Diagnosis Of Disease Research Articles

Myeloperoxidase as a biomarker in periodontal disease: electrochemical detection using printed screen graphene electrodes.

Periodontal disease is a common oral health issue marked by inflammation and the breakdown of tissues. Early detection of biomarkers associated with periodontal disease (PD) can significantly aid in timely diagnosis and intervention. Myeloperoxidase (MPO) is an enzyme abundantly present in neutrophils and has been associated in the pathogenesis of PD. Here, we present a novel approach for the electrochemical detection of MPO using printed screen graphene electrodes coupled with principal component analysis (PCA) for data analysis. We employed cyclic voltammetry to characterize the electrochemical behavior of MPO using potassium ferrocyanide and hydrogen peroxide. The process was controlled by species diffusion on the electrode surface using a scan rate spanning from 10 to 400mVs-1. In addition, we investigated the detection of hydrogen peroxide, a substrate of MPO, as a method to indirectly asses MPO electroactivity, leveraging a redox potential of - 500mV. Saliva samples were collected and analyzed using the developed electrochemical sensor, followed by principal component analysis to differentiate between healthy and diseased samples based on MPO levels. Our findings demonstrate the feasibility of using printed screen graphene electrodes for the sensitive and selective detection of MPO, offering a promising approach for early diagnosis and monitoring of periodontal disease. In conclusion, our results highlight the potential of MPO as a robust biomarker for periodontal disease and highlight the utility of electrochemical sensing coupled with PCA analysis for sensitive and specific detection in clinical settings.

Identifying robust biomarkers for the diagnosis and subtype distinction of inflammatory bowel disease through comprehensive serum metabolomic profiling

Inflammatory Bowel Disease (IBD), Crohn's disease (CD) and ulcerative colitis (UC), requires a combination of procedures and tests in diagnosis and discrimination. This study aimed to delineate specific serum metabolomic biomarkers that diagnose IBD and differentiate IBD subgroups. Serum samples and clinical metadata of the participants, IBD patients and Normal Controls (NC), were collected. Untargeted and targeted metabolomic analyses by high-resolution mass spectrometry and multivariate statistical approaches were applied. Further, Receiver Operating Characteristic (ROC) curves, pathways, and network analyses were conducted. Distinct clustering separated IBD patients from the NC, although the CD and UC subgroups overlapped in the non-targeted profiling. Targeted metabolomics revealed elevated tryptophan and indole-3-acetic acid levels and reduced primary-to-secondary bile acid ratios in both CD and UC patients. The differences in specific tryptophan metabolites between CD and UC were identified. The ROC analysis underscored the discriminatory power of the biomarkers (AUC values: NC vs. CD = 0.9738; NC vs. UC = 0.9887; UC vs. CD = 0.7140). Pathway analysis revealed alterations in glycerolipid metabolism, markedly differentiating UC from CD. Network analysis correlated metabolomic markers with the clinical phenotypes of IBD. Serum metabolomic biomarkers can precisely identify IBD, discriminate IBD subtypes, and further reveal the phenotypes of IBD.

Mango fruit diseases severity estimation based on image segmentation and deep learning

Plant disease severity is the ratio between the surface area of disease symptoms and the total surface area of the plant unit (e.g. fruit, leaf). It is related to plant disease diagnosis and has several advantages for farmers. It is therefore a key element in the protection and management of plant diseases. In the literature, there are three proposed categories of plant disease severity determination solutions: solutions based on segmentation algorithms, solutions based on classical ML algorithms and those based on DL algorithms. Despite their several advantages, these solutions have a number of limitations, including i) subjectivity in data labeling, ii) loss of information on disease lesion contours during (manual) data labeling, and iii) the proposed solutions have focused on estimating plant disease severity from leaves, although diseases can also affect other parts of the plant, such as fruits. In this paper, we present a solution for estimating the severity of four mango fruit diseases, namely Alternariose, Anthracnose, Aspergillus rot and Stem rot. This solution is based on ResNet50 CNN and uses a dataset automatically labeled by a proposed algorithm based on two segmentation algorithms such as image color space segmentation and image thresholding. The solution has achieved an accuracy, a precision and a F1_score of 97.82%, 97.09% and 97.79%, respectively, on test data It is then deployed in a mobile application with a diagnostic solution we previously proposed. This mobile application will help mango growers, particularly those in Sahelian countries like Senegal, to manage their mango diseases earlier.

Surface-Enhanced Raman Scattering (SERS) for exosome detection.

