Human fungal infections represent a rapidly emerging global health threat,especially threatening immunocompromised populations,highlighting the urgent need for accurate and timely diagnostic approaches to reduce morbi...Human fungal infections represent a rapidly emerging global health threat,especially threatening immunocompromised populations,highlighting the urgent need for accurate and timely diagnostic approaches to reduce morbidity and mortality.This review synthesizes recent advances in diagnostic methodologies,including serological assays,point-of-care diagnostics,polymerase chain reaction(PCR)-based and sequencing technologies,as well as artificial intelligence(AI)-and machine learning(ML)-powered tools.Emerging diagnostic approaches have demonstrated notable improvements in detection accuracy,turnaround time,and antifungal resistance profiling capabilities,especially for drug-resistant strains.Nevertheless,substantial challenges persist in terms of standardization,scalability,cost-effectiveness,and implementation,particularly in resource-constrained settings.Future efforts should be directed toward the continuous innovation of rapid,sensitive,and multiplex diagnostic platforms for the simultaneous detection of fungi,bacteria,and viruses.Such advances may accelerate result acquisition,enhance diagnostic accuracy,support the development of more targeted therapeutic strategies,and ultimately improve clinical outcomes for patients.展开更多
Understanding the complex plasma dynamics in ultra-intense relativistic laser-solid interactions is of fundamental importance for applications of laser-plasma-based particle accelerators,the creation of high-energy-de...Understanding the complex plasma dynamics in ultra-intense relativistic laser-solid interactions is of fundamental importance for applications of laser-plasma-based particle accelerators,the creation of high-energy-density matter,understanding planetary science,and laser-driven fusion energy.However,experimental efforts in this regime have been limited by the lack of accessibility of over-critical densities and the poor spatiotemporal resolution of conventional diagnostics.Over the last decade,the advent of femtosecond brilliant hard X-ray free-electron lasers(XFELs)has opened new horizons to overcome these limitations.Here,for the first time,we present full-scale spatiotemporal measurements of solid-density plasma dynamics,including preplasma generation with tens of nanometer scale length driven by the leading edge of a relativistic laser pulse,ultrafast heating and ionization at the main pulse arrival,the laser-driven blast wave,and transient surface return current-induced compression dynamics up to hundreds of picoseconds after interaction.These observations are enabled by utilizing a novel combination of advanced X-ray diagnostics including small-angle X-ray scattering,resonant X-ray emission spectroscopy,and propagation-based X-ray phase-contrast imaging simultaneously at the European XFEL-HED beamline station.展开更多
Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dim...Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dimensional healthcare data,encompassing genomic,transcriptomic,and other omics profiles,as well as radiological imaging and histopathological slides,makes this approach increasingly important because,when examined separately,these data sources only offer a fragmented picture of intricate disease processes.Multimodal deep learning leverages the complementary properties of multiple data modalities to enable more accurate prognostic modeling,more robust disease characterization,and improved treatment decision-making.This review provides a comprehensive overview of the current state of multimodal deep learning approaches in medical diagnosis.We classify and examine important application domains,such as(1)radiology,where automated report generation and lesion detection are facilitated by image-text integration;(2)histopathology,where fusion models improve tumor classification and grading;and(3)multi-omics,where molecular subtypes and latent biomarkers are revealed through cross-modal learning.We provide an overview of representative research,methodological advancements,and clinical consequences for each domain.Additionally,we critically analyzed the fundamental issues preventing wider adoption,including computational complexity(particularly in training scalable,multi-branch networks),data heterogeneity(resulting from modality-specific noise,resolution variations,and inconsistent annotations),and the challenge of maintaining significant cross-modal correlations during fusion.These problems impede interpretability,which is crucial for clinical trust and use,in addition to performance and generalizability.Lastly,we outline important areas for future research,including the development of standardized protocols for harmonizing data,the creation of lightweight and interpretable fusion architectures,the integration of real-time clinical decision support systems,and the promotion of cooperation for federated multimodal learning.Our goal is to provide researchers and clinicians with a concise overview of the field’s present state,enduring constraints,and exciting directions for further research through this review.展开更多
In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has...In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has emerged as a transformative tool in health care,offering rapid,sensitive,and specific identification of microorganisms.This editorial provides a comprehensive overview of LOC technology,highlighting its principles,advantages,applications,challenges,and future directions.Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics,epidemiology,and food safety.Comparative studies have underscored the superiority of LOC technology over traditional methods,showcasing improvements in speed,accuracy,and portability.The future integration of LOC with biosensors,artificial intelligence,and data analytics promises further innovation and expansion.This call to action emphasizes the importance of continued research,investment,and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.展开更多
This article provides a short review on the importance of the detailed analysis of a Langmuir probe I-V trace in a multi-Maxwellian plasma,and discuss proper procedures analyzing Langmuir probe I-V traces in bi-Maxwel...This article provides a short review on the importance of the detailed analysis of a Langmuir probe I-V trace in a multi-Maxwellian plasma,and discuss proper procedures analyzing Langmuir probe I-V traces in bi-Maxwellian and triple-Maxwellian Electron Energy Distribution Function(EEDF)plasmas.Discus⁃sion and demonstration of procedures include the treatment of the ion saturation current,electron saturation cur⁃rent,space-charge effects on the I-V trace,and most importantly how to properly isolate and fit for each electron group present in an I-V trace reflecting a mult-Maxwellian EEDF,as well as how having a multi-Maxwellian EEDF affects the procedures of treating the ion and electron saturation currents.Shortcomings of common improp⁃er procedures are discussed and demonstrated with simulated I-V traces to show how these procedures gives false measurements.展开更多
