The FDA NIH Biomarkers, EndpointS, and other Tools (BEST) resource in neuro-oncology

Standards of Fluid Biomarker Collection and Pre-analytical Processes in Humans and Mice: Recommendations by the Ataxia Global Initiative Working Group on Biomarkers

The Ataxia Global Initiative (AGI) aims to serve as a platform to facilitate clinical trial readiness for the hereditary ataxias. Clinical trials for these diseases have been hampered by the lack of objective measures to study disease onset, progression, and treatment efficacy. While these issues are not unique to the genetic ataxias, the relative rarity of these diseases makes the need for such measures even more pressing to achieve statistical power in clinical trials. In this report, we have described the efforts of the AGI fluid biomarker working group (WG) in developing uniform protocols for biomarker sampling and storage, both for human and preclinical studies in mice. By reducing collection variability, we anticipate reduced noise in downstream biomarker analysis that will improve statistical power and minimize the necessary sample size. The emphasis has been on defining and standardizing the sampling and pre-analytical work-up of minimal set of biological samples, specifically blood plasma and serum, keeping in mind the need for harmonization of collection and storage that can be achieved with relatively limited cost and resources. An optional package is detailed for those centers that have the resources and commitment for additional biofluids/sample processing and storage. Finally, we have delineated similar standardized protocols for mice that will be important for preclinical studies in the field.

Electrochemical biosensors for early diagnosis of glioblastoma

Glioblastoma (GBM) is a highly aggressive and life-threatening neurological malignancy of predominant astrocyte origin. This type of neoplasm can develop in either the brain or the spine and is also known as glioblastoma multiforme. Although current diagnostic methods such as magnetic resonance imaging (MRI) and positron emission tomography (PET) facilitate tumor location, these approaches are unable to assess disease severity. Furthermore, interpretation of imaging studies requires significant expertise which can have substantial inter-observer variability, thus challenging diagnosis and potentially delaying treatment. In contrast, biosensing systems offer a promising alternative to these traditional approaches. These technologies can continuously monitor specific molecules, providing valuable real-time data on treatment response, and could significantly improve patient outcomes. Among various types of biosensors, electrochemical systems are preferred over other types, as they do not require expensive or complex equipment or procedures and can be made with readily available materials and methods. Moreover, electrochemical biosensors can detect very small amounts of analytes with high accuracy and specificity by using various signal amplification strategies and recognition elements. Considering the advantages of electrochemical biosensors compared to other biosensing methods, we aim to highlight the potential application(s) of these sensors for GBM theranostics. The review's innovative insights are expected to antecede the development of novel biosensors and associated diagnostic platforms, ultimately restructuring GBM detection strategies.

Transcranial Magnetic Stimulation-Electroencephalography for Biomarker Discovery in Psychiatry

Current diagnosis and treatment of psychiatric illnesses are still based on behavioral observations and self-reports, commonly leading to prolonged untreated illness. Biological markers (biomarkers) may offer an opportunity to revolutionize clinical psychiatry practice by helping provide faster and potentially more effective therapies. Transcranial magnetic stimulation concurrent with electroencephalography (TMS-EEG) is a noninvasive brain mapping methodology that can assess the functions and dynamics of specific brain circuitries in awake humans and aid in biomarker discovery. This article provides an overview of TMS-EEG-based biomarkers that may hold potential in psychiatry. The methodological readiness of the TMS-EEG approach and steps in the validation of TMS-EEG biomarkers for clinical utility are discussed. Biomarker discovery with TMS-EEG is in the early stages, and several validation steps are still required before clinical implementations are realized. Thus far, TMS-EEG predictors of response to magnetic brain stimulation treatments in particular have shown promise for translation to clinical practice. Larger-scale studies can confirm validation followed by biomarker-informed trials to assess added value compared to existing practice.

Unlocking the potential of oncology biomarkers: advancements in clinical theranostics

INTRODUCTION

Cancer biomarkers have revolutionized the field of oncology by providing valuable insights into tumor changes and aiding in screening, diagnosis, prognosis, treatment prediction, and risk assessment. The emergence of "omic" technologies has enabled biomarkers to become reliable and accurate predictors of outcomes during cancer treatment.

CONTENT

In this review, we highlight the clinical utility of biomarkers in cancer identification and motivate researchers to establish a personalized/precision approach in oncology. By extending a multidisciplinary technology-based approach, biomarkers offer an alternative to traditional techniques, fulfilling the goal of cancer therapeutics to find a needle in a haystack.

SUMMARY AND OUTLOOK

We target different forms of cancer to establish a dynamic role of biomarkers in understanding the spectrum of malignancies and their biochemical and molecular characterization, emphasizing their prospective contribution to cancer screening. Biomarkers offer a promising avenue for the early detection of human cancers and the exploration of novel technologies to predict disease severity, facilitating maximum survival and minimum mortality rates. This review provides a comprehensive overview of the potential of biomarkers in oncology and highlights their prospects in advancing cancer diagnosis and treatment.

Developing medicines for the pre-symptomatic stage of degenerative neurological conditions: Challenges and opportunities

Neurodegenerative disorders have a devastating disease course and an increasing prevalence due to prolonged life expectancy. In the last decades, it has become increasingly clear that these diseases often start much earlier, before the onset of symptoms, creating a potential window for pre-symptomatic treatment, a strategy that is desirable from both the biologic and the ethical point of view. However, studying treatments for a pre-symptomatic population presents objective difficulties. This article intends to give a perspective about opportunities and challenges of pre-symptomatic prevention of neurodegenerative diseases. Besides the requirement for biomarkers that would facilitate both the selection of study population and demonstrating a treatment effect, further considerations about balancing benefits, risks and uncertainties pertaining a pre-symptomatic population will be examined.

Brain Tumor Imaging without Gadolinium-based Contrast Agents: Feasible or Fantasy?

Gadolinium-based contrast agents (GBCAs) form the cornerstone of current primary brain tumor MRI protocols at all stages of the patient journey. Though an imperfect measure of tumor grade, GBCAs are repeatedly used for diagnosis and monitoring. In practice, however, radiologists will encounter situations where GBCA injection is not needed or of doubtful benefit. Reducing GBCA administration could improve the patient burden of (repeated) imaging (especially in vulnerable patient groups, such as children), minimize risks of putative side effects, and benefit costs, logistics, and the environmental footprint. On the basis of the current literature, imaging strategies to reduce GBCA exposure for pediatric and adult patients with primary brain tumors will be reviewed. Early postoperative MRI and fixed-interval imaging of gliomas are examples of GBCA exposure with uncertain survival benefits. Half-dose GBCAs for gliomas and T2-weighted imaging alone for meningiomas are among options to reduce GBCA use. While most imaging guidelines recommend using GBCAs at all stages of diagnosis and treatment, non-contrast-enhanced sequences, such as the arterial spin labeling, have shown a great potential. Artificial intelligence methods to generate synthetic postcontrast images from decreased-dose or non-GBCA scans have shown promise to replace GBCA-dependent approaches. This review is focused on pediatric and adult gliomas and meningiomas. Special attention is paid to the quality and real-life applicability of the reviewed literature.

Recommendations for the collection and annotation of biosamples for analysis of biomarkers in neurofibromatosis and schwannomatosis clinical trials

INTRODUCTION

Neurofibromatosis 1 and schwannomatosis are characterized by potential lifelong morbidity and life-threatening complications. To date, however, diagnostic and predictive biomarkers are an unmet need in this patient population. The inclusion of biomarker discovery correlatives in neurofibromatosis 1/schwannomatosis clinical trials enables study of low-incidence disease. The implementation of a common data model would further enhance biomarker discovery by enabling effective concatenation of data from multiple studies.

METHODS

The Response Evaluation in Neurofibromatosis and Schwannomatosis biomarker working group reviewed published data on emerging trends in neurofibromatosis 1 and schwannomatosis biomarker research and developed recommendations in a series of consensus meetings.

RESULTS

Liquid biopsy has emerged as a promising assay for neurofibromatosis 1/schwannomatosis biomarker discovery and validation. In addition, we review recommendations for a range of biomarkers in clinical trials, neurofibromatosis 1/schwannomatosis-specific data annotations, and common data models for data integration.

