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

Simultaneous detection of trace protein biomarkers from a single drop of blood using AI-enhanced smartphone-based digital microscopy

The detection of early-stage diseases is often impeded by the low concentrations of protein biomarkers, necessitating sophisticated and costly technologies. In response, we have developed an advanced cyber-physical system that integrates blood plasma separation, biomarker detection, and analysis on a single microfluidic platform. This novel system enables the simultaneous detection of Neuron-Specific Enolase (NSE) and Carcinoembryonic Antigen (CEA) with high specificity and accuracy. Functionalized carbon dots (CDs) are employed for fluorescence-based quantification due to their superior biocompatibility, photostability, and resistance to photobleaching. The emission properties are optimized at 460 nm (blue) and 580 nm (yellow), yielding robust quantum efficiencies. The precise synthesis of CDs ensures reproducible fluorescence response with minimal background interference. A portable, smartphone-based fluorescence microscope equipped with 1000X magnification and UV excitation facilitates high-resolution image acquisition, serving as a low-cost alternative to conventional microscopy. Artificial intelligence algorithms are integrated for automated image analysis, enabling accurate quantification of biomarker concentrations. The system demonstrates impressive detection limits of 0.4 ng/mL and 0.9 ng/mL for CEA and NSE, respectively. The entire assay workflow, from sample introduction to result generation, is completed in 10 min. This integrated, portable diagnostic platform offers a transformative solution for point-of-care biomarker detection, particularly in resource-constrained settings, with the potential to democratize early disease screening and significantly reduce healthcare burdens globally.

Precision Medicine in High-Grade Serous Ovarian Cancer: Targeted Therapies and the Challenge of Chemoresistance

The poor prognosis for high-grade serous ovarian cancer (HGSOC), the dominant subtype of ovarian cancer, reflects its aggressive nature, late diagnosis, and the highest mortality rate among all gynaecologic cancers. Apart from late diagnosis, the main reason for the poor prognosis and its unsuccessful treatment is primarily the emergence of chemoresistance to carboplatin. Although there is a good response to primary treatment, the disease recurs in 80% of cases, at which point it is largely resistant to carboplatin. The introduction of novel targeted therapies in the second decade of the 21st century has begun to transform the treatment of HGSOC, although their impact on overall survival remains unsatisfactory. Targeting the specific pathways known to be abnormally activated in HGSOC is especially difficult due to the molecular diversity of its subtypes. Moreover, a range of molecular changes are associated with acquired chemoresistance, e.g., reversion of BRCA1 and BRCA2 germline alleles. In this review, we examine the advantages and disadvantages of approved targeted therapies, including bevacizumab, PARP inhibitors (PARPis), and treatments targeting cells with neurotrophic tyrosine receptor kinase (NTRK), B-rapidly accelerated fibrosarcoma (BRAF), and rearranged during transfection (RET) gene alterations, as well as antibody-drug conjugates. Additionally, we explore promising new targets under investigation in ongoing clinical trials, such as immune checkpoint inhibitors, anti-angiogenic agents, phosphatidylinositol-3-kinase (PI3K) inhibitors, Wee1 kinase inhibitors, and ataxia telangiectasia and Rad3-related protein (ATR) inhibitors for platinum-resistant disease. Despite the development of new targeted therapies, carboplatin remains the fundamental medicine in HGSOC therapy. The correct choice of treatment strategy for better survival of patients with advanced HGSOC should therefore include a prediction of patients' risks of developing chemoresistance to platinum-based chemotherapy. Moreover, effective targeted therapy requires the selection of patients who are likely to derive clinical benefit while minimizing potential adverse effects, underscoring the essence of precision medicine.