Radiation doses to patients in diagnostics and interventional radiology need to be optimized to comply with the principles of radiation protection in medical practice. This involves using specific detectors with respe...Radiation doses to patients in diagnostics and interventional radiology need to be optimized to comply with the principles of radiation protection in medical practice. This involves using specific detectors with respective diagnostic beams to carry out quality control/quality assurance tests needed to optimize patient doses in the hospital. Semiconductor detectors are used in dosimetry to verify the equipment performance and dose to patients. This work aims to assess the performance, energy dependence, and response of five commercially available semiconductor detectors in RQR, RQR-M, RQA, and RQT at Secondary Standard Dosimetry for clinical applications. The diagnostic beams were generated using Exradin A4 reference ion chamber and PTW electrometer. The ambient temperature and pressure were noted for KTP correction. The detectors designed for RQR showed good performance in RQT beams and vice versa. The detectors designed for RQR-M displayed high energy dependency in other diagnostic beams. The type of diagnostic beam quality determines the response of semiconductor detectors. Therefore, a detector should be calibrated according to the beam qualities to be measured.展开更多
Artificial Intelligence(AI)is fundamentally transforming medical diagnostics,driving advancements that enhance accuracy,efficiency,and personalized patient care.This narrative review explores AI integration across var...Artificial Intelligence(AI)is fundamentally transforming medical diagnostics,driving advancements that enhance accuracy,efficiency,and personalized patient care.This narrative review explores AI integration across various diagnostic domains,emphasizing its role in improving clinical decision-making.The evolution of medical diagnostics from traditional observational methods to sophisticated imaging,laboratory tests,and molecular diagnostics lays the foundation for understanding AI’s impact.Modern diagnostics are inherently complex,influenced by multifactorial disease presentations,patient variability,cognitive biases,and systemic factors like data overload and interdisciplinary collaboration.AI-enhanced clinical decision support systems utilize both knowledge-based and non-knowledge-based approaches,employing machine learning and deep learning algorithms to analyze vast datasets,identify patterns,and generate accurate differential diagnoses.AI’s potential in diagnostics is demonstrated through applications in genomics,predictive analytics,and early disease detection,with successful case studies in oncology,radiology,pathology,ophthalmology,dermatology,gastroenterology,and psychiatry.These applications demonstrate AI’s ability to process complex medical data,facilitate early intervention,and extend specialized care to underserved populations.However,integrating AI into diagnostics faces significant limitations,including technical challenges related to data quality and system integration,regulatory hurdles,ethical concerns about transparency and bias,and risks of misinformation and overreliance.Addressing these challenges requires robust regulatory frameworks,ethical guidelines,and continuous advancements in AI technology.The future of AI in diagnostics promises further innovations in multimodal AI,genomic data integration,and expanding access to high-quality diagnostic services globally.Responsible and ethical implementation of AI will be crucial to fully realize its potential,ensuring AI serves as a powerful ally in achieving diagnostic excellence and improving global health care outcomes.This narrative review emphasizes AI’s pivotal role in shaping the future of medical diagnostics,advocating for sustained investment and collaborative efforts to harness its benefits effectively.展开更多
As a critical technology for industrial system reliability and safety,machine monitoring and fault diagnostics have advanced transformatively with large language models(LLMs).This paper reviews LLM-based monitoring an...As a critical technology for industrial system reliability and safety,machine monitoring and fault diagnostics have advanced transformatively with large language models(LLMs).This paper reviews LLM-based monitoring and diagnostics methodologies,categorizing them into in-context learning,fine-tuning,retrievalaugmented generation,multimodal learning,and time series approaches,analyzing advances in diagnostics and decision support.It identifies bottlenecks like limited industrial data and edge deployment issues,proposing a three-stage roadmap to highlight LLMs’potential in shaping adaptive,interpretable PHM frameworks.展开更多
Background:Head and neck cancers(HNC)account for a significant global health burden,with increasing incidence rates and complex treatment requirements.Traditional diagnostic and therapeutic approaches,while effective,...Background:Head and neck cancers(HNC)account for a significant global health burden,with increasing incidence rates and complex treatment requirements.Traditional diagnostic and therapeutic approaches,while effective,often result in substantial morbidity and limitations in personalized care.This review provides a comprehensive overview of the latest innovations in diagnostics and therapeutic strategies for HNC from 2015 to 2024.Methods:A review of literature focused on pe-reviewed journals,clinical trial databases,and oncology conference proceedings.Key areas include molecular diagnostics,imaging technologies,minimally invasive surgeries,and innovative therapeutic strategies.Results:Technologies like liquid biopsy next-generation sequencing(NGS)have greatly improved diagnostic accuracy and personalization in HNC care.These advancements have improved survival rates and enhanced patients’quality of life.Personalized therapeutic approaches,including immune checkpoint inhibitors,precision radiation therapy,and surgery,have led to enhanced treatment efficacy while reducing side effects.The integration of AI and machine learning into diagnostics and treatment planning shows promise in optimizing clinical decision-making and predicting treatment outcomes.Conclusion:The current innovations in diagnostics and therapeutics are reshaping the management of head and neck cancer,offering more tailored and effective approaches to care.Overall,the continuous integration of these innovations in clinical practice is reshaping HNC treatment and improving patient outcomes and survival rates.Future research should focus on further refining these technologies,addressing challenges related to accessibility,and exploring their long-term clinical benefits in diverse patient populations.展开更多
Electrochemical impedance spectroscopy(EIS)offers valuable insights into the dynamic behaviors of lithium-ion batteries,making it a powerful and non-invasive tool for evaluating battery health.However,EIS falls short ...Electrochemical impedance spectroscopy(EIS)offers valuable insights into the dynamic behaviors of lithium-ion batteries,making it a powerful and non-invasive tool for evaluating battery health.However,EIS falls short in quantitatively determining the degree of specific degradation modes,which are essential for improving battery lifespan.This study introduces a novel approach employing deep neural networks enhanced by an attention mechanism to identify the degree of degradation modes.The proposed method can automatically determine the most relevant frequency ranges for each degradation mode,which can link impedance characteristics to battery degradation.To overcome the limitation of scarce labeled experimental data,simulation results derived from mechanistic models are incorporated into the model.Validation results demonstrate that the proposed method could achieve root mean square errors below 3%for estimating loss of lithium inventory and loss of active material of the positive electrode,and below 4%for estimating loss of active material of the negative electrode while requiring only 25%of early-stage experimental degradation data.By integrating simulation results,the proposed method achieves a reduction in maximum estimation error ranging from 42.92%to 66.30%across different temperatures and operating conditions compared to the baseline model trained solely on experimental data.展开更多