CONCLUSION

These Response Evaluation in Neurofibromatosis and Schwannomatosis consensus guidelines are intended to provide best practices for the inclusion of biomarker studies in neurofibromatosis 1/schwannomatosis clinical trials, data, and sample annotation and to lay a framework for data harmonization and concatenation between trials.

TheMarker: a comprehensive database of therapeutic biomarkers

Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.

Which Psychological and Psychosocial Constructs Are Important to Measure in Future Tendinopathy Clinical Trials? A Modified International Delphi Study With Expert Clinician/Researchers and People With Tendinopathy

OBJECTIVE: To identify which psychological and psychosocial constructs to include in a core outcome set to guide future clinical trials in the tendinopathy field. DESIGN: Modified International Delphi study. METHODS: In 3 online Delphi rounds, we presented 35 psychological and psychosocial constructs to an international panel of 38 clinician/researchers and people with tendinopathy. Using a 9-point Likert scale (1 = not important to include, 9 = critical to include), consensus for construct inclusion required ≥70% of respondents rating "extremely critical to include" (score ≥7) and ≤15% rating "not important to include" (score ≤3). Consensus for exclusion required ≥70% of respondents rating "not important to include" (score ≤3) and ≤15% of rating "critical to include" (score ≥7). RESULTS: Thirty-six participants (95% of 38) completed round 1, 90% (n = 34) completed round 2, and 87% (n = 33) completed round 3. Four constructs were deemed important to include as part of a core outcome set: kinesiophobia (82%, median: 8, interquartile range [IQR]: 1.0), pain beliefs (76%, median: -7, IQR: 1.0), pain-related self-efficacy (71%, median: 7, IQR: 2.0), and fear-avoidance beliefs (73%, median: -7, IQR: 1.0). Six constructs were deemed not important to include: perceived injustice (82%), individual attitudes of family members (74%), social isolation and loneliness (73%), job satisfaction (73%), coping (70%), and educational attainment (70%). Clinician/researchers and people with tendinopathy reached consensus that kinesiophobia, pain beliefs, pain self-efficacy, and fear-avoidance beliefs were important psychological constructs to measure in tendinopathy clinical trials. J Orthop Sports Phys Ther 2024;54(1):1-12. Epub 20 September 2023. doi:10.2519/jospt.2023.11903.

Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies

Background

Immunotherapy is an effective "precision medicine" treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma.

Methods

A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered: CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis.

Results

Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed.

Conclusions

Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability.

Artificial Intelligence Applications in Glioma With 1p/19q Co-Deletion: A Systematic Review

As an important genomic marker for oligodendrogliomas, early determination of 1p/19q co-deletion status is critical for guiding therapy and predicting prognosis in patients with glioma. The purpose of this study is to systematically review the literature concerning the magnetic resonance imaging (MRI) with artificial intelligence (AI) methods for predicting 1p/19q co-deletion status in glioma. PubMed, Scopus, Embase, and IEEE Xplore were searched in accordance with the Preferred Reporting Items for systematic reviews and meta-analyses guidelines. Methodological quality of studies was assessed according to the Quality Assessment of Diagnostic Accuracy Studies-2. Finally, 28 studies were included in the quantitative analysis. Diagnostic test accuracy reached an area under the ROC curve of 0.71-0.98 were reported in 24 studies. The remaining four studies with no available AUC provided an accuracy of 0.75-0. 89. The included studies varied widely in terms of imaging sequences, input features, and modeling methods. The current review highlighted that integrating MRI with AI technology is a potential tool for determination 1p/19q status pre-operatively and noninvasively, which can possibly help clinical decision-making. However, the reliability and feasibility of this approach still need to be further validated and improved in a real clinical setting. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: 2.

Salivary interleukin-1β as a biomarker to differentiate between periodontal health, gingivitis, and periodontitis

BACKGROUND

Periodontal diagnosis is based on recording clinical parameters including bleeding on probing (BOP), probing pocket depth (PPD), and clinical attachment loss (CAL). These techniques may be prone to errors due to different factors. Available biomarkers in the oral biofluid such as interleukin (IL)-1β could provide solutions for these issues. The study aimed to determine the potential of salivary IL-1β to differentiate periodontal health from disease and between gingivitis and periodontitis.

METHODS

Patients with gingivitis (N.=25), periodontitis (N.=50), and healthy periodontium (N.=25) were recruited for this study. For each patient, whole unstimulated saliva was collected followed by recording periodontal parameters namely; Plaque Index (PI), BOP, PPD, CAL. Level of salivary IL-1β was assayed by using enzyme-linked immunosorbent assays. Sensitivity and specificity of IL-1β, to differentiate any given condition, was determined by Receiver operating characteristic curve and area under the curve (AUC).

RESULTS

Both BOP and PI were significantly higher in association with gingivitis and periodontitis groups as compared to controls. Concentration of salivary IL-1β in periodontal health was significantly lower than gingivitis and periodontitis groups. The biochemical analyses showed that salivary IL-1β differentiated periodontal health from gingivitis (AUC 0.949) and periodontitis (AUC 0.852) but could not discriminate gingivitis from periodontitis (AUC 0.532). The proposed cut-off points to differentiate periodontal health from gingivitis was 103.8 pg/mL, while the value of the biomarker to differentiate periodontal health from periodontitis was 102.0 pg/mL.

CONCLUSIONS

Salivary IL-1β could be a reliable biomarker with a good level of accuracy to differentiate periodontal health from disease but not to discriminate gingivitis from periodontitis.

Filter and Wrapper Stacking Ensemble (FWSE): a robust approach for reliable biomarker discovery in high-dimensional omics data

Selecting informative features, such as accurate biomarkers for disease diagnosis, prognosis and response to treatment, is an essential task in the field of bioinformatics. Medical data often contain thousands of features and identifying potential biomarkers is challenging due to small number of samples in the data, method dependence and non-reproducibility. This paper proposes a novel ensemble feature selection method, named Filter and Wrapper Stacking Ensemble (FWSE), to identify reproducible biomarkers from high-dimensional omics data. In FWSE, filter feature selection methods are run on numerous subsets of the data to eliminate irrelevant features, and then wrapper feature selection methods are applied to rank the top features. The method was validated on four high-dimensional medical datasets related to mental illnesses and cancer. The results indicate that the features selected by FWSE are stable and statistically more significant than the ones obtained by existing methods while also demonstrating biological relevance. Furthermore, FWSE is a generic method, applicable to various high-dimensional datasets in the fields of machine intelligence and bioinformatics.

Fiber-specific micro- and macroscopic white matter alterations in progressive supranuclear palsy and corticobasal syndrome

Progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS) are characterized by progressive white matter (WM) alterations associated with the prion-like spreading of four-repeat tau, which has been pathologically confirmed. It has been challenging to monitor the WM degeneration patterns underlying the clinical deficits in vivo. Here, a fiber-specific fiber density and fiber cross-section, and their combined measure estimated using fixel-based analysis (FBA), were cross-sectionally and longitudinally assessed in PSP (n = 20), CBS (n = 17), and healthy controls (n = 20). FBA indicated disease-specific progression patterns of fiber density loss and subsequent bundle atrophy consistent with the tau propagation patterns previously suggested in a histopathological study. This consistency suggests the new insight that FBA can monitor the progressive tau-related WM changes in vivo. Furthermore, fixel-wise metrics indicated strong correlations with motor and cognitive dysfunction and the classifiability of highly overlapping diseases. Our findings might also provide a tool to monitor clinical decline and classify both diseases.

Moderate evidence exists for four microRNAs as potential biomarkers for tendinopathies and degenerative tendon ruptures at the upper extremity in elderly patients: conclusion of a systematic review with best-evidence synthesis

PURPOSE

The aim of this systematic review was to investigate tendon-specific microRNAs (miRNAs) as biomarkers for the detection of tendinopathies or degenerative tendon ruptures. Also, their regulatory mechanisms within the tendon pathophysiology were summarized.