Exosomes, nanoscale extracellular vesicles secreted by various cells, are abundantly present in biological fluids. They have been identified as carriers of specific molecules, suggesting their potential role in early disease detection. However, their clinical application is hindered by several challenges, including the need for large sample volumes for enrichment, limitations of traditional detection methods, and the complexity involved in phenotype analysis and separation. This review aims to explore the application of Surface-Enhanced Raman Scattering (SERS) technology in exosome detection. SERS, known for its unique photonic properties and high sensitivity, offers a promising solution for detecting exosomes without the need for large sample volumes or extensive phenotypic analysis. This review focuses on the real-time and non-invasive assessment capabilities of SERS in exosome detection, providing insights into its potential for early disease diagnosis. The review concludes by emphasizing the potential of SERS-based exosome detection in advancing early disease diagnosis. By overcoming existing challenges, SERS technology offers a promising approach for the development of sensitive and specific diagnostic assays, contributing to better patient outcomes and personalized medicine.

Understanding the physiological mechanisms and therapeutic targets of diseases: Lipidomics strategies.

As a pivotal branch of metabolomics, lipidomics studies global changes in lipid metabolism under different physiological and pathological conditions or drug interventions, discovers key lipid markers, and elaborates the associated lipid metabolism network. There are a considerable number of lipids in the host, which act on various functional networks such as metabolism and immune regulation. As an indispensable research method, lipidomics plays a key character in the analysis of lipid composition in organisms, the elaboration of the physiological mechanism of lipids, and the decoding of their character in the occurrence and development of diseases by exploring the character of lipids in the host environmental network. As an essential means of driving lipidomics research, High-throughput and High-resolution mass spectrometry is helpful in exploring disease phenotypic characteristics, diagnosing disease biomarkers, regulating related metabolic pathways, and discovering related active components. In this paper, we discuss the specific role of lipidomics in the analysis of disease diagnosis, prognosis and treatment, which is conducive to the realization of accurate and personalized medicine.

Cracking the code: an integrated electronic medical record approach to early diagnosis of genetic kidney disease in children with microscopic haematuria.

Microscopic haematuria (MH) is a common incidental finding in childhood that typically resolves spontaneously but, if persistent, can be a sign of underlying genetic kidney disease. Best practice indicates that all MH samples should be followed up to ensure resolution. Repeat sampling often does not occur, and an opportunity to diagnose and manage genetic kidney disease is missed, with substantial resultant preventable morbidity. The quality gap between recommended follow-up and actual practice represented an opportunity to improve care. A retrospective audit of the electronic medical record (EMR) was carried out to identify patients with documented MH but in whom follow-up results within the reference range were lacking. Digital tools were used to link bulk communication functions within the EMR. Families were contacted and offered repeat testing. Persistent findings of MH were referred to the Kidney Genomics Clinic (KGC). There were 1,335 children/families contacted. The overall response rate was 21%. Two hundred sixty-two children had normal repeat urinalysis. There were 16 children with MH. Fifteen underwent genomic testing. Five children were diagnosed with Alport syndrome (AS), and subsequently, two family members were. It is feasible to support the early diagnosis of genetic kidney disease using digital health tools. The lack of true interoperability within healthcare systems remains a barrier. This issue prevented the solution from being fully digital and created a manual element of follow-up. Prospective digital solutions are being explored to improve follow-up rates of incidental findings of MH and facilitate early diagnosis of genetic kidney disease.

Vigorously advancing the application of AI in the diagnosis and treatment of ocular surface and tear diseases

Ocular surface and tear diseases are among the most common and significant ocular conditions affecting eye health. In recent years, research and clinical diagnosis and treatment of ocular surface and tear diseases have rapidly developed in China, but numerous challenges remain. Breakthroughs in medical artificial intelligence (AI) offer new methods and approaches to address these difficulties. This article, based on the current state of AI applications for ocular surface and tear diseases in China and the major challenges, outlines future directions for progress. With the construction of shared multimodal databases, development of AI foundational models, improvement of AI evaluation systems, promotion of AI for comprehensive disease management throughout the entire lifecycle, and training of multidisciplinary talents, significant advances of the application of AI in the diagnosis and treatment of ocular surface and tear diseases would be achieved.

Tutorial on impedance and dielectric spectroscopy for single-cell characterisation on microfluidic platforms: theory, practice, and recent advances.

