Point-of-care testing(POCT)refers to a category of diagnostic tests that are performed at or near to the site of the patients(also called bedside testing)and is capable of obtaining accurate results in a short time by...Point-of-care testing(POCT)refers to a category of diagnostic tests that are performed at or near to the site of the patients(also called bedside testing)and is capable of obtaining accurate results in a short time by using portable diagnostic devices,avoiding sending samples to the medical laboratories.It has been extensively explored for diagnosing and monitoring patients’diseases and health conditions with the assistance of development in biochemistry and microfluidics.Microfluidic paper-based analytical devices(μPADs)have gained dramatic popularity in POCT because of their simplicity,user-friendly,fast and accurate result reading and low cost.SeveralμPADs have been successfully commercialized and received excellent feedback during the past several decades.This review briefly discusses the main types ofμPADs,preparation methods and their detection principles,followed by a few representative examples.The future perspectives of the development inμPADs are also provided.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
Nickel(II)as one of the primary categories of heavy metals can lead to serious health problems if achieving the critical levels in the water.Thus,it is vital to propose a stable,reliable,and economical approach for de...Nickel(II)as one of the primary categories of heavy metals can lead to serious health problems if achieving the critical levels in the water.Thus,it is vital to propose a stable,reliable,and economical approach for detecting Ni ions.The microfluidic paper-based analytical devices(µPADs)are potential candidates for the detection of water quality parameters including pH,heavy ions,nitrite and so on.However,it suffers from a huge error caused by the environment and artificial mistakes.In this study,we proposed an improved technique route to increase the stability and reliability of microfluidic paper-based analytical devices.The main technique points include a stable light source,a matched camera,improved reliability of the devices,and effective calculated methods.Finally,we established 15 standard curves that could be used to detect nickel ions and obtained uniform colorimetric results with reliability and repeatability.With those improvements,the relative errors for the five types of real water samples from the Zhongshan industrial parks were reduced to 0.26%,14.78%,24.20%,50.29%and 3.53%,respectively.These results were conducive to exploring this technique for the detection of nickel ions in wastewater from the Zhongshan industrial parks.The results demonstrated that the above technique route is promising for the detection of other heavy metal ions in industrial effluent.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
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
The population with metabolic dysfunction-associated fatty liver disease(MAFLD)is increasingly common worldwide.Identification of people at risk of progression to advanced stages is necessary to timely offer intervent...The population with metabolic dysfunction-associated fatty liver disease(MAFLD)is increasingly common worldwide.Identification of people at risk of progression to advanced stages is necessary to timely offer interventions and appropriate care.Liver biopsy is currently considered the gold standard for the diagnosis and staging of MAFLD,but it has associated risks and limitations.This has spurred the exploration of non-invasive diagnostics for MAFLD,especially for steatohepatitis and fibrosis.These non-invasive approaches mostly include biomarkers and algorithms derived from anthropometric measurements,serum tests,imaging or stool metagenome profiling.However,they still need rigorous and widespread clinical validation for the diagnostic performance.展开更多
Interpreting experimental diagnostics data in tokamaks,while considering non-ideal effects,is challenging due to the complexity of plasmas.To address this challenge,a general synthetic diagnostics(GSD)platform has bee...Interpreting experimental diagnostics data in tokamaks,while considering non-ideal effects,is challenging due to the complexity of plasmas.To address this challenge,a general synthetic diagnostics(GSD)platform has been established that facilitates microwave imaging reflectometry and electron cyclotron emission imaging.This platform utilizes plasma profiles as input and incorporates the finite-difference time domain,ray tracing and the radiative transfer equation to calculate the propagation of plasma spontaneous radiation and the external electromagnetic field in plasmas.Benchmark tests for classical cases have been conducted to verify the accuracy of every core module in the GSD platform.Finally,2D imaging of a typical electron temperature distribution is reproduced by this platform and the results are consistent with the given real experimental data.This platform also has the potential to be extended to 3D electromagnetic field simulations and other microwave diagnostics such as cross-polarization scattering.展开更多