METHODS

A systematic literature research was performed using the PRISMA guidelines. The search was conducted in the Pubmed database. The SIGN checklist was used to assess the study quality of the included original studies. To determine the evidence and direction of the miRNA expression rates, a best-evidence synthesis was carried out, whereby only studies with at least a borderline methodological quality were considered for validity purposes.

RESULTS

Three thousand three hundred seventy studies were reviewed from which 22 fulfilled the inclusion criteria. Moderate evidence was found for miR-140-3p and miR-425-5p as potential biomarkers for tendinopathies as well as for miR-25-3p, miR-29a-3p, miR-140-3p, and miR-425-5p for the detection of degenerative tendon ruptures. This evidence applies to tendons at the upper extremity in elderly patients. All miRNAs were associated with inflammatory cytokines as interleukin-6 or interleukin-1ß and tumor necrosis factor alpha.

CONCLUSIONS

Moderate evidence exists for four miRNAs as potential biomarkers for tendinopathies and degenerative tendon ruptures at the upper extremity in elderly patients. The identified miRNAs are associated with inflammatory processes.

Exploring physical and biological manifestations of burnout and post-traumatic stress disorder symptoms in healthcare workers: a scoping review protocol

INTRODUCTION

The COVID-19 pandemic has strained the mental and physical well-being of healthcare workers (HCW). Increased work-related stress and limited resources have increased symptoms of anxiety, depression, insomnia and post-traumatic stress disorder (PTSD) in this population. Stress-related disorders have been strongly associated with long-term consequences, including cardiometabolic disorders, endocrine disorders and premature mortality. This scoping review aims to explore available literature on burnout, PTSD, and other mental health-associated symptoms in HCW to synthesise relationships with physiological and biological biomarkers that may be associated with increased risk of disease, creating an opportunity to summarise current biomarker knowledge and identify gaps in this literature.

METHODS AND ANALYSIS

This scoping review uses the Arksey and O'Malley six-step scoping review methodology framework. The research team will select appropriate primary sources using a search strategy developed in collaboration with a health sciences librarian. Three reviewers will initially screen the title and abstracts obtained from the literature searches, and two reviewers will conduct independent reviews of full-text studies for inclusion. The research team will be reviewing literature focusing on which burnout and/or PTSD-associated physiological and biological biomarkers have been studied, the methodologies used to study them and the correlations between the biomarkers and HCW experiencing burnout/PTSD. Data extraction forms will be completed by two reviewers for included studies and will guide literature synthesis and analysis to determine common themes.

ETHICS AND DISSEMINATION

This review does not require ethical approval. We expect results from this scoping review to identify gaps in the literature and encourage future research regarding improving biological and physiological biomarker research in HCW. Preliminary results and general themes will be communicated back to stakeholders. Results will be disseminated through peer-reviewed publications, policy briefs and conferences as well as presented to stakeholders to an effort to invest in HCW mental and physical health.

Protein Biomarkers in Chronic Kidney Disease in Children-What Do We Know So Far?

Chronic kidney disease (CKD) in children is a major concern of medical care and public health as it is related to high morbidity and mortality due to progression to end-stage kidney disease (ESKD). It is essential to identify patients with a risk of developing CKD to implement therapeutic interventions. Unfortunately, conventional markers of CKD, such as serum creatinine, glomerular filtration rate (GFR) and proteinuria, have many limitations in serving as an early and specific diagnostic tool for this condition. Despite the above, they are still the most frequently utilized as we do not have better. Studies from the last decade identified multiple CKD blood and urine protein biomarkers but mostly assessed the adult population. This article outlines some recent achievements and new perspectives in finding a set of protein biomarkers that might improve our ability to prognose CKD progression in children, monitor the response to treatment, or even become a potential therapeutic target.

The experience of interval scans for adults living with primary malignant brain tumors

PURPOSE

People with primary malignant brain tumors (PMBT) undergo anti-tumor treatment and are followed up with MRI interval scans. There are potential burdens and benefits to interval scanning, yet high-quality evidence to suggest whether scans are beneficial or alter outcomes of importance for patients is lacking. We aimed to gain an in-depth understanding of how adults living with PMBTs experience and cope with interval scanning.

METHODS

Twelve patients diagnosed with WHO grade III or IV PMBT from two sites in the UK took part. Using a semi-structured interview guide, they were asked about their experiences of interval scans. A constructivist grounded theory approach was used to analyze data.

RESULTS

Although most participants found interval scans uncomfortable, they accepted that scans were something that they had to do and were using various coping methods to get through the MRI scan. All participants said that the wait between their scan and results was the most difficult part. Despite the difficulties they experienced, all participants said that they would rather have interval scans than wait for a change in their symptoms. Most of the time, scans provided relief, gave participants some certainty in an uncertain situation, and a short-term sense of control over their lives.

CONCLUSION

The present study shows that interval scanning is important and highly valued by patients living with PMBT. Although interval scans are anxiety provoking, they appear to help people living with PMBT cope with the uncertainty of their condition.

Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review

The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification. A systematic search was conducted using PubMed to identify relevant studies published between 2017 and 2023. The selected studies were analyzed to better understand the concept of biomarker identification, evaluate machine learning methods, assess the level of research activity, and highlight the application of these methods in cancer research and treatment. Furthermore, existing obstacles and concerns are discussed to identify prospective research areas. We believe that this review will serve as a valuable resource for researchers, providing insights into the methods and approaches used in biomarker discovery and identifying future research opportunities.

Exploring Physical and Biological Manifestations of Burnout and Post-Traumatic Stress Disorder Symptoms in Healthcare Workers: A Scoping Review Protocol

Introduction

The COVID-19 pandemic has strained the mental and physical well-being of healthcare workers (HCW). Increased work-related stress and limited resources has increased symptoms of anxiety, depression, insomnia, and post-traumatic stress disorder (PTSD) in this population. Stress-related disorders have been strongly associated with long-term consequences including cardiometabolic disorders, endocrine disorders and premature mortality. This scoping review aims to explore available literature on burnout, PTSD, and other mental health-associated symptoms in HCW to synthesize relationships with physiological and biological biomarkers that may be associated with increased risk of disease, creating an opportunity to summarize current biomarker knowledge and identify gaps in this literature.

Methods and Analysis

This scoping review uses the Arksey and O'Malley six-step scoping review methodology framework. The research team will select appropriate primary sources using a search strategy developed in collaboration with a health sciences librarian. Three reviewers will initially screen the title and abstracts obtained from the literature searches, and two reviewers will conduct independent reviews of full-text studies for inclusion. The research team will be reviewing literature focusing on which burnout and/or PTSD-associated physiological and biological biomarkers have been studied, the methodologies used to study them and the correlations between the biomarkers and HCW experiencing burnout/PTSD. Data extraction forms will be completed by two reviewers for included studies and will guide literature synthesis and analysis to determine common themes.

Ethics and Dissemination

This review does not require ethical approval. We expect results from this scoping review to identify gaps in the literature and encourage future research regarding improving biologic and physiologic biomarker research in HCW. Preliminary results and general themes will be communicated back to stakeholders. Results will be disseminated through peer-reviewed publications, policy briefs, and conferences, as well as presented to stakeholders to an effort to invest in HCW mental and physical health.

Strengths and Limitations of This Study

This will be the first scoping review to assess the current understanding of the biologic and physiological impact of burnout on healthcare workers. The target population is restricted to healthcare workers; however, identified research gaps may be used to guide future studies in other high-burnout occupations and industries.This scoping review will be guided by the Arksey and O'Malley six-step methodological framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Review checklist.Both peer reviewed manuscript and pre-prints/abstracts will be evaluated, but studies that have not been peer reviewed will be notated in the summary table. Conference abstracts are excluded.Preliminary and final themes and results identified by this scoping review will be communicated to stakeholders, including hospital staff and HCW, to ensure agreement with our interpretation and to convey knowledge gained with our population of interest.This review will advance the field's current understanding of mechanisms connecting the burnout and pathogenic stress to biologic and physiologic outcomes in healthcare workers and provide researchers with gaps in the literature to inform opportunities for future research.