Precision Medicine in Rheumatology: The Role of Biomarkers in Diagnosis and Treatment Optimization

Rheumatic diseases encompass a wide range of autoimmune and inflammatory disorders, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), psoriatic arthritis (PsA), and systemic sclerosis (SSc). These conditions often result in chronic pain, disability, and reduced quality of life, with unpredictable disease courses that may lead to joint destruction, organ damage, or systemic complications. Biomarkers, defined as measurable indicators of biological processes or conditions, have the potential to transform clinical practice by improving disease diagnosis, monitoring, prognosis, and treatment decisions. While significant strides have been made in identifying and validating biomarkers in rheumatic diseases, challenges remain in their standardization, clinical utility, and integration into routine practice. This review provides an overview of the current state of biomarkers in rheumatic diseases, their roles in clinical settings, and the emerging advancements in the field.

Biomarkers in orofacial pain conditions: A narrative review

Orofacial pain conditions, including temporomandibular disorder, migraine, dental pain, and trigeminal neuralgia, are complex, multifactorial disorders with significant impacts on patients' quality of life. As understanding of the pathophysiology of these conditions has deepened, the role of molecular and genetic biomarkers in diagnosing, monitoring, and potentially treating orofacial pain has garnered increasing interest. This scoping review provides a comprehensive overview of the current state of research on biomarkers associated with orofacial pain conditions. By analyzing existing literature, we identify key biomarkers linked to inflammation, neural activity, and tissue degradation that are common across multiple conditions, as well as those specific to particular disorders. Our findings underscore the potential of these biomarkers to guide the development of personalized therapeutic strategies. However, the review also highlights the challenges faced by current biomarker research, including heterogeneity in study designs, small sample sizes, and a lack of longitudinal data. Addressing these challenges is critical for translating biomarker research into clinical practice and improving outcomes for patients with orofacial pain.

Comprehensive analysis of intervention and control studies for the computational identification of dengue biomarker genes

Dengue fever, caused by the dengue virus (DENV), presents a significant global health concern, with millions of cases reported annually. Despite significant progress in understanding Dengue fever, effective prognosis and treatment remain elusive due to the complex clinical presentations and limitations in current diagnostic methods. The virus, transmitted primarily by the Aedes aegypti mosquito, exists in four closely related forms, each capable of causing flu-like symptoms ranging from mild febrile illness to severe manifestations such as plasma leakage and hemorrhagic fever. Although advancements in diagnostic techniques have been made, early detection of severe dengue remains difficult due to the complexity of its clinical presentations. This study conducted a comprehensive analysis of differential gene expression in dengue fever patients using multiple microarray datasets from the NCBI GEO database. Through bioinformatics approaches, 163 potential biomarker genes were identified, with some overlapping previously reported biomarkers and others representing novel candidates. Notably, AURKA, BUB1, BUB1B, BUB3, CCNA2, CCNB2, CDC6, CDK1, CENPE, EXO1, NEK2, ZWINT, and STAT1 were among the most significant biomarkers. These genes are involved in critical cellular processes, such as cell cycle regulation and mitotic checkpoint control, which are essential for immune cell function and response. Functional enrichment analysis revealed that the dysregulated genes were predominantly associated with immune response to the virus, cell division, and RNA processing. Key regulatory genes such as AURKA, BUB1, BUB3, and CDK1 are found to be involved in cell cycle regulation and have roles in immune-related pathways, underscoring their importance in the host immune response to Dengue virus infection. This study provides novel insights into the molecular mechanisms underlying Dengue fever pathogenesis, highlighting key regulatory genes such as AURKA and CDK1 that could serve as potential biomarkers for early diagnosis and targets for therapeutic intervention, paving the way for improved management of the disease.

