Biodiagnostics in an era of global pandemics-From biosensing materials to data management

Engineering Innervated Musculoskeletal Tissues for Regenerative Orthopedics and Disease Modeling

Musculoskeletal (MSK) disorders significantly burden patients and society, resulting in high healthcare costs and productivity loss. These disorders are the leading cause of physical disability, and their prevalence is expected to increase as sedentary lifestyles become common and the global population of the elderly increases. Proper innervation is critical to maintaining MSK function, and nerve damage or dysfunction underlies various MSK disorders, underscoring the potential of restoring nerve function in MSK disorder treatment. However, most MSK tissue engineering strategies have overlooked the significance of innervation. This review first expounds upon innervation in the MSK system and its importance in maintaining MSK homeostasis and functions. This will be followed by strategies for engineering MSK tissues that induce post-implantation in situ innervation or are pre-innervated. Subsequently, research progress in modeling MSK disorders using innervated MSK organoids and organs-on-chips (OoCs) is analyzed. Finally, the future development of engineering innervated MSK tissues to treat MSK disorders and recapitulate disease mechanisms is discussed. This review provides valuable insights into the underlying principles, engineering methods, and applications of innervated MSK tissues, paving the way for the development of targeted, efficacious therapies for various MSK conditions.

Advances in Detecting Cystic Echinococcosis in Intermediate Hosts and New Diagnostic Tools: A Literature Review

Cystic echinococcosis (CE) is a zoonotic disease affecting humans and animals. Despite a lack of clarity about many details of parasite-intermediate host interactions, the nature of the immune responses triggered by hydatid infection has revealed new perspectives. This study discusses the latest advances in elucidating the immunologic mechanism of echinococcosis and its detection and potential approaches to enhance serodiagnosis accuracy. Moreover, nanobiosensors have been evaluated according to their potential to improve treatment efficiency and aid in an early diagnosis of cystic echinococcosis. The serum of an intermediate host can diagnose CE by analyzing antibodies induced by Echinococcus granulosus. Among the most notable features of this method are its noninvasive ability and high sensitivity, both of which make it an excellent tool for clinical diagnosis. Several serological tests, including ELISAs and immunoblotting, can detect these antibodies to assess the disease's state and determine the treatment outcome. A thorough understanding of what cross-reactivity means and the stage of the disease are crucial to interpreting serological results. Nanobiosensors have also proven better than conventional biosensors in detecting hydatid cysts. Additionally, they are highly sensitive and versatile when detecting specific biomarkers, improving diagnostic accuracy. These immunomodulatory molecules, induced by E. granulosus, are a good candidate for diagnosing cystic echinococcosis because they alter intermediate host immune responses. Hydatid cyst detection is also enhanced through nanobiosensors, which provide better accuracy.

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

Integrating Paper-Based Microfluidics and Lateral Flow Strip into Nucleic Acid Amplification Device toward Rapid, Low-Cost, and Visual Diagnosis of Multiple Mycobacteria

Efficient diagnosis of mycobacterial infections can effectively manage and prevent the transmission of infectious diseases. Unfortunately, existing diagnostic strategies are challenged by long assay times, high costs, and highly specialized expertise to distinguish between pulmonary tuberculosis (PTB) and nontuberculous mycobacterial pulmonary diseases (NTM-PDs). Herein, in this study, an optimized 3D paper-based analytical device (µPAD) is incorporated with a closed lateral flow (LF) strip into a loop-mediated isothermal amplification (LAMP) device (3D-µPAD-LF-LAMP) for rapid, low-cost, and visual detection of pathogenic mycobacteria. The platform's microfluidic feature enhanced the nucleic acid amplification, thereby reducing the costs and time as compared to boiling, easyMAG, and QIAGEN techniques. Moreover, the LF unit is specifically designed to minimize aerosol contamination for a user-friendly and visual readout. 3D-µPAD-LF-LAMP is optimized and assessed using standard strains, demonstrating a limit of detection (LOD) down to 10 fg reaction-1 . In a cohort of 815 patients, 3D-µPAD-LF-LAMP displays significantly better sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and diagnostic accuracy than conventional bacterial culture and Xpert techniques. Collectively, 3D-µPAD-LF-LAMP demonstrates enhanced accessibility, efficiency, and practicality for the diagnosis of multiple pathogenic mycobacteria, which can be applied across diverse clinical settings, thereby ultimately improving public health outcomes.

