Detection Of Infectious Diseases Research Articles

AI-driven health data analytics for early detection of infectious diseases: A conceptual exploration of U.S. public health strategies

This review paper explores the role of AI-driven health data analytics in enhancing the early detection and response to infectious diseases within U.S. public health strategies. It begins by outlining the importance of timely detection in controlling outbreaks and discusses current public health systems and their limitations, including passive surveillance and data fragmentation. The paper highlights innovative AI technologies such as machine learning, natural language processing, and predictive modeling, which can significantly improve disease monitoring and forecasting. By analyzing vast datasets in real time, these technologies facilitate quicker identification of emerging health threats and enable tailored public health responses. Furthermore, the review presents recommendations for integrating AI innovations into existing public health strategies, emphasizing improved data interoperability, investment in infrastructure, stakeholder collaboration, community engagement, and real-world pilot programs. Ultimately, the findings underscore the potential of AI-driven health data analytics to transform infectious disease management, thereby safeguarding public health.

Strengthening medical facility responses to respiratory infectious diseases: Global trends, challenges, and innovations post-COVID-19.

Respiratory infectious diseases have long been a serious public health problem. This study explores the significance of respiratory infectious disease prevention and control systems worldwide, particularly during and after the COVID-19 pandemic. The epidemiology of many respiratory diseases such as influenza changed over the past two years, and the incidence of pathogens such as Mycoplasma pneumoniae and Bordetella pertussis has also increased. Based on influenza surveillance data in China, the influenza season in 2023 was notably delayed and extended, with A(H1N1) pdm09 being the predominant strain. This editorial also reviewed the gradual establishment of China's infectious disease prevention and control system following the 2003 SARS outbreak, highlighting the role of medical facilities in managing public health emergencies, conducting infectious disease pre-screening, and reporting cases to networks in real time. In the future, China will further develop an intelligent, multi-trigger infectious disease surveillance and early warning system to increase the early detection of unknown infectious diseases and optimize the allocation of medical resources. A robust infectious disease control system is crucial to addressing future pandemic threats.

CMOS Point-of-Care Diagnostics Technologies: Recent Advances and Future Prospects

This review provides a comprehensive overview of point-of-care (PoC) devices across several key diagnostic applications, including blood analysis, infectious disease detection, neural interfaces, and commercialized integrated circuits (ICs). In the blood analysis section, the focus is on biomarkers such as glucose, dopamine, and aptamers, and their respective detection techniques. The infectious disease section explores PoC technologies for detecting pathogens, RNA, and DNA, highlighting innovations in molecular diagnostics. The neural interface section reviews advancements in neural recording and stimulation for therapeutic applications. Finally, a survey of commercialized ICs from companies such as Abbott and Medtronic is presented, showcasing existing PoC devices already in widespread clinical use. This review emphasizes the role of complementary metal-oxide-semiconductor (CMOS) technology in enabling compact, efficient diagnostic systems and offers insights into the current and future landscape of PoC devices.

The Rise of Biochemical Sensors: Technology for Real-Time Health Monitoring (Applications and Future Scope)

Biochemical sensors integrated into wearable health technology represent one of the most transformative innovations of the 21st century. This review delves into the evolution, principles, and real-time applications of biochemical sensors and their role in personalized health monitoring. Wearable biochemical sensors, capable of continuously measuring various biomarkers like glucose, lactate, cortisol, and electrolytes, are revolutionizing healthcare by enabling proactive management of chronic diseases, including diabetes, cardiovascular disorders, and mental health issues. Advances in biosensing technologies, coupled with the use of AI and machine learning algorithms, have enhanced the sensitivity and accuracy of these devices, ensuring that critical health data is available in real-time. From glucose monitoring devices like Abbott's FreeStyle Libre to the latest nanomaterial-based sensors, these innovations are reshaping healthcare delivery by shifting the focus from hospital-centered treatments to patient-centric, continuous monitoring systems. This review provides an in-depth analysis of the technological advancements, challenges, and future directions in biochemical sensors, focusing on key technologies such as electrochemical, optical, and enzymatic sensors. It also highlights the critical role of AI in interpreting the complex data generated by these sensors, paving the way for more efficient diagnostics and predictive healthcare models. Furthermore, the paper explores how these sensors have been applied in infectious disease detection, particularly during the COVID-19 pandemic, and discusses their potential to enhance global health surveillance systems. In conclusion, wearable biochemical sensors represent a significant leap forward in the pursuit of personalized medicine, offering real-time diagnostics and timely interventions for disease management. The future of healthcare is closely tied to the ongoing innovations in sensor technology, with the promise of even more advanced and multifunctional devices on the horizon.

