Determining topical product bioequivalence with stimulated Raman scattering microscopy.

Generic drugs are essential for affordable medicine and improving accessibility to treatments. Bioequivalence (BE) is typically demonstrated by assessing a generic product's pharmacokinetics (PK) relative to a reference-listed drug (RLD). Accurately estimating cutaneous PK (cPK) at or near the site of action can be challenging for locally acting topical products. Certain cPK approaches are available for assessing local bioavailability (BA) in the skin. Stimulated Raman scattering (SRS) microscopy has unique capabilities enabling continuous, high spatial and temporal resolution and quantitative imaging of drugs within the skin. In this paper, we developed an approach based on SRS and a polymer-based standard reference for the evaluation of topical product BA and BE in human skin ex vivo. BE assessment of tazarotene-containing formulations was achieved using cPK parameters obtained within different skin microstructures. The establishment of BE between the RLD and an approved generic product was successfully demonstrated. Interestingly, within the constraints of the current study design the results suggest similar BA between the tested gel formulation and the reference cream formulation, despite the differences in the formulation/dosage form. Another formulation containing polyethylene glycol as the vehicle was demonstrated to be not bioequivalent to the RLD. Compared to using the SRS approach without a standard reference, the developed approach enabled more consistent and reproducible results, which is crucial in BE assessment. The abundant information from the developed approach can help to systematically identify key areas of study design that will enable a better comparison of topical products and support an assessment of BE.

A Polarity-Sensitive Far-Red Fluorescent Probe for Glucose Sensing through Skin.

The field of glucose biosensors for diabetes management has been of great interest over the past 60 years. Continuous glucose monitoring (CGM) is important to continuously track the glucose level to provide better management of the disease. Concanavalin A (ConA) can reversibly bind to glucose and mannose molecules and form a glucose biosensor via competitive binding. Here, we developed a glucose biosensor using ConA and a fluorescent probe, which generated a fluorescent intensity change based on solvatochromism, the reversible change in the emission spectrum dependent on the polarity of the solvent. The direction in which the wavelength shifts as the solvent polarity increases can be defined as positive (red-shift), negative (blue-shift), or a combination of the two, referred to as reverse. To translate this biosensor to a subcutaneously implanted format, Cyanine 5.5 (Cy5.5)-labeled small mannose molecules were used, which allows for the far-red excitation wavelength range to increase the skin penetration depth of the light source and returned emission. Three Cy5.5-labeled small mannose molecules were synthesized and compared when used as the competing ligand in the competitive binding biosensor. We explored the polarity-sensitive nature of the competing ligands and examined the biosensor's glucose response. Cy5.5-mannotetraose performed best as a biosensor, allowing for the detection of glucose from 25 to 400 mg/dL. Thus, this assay is responsive to glucose within the physiologic range when its concentration is increased to levels needed for an implantable design. The biosensor response is not statistically different when placed under different skin pigmentations when comparing the percent increase in fluorescence intensity. This shows the ability of the biosensor to produce a repeatable signal across the physiologic range for subcutaneous glucose monitoring under various skin tones.

Exploring the Clinical Utility of Raman Spectroscopy for Point-of-Care Cardiovascular Disease Biomarker Detection.

A variety of innovative point-of-care (POC) solutions using Raman systems have been explored. However, the vast effort is in assay development, while studies of the characteristics required for Raman spectrometers to function in POC applications are lacking. In this study, we tested and compared the performance of eight commercial Raman spectrometers ranging in size from benchtop Raman microscopes to portable and handheld Raman spectrometers using paper fluidic cartridges, including their ability to detect cardiac troponin I and heart fatty acid binding protein, both of which are well-established biomarkers for evaluating cardiovascular health. Each spectrometer was evaluated in terms of excitation wavelength, laser characteristics, and ease of use to investigate POC utility. We found that the Raman spectrometers equipped with 780 and 785 nm laser sources exhibited a reduced background signal and provided higher sensitivity compared to those with 633 and 638 nm laser sources. Furthermore, the spectrometer equipped with the single acquisition line readout functionality showed improved performance when compared to the point scan spectrometers and allowed measurements to be made faster and easier. The portable and handheld spectrometers also showed similar detection sensitivity to the gold standard instrument. Lastly, we reduced the laser power for the spectrometer with single acquisition line readout capability to explore the system performance at a laser power that change the classification from a Class 3B laser device to a Class 3R device and found that it showed comparable performance. Overall, these findings show that portable Raman spectrometers have the potential to be used in POC settings with accuracy comparable to laboratory-grade instruments, are relatively low-cost, provide fast signal readout, are easy to use, and can facilitate access for underserved communities.