Paper-based microchips have different advantages,such as better biocompatibility,simple production,and easy handling,making them promising candidates for clinical diagnosis and other fields.This study describes ametho...Paper-based microchips have different advantages,such as better biocompatibility,simple production,and easy handling,making them promising candidates for clinical diagnosis and other fields.This study describes amethod developed to fabricate modular three-dimensional(3D)paper-based microfluidic chips based on projection-based 3D printing(PBP)technology.A series of two-dimensional(2D)paper-based microfluidic modules was designed and fabricated.After evaluating the effect of exposure time on the accuracy of the flow channel,the resolution of this channel was experimentally analyzed.Furthermore,several 3D paper-based microfluidic chips were assembled based on the 2D ones using different methods,with good channel connectivity.Scaffold-based 2D and hydrogel-based 3D cell culture systems based on 3D paper-based microfluidic chips were verified to be feasible.Furthermore,by combining extrusion 3D bioprinting technology and the proposed 3D paper-based microfluidic chips,multiorgan microfluidic chips were established by directly printing 3D hydrogel structures on 3D paperbased microfluidic chips,confirming that the prepared modular 3D paper-based microfluidic chip is potentially applicable in various biomedical applications.展开更多
Sweat contains numerous vital biomarkers such as metabolites,electrolytes,proteins,nucleic acids and antigens that reflect hydration status,exhaustion,nutrition,and physiological changes.Conventional healthcare diagno...Sweat contains numerous vital biomarkers such as metabolites,electrolytes,proteins,nucleic acids and antigens that reflect hydration status,exhaustion,nutrition,and physiological changes.Conventional healthcare diagnosis relies on disease diagnostics in sophisticated centralized laboratories with invasive sample collection(e.g.,chemical analyses,plasma separation via centrifugation,tissue biopsy,etc.).Cutting-edge point-of-care diagnostics for sweat biomarker analysis allow for non-invasive monitoring of physiologically related biomarkers in sweat and real-time health status tracking.Moreover,using advanced nanoarchitectures,including nanostructured platforms and nanoparticles,can enhance the specificity,sensitivity,wearability and widen the sensing modality of sweat biosensors.Herein,we comprehensively review the secretory mechanisms,clinical uses of sweat biomarkers,and the design,principle,and latest technologies of sweat biosensors.With an emphasis on cutting-edge technologies for sweat biomarker analysis,this review chronicles the issues associated with the current sweat biomarkers analysis of sweat biomarkers and provides insights into strategies for enhancing the translation of such biosensors into routine clinical practice.展开更多
OBJECTIVE:To reach consensus on the diagnostic criteria of syndrome of dampness obstruction in idiopathic membranous nephropathy(IMN)patients by literature research and expert investigation(interviews and a Delphi met...OBJECTIVE:To reach consensus on the diagnostic criteria of syndrome of dampness obstruction in idiopathic membranous nephropathy(IMN)patients by literature research and expert investigation(interviews and a Delphi method).METHODS:Our study was consistent with T/CACM 1336-2020.We searched the monographs and references published in the past 40 years(1983-2022),and established the diagnostic criteria pool of waterdampness syndrome and dampness-turbidity syndrome in Traditional Chinese Medicine(TCM)based on literature by using frequency statistics and correlation analysis.Expert investigation(interview method and two rounds of Delphi method)was used to form the diagnostic criteria of water-dampness syndrome and dampnessturbidity syndrome of idiopathic membranous nephropathy.Clinical diagnostic test research was carried out,and compared with“Diagnostic Criteria for dampness syndrome”(T/CACM 1454-2023)to evaluate the authenticity,reliability and clinical application value of the standard.RESULTS:A total of 122 relevant guides,standards,monographs and documents were included through searching books and Chinese databases.Four experts were interviewed and two rounds of delphi method(75 experts nationwide)were carried out.The experts'opinions are relatively concentrated