Introducing AVAC as an ultra-sensitive platform with broad dynamical range for high-throughput multiplexed biomarker detection using digital counting of plasmonic nanoparticles

Accurate detection and quantification of biomarkers at ultra-low levels is critical for disease diagnosis and effective treatment. Traditional detection technologies often lack the sensitivity, specificity, throughput, or multiplexing capacity required for comprehensive diagnostics, providing only a subset of these requirements. Here, we introduce AVAC, an automated optical technology for rapid and accurate biomarker detection with ultra-high sensitivity that significantly outperforms standard clinical assays. The core of this technology is the digital counting of plasmonic nanoparticles used as optical labels, enabling multiplexed, high-throughput detection of biomarkers. Validation studies demonstrate AVAC's high accuracy, with 98.2% specificity and detection limits as low as 26 fg/mL for HIV p24 protein and a quantification range of 160 fg/mL to 850 pg/mL for interleukin-6 (IL-6). The technology supports multiplexed assays without compromising sensitivity, as demonstrated by the simultaneous detection of three key biomarkers associated with cardiovascular disease. A counting range spanning more than four orders of magnitude ensures robust detection from ultra-low to high biomarker concentrations, and its ability to analyze up to 1,000 samples per hour provides high throughput suitable for large laboratories. With its unique combination of capabilities, this versatile platform has significant potential to advance biomarker-based diagnostics in clinical and research settings.

Exploring Biomarkers for Malaria: Advances in Early Detection and Asymptomatic Diagnosis

Malaria is a tropical disease caused by the Plasmodium parasite, which was responsible for 249 million cases worldwide in 2022. Malaria is currently diagnosed using RDTs, PCR-based methods, or blood smear microscopy. Ideal biomarkers have been identified for malaria, with the potential for improving treatment, diagnosis, and overall clinical outcomes. This review discusses the types of existing biomarkers and the opportunities for new biomarkers to be used as diagnostic components in detecting Plasmodium, including in terms of sensitivity, detection limit, specificity, and the species of Plasmodium that can be detected. Following a comparison, five main ideal malaria biomarkers were identified, namely HRP2, pLDH, hemozoin, aldolase, and pGDH. These biomarkers distinguished themselves markedly from the others in terms of specificity in Plasmodium detection, sensitivity in analysis, and the use of non-invasive samples. Several other biomarkers, such as CRP, Ang-1, Ang-2, and PCT, show potential for malaria detection in terms of their ability to differentiate disease severity, and the levels of these biomarkers can be determined in the body for comparison with malaria parasitemia. Of the five ideal biomarkers, hemozoin and aldolase can still be developed regarding the types of samples used and their sensitivity to different Plasmodium species. Further research on the biomarkers CRP, Ang-1, Ang-2, and PCT is still needed to evaluate their potential.

Innovative laboratory techniques shaping cancer diagnosis and treatment in developing countries

Cancer is a major global health challenge, with approximately 19.3 million new cases and 10 million deaths estimated by 2020. Laboratory advancements in cancer detection have transformed diagnostic capabilities, particularly through the use of biomarkers that play crucial roles in risk assessment, therapy selection, and disease monitoring. Tumor histology, single-cell technology, flow cytometry, molecular imaging, liquid biopsy, immunoassays, and molecular diagnostics have emerged as pivotal tools for cancer detection. The integration of artificial intelligence, particularly deep learning and convolutional neural networks, has enhanced the diagnostic accuracy and data analysis capabilities. However, developing countries face significant challenges including financial constraints, inadequate healthcare infrastructure, and limited access to advanced diagnostic technologies. The impact of COVID-19 has further complicated cancer management in resource-limited settings. Future research should focus on precision medicine and early cancer diagnosis through sophisticated laboratory techniques to improve prognosis and health outcomes. This review examines the evolving landscape of cancer detection, focusing on laboratory research breakthroughs and limitations in developing countries, while providing recommendations for advancing tumor diagnostics in resource-constrained environments.