[Effects of Different Planting Years of Dendrocalamus brandisii on Soil Fungal Community].

To explore the effect of soil fungal community under different planting years in Dendrocalamus brandisii, the soil samples from D. brandisii with different planting years (5, 10, 20, and 40 a) were taken as the research object. The soil fungal community structure, diversity, and its functional groups of different planting years were analyzed using high-throughput sequencing technology and the FUNGuild fungal function prediction tool, and the main soil environmental factors influencing the variations in soil fungal community were examined. The results showed that the dominant fungal communities at the phylum level were Ascomycota, Basidiomycota, Mortierellomycota, and Mucoromycota. The relative abundance of Mortierellomycota decreased and then increased with the increase in planting years, and there was a significant difference among different planting years (P<0.05). The dominant fungal communities at the class level were Sordariomycetes, Agaricomycetes, Eurotiomycetes, and Mortierellomycetes. The relative abundance of Sordariomycetes and Dothideomycetes decreased and then increased with the increase in planting years, and there were significant differences among different planting years (P<0.01). The Richness index and Shannon index of soil fungi increased and then decreased with the increase in planting years, and the Richness index and Shannon index in 10 a were significantly higher than those of other planting years. Non-metric multidimensional scaling (NMDS) and analysis of similarities (ANOSIM) showed that there were significant differences in soil fungal community structure with different planting years. The functional prediction with FUNGuild showed that the main functional trophic types of soil fungi in D. brandisii were pathotroph, symbiotroph, and saprotroph, and the most dominant functional group was endophyte-litter saprotroph-soil saprotroph-undefined saprotroph. The relative abundance of endophytes gradually increased with the increase in planting years. Correlation analysis showed that pH, total potassium (TK), and nitrate nitrogen (NO-3-N) were the main soil environmental factors affecting the change in fungal community. In summary, the planting year of D. brandisii has changed soil environmental factors and has thus changed the structure, diversity, and functional groups of soil fungal communities.

Unsupervised Cross-Modality Adaptation via Dual Structural-Oriented Guidance for 3D Medical Image Segmentation.

Deep convolutional neural networks (CNNs) have achieved impressive performance in medical image segmentation; however, their performance could degrade significantly when being deployed to unseen data with heterogeneous characteristics. Unsupervised domain adaptation (UDA) is a promising solution to tackle this problem. In this work, we present a novel UDA method, named dual adaptation-guiding network (DAG-Net), which incorporates two highly effective and complementary structural-oriented guidance in training to collaboratively adapt a segmentation model from a labelled source domain to an unlabeled target domain. Specifically, our DAG-Net consists of two core modules: 1) Fourier-based contrastive style augmentation (FCSA) which implicitly guides the segmentation network to focus on learning modality-insensitive and structural-relevant features, and 2) residual space alignment (RSA) which provides explicit guidance to enhance the geometric continuity of the prediction in the target modality based on a 3D prior of inter-slice correlation. We have extensively evaluated our method with cardiac substructure and abdominal multi-organ segmentation for bidirectional cross-modality adaptation between MRI and CT images. Experimental results on two different tasks demonstrate that our DAG-Net greatly outperforms the state-of-the-art UDA approaches for 3D medical image segmentation on unlabeled target images.

Electrocatalytic NAD+ reduction via hydrogen atom-coupled electron transfer.