Cell analysis plays an important role in disease diagnosis. However, many characterisation techniques are labour intensive, expensive and time-consuming. Impedance and dielectric spectroscopy (IDS) offers a new approach by using varying electrical current and electric field propagation responses to probe cell physiology. This review aims to explore the theoretical foundations, practical applications, and advancements in IDS for single-cell analysis, particularly when integrated with microfluidic technologies. It highlights recent developments in electrode configurations, calibration techniques, and data analysis methodologies, emphasising their importance in enhancing sensitivity and selectivity. The review identifies key trends, including the shift towards high-throughput and precise single-cell analysis, and discusses the challenges and potential solutions in this field. The implications of these findings suggest significant near-future advances in biomedical research, diagnostics, and therapeutic monitoring. This paper serves as a comprehensive reference for researchers in different fields to make a bridge between theoretical research and practical implementation in single-cell analysis.

A cross-sectional survey of blindness and visual impairment among adults in 13 regions of China from 2021 to 2022

Objective: To investigate the prevalence of blindness and visual impairment in adults and the related causes in China. Methods: A population-based cross-sectional survey was conducted during 2021 and 2022 among 13 provinces, municipalities, and autonomous regions, including Beijing, Hebei, Jiangsu, Shanghai, Guangdong, Henan, Shanxi, Ningxia Hui Autonomous Region, Chongqing, Yunnan, Sichuan, Heilongjiang, and Liaoning. Respondents were adults aged 18 years and above, and were classified into groups of young adults (18 to 39 years), middle-age adults (40 to 59 years), and the elderly (≥60 years). The examinations included visual acuity measurement, non-contact tonometry, slit-lamp examination, fundus photography, automatic refraction (non-mydriasis), and demographics (questionnaire). The diagnosis and distribution of eye diseases were analyzed. The Cochran-Armitage test was used for statistical processing. Results: A total of 93 123 adults with a mean age of 48.8 years (range, 18 to 101 years) completed the survey, and the response rate was 76.2% (93 123/122 191). There were 37 725 males (40.5%) and 55 398 females (59.5%), while 32 357 (34.75%) were aged 18 to 39, 29 127 (31.28%) were aged 40 to 59, and 31 639 (33.97%) were aged≥60. According to the best-corrected visual acuity (BCVA) in the better eye of 93 123 adults, the blindness, moderate to severe visual impairment (low vision), and mild visual impairment were found in 280, 2 270, and 4 679 patients, respectively. Based on BCVA of the worse eye, the top three causes of blindness and low vision were cataract (1 271/2 982, 42.62%), uncorrected refractive error (433/2 982, 14.52%), and pathological myopia (90/2 982, 3.02%), without considering trauma or other undefined fundus diseases. Conclusions: From 2021 to 2022, the prevalence rates of blindness and low vision in Chinese adults were 0.30% and 2.44%, respectively. Cataract was the primary eye disease leading to blindness and low vision, and pathological myopia became the major cause of irreversible blindness in the population.

Mechanical Compatibility in Stitch Configuration and Sensor Adhesion for High-Fidelity Pulse Wave Monitoring.

Wearable electronics can achieve high-fidelity monitoring of pulse waveforms on the body surface enabling early diagnosis of cardiovascular diseases (CVDs). Textile-based wearable devices offer advantages in terms of high permeability and comfort. However, knitted strain sensors struggle to capture small-range deformation signals due to stress dissipation during friction and slip of yarns within the textiles. They are optimized for mechanical adaptability and adhesive capability. In this work, the stitch configurations of knitted structure are adjusted to optimize the energy dissipation ratio during deformation and waveform fitting performance. These electric-mechanical results enabled the selection of the most suitable knitted structure for the clinical diagnosis. On the other hand, the sensor's adhesion is optimized with respect to electrical-force-strain coupling and energy transfer efficiency at the interface between skin and sensor. The balance between the storage modulus and loss modulus are adjusted via the crosslinking degree of the polyacrylamide (PAAm) hydrogel network. As a result, the optimized knitted sensor enables stable collection of pulse waveforms from the radial and dorsalis pedis arteries. In human patient evaluations, the knitting-based strain sensor can distinguish patients with different potential CVD risks through extracted characteristic indicators.