Wrought and laser powder bed fusion(LPBF)Ti−6Al−4V(Ti-6-4)specimens were comparatively evaluated,with the objective to determine LPBF Ti−6Al−4V’s suitability for biomedical applications.Testing included nanoindentati...Wrought and laser powder bed fusion(LPBF)Ti−6Al−4V(Ti-6-4)specimens were comparatively evaluated,with the objective to determine LPBF Ti−6Al−4V’s suitability for biomedical applications.Testing included nanoindentation,cyclic polarization in simulated body fluid(SBF,37°C),and dry and SBF“ball-on-plate”sliding.Wrought Ti-6-4 exhibited a lamellarα+βmicrostructure,whereas LPBF Ti-6-4 displayed a fine-grainedα′-martensite microstructure.LPBF Ti-6-4 demonstrated~3%higher indentation modulus and~32%higher hardness,while wrought Ti-6-4 showed~8%higher plasticity.Both alloys exhibited low corrosion rates(10−5 mA/cm^(2)order)and true passivity(10−4 mA/cm^(2)order).No localized corrosion was observed in either two alloys,except for occasional metastable pitting in the LPBF alloy.However,LPBF Ti-6-4 presented higher corrosion rate and passive current,ascribed to its martensitic structure.During dry sliding,LPBF Ti-6-4 exhibited~14%lower volume loss compared to wrought Ti-6-4.Sliding in SBF increased volume losses for both alloys,with wear resistances nearly equalized,as the advantage of LPBF Ti-6-4 decreased due to more intense wear-accelerated corrosion induced by the stressed martensite.Overall,the results demonstrate the suitability of LPBF Ti-6-4 for biomedical uses.展开更多
Accurate real-time monitoring of internal temperature in lithium-ion batteries remains critical for preventing thermal runaway,as conventional approaches sacrifice either computational efficiency or cross-scenario rob...Accurate real-time monitoring of internal temperature in lithium-ion batteries remains critical for preventing thermal runaway,as conventional approaches sacrifice either computational efficiency or cross-scenario robustness.We present a generalized fuzzy physics-informed framework that distills thermally sensitive electrochemical processes while circumventing redundant physical constraints,thereby establishing an explicit mechanism-constrained mapping between frequency-domain signals and internal temperature.This framework facilitates online thermal estimation,with dynamic validations in LiFePO_4/graphite 18650-type cells confirming real-time capability with near-instantaneous acquisition(~6 s per measurement),exceptional accuracy(±0.5℃) within the operational temperature range(30-50℃),and operational resilience across 20 %-80 % state-of-charge.The framework maintains predictive fidelity(±1.0℃ at 30℃ and ±4.0℃ at 60℃,95 % prediction intervals) across 80 %-100 % state-of-health while demonstrating adaptability to cathode materials and structural architectures.This strategy resolves the competing imperatives of physical interpretability,computational efficiency,and crossscenario generalizability,offering a universal paradigm for embedded thermal management in safetycritical applications.展开更多
With the rapid development of science and technology,the application of artificial intelligence(AI)technology in medical education has become increasingly widespread in the digital age,bringing new opportunities and c...With the rapid development of science and technology,the application of artificial intelligence(AI)technology in medical education has become increasingly widespread in the digital age,bringing new opportunities and challenges to China’s higher education of traditional Chinese medicine(TCM).In the context of digital education,it is of great significance to construct a teaching model that integrates AI technology with the characteristics of the diagnostics of traditional Chinese medicine,in order to improve the quality of curriculum teaching in the future.This article aims to introduce how to organically integrate AI technology with diagnostics of traditional Chinese medicine teaching based on the characteristics of the discipline,to achieve teaching mode reform,therefore to improve the teaching quality of traditional Chinese medicine education,and cultivate high-quality TCM talents that meet the needs of the new era.展开更多
In Unani medicine,Bawl(urine)is recognized as a key diagnostic tool,with humoural imbalances assessed via parameters like color,consistency,sediment,clarity,froth,odor,and volume.This conceptual review explores how th...In Unani medicine,Bawl(urine)is recognized as a key diagnostic tool,with humoural imbalances assessed via parameters like color,consistency,sediment,clarity,froth,odor,and volume.This conceptual review explores how these classical diagnostic indicators may be contextualized alongside modern urinalysis markers(e.g.,bilirubin,protein,ketones,and sedimentation)and examined through emerging artificial intelligence(AI)frameworks.Potential applications include ResNet-18 for color classification,You Only Look Once version 8(YOLOv8)for sediment detection,long short-term memory(LSTM)for viscosity estimation,and EfficientDet for froth analysis,with standardized urine images/videos forming the basis of future datasets.Additionally,a comparative ontology is proposed to align Unani perspectives with diagnostic approaches in traditional Chinese medicine,encouraging cross-system integration.By synthesizing classical epistemology with computational intelligence,this review highlights pathways for developing AI-based decision support systems to promote personalized,accessible,and telemedicine-enabled healthcare.展开更多
Objective:To retrospectively evaluate the diagnostic efficacy of traditional MRI and T2 Mapping quantitative imaging technology for knee joint cartilage injury,clarify the differences in diagnostic value of the two im...Objective:To retrospectively evaluate the diagnostic efficacy of traditional MRI and T2 Mapping quantitative imaging technology for knee joint cartilage injury,clarify the differences in diagnostic value of the two imaging methods in different injury grades and different cartilage subregions,and provide evidence-based basis for the accurate diagnosis of clinical cartilage injury.Methods:Clinical and imaging data of 286 patients with knee joint lesions admitted to the Affiliated Hospital of Xiangtan Medicine and Health Vocational College from January 2020 to June 2023 were collected retrospectively.All patients underwent both traditional MRI sequences and T2 Mapping sequences.The knee joint cartilage was divided into 14 subregions.Two senior radiologists independently diagnosed the images of the two imaging technologies using a blind method and recorded the cartilage injury grades.The sensitivity,specificity,accuracy,positive predictive value,negative predictive value,and area under the receiver operating characteristic curve(AUC)of the two technologies for diagnosing cartilage injury were calculated and compared,and the differences in their diagnostic efficacy in different injury grades and different subregions were analyzed.Results:A total of 4004 cartilage subregions from 286 patients were included in the analysis,including 1836 injured subregions and 2168 normal subregions.The overall sensitivity(89.7%),accuracy(91.2%),and AUC(0.946)of T2 Mapping quantitative imaging for diagnosing cartilage injury were significantly higher than those of traditional MRI(76.3%,82.5%,and 0.852 respectively),with statistically significant differences(p<0.001);there was no significant difference in specificity between the two(93.5%vs 90.8%,p=0.062).Subgroup analysis showed that T2 Mapping had the most significant diagnostic advantage in early cartilage injury(Grade 1),with sensitivity(78.5%)33.2%higher than that of traditional MRI(45.3%)(p<0.001).Conclusion:The diagnostic efficacy of T2 Mapping quantitative imaging for knee joint cartilage injury is significantly superior to that of traditional MRI,especially in the detection of early cartilage injury and accurate evaluation of weight-bearing area injury.Data verify its clinical applicability and reliability.It can be used as an important supplementary method to traditional MRI,and is recommended for the early diagnosis,grading evaluation,and clinical follow-up of cartilage injury.展开更多