B-152 Analysis of carbonyl compounds in exhaled breath for detection of COVID-19

Abstract Background COVID-19 pandemic has caused enormous loss of lives and economic disruption. The global crisis has resulted in more than 700 million infections and 7 million deaths worldwide. Rapid screening and detection of highly infectious respiratory diseases is a key for preventing and curbing future epidemic and pandemic. The objective of this work is to develop a novel breath analysis technique for detection of COVID-19 using chemoselective capture of carbonyl compounds in exhaled breath for analysis by gas chromatography-mass spectrometry (GC-MS). Respiratory viral infection causes oxidative stress and inflammation in its host body which leads to production of higher levels of some carbonyl compounds through oxidation of lipids and could be detected in exhaled breath. This breath analysis technique can be rapidly adapted for screening infectious respiratory diseases for future epidemic and potential pandemic. Methods A total of 237 subjects were enrolled in this study. Of these, 142 were COVID-19 positive and 95 were negative. One liter exhaled breath in Tedlar bags from both COVID-19 positive and negative subjects confirmed by reverse transcriptase polymerase chain reaction between April 2022 and September 2023. The collected one liter breath samples were completely evacuated through a cartridge packed with silica particles in 2-3 minutes. Silica particles were coated with a reactive aminooxy agent for capture of carbonyl compounds in the breath samples through oximation reactions. The captured carbonyl compounds were eluted by methanol and the eluted solutions were analyzed by GC-MS for calculation of each compound concentration in the exhaled breath samples. Biostatistics was used to identify significant carbonyl compounds (defined as p value < 0.05) as markers of CPOVID-19 infection. A machine learning algorithm was implemented with the captured carbonyl compounds as input features for differentiation of COVID-19 positive from negative subjects. Results A total of more than 30 carbonyl compounds were detected in exhaled breath samples of all subjects. 10 features including 3 individual carbonyl compounds and 7 compound ratios show significant differences (p value <0.05) between COVID-19 positive and negative groups. The developed machine learning and artificial intelligence (AI) algorithms successfully separated COVOD-19 positive from negative with an accuracy of 95.4%, sensitivity of 96.5%, specificity of 93.7%, and AUROC=0.974. Conclusions The analysis of carbonyl compounds in exhaled breath by GC-MS has great potential for non-invasive detection of COVID-19. The method could be rapidly adapted for detection of other respiratory infectious diseases with a small sample size and data set for training of the machine learning and AI algorithms.

Plasmonic-Enhanced Colorimetric Lateral Flow Immunoassays Using Bimetallic Silver-Coated Gold Nanostars.

The colorimetric lateral flow immunoassay (cLFIA) has gained widespread attention as a point-of-care testing (POCT) technique due to its low cost, short analysis time, portability, and capability of being performed by unskilled operators with minimal requirement of reagents. However, the low analytical sensitivity of conventional LFIA based on colloidal gold nanospheres limits their applications for sensitive detection of trace amounts of target analytes. In this study, we introduced a novel plasmonic-enhanced colorimetric LFIA (PE-cLFIA) platform featuring bimetallic silver-coated gold nanostars (BGNS) with exceptional optical properties, leading to ultrahigh visual color brightness. The BGNS-based PE-cLFIA was successfully applied to detect a model analyte, low-calcium response V (LcrV), a virulence protein factor found in Yersinia pestis, the causative agent of bubonic plague. The PE-cLFIA sensing using BGNS-3 composed of 45 nm silver thickness showed a high visual colorimetric sensitivity with a detection limit as low as 13.7 pg/mL, which was around 50 times more sensitive than that of a traditional gold nanoparticle-based LFIA. In addition, the antibody-conjugated BGNS-3 showed excellent stability over 6 months. To illustrate the potential for clinical applications, we demonstrated that our LFIA platform for detecting LcrV spiked in human serum without any sample preprocessing exhibited a detection limit of 22.8 pg/mL. These results open up new opportunities for developing hybrid nanoparticle systems for sensitive POCT PE-cLFIA screening for infectious disease detection.