Electrophysiological and Clinical Predictors of Methylphenidate, Guanfacine, and Combined Treatment Outcomes in Children With Attention-Deficit/Hyperactivity Disorder

OBJECTIVE

The combination of d-methylphenidate and guanfacine (an α-2A agonist) has emerged as a potential alternative to either monotherapy in children with attention-deficit/hyperactivity disorder (ADHD), but it is unclear what predicts response to these treatments. This study is the first to investigate pretreatment clinical and electroencephalography (EEG) profiles as predictors of treatment outcome in children randomized to these different medications.

METHOD

A total of 181 children with ADHD (aged 7-14 years; 123 boys) completed an 8-week randomized, double-blind, comparative study with d-methylphenidate, guanfacine, or combined treatments. Pretreatment assessments included ratings on ADHD, anxiety, and oppositional behavior. EEG activity from cortical sources localized within midfrontal and midoccipital regions was measured during a spatial working memory task with encoding, maintenance, and retrieval phases. Analyses tested whether pretreatment clinical and EEG measures predicted treatment-related change in ADHD severity.

RESULTS

Higher pretreatment hyperactivity-impulsivity and oppositional symptoms and lower anxiety predicted greater ADHD improvements across all medication groups. Pretreatment event-related midfrontal beta power predicted treatment outcome with combined and monotherapy treatments, albeit in different directions. Weaker beta modulations predicted improvements with combined treatment, whereas stronger modulation during encoding and retrieval predicted improvements with d-methylphenidate and guanfacine, respectively. A multivariate model including EEG and clinical measures explained twice as much variance in ADHD improvement with guanfacine and combined treatment (R2= 0.34-0.41) as clinical measures alone (R2 = 0.14-.21).

CONCLUSION

We identified treatment-specific and shared predictors of response to different pharmacotherapies in children with ADHD. If replicated, these findings would suggest that aggregating information from clinical and brain measures may aid personalized treatment decisions in ADHD.

CLINICAL TRIAL REGISTRATION INFORMATION

Single Versus Combination Medication Treatment for Children With Attention Deficit Hyperactivity Disorder; https://clinicaltrials.gov; NCT00429273.

Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives

Biomarker discovery and development are popular for detecting the subtle diseases. However, biomarkers are needed to be validated and approved, and even fewer are ever used clinically. Imaging biomarkers have a crucial role in the treatment of cancer patients because they provide objective information on tumor biology, the tumor's habitat, and the tumor's signature in the environment. Tumor changes in response to an intervention complement molecular and genomic translational diagnosis as well as quantitative information. Neuro-oncology has become more prominent in diagnostics and targeted therapies. The classification of tumors has been actively updated, and drug discovery, and delivery in nanoimmunotherapies are advancing in the field of target therapy research. It is important that biomarkers and diagnostic implements be developed and used to assess the prognosis or late effects of long-term survivors. An improved realization of cancer biology has transformed its management with an increasing emphasis on a personalized approach in precision medicine. In the first part, we discuss the biomarker categories in relation to the courses of a disease and specific clinical contexts, including that patients and specimens should both directly reflect the target population and intended use. In the second part, we present the CT perfusion approach that provides quantitative and qualitative data that has been successfully applied to the clinical diagnosis, treatment and application. Furthermore, the novel and promising multiparametric MR imageing approach will provide deeper insights regarding the tumor microenvironment in the immune response. Additionally, we briefly remark new tactics based on MRI and PET for converging on imaging biomarkers combined with applications of bioinformatics in artificial intelligence. In the third part, we briefly address new approaches based on theranostics in precision medicine. These sophisticated techniques merge achievable standardizations into an applicatory apparatus for primarily a diagnostic implementation and tracking radioactive drugs to identify and to deliver therapies in an individualized medicine paradigm. In this article, we describe the critical principles for imaging biomarker characterization and discuss the current status of CT, MRI and PET in finiding imaging biomarkers of early disease.

Potential biomarkers for late-onset and term preeclampsia: A scoping review

Preeclampsia is a progressive, multisystem pregnancy disorder. According to the time of onset or delivery, preeclampsia has been subclassified into early-onset (<34 weeks) and late-onset (≥34 weeks), or preterm (<37 weeks) and term (≥37 weeks). Preterm preeclampsia can be effectively predicted at 11–13 weeks well before onset, and its incidence can be reduced by preventively using low-dose aspirin. However, late-onset and term preeclampsia are more prevalent than early forms and still lack effective predictive and preventive measures. This scoping review aims to systematically identify the evidence of predictive biomarkers reported in late-onset and term preeclampsia. This study was conducted based on the guidance of the Joanna Briggs Institute (JBI) methodology for scoping reviews. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews (PRISMA-ScR) was used to guide the study. The following databases were searched for related studies: PubMed, Web of Science, Scopus, and ProQuest. Search terms contain “preeclampsia,” “late-onset,” “term,” “biomarker,” or “marker,” and other synonyms combined as appropriate using the Boolean operators “AND” and “OR.” The search was restricted to articles published in English from 2012 to August 2022. Publications were selected if study participants were pregnant women and biomarkers were detected in maternal blood or urine samples before late-onset or term preeclampsia diagnosis. The search retrieved 4,257 records, of which 125 studies were included in the final assessment. The results demonstrate that no single molecular biomarker presents sufficient clinical sensitivity and specificity for screening late-onset and term preeclampsia. Multivariable models combining maternal risk factors with biochemical and/or biophysical markers generate higher detection rates, but they need more effective biomarkers and validation data for clinical utility. This review proposes that further research into novel biomarkers for late-onset and term preeclampsia is warranted and important to find strategies to predict this complication. Other critical factors to help identify candidate markers should be considered, such as a consensus on defining preeclampsia subtypes, optimal testing time, and sample types.

Radiomic features from dynamic susceptibility contrast perfusion-weighted imaging improve the three-class prediction of molecular subtypes in patients with adult diffuse gliomas

OBJECTIVES

To investigate whether radiomic features extracted from dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) can improve the prediction of the molecular subtypes of adult diffuse gliomas, and to further develop and validate a multimodal radiomic model by integrating radiomic features from conventional and perfusion MRI.

METHODS

We extracted 1197 radiomic features from each sequence of conventional MRI and DSC-PWI, respectively. The Boruta algorithm was used for feature selection and combination, and a three-class random forest method was applied to construct the models. We also constructed a combined model by integrating radiomic features and clinical metrics. The models' diagnostic performance for discriminating the molecular subtypes (IDH wild type [IDHwt], IDH mutant and 1p/19q-noncodeleted [IDHmut-noncodel], and IDH mutant and 1p/19q-codeleted [IDHmut-codel]) was compared using AUCs in the validation set.

RESULTS

We included 272 patients (training set, n = 166; validation set, n = 106) with grade II-IV gliomas (mean age, 48.7 years; range, 19-77 years). The proportions of the molecular subtypes were 66.2% IDHwt, 15.1% IDHmut-noncodel, and 18.8% IDHmut-codel. Nineteen radiomic features (13 from conventional MRI and 6 from DSC-PWI) were selected to build the multimodal radiomic model. In the validation set, the multimodal radiomic model showed better performance than the conventional radiomic model did in predicting the IDHwt and IDHmut-codel subtypes, which was comparable to the conventional radiomic model in predicting the IDHmut-noncodel subtype. The multimodal radiomic model yielded similar performance as the combined model in predicting the three molecular subtypes.

CONCLUSIONS

Adding DSC-PWI to conventional MRI can improve molecular subtype prediction in patients with diffuse gliomas.

KEY POINTS

• The multimodal radiomic model outperformed conventional MRI when predicting both the IDH wild type and IDH mutant and 1p/19q-codeleted subtypes of gliomas. • The multimodal radiomic model showed comparable performance to the combined model in the prediction of the three molecular subtypes. • Radiomic features from T1-weighted gadolinium contrast-enhanced and relative cerebral blood volume images played an important role in the prediction of molecular subtypes.