Using ChatGPT-4 in visual field test assessment

ABSTRACT Clinical relevance Visual field testing is essential in the diagnosis and management of various ophthalmic diseases, particularly glaucoma. Integrating ChatGPT-4 into the interpretation of these tests has the potential to aid clinical decision making and improve efficiency and accessibility in clinical practice. Background This study aims to evaluate the capability of ChatGPT-4 in interpreting visual field tests. Method A total of 30 patient visual field printouts, either with or without defects, were included in this study. The performance of ChatGPT-4 in identifying test name, pattern, reliability indices, total deviation map, pattern deviation map and greyscale map was evaluated and compared with that of 2 experienced glaucoma consultants. The study also focused on the ability of ChatGPT to categorise tests as ‘normal’ or suggest diagnosis by interpreting tests accurately. Results The results showed that ChatGPT-4 was highly accurate in identifying test names (100%), patterns (90%) and global visual field indices (96.7%). It also accurately classified tests as reliable or unreliable (93.3%).The model provided 66.7% and 30% accurate and adequate answers in interpreting deviation and greyscale maps, respectively. In addition, in 33.3% of tests, it was able to accurately interpret the visual field test and classify it as ‘normal’ or suggest a diagnosis. Conclusion The study highlights the potential of large language models like ChatGPT-4 in assessing visual field tests. ChatGPT-4 could interpret numeric data on tests accurately. However, it was inadequate in interpreting deviation and greyscale maps and suggesting a diagnosis according to the defects.

Epidemiology and clinical outcomes of hospitalized Hispanic patients with IBD: results of a large national cohort study

IntroductionInflammatory bowel disease (IBD) has historically been seen as predominantly affecting non-Hispanic Whites (NHW). Hispanics are the largest minority group in the USA, yet they remain grossly underrepresented in studies of IBD. With this study, we aimed to better understand the epidemiology of hospitalized Hispanic patients with IBD in the US.MethodsThis was a retrospective cohort study utilizing the National Inpatient Sample, the largest publicly available all-payer inpatient care database in the United States. We compared demographics, hospitalization characteristics, clinical outcomes, and year-to-year trends from 2016 to 2020 in Hispanic and NHW with a primary diagnosis of inflammatory bowel disease, Crohn’s disease, or ulcerative colitis.ResultsNHWs hospitalized with a primary diagnosis of IBD had significantly higher rates of hospitalization than Hispanics (122.67 vs 71.12, P < 0.01). While hospitalized Hispanics with IBD are more likely to be in the lowest quartile for household income (31.6% vs 19.3%, P < 0.01), have a younger median age (37.0 vs 45.0, P < 0.01), and be uninsured (4.3% vs 8.8%, P < 0.01) compared to NHW. Length of admission was similar, yet NHWs had higher rates of mortality (0.3% vs 0.2%, P = 0.01), while total charges for hospitalizations were significantly higher for Hispanic patients (P < 0.01).DiscussionTo our knowledge, this is one of the largest US-based studies of Hispanics with IBD. Our findings suggest that among hospitalized IBD patients, Hispanics are more likely to be younger, uninsured, have a lower household income, and are less likely to undergo surgery while having higher hospital charges.

DNA-Enabled Self-Resetting Electrochemical Sensor for microRNA Detection.

Nucleic acids, such as microRNA and circulating tumor DNA, are widely utilized potential biomarkers for early disease diagnosis. Electrochemical sensors with high sensitivity play a significant role in quantitative bioanalysis, however, target molecules during detection usually bind to probes and limit their reusability. Powered by DNA/RNA fuel and enzyme-based fuel consumption unit, dissipative DNA systems can perform periodic tasks in a self-resettable manner. Here, a self-resetting electrochemical sensor is reported for miRNA detection based on dissipative DNA networks. The target microRNA-21 (miRNA-21) invades the incumbent strand and activates the strand displacement process. Released ferrocene-modified strands bind to the probe on the electrode surface for accurate detection. The miRNA within RNA/DNA heteroduplex will then be digested by RNase H to free the incumbent strand which replaces the ferrocene strand and restores the probe to the initial state. It is found that the detection limit of this self-resetting electrochemical sensor is 0.35 pM for the third cycle. The self-resetting electrochemical sensor can not only achieve lower detection limits, and maintain repeatability and stability in complex matrices, but also reduce the detection cost of electrochemical sensors. It is envisioned that many real-life applications can be initiated such as analyzing various oligonucleotide biomarkers during early cancer diagnosis and bioanalysis.

PNL: a software to build polygenic risk scores using a Super Learner approach based on PairNet, a Convolutional Neural Network.