and the differences are small.Based on the weight of each index,the diagnostic criteria indexes of water-dampness syndrome and dampness-turbidity syndrome were selected.After discussion by the core group members,the diagnostic model of"necessary symptoms and optional symptoms"was established,and the final diagnostic criteria of waterdampness syndrome and dampness-turbidity syndrome were established.One hundred and ninety-one inpatients and outpatients of Guangdong Provincial Hospital of Chinese Medicine from January 2021 to February 2023 were included in Diagnostic test study.There was no statistical difference in gender,age and course of disease(P>0.05).The sensitivity and specificity of the trial standard were 90.34%and 73.33%respectively,while the sensitivity and specificity of T/CACM 1454-2023 were 99.43%and 6.67%,respectively.CONCLUSIONS:The consensus-based diagnostic criteria for IMN can be widely incorporated in TCM.A further clinical study will be conducted to analyze the diagnosis value and cut-off score of our IMN criteria.展开更多
This study investigates the variability in cancer diagnosis across different tissues and organs, with a focus on the role of diagnostic methods such as Hematoxylin and Eosin (H&E) staining and immunohistochemistry...This study investigates the variability in cancer diagnosis across different tissues and organs, with a focus on the role of diagnostic methods such as Hematoxylin and Eosin (H&E) staining and immunohistochemistry (IHC). The predominance of female breast cancer (30%) aligns with global trends, underscoring the need for robust diagnostic protocols, particularly in developing regions. Other prevalent cancers, including skin, stomach, and cervix uteri, reflect a mix of environmental, genetic, and infectious factors. The underrepresentation of gallbladder and thyroid cancers (<1%) suggests potential underdiagnosis or lower prevalence. Age distribution data indicate peak cancer incidence in individuals aged 31 - 45 years, with gender-specific cancers like breast and cervical cancer predominantly affecting females (63.4%). The analysis also highlights significant diagnostic gaps, as 61.2% of cases did not undergo IHC testing due to resource constraints, leading to potential biases in cancer prevalence and diagnostic accuracy. The study emphasizes the complementary role of IHC in confirming ambiguous H&E findings, with strong alignment observed when both methods were used. However, the absence of IHC in many cases limits the robustness of conclusions, suggesting the need for increased access to IHC testing. The findings advocate for integrating IHC into routine diagnostics, expanding diagnostic capabilities, and improving sample sizes to ensure more reliable and comprehensive cancer data.展开更多
Peritoneal tuberculosis is the most common digestive location of tuberculosis. Its diagnosis is often based on a combination of clinical and biological arguments, and confirmed by bacteriology which is rarely availabl...Peritoneal tuberculosis is the most common digestive location of tuberculosis. Its diagnosis is often based on a combination of clinical and biological arguments, and confirmed by bacteriology which is rarely available. In Congo there is little published data on this entity. Objectives: To describe the epidemiological, diagnostic, and progression characteristics of peritoneal tuberculosis at the university hospital center in Brazzaville. Patients and Methods: This study is a descriptive and retrospective analysis conducted from January 1, 2015, to December 31, 2021, in the Gastroenterology and Internal Medicine department of the CHU of Brazzaville. It included all patients hospitalized during this period with a confirmed diagnosis of peritoneal tuberculosis, encompassing 54 records that met the inclusion criteria. Results: Out of the study period, 54 records that fulfilled the inclusion criteria were analyzed. The annual incidence of peritoneal tuberculosis was 7.7 patients, with a prevalence of 1.4%, showing a male predominance of 61% and an average age of 39.93 ± 14.62 years. The primary symptoms were abdominal bloating and abdominal pain, present in 100% and 74% of cases, respectively. The clinical presentation was primarily characterized by febrile ascites observed in all patients. HIV co-infection was noted in 29.6% of cases. Anemia was present in 79.6% of patients, and an elevated sedimentation rate was observed in 74% of cases. The tuberculin skin test returned positive in 50% of cases. The ascitic fluid was exudative, rich in proteins and white blood cells (exceeding 1000/mm3, predominantly lymphocytes) in the majority of cases (100%, 83.3%, 83.3%, respectively). The diagnosis was deemed highly probable based on the clinical and paraclinical signs and the favorable response to treatment in 79.6% of cases. There were instances of pleural involvement (33.3%) and lymph node involvement (pulmonary 22.2% and lymph node 16.6%). Treatment outcomes were favorable in 37% of cases, with a mortality rate of 9%. Conclusion: Peritoneal tuberculosis is prevalent in Brazzaville, predominantly affecting young males. The diagnosis relies chiefly on a combination of clinical, paraclinical, and progression indicators.展开更多
