1. TP53 Official Symbol: TP53 and Name: tumor protein p53 [Homo sapiens (human)] Other Aliases: BCC7, BMFS5, LFS1, P53, TRP53 Other Designations: cellular tumor antigen p53; antigen NY-CO-13; mutant tumor protein 53; phosphoprotein p53; transformation-related protein 53; tumor protein 53; tumor supressor p53 Chromosome: 17; Location: 17p13.1 Annotation: Chromosome 17 NC_000017.11 (7668421..7687490, complement) MIM: 191170 ID: 7157

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1. AGTR1 Official Symbol: AGTR1 and Name: angiotensin II receptor type 1 [Homo sapiens (human)] Other Aliases: AG2SB, AT1, AT1AR, AT1B, AT1BR, AT1R, AT2R1, ATR1, HAT1R, AGTR1 Other Designations: type-1 angiotensin II receptor; AT1 receptor; type-1B angiotensin II receptor Chromosome: 3; Location: 3q24 Annotation: Chromosome 3 NC_000003.12 (148697903..148743003) MIM: 106165 ID: 185

No detailed information available.

1. FGF19 Official Symbol: FGF19 and Name: fibroblast growth factor 19 [Homo sapiens (human)] Other Designations: fibroblast growth factor 19; FGF-19 Chromosome: 11; Location: 11q13.3 Annotation: Chromosome 11 NC_000011.10 (69698238..69704022, complement) MIM: 603891 ID: 9965

1. ICOS Official Symbol: ICOS and Name: inducible T cell costimulator [Homo sapiens (human)] Other Aliases: AILIM, CD278, CVID1 Other Designations: inducible T-cell costimulator; activation-inducible lymphocyte immunomediatory molecule; inducible T-cell co-stimulator; inducible costimulator Chromosome: 2; Location: 2q33.2 Annotation: Chromosome 2 NC_000002.12 (203936763..203961577) MIM: 604558 ID: 29851

1. KLK3 Official Symbol: KLK3 and Name: kallikrein related peptidase 3 [Homo sapiens (human)] Other Aliases: APS, KLK2A1, PSA, hK3 Other Designations: prostate-specific antigen; P-30 antigen; gamma-seminoprotein; kallikrein-3; semenogelase; seminin Chromosome: 19; Location: 19q13.33 Annotation: Chromosome 19 NC_000019.10 (50854915..50860764) MIM: 176820 ID: 354

No detailed information available.

1. GNAQ Official Symbol: GNAQ and Name: G protein subunit alpha q [Homo sapiens (human)] Other Aliases: CMAL, G-ALPHA-q, GAQ, SWS Other Designations: guanine nucleotide-binding protein G(q) subunit alpha; epididymis secretory sperm binding protein; guanine nucleotide binding protein (G protein), q polypeptide; guanine nucleotide-binding protein alpha-q Chromosome: 9; Location: 9q21.2 Annotation: Chromosome 9 NC_000009.12 (77716097..78031811, complement) MIM: 600998 ID: 2776

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1. Gnaq Official Symbol: Gnaq and Name: guanine nucleotide binding protein, alpha q polypeptide [Mus musculus (house mouse)] Other Aliases: 1110005L02Rik, 6230401I02Rik, Dsk1, Dsk10, Galphaq, Gq, GqI Other Designations: guanine nucleotide-binding protein G(q) subunit alpha Chromosome: 19; Location: 19 11.01 cM Annotation: Chromosome 19 NC_000085.7 (16110048..16365884) ID: 14682

1. PTGFR Official Symbol: PTGFR and Name: prostaglandin F receptor [Homo sapiens (human)] Other Aliases: FP Other Designations: prostaglandin F2-alpha receptor; FP prostanoid receptor; PGF receptor; PGF2 alpha receptor; prostaglandin receptor (2-alpha); prostanoid FP receptor Chromosome: 1; Location: 1p31.1 Annotation: Chromosome 1 NC_000001.11 (78490974..78540701) MIM: 600563 ID: 5737

1. Nppc Official Symbol: Nppc and Name: natriuretic peptide C [Rattus norvegicus (Norway rat)] Other Designations: C-type natriuretic peptide; natriuretic peptide precursor C; natriuretic peptide precursor type C Chromosome: 9; Location: 9q35 Annotation: Chromosome 9 NC_086027.1 (94767986..94772186, complement) ID: 114593