Polygenic risk scores (PRS) hold promise for early disease diagnosis and personalized treatment, but their overall discriminative power remains limited for many diseases in the general population. As a result, numerous novel PRS modeling techniques have been developed to improve predictive performance, but determining the most effective method for a specific application remains uncertain until tested. Hence, we introduce a novel, versatile tool for building an optimized PRS model by integrating candidate models from multiple existing PRS building methods that use target population data and/or incorporating information from other populations through a trans-ethnic approach. Our tool, PNL is based on PairNet algorithm, a Convolutional Neural Network with low computation complexity through simple paring operation. In the case studies for asthma, type 2 diabetes, and vertigo, the optimal PRS model generated with PNL using only TWB data achieved AUCs that matched or improved the best results using other methods individually. Incorporating UKBB data further improved performance of PNL for asthma and type 2 diabetes. For vertigo, unlike the other diseases, individual method analysis showed that UKBB data alone generally produced lower AUCs compared to TWB data alone. As a result, incorporating UKBB data did not improve AUC with PNL, suggesting that increasing the number of candidate models does not necessarily result in higher AUC values, alleviating concerns about overfitting. The python code for PairNet algorithm incorporated in PNL is freely available on: https://github.com/FannLab/pairnet. An archived, citable version is stored on: https://doi.org/10.5281/zenodo.14838227. Correspondence should be addressed to corresponding authors. Detailed implementation procedures can be found in the Supplementary Materials.

Reliability-enhanced data cleaning in biomedical machine learning using inductive conformal prediction.

Accurately labeling large datasets is important for biomedical machine learning yet challenging while modern data augmentation methods may generate noise in the training data, which may deteriorate machine learning model performance. Existing approaches addressing noisy training data typically rely on strict modeling assumptions, classification models and well-curated dataset. To address these, we propose a novel reliability-based training-data-cleaning method employing inductive conformal prediction (ICP). This method uses a small set of well-curated training data and leverages ICP-calculated reliability metrics to selectively correct mislabeled data and outliers within vast quantities of noisy training data. The efficacy is validated across three classification tasks with distinct modalities: filtering drug-induced-liver-injury (DILI) literature with free-text title and abstract, predicting ICU admission of COVID-19 patients through CT radiomics and electronic health records, and subtyping breast cancer using RNA-sequencing data. Varying levels of noise to the training labels were introduced via label permutation. Our training-data-cleaning method significantly enhanced the downstream classification performance (paired t-tests, p≤0.05 among 30 random train/test partitions): significant accuracy enhancement in 86 out of 96 DILI experiments (up to 11.4% increase from 0.812 to 0.905), significant AUROC and AUPRC enhancements in all 48 COVID-19 experiments (up to 23.8% increase from 0.597 to 0.739 for AUROC, and 69.8% increase from 0.183 to 0.311 for AUPRC), and significant accuracy and macro-average F1-score improvements in 47 out of 48 RNA-sequencing experiments (up to 74.6% increase from 0.351 to 0.613 for accuracy, and 89.0% increase from 0.267 to 0.505 for F1-score). The improvement can be both statistically and clinically significant for information retrieval, disease diagnosis and prognosis. The method offers the potential to substantially boost classification performance in biomedical machine learning tasks without necessitating an excessive volume of well-curated training data or strong data distribution and modeling assumptions in existing semi-supervised learning methods.

Consensus on early detection of congenital heart disease

Congenital heart diseases are the most frequent birth defects, and one of the main causes of perinatal morbidity and death. Early diagnosis allows a timely treatment and improves the patients' prognosis. Early detection includes screening studies during the first and second trimesters of pregnancy, a neonatal complete clinical examination and the neonatal pulse oximetry test. The aim of this consensus is to present recommendations for early detection and diagnosis of congenital heart disease.

Heart2Heart: a digital peer support programme for people with heart disease: protocol for a community-based, investigator-blinded randomised controlled trial conducted in Australia

IntroductionCardiac rehabilitation is known to reduce morbidity and improve quality of life in people living with heart disease, however, adherence, access and completion of these programmes is suboptimal. Peer support...

High Impact Clinical Applications of Cardiac Magnetic Resonance Imaging in Women: A Review.

The diagnosis of cardiovascular disease in women poses an ongoing challenge due to lack of knowledge about sex differences in the manifestations of cardiovascular disease, since women have been underrepresented in cardiovascular research studies that guide current practice. The purpose of this article is to review a spectrum of cardiovascular disorders which occur exclusively or more frequently in women and to highlight the role that cardiovascular magnetic resonance (MR) plays in diagnosing and prognosticating these disorders. Specifically, this review focuses on cardio-oncologic, ischemic, inflammatory, autoimmune, peri-partum, and genetic manifestations of cardiomyopathy in women. We strive to draw attention to the added diagnostic value provided by cardiac MR, compared against alternative imaging modalities, and propose opportunities for further research on sex differences in imaging and diagnosing cardiovascular diseases. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 3.