According to the 2024 global cancer data from GLOBOCAN,liver cancer ranks the 6th most common malignancy and the 3rd leading cause of cancer-related mortality worldwide[1].Among these cases,hepatocellular carcinoma(HC...According to the 2024 global cancer data from GLOBOCAN,liver cancer ranks the 6th most common malignancy and the 3rd leading cause of cancer-related mortality worldwide[1].Among these cases,hepatocellular carcinoma(HCC)accounts for approximately 85%−90%[2,3].Its incidence and mortality rates remain persistently high worldwide.However,China has the highest incidence and mortality rates of the disease in the world[4].And the majority of patients are diagnosed at intermediate or advanced stages.Thus,identifying novel tumor biomarkers for early detection and implementing precision therapy has long been a key focus of research.展开更多
This narrative review examines recent advances in salivary biomarkers for oral squamous cell carcinoma(OSCC),a major subtype of oral cancer with persistently low five-year survival rates due to delayed diagnosis.Saliv...This narrative review examines recent advances in salivary biomarkers for oral squamous cell carcinoma(OSCC),a major subtype of oral cancer with persistently low five-year survival rates due to delayed diagnosis.Saliva has emerged as a noninvasive diagnostic medium capable of reflecting both local tumor activity and systemic physiological changes.Various salivary biomarkers,including microRNAs,cytokines,proteins,metabolites,and exosomes,have been linked to oncogenic signaling pathways involved in tumor progression,immune modulation,and therapeutic resistance.Advances in quantitative polymerase chain reaction,mass spectrometry,and next-generation sequencing have enabled comprehensive biomarker profiling,while point-of-care detection systems and saliva-based omics platforms are accelerating clinical translation.Remaining challenges include variability in salivary composition,lack of standardized collection protocols,and insufficient validation across large patient cohorts.This review highlights the mechanistic relevance,diagnostic potential,and translational challenges of salivary biomarkers in OSCC.展开更多
Oxidative stress significantly contributes to secondary damage after spinal cord injury.Despite its importance,research on oxidative stress in spinal cord injury remains limited.Investigating the expression and regula...Oxidative stress significantly contributes to secondary damage after spinal cord injury.Despite its importance,research on oxidative stress in spinal cord injury remains limited.Investigating the expression and regulation of oxidative stress-related genes could enhance the diagnosis and treatment of spinal cord injury.In this study,we analyzed the sequencing data of human blood samples and injured mouse spinal cord tissue that were sourced from GEO databases and identified diagnostic biomarkers associated with the severity of spinal cord injury.We also explored the expression patterns of oxidative stress-related genes,potential regulatory mechanisms,and therapeutic drugs.To validate our findings,we performed immunofluorescence and quantitative polymerase chain reaction to assess gene expression in the injured spinal cord.Our results revealed biomarkers associated with oxidative stress and immune responses across different levels of spinal cord injury in humans.We identified differentially expressed oxidative stress-related genes and key hub genes in injured mouse spinal cord tissue and revealed their temporal expression patterns at both the tissue and single-cell levels.We also clarified the signaling pathways associated with oxidative stress and identified ligand-receptor pairs among various cell types at different time points after injury.Furthermore,we discovered microRNAs,long non-coding RNAs,and transcription factors that regulate these hub genes and revealed their roles in modulating gene expression at various stages after spinal cord injury.We also identified drugs targeting these hub genes.The findings from this study not only aid in identifying diagnostic biomarkers that reflect the severity of spinal cord injury,but also provide insights into the expression dynamics of oxidative stress-related genes.In addition,the study reveals potential regulatory mechanisms and identifies potential drugs to treat patients with spinal cord injury.展开更多
A duty in development of an on-line fault detection algorithm is to make it associate with estimation of engine s health degradation. For this purpose,an on-line diagnostic algorithm is put forward. Using a tracking f...A duty in development of an on-line fault detection algorithm is to make it associate with estimation of engine s health degradation. For this purpose,an on-line diagnostic algorithm is put forward. Using a tracking filter to estimate the engine s health condition over its lifetime,can be reconstructed an onboard model,which is then made to match a real aircraft gas turbine engine. Finally,a bank of Kalman filters is applied in fault detection and isola-tion (FDI) of sensors for the engine. Through the bank...展开更多
Helicobacter pylori(H.pylori)infection is highly prevalent in the human population and may lead to severe gastrointestinal pathology including gastric and duodenal ulcers,mucosa associated tissue lymphoma and gastric ...Helicobacter pylori(H.pylori)infection is highly prevalent in the human population and may lead to severe gastrointestinal pathology including gastric and duodenal ulcers,mucosa associated tissue lymphoma and gastric adenocarcinoma.In recent years,an alarming increase in antimicrobial resistance and subsequently failing empiric H.pylori eradication therapies have been noted worldwide,also in many European countries.Therefore,rapid and accurate determination of H.pylori’s antibiotic susceptibility prior to the administration of eradication regimens becomes ever more important.Traditionally,detection of H.pylori and its antimicrobial resistance is done by culture and phenotypic drug susceptibility testing that are cumbersome with a long turn-around-time.Recent advances in diagnostics provide new tools,like real-time polymerase chain reaction(PCR)and line probe assays,to diagnose H.pylori infection and antimicrobial resistance to certain antibiotics,directly from clinical specimens.Moreover,high-throughput whole genome sequencing technologies allow the rapid analysis of the pathogen’s genome,thereby allowing identification of resistance mutations and associated antibiotic resistance.In the first part of this review,we will give an overview on currently available diagnostic methods for detection of H.pylori and its drug resistance and their implementation in H.pylori management.The second part of the review focusses on the use of next generation sequencing technology in H.pylori research.To this end,we conducted a literature search for original research articles in English using the terms“Helicobacter”,“transcriptomic”,“transcriptome”,“next generation sequencing”and“whole genome sequencing”.This review is aimed to bridge the gap between current diagnostic practice(histology,rapid urease test,H.pylori culture,PCR and line probe assays)and new sequencing technologies and their potential implementation in diagnostic laboratory settings in order to complement the currently recommended H.pylori management guidelines and subsequently improve public health.展开更多
基金supported by the MOST Key R&D Program of China(grant number 2022YFC2303500 to X.H.)the National Natural Science Foundation of China(grant numbers 32570236,32170195,and 32311530119 to C.C.and 32470200 to X.H.)+1 种基金Shanghai Science and Technology Innovation Action Plan 2023“Basic Research Project”(grant number 23JC1404200 to C.C.)the Foundation of State Key Laboratory of Pathogen and Biosecurity(grant number SKLPBS2236 to C.C.).