Neonatal Infectious Disease: A Major Contributor to Infant Mortality Requiring Advances in Point-of-Care Diagnosis.

Neonatal infectious disease continues to result in high rates of infant morbidity and mortality. Early- and late-onset disease represent difficult to detect and difficult to treat illnesses, particularly when antimicrobial resistant pathogens are present. Newborns are immunodeficient and are at increased risk of vertical and horizontal infection, with preterm infants increasingly susceptible. Additional risk factors associated with infection include prolonged use of a central catheter and/or ventilation, congenital abnormalities, admittance to intensive care units, and the use of broad-spectrum antibiotics. There is increasing recognition of the importance of the host microbiome and dysbiosis on neonatal infectious disease, including necrotising enterocolitis and sepsis in patients. Current diagnostic methods rely on blood culture, which is unreliable, time consuming, and can result in false negatives. There is a lack of accurate and reliable diagnostic tools available for the early detection of infectious disease in infants; therefore, efficient triage and treatment remains challenging. The application of biomarkers, machine learning, artificial intelligence, biosensors, and microfluidics technology, may offer improved diagnostic methodologies. Point-of-care devices, such diagnostic methodologies, may provide fast, reliable, and accurate diagnostic aids for neonatal patients. This review will discuss neonatal infectious disease as impacted by antimicrobial resistance and will highlight novel point-of-care diagnostic options.

New progress in laboratory detection of respiratory infectious diseases in children

Respiratory infectious disease has become ahead of all the children's diseases, with the trend of continuously increasing global incidence, antimicrobial resistance and simultaneous infection with multiple pathogens. Diagnosis of this disease is mainly based on clinical symptoms and pathogenic detection. However, there are some differences in clinical manifestations, progression and prognosis between pediatric patients and adults, which prompting clinical diagnosis mainly depending on clinical laboratory test. Therefore, fast, convenient and accurate methods are urgently needed to clarify the type of infectious pathogen and carry out differentiated treatment, and reduce the burden on families and public health-care systems in schools. This article aims to elaborate the laboratory methods of children's respiratory infectious diseases and explore the opportunities and challenges, which can provide ideas for prevention, early screening and diagnosis, prognosis and treatment monitoring.

Public Health Initiatives of Wearable Sensors for Health Monitoring and Early Heart Disease Detection

The science of preserving and enhancing individual and community health is known as public health. In order to accomplish this task, healthy lifestyle promotion, disease and injury prevention research, and the detection, prevention, and management of infectious diseases are used. Early detection and treatment of health disease can improve the prognosis for the condition worldwide. However, the massive volume of data needed poses a problem to the current automatic algorithms for diagnosing health illness. This study presents a medical gadget that uses the Internet of Things to gather cardiac data from individuals both before and after of heart illness. Technology is developing at a quick pace, leading to the establishment of many methodologies and ongoing research into solutions for problems that arise in a variety of industries. Preprocessing techniques are employed to effectively classify collected health data because the human body generates enormous amounts of data all the time. Furthermore, the most important phase is accurately classifying health data, which is necessary for diagnosis. The Deep Convolutional Neural Network (DCNN) is among the greatest and most efficient methods for categorising medical data. The results of the simulation in experimental research demonstrate that following this advise improves classification accuracy.