Nicotinamide adenine dinucleotide cofactor (NAD(P)H) is regarded as an important energy carrier and charge transfer mediator. Enzyme-catalyzed NADPH production in natural photosynthesis proceeds via a hydride transfer mechanism. Selective and effective regeneration of NAD(P)H from its oxidized form by artificial catalysts remains challenging due to the formation of byproducts. Herein, electrocatalytic NADH regeneration and the reaction mechanism on metal and carbon electrodes are studied. We find that the selectivity of bioactive 1,4-NADH is relatively high on Cu, Fe, and Co electrodes without forming commonly reported NAD2 byproducts. In contrast, more NAD2 side product is formed with the carbon electrode. ADP-ribose is confirmed to be a side product caused by the fragmentation reaction of NAD+. Based on H/D isotope effects and electron paramagnetic resonance analysis, it is proposed that the formation of NADH on these metal electrodes proceeds via a hydrogen atom-coupled electron transfer (HadCET) mechanism, in contrast to the direct electron-transfer and NAD˙ radical pathway on carbon electrodes, which leads to more by-product, NAD2. This work sheds light on the mechanism of electrocatalytic NADH regeneration, which is different from biocatalysis.

Portable, multi-modal Raman and fluorescence spectroscopic platform for point-of-care applications.

Significance: Point-of-care (POC) platforms utilizing optical biosensing strategies can achieve on-site detection of biomarkers to improve the quality of care for patients in low-resource settings. Aim: We aimed to develop a portable, multi-modal spectroscopic platform capable of performing Raman and fluorescence measurements from a single sample site. Approach: We designed the spectroscopic platform in OpticStudio using commercial optical components and built the system on a portable optical breadboard. Two excitation and collection arms were utilized to detect the two optical signals. The multi-modal functionality was validated using ratiometric Raman/fluorescence samples, and the potential utility was demonstrated using a model bioassay for cardiac troponin I. Results: The designed spectroscopic platform achieved a spectral resolution of 0.67 ± 0.2 nm across the Raman detection range (660 to 770 nm). The ratiometric Raman/fluorescence samples demonstrated no crosstalk between the two detector arms across a gradient of high molar concentrations. Testing of the model bioassay response showed that the integrated approach improved the linearity of the calibration curve from (R2 = 0.977) for the Raman only and (R2 = 0.972) for the fluorescence only to (R2 = 0.988) for the multi-modal approach. Conclusion: These findings demonstrate the potential impact of a multi-modal POC spectroscopic platform to improve the sensitivity and robustness necessary for biomarker detection.

Deep Learning for Automatic Upper Airway Obstruction Detection by Analysis of Flow-Volume Curve.

BACKGROUND: Due to the similar symptoms of upper airway obstruction to asthma, misdiagnosis is common. Spirometry is a cost-effective screening test for upper airway obstruction and its characteristic patterns involving fixed, variable intrathoracic and extrathoracic lesions. We aimed to develop a deep learning model to detect upper airway obstruction patterns and compared its performance with that of lung function clinicians. METHODS: Spirometry records were reviewed to detect the possible condition of airway stenosis. Then they were confirmed by the gold standard (e.g., computed tomography, endoscopy, or clinic diagnosis of upper airway obstruction). Images and indices derived from flow-volume curves were used for training and testing the model. Clinicians determined cases using spirometry records from the test set. The deep learning model evaluated the same data. RESULTS: Of 45,831 patients' spirometry records, 564 subjects with curves suggesting upper airway obstruction, after verified by the gold standard, 351 patients were confirmed. These cases and another 200 cases without airway stenosis were used as the training and testing sets. 432 clinicians evaluated 20 cases of each of the three patterns and 20 no airway stenosis cases (n = 80). They assigned an accuracy of 41.2% (±15.4) (interquartile range: 27.5-52.5%), with poor agreements (κ = 0.12). For the same cases, the model generated a correct detection of 81.3% (p < 0.0001). CONCLUSIONS: Deep learning could detect upper airway obstruction patterns from other classic patterns of ventilatory defects with high accuracy, whereas clinicians presented marked errors and variabilities. The model may serve as a support tool to enhance clinicians' correct diagnosis of upper airway obstruction using spirometry.

Deep learning for spirometry quality assurance with spirometric indices and curves.