AuNPs/CNC Nanocomposite with A "Dual Dispersion" Effect for LDI-TOF MS Analysis of Intact Proteins in NSCLC Serum Exosomes

Detecting exosomal markers using laser desorption/ionization time-of-flight mass spectrometry (LDI-TOF MS) is a novel approach for examining liquid biopsies of non-small cell lung cancer (NSCLC) samples. However, LDI-TOF MS is limited by low sensitivity and poor reproducibility when analyzing intact proteins directly. In this report, gold nanoparticles/cellulose nanocrystals (AuNPs/CNC) is introduced as the matrix for direct analysis of intact proteins in NSCLC serum exosomes. AuNPs/CNC with "dual dispersion" effects dispersed and stabilized AuNPs and improved ion inhibition effects caused by protein aggregation. These features increased the signal-to-noise ratio of [M+H]+ peaks by two orders of magnitude and lowered the detection limit of intact proteins to 0.01 mg mL-1. The coefficient of variation with or without AuNPs/CNC is measured as 10.2% and 32.5%, respectively. The excellent reproducibility yielded a linear relationship (y = 15.41x - 7.983, R2 = 0.989) over the protein concentration range of 0.01 to 20 mg mL-1. Finally, AuNPs/CNC-assisted LDI-TOF MS provides clinically relevant fingerprint information of exosomal proteins in NSCLC serum, and characteristic proteins S100 calcium-binding protein A10, Urokinase plasminogen activator surface receptor, Plasma protease C1 inhibitor, Tyrosine-protein kinase Fgr and Mannose-binding lectin associated serine protease 2 represented excellent predictive biomarkers of NSCLC risk.

Diagnosis of Esophageal Squamous Cell Carcinoma by High-Performance Serum Metabolic Fingerprints: A Retrospective Study

Esophageal squamous cell carcinoma (ESCC) is a highly prevalent and aggressive malignancy, and timely diagnosis of ESCC contributes to an increased cancer survival rate. However, current detection methods for ESCC mainly rely on endoscopic examination, limited by a relatively low participation rate. Herein, ferric-particle-enhanced laser desorption/ionization mass spectrometry (FPELDI MS) is utilized to record the serum metabolic fingerprints (SMFs) from a retrospective cohort (523 non-ESCC participants and 462 ESCC patients) to build diagnostic models toward ESCC. The PFELDI MS achieved high speed (≈30 s per sample), desirable reproducibility (coefficients of variation < 15%), and high throughput (985 samples with ≈124 200 data points for each spectrum). Desirable diagnostic performance with area-under-the-curves (AUCs) of 0.925-0.966 is obtained through machine learning of SMFs. Further, a metabolic biomarker panel is constructed, exhibiting superior diagnostic sensitivity (72.2-79.4%, p < 0.05) as compared with clinical protein biomarker tests (4.3-22.9%). Notably, the biomarker panel afforded an AUC of 0.844 (95% confidence interval [CI]: 0.806-0.880) toward early ESCC diagnosis. This work highlighted the potential of metabolic analysis for accurate screening and early detection of ESCC and offered insights into the metabolic characterization of diseases including but not limited to ESCC.

Artificial Intelligence for Surface-Enhanced Raman Spectroscopy

Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

Next-Generation Sequencing for the Detection of Microbial Agents in Avian Clinical Samples