Diagnostic, Prognostic and Mechanistic Biomarkers of COVID-19 Identified by Mass Spectrometric Metabolomics

A number of studies have assessed the impact of SARS-CoV-2 infection and COVID-19 severity on the metabolome of exhaled air, saliva, plasma, and urine to identify diagnostic and prognostic biomarkers. In spite of the richness of the literature, there is no consensus about the utility of metabolomic analyses for the management of COVID-19, calling for a critical assessment of the literature. We identified mass spectrometric metabolomic studies on specimens from SARS-CoV2-infected patients and subjected them to a cross-study comparison. We compared the clinical design, technical aspects, and statistical analyses of published studies with the purpose to identify the most relevant biomarkers. Several among the metabolites that are under- or overrepresented in the plasma from patients with COVID-19 may directly contribute to excessive inflammatory reactions and deficient immune control of SARS-CoV2, hence unraveling important mechanistic connections between whole-body metabolism and the course of the disease. Altogether, it appears that mass spectrometric approaches have a high potential for biomarker discovery, especially if they are subjected to methodological standardization.

Potential Pathophysiological Pathways in the Complex Relationships between OSA and Cancer

Several epidemiological and clinical studies have suggested a relationship between obstructive sleep apnea (OSA) and a higher incidence or severity of cancer. This relationship appears to be dependent on a myriad of factors. These include non-modifiable factors, such as age and gender; and modifiable or preventable factors, such as specific comorbidities (especially obesity), the use of particular treatments, and, above all, the histological type or location of the cancer. Heterogeneity in the relationship between OSA and cancer is also related to the influences of intermittent hypoxemia (a hallmark feature of OSA), among others, on metabolism and the microenvironment of different types of tumoral cells. The hypoxia inducible transcription factor (HIF-1α), a molecule activated and expressed in situations of hypoxemia, seems to be key to enabling a variety of pathophysiological mechanisms that are becoming increasingly better recognized. These mechanisms appear to be operationally involved via alterations in different cellular functions (mainly involving the immune system) and molecular functions, and by inducing modifications in the microbiome. This, in turn, may individually or collectively increase the risk of cancer, which is then, further modulated by the genetic susceptibility of the individual. Here, we provide an updated and brief review of the different pathophysiological pathways that have been identified and could explain the relationship between OSA and cancer. We also identify future challenges that need to be overcome in this intriguing field of research.

Circulatory Biomarkers and Diabetic Retinopathy in Racial and Ethnic Populations

Clinical staging systems for diagnosis and treatment of diabetic retinopathy (DR) must closely relate to endpoints that are both relevant for patients and feasible for physicians to implement. Current DR staging systems for clinical eye care and research provide detailed phenotypic characterization to predict patient outcomes in diabetes but have limitations. Biochemical biomarkers provide a rich pool of potential candidates for new DR staging systems that can be readily measured in accessible fluids. Circulating biomarkers that are specific to the retina and relate to angiogenesis and inflammation have been suggested as relevant for DR. Although there is a lack of multi-ethnic studies evaluating circulatory biomarkers in DR, variability in circulatory biomarkers have been reported in people from different ethnic and racial backgrounds. Therefore, there is a need for future studies to evaluate individual or combinations of biomarkers in diverse populations with DR from different ethnic and racial backgrounds.

Comparative accuracy of endosonographic shear wave elastography and transcutaneous liver stiffness measurement: a pilot study

BACKGROUND AND AIMS

Vibration-controlled transient elastography (VCTE) is a validated test for assessing liver fibrosis but may be unreliable in select patients, including those with morbid obesity. The limitations of VCTE may be overcome by EUS-guided shear wave elastography (EUS-SWE).

METHODS

This single-center, prospective, nonrandomized tandem study compared the diagnostic accuracy of EUS-SWE and VCTE in consecutive patients undergoing liver biopsy sampling because of unreliable noninvasive testing. EUS-SWE of the left and right lobes were separately performed and then compared with VCTE. Liver elasticity cutoffs for different stages of fibrosis were estimated in 3 ways: optimized sensitivity and specificity using the Youden index; and with sensitivity and specificity fixed at 90% each, Diagnostic accuracy for fibrosis was compared with liver histology using the area under the receiver-operating characteristic curve (AUROC). The primary outcome was the diagnostic accuracy of EUS-SWE for advanced fibrosis. Secondary outcomes were diagnostic accuracy of VCTE, EUS-SWE for left and right hepatic lobes for significant/advanced fibrosis, and cirrhosis.

RESULTS

Forty-two patients (39 men, aged 54.5 ± 12.1 years) underwent EUS-SWE, VCTE, and liver biopsy sampling. The cross-validated AUROCs for advanced fibrosis were as follows: VCTE, .87 (95% confidence interval [CI], .76-.97); EUS-SWE left lobe, .8 (95% CI, .64-.96); and EUS-SWE right lobe, .78 (95% CI, .62-.95). The corresponding AUROCs for cirrhosis were as follows: VCTE, .9 (95% CI, .83-.97); EUS-SWE left lobe, .96 (95% CI, .9-1); and EUS-SWE right lobe, .9 (95% CI, .8-1). VCTE was unreliable in 8 patients who successfully underwent EUS-SWE. There was no statistically significant difference in the AUROCs for EUS-SWE and VCTE.

CONCLUSIONS

EUS-SWE correlates well with liver histology and is a safe and reliable diagnostic test for assessing liver fibrosis with accuracy comparable with VCTE. (Clinical trial registration number: NCT04533932.).

Deciphering the Function of New Therapeutic Targets and Prospective Biomarkers in the Management of Psoriasis

Psoriasis is an immune-mediated skin condition affecting people worldwide, presenting at any age, and leading to a substantial burden physically and mentally. The innate and adaptive immune systems interact intricately with the pathomechanisms that underlie disease. T cells can interact with keratinocytes, macrophages, and dendritic cells through the cytokines they secrete. According to recent research, psoriasis flare-ups can cause systemic inflammation and various other co-morbidities, including depression, psoriatic arthritis, and cardio-metabolic syndrome. Additionally, several auto-inflammatory and auto-immune illnesses may be linked to psoriasis. Although psoriasis has no proven treatment, care must strive by treating patients as soon as the disease surfaces, finding and preventing concurrent multimorbidity, recognising and reducing bodily and psychological distress, requiring behavioural modifications, and treating each patient individually. Biomarkers are traits that are assessed at any time along the clinical continuum, from the early stages of a disease through the beginning of treatment (the foundation of precision medicine) to the late stages of treatment (outcomes and endpoints). Systemic therapies that are frequently used to treat psoriasis provide a variety of outcomes. Targeted therapy selection, better patient outcomes, and more cost-effective healthcare would be made possible by biomarkers that reliably predict effectiveness and safety. This review is an attempt to understand the role of Antimicrobial peptides (AMP), Interleukin-38 (IL-38), autophagy 5 (ATG5) protein and squamous cell carcinoma antigen (SCCA) as biomarkers of psoriasis.

Extracellular Vesicle RNA Contents as Biomarkers for Ocular Diseases

Extracellular vesicles (EVs) are small vesicles secreted from cells into extracellular space. EVs contain proteins, lipids, and nucleic acids of the cells from which they originate. For this reason, EVs are being studied for use as biomarkers as they can be surrogates for the status of the cell from which they are secreted. Moreover, EVs are found in numerous biofluids and can be taken up by other cells, which allows for transfer of functional cargo, like RNAs, and changes in gene regulation in the recipient cell. Several potential RNA biomarkers have been identified in many diseases, and there is great potential in the vision field for extracellular RNA biomarkers as a diagnostic tool as well as a measure for treatment efficacy.

Feasibility of evaluating the histologic and genetic subtypes of WHO grade II-IV gliomas by diffusion-weighted imaging

BACKGROUND

To explore the feasibility of diffusion-weighted imaging (DWI) metrics to predict the histologic subtypes and genetic status of gliomas (e.g., IDH, MGMT, and TERT) noninvasively.

METHODS

One hundred and eleven patients with pathologically confirmed WHO grade II-IV gliomas were recruited retrospectively. Apparent diffusion coefficient (ADC) values were measured in solid parts of gliomas on co-registered T2-weighted images and were compared with each other in terms of WHO grading and genotypes using t-tests. Receiver operating characteristic analysis was performed to assess the diagnostic performances of ADC. Subsequently, multiple linear regression was used to find independent variables, which can directly affect ADC values.