BACKGROUND: Spirometry quality assurance is a challenging task across levels of healthcare tiers, especially in primary care. Deep learning may serve as a support tool for enhancing spirometry quality. We aimed to develop a high accuracy and sensitive deep learning-based model aiming at assisting high-quality spirometry assurance. METHODS: Spirometry PDF files retrieved from one hospital between October 2017 and October 2020 were labeled according to ATS/ERS 2019 criteria and divided into training and internal test sets. Additional files from three hospitals were used for external testing. A deep learning-based model was constructed and assessed to determine acceptability, usability, and quality rating for FEV1 and FVC. System warning messages and patient instructions were also generated for general practitioners (GPs). RESULTS: A total of 16,502 files were labeled. Of these, 4592 curves were assigned to the internal test set, the remaining constituted the training set. In the internal test set, the model generated 95.1%, 92.4%, and 94.3% accuracy for FEV1 acceptability, usability, and rating. The accuracy for FVC acceptability, usability, and rating were 93.6%, 94.3%, and 92.2%. With the assistance of the model, the performance of GPs in terms of monthly percentages of good quality (A, B, or C grades) tests for FEV1 and FVC was higher by ~ 21% and ~ 36%, respectively. CONCLUSION: The proposed model assisted GPs in spirometry quality assurance, resulting in enhancing the performance of GPs in quality control of spirometry.

The complete chloroplast genome of Indosasa hispida 'Rainbow' (Poaceae, Bambuseae): an ornamental bamboo species in horticulture.

Indosasa hispida 'Rainbow' is a new horticultural plant variety for anthocyanin production, which has great ornamental value and huge market potential. The chloroplast genome is 139,690 bp in length, containing a large single-copy region (LSC) of 83,268 bp, a small single-copy region (SSC) of 12,830 bp, and a pair of 21,796 bp inverted repeats region (IR). The GC content of chloroplast genome is 38.9%. There are 130 genes in the cp genome, including 83 protein-coding genes, 8 ribosomal RNA genes, and 39 transfer RNA genes. In addition, phylogenetic analysis firmly supported that I. hispida 'Rainbow' constituted that a sister species with Pleioblastus maculatus.

Nonalloy Model-Based Ternary Organic Solar Cells.

Ternary blending based on an alloy-like model has been proved as an efficient strategy for high-efficiency organic solar cells (OSCs). However, the third component that possesses excellent miscibility with host materials in the alloy-like model may trigger adverse effects for the active layer, especially at a high doping ratio. In this work, we propose a new concept of nonalloy model for the ternary OSCs in which the third component presents moderate miscibility with the acceptor and distributes at the interspace between donor and acceptor domains. The nonalloy model is constructed based on the PM6:Y6 system, and a Y6 analogue (BTP-MCA) is synthesized as the third component. The BTP-MCA can maintain initial excellent morphology of the active layer and enhance the morphological stability by acting as a frame around the host materials. As a result, ternary OSCs based on the PM6:Y6:BTP-MCA blend exhibit an impressive efficiency of 17.0% with a high open-circuit voltage of 0.87 V. Moreover, the devices present a high doping tolerance (keeping high efficiency with a doping ratio of 50%) and improved stability. This work indicates that the nonalloy model can be a promising method to fabricate efficient and stable ternary OSCs apart from the conventional alloy-like model.

Spectrally multiplexed assay using gap enhanced nanoparticle for detection of a myocardial infarction biomarker panel.

Multiplexed assays are essential for the detection of biomarker panels. Differentiating signals from different biomarkers in a single test zone makes the detection more efficient. In this paper, a new method is designed for the synthesis of gap-enhanced nanoparticles (GeNPs) using Raman reporter molecules (RRM) and 6-amino-1-hexanethiol (6-AHT) as the spacer. The GeNPs show a nanometer-size gap, generate strong surface-enhanced Raman scattering (SERS) attributed to the gap, and exhibit discriminative spectral peaks. The strong Au-S bonds on both core and shell sides and the covalent bond between RRM and 6-AHT led to a stable structure, which ensured the stable SERS signal generation from the GeNPs. Using the GeNPs, a spectrally multiplexed assay for the detection of a biomarker panel is developed. The biomarker panel is composed of cardiac troponin I (cTnI), copeptin, and heart-type fatty acid-binding protein (h-FABP), which improves myocardial infarction (MI) diagnostic performance. A paper-based platform that is more amenable to point-of-care diagnostic analysis is used. The developed single biomarker assay achieves limits of detection of 0.01 ng mL-1, 0.86 ng mL-1, 0.004 ng mL-1 for cTnI, h-FABP, and copeptin in buffer solutions. The dynamic range of the assay in human serum samples also covers the clinically relevant range of the biomarkers. The cross interference in the multiplexed assay is low. These results show the strong potential of the developed GeNPs in multiplexed detection of biomarkers and the developed simple-to-use multiplexed assay in the diagnosis of MI at the point of care.