文摘Human fungal infections represent a rapidly emerging global health threat,especially threatening immunocompromised populations,highlighting the urgent need for accurate and timely diagnostic approaches to reduce morbidity and mortality.This review synthesizes recent advances in diagnostic methodologies,including serological assays,point-of-care diagnostics,polymerase chain reaction(PCR)-based and sequencing technologies,as well as artificial intelligence(AI)-and machine learning(ML)-powered tools.Emerging diagnostic approaches have demonstrated notable improvements in detection accuracy,turnaround time,and antifungal resistance profiling capabilities,especially for drug-resistant strains.Nevertheless,substantial challenges persist in terms of standardization,scalability,cost-effectiveness,and implementation,particularly in resource-constrained settings.Future efforts should be directed toward the continuous innovation of rapid,sensitive,and multiplex diagnostic platforms for the simultaneous detection of fungi,bacteria,and viruses.Such advances may accelerate result acquisition,enhance diagnostic accuracy,support the development of more targeted therapeutic strategies,and ultimately improve clinical outcomes for patients.
基金funding from Grant No. HIDSS-0002 DASHH (Data Science in Hamburg-Helmholtz Graduate School for the Structure of Matter)partially supported by the Helmholtz Imaging platform through the project “Smart Phase.”
文摘Understanding the complex plasma dynamics in ultra-intense relativistic laser-solid interactions is of fundamental importance for applications of laser-plasma-based particle accelerators,the creation of high-energy-density matter,understanding planetary science,and laser-driven fusion energy.However,experimental efforts in this regime have been limited by the lack of accessibility of over-critical densities and the poor spatiotemporal resolution of conventional diagnostics.Over the last decade,the advent of femtosecond brilliant hard X-ray free-electron lasers(XFELs)has opened new horizons to overcome these limitations.Here,for the first time,we present full-scale spatiotemporal measurements of solid-density plasma dynamics,including preplasma generation with tens of nanometer scale length driven by the leading edge of a relativistic laser pulse,ultrafast heating and ionization at the main pulse arrival,the laser-driven blast wave,and transient surface return current-induced compression dynamics up to hundreds of picoseconds after interaction.These observations are enabled by utilizing a novel combination of advanced X-ray diagnostics including small-angle X-ray scattering,resonant X-ray emission spectroscopy,and propagation-based X-ray phase-contrast imaging simultaneously at the European XFEL-HED beamline station.
文摘Multimodal deep learning has emerged as a key paradigm in contemporary medical diagnostics,advancing precision medicine by enabling integration and learning from diverse data sources.The exponential growth of high-dimensional healthcare data,encompassing genomic,transcriptomic,and other omics profiles,as well as radiological imaging and histopathological slides,makes this approach increasingly important because,when examined separately,these data sources only offer a fragmented picture of intricate disease processes.Multimodal deep learning leverages the complementary properties of multiple data modalities to enable more accurate prognostic modeling,more robust disease characterization,and improved treatment decision-making.This review provides a comprehensive overview of the current state of multimodal deep learning approaches in medical diagnosis.We classify and examine important application domains,such as(1)radiology,where automated report generation and lesion detection are facilitated by image-text integration;(2)histopathology,where fusion models improve tumor classification and grading;and(3)multi-omics,where molecular subtypes and latent biomarkers are revealed through cross-modal learning.We provide an overview of representative research,methodological advancements,and clinical consequences for each domain.Additionally,we critically analyzed the fundamental issues preventing wider adoption,including computational complexity(particularly in training scalable,multi-branch networks),data heterogeneity(resulting from modality-specific noise,resolution variations,and inconsistent annotations),and the challenge of maintaining significant cross-modal correlations during fusion.These problems impede interpretability,which is crucial for clinical trust and use,in addition to performance and generalizability.Lastly,we outline important areas for future research,including the development of standardized protocols for harmonizing data,the creation of lightweight and interpretable fusion architectures,the integration of real-time clinical decision support systems,and the promotion of cooperation for federated multimodal learning.Our goal is to provide researchers and clinicians with a concise overview of the field’s present state,enduring constraints,and exciting directions for further research through this review.
文摘In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has emerged as a transformative tool in health care,offering rapid,sensitive,and specific identification of microorganisms.This editorial provides a comprehensive overview of LOC technology,highlighting its principles,advantages,applications,challenges,and future directions.Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics,epidemiology,and food safety.Comparative studies have underscored the superiority of LOC technology over traditional methods,showcasing improvements in speed,accuracy,and portability.The future integration of LOC with biosensors,artificial intelligence,and data analytics promises further innovation and expansion.This call to action emphasizes the importance of continued research,investment,and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.
文摘This article provides a short review on the importance of the detailed analysis of a Langmuir probe I-V trace in a multi-Maxwellian plasma,and discuss proper procedures analyzing Langmuir probe I-V traces in bi-Maxwellian and triple-Maxwellian Electron Energy Distribution Function(EEDF)plasmas.Discus⁃sion and demonstration of procedures include the treatment of the ion saturation current,electron saturation cur⁃rent,space-charge effects on the I-V trace,and most importantly how to properly isolate and fit for each electron group present in an I-V trace reflecting a mult-Maxwellian EEDF,as well as how having a multi-Maxwellian EEDF affects the procedures of treating the ion and electron saturation currents.Shortcomings of common improp⁃er procedures are discussed and demonstrated with simulated I-V traces to show how these procedures gives false measurements.
