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HR leaders and corporate benefits managers must balance organizational costs with decisions about which new tools and treatments will be covered by their employee health insurance plans. Getting it right can mean the difference between life and death for cancer patients. Most HR leaders, however, are not experts in cancer treatment and do not know how to make sure their plan benefits do not create roadblocks to treatment.

A total of 295 people who were diagnosed with cancer from 2019 to 2022 participated in the 2023 CancerCare Biomarker Survey, which was conducted in January 2023.

CancerCare’s 2023 survey of cancer patients found that biomarker testing helped doctors tailor therapy for nearly all the patients (93%) whose cancers were tested over the past three years. Two in 10 cancer patients (20%) avoided unnecessary chemotherapy and/or radiation and one in 10 (10%) became eligible for a clinical trial because of biomarker testing.

Biomarker testing is a necessary tool in the advancing world of precision cancer treatment. Despite the significant and demonstrable benefits to surveyed patients, three out of 10 respondents (29%) who received biomarker testing did not have the test covered by their insurance. Some survey respondents reported that biomarker test coverage was originally denied and they had to fight to get it covered. Others had to find ways to pay out-of-pocket or seek financial assistance to cover the cost of the testing.

Unfortunately, health insurance plans often limit cancer patients’ access to recommended biomarker testing, impose burdensome prior authorization (PA) protocols or require unaffordable cost-sharing, which can prevent or delay cancer patients’ access to optimal treatments. PA, a significant source of roadblocks to timely testing and treatment, was required by a quarter (25%) of the cancer patients surveyed.

Biomarker testing is increasingly a health care equity issue and there are significant gaps in the rate of biomarker testing between black and white lung and colorectal cancer patients, which can lead to disparities in clinical trial participation and hinder access to the most effective treatments. A key way to address these barriers is to broaden insurance coverage of biomarker testing, as recommended by medical experts.

This paper aims to compare two diagnostic performance measures, i.e. signal‐to‐noise ratio (S/N ratio) and partial area under receiver operating characteristic curves (pAUC). It proposes the use of S/N ratio rather than pAUC for establishing optimal cut‐off point for diagnostic biomarkers.

This paper discusses the properties, uses, advantages and shortcomings of the two performance measures, namely the partial area under receiver operating characteristic curve (pAUC) and Taguchi's signal‐to‐noise (S/N) ratio. The benefits of S/N ratio have been illustrated in a sample of four biomarkers, each having five cut‐off points. The S/N ratio is compared to the pAUC index. The SAS software is employed to calculate pAUC and AUC.

This paper shows that S/N ratio can be used as a measure of diagnostic accuracy. The cut‐off point with the highest S/N ratio is the optimal cut‐off point for the biomarker. The proposed method has the advantages of being easier, more practical and less costly than that of pAUC.

This paper includes implications for the development of a more practical, equally powerful and less costly means of measuring clinical accuracy thereby reducing the costs and risks resulting from wrong selection of cut‐off point can be decreased.

This paper supports suggestions in the recent literature to replace pAUC with a new, more meaningful index.

This paper examines the potential of “biomarkers” to provide immutable identification for food products (chocolate), providing traceability and visibility in the supply chain from retail product back to farm.

This research uses qualitative data collection, including fieldwork at cocoa farms and chocolate manufacturers in Ecuador and the Netherlands and semi-structured interviews with industry professionals to identify challenges and create a supply chain map from cocoa plant to retailer, validated by area experts. A library of biomarkers is created using DNA collected from fieldwork and the International Cocoa Quarantine Centre, holders of cocoa varieties from known locations around the world. Matching sample biomarkers with those in the library enables identification of origins of cocoa used in a product, even when it comes from multiple different sources and has been processed.

Supply chain mapping and interviews identify areas of the cocoa supply chain that lack the visibility required for management to guarantee sustainability and quality. A decoupling point, where smaller farms/traders’ goods are combined to create larger economic units, obscures product origins and limits visibility. These factors underpin a potential boundary condition to institutional theory in the industry’s fatalism to environmental and human abuses in the face of rising institutional pressures. Biomarkers reliably identify product origin, including specific farms and (fermentation) processing locations, providing visibility and facilitating control and trust when purchasing cocoa.

The biomarker “meta-barcoding” of cocoa beans used in chocolate manufacturing accurately identifies the farm, production facility or cooperative, where a cocoa product came from. A controlled data set of biomarkers of registered locations is required for audit to link chocolate products to origin.

Where biomarkers can be produced from organic products, they offer a method for closing visibility gaps, enabling responsible sourcing. Labels (QR codes, barcodes, etc.) can be swapped and products tampered with, but biological markers reduce reliance on physical tags, diminishing the potential for fraud. Biomarkers identify product composition, pinpointing specific farm(s) of origin for cocoa in chocolate, allowing targeted audits of suppliers and identifying if cocoa of unknown origin is present. Labour and environmental abuses exist in many supply chains and enabling upstream visibility may help firms address these challenges.