Dose-Dependent PFESA-BP2 Exposure Increases Risk of Liver Toxicity and Hepatocellular Carcinoma

Per- and polyfluoroalkyl substances (PFASs) are persistent and highly bioaccumulative emerging environmental contaminants of concern that display significant toxic and carcinogenic effects. An emerging PFAS is PFESA-BP2, a polyfluoroalkyl ether sulfonic acid found in drinking water and the serum of humans and animals. While PFESA-BP2-induced liver and intestinal toxicity has been demonstrated, the toxicological mechanisms and carcinogenic potential of PFESA-BP2 have remained relatively understudied. Here, we studied how different doses of PFESA-BP2 affect gene activity related to liver toxicity and the risk of liver cancer such as hepatocellular carcinoma (HCC) in mice exposed to PFESA-BP2 once daily through oral gavage for seven days. An analysis of key hepatic pathways suggested increased risk of hepatotoxicity as a result of PFESA-BP2 exposure. Increased oxidative stress response was associated with all concentrations of exposure. Liver toxicity pathways, including PXR/RXR activation and hepatic fibrosis, showed dose-dependent alteration with activation primarily at low doses, suggesting an increased risk of hepatic inflammation and injury. Additionally, an analysis of carcinogenic and HCC-specific pathways suggested PFESA-BP2-induced risk of liver cancer, particularly at low doses. Low-dose PFESA-BP2 exposure (0.03 and 0.3 mg/kg-day) was associated with an increased risk of HCC carcinogenesis, as indicated by the activation of tumor-related and HCC-associated pathways. In contrast, these pathways were inhibited at high doses (3.0 and 6.0 mg/kg-day), accompanied by the activation of HCC-suppressive pathways. The increased risk of HCC development at low doses was mechanistically linked to the activation of signaling pathways such as HIF, EGF, NOTCH4, HGF, and VEGF. Biomarkers linked to liver cancer risk, prognoses, and diagnoses were also identified as a result of exposure. Overall, our findings on liver carcinogenic and hepatotoxic pathway activation patterns suggest that PFESA-BP2 increases the risk of liver toxicity and HCC development, particularly at low doses.

"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.

The efficacy of novel biomarkers for the early detection and management of acute kidney injury: A systematic review

Acute kidney injury (AKI) is a frequent clinical complication lacking early diagnostic tests and effective treatments. Novel biomarkers have shown promise for enabling earlier detection, risk stratification, and guiding management of AKI. We conducted a systematic review to synthesize evidence on the efficacy of novel biomarkers for AKI detection and management. Database searches yielded 17 relevant studies which were critically appraised. Key themes were biomarker efficacy in predicting AKI risk and severity before functional changes; potential to improve clinical management through earlier diagnosis, prognostic enrichment, and guiding interventions; emerging roles as therapeutic targets and prognostic tools; and ongoing challenges requiring further validation. Overall, novel biomarkers like neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and cell cycle arrest markers ([TIMP-2] •[IGFBP7]) demonstrate capability for very early AKI prediction and accurate risk stratification. Their incorporation has potential to facilitate timely targeted interventions and personalized management. However, factors influencing biomarker performance, optimal cutoffs, cost-effectiveness, and impact on patient outcomes require robust validation across diverse settings before widespread implementation. Addressing these limitations through ongoing research can help translate novel biomarkers into improved detection, prognosis, and management of AKI in clinical practice.

Electrochemical and Optical Carbon Dots and Glassy Carbon Biosensors: A Review on Their Development and Applications in Early Cancer Detection

Cancer remains one of the leading causes of mortality worldwide, making early detection a critical factor in improving patient outcomes and survival rates. Developing advanced biosensors is essential for achieving early detection and accurate cancer diagnosis. This review offers a comprehensive overview of the development and application of carbon dots (CDs) and glassy carbon (GC) biosensors for early cancer detection. It covers the synthesis of CDs and GC, electrode fabrication methods, and electrochemical and optical transduction principles. This review explores various biosensors, including enzymatic and non-enzymatic, and discusses key biomarkers relevant to cancer detection. It also examines characterization techniques for electrochemical and optical biosensors, such as electrochemical impedance spectroscopy, cyclic voltammetry, UV-VIS, and confocal microscopy. The findings highlight the advancements in biosensor performance, emphasizing improvements in sensitivity, selectivity, and stability, as well as underscoring the potential of integrating different transduction methods and characterization approaches to enhance early cancer detection.