文摘Radiation doses to patients in diagnostics and interventional radiology need to be optimized to comply with the principles of radiation protection in medical practice. This involves using specific detectors with respective diagnostic beams to carry out quality control/quality assurance tests needed to optimize patient doses in the hospital. Semiconductor detectors are used in dosimetry to verify the equipment performance and dose to patients. This work aims to assess the performance, energy dependence, and response of five commercially available semiconductor detectors in RQR, RQR-M, RQA, and RQT at Secondary Standard Dosimetry for clinical applications. The diagnostic beams were generated using Exradin A4 reference ion chamber and PTW electrometer. The ambient temperature and pressure were noted for KTP correction. The detectors designed for RQR showed good performance in RQT beams and vice versa. The detectors designed for RQR-M displayed high energy dependency in other diagnostic beams. The type of diagnostic beam quality determines the response of semiconductor detectors. Therefore, a detector should be calibrated according to the beam qualities to be measured.
文摘Artificial Intelligence(AI)is fundamentally transforming medical diagnostics,driving advancements that enhance accuracy,efficiency,and personalized patient care.This narrative review explores AI integration across various diagnostic domains,emphasizing its role in improving clinical decision-making.The evolution of medical diagnostics from traditional observational methods to sophisticated imaging,laboratory tests,and molecular diagnostics lays the foundation for understanding AI’s impact.Modern diagnostics are inherently complex,influenced by multifactorial disease presentations,patient variability,cognitive biases,and systemic factors like data overload and interdisciplinary collaboration.AI-enhanced clinical decision support systems utilize both knowledge-based and non-knowledge-based approaches,employing machine learning and deep learning algorithms to analyze vast datasets,identify patterns,and generate accurate differential diagnoses.AI’s potential in diagnostics is demonstrated through applications in genomics,predictive analytics,and early disease detection,with successful case studies in oncology,radiology,pathology,ophthalmology,dermatology,gastroenterology,and psychiatry.These applications demonstrate AI’s ability to process complex medical data,facilitate early intervention,and extend specialized care to underserved populations.However,integrating AI into diagnostics faces significant limitations,including technical challenges related to data quality and system integration,regulatory hurdles,ethical concerns about transparency and bias,and risks of misinformation and overreliance.Addressing these challenges requires robust regulatory frameworks,ethical guidelines,and continuous advancements in AI technology.The future of AI in diagnostics promises further innovations in multimodal AI,genomic data integration,and expanding access to high-quality diagnostic services globally.Responsible and ethical implementation of AI will be crucial to fully realize its potential,ensuring AI serves as a powerful ally in achieving diagnostic excellence and improving global health care outcomes.This narrative review emphasizes AI’s pivotal role in shaping the future of medical diagnostics,advocating for sustained investment and collaborative efforts to harness its benefits effectively.
文摘As a critical technology for industrial system reliability and safety,machine monitoring and fault diagnostics have advanced transformatively with large language models(LLMs).This paper reviews LLM-based monitoring and diagnostics methodologies,categorizing them into in-context learning,fine-tuning,retrievalaugmented generation,multimodal learning,and time series approaches,analyzing advances in diagnostics and decision support.It identifies bottlenecks like limited industrial data and edge deployment issues,proposing a three-stage roadmap to highlight LLMs’potential in shaping adaptive,interpretable PHM frameworks.
文摘Background:Head and neck cancers(HNC)account for a significant global health burden,with increasing incidence rates and complex treatment requirements.Traditional diagnostic and therapeutic approaches,while effective,often result in substantial morbidity and limitations in personalized care.This review provides a comprehensive overview of the latest innovations in diagnostics and therapeutic strategies for HNC from 2015 to 2024.Methods:A review of literature focused on pe-reviewed journals,clinical trial databases,and oncology conference proceedings.Key areas include molecular diagnostics,imaging technologies,minimally invasive surgeries,and innovative therapeutic strategies.Results:Technologies like liquid biopsy next-generation sequencing(NGS)have greatly improved diagnostic accuracy and personalization in HNC care.These advancements have improved survival rates and enhanced patients’quality of life.Personalized therapeutic approaches,including immune checkpoint inhibitors,precision radiation therapy,and surgery,have led to enhanced treatment efficacy while reducing side effects.The integration of AI and machine learning into diagnostics and treatment planning shows promise in optimizing clinical decision-making and predicting treatment outcomes.Conclusion:The current innovations in diagnostics and therapeutics are reshaping the management of head and neck cancer,offering more tailored and effective approaches to care.Overall,the continuous integration of these innovations in clinical practice is reshaping HNC treatment and improving patient outcomes and survival rates.Future research should focus on further refining these technologies,addressing challenges related to accessibility,and exploring their long-term clinical benefits in diverse patient populations.
基金supported by the National Key R&D Program of China(2024YFB2505003).
文摘Electrochemical impedance spectroscopy(EIS)offers valuable insights into the dynamic behaviors of lithium-ion batteries,making it a powerful and non-invasive tool for evaluating battery health.However,EIS falls short in quantitatively determining the degree of specific degradation modes,which are essential for improving battery lifespan.This study introduces a novel approach employing deep neural networks enhanced by an attention mechanism to identify the degree of degradation modes.The proposed method can automatically determine the most relevant frequency ranges for each degradation mode,which can link impedance characteristics to battery degradation.To overcome the limitation of scarce labeled experimental data,simulation results derived from mechanistic models are incorporated into the model.Validation results demonstrate that the proposed method could achieve root mean square errors below 3%for estimating loss of lithium inventory and loss of active material of the positive electrode,and below 4%for estimating loss of active material of the negative electrode while requiring only 25%of early-stage experimental degradation data.By integrating simulation results,the proposed method achieves a reduction in maximum estimation error ranging from 42.92%to 66.30%across different temperatures and operating conditions compared to the baseline model trained solely on experimental data.