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Wearable biosensors can enable continuous human data capture, facilitating development of real-world Human-Robot Interaction (HRI) systems. However, a lack of standardized libraries and implementations adds extraneous complexity to HRI system designs, and precludes collaboration across disciplines and institutions. Here, we introduce a novel wearable biosensor package for the Robot Operating System 2 (ROS 2) system. The ROS2 officially supports real-time computing and multi-robot systems, and thus provides easy-to-use and reliable streaming data from multiple nodes. The package standardizes biosensor HRI integration, lowers the technical barrier of entry, and expands the biosensor ecosystem into the robotics field. Each biosensor package node follows a generalized node and topic structure concentrated on ease of use. Current package capabilities, listed by biosensor, highlight package standardization. Collected example data demonstrate a full integration of each biosensor into ROS2. We expect that standardization of this biosensors package for ROS2 will greatly simplify use and cross-collaboration across many disciplines. The wearable biosensor package is made publicly available on GitHub at \https://github.com/SMARTlab-Purdue/ros2-foxy-wearable-biosensors.

The study proposes a new efficient wireless biosensor based on magnetoelastic waves for the detection of antibodies in human plasma, aiming at the serological diagnosis of COVID-19. The biosensor was functionalized with the N antigen - nucleocapsid phosphoprotein of the SARS-CoV-2 virus. Validation analyses, by sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE), Western blotting, atomic force microscopy (AFM), scanning electron microscopy (SEM), micro-Raman spectroscopy, confirmed the selectivity and effective surface functionalization of the biosensor. The research successfully obtained, expressed and purified the recombinant antigen, while plasma samples from COVID-19 positive and negative patients were used to test the performance of the biosensor. A comparison of performance with the ELISA method revealed equivalent diagnostic power. These results indicate the robustness of the biosensor in reliably differentiating between positive and negative samples, highlighting its potential as an efficient and low-cost tool for the serological diagnosis of COVID-19. In addition to being fast to execute and having the potential for automation in large-scale diagnostic studies, the biosensor fills a significant gap in existing SARS-CoV-2 detection approaches.

Biosensors are essential tools which have been traditionally used to monitor environmental pollution, detect the presence of toxic elements and biohazardous bacteria or virus in organic matter and biomolecules for clinical diagnostics. In the last couple of decades, the scientific community has witnessed their widespread application in the fields of military, health care, industrial process control, environmental monitoring, food-quality control, and microbiology. Biosensor technology has greatly evolved from the in vitro studies based on the biosensing ability of organic beings to the highly sophisticated world of nanofabrication enabled miniaturized biosensors. The incorporation of nanotechnology in the vast field of biosensing has led to the development of novel sensors and sensing mechanisms, as well as an increase in the sensitivity and performance of the existing biosensors. Additionally, the nanoscale dimension further assists the development of sensors for rapid and simple detection in vivo as well as the ability to probe single-biomolecules and obtain critical information for their detection and analysis. However, the major drawbacks of this include, but are not limited to potential toxicities associated with the unavoidable release of nanoparticles into the environment, miniaturization induced unreliability, lack of automation, and difficulty of integrating the nanostructured-based biosensors as well as unreliable transduction signals from these devices. Although the field of biosensors is vast, we intend to explore various nanotechnology enabled biosensors as part of this review article and provide a brief description of their fundamental working principles and potential applications.

Integrated nanophotonic biosensors offer a promising route toward future biomedical detection applications that may enable inexpensive, portable, and sensitive diagnosis of diseases with a small amount of biological samples for convenient early-stage screening of fatal diseases. However, the current photonic biosensor designs are not suitable for highly integrated and multiplexing device architectures that can achieve the detection of complex combinations of many biomarkers. Here, we propose a topological scheme for the integration of miniature biosensors in photonic crystal chips that can meet the above requirement. Using photonic topological edge states as robust one-dimensional waveguides that connect many photonic biosensors, we propose here the topologically integrated photonic biosensor circuits. We demonstrate that the performance of the topologically integrated photonic biosensors is much more robust against disorders than that of the photonic biosensors connected by the normal photonic waveguides, due to the robust transport of photons along the edge channel. Since disorders arising from the fabrication imperfection and the random distribution of the biomarkers are inevitable in genuine devices, resilience against disorders is a necessity for on-chip integration of biosensors. The topological scheme proposed here thus opens a promising path toward reliable integration of photonic biosensors for next-generation biomedical applications.

Physical limit of molecular sensing has been extensively studied in biological systems. Biosensors are engineered equivalents of molecular sensors in living systems and play critical role in disease diagnosis and management. Investigation into the physical limits of biosensors could have major beneficial impact on early disease diagnosis. Here, we present an extension of the classical works on molecular sensing limits of living systems to the realm of biosensors. Two approaches are proposed to estimate concentration with noisy biosensors. We find a trade-off between precision and accuracy.