Objective To qualitatively assess the diagnostic performance of dynamic contrast enhancement(DCE),diffusionweighted imaging(DWI),and T2-weighted imaging(T2WI),alone or in combination,in the evaluation of breast cancer...Objective To qualitatively assess the diagnostic performance of dynamic contrast enhancement(DCE),diffusionweighted imaging(DWI),and T2-weighted imaging(T2WI),alone or in combination,in the evaluation of breast cancer.Methods We retrospectively reviewed the records of 394 consecutive patients with pathologically confirmed breast lesions who had undergone 3-T magnetic resonance imaging(MRI).The morphological characteristics of breast lesions were evaluated using DCE,DWI,and T2WI based on BI-RADS lexicon descriptors by trained radiologists.Patients were categorized into mass and non-mass groups based on MRI characteristics of the lesions,and the differences between benign and malignant lesions in each group were compared.Clinical prediction models for breast cancer diagnosis were constructed using logistic regression analysis.Diagnostic efficacies were compared using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results For mass-like lesions,all the morphological parameters significantly differentiated benign and malignant lesions on consensus DCE,DWI,and T2WI(P<0.05).The combined method(DCE+DWI+T2WI)had a higher AUC(0.865)than any of the individual modality(DCE:0.786;DWI:0.793;T2WI:0.809)(P<0.05).For non-mass-like lesions,DWI signal intensity was a significant predictor of malignancy(P=0.036),but the model using DWI alone had a low AUC(0.669).Conclusion Morphological assessment using the combination of DCE,DWI,and T2WI provides better diagnostic value in differentiating benign and malignant breast mass-like lesions than assessment with only one of the modalities.展开更多
文摘Point-of-care testing(POCT)refers to a category of diagnostic tests that are performed at or near to the site of the patients(also called bedside testing)and is capable of obtaining accurate results in a short time by using portable diagnostic devices,avoiding sending samples to the medical laboratories.It has been extensively explored for diagnosing and monitoring patients’diseases and health conditions with the assistance of development in biochemistry and microfluidics.Microfluidic paper-based analytical devices(μPADs)have gained dramatic popularity in POCT because of their simplicity,user-friendly,fast and accurate result reading and low cost.SeveralμPADs have been successfully commercialized and received excellent feedback during the past several decades.This review briefly discusses the main types ofμPADs,preparation methods and their detection principles,followed by a few representative examples.The future perspectives of the development inμPADs are also provided.
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.
基金funded by the Beijing Natural Science Foundation[Grant No.Z210006]the National Natural Science Foundation of China[Grant No.62275061].
文摘Nickel(II)as one of the primary categories of heavy metals can lead to serious health problems if achieving the critical levels in the water.Thus,it is vital to propose a stable,reliable,and economical approach for detecting Ni ions.The microfluidic paper-based analytical devices(µPADs)are potential candidates for the detection of water quality parameters including pH,heavy ions,nitrite and so on.However,it suffers from a huge error caused by the environment and artificial mistakes.In this study,we proposed an improved technique route to increase the stability and reliability of microfluidic paper-based analytical devices.The main technique points include a stable light source,a matched camera,improved reliability of the devices,and effective calculated methods.Finally,we established 15 standard curves that could be used to detect nickel ions and obtained uniform colorimetric results with reliability and repeatability.With those improvements,the relative errors for the five types of real water samples from the Zhongshan industrial parks were reduced to 0.26%,14.78%,24.20%,50.29%and 3.53%,respectively.These results were conducive to exploring this technique for the detection of nickel ions in wastewater from the Zhongshan industrial parks.The results demonstrated that the above technique route is promising for the detection of other heavy metal ions in industrial effluent.