Erratum: Author Correction: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.

[This corrects the article DOI: 10.1038/s42256-021-00421-z.].

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence.

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

Isomeric anthracene diimide polymers.

N-type semiconducting polymers are attractive for organic electronics, but desirable electron-deficient units for synthesizing such polymers are still lacking. As a cousin of rylene diimides such as naphthalene diimide (NDI) and perylene diimide (PDI), anthracene diimide (ADI) is a promising candidate; its polymers, however, have not been achieved yet because of synthetic challenges for its polymerizable monomers. Herein, we present ingenious synthesis of two dibromide ADI monomers with dibromination at differently symmetrical positions of the ADI core, which are further employed to construct ADI polymers. More interestingly, the two obtained ADI polymers possess the same main-chain and alkyl-chain structures but different backbone conformations owing to varied linking positions between repeating units. This feature enables their different optoelectronic properties and film-state packing behavior. The ADI polymers offer first examples of conjugated polymer conformational isomers and are highly promising as a new class of n-type semiconductors for various organic electronics applications.

Deep learning for predicting COVID-19 malignant progression.

As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosis and treatment, which has greatly challenged public medical systems. Treatment priority is often determined by the symptom severity based on first assessment. However, clinical observation suggests that some patients with mild symptoms may quickly deteriorate. Hence, it is crucial to identify patient early deterioration to optimize treatment strategy. To this end, we develop an early-warning system with deep learning techniques to predict COVID-19 malignant progression. Our method leverages CT scans and the clinical data of outpatients and achieves an AUC of 0.920 in the single-center study. We also propose a domain adaptation approach to improve the generalization of our model and achieve an average AUC of 0.874 in the multicenter study. Moreover, our model automatically identifies crucial indicators that contribute to the malignant progression, including Troponin, Brain natriuretic peptide, White cell count, Aspartate aminotransferase, Creatinine, and Hypersensitive C-reactive protein.

Paper Microfluidic Device with a Horizontal Motion Valve and a Localized Delay for Automatic Control of a Multistep Assay.

A microfluidic paper-based analytical device (µPAD) is a cost-effective platform to implement assays, especially for point-of-care testing. Developing µPADs with fluidic control is important to implement multistep assays and provide high sensitivities. However, current localized delays in µPADs made of sucrose have a limited ability to decrease the flow rate. In addition, existing µPADs for automatic multistep assays are limited by their need for auxiliary instruments, their false activation, or their unavoidable tradeoff between available fluid volumes and temporal differences between steps. Here, a novel µPAD composed of a localized dissolvable delay and a horizontal motion mechanical valve for use as an automatic multistep assay is reported. A mixture of fructose and sucrose was used in the localized dissolvable delay and it provided an effective decrease in the flow rate to ensure adequate sensitivity in an assay. The dissolvable delay effectively doubled the flow time. A mechanical valve using a horizontal movement was developed to automatically implement a multistep process. Two-step and four-step processes were enabled with the µPAD. Cardiac troponin I (cTnI), a gold-standard biomarker for myocardial infarction, was used as a model analyte to show the performance of the developed µPAD in an assay. The designed µPAD, with the simple-to-make localized dissolvable delay and the robust mechanical valve, provides the potential to automatically implement high-performance multistep assays toward a versatile platform for point-of-care diagnostics.

Synthesis of SERS-active core-satellite nanoparticles using heterobifunctional PEG linkers.