By describing a method for firms in cocoa supply chains to scientifically track their cocoa back to the farm level, the research shows that organizations can conduct social audits for child labour and environmental abuses at specific farms proven to be in their supply chains. This provides a method for delivering supply chain visibility (SCV) for firms serious about tackling such problems.

This paper provides one of the very first examples of biomarkers for agricultural SCV. An in-depth study of stakeholders from the cocoa and chocolate industry elucidates problematic areas in cocoa supply chains. Biomarkers provide a unique biological product identifier. Biomarkers can support efforts to address environmental and social sustainability issues such as child labour, modern slavery and deforestation by providing visibility into previously hidden areas of the supply chain.

Live non-invasive monitoring of biomarkers is of great importance for the medical community. Moreover, some studies suggest that there is a substantial business gap in the development of mass-production commercial sweat-analysing wearables with great revenue potential. The objective of this work is to quantify the concentration of biomarkers that reaches the area of the garment where a sensor is positioned to advance the development of commercial sweat-analysing garments.

Computational analysis of the microfluidic transport of biomarkers within eccrine sweat glands provides a powerful way to explore the potential for quantitative measurements of biomarkers that can be related to the health and/or the physical activity parameters of an individual. The numerical modelling of sweat glands and the interaction of sweat with a textile layer remain however rather unexplored. This work presents a simulation of the production of sweat in the eccrine gland, reabsorption from the dermal duct into the surrounding skin and diffusion within an overlying garment.

The model represents satisfactorily the relationship between the biomarker concentration and the flow rate of sweat. The biomarker distribution across an overlying garment has also been calculated and subsequently compared to the minimum amount detectable by a sensor previously reported in the literature. The model can thus be utilized to check whether or not a given sensor can detect the minimum biomarker concentration threshold accumulated on a particular type of garment.

The present work presents to the best of our knowledge, the earliest numerical models of the sweat gland carried out so far. The model describes the flow of human sweat along the sweat duct and on to an overlying piece of garment. The model considers complex phenomena, such as reabsorption of sweat into the skin layers surrounding the duct, and the structure of the fibres composing the garment. Biomarker concentration maps are obtained to check whether sensors can detect the threshold concentration that triggers an electric signal. This model finds application in the development of smart textiles.

The purpose of this study was to design a paper strip-based non-invasive urine analysis system for the qualitative detection of biomarker aquaporin-1 (AQP1) in renal cancer (RC). RC accounts for 3% of all cancers and 85% of all kidney tumors and mainly originates from the kidney cortex. In recent times, higher urine concentration of AQP1 in patients with RC was confirmed as a specific biomarker of the disease. Hence, the noninvasive, user-friendly and self-diagnostic method is required for the detection of aquaporin biomarkers in RC.

The present research work was focused on the development and characterization of a dye conjugated cyclodextrin-based miniaturized system for impregnation on Whatman filter paper to identify RC using AQP1 biomarker present in urine samples.

It was observed that the test strip dipped into the urine sample, and the yellow color intensity increased with a decrease in AQP1 concentration due to the transformation of the dye system of free basic form into bound acidic form. The Hue-Saturation-Value profiling was used to observe the effect of color change using a smartphone application. The paper strip-based urine analysis system is highly sensitive for the detection of AQP1 in the range of 10 to 1,000 ng.

The successful validation indicated that this biosensor is likely to contribute to the development of point-of-care, novel, personalized diagnostics and ensure prolonged survival of RC patients in the near future.

This paper aims to offer insights into the impact of digitization technology on consumer goods manufacturers and retail organizations. The authors propose that the “next phase” of digitization will entail the employment of digitization technology to offer consumers personalized product offerings and recommendations based on their internal biomarkers.

The authors draw on past investigations into digitization and their retailing experience to speculate on how the next phase of digitization will affect both consumer goods manufacturers and retailers.

The next phase of digitization will entail the use of nutrigenomics (DNA sequencing), exhaled breath analysis, fitness tracker devices, sensory patches, radio frequency identification tags and quantum ID tags to create customized and recommend products, and support product-to-customer communication regarding authenticity.

Consumers will increasingly rely on technology to inform them of their bodily needs and to receive personalized solutions to help satisfy those needs. Consumer behavior theories must be reconsidered because consumers will become more passive participants in retail consumption as they rely on technology for need-recognition and product-fulfillment.

Digitization technologies that use consumers’ biomarkers for new product creation or product recommendation raise new risks and uncertainty. For example, the legal implications of an incorrect product recommendation based on customer biomarkers are unknown. Furthermore, retailers would need to maintain data privacy of biomarker data and be responsible for data breaches.