基金supported by the Ministry of Education and Science of Ukraine(No.0123U101834)support in the framework of the“EU Next generation EU through the Recovery and Resilience Plan for Slovakia”(Nos.09I03-03-V01-00061 and 09I03-03-V01-00099)。
文摘Wrought and laser powder bed fusion(LPBF)Ti−6Al−4V(Ti-6-4)specimens were comparatively evaluated,with the objective to determine LPBF Ti−6Al−4V’s suitability for biomedical applications.Testing included nanoindentation,cyclic polarization in simulated body fluid(SBF,37°C),and dry and SBF“ball-on-plate”sliding.Wrought Ti-6-4 exhibited a lamellarα+βmicrostructure,whereas LPBF Ti-6-4 displayed a fine-grainedα′-martensite microstructure.LPBF Ti-6-4 demonstrated~3%higher indentation modulus and~32%higher hardness,while wrought Ti-6-4 showed~8%higher plasticity.Both alloys exhibited low corrosion rates(10−5 mA/cm^(2)order)and true passivity(10−4 mA/cm^(2)order).No localized corrosion was observed in either two alloys,except for occasional metastable pitting in the LPBF alloy.However,LPBF Ti-6-4 presented higher corrosion rate and passive current,ascribed to its martensitic structure.During dry sliding,LPBF Ti-6-4 exhibited~14%lower volume loss compared to wrought Ti-6-4.Sliding in SBF increased volume losses for both alloys,with wear resistances nearly equalized,as the advantage of LPBF Ti-6-4 decreased due to more intense wear-accelerated corrosion induced by the stressed martensite.Overall,the results demonstrate the suitability of LPBF Ti-6-4 for biomedical uses.
基金supported by the National Natural Science Foundation of China (Grant No.22109008)。
文摘Accurate real-time monitoring of internal temperature in lithium-ion batteries remains critical for preventing thermal runaway,as conventional approaches sacrifice either computational efficiency or cross-scenario robustness.We present a generalized fuzzy physics-informed framework that distills thermally sensitive electrochemical processes while circumventing redundant physical constraints,thereby establishing an explicit mechanism-constrained mapping between frequency-domain signals and internal temperature.This framework facilitates online thermal estimation,with dynamic validations in LiFePO_4/graphite 18650-type cells confirming real-time capability with near-instantaneous acquisition(~6 s per measurement),exceptional accuracy(±0.5℃) within the operational temperature range(30-50℃),and operational resilience across 20 %-80 % state-of-charge.The framework maintains predictive fidelity(±1.0℃ at 30℃ and ±4.0℃ at 60℃,95 % prediction intervals) across 80 %-100 % state-of-health while demonstrating adaptability to cathode materials and structural architectures.This strategy resolves the competing imperatives of physical interpretability,computational efficiency,and crossscenario generalizability,offering a universal paradigm for embedded thermal management in safetycritical applications.
文摘With the rapid development of science and technology,the application of artificial intelligence(AI)technology in medical education has become increasingly widespread in the digital age,bringing new opportunities and challenges to China’s higher education of traditional Chinese medicine(TCM).In the context of digital education,it is of great significance to construct a teaching model that integrates AI technology with the characteristics of the diagnostics of traditional Chinese medicine,in order to improve the quality of curriculum teaching in the future.This article aims to introduce how to organically integrate AI technology with diagnostics of traditional Chinese medicine teaching based on the characteristics of the discipline,to achieve teaching mode reform,therefore to improve the teaching quality of traditional Chinese medicine education,and cultivate high-quality TCM talents that meet the needs of the new era.
文摘In Unani medicine,Bawl(urine)is recognized as a key diagnostic tool,with humoural imbalances assessed via parameters like color,consistency,sediment,clarity,froth,odor,and volume.This conceptual review explores how these classical diagnostic indicators may be contextualized alongside modern urinalysis markers(e.g.,bilirubin,protein,ketones,and sedimentation)and examined through emerging artificial intelligence(AI)frameworks.Potential applications include ResNet-18 for color classification,You Only Look Once version 8(YOLOv8)for sediment detection,long short-term memory(LSTM)for viscosity estimation,and EfficientDet for froth analysis,with standardized urine images/videos forming the basis of future datasets.Additionally,a comparative ontology is proposed to align Unani perspectives with diagnostic approaches in traditional Chinese medicine,encouraging cross-system integration.By synthesizing classical epistemology with computational intelligence,this review highlights pathways for developing AI-based decision support systems to promote personalized,accessible,and telemedicine-enabled healthcare.
基金Application Research of MRI Physiological Quantitative Imaging Technology in the Diagnosis of Cartilage Injury(Project No.:RCYJ2021-04)。
文摘Objective:To retrospectively evaluate the diagnostic efficacy of traditional MRI and T2 Mapping quantitative imaging technology for knee joint cartilage injury,clarify the differences in diagnostic value of the two imaging methods in different injury grades and different cartilage subregions,and provide evidence-based basis for the accurate diagnosis of clinical cartilage injury.Methods:Clinical and imaging data of 286 patients with knee joint lesions admitted to the Affiliated Hospital of Xiangtan Medicine and Health Vocational College from January 2020 to June 2023 were collected retrospectively.All patients underwent both traditional MRI sequences and T2 Mapping sequences.The knee joint cartilage was divided into 14 subregions.Two senior radiologists independently diagnosed the images of the two imaging technologies using a blind method and recorded the cartilage injury grades.The sensitivity,specificity,accuracy,positive predictive value,negative predictive value,and area under the receiver operating characteristic curve(AUC)of the two technologies for diagnosing cartilage injury were calculated and compared,and the differences in their diagnostic efficacy in different injury grades and different subregions were analyzed.Results:A total of 4004 cartilage subregions from 286 patients were included in the analysis,including 1836 injured subregions and 2168 normal subregions.The overall sensitivity(89.7%),accuracy(91.2%),and AUC(0.946)of T2 Mapping quantitative imaging for diagnosing cartilage injury were significantly higher than those of traditional MRI(76.3%,82.5%,and 0.852 respectively),with statistically significant differences(p<0.001);there was no significant difference in specificity between the two(93.5%vs 90.8%,p=0.062).Subgroup analysis showed that T2 Mapping had the most significant diagnostic advantage in early cartilage injury(Grade 1),with sensitivity(78.5%)33.2%higher than that of traditional MRI(45.3%)(p<0.001).Conclusion:The diagnostic efficacy of T2 Mapping quantitative imaging for knee joint cartilage injury is significantly superior to that of traditional MRI,especially in the detection of early cartilage injury and accurate evaluation of weight-bearing area injury.Data verify its clinical applicability and reliability.It can be used as an important supplementary method to traditional MRI,and is recommended for the early diagnosis,grading evaluation,and clinical follow-up of cartilage injury.
基金supported by a grant from the Central Level Public Welfare Research Institutes Basic Research Expenses of Chinese Academy of Medical Sciences(No.2023-RW320-05)。
文摘According to the 2024 global cancer data from GLOBOCAN,liver cancer ranks the 6th most common malignancy and the 3rd leading cause of cancer-related mortality worldwide[1].Among these cases,hepatocellular carcinoma(HCC)accounts for approximately 85%−90%[2,3].Its incidence and mortality rates remain persistently high worldwide.However,China has the highest incidence and mortality rates of the disease in the world[4].And the majority of patients are diagnosed at intermediate or advanced stages.Thus,identifying novel tumor biomarkers for early detection and implementing precision therapy has long been a key focus of research.
基金supported by the College of Oral Medicine,Taipei Medical University,Taipei,Taiwan(Grant No.TMUCOM202502)supported by Taipei Medical University Hospital,Taipei,Taiwan(Grant No.114TMUH-NE-05).
文摘This narrative review examines recent advances in salivary biomarkers for oral squamous cell carcinoma(OSCC),a major subtype of oral cancer with persistently low five-year survival rates due to delayed diagnosis.Saliva has emerged as a noninvasive diagnostic medium capable of reflecting both local tumor activity and systemic physiological changes.Various salivary biomarkers,including microRNAs,cytokines,proteins,metabolites,and exosomes,have been linked to oncogenic signaling pathways involved in tumor progression,immune modulation,and therapeutic resistance.Advances in quantitative polymerase chain reaction,mass spectrometry,and next-generation sequencing have enabled comprehensive biomarker profiling,while point-of-care detection systems and saliva-based omics platforms are accelerating clinical translation.Remaining challenges include variability in salivary composition,lack of standardized collection protocols,and insufficient validation across large patient cohorts.This review highlights the mechanistic relevance,diagnostic potential,and translational challenges of salivary biomarkers in OSCC.
基金supported by Shenzhen Science and Technology Program, No. JCYJ20230807110259002 (to JL)The Seventh Affiliated Hospital of Sun Yat-sen University, No. ZSQYRSFPD0050 (to JL)The Postdoctoral Fellowship Program of CPSF, No. GZC20242074 (to KT)
文摘Oxidative stress significantly contributes to secondary damage after spinal cord injury.Despite its importance,research on oxidative stress in spinal cord injury remains limited.Investigating the expression and regulation of oxidative stress-related genes could enhance the diagnosis and treatment of spinal cord injury.In this study,we analyzed the sequencing data of human blood samples and injured mouse spinal cord tissue that were sourced from GEO databases and identified diagnostic biomarkers associated with the severity of spinal cord injury.We also explored the expression patterns of oxidative stress-related genes,potential regulatory mechanisms,and therapeutic drugs.To validate our findings,we performed immunofluorescence and quantitative polymerase chain reaction to assess gene expression in the injured spinal cord.Our results revealed biomarkers associated with oxidative stress and immune responses across different levels of spinal cord injury in humans.We identified differentially expressed oxidative stress-related genes and key hub genes in injured mouse spinal cord tissue and revealed their temporal expression patterns at both the tissue and single-cell levels.We also clarified the signaling pathways associated with oxidative stress and identified ligand-receptor pairs among various cell types at different time points after injury.Furthermore,we discovered microRNAs,long non-coding RNAs,and transcription factors that regulate these hub genes and revealed their roles in modulating gene expression at various stages after spinal cord injury.We also identified drugs targeting these hub genes.The findings from this study not only aid in identifying diagnostic biomarkers that reflect the severity of spinal cord injury,but also provide insights into the expression dynamics of oxidative stress-related genes.In addition,the study reveals potential regulatory mechanisms and identifies potential drugs to treat patients with spinal cord injury.
文摘A duty in development of an on-line fault detection algorithm is to make it associate with estimation of engine s health degradation. For this purpose,an on-line diagnostic algorithm is put forward. Using a tracking filter to estimate the engine s health condition over its lifetime,can be reconstructed an onboard model,which is then made to match a real aircraft gas turbine engine. Finally,a bank of Kalman filters is applied in fault detection and isola-tion (FDI) of sensors for the engine. Through the bank...
文摘Helicobacter pylori(H.pylori)infection is highly prevalent in the human population and may lead to severe gastrointestinal pathology including gastric and duodenal ulcers,mucosa associated tissue lymphoma and gastric adenocarcinoma.In recent years,an alarming increase in antimicrobial resistance and subsequently failing empiric H.pylori eradication therapies have been noted worldwide,also in many European countries.Therefore,rapid and accurate determination of H.pylori’s antibiotic susceptibility prior to the administration of eradication regimens becomes ever more important.Traditionally,detection of H.pylori and its antimicrobial resistance is done by culture and phenotypic drug susceptibility testing that are cumbersome with a long turn-around-time.Recent advances in diagnostics provide new tools,like real-time polymerase chain reaction(PCR)and line probe assays,to diagnose H.pylori infection and antimicrobial resistance to certain antibiotics,directly from clinical specimens.Moreover,high-throughput whole genome sequencing technologies allow the rapid analysis of the pathogen’s genome,thereby allowing identification of resistance mutations and associated antibiotic resistance.In the first part of this review,we will give an overview on currently available diagnostic methods for detection of H.pylori and its drug resistance and their implementation in H.pylori management.The second part of the review focusses on the use of next generation sequencing technology in H.pylori research.To this end,we conducted a literature search for original research articles in English using the terms“Helicobacter”,“transcriptomic”,“transcriptome”,“next generation sequencing”and“whole genome sequencing”.This review is aimed to bridge the gap between current diagnostic practice(histology,rapid urease test,H.pylori culture,PCR and line probe assays)and new sequencing technologies and their potential implementation in diagnostic laboratory settings in order to complement the currently recommended H.pylori management guidelines and subsequently improve public health.