Surface-enhanced Raman scattering (SERS) is a sensitive analytical technique capable of magnifying the vibrational intensity of molecules adsorbed onto the surface of metallic nanostructures. Various solution-based SERS-active metallic nanostructures have been designed to generate substantial SERS signal enhancements. However, most of these SERS substrates rely on the chemical aggregation of metallic nanostructures to create strong signals. While this can induce high SERS intensities through plasmonic coupling, most chemically aggregated assemblies suffer from poor signal reproducibility and reduced long-term stability. To overcome these issues, here we report for the first time the synthesis of gold core-satellite nanoparticles (CSNPs) for robust SERS signal generation. The novel CSNP assemblies consist of a 30 nm spherical gold core linked to 18 nm satellite particles via linear heterobifunctional thiol-amine terminated PEG chains. We explore the effects that the varying chain lengths have on SERS hot-spot generation, signal reproducibility and long-term activity. The chain length was varied by using PEGs with different molecular weights (1000 Da, 2000 Da, and 3500 Da). The CSNPs were characterized via UV-Vis spectrophotometry, transmission electron microscopy (TEM), ζ-potential measurements, and lastly SERS measurements. The versatility of the synthesized SERS-active CSNPs was revealed through characterization of optical stability and SERS enhancement at 0, 1, 3, 5, 7 and 14 days.

Deep Learning for Detecting Cerebral Aneurysms with CT Angiography.

Background Cerebral aneurysm detection is a challenging task. Deep learning may become a supportive tool for more accurate interpretation. Purpose To develop a highly sensitive deep learning-based algorithm that assists in the detection of cerebral aneurysms on CT angiography images. Materials and Methods Head CT angiography images were retrospectively retrieved from two hospital databases acquired across four different scanners between January 2015 and June 2019. The data were divided into training and validation sets; 400 additional independent CT angiograms acquired between July and December 2019 were used for external validation. A deep learning-based algorithm was constructed and assessed. Both internal and external validation were performed. Jackknife alternative free-response receiver operating characteristic analysis was performed. Results A total of 1068 patients (mean age, 57 years ± 11 [standard deviation]; 660 women) were evaluated for a total of 1068 CT angiograms encompassing 1337 cerebral aneurysms. Of these, 534 CT angiograms (688 aneurysms) were assigned to the training set, and the remaining 534 CT angiograms (649 aneurysms) constituted the validation set. The sensitivity of the proposed algorithm for detecting cerebral aneurysms was 97.5% (633 of 649; 95% CI: 96.0, 98.6). Moreover, eight new aneurysms that had been overlooked in the initial reports were detected (1.2%, eight of 649). With the aid of the algorithm, the overall performance of radiologists in terms of area under the weighted alternative free-response receiver operating characteristic curve was higher by 0.01 (95% CI: 0.00, 0.03). Conclusion The proposed deep learning algorithm assisted radiologists in detecting cerebral aneurysms on CT angiography images, resulting in a higher detection rate. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kallmes and Erickson in this issue.

A review of biosensor technologies for blood biomarkers toward monitoring cardiovascular diseases at the point-of-care.

Cardiovascular diseases (CVDs) cause significant mortality globally. Notably, CVDs disproportionately negatively impact underserved populations, such as those that are economically disadvantaged and often located in remote regions. Devices to measure cardiac biomarkers have traditionally been focused on large instruments in a central laboratory but the development of affordable, portable devices that measure multiple cardiac biomarkers at the point-of-care (POC) are needed to improve clinical outcomes for patients, especially in underserved populations. Considering the enormity of the global CVD problem, complexity of CVDs, and the large candidate pool of biomarkers, it is of great interest to evaluate and compare biomarker performance and identify potential multiplexed panels that can be used in combination with affordable and robust biosensors at the POC toward improved patient care. This review focuses on describing the known and emerging CVD biosensing technologies for analysis of cardiac biomarkers from blood. Initially, the global burden of CVDs and the standard of care for the primary CVD categories, namely heart failure (HF) and acute coronary syndrome (ACS) including myocardial infarction (MI) are discussed. The latest United States, Canadian and European society guidelines recommended standalone, emerging, and add-on cardiac biomarkers, as well as their combinations are then described for the prognosis, diagnosis, and risk stratification of CVDs. Finally, both commercial in vitro biosensing devices and recent state-of-art techniques for detection of cardiac biomarkers are reviewed that leverage single and multiplexed panels of cardiac biomarkers with a view toward affordable, compact devices with excellent performance for POC diagnosis and monitoring.