The research explores how digitization will affect consumers’ in-store experiences with consumer goods products.

Gliomas are common intracranial tumors with the characteristic of diffuse and invasive growth. The prognosis is poor, and the recurrence rate and mortality are higher. With the development of big data technology, many methods such as natural language processing, computer vision and image processing have been deeply applied in the medical field. This can help clinicians to provide personalized and precise diagnosis and therapeutic schedule for patients with different type of gliomas to achieve the best therapeutic effect. The purpose of this paper is to summarize and extract useful information from published research results by conducting a secondary analysis of the literature.

The PubMed and China National Knowledge Infrastructure (CNKI) literature database were used to retrieve published Chinese and English research papers about human gliomas. Comprehensive analysis was applied to conduct this research. The factors affecting survival and prognosis were screened and analyzed respectively in this paper, and different methods for multidimensional data of patients were discussed.

This paper identified biomarkers and therapeutic modalities associated with prognosis for different grade of gliomas. This paper investigated the relationship among these clinical prognostic factors and different histopathologic tying and grade of gliomas by comprehensive analysis. This paper summarizes the research progress of biomarker in medical imaging and genomics of gliomas to improve prognosis and the current status of treatment in China.

Combined with multimodal data such as genomics data, medical image data and clinical information data, this paper comprehensively analyzed the prognostic factors of glioma and provided guidance and evidence for rational treatment planning and improvement of clinical treatment prognosis.

Meteorin like-peptide (Metrnl) modulates energy hemostasis and relieves inflammation and oxidative stress. This study aims to investigate the relationship between Metrnl levels and inflammatory cytokines, oxidative stress biomarkers and body composition parameters in obese type 2 diabetic patients.

This analytical cross-sectional study was carried out between August 2020 and March 2021on 93 people (n = 32 obese type 2 diabetic patients, n = 31 healthy obese, n = 30 healthy normal weight). Serum Metrnl levels were measured by enzyme-linked immunosorbent assay. Serum levels of interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), high-sensitivity C-reactive protein (hs-CRP), malondialdehyde and total antioxidant capacity were measured using standard methods. Body composition was assessed by bioelectrical impedance analysis. p-Value less than 0.05 was considered significant.

Serum Metrnl levels were lower in obese diabetic patients than in healthy normal-weight subjects (82.5 [72.4–94.5] ng/mL compared to 154 [129–189] ng/mL). The difference in Metrnl levels between the two groups was significant (p < 0.001). The difference in Metrnl levels between the two groups was significant. Moreover, a significant correlation between Metrnl level and both TNF-a and hs-CRP was detected (p = 0.006 and p < 0.001, respectively) and the correlation between Metrnl and IL-6 was borderline (p = 0.051). A negative correlation was revealed between Metrnl level and anthropometric measurements and body composition (p < 0.05) with the exception of fat-free mass and skeletal muscle mass.

To discover sufficient evidence for the therapeutic function of Metrnl and its use as a prognostic biomarker in the management of type 2 diabetes mellitus, future clinical studies are needed to highlight other factors influencing Metrnl serum levels.

This review provides an overview of recent advances in electrochemical sensors for analyte detection in saliva, highlighting their potential applications in diagnostics and health care. The purpose of this paper is to summarize the current state of the field, identify challenges and limitations and discuss future prospects for the development of saliva-based electrochemical sensors.

The paper reviews relevant literature and research articles to examine the latest developments in electrochemical sensing technologies for saliva analysis. It explores the use of various electrode materials, including carbon nanomaterial, metal nanoparticles and conducting polymers, as well as the integration of microfluidics, lab-on-a-chip (LOC) devices and wearable/implantable technologies. The design and fabrication methodologies used in these sensors are discussed, along with sample preparation techniques and biorecognition elements for enhancing sensor performance.

Electrochemical sensors for salivary analyte detection have demonstrated excellent potential for noninvasive, rapid and cost-effective diagnostics. Recent advancements have resulted in improved sensor selectivity, stability, sensitivity and compatibility with complex saliva samples. Integration with microfluidics and LOC technologies has shown promise in enhancing sensor efficiency and accuracy. In addition, wearable and implantable sensors enable continuous, real-time monitoring of salivary analytes, opening new avenues for personalized health care and disease management.

This review presents an up-to-date overview of electrochemical sensors for analyte detection in saliva, offering insights into their design, fabrication and performance. It highlights the originality and value of integrating electrochemical sensing with microfluidics, wearable/implantable technologies and point-of-care testing platforms. The review also identifies challenges and limitations, such as interference from other saliva components and the need for improved stability and reproducibility. Future prospects include the development of novel microfluidic devices, advanced materials and user-friendly diagnostic devices to unlock the full potential of saliva-based electrochemical sensing in clinical practice.