Work-related stress (WRS) is associated with the development of various health issues and long-term absence from the workplace. Adequate measurement of WRS is essential to assess its prevalence, risks, and effectiveness of preventive interventions. The aim of this review was to provide an overview of different categories of WRS assessment: 1) self-assessment, 2) external assessment, and 3) biomarkers.

The databases MEDLINE, PsycINFO, EMBASE, CINAHL, and Web of Science have been searched until July 2024 for studies comprising self-assessment or external assessment of WRS, and WRS biomarkers. The self-assessment studies were further evaluated following the COSMIN guidelines.

In this review, a total of 15,749 articles were screened. The final analysis included 53 studies on self-assessment of WRS, 33 articles on external assessment of WRS and 167 articles on stress biomarkers. Within self-assessment studies, four instruments were included in the analysis: Job Content Questionnaire, Effort Reward Imbalance Questionnaire, Copenhagen Psychosocial Questionnaire II and the Demand-Control-Support Questionnaire. The studies applying external assessment used job-exposure matrices, work register data, ethnography, digital tools, and external observation. The identified WRS biomarkers were associated with the sympathetic adrenal medullary axis, the hypothalamic pituitary adrenal axis, immune response and inflammation, and haemostatic, metabolic and (epi)genetic biomarkers.

The available evidence does not support the claim that there is a singular golden standard for assessing WRS. Inclusion of objective parameters and the interaction with subjective parameters and biological markers has to be studied to receive a broader view of WRS.

Although studies have evaluated the presence of cell-free DNA and neutrophil extracellular traps (NETs) in acute respiratory distress syndrome (ARDS), the kinetics of NET formation during the early ICU admission and whether plasma NET markers correlate with clinical outcomes in patients with moderate-to-severe hypoxemia remain unknown. We sought to determine whether serial plasma NET marker levels in study participants collected over 48 h post enrollment predict disease severity and mortality in non-COVID-19 ARDS patients.

We obtained previously collected plasma samples (trial enrollment, 24 h, 48 h) from 200 randomly selected ARDS participants in the completed Reevaluation of Systemic Early Neuromuscular Blockade (ROSE) Trial, as well as from 20 healthy control donors. We determined plasma levels of surrogate biomarkers for NETs using a cell-free DNA fluorescence assay and a plasma myeloperoxidase (MPO)-DNA complex ELISA. We correlated these surrogate biomarker levels with clinical outcomes from the ROSE trial study participants.

ROSE plasma samples demonstrated significantly higher NET levels compared to healthy donor controls. Individual study participant NET levels did not change over the forty-eight hours after trial enrollment. Higher levels of both surrogate markers correlated with fewer ventilator-free days, but only cell free-DNA correlated with mortality and higher illness severity scores.

Surrogate markers for plasma NET levels measured in patients with moderate or severe ARDS correlate directly with adverse clinical outcomes and may serve as biomarkers for predicting severe disease. Further studies of surrogate biomarkers for NET formation in moderate-to-severe ARDS are warranted.

Acute pain, a critical aspect of patient care, presents a challenge due to its subjective nature and complex biological underpinnings. Biomarkers for acute pain promise a paradigm shift in how pain is perceived, diagnosed, and managed. The study of genetic, inflammatory, and neurotransmission markers associated with pain experience may hold the key for the development of personalized and effective pain management strategies. This narrative review explores the neurobiological pathways of acute pain, encompassing inflammatory responses and neurotransmission mechanisms. It synthesizes current research on the identification and clinical application of biomarkers, emphasizing their potential to enhance diagnostic precision, treatment effectiveness, and risk prediction. We underscore the promising role of acute pain biomarkers in identifying patients at risk for developing acute and potentially chronic pain, predicting patients' response to pharmacological interventions, and aiding in the development of novel therapeutic and pain preventive strategies. The evolving landscape of biomarker research not only deepens our understanding of pain mechanisms but also lays the foundation for more tailored and patient-specific healthcare interventions.

The highly sensitive detection and imaging of biomarkers are critical for early diagnosis, treatment, and prognosis monitoring. The unique size and structure of fluorescent nanomaterials provide key benefits such as excellent photostability, high fluorescence quantum yield, and tunable excitation and emission wavelengths. These properties have led to the widespread application of nanomaterials in fluorescent biomarkers detection and imaging. In this review, we began by introducing the composition of fluorescent probes and discussing the underlying sensing mechanisms. We then summarized recent advances in the use of fluorescent nanomaterials such as quantum dots (QDs), metal nanoclusters (MNCs), carbon dots (CDs), and metal-organic frameworks (MOFs) for biomarker detection and imaging. Additionally, we highlighted the applications of fluorescent nanomaterials in the detection and imaging of small molecules, biomacromolecules, and various biomarkers, including metal ions, bacteria, and circulating tumor cells (CTCs). The challenges and future prospects of fluorescent nanomaterials in biomarker detection and imaging were also discussed. We anticipate that fluorescent nanomaterials will have profound implications for clinical biomarker detection and imaging, with considerable application in both academic research and industrial applications.

The complex two-way relationship between diabetes mellitus (DM) and cancer poses a significant global health challenge. Shared mechanisms such as hyperinsulinemia, chronic inflammation, and oxidative stress create an environment that fosters cancer development, increasing the risk for certain cancers in individuals with diabetes, including pancreatic, colorectal, breast, liver, and endometrial malignancies. In this context, biomarkers emerge as essential tools, offering a means to untangle the connections between these two conditions by providing insights into early detection, diagnosis, prognosis, and treatment monitoring. For diabetic patients, biomarkers are particularly valuable as they help differentiate between changes caused by cancer and those driven by metabolic imbalances, illuminating disease evolution. This review examines the unique challenges encountered by diabetic patients with cancer, emphasizing the contributions of targeted biomarkers in identifying cancer subtypes, predicting outcomes, and guiding treatment decisions. We explore organ-specific biomarker profiles across various cancers, including pancreatic, colorectal, breast, liver, and lung, highlighting their potential to enhance diagnostic precision and enable personalized treatment strategies. Ultimately, we aim to illustrate how a deeper understanding of biomarker signatures can inform innovative clinical approaches and improve care for patients facing the dual burden of diabetes and cancer.