RESULTS

The values of overall mean ADC (omADC) and normalized ADC (nADC) of high grade gliomas and IDH wildtype gliomas were lower than low grade gliomas and IDH mutated gliomas (P < 0.05). nADC values showed better diagnostic performance than omADC in identifying tumor grade (AUC: 0.787 vs. 0.750) and IDH status (AUC: 0.836 vs. 0.777). ADC values had limited abilities in distinguishing TERT status (AUC = 0.607 for nADC and 0.617 for omADC) and MGMT status (AUC = 0.651 for nADC). Only tumor grade and IDH status were tightly associated with ADC values.

CONCLUSION

DWI metrics can predict glioma grading and IDH mutation noninvasively, but have limited use in detecting TERT mutation and MGMT methylation.

Study in Parkinson's disease of exercise phase 3 (SPARX3): study protocol for a randomized controlled trial

BACKGROUND

To date, no medication has slowed the progression of Parkinson's disease (PD). Preclinical, epidemiological, and experimental data on humans all support many benefits of endurance exercise among persons with PD. The key question is whether there is a definitive additional benefit of exercising at high intensity, in terms of slowing disease progression, beyond the well-documented benefit of endurance training on a treadmill for fitness, gait, and functional mobility. This study will determine the efficacy of high-intensity endurance exercise as first-line therapy for persons diagnosed with PD within 3 years, and untreated with symptomatic therapy at baseline.

METHODS

This is a multicenter, randomized, evaluator-blinded study of endurance exercise training. The exercise intervention will be delivered by treadmill at 2 doses over 18 months: moderate intensity (4 days/week for 30 min per session at 60-65% maximum heart rate) and high intensity (4 days/week for 30 min per session at 80-85% maximum heart rate). We will randomize 370 participants and follow them at multiple time points for 24 months. The primary outcome is the Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) motor score (Part III) with the primary analysis assessing the change in MDS-UPDRS motor score (Part III) over 12 months, or until initiation of symptomatic antiparkinsonian treatment if before 12 months. Secondary outcomes are striatal dopamine transporter binding, 6-min walk distance, number of daily steps, cognitive function, physical fitness, quality of life, time to initiate dopaminergic medication, circulating levels of C-reactive protein (CRP), and brain-derived neurotrophic factor (BDNF). Tertiary outcomes are walking stride length and turning velocity.

DISCUSSION

SPARX3 is a Phase 3 clinical trial designed to determine the efficacy of high-intensity, endurance treadmill exercise to slow the progression of PD as measured by the MDS-UPDRS motor score. Establishing whether high-intensity endurance treadmill exercise can slow the progression of PD would mark a significant breakthrough in treating PD. It would have a meaningful impact on the quality of life of people with PD, their caregivers and public health.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04284436 . Registered on February 25, 2020.

Research Question, Objectives, and Endpoints in Clinical and Oncological Research: A Comprehensive Review

Clinical research is a systematic process of conducting research work to find solutions for human health-related problems. It is applied to understand the disease process and assist in the diagnosis, treatment, and prevention. Currently, we are experiencing global unrest caused by the coronavirus disease (COVID-19) pandemic. The novel severe acute respiratory syndrome coronavirus (SARS-CoV-2) has been responsible for the deaths of more than 50 million people worldwide. Also, it has resulted in severe morbidity among the affected population. The cause of such a huge amount of influence on human health by the pandemic was the unavailability of drugs and therapeutic interventions to treat and manage the disease. Cancer is a disease condition wherein the normal cell function is deranged, and the cells multiply in an uncontrolled manner. Based on recent reports by the World Health Organization (WHO), cancer is the second leading cause of death globally. Moreover, the rates of cancers have shown an increasing trend in the past decade. Therefore, it is essential to improve the understanding concerning clinical research to address the health concerns of humans. In this review, we comprehensively discuss critical aspects of clinical research that include the research question, research objectives, patient-reported outcome measures (PROMs), intention-to-treat and per-protocol analysis, and endpoints in clinical and oncological research.

Deciphering clinical significance of BCL11A isoforms and protein expression roles in triple-negative breast cancer subtype

Purpose

Triple negative breast cancer (TNBC) is an aggressive clinical tumor, accounting for about 25% of breast cancer (BC) related deaths. Chemotherapy is the only therapeutic option to treat TNBC, hence a detailed understanding of the biology and its categorization is required. To investigate the clinical relevance of BCL11A in TNBC subtype, we focused on gene and protein expression and its mutational status in a large cohort of this molecular subtype.

Methods

Gene expression profiling of BCL11A and its isoforms (BCL11A-XL, BCL11A-L and BCL11A-S) has been determined in Luminal A, Luminal B, HER2-enriched and TNBC subtypes. BCL11A protein expression has been analyzed by immunohistochemistry (IHC) and its mutational status by Sanger sequencing.

Results

In our study, BCL11A was significantly overexpressed in TNBC both at transcriptional and translational levels compared to other BC molecular subtypes. A total of 404 TNBCs were selected and examined showing a high prevalence of BCL11A-XL (37.3%) and BCL11A-L (31.4%) isoform expression in TNBC, associated with a 26% of BCL11A protein expression levels. BCL11A protein expression predicts scarce LIV (HR = 0.52; 95% CI, 0.29–0.92, P = 0.03) and AR downregulation (HR = 0.37; 95% CI, 0.16–0.88; P = 0.02), as well as a higher proliferative index in TNBC cells. BCL11A-L expression is associated with more aggressive TNBC histological types, such as medullary and metaplastic carcinoma.

Conclusion

Our finding showed that BCL11A protein expression acts as an unfavorable prognostic factor in TNBC patients, especially in non luminal TNBCs subgroups. These results may yield a better treatment strategy by providing a new parameter for TNBC classification.

Predictive Significance of High-Sensitivity C-Reactive Protein Combined with Homocysteine for Coronary Heart Disease in Patients with Anxiety Disorders

Background

Currently, there are few studies on biomarkers for predicting coronary heart disease (CHD) with anxiety disorders.

Objective

To explore risk factors and investigate the predictive value of common clinical peripheral blood indicators, such as high-sensitivity C-reactive protein (hs-CRP) and homocysteine (Hcy) for CHD patients with anxiety disorders.

Methods

One hundred fifty-three hospitalized patients with chest pain as the main symptom and a Hamilton Anxiety Scale score > 14 were recruited from October 2020 to September 2021 in the hospital. Then, they were divided into an anxiety disorder with CHD group (observation group, n = 64) and a simple anxiety disorder group (control group, n = 89), according to coronary angiography (CAG) findings. Patients' demographic and clinical messages were collected and compared. Diabetes mellitus and hypertension, body mass index (BMI), and peripheral blood interleukin-6 (IL-6), high-sensitivity C-reactive protein (hs-CRP), homocysteine (Hcy), fibrinogen, D-dimer, cortisol, and norepinephrine expression levels were compared. Binary logistic regression analysis screened independent risk factors of CHD patients with anxiety disorders. The effectiveness of independent risk factors in predicting CHD with anxiety disorders was analyzed using receiver operating characteristic (ROC) curves.

Results

IL-6, hs-CRP, and Hcy levels of anxiety disorder in the CHD group were significantly higher than those in the simple anxiety disorder group. Binary multiple logistic regression analysis indicated that IL-6, hs-CRP, and Hcy were independent risk factors for CHD in patients with anxiety disorders. hs-CRP and Hcy levels were positively correlated with the Gensini score. ROC curve analysis indicated that the detection of hs-CRP or Hcy alone or the combined detection of the 2 had clinical predictive value for CHD in patients with anxiety disorders, and the area under the curve (AUC) of the combined detection of the 2 was significantly larger than that of any single factor alone (vs. hs-CRP, P = 0.045; vs. Hcy, P = 0.045).

Conclusion

IL-6, hs-CRP, and Hcy are related to CHD with anxiety disorders. Serum levels of the combined detection of hs-CRP and Hcy have a high clinical predictive value for CHD in patients with anxiety disorders.