Using a Convolutional Neural Network for Early Infectious Disease Detection During Public Health Emergencies

Proactively detecting infectious illnesses aids in delivering superior therapy and improves preventing and managing such diseases. This work proposed a Convolutional Neural Network for early Infectious Disease Detection during Public Health Emergencies (CNN-IDD-PHE). The objective is to mitigate the significant damages that Public Health Emergencies (PHE) inflicted on individuals' well-being, everyday routines, and the whole national economy. Statistics on Tuberculosis (TB) cases in a city were gathered from July 2020 to 2022. The Structural Equation Model (SEM) is designed to ascertain the correlation between latent and observed variables by identifying the appropriate indicators and estimating the parameters. A prediction model using Convolutional Neural Network (CNN) has been developed. The strategy's efficacy is validated by assessing the loss value and accuracy of the detection model during both the training and testing phases. Hence, using CNN in Deep Learning (DL) for early warning systems performs better in predicting and alerting Public Health (PH) situations. This advancement is of great importance in enhancing the capabilities of early warning systems.

Early Detection of Infectious Diseases among the Refugees of UNHCR in South Tangerang, Banten; the Problems and Strategies to Prevent the Disease's Transmission

The previous study at Puskesmas Pisangan, Ciputat had reported that among 23.8 % patient of the UNHCR was infected by malaria Plasmodium vivax, and one patient with bacterial urinary infection. However, the result can not represent the actual case of the disease, because of the lack number of participant to visit the Puskesmas since the Covid-19 pandemic which had been contributing to decrease number of the patients. The study purposed to improve data and information about parasitic infection, and to design strategy in early detection and prevention to the disease. Design of the study was approached in cross-sectional with a total sampling method of the UNHCR out patients visiting the Puskesmas Pisangan and Cirendeu.We collected specimen of feces, urine, and blood, and performed blood diff-count, rapid diagnostic, microscopic, dipstick, and bacterial culture. The study revealed some parasitic and bacterial infections as defined: five cases (17.24%) of malaria, which is suspected as imported cases; Enterobacteriacea family as non-specific bacteria of negative gram in urine; also Entamoeba coli in stool. This finding was confirmed 17.24% of leucocytosis in blood diffcount and 24.14 % in urinalysis. By nationality, Sudanese was detected the most prevalent 10.34% of parasitic infections, followed by Somalia (6.9%), Yaman (3.45%), and Afghanistan (3.45%) respectively. While mosquitoes and poor living conditions were also contributed as the major potential risk of transmission to the diseases. In conclusion, early detection, health screening, vaccination, access to primary, and upgraded levels of healthcare are important for diseases control and management to prevent transmission.

An automated syringe-based PoC RT-LAMP LFB platform for infectious disease detection from saliva

Decentralized Point-of-Care (PoC) diagnostics hold momentous potential for rapid and accessible viral infection disease detection. Presented is a unique design application of an easy-to-use (plug-and-play) platform for viral detection. The platform leverages a simplified multiplex Reverse-Transcription Loop-mediated Isothermal Amplification (RT-LAMP) Lateral Flow Biosensor (LFB) assay with a lyophilized master mix, eliminating the need for RNA isolation or special reporting equipment. A user-friendly Saliva Measuring Tube (SMT) ensures accurate saliva volume self-collection, and a Syringe-based PoC (SPoC) platform automates sample treatment, reagent mixing, and temperature control using readily available components and consumables. The platform’s performance was evaluated by multiplexed detection of the SARS-CoV-2 N2 target gene and human ACTB gene from saliva samples. The SPoC platform achieved a detection limit of spiked 500 copies/mL for SARS-CoV-2 and consistent internal control readout. The presented PoC system offers a promising initial step for further development toward a decentralized solution for viral infection testing.

Performance of three multiplex real-time PCR assays for simultaneous detection of 12 infectious pathogens in mice affected with respiratory and digestive diseases.