Milk protein contains high concentrations of branched-chain amino acids (BCAA) that play a critical role in anabolism and are implicated in the onset of obesity and chronic disease. Characterizing BCAA catabolism in the postprandial phase could elucidate the impact of protein intake on obesity risk established in the "early protein hypothesis."

To examine the acute effects of protein content of young child formulas as test meals on BCAA catabolism, observing postprandial plasma concentrations of BCAA in relation to their degradation products.

The TOMI Add-On Study is a randomized, double-blind crossover study in which 27 healthy adults consumed 2 isocaloric young child formulas with alternating higher (HP) and lower (LP) protein and fat content as test meals during separate interventions, while 9 blood samples were obtained over 5 hours. BCAA, branched-chain α-keto acids (BCKA), and acylcarnitines were analyzed using a fully targeted HPLC-ESI-MS/MS approach.

Mean concentrations of BCAA, BCKA, and acylcarnitines were significantly higher after HP than LP over the 5 postprandial hours, except for the BCKA α-ketoisovalerate (KIVA). The latter metabolite showed higher postprandial concentrations after LP. With increasing mean concentrations of BCAA, concentrations of corresponding BCKA, acylcarnitines, and urea increased until a breakpoint was reached, after which concentrations of degradation products decreased (for all metabolites except valine and KIVA and Carn C4:0-iso).

BCAA catabolism is markedly influenced by protein content of the test meal. We present novel evidence for the apparent saturation of the BCAA degradation pathway in the acute postprandial phase up to 5 hours after consumption.

Biomarkers play a pivotal role in the selection and enrollment of trial participants. Particularly, predictive biomarkers help tailor medical care to individual patients; however, also prognostic biomarkers require consideration at the design stage. At the time of initiating a clinical trial, there may be uncertainty about whether a biomarker is predictive or prognostic, and the trial design may need to account for this. Relevant discussions between drug developers and regulators on the role of a biomarker in a specific drug development program are expected to take place during Scientific Advice (SA) procedures. SA procedures at the European Medicines Agency from January 1, 2018, to December 31, 2020, were systematically searched for methodological discussions around the use of predictive or prognostic biomarkers. The final analysis included 45 SA procedures for which key characteristics were summarized quantitatively. Selected methodological issues such as the cutoff selection of continuous biomarkers or study design considerations were elaborated in a qualitative summary. Our results identify commonly encountered points for discussion between drug developers and the European Medicines Agency for biomarker-informed patient selection and enrollment. Identified topics addressed during SA procedures include cutoff selection, study design, multiplicity control, and data-driven subgroup selection. The majority of the identified 45 SA procedures concern development programs in oncology. Addressing these issues upfront will allow for an improved dialogue between drug developers and regulators and support the drug development program and ultimately patient-centered access to medicines.

Both elevated and blunted cortisol responses have been associated with depression. Previous Mendelian randomization (MR) studies have largely ruled out cortisol as a cause of depression. Based on the attenuation hypothesis, this MR study used depression as exposure to assess whether cortisol might be a consequence and therefore a biomarker of depression.

Strong (P < 5 × 10-8) and independent (r2 < 0.001) single nucleotide polymorphisms (SNPs) associated with broadly defined depression (294,322 cases, 741,438 controls) were used as instruments. These were applied to genetic associations with morning, fasting, and random plasma cortisol in the CORtisol NETwork (CORNET) consortium (n = 25,314), METabolic Syndrome in Men (METSIM) study (n = 6667), and Canadian Longitudinal Study on Aging (CLSA) cohort (n = 8299). Multivariable MR, adjusting for childhood maltreatment and major mental disorders, was conducted to address potential horizontal pleiotropy from dichotomous depression. Instruments were also selected by evidence of colocalization with major depressive disorder to address non-specificity.

Using 133 SNPs as instruments, depression was inversely associated with morning plasma cortisol (β per log-odds of genetic liability to depression = -0.107 [95 % CI, -0.181 to -0.032]) in the CORNET consortium. Replication in the METSIM study (β = -0.203 [95 % CI, -0.367 to -0.040]) and CLSA cohort (β = -0.091 [95 % CI, -0.220 to 0.039]) showed consistent but not always significant associations. Multivariable MR and follow-up analysis incorporating colocalization supported these findings.

Consistent with the attenuation hypothesis, blunted cortisol response appeared to be a consequence and potentially a biomarker of depression. Future studies are needed to provide more interpretable effect sizes and validate other biomarker measures.

Identifying clinical or biological risk factors for disease plays a critical role in enabling earlier disease diagnosis, prognostic outcomes assessment, and may inform disease prevention or monitoring practices. One framework commonly examined is understanding the association between a risk factor ever occurring in follow-up and the future risk of an outcome. If such an association is found, researchers are often asked to validate the finding. External validation is often infeasible, and validation may only be performed internally. However, the performance of internal validation methods in the setting of a time-dependent binary indicator and a time-to-event outcome has not been well-studied. We emulated a dataset motivated by real-world serial biomarker observations and performed extensive simulation studies to evaluate the performance of a resampling-based method to internally validate the association between a time-dependent binary indicator and a time-to-event outcome. We found the resampling-based method achieved optimal power for validating such an association while maintaining good Type I error control.

The study by Ohno et al provides valuable insights into the role of leucine-rich alpha-2-glycoprotein (LRG) as a potential biomarker for identifying small bowel lesions in Crohn's disease (CD). However, several methodological challenges hinder its immediate use in clinical practice. Notably, the current research was retrospective, lacks comparative studies with fecal calprotectin, and did not provide long-term predictive data. Further prospective studies are needed to improve the applicability of LRG. Moreover, integrating LRG with additional biomarkers and employing artificial intelligence techniques may improve its effectiveness in disease monitoring. Future research should address interobserver variability, assess LRG's cost-effectiveness, and standardize endoscopic healing definitions to ensure broader applicability. Advancing these areas is vital for establishing LRG's role in precision medicine strategies for the management of CD.