Direct-targeted next-generation sequencing (tNGS), with its undoubtedly superior diagnostic capacity over real-time PCR (RT-PCR), and direct-non-targeted NGS (ntNGS), with its higher capacity to identify and characterize multiple agents, are both likely to become diagnostic methods of choice in the future. tNGS is a rapid and sensitive method for precise characterization of suspected agents. ntNGS, also known as agnostic diagnosis, does not require a hypothesis and has been used to identify unsuspected infections in clinical samples. Implemented in the form of multiplexed total DNA metagenomics or as total RNA sequencing, the approach produces comprehensive and actionable reports that allow semi-quantitative identification of most of the agents present in respiratory, cloacal, and tissue samples. The diagnostic benefits of the use of direct tNGS and ntNGS are high specificity, compatibility with different types of clinical samples (fresh, frozen, FTA cards, and paraffin-embedded), production of nearly complete infection profiles (viruses, bacteria, fungus, and parasites), production of "semi-quantitative" information, direct agent genotyping, and infectious agent mutational information. The achievements of NGS in terms of diagnosing poultry problems are described here, along with future applications. Multiplexing, development of standard operating procedures, robotics, sequencing kits, automated bioinformatics, cloud computing, and artificial intelligence (AI) are disciplines converging toward the use of this technology for active surveillance in poultry farms. Other advances in human and veterinary NGS sequencing are likely to be adaptable to avian species in the future.

Comprehensive Metabolic Fingerprints Characterize Neuromyelitis Optica Spectrum Disorder by Nanoparticle-Enhanced Laser Desorption/Ionization Mass Spectrometry

Timely screening of neuromyelitis optica spectrum disorder (NMOSD) and differential diagnosis from myelin oligodendrocyte glycoprotein associated disorder (MOGAD) are the keys to improving the quality of life of patients. Metabolic disturbance occurs with the development of NMOSD. Still, advanced tools are required to probe the metabolic phenotype of NMOSD. Here, we developed a fast nanoparticle-enhanced laser desorption/ionization mass spectrometry assay for multiplexing metabolic fingerprints (MFs) from trace plasma and cerebrospinal fluid (CSF) samples in 30 s. Machine learning of the plasma MFs achieved the timely screening of NMOSD from healthy donors with an area under receiver operator characteristic curve (AUROC) of 0.998, and it comprehensively revealed the dysregulated neurotransmitter and energy metabolisms. Combining comprehensive MFs from both plasma and CSF, we constructed an integrated panel for differential diagnosis of NMOSD versus MOGAD with an AUROC of 0.923. This approach demonstrated performance superior to that of human experts in classifying two diseases, especially in antibody assay-limited regions. Together, this approach provides an advanced nanomaterial-based tool for identifying vulnerable populations below the antibody threshold of aquaporin-4 positivity.

Sensitivity and specificity of mid-upper arm circumference for assessment of severe acute malnutrition among children ages 6 to 59 months: Systematic review and meta-analysis

OBJECTIVES

Sensitivity is the proportion of people classified as diseased (i.e., no false negatives). A test with low sensitivity can be thought of as being too cautious in finding a positive result.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines were followed for this systematic review and meta-analysis. The databases used were PubMed, Google Scholar, Jane, and African Journals Online. The search terms used were "sensitivity" and "specificity of and mid-upper arm circumference" (MUAC). A Joanna Briggs Institute meta-analysis and checklist for diagnostic test accuracy studies was used for the critical appraisal of the studies. The meta-analysis was conducted using STATA, version 14, software. The pooled sensitivity was computed to present the pooled sensitivity at a 95% confidence interval (CI).

RESULTS

A total of 11 individual studies were included in the meta-analysis. The lowest sensitivity of MUAC with the detection of severe acute malnutrition (SAM) was 5% in Vietnam, and the highest sensitivity was at 43.2% in India. The pooled sensitivity of MUAC among children aged <5 y to determine SAM was 20.7% (range, 13.24%-28.25%; P = 0.001). Based on the pooled specificity of MUAC, the detection of SAM was 97.636% (95% CI, 96.339%-98.932%; P = 0.001), and the pooled optimal cutoff point to diagnose SAM was 13.23 cm (95% CI, 12.692-13.763 cm; P = 0.001).

CONCLUSIONS

The sensitivity of MUAC is lower compared with the specificity to detect SAM, and varies from area to area.