Skin Tape Stripping Reveals Distinct Biomarker Profiles in Chronic Hand Eczema of Patients With and Without Comorbid Atopic Dermatitis

INTRODUCTION

Chronic hand eczema (CHE) is a highly prevalent inflammatory skin condition which is often resistant to conventional treatments. Molecular insights of CHE remain limited. Tape stripping combined with high-throughput RNA sequencing can now provide a better insight into CHE pathogenesis in a minimally invasive fashion.

METHODS

We collected tape strip samples from lesional and non-lesional skin of 66 patients with moderate-to-severe CHE, comprising 33 with and 33 without comorbid atopic dermatitis (AD), and performed bulk RNA sequencing. Results were compared to tape strips from palmar skin of age/race/sex-matched healthy controls (HC). Differentially expressed genes (DEGs) (fold change/FCH > 1.5 and false discovery rate/FDR < 0.05) were calculated and correlated with clinical severity scores including hand eczema severity index (HECSI) and modified total lesion symptoms score (mTLSS).

RESULTS

Tape strip isolates detected a common phenotype in CHE lesions regardless of AD status, including upregulated type-1 (IL12RB2, IFNGR1, IFNGR2, MX1) and type-2-associated inflammatory mediators (CCL22, CCL24, OX40/TNFRSF4, TSLPR/CRLF2, GATA3), paralleled by downregulated epidermal barrier markers (i.e., FLG or LORICRIN). Non-lesional skin demonstrated a similar, albeit milder, dysregulation pattern, with additional reduction in type-17 pathways. Lesional skin of CHE patients without AD showed greater skewing towards type-1 immunity (IL15RA, CXCL9), while CHE from AD patients showed a more pronounced type-2 inflammatory pattern (IL13, CCL17) and their gene expression biomarkers had greater and more significant correlations with clinical severity markers.

CONCLUSION

Tape stripping can capture detailed immune and skin barrier abnormalities in CHE and identify potential novel subtype-specific treatment targets. Stronger correlations in patients with AD suggest a more homogenous disease phenotype than in CHE non-AD patients.

TRIAL REGISTRATION

NCT03728504.

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.

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.

Clinical and omics biomarkers in osteoarthritis diagnosis and treatment

Osteoarthritis (OA) is a prevalent degenerative joint disease that significantly impacts the quality of life for hundreds of millions, and is a major cause of disability. Despite this, diagnostic and therapeutic options for OA are still limited. With advances in molecular biology, an increasing number of OA biomarkers have been identified, which not only enhances our understanding of OA pathogenesis, but also offers new approaches for OA diagnosis and treatment. This review discussed the research progress on traditional OA biomarkers, and analyzed the application of various omics, including genomics, transcriptomics, proteomics, and metabolomics, in the diagnosis and treatment of OA. Furthermore, we explored how integrating multi-omics methods can reveal interactions among different biomolecules and their roles in the development of OA. This emerging interdisciplinary approach not only provides a more comprehensive understanding of the fundamental biological characteristics of OA, but also aids in identifying new integrated biomarkers, thereby allowing for more accurate predictions of disease progression and treatment responses. The identification and development of biomarkers offer new perspectives in understanding OA, enhancing the specificity and sensitivity of biological diagnostic markers, providing a basis for the design of targeted drugs, and ultimately advancing the development of precision diagnosis and treatment strategies in clinical OA. This study provides an overview of both commonly used and emerging biomarkers of OA which is beneficial for a more accurate, timely, effective clinical diagnosis and treatment for OA.