Characteristics of tumors and patients can be used as predictive biomarkers to guide treatment choice. Although many potential biomarkers are evaluated each year, only few will eventually be used since evidence is usually based on small studies leading to inconclusive results. Such data are often analyzed with Cox proportional hazards regression using a multiplicative interaction term between biomarker and treatment, with insufficient power and possibly biased results. Instead of analyzing patients who do (cases) and do not experience (non-cases) the survival event of interest, case-only analysis with logistic regression has been proposed, however with unknown small sample properties. We evaluated the performance of case-only analysis with bias-eliminating Firth correction and confidence intervals obtained with a profile likelihood method in a simulation study tailored to breast cancer. Our results show that this approach is generally inferior to the full cohort analysis but has acceptable properties when the marker is protective or null among patients treated with the standard treatment, the event rate is low (e.g., a rare event and a protective marker) and treatment assignment is independent of the marker level (e.g., in randomized studies). In such situations, the case-only design offers substantial cost savings. However, the model is sensitive to these assumptions.

Background/Objectives: Currently, the diagnosis of autism spectrum disorder (ASD) relies on behavioral observations, frequently causing delays in early identification. Prognostic markers are essential for customizing therapy and monitoring progress. However, there are currently no recognized biomarkers for ASD. The current systematic review aims to analyze studies on the intestinal metabolome in children (both autistic and non-autistic) to identify potential metabolites for diagnostic and prognostic purposes. Methods: We searched Medline, Scopus, Embase, and Web of Science for relevant publications. Results: We identified 11 studies examining the gut metabolome that distinguished between autistic and non-autistic children. These studies also revealed connections between gut metabolites, developmental scores, and symptoms. The substances identified were associated with metabolic pathways such as amino acids, vitamins, lipids, oxidative stress, glycans, xenobiotics, and nucleotides. Conclusions: These findings suggest metabolic changes that may be linked to the causes or development of autism. Although these observations came from a few reports, only high-quality studies were included in this review. Further research is essential to confirm the identified substances as biomarkers.

Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is a prevalent chronic liver condition characterized by lipid accumulation and inflammation, often progressing to severe liver damage. We aim to review the pathophysiology, diagnostics, and clinical care of MASLD, and review highlights of advances in proteomic technologies. Recent advances in proteomics technologies have improved the identification of novel biomarkers and therapeutic targets, offering insight into the molecular mechanisms underlying MASLD progression. We focus on the application of mass spectrometry-based proteomics including single cell proteomics, proteogenomics, extracellular vesicle (EV-omics), and exposomics for biomarker discovery, emphasizing the potential of blood-based panels for noninvasive diagnosis and personalized medicine. Future research directions are presented to develop targeted therapies and improve clinical outcomes for MASLD patients.

Hematological malignancies present substantial challenges in clinical practice due to their heterogeneity and complex biological profiles. In these diseases, biomarkers - measurable indicators of biological states - are indispensable for diagnosis, prognosis, and therapeutic decision-making. Emerging biomarkers are significantly improving outcomes in hematological cancers by enhancing early detection, refining prognostic assessments, enabling personalized treatment approaches, and optimizing overall patient management. This progress translates into better clinical outcomes and more effective strategies to treat and manage malignancies. The field of biomarker discovery has developed from basic morphological and cytogenetic markers to advanced molecular techniques, including polymerase chain reaction (PCR) and next-generation sequencing (NGS), which have significantly enhanced diagnostic accuracy and led to the development of targeted therapies. Additionally, the recent advent of technologies like mass spectrometry and single-cell RNA sequencing enables comprehensive molecular profiling and reveals novel biomarkers that were previously undetectable. Our aim in this manuscript is to provide a comprehensive overview of recent and novel immunohematological biomarkers, their diagnostic and therapeutic applications, and the future directions of this field.

Breast cancer (BC) is one of the most significant neoplasms globally due to its high incidence and mortality, particularly among females. As a highly heterogeneous pathology, biomarkers are essential for characterizing specific tumors. Currently, several biological processes are well-described in the context of this neoplasm, such as alterations in BRCA1/2, HER, and pathways involving estrogen and progesterone hormone receptors. These studies have enabled the use of these findings as more precise methods for diagnosis, prognosis, and treatment. However, beyond patients who do not exhibit these classic markers, some individuals within the same risk group respond differently to treatment. Therefore, the search for biological markers that can improve diagnosis, aid in stratification, or serve as therapeutic targets is continuous and urgent. Genetic signatures have led to molecular tests currently used in clinical practice, though certain limitations persist. Understanding genetic and epigenetic mechanisms facilitates the identification of potential biomarkers. Biomarker targets must undergo experimental and clinical trials on samples of significant size before reaching clinical utility. In this review, we compile the classical markers and describe the potential use of other markers associated with the biological processes of this neoplasm.

Peri-implantitis is implant-associated inflammation that leads to irreversible loss of surrounding bone. Early diagnosis increases the success of peri-implantitis treatment. Despite various studies associated with this most common complication, early detection of the onset of peri-implantitis remains a major challenge. Molecular biomarkers are applicable detectors for the early detection of numerous diseases and monitoring their development. The present study aimed to predict interactome networks of up/down regulated proteins and analyze drug-gene interaction in peri-implantitis to identify the diagnostic and druggable genes.

In this in silico study, a suitable gene expression profile related to peri-implantitis was retrieved from Gene Expression Omnibus. Screening differentially expressed genes (DEGs) was carried out based on the cut-off criteria |log2 (fold change)|>2 and P < 0.05. Interactome networks were constructed and analyzed by the STRING database (Version: 12.0) and the Cytoscape software (version: 3.9.1). Finally, to investigate drug-gene interaction, detected hub genes were analyzed by DGIdb (version: 5.0.6).