An antibody-free platform for multiplexed, sensitive quantification of protein biomarkers in complex biomatrices

Sensitive, multiplexed protein quantification remains challenging despite recent advancements in LC-MS assays for targeted protein biomarker quantification. High-sensitivity protein biomarker measurements usually require immuno-affinity enrichment of target protein; a process which is highly dependent on capture reagent and limited in capability to measure multiple analytes. Herein, we report a novel antibody-free platform, which measures multiple biomarkers from complex matrices employing a strategically optimized solid-phase extraction cleanup and orthogonal multidimensional LC-MS. Eight human protein biomarkers with different specifications were spiked into canine plasma as a model investigation system. The developed strategy achieved the desired sensitivity, robustness, and throughput via the following steps: (1) post digestion mixed-mode cation exchange-reverse phase SPE enrichment cleaned up the sample initially; (2) rapid, high-pH peptide fractionation further eliminated background components efficiently while selectively enriched signature peptides (SP) to provide sufficient sensitivity for multiple targets; and (3) trapping-micro-LC-MS analysis delivered high sensitivity comparable to a nano-LC-MS method but with much better robustness and throughput for the final analysis. Compared with a conventional LC-MS assay with direct protein digestion and limited clean-up, analysis with this antibody-free platform improved the LLOQ by 1-2 orders of magnitude for the eight protein biomarkers, reaching as low as 5 ng/mL in plasma, with feasible robustness and throughput. This platform was applied for the quantification of biomarkers of respiratory conditions in patients with various lung diseases, demonstrating real-world applicability.

Advances in Traumatic Brain Injury Biomarkers

Traumatic brain injury (TBI) is increasingly a major cause of disability across the globe. The current methods of diagnosis are inadequate at classifying patients and prognosis. TBI is a diagnostic and therapeutic challenge. There is no Food and Drug Administration (FDA)-approved treatment for TBI yet. It took about 16 years of preclinical research to develop accurate and objective diagnostic measures for TBI. Two brain-specific protein biomarkers, namely, ubiquitin C-terminal hydrolase-L1 and glial fibrillary acidic protein, have been extensively characterized. Recently, the two biomarkers were approved by the FDA as the first blood-based biomarker, Brain Trauma Indicator™ (BTI™), via the Breakthrough Devices Program. This scoping review presents (i) TBI diagnosis challenges, (ii) the process behind the FDA approval of biomarkers, and (iii) known unknowns in TBI biomarker biology. The current lag in TBI incidence and hospitalization can be reduced if digital biomarkers such as hard fall detection are standardized and used as a mechanism to alert paramedics to an unresponsive trauma patient.

An Unsupervised Data-driven Anomaly Detection Approach for Detection of Adverse Health Conditions in People Living with Dementia: Cohort Study (Preprint)

Background

Sensor-based remote health monitoring can be used for the timely detection of health deterioration in people living with dementia with minimal impact on their day-to-day living. Anomaly detection approaches have been widely applied in various domains, including remote health monitoring. However, current approaches are challenged by noisy, multivariate data and low generalizability.

Objective

This study aims to develop an online, lightweight unsupervised learning–based approach to detect anomalies representing adverse health conditions using activity changes in people living with dementia. We demonstrated its effectiveness over state-of-the-art methods on a real-world data set of 9363 days collected from 15 participant households by the UK Dementia Research Institute between August 2019 and July 2021. Our approach was applied to household movement data to detect urinary tract infections (UTIs) and hospitalizations.

Methods

We propose and evaluate a solution based on Contextual Matrix Profile (CMP), an exact, ultrafast distance-based anomaly detection algorithm. Using daily aggregated household movement data collected via passive infrared sensors, we generated CMPs for location-wise sensor counts, duration, and change in hourly movement patterns for each patient. We computed a normalized anomaly score in 2 ways: by combining univariate CMPs and by developing a multidimensional CMP. The performance of our method was evaluated relative to Angle-Based Outlier Detection, Copula-Based Outlier Detection, and Lightweight Online Detector of Anomalies. We used the multidimensional CMP to discover and present the important features associated with adverse health conditions in people living with dementia.

Results

The multidimensional CMP yielded, on average, 84.3% recall with 32.1 alerts, or a 5.1% alert rate, offering the best balance of recall and relative precision compared with Copula-Based and Angle-Based Outlier Detection and Lightweight Online Detector of Anomalies when evaluated for UTI and hospitalization. Midnight to 6 AM bathroom activity was shown to be the most important cross-patient digital biomarker of anomalies indicative of UTI, contributing approximately 30% to the anomaly score. We also demonstrated how CMP-based anomaly scoring can be used for a cross-patient view of anomaly patterns.

Conclusions

To the best of our knowledge, this is the first real-world study to adapt the CMP to continuous anomaly detection in a health care scenario. The CMP inherits the speed, accuracy, and simplicity of the Matrix Profile, providing configurability, the ability to denoise and detect patterns, and explainability to clinical practitioners. We addressed the need for anomaly scoring in multivariate time series health care data by developing the multidimensional CMP. With high sensitivity, a low alert rate, better overall performance than state-of-the-art methods, and the ability to discover digital biomarkers of anomalies, the CMP is a clinically meaningful unsupervised anomaly detection technique extensible to multimodal data for dementia and other health care scenarios.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 1: Perfusion and Diffusion Techniques

Objective

Summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and highlight the latest bench-to-bedside developments.

Methods

Experts in advanced MRI techniques applied to high-grade glioma treatment response assessment convened through a European framework. Current evidence regarding the potential for monitoring biomarkers in adult high-grade glioma is reviewed, and individual modalities of perfusion, permeability, and microstructure imaging are discussed (in Part 1 of two). In Part 2, we discuss modalities related to metabolism and/or chemical composition, appraise the clinic readiness of the individual modalities, and consider post-processing methodologies involving the combination of MRI approaches (multiparametric imaging) or machine learning (radiomics).

Results

High-grade glioma vasculature exhibits increased perfusion, blood volume, and permeability compared with normal brain tissue. Measures of cerebral blood volume derived from dynamic susceptibility contrast-enhanced MRI have consistently provided information about brain tumor growth and response to treatment; it is the most clinically validated advanced technique. Clinical studies have proven the potential of dynamic contrast-enhanced MRI for distinguishing post-treatment related effects from recurrence, but the optimal acquisition protocol, mode of analysis, parameter of highest diagnostic value, and optimal cut-off points remain to be established. Arterial spin labeling techniques do not require the injection of a contrast agent, and repeated measurements of cerebral blood flow can be performed. The absence of potential gadolinium deposition effects allows widespread use in pediatric patients and those with impaired renal function. More data are necessary to establish clinical validity as monitoring biomarkers. Diffusion-weighted imaging, apparent diffusion coefficient analysis, diffusion tensor or kurtosis imaging, intravoxel incoherent motion, and other microstructural modeling approaches also allow treatment response assessment; more robust data are required to validate these alone or when applied to post-processing methodologies.

Conclusion

Considerable progress has been made in the development of these monitoring biomarkers. Many techniques are in their infancy, whereas others have generated a larger body of evidence for clinical application.

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 2: Spectroscopy, Chemical Exchange Saturation, Multiparametric Imaging, and Radiomics

Objective

To summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and to highlight the latest bench-to-bedside developments.

Methods

The current evidence regarding the potential for monitoring biomarkers was reviewed and individual modalities of metabolism and/or chemical composition imaging discussed. Perfusion, permeability, and microstructure imaging were similarly analyzed in Part 1 of this two-part review article and are valuable reading as background to this article. We appraise the clinic readiness of all the individual modalities and consider methodologies involving machine learning (radiomics) and the combination of MRI approaches (multiparametric imaging).

Results

The biochemical composition of high-grade gliomas is markedly different from healthy brain tissue. Magnetic resonance spectroscopy allows the simultaneous acquisition of an array of metabolic alterations, with choline-based ratios appearing to be consistently discriminatory in treatment response assessment, although challenges remain despite this being a mature technique. Promising directions relate to ultra-high field strengths, 2-hydroxyglutarate analysis, and the use of non-proton nuclei. Labile protons on endogenous proteins can be selectively targeted with chemical exchange saturation transfer to give high resolution images. The body of evidence for clinical application of amide proton transfer imaging has been building for a decade, but more evidence is required to confirm chemical exchange saturation transfer use as a monitoring biomarker. Multiparametric methodologies, including the incorporation of nuclear medicine techniques, combine probes measuring different tumor properties. Although potentially synergistic, the limitations of each individual modality also can be compounded, particularly in the absence of standardization. Machine learning requires large datasets with high-quality annotation; there is currently low-level evidence for monitoring biomarker clinical application.