Diagnosis of Unruptured Intracranial Aneurysm by High-Performance Serum Metabolic Fingerprints

Unruptured intracranial aneurysm (UIA) is a high-risk cerebrovascular saccular dilatation, the effective medical management of which depends on high-performance diagnosis. However, most UIAs are diagnosed incidentally during neurovascular imaging modalities, which are time-consuming and harmful (e.g., radiation). Serum metabolic fingerprints is a promising alternative for early diagnosis of UIA. Here, nanoparticle enhanced laser desorption/ionization mass spectrometry is applied to obtain high-performance UIA-specific serum metabolic fingerprints. Diagnostic performance with an area-under-the-curve (AUC) of 0.842 (95% confidence interval (CI): 0.783-0.891) is achieved by the constructed machine learning (ML) model, including ML algorithm selection and feature selection. Lactate, glutamine, homoarginine, and 3-methylglutaconic acid are identified as the metabolic biomarker panel, which showed satisfactory diagnosis (AUC of 0.812, 95% CI: 0.727-0.897) and effective growth risk assessment (p<0.05, two-tailed t-test) of UIAs. This work aims to promote the diagnostics of UIAs and metabolic biomarker screening for medical management.

Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification

Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host-derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints-based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550-0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs-DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs-DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID-2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID-19 management (AUC of 0.677-0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.

Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers

Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer.

Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward

Coronavirus disease 2019 (COVID-19) is a transmitted respiratory disease caused by the infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although humankind has experienced several outbreaks of infectious diseases, the COVID-19 pandemic has the highest rate of infection and has had high levels of social and economic repercussions. The current COVID-19 pandemic has highlighted the limitations of existing virological tests, which have failed to be adopted at a rate to properly slow the rapid spread of SARS-CoV-2. Pandemic preparedness has developed as a focus of many governments around the world in the event of a future outbreak. Despite the largely widespread availability of vaccines, the importance of testing has not diminished to monitor the evolution of the virus and the resulting stages of the pandemic. Therefore, developing diagnostic technology that serves as a line of defense has become imperative. In particular, that test should satisfy three criteria to be widely adopted: simplicity, economic feasibility, and accessibility. At the heart of it all, it must enable early diagnosis in the course of infection to reduce spread. However, diagnostic manufacturers need guidance on the optimal characteristics of a virological test to ensure pandemic preparedness and to aid in the effective treatment of viral infections. Nanomaterials are a decisive element in developing COVID-19 diagnostic kits as well as a key contributor to enhance the performance of existing tests. Our objective is to develop a profile of the criteria that should be available in a platform as the target product. In this work, virus detection tests were evaluated from the perspective of the COVID-19 pandemic, and then we generalized the requirements to develop a target product profile for a platform for virus detection.

Sensors for Volatile Organic Compounds

This paper provides an overview of recent developments in the field of volatile organic compound (VOC) sensors, which are finding uses in healthcare, safety, environmental monitoring, food and agriculture, oil industry, and other fields. It starts by briefly explaining the basics of VOC sensing and reviewing the currently available and quickly progressing VOC sensing approaches. It then discusses the main trends in materials' design with special attention to nanostructuring and nanohybridization. Emerging sensing materials and strategies are highlighted and their involvement in the different types of sensing technologies is discussed, including optical, electrical, and gravimetric sensors. The review also provides detailed discussions about the main limitations of the field and offers potential solutions. The status of the field and suggestions of promising directions for future development are summarized.

Bioinspired Triboelectric Nanosensors for Self-Powered Wearable Applications

The sustainable operation of wearable sensors plays an important role in continuous and longtime health monitoring. Conventional batteries, which are bulky and rigid, do not satisfy these requirements and, rather, cause additional economic burdens and environmental problems by regular replacement of power sources. This article provides a review on an alternative solution in the form of self-powered devices that can harvest energy from the surrounding environment to support the operation of the wearable sensor. The Review starts with an introduction of the self-powered triboelectric nanosensors (TENSs) and its two independent modules: the energy harvester and the sensing module. The Review continues with the TENS-related bioinspired designs for wearable applications, while providing a bird's-eye view of their characteristics and applications. The ongoing challenges and prospects for providing personal healthcare with self-powered TENS are presented and discussed.