A total of 216 genes were identified as DEGs (129 down-regulated and 87 up-regulated genes) in peri-implantitis. Regarding Cytoscape analysis, FCGR3B, CSF3R, AQP9, TREM1, and P2RY13 were the top 5 hub nodes of up-regulated DEGs, and CXCL10, OASL, IFIT1, RSAD2, and ISG15 were the top 5 hub nodes of down-regulated DEGs. Among these key nods, AQP9, CSF3R, CXCL10, IFIT1, ISG15, OASL, and, FCGR3B were therapeutic targets and had approved drugs.

In this research, seven genes have been identified as druggable genes in peri-implantitis which can be used to treat and diagnose this disease. However, these results and identified genes need to be validated by clinical or experimental methods.

Analytes, from sample preparation, until entering an analytical instrument, are prone to adsorb to surfaces, driven by the chemical properties of the surface and the liquids they are dissolved in. This problem can be addressed with internal standards when a single or few known analytes are quantified that are usually not available in omics. However, minimal to no loss of analytes is the aim. Here, we present a novel assay for qualifying and quantifying interactions responsible for adsorption of molecules to surfaces (APS) by using LC-MS/MS-based differential quantitative analysis. To reflect a broad range of chemical interactions with surfaces, a reference mixture of thousands of tryptic peptides, with known compositions was selected, representing a variety of different chemical characteristics. The assay was tested by investigating the adsorption properties of several different vials with different surface chemistries. A significant number of hydrophobic peptides adsorbed to conventional polypropylene vials. In contrast, only few peptides adsorbed to polypropylene vials, assigned as low-protein-binding. The highest number of peptides adsorbed to glass vials driven by electrostatic interactions. In summary, the new assay is suitable to characterize adsorption properties of different surfaces and to approximate the loss of analytes during sample preparation.

Reproductive efficiency is crucial for animal agriculture. This economically important aspect can be influenced by environmental burdens, nutritional imbalance, and gonadal or gametic malformations of genetic origin. Successful implementation of genomic-driven selective breeding in cattle depends on the reproductive performance of artificial insemination (AI) sires with valuable genomic production traits. Reproduction is traditionally viewed as a complex set of polygenic traits that are negatively impacted by using a small number of often closely related sires selected for AI due to their superior genetics. Despite recent progress, it remains difficult to define relationships between sire genome and variation in sperm phenotypes, even though several types of heritable, non-compensable sperm defects have been identified. In this review, we discuss the concept of sperm quality biomarker discovery and genomics of male fertility. We also outline a multidisciplinary genome-to-phenome approach for investigating heritable mutations and their impacts on bull fertility, sperm phenotypes and paternal contributions to early pregnancy. High-precision phenotyping requires novel, state-of-the-art instrumentation for sperm quality evaluation and development of new biomarkers of sperm quality in farm animals, with potential for incorporation into andrology-specific machine learning protocols and translation to human andrology. We conclude that reproduction is a complex phenotype that can be deciphered and explored for more precise male fertility evaluation and higher reproductive efficiency.

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.

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.

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.

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.

Pulmonary arterial hypertension is a complex and heterogeneous condition, associated with a considerable diagnostic delay, diminished exercise capacity, and poor outcomes. In pulmonary arterial hypertension, biomarker research has become a subject of intense inquiry, and novel circulating biomarkers acknowledged in a multitude of mechanistic pathways are emerging. Beyond the widely used natriuretic peptides, novel biomarkers may provide deeper pathophysiological understanding, support clinical decision-making, and prompt the incorporation of precision medicine by enabling a more precise individual phenotyping. In this state-of-the-art review, the recent advances in circulating biomarkers in pulmonary arterial hypertension from a clinical perspective are discussed, with particular emphasis on the current state of knowledge, gaps in evidence, and future perspectives.

Peptides carry important functions in normal physiological and pathophysiological processes and can serve as clinically useful biomarkers. Given the ability to diffuse passively across endothelial barriers, endogenous peptides can be examined in several body fluids, including among others urine, blood, and cerebrospinal fluid. This review article provides an update on the recently published literature that reports on investigating native peptides in body fluids using mass spectrometry-based platforms, specifically those studies that focus on the application of peptides as biomarkers to improve clinical management. We emphasize on the critical evaluation of their clinical value, how close they are to implementation, and the associated challenges and potential solutions to facilitate clinical implementation. During the last 5 years, numerous studies have been published, demonstrating the increased interest in mass spectrometry for the assessment of endogenous peptides as potential biomarkers. Importantly, the presence of few successful examples of implementation in patients' management and/or in the context of clinical trials indicates that the peptide biomarker field is evolving. Nevertheless, most studies still report evidence based on small sample size, while validation phases are frequently missing. Therefore, a gap between discovery and implementation still exists.

Cancer is a leading cause of world-wide death and a major subject of clinical studies focused on the identification of new diagnostic tools. An in-depth meta-analysis of 244 clinical metabolomics studies of human serum samples highlights a reproducibility crisis. A total of 2,206 unique metabolites were reported as statistically significant across the 244 studies, but 72% (1,582) of these metabolites were identified by only one study. Further analysis shows a random disparate disagreement in reported directions of metabolite concentration changes when detected by multiple studies. Statistical models revealed that 1,867 of the 2,206 metabolites (85%) are simply statistical noise. Only 3 to 12% of these metabolites reach the threshold of statistical significance for a specific cancer type. Our findings demonstrate the absence of a detectable metabolic response to cancer and provide evidence of a serious need by the metabolomics community to establish widely accepted best practices to improve future outcomes.

The identification and characterization of glycopeptides through LC-MS/MS and advanced enrichment techniques are crucial for advancing clinical glycoproteomics, significantly impacting the discovery of disease biomarkers and therapeutic targets. Despite progress in enrichment methods like Lectin Affinity Chromatography (LAC), Hydrophilic Interaction Liquid Chromatography (HILIC), and Electrostatic Repulsion Hydrophilic Interaction Chromatography (ERLIC), issues with specificity, efficiency, and scalability remain, impeding thorough analysis of complex glycosylation patterns crucial for disease understanding.

This review explores the current challenges and innovative solutions in glycopeptide enrichment and mass spectrometry analysis, highlighting the importance of novel materials and computational advances for improving sensitivity and specificity. It outlines the potential future directions of these technologies in clinical glycoproteomics, emphasizing their transformative impact on medical diagnostics and therapeutic strategies.

The application of innovative materials such as Metal-Organic Frameworks (MOFs), Covalent Organic Frameworks (COFs), functional nanomaterials, and online enrichment shows promise in addressing challenges associated with glycoproteomics analysis by providing more selective and robust enrichment platforms. Moreover, the integration of artificial intelligence and machine learning is revolutionizing glycoproteomics by enhancing the processing and interpretation of extensive data from LC-MS/MS, boosting biomarker discovery, and improving predictive accuracy, thus supporting personalized medicine.

The most recognizable implications of tissue aging manifest themselves on the skin. Skin laxity, roughness, pigmentation disorders, age spots, wrinkles, telangiectasia or hair graying are symptoms of physiological aging. Development of the senescent phenotype depends on the interaction between aging cells and remodeling of the skin's extracellular matrix (ECM) that contains collagen and elastic fiber. Aging changes occur due to the combination of both endogenous (gene mutation, cellular metabolism or hormonal agents) and exogenous factors (ultraviolet light, environmental pollutants, and unsuitable diet). However, overproduction of mitochondrial reactive oxygen species (ROS) is a key factor driving cellular senescence. Aging theories have disclosed a range of diverse molecular mechanisms that are associated with cellular senescence of the body. Theories best supported by evidence include protein glycation, oxidative stress, telomere shortening, cell cycle arrest, and a limited number of cell divisions. Accumulation of the ECM damage is suggested to be a key factor in skin aging. Every cell indicates a functional and morphological change that may be used as a biomarker of senescence. Senescence-associated β-galactosidase (SA-β-gal), cell cycle inhibitors (p16INK4a, p21CIP1, p27, p53), DNA segments with chromatin alterations reinforcing senescence (DNA-SCARS), senescence-associated heterochromatin foci (SAHF), shortening of telomeres or downregulation of lamina B1 constitute just an example of aging biomarkers known so far. Aging may also be assessed non-invasively through measuring the skin fluorescence of advanced glycation end-products (AGEs). This review summarizes the recent knowledge on the pathogenesis and clinical conditions of skin aging as well as biomarkers of skin senescence.

A biomarker is defined as a characteristic that is measured as an indicator of a normal biological or pathological process, a response to an exposure or intervention. Biomarkers with a diagnostic approach must identify not only the presence but also the absence of the disease with high precision, so having the biological source of the said marker is of vital importance to ensure precision and accuracy; the aim was to carry out a review of its diagnostic potential. The search strategy was carried out in three databases: PubMed, ScienceDirect, and Scopus. The keywords that were used were as follows: "gingival crevicular fluid", "Biomarker", and "Diagnosis", using the Boolean operator "AND". The filter was used at 10 years. Within the type of molecules most studied, the cytokine family was the most abundant with 25.42% of the studies, followed by metalloproteinases and proteins with 16.9% each one. Studies that included RNA-type genetic material were less frequently found. As has been demonstrated, the use of GCF as a source of biomolecules for diagnostic use has been increasing, both for oral diseases, which reflects the local conditions of the disease; it also has the ability to reflect the development of distant diseases; and this is because GCF is a blood ultrafiltrate.

Hepatocellular carcinoma (HCC) is a leading contributor to cancer-related deaths worldwide and presents significant challenges in diagnosis and treatment due to its heterogeneous nature. The discovery of biomarkers has become crucial in addressing these challenges, promising early detection, precise diagnosis, and personalized treatment plans. Key biomarkers, such as alpha fetoprotein (AFP) glypican 3 (GPC3) and des gamma carboxy prothrombin (DCP) have shown potential in improving clinical results. Progress in proteomic technologies, including next-generation sequencing (NGS), mass spectrometry, and liquid biopsies detecting circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), has deepened our understanding of HCC's molecular landscape. Immunological markers, like PD-L1 expression and tumor-infiltrating lymphocytes (TILs), also play a crucial role in guiding immunotherapy decisions. Despite these advancements, challenges remain in biomarker validation, standardization, integration into clinical practice, and cost-related barriers. Emerging technologies like single-cell sequencing and machine learning offer promising avenues for further exploration. Continued investment in research and collaboration among researchers, healthcare providers, and policymakers is vital to harness the potential of biomarkers fully, ultimately revolutionizing HCC management and improving patient outcomes through personalized treatment approaches.

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.

In pig production, the optimization of artificial insemination (AI) efficiency significantly relies on the accurate assessment of semen quality and fertility of boars. Traditional methods such as conventional seminogram techniques, although long-standing, exhibit limited sensitivity in predicting boar fertility, warranting the exploration of novel molecular markers. This review synthesizes the current knowledge on the utilization of molecular markers for semen quality evaluation and male fertility prediction in boars, providing an in-depth examination of molecular markers in this context. Specifically, the present work delves into the potential of OMICs technologies, encompassing genetic and genomic approaches, transcriptomics, proteomics, and metabolomics. A diverse array of molecular markers, including genomic regions associated with sperm quality and male fertility, chromatin integrity, mitochondrial DNA content, mRNA and non-coding RNA signatures, as well as proteins and metabolites in sperm and seminal plasma, are identified as promising molecular markers for fertility prediction in boars. Furthermore, the need of validating biomarkers and their practical implementation in AI centres is here emphasized. Addressing these considerations and integrating molecular markers within the swine breeding field holds the potential to enhance reproductive management practices and optimize productivity in boar breeding programs. This integration can significantly improve overall efficiency within the pig breeding industry.