Research quality can be improved with reliable and reproducible experimental results when animal experiments are conducted using laboratory animals with guaranteed microbiological and genetic quality through health monitoring. Therefore, health monitoring requires the rapid and accurate diagnosis of infectious diseases in laboratory animals. This study presents a performance evaluation of a commercially available multiplex real-time PCR (mRT-PCR) assay for the rapid detection of 12 infectious pathogens (Set 1: Sendai virus [SeV, formally murine respirovirus], Mycoplasma spp., Rodentibacter pneumotropicus, and Rodentibacter heylii; Set 2: Helicobacter spp., Murine norovirus [MNV], Murine hepatitis virus [MHV], and Salmonella spp.; Set 3: Staphylococcus aureus, Streptobacillus moniliformis, Corynebacterium kutscheri, and Pseudomonas aeruginosa). To evaluate the efficacy of the mRT-PCR assay, 102 clinical samples encompassing fecal and cecal specimens were analyzed. The resulting data were then compared with the findings from sequence analysis for validation. The assay's detection limit ranged from 1 to 100 copies per reaction. Specificity testing involving various viruses and bacteria indicated no cross-reactivity between strains. Additionally, the assay exhibited good reproducibility, with mean coefficients of variation for inter- and intra assay variation below 3%. The overall positive rate was 52.9% (n = 54), with the mRT-PCR assay findings matching sequence analysis results (κ = 1). MHV (n = 29, 28.4%) was the most prevalent pathogen, followed by Helicobacter spp. (n = 28, 27.5%), R. heylii (n = 18, 17.6%), Mycoplasma spp. (n = 14, 13.7%), MNV (n = 12, 11.8%), S. aureus (n = 9, 8.8%), P. aeruginosa (n = 4, 3.9%), and R. pneumotropicus (n = 1, 0.9%). This assay offers a rapid turnaround time of 100 min, including 30 min for DNA preparation and 70 min for target DNA/RNA amplification. It ensures accuracy, minimizing false positives or negatives, making it a convenient tool for the simultaneous detection of infectious diseases in many samples. Overall, the propose‑d assay holds promise for the effective detection of the most important pathogens in laboratory animal health monitoring.

Borne of necessity: pharmacy-based harm reduction and express sexually transmitted infection services

BackgroundHIV, hepatitis C virus (HCV), Sexually Transmitted Infections (STIs), and substance use disorder are interrelated epidemics. Augmented services to respond to this “syndemic” are hampered by shortages of health care workers, especially in rural areas. In an Indian Health Service hospital in rural Minnesota, the pharmacy sought to integrate harm reduction and express STI services into its scope of practice. ObjectivesProvide pharmacy-based harm reduction and express STI services to increase access to care for community members, especially those without a primary care provider. MethodsThe program was designed with input from tribal counterparts and internal medical staff. The pharmacy window was made the intake point for services for patient education, harm reduction materials, and STI testing and treatment. Collaborative practice agreements and standing orders greatly expanded the pharmacy’s ability to deliver care. Later in the program, the pharmacy was able to introduce patient incentives. ResultsFrom October 2022 to November 2023, the program had 500 visits from 101 unique patients with a median age of 36. Among users of the service, 71% did not have a primary care provider. Once patient incentives were introduced, express STI testing increased over 10-fold. The laboratory panels had a 44% positivity rate for either an STI or HCV. ConclusionsPharmacy can be an accessible and effective means of delivering harm reduction, STI, and HCV services. Patient incentives may greatly increase testing and detection of infectious diseases among patients who may otherwise not seek care.

Clinicopathological Aspects of Dilation and Curettage (D&C) Biopsies Taken from Patients Living at High Altitude in Taif, KSA, with a Special Emphasis on Chronic Endometritis.