Conclusion

Advanced MRI techniques show huge promise in treatment response assessment. The clinical readiness analysis highlights that most monitoring biomarkers require standardized international consensus guidelines, with more facilitation regarding technique implementation and reporting in the clinic.

Predicting 1p/19q co-deletion status from magnetic resonance imaging using deep learning in adult-type diffuse lower-grade gliomas: a discovery and validation study

Determination of 1p/19q co-deletion status is important for the classification, prognostication, and personalized therapy in diffuse lower-grade gliomas (LGG). We developed and validated a deep learning imaging signature (DLIS) from preoperative magnetic resonance imaging (MRI) for predicting the 1p/19q status in patients with LGG. The DLIS was constructed on a training dataset (n = 330) and validated on both an internal validation dataset (n = 123) and a public TCIA dataset (n = 102). The receiver operating characteristic (ROC) analysis and precision recall curves (PRC) were used to measure the classification performance. The area under ROC curves (AUC) of the DLIS was 0.999 for training dataset, 0.986 for validation dataset, and 0.983 for testing dataset. The F1-score of the prediction model was 0.992 for training dataset, 0.940 for validation dataset, and 0.925 for testing dataset. Our data suggests that DLIS could be used to predict the 1p/19q status from preoperative imaging in patients with LGG. The imaging-based deep learning has the potential to be a noninvasive tool predictive of molecular markers in adult diffuse gliomas.

Alzheimer Disease Biomarkers in Clinical Practice: A Blood-Based Diagnostic Revolution

An estimated 6.1 million Americans live with cognitive impairment-a number that is expected to triple by 2050. Alzheimer disease (AD) is the most common cause of impairment. The development of blood-based biomarkers capable of detecting pathological changes of AD in living patients has the potential to revolutionize the diagnostic approach to cognitive impairment by enabling screening for AD using accessible, non-invasive measures of amyloid and tau neuropathology, with accuracy that increasingly approaches that seen with "gold standard" positron emission tomography and cerebrospinal fluid measures. Demand for biomarker testing is expected to intensify with the emergence of effective treatments for AD and related dementias. Clinicians in all fields must prepare to meet this demand. Primary care practitioners are well positioned to support dementia diagnosis and management, including the application and interpretation of biomarkers. This article reviews the current uses of AD biomarkers and the potential applications of emerging blood-based AD biomarkers in clinical practice.

Toward the Next Generation of High-Grade Glioma Clinical Trials in the Era of Precision Medicine

ABSTRACT

In the era of precision medicine, there is a desire to harness our improved understanding of genomic and molecular underpinnings of gliomas to develop therapies that can be tailored to individual patients and tumors. With the rapid development of novel therapies, there has been a growing need to develop smart clinical trials that are designed to efficiently test promising agents, identify therapies likely to benefit patients, and discard ineffective therapies. We review clinical trial design in gliomas and developments designed to address the unique challenges of precision medicine. To provide an overview of this topic, we examine considerations for endpoints and response assessment, biomarkers, and novel clinical trial designs such as adaptive platform trials in the testing of new therapies for glioma patients.

Biomarkers for differentiating grade II meningiomas from grade I: a systematic review

INTRODUCTION

There are a number of prognostic markers (methylation, CDKN2A/B) described to be useful for the stratification of meningiomas. However, there are currently no clinically validated biomarkers for the preoperative prediction of meningioma grade, which is determined by the histological analysis of tissue obtained from surgery. Accurate preoperative biomarkers would inform the pre-surgical assessment of these tumours, their grade and prognosis and refine the decision-making process for treatment. This review is focused on the more controversial grade II tumours, where debate still surrounds the need for adjuvant therapy, repeat surgery and frequency of follow up.

METHODS

We evaluated current literature for potential grade II meningioma clinical biomarkers, focusing on radiological, biochemical (blood assays) and immunohistochemical markers for diagnosis and prognosis, and how they can be used to differentiate them from grade I meningiomas using the post-2016 WHO classification. To do this, we conducted a PUBMED, SCOPUS, OVID SP, SciELO, and INFORMA search using the keywords; 'biomarker', 'diagnosis', 'atypical', 'meningioma', 'prognosis', 'grade I', 'grade 1', 'grade II' and 'grade 2'.

RESULTS

We identified 1779 papers, 20 of which were eligible for systematic review according to the defined inclusion and exclusion criteria. From the review, we identified radiological characteristics (irregular tumour shape, tumour growth rate faster than 3cm³/year, high peri-tumoural blood flow), blood markers (low serum TIMP1/2, high serum HER2, high plasma Fibulin-2) and histological markers (low H3K27me3, low SMARCE1, low AKAP12, high ARIDB4) that may aid in differentiating grade II from grade I meningiomas.

CONCLUSION

Being able to predict meningioma grade at presentation using the radiological and blood markers described may influence management as the likely grade II tumours will be followed up or treated more aggressively, while the histological markers may prognosticate progression or post-treatment recurrence. This to an extent offers a more personalised treatment approach for patients.

Necrotizing Enterocolitis

Necrotizing enterocolitis (NEC) is an inflammatory disease affecting premature infants. Intestinal microbial composition may play a key role in determining which infants are predisposed to NEC and when infants are at highest risk of developing NEC. It is unclear how to optimize antibiotic therapy in preterm infants to prevent NEC and how to optimize antibiotic regimens to treat neonates with NEC. This article discusses risk factors for NEC, how dysbiosis in preterm infants plays a role in the pathogenesis of NEC, and how probiotic and antibiotic therapy may be used to prevent and/or treat NEC and its sequelae.

Perspectives on statistical strategies for the regulatory biomarker qualification process

Qualification of a biomarker for use in a medical product development program requires a statistical strategy that aligns available evidence with the proposed context of use (COU), identifies any data gaps to be filled and plans any additional research required to support the qualification. Accumulating, interpreting and analyzing available data is outlined, step-by-step, illustrated by a qualified enrichment biomarker example and a safety biomarker in the process of qualification. The detailed steps aid requestors seeking qualification of biomarkers, allowing them to organize the available evidence and identify potential gaps. This provides a statistical perspective for assessing evidence that parallels clinical considerations and is intended to guide the overall evaluation of evidentiary criteria to support a specific biomarker COU.

Raman Spectral Signatures of Serum-Derived Extracellular Vesicle-Enriched Isolates May Support the Diagnosis of CNS Tumors

Investigating the molecular composition of small extracellular vesicles (sEVs) for tumor diagnostic purposes is becoming increasingly popular, especially for diseases for which diagnosis is challenging, such as central nervous system (CNS) malignancies. Thorough examination of the molecular content of sEVs by Raman spectroscopy is a promising but hitherto barely explored approach for these tumor types. We attempt to reveal the potential role of serum-derived sEVs in diagnosing CNS tumors through Raman spectroscopic analyses using a relevant number of clinical samples. A total of 138 serum samples were obtained from four patient groups (glioblastoma multiforme, non-small-cell lung cancer brain metastasis, meningioma and lumbar disc herniation as control). After isolation, characterization and Raman spectroscopic assessment of sEVs, the Principal Component Analysis-Support Vector Machine (PCA-SVM) algorithm was performed on the Raman spectra for pairwise classifications. Classification accuracy (CA), sensitivity, specificity and the Area Under the Curve (AUC) value derived from Receiver Operating Characteristic (ROC) analyses were used to evaluate the performance of classification. The groups compared were distinguishable with 82.9-92.5% CA, 80-95% sensitivity and 80-90% specificity. AUC scores in the range of 0.82-0.9 suggest excellent and outstanding classification performance. Our results support that Raman spectroscopic analysis of sEV-enriched isolates from serum is a promising method that could be further developed in order to be applicable in the diagnosis of CNS tumors.