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.
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Supported by The National Natural Science Foundation of China,No.82104525.
文摘The population with metabolic dysfunction-associated fatty liver disease(MAFLD)is increasingly common worldwide.Identification of people at risk of progression to advanced stages is necessary to timely offer interventions and appropriate care.Liver biopsy is currently considered the gold standard for the diagnosis and staging of MAFLD,but it has associated risks and limitations.This has spurred the exploration of non-invasive diagnostics for MAFLD,especially for steatohepatitis and fibrosis.These non-invasive approaches mostly include biomarkers and algorithms derived from anthropometric measurements,serum tests,imaging or stool metagenome profiling.However,they still need rigorous and widespread clinical validation for the diagnostic performance.
基金supported by the National Magnetic Confinement Fusion Energy Program of China(No.2019YFE03020001)the Collaborative Innovation Program of Hefei Science Center,CAS(No.2021HSC-CIP010)the Fundamental Research Funds for the Central Universities。
文摘Interpreting experimental diagnostics data in tokamaks,while considering non-ideal effects,is challenging due to the complexity of plasmas.To address this challenge,a general synthetic diagnostics(GSD)platform has been established that facilitates microwave imaging reflectometry and electron cyclotron emission imaging.This platform utilizes plasma profiles as input and incorporates the finite-difference time domain,ray tracing and the radiative transfer equation to calculate the propagation of plasma spontaneous radiation and the external electromagnetic field in plasmas.Benchmark tests for classical cases have been conducted to verify the accuracy of every core module in the GSD platform.Finally,2D imaging of a typical electron temperature distribution is reproduced by this platform and the results are consistent with the given real experimental data.This platform also has the potential to be extended to 3D electromagnetic field simulations and other microwave diagnostics such as cross-polarization scattering.
基金sponsored by the National Natural Science Foundation of China(No.52235007,YH)the Science Fund for Creative Research Groups of the National Natural Science Foundation of China(No.T2121004,YH)+3 种基金the NationalNatural Science Foundation of China(No.52305300,MJX)the Fellowship of China Postdoctoral Science Foundation(No.2022M722826,MJX)the National Natural Science Foundation of China(No.82203602,JW)the Zhejiang Provincial Natural Science Foundation of China(No.LQ22H160020,JW)。
文摘Paper-based microchips have different advantages,such as better biocompatibility,simple production,and easy handling,making them promising candidates for clinical diagnosis and other fields.This study describes amethod developed to fabricate modular three-dimensional(3D)paper-based microfluidic chips based on projection-based 3D printing(PBP)technology.A series of two-dimensional(2D)paper-based microfluidic modules was designed and fabricated.After evaluating the effect of exposure time on the accuracy of the flow channel,the resolution of this channel was experimentally analyzed.Furthermore,several 3D paper-based microfluidic chips were assembled based on the 2D ones using different methods,with good channel connectivity.Scaffold-based 2D and hydrogel-based 3D cell culture systems based on 3D paper-based microfluidic chips were verified to be feasible.Furthermore,by combining extrusion 3D bioprinting technology and the proposed 3D paper-based microfluidic chips,multiorgan microfluidic chips were established by directly printing 3D hydrogel structures on 3D paperbased microfluidic chips,confirming that the prepared modular 3D paper-based microfluidic chip is potentially applicable in various biomedical applications.
基金supported by the JSPS fellowship to M.K.M(Grant Number P20039)support from JