credibility of numerous emerging biomarkers associated with HFpEF. We conducted a thorough analysis of the available medical literature and selected the biomarkers which yielded the maximum amount of published information. After reviewing the current literature we conclude that there are no biomarkers at present which are superior to natriuretic peptides in terms of diagnosis and prognosis of HFpEF. However biomarkers like Suppression of tumorigenicity2, Galectin3 and microRNAs are promising and can be researched further for future use. Although newer individual biomarkers may not be useful in diagnosing and prognosis of HFpEF, we believe that a specific biomarker profile may be identified in each phenotype,which can be used in future.

Detection of genomic alterations in liquid biopsies from patients with non-small cell lung cancer using FoundationOne Liquid CDx: a cost-effectiveness analysis

OBJECTIVE

Liquid biopsy (LB) is a non-invasive technique to detect genetic alterations by next-generation sequencing (NGS) when tissue biopsy is not available. This study aims to estimate in the Spanish setting, the cost-effectiveness of using FoundationOne Liquid CDx (F1L CDx), a novel blood-derived LB test based on NGS, versus non-molecular diagnosis (non-mDx) in patients with advanced non-small cell lung cancer (NSCLC) in whom tissue sampling is not feasible.

METHODS

A joint model was developed combining a decision-tree with partitioned survival models to calculate the costs and health outcomes over a lifetime horizon, comparing F1L CDx in LB versus non-mDx. Only direct costs (expressed in € of 2023) were included and a 3% discount rate for future costs and effects was considered. Health outcomes were expressed in Life Years (LYs) and Quality-Adjusted Life Years (QALYs). Utilities and treatment efficacy were obtained from the literature. An expert panel of 11 Spanish oncologists determined the treatment allocation and validated all model inputs and assumptions. Several sensitivity analyses were performed to assess the robustness of the results.

RESULTS

In a hypothetical cohort of 1,000 patients, LB using F1L CDx would detect 386 alterations, so those patients could be treated with targeted therapies or enrolled in clinical trials. Cost-effectiveness results showed that F1L CDx provides greater effectiveness than non-mDx (+383.95 LYs and +305.94 QALYs), with an additional cost of €2,898,308. The incremental cost-utility ratio was €9,473/QALY gained. The probabilistic sensitivity analysis confirmed the robustness of the cost-effectiveness results.

LIMITATIONS

Various limitations inherent to cost-effectiveness analyses were described.

CONCLUSION

LB with F1L CDx test is a cost-effective strategy in Spain for patients with advanced NSCLC without tissue sample available for molecular diagnosis, improving the personalized treatment of these patients.