Chronic endometritis (CE) is a persistent inflammation of the uterine lining. Although it has a minimal clinical presentation, CE adversely affects the reproductive ability of women. The aims of this study were to detect pathological endometrial patterns in D&C biopsies and to evaluate chronic endometritis in patients living in a high-altitude area (1800 m above sea level) in order to determine the clinical pathological features and prevalence. A cross-sectional study conducted at King Faisal Maternity Hospital included 100 samples of D&C biopsies from women complaining of various gynecological symptoms not due to gestational causes. The biopsies underwent tissue processing, H&E staining, and CD138 detection. Blood samples were taken for serological detection of infectious diseases, complete blood count, and chemical parameters. The mean age of women in the study with CE was 48.5 ± 8.5 years, and that of those without CE was 46.9 ± 9.7 years. The most common complaints were abnormal uterine bleeding, accounting for 83%. CE was present in 8% of cases, and there was a nonsignificant difference in hematological parameters between women with CE and those with other pathological diagnoses. There were also nonsignificant differences in chemical parameters, except for FSH and LH levels, which showed a significant difference, with p-values of 0.05 and 0.02, respectively. It can be concluded that the most common gynecological complaint of women in this study was abnormal uterine bleeding. The most commonly diagnosed pathological endometrial disorder in D&C biopsies was disordered proliferative endometrium, followed by endometrial polyps and endometrial hyperplasia. All of these are usually associated with hormonal disturbance, which appeared to be very common in the women in this study. The prevalence of chronic endometritis detected in our study was 8%, which is relatively high.

Automated quantification of SARS-CoV-2 pneumonia with large vision model knowledge adaptation

BackgroundLarge vision models (LVM) pretrained by large datasets have demonstrated their enormous capacity to understand visual patterns and capture semantic information from images. We proposed a novel method of knowledge domain adaptation with pretrained LVM for a low-cost artificial intelligence (AI) model to quantify the severity of SARS-CoV-2 pneumonia based on frontal chest X-ray (CXR) images. MethodsOur method used the pretrained LVMs as the primary feature extractor and self-supervised contrastive learning for domain adaptation. An encoder with a 2048-dimensional feature vector output was first trained by self-supervised learning for knowledge domain adaptation. Then a multi-layer perceptron (MLP) was trained for the final severity prediction. A dataset with 2599 CXR images was used for model training and evaluation. ResultsThe model based on the pretrained vision transformer (ViT) and self-supervised learning achieved the best performance in cross validation, with mean squared error (MSE) of 23.83 (95 % CI 22.67–25.00) and mean absolute error (MAE) of 3.64 (95 % CI 3.54–3.73). Its prediction correlation has the R2 of 0.81 (95 % CI 0.79–0.82) and Spearman ρ of 0.80 (95 % CI 0.77–0.81), which are comparable to the current state-of-the-art (SOTA) methods trained by much larger CXR datasets. ConclusionThe proposed new method has achieved the SOTA performance to quantify the severity of SARS-CoV-2 pneumonia at a significantly lower cost. The method can be extended to other infectious disease detection or quantification to expedite the application of AI in medical research.

Electrochemical Label-free Methods for Ultrasensitive Multiplex Protein Profiling of Infectious Diseases.

Electrochemical detection methods are the more appropriate detection methods when it comes to the sensitive and specific determination of biomarkers. Biomarkers are the biological targets for disease diagnosis and monitoring. This review focuses on recent advances in label-free detection of biomarkers for infectious disease diagnosis. The current state of the art for rapid detection of infectious diseases and their clinical applications and challenges were discussed. Label-free electroanalytical methods are probably the most promising means to achieve this. We are currently in the early stages of the emerging technology of using label-free electrochemistry of proteins to develop biosensors. To date, antibody-based biosensors have been intensively developed, although many improvements in reproducibility and sensitivity are still needed. Moreover, there is no doubt that a growing number of aptamers and hopefully label-free biosensors based on nanomaterials will soon be used for disease diagnosis and therapy monitoring. And also here in this review article, we have discussed recent developments in the diagnosis of bacterial and viral infections, as well as the current status of the use of label-free electrochemical methods for monitoring inflammatory diseases.

Mini-Program Enabled IoT Intelligent Molecular Diagnostic Device for Co-Detection and Spatiotemporal Mapping of Infectious Disease Pathogens.