Multimodal imaging unfolds as an innovative approach that synergistically employs a spectrum of imaging techniques either simultaneously or sequentially. The integration of computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), positron emission tomography (PET), and optical imaging (OI) results in a comprehensive and complementary understanding of complex biological processes. This innovative approach combines the strengths of each method and overcoming their individual limitations. By harmoniously blending data from these modalities, it significantly improves the accuracy of cancer diagnosis and aids in treatment decision-making processes. Nanoparticles possess a high potential for facile functionalization with radioactive isotopes and a wide array of contrast agents. This strategic modification serves to augment signal amplification, significantly enhance image sensitivity, and elevate contrast indices. Such tailored nanoparticles constructs exhibit a promising avenue for advancing imaging modalities in both preclinical and clinical setting. Furthermore, nanoparticles function as a unified nanoplatform for the co-localization of imaging agents and therapeutic payloads, thereby optimizing the efficiency of cancer management strategies. Consequently, radiolabeled nanoparticles exhibit substantial potential in driving forward the realms of multimodal imaging and theranostic applications. This review discusses the potential applications of molecular imaging in cancer diagnosis, the utilization of nanotechnology-based radiolabeled materials in multimodal imaging and theranostic applications, as well as recent advancements in this field. It also highlights challenges including cytotoxicity and regulatory compliance, essential considerations for effective clinical translation of nanoradiopharmaceuticals in multimodal imaging and theranostic applications.

Background/Objectives: Spinal tuberculosis (STB) is frequently misdiagnosed due to the multitude of symptoms it presents with. This review aimed to investigate the biomarkers that have the potential to accurately diagnose spinal TB in its early stages. Methods: A systematic search was conducted across multiple databases, yielding a diverse range of biomarkers categorized into complete blood count parameters, host inflammatory responses, bacterial antigens, and RNA-based markers. This review included studies on spinal tuberculosis patients, including blood serum biomarkers, while exclusion criteria included pediatric cases, cerebrospinal fluid or imaging biomarkers, co-infection with other bacteria, viruses, comorbidities, tumors, immune diseases, HIV infection, metabolic disorders, animal studies, opinion papers, and biomarkers relevant to health problems outside the disease. QUADAS-2 was used as a quality assessment tool for this review. This review identifies several promising biomarkers with significant diagnostic potential. Results: The neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), IFN-γ, CXCR3, CXCL9, CXCL10, PSMB9, STAT1, TAP1, and specific miRNA combinations demonstrated noteworthy diagnostic accuracy in distinguishing STB from other spinal pathologies. Additionally, these biomarkers offer insights into disease severity and progression. The review also highlighted the importance of combining multiple biomarkers to enhance diagnostic precision. This comprehensive systematic review underscores the potential of biomarkers to revolutionize the diagnosis of spinal tuberculosis. By integrating these markers into clinical practice, healthcare providers can achieve earlier and more accurate diagnosis, leading to improved patient care and outcomes. Conclusions: The combination of multiple biomarkers, including NLR, PSMB9, STAT1, and specific miRNAs, demonstrates promising diagnostic accuracy.

Over the last decade, a new paradigm for cancer therapies has emerged which leverages the immune system to act against the tumor. The novel mechanism of action of these immunotherapies has also introduced new challenges to drug development. Biomarkers play a key role in several areas of early clinical development of immunotherapies including the demonstration of mechanism of action, dose finding and dose optimization, mitigation and prevention of adverse reactions, and patient enrichment and indication prioritization. We discuss statistical principles and methods for establishing the prognostic, predictive aspect of a (set of) biomarker and for linking the change in biomarkers to clinical efficacy in the context of early development studies. The methods discussed are meant to avoid bias and produce robust and reproducible conclusions. This review is targeted to drug developers and data scientists interested in the strategic usage and analysis of biomarkers in the context of immunotherapies.

This review explores the evolving landscape of blood biomarkers in the diagnosis of tuberculosis (TB), focusing on biomarkers derived both from the pathogen and the host. These biomarkers provide critical insights that can improve diagnostic accuracy and timeliness, essential for effective TB management. The document highlights recent advancements in molecular techniques that have enhanced the detection and characterization of specific biomarkers. It also discusses the integration of these biomarkers into clinical practice, emphasizing their potential to revolutionize TB diagnostics by enabling more precise detection and monitoring of the disease progression. Challenges such as variability in biomarker expression and the need for standardized validation processes are addressed to ensure reliability across different populations and settings. The review calls for further research to refine these biomarkers and fully harness their potential in the fight against TB, suggesting a multidisciplinary approach to overcome existing barriers and optimize diagnostic strategies. This comprehensive analysis underscores the significance of blood biomarkers as invaluable tools in the global effort to control and eliminate TB.

RASopathies are a group of related genetic disorders caused by mutations in genes within the RAS/MAPK signaling pathway. This pathway is crucial for cell division, growth, and differentiation, and its disruption can lead to a variety of developmental and health issues. RASopathies present diverse clinical features and pose significant diagnostic and therapeutic challenges. Studying the landscape of biomarkers in RASopathies has the potential to improve both clinical practices and the understanding of these disorders. This review provides an overview of recent discoveries in RASopathy molecular profiling, which extend beyond traditional gene mutation analysis. mRNAs, non-coding RNAs, protein expression patterns, and post-translational modifications characteristic of RASopathy patients within pivotal signaling pathways such as the RAS/MAPK, PI3K/AKT/mTOR, and Rho/ROCK/LIMK2/cofilin pathways are summarized. Additionally, the field of metabolomics holds potential for uncovering metabolic signatures associated with specific RASopathies, which are crucial for developing precision medicine. Beyond molecular markers, we also examine the role of histological characteristics and non-invasive physiological assessments in identifying potential biomarkers, as they provide evidence of the disease's effects on various systems. Here, we synthesize key findings and illuminate promising avenues for future research in RASopathy biomarker discovery, underscoring rigorous validation and clinical translation.