Implications of Hydrocephalus on [18F]-Fluorodeoxyglucose Positron Emission Tomography Statistical Parametric Mapping Analysis in Neurodegenerative Disease Evaluation.

Brain [18F] fluorodeoxyglucose positron emission tomography (FDG-PET) is critical in the diagnosis of neurodegenerative disease. Quantitative analysis with statistical parametric mapping (SPM) has been shown to improve diagnostic accuracy of FDG-PET and has been incorporated in clinical workflows. This study aimed to assess the effects of hydrocephalus on FDG PET SPM analysis accuracy, focusing on cingulate gyrus regions, which are of particular interest in dementia evaluation, and also are adjacent to the lateral ventricles. In this retrospective IRB-approved study, patients who underwent brain FDG-PET/CT or PET/MRI were evaluated. Inclusion criteria were clinical history of cognitive impairment/suspected neurodegenerative disease and MRI evidence of communicating hydrocephalus. Region-specific Z-scores for the anterior, middle, and posterior cingulate gyri (ACG, MCG, PCG), as well as for the cerebellum were generated using SPM analysis. Blinded expert qualitative assessment was performed for each anatomic region. Kappa coefficients were computed to evaluate agreement between quantitative and qualitative results. Paired nonparametric t-tests assessed Z-score differences between cingulate and cerebellar regions. The study included 48 patients (17 females, mean age 76). SPM analysis found significantly lower cingulate Z-scores compared to the cerebellum [-4.3 (ACG), -6.9 (MCG), and -3.2 (PCG), -1.2 (CBL) p < 0.0001]. Similar results were observed in the signed rank tests comparing cingulate regions to cerebellum [ACG -3.2 (SD 2.1), MCG -5.7 (SD 3.6), PCG -1.9 (SD 2.4), p < 0.001 for all 3 cingulate regions]. Kappa coefficients indicated poor agreement between SPM and qualitative assessments (kappa 0.05-0.19, p-values 0.078-0.479). Our study highlights hydrocephalus as an important pitfall of FDG-PET SPM, particularly when analyzing the cingulate regions, integral to the clinical evaluation of dementia. Awareness of this pitfall can improve diagnostic accuracy and thus improve clinical outcomes in this growing patient population. FDG= [18F]fluorodeoxyglucose, PET= Positron Emission Tomography, SPM:=Statistical Parametric Mapping, SUV:= Standardized Uptake Values, NPH= Normal Pressure Hydrocephalus, MRI= Magnetic Resonance Imaging, CT= Computed Tomography, ACG= Anterior Cingulate Gyrus, MCG= Middle Cingulate Gyrus, PCG= Posterior Cingulate Gyrus, CBL= Cerebellum.

Facile and Ultrafast Microfluidic Photothermal PCR for Autonomous and Quantitative Point-of-Care Pathogen Detection.

Point-of-care (POC) pathogen detection is highly desirable in diverse fields such as infectious disease diagnosis, food safety testing, and environmental monitoring. Herein, the study seeks to address this critical need by developing an automated microfluidic photothermal quantitative polymerase chain reaction (AMP-qPCR) system in a greatly simplified format. A key element of AMP-qPCR is an architecture that combines the design of a clockwork-like, magnetically-driven multi-chamber cartridge with the use of a cheap black tape beneath the PCR chamber as a fast photothermal-responsive engine. This not only enables the unprocessed sample to be lysed, purified, and subjected to real-time fluorescence PCR in an ultracompact and autonomous manner but also eliminates the need for sophisticated photonic material/device fabrication that is frequently required for performing ultrafast photothermal PCR. It is shown that AMP-qPCR can accomplish high-efficient bacterial DNA extraction and quantitative PCR within 18.5min, enabling accurate quantification of bacteria concentration from 108 to 102 CFU·mL-1. Furthermore, its practical applicability is demonstrated in detecting Neisseria gonorrhoeae from sexually transmitted infection-suspected patients by using clinical urine and cervical swab specimens, exhibiting matched performance to the benchtop automated machine. The presented platform enhances the availability of POC molecular diagnostics for on-site and in-home testing.