Diagnosis, Severity, and Prognosis from Potential Biomarkers of COVID-19 in Urine: A Review of Clinical and Omics Results

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has spurred an extraordinary scientific effort to better understand the disease's pathophysiology and develop diagnostic and prognostic tools to guide more precise and effective clinical management. Among the biological samples analyzed for biomarker identification, urine stands out due to its low risk of infection, non-invasive collection, and suitability for frequent, large-volume sampling. Integrating data from omics studies with standard biochemical analyses offers a deeper and more comprehensive understanding of COVID-19. This review aims to provide a detailed summary of studies published to date that have applied omics and clinical analyses on urine samples to identify potential biomarkers for COVID-19. In July 2024, an advanced search was conducted in Web of Science using the query: "covid* (Topic) AND urine (Topic) AND metabol* (Topic)". The search included results published up to 14 October 2024. The studies retrieved from this digital search were evaluated through a two-step screening process: first by reviewing titles and abstracts for eligibility, and then by retrieving and assessing the full texts of articles that met the specific criteria. The initial search retrieved 913 studies, of which 45 articles were ultimately included in this review. The most robust biomarkers identified include kynurenine, neopterin, total proteins, red blood cells, ACE2, citric acid, ketone bodies, hypoxanthine, amino acids, and glucose. The biological causes underlying these alterations reflect the multisystemic impact of COVID-19, highlighting key processes such as systemic inflammation, renal dysfunction, critical hypoxia, and metabolic stress.

CHI3L1 in Multiple Sclerosis-From Bench to Clinic

Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system (CNS) with a complex and not fully understood etiopathological background involving inflammatory and neurodegenerative processes. CHI3L1 has been implicated in pathological conditions such as inflammation, injury, and neurodegeneration, and is likely to play a role in the physiological development of the CNS. CHI3L1 is primarily produced by CNS macrophages, microglia, and activated astrocytes. The CHI3L1 expression pattern in MS lesions might support the important role of astrocytes in modulating inflammatory processes in this disease. The potential applications of CHI3L1 as a biomarker in MS are multifactorial. The measurement of CHI3L1 in body fluids might find its role in the early diagnosis of MS. In further stages, the monitoring of CHI3L1 levels might provide information on disease severity and progression, enabling a better adjustment of therapeutic strategies. Importantly, CHI3L1 might potentially serve as a marker of ongoing glial activation, reflecting the dynamic response of the CNS cells to the inflammatory processes in MS. Although preliminary findings have been promising, further research is needed to validate the utility of CHI3L1 measurements in the diagnosis and prediction of the progression of MS. Additionally, comparisons with other biomarkers might be useful in clinical practice.

Rectal Cancer: Exploring Predictive Biomarkers Through Molecular Pathways Involved in Carcinogenesis

In 2022, colorectal cancer (CCR) had the second-highest incidence in Europe, preceded only by breast cancer [...].

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.

Markers of inflammation in patients with generalized anxiety disorder

Purpose: The aim of this study was to analyze the systemic immune response index, systemic immune inflammation index, neutrophil/HDL, lymphocyte/HDL, platelet/HDL, and monocyte/HDL ratio, used as inflammatory markers in patients with generalized anxiety disorder (GAD). Materials and Methods: A total of 864 participants, including 432 healthy controls and 432 patients diagnosed with GAD, were included in this study. High-density lipoprotein (HDL-c) levels, neutrophil, monocyte, lymphocyte, and platelet counts; systemic inflammatory response index (SIRI), systemic immune-inflammatory index (SII), neutrophil/high-density lipoprotein ratio (NHR), lymphocyte/high-density lipoprotein ratio (LHR), monocyte/high-density lipoprotein ratio (MHR), and platelet/high-density lipoprotein ratio (PHR) were calculated. Results: The median age of the case group was 35.50 (interquartile range: 28.00-44.00), while the median age of the control group was 35.00 (interquartile range: 28.00-44.00). In the GAD group, neutrophils, monocytes, SII, SIRI, NHR, LHR, MHR, and PHR were significantly higher, while platelets and HDL were significantly lower. Conclusions: This study highlights that elevated systemic inflammation markers, such as SII and SIRI, along with altered HDL ratios, may be associated with GAD. The findings support the hypothesis that inflammation plays a role in the etiopathogenesis of GAD, potentially contributing to both diagnosis and management.

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.

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.

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.