With the rapid adoption of immunotherapy for the treatment of cancer comes the pressing need for readily accessible biomarkers to guide immunotherapeutic strategies and offer insights into outcomes with specific treatments. Regular sampling of solid tumour tissues outside of melanoma for immune monitoring is not often feasible; conversely, routine, frequent interrogation of circulating immune biomarkers is entirely possible. As immunotherapies and immune checkpoint inhibitors, in particular, are more widely used in first-line, neoadjuvant, and metastatic settings, the discovery and validation of peripheral immune biomarkers are urgently needed across solid tumour types for improved prediction and prognostication of clinical outcomes in response to immunotherapy, as well as elucidation of mechanistic underpinnings of the intervention. Careful experimental design, encompassing both retrospective and prospective studies, is required in such biomarker identification studies, and concerted efforts are essential for their advancement into clinical settings.

In this review, we summarize shared immune features between the tumour microenvironment and systemic circulation, evaluate exploratory peripheral immune biomarker studies, and discuss associations between candidate biomarkers with clinical outcomes. We also consider integration of multiple peripheral immune parameters for better prediction and prognostication and discuss considerations in study design to further evaluate the clinical utility of candidate peripheral immune biomarkers for immunotherapy of solid tumours.

Peripheral immune biomarkers are critical for improved prediction and prognostication of clinical outcomes for patients with solid tumours treated with immune checkpoint inhibition. Candidate peripheral biomarkers, such as cytokines, soluble factors, and immune cells, have potential as biomarkers to guide immunotherapy of solid tumours. Multiple peripheral immune parameters may be integrated to improve prediction and prognostication. The potential of peripheral immune biomarkers to guide immunotherapy of solid tumours requires critical work in biomarker discovery, validation, and standardization.

An inadequate biomarker validation can affect many patients' diagnosis, treatment, and follow-up. Therefore, special interest should be placed on performing these analyses correctly so that biomarkers can be applicable to patients and evidence of their clinical usefulness can be generated. A methodological work on the concept of biomarkers is presented, as well as the difficulties associated with the methodological approach to their development, validation, and implementation in clinical practice.

The tumor microenvironment (TME) plays a fundamental role in tumorigenesis, tumor progression, and anti-cancer immunity potential of emerging cancer therapeutics. Understanding inter-patient TME heterogeneity, however, remains a challenge to efficient drug development. This article applies recent advances in machine learning (ML) for survival analysis to a retrospective study of NSCLC patients who received definitive surgical resection and immune pathology following surgery. ML methods are compared for their effectiveness in identifying prognostic subtypes. Six survival models, including Cox regression and five survival machine learning methods, were calibrated and applied to predict survival for NSCLC patients based on PD-L1 expression, CD3 expression, and ten baseline patient characteristics. Prognostic subregions of the biomarker space are delineated for each method using synthetic patient data augmentation and compared between models for overall survival concordance. A total of 423 NSCLC patients (46% female; median age [inter quantile range]: 67 [60-73]) treated with definite surgical resection were included in the study. And 219 (52%) patients experienced events during the observation period consisting of a maximum follow-up of 10 years and median follow up 78 months. The random survival forest (RSF) achieved the highest predictive accuracy, with a C-index of 0.84. The resultant biomarker subtypes demonstrate that patients with high PD-L1 expression combined with low CD3 counts experience higher risk of death within five-years of surgical resection.

Predictive biomarkers are essential for personalized medicine since they select the best treatment for a specific patient. However, of all biomarkers that are evaluated, only few are eventually used in clinical practice. Many promising biomarkers may be erroneously abandoned because they are investigated in small studies using standard statistical techniques which can cause small sample bias or lack of power. The standard technique for failure time endpoints is Cox proportional hazards regression with a multiplicative interaction term between binary variables of biomarker and treatment. Properties of this model in small studies have not been evaluated so far, therefore we performed a simulation study to understand its small sample behavior. As a remedy, we applied a Firth correction to the score function of the Cox model and obtained confidence intervals (CI) using a profile likelihood (PL) approach. These methods are generally recommended for small studies of different design. Our results show that a Cox model estimates the biomarker-treatment interaction term and the treatment effect in one of the biomarker subgroups with bias, and overestimates their standard errors. Bias is however reduced and power is increased with Firth correction and PL CIs. Hence, the modified Cox model and PL CI should be used instead of a standard Cox model with Wald based CI in small studies of predictive biomarkers.

Mechanical ventilation, a lifesaving intervention in critical care, can lead to damage in the extracellular matrix (ECM), triggering inflammation and ventilator-induced lung injury (VILI), particularly in conditions such as acute respiratory distress syndrome (ARDS). This review discusses the detailed structure of the ECM in healthy and ARDS-affected lungs under mechanical ventilation, aiming to bridge the gap between experimental insights and clinical practice by offering a thorough understanding of lung ECM organization and the dynamics of its alteration during mechanical ventilation.

Focusing on the clinical implications, we explore the potential of precise interventions targeting the ECM and cellular signaling pathways to mitigate lung damage, reduce inflammation, and ultimately improve outcomes for critically ill patients. By analyzing a range of experimental studies and clinical papers, particular attention is paid to the roles of matrix metalloproteinases (MMPs), integrins, and other molecules in ECM damage and VILI. This synthesis not only sheds light on the structural changes induced by mechanical stress but also underscores the importance of cellular responses such as inflammation, fibrosis, and excessive activation of MMPs.

This review emphasizes the significance of mechanical cues transduced by integrins and their impact on cellular behavior during ventilation, offering insights into the complex interactions between mechanical ventilation, ECM damage, and cellular signaling. By understanding these mechanisms, healthcare professionals in critical care can anticipate the consequences of mechanical ventilation and use targeted strategies to prevent or minimize ECM damage, ultimately leading to better patient management and outcomes in critical care settings.

Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive nature and the implications in precision cancer management. Recently, liquid biopsy has been further expanded to profile glycoproteins, which are the products of post-translational modifications of proteins and play key roles in both normal and pathological processes, including cancers. The advancements in chemical and mass spectrometry-based technologies and artificial intelligence-based platforms have enabled extensive studies of cancer and organ-specific changes in glycans and glycoproteins through glycomics and glycoproteomics. Glycoproteomic analysis has emerged as a promising tool for biomarker discovery and development in early detection of cancers and prediction of treatment efficacy including response to immunotherapies. These biomarkers could play a crucial role in aiding in early intervention and personalized therapy decisions. In this review, we summarize the significant advance in cancer glycoproteomic biomarker studies and the promise and challenges in integration into clinical practice to improve cancer patient care.

Chronic inflammatory diseases (CIDs) are debilitating and potentially lethal illnesses that affect a large proportion of the global population. Osteopathic manipulative treatment (OMT) is a manual therapy technique developed and performed by osteopathic physicians that facilitates the body's innate healing processes. Therefore, OMT may prove a beneficial anti-inflammatory modality useful in the management and treatment of CIDs. This work aims to objectively evaluate the therapeutic benefits of OMT in patients with various CIDs. In this review, a structured literature search was performed. The included studies involving asthma, chronic obstructive pulmonary disease, irritable bowel syndrome, ankylosing spondylitis, and peripheral arterial disease were selected for this work. Various OMT modalities, including lymphatic, still, counterstain, and muscle energy techniques, were utilized. Control treatments included sham techniques, routine care, or no treatment. OMT utilization led to variable patient outcomes in individuals with pathologies linked to CID.

In recent years, EVs have emerged as promising vehicles for coding and non-coding RNAs (ncRNAs), which have demonstrated remarkable potential as biomarkers for various diseases, including chronic liver diseases (CLDs). EVs are small, membrane-bound particles released by cells, carrying an arsenal of ncRNAs, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and other ncRNA species, such as piRNAs, circRNAs, and tsRNAs. These ncRNAs act as key regulators of gene expression, splicing, and translation, providing a comprehensive molecular snapshot of the cells of origin. The non-invasive nature of EV sampling, typically via blood or serum collection, makes them highly attractive candidates for clinical biomarker applications. Moreover, EV-encapsulated ncRNAs offer unique advantages over traditional cell-free ncRNAs due to their enhanced stability within the EVs, hence allowing for their detection in circulation for extended periods and enabling more sensitive and reliable biomarker measurements. Numerous studies have investigated the potential of EV-enclosed ncRNAs as biomarkers for CLD. MiRNAs, in particular, have gained significant attention due to their ability to rapidly respond to changes in cellular stress and inflammation, hallmarks of CLD pathogenesis. Elevated levels of specific miRNAs have been consistently associated with various CLD subtypes, including metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction-associated steatohepatitis (MASH), and chronic hepatitis B and C. LncRNAs have also emerged as promising biomarkers for CLD. These transcripts are involved in a wide range of cellular processes, including liver regeneration, fibrosis, and cancer progression. Studies have shown that lncRNA expression profiles can distinguish between different CLD subtypes, providing valuable insights into disease progression and therapeutic response. Promising EV-enclosed ncRNA biomarkers for CLD included miR-122 (elevated levels of miR-122 are associated with MASLD progression and liver fibrosis), miR-21 (increased expression of miR-21 is linked to liver inflammation and fibrosis in CLD patients), miR-192 (elevated levels of miR-192 are associated with more advanced stages of CLD, including cirrhosis and HCC), LncRNA HOTAIR (increased HOTAIR expression is associated with MASLD progression and MASH development), and LncRNA H19 (dysregulation of H19 expression is linked to liver fibrosis and HCC progression). In the present review, we focus on the EV-enclosed ncRNAs as promising tools for the diagnosis and monitoring of CLD of various etiologies.

Acute kidney injury is a prevalent disease in hospitalized patients and is continuously increasing worldwide. Various efforts have been made to define and classify acute kidney injury to understand the progression of this disease. Furthermore, deviations from structure and kidney function and the current diagnostic guidelines are not adequately placed due to baseline serum creatinine values, which are rarely known and estimated based on glomerular function rate, resulting in misclassification of acute kidney injury staging. Hence, the current guidelines are still developing to improve and understand the clinical implications of risk factors and earlier predictive biomarkers of acute kidney injury. Yet, studies have indicated disadvantages and limitations with the current acute kidney injury biomarkers, including lack of sensitivity and specificity. Therefore, the present narrative review brings together the most current evidenced-based practice and literature associated with the limitations of the gold standard for acute kidney injury diagnoses, the need for novel acute kidney injury biomarkers, and the process for biomarkers to be qualified for diagnostic use under the following sections and themes. The introduction section situates the anatomy and normal and abnormal kidney functions related to acute kidney injury disorders. Guidelines in providing acute kidney injury definitions and classification are then considered, followed by a discussion of the disadvantages of standard markers used to diagnose acute kidney injury. Characteristics of an ideal acute kidney injury biomarker are discussed concerning sensitivity, specificity, and anatomic location of injury. A particular focus on the role and function of emerging biomarkers is discussed in relation to their applications and significance to the prognosis and severity of acute kidney injury. Findings show emerging markers are early indicators of acute kidney injury prediction in different clinical settings. Finally, the process required for a biomarker to be applied for diagnostic use is explained.

Adenosine triphosphate (ATP) is a small molecule that is released to the urine from bladder urothelial cells and the bladder mucosal band of the human body. In certain cases, ATP can serve as a biomarker in bladder disease. For the practical applicability of luminescent sensors for ATP in urine, it is significant to find a new strategy for making the detection progress simple and available for in-field urine analysis. Here, a novel luminescent lanthanide coordination polymer (Tb-BPA) was designed and synthesized for quick and sensitive detection of ATP through luminescence quenching with a quenching constant of 4.90 × 103 M-1 and a detection limit of 0.55 × 10-6 M. Besides, Tb-BPA has excellent anti-interference ability and can detect ATP in simulated urine with a small relative standard deviation (<4%). Moreover, the luminescent polyacrylonitrile nanofiber films modified by Tb-BPA were prepared by electrospinning and were used for ATP visual detection. Notably, this film is easy to recover and reuse, and maintains good detection performance after at least 7 cycles.