Effective detection of infectious pathogens is crucial for disease prevention and control. We present an innovative Internet of Things (IoT) molecular diagnostic device featuring a WeChat mini-program for simultaneous detection and spatiotemporal mapping of respiratory pathogens. Leveraging social software's widespread usage, our device integrates seamlessly with WeChat, eliminating the need for app downloads and installations. Through a comprehensive detection system, including a user-friendly mini-program, a portable Point-of-Care fluorescence detector, and a diagnostic information management platform (EzDx Cloud), we demonstrate high sensitivity and specificity in detecting common respiratory viruses. Our SARS-CoV-2/H1N1 combo test kit, developed using a novel one-tube/one-step loop-mediated isothermal amplification-CRISPR method, shows remarkable performance. We address challenges in at-home nucleic acid testing by providing a cost-effective solution capable of detecting multiple pathogens simultaneously. Our system's versatility accommodates various assays operating at different temperatures and fluorescence intensities, offering significant advantages over traditional methods. Moreover, integration with EzDx Cloud facilitates disease monitoring and early warning systems, enhancing public health management. This study highlights the potential of our IoT molecular diagnostic device in revolutionizing infectious disease detection and control, with wide-ranging applications in both human and animal population.

An Ultra-Compact and Low-Cost LAMP-Based Virus Detection Device.

Timely and accurate detection of viruses is crucial for infection diagnosis and treatment. However, it remains a challenge to develop a portable device that meets the requirement of being portable, powerless, user-friendly, reusable, and low-cost. This work reports a compact ∅30 × 48 mm portable powerless isothermal amplification detection device (material cost ∼$1 USD) relying on LAMP (Loop-Mediated Isothermal Amplification). We have proposed chromatographic-strip-based microporous permeation technology which can precisely control the water flow rate to regulate the exothermic reaction. This powerless heating combined with phase-change materials can maintain a constant temperature between 50 and 70 °C for a duration of up to 49.8 min. Compared with the conventional methods, it avoids the use of an additional insulation layer for heat preservation, greatly reducing the size and cost. We have also deployed a color card and a corresponding algorithm to facilitate color recognition, data analysis, and storage using a mobile phone. The experimental results demonstrate that our device exhibits the same limit of detection (LOD) as the ProFlex PCR for SARS-CoV-2 pseudovirus samples, with that for both being 103 copies/μL, verifying its effectiveness and reliability. This work offers a timely, low-cost, and easy way for respiratory infectious disease detection, which could provide support in curbing virus transmission and protecting the health of humans and animals, especially in remote mountainous areas without access to electricity or trained professionals.

Robust detection of infectious disease, autoimmunity, and cancer from the paratope networks of adaptive immune receptors.

Liquid biopsies based on peripheral blood offer a minimally invasive alternative to solid tissue biopsies for the detection of diseases, primarily cancers. However, such tests currently consider only the serum component of blood, overlooking a potentially rich source of biomarkers: adaptive immune receptors (AIRs) expressed on circulating B and T cells. Machine learning-based classifiers trained on AIRs have been reported to accurately identify not only cancers but also autoimmune and infectious diseases as well. However, when using the conventional "clonotype cluster" representation of AIRs, individuals within a disease or healthy cohort exhibit vastly different features, limiting the generalizability of these classifiers. This study aimed to address the challenge of classifying specific diseases from circulating B or T cells by developing a novel representation of AIRs based on similarity networks constructed from their antigen-binding regions (paratopes). Features based on this novel representation, paratope cluster occupancies (PCOs), significantly improved disease classification performance for infectious disease, autoimmune disease, and cancer. Under identical methodological conditions, classifiers trained on PCOs achieved a mean AUC of 0.893 when applied to new individuals, outperforming clonotype cluster-based classifiers (AUC 0.714) and the best-performing published classifier (AUC 0.777). Surprisingly, for cancer patients, we observed that "healthy-biased" AIRs were predicted to target known cancer-associated antigens at dramatically higher rates than healthy AIRs as a whole (Z scores >75), suggesting an overlooked reservoir of cancer-targeting immune cells that could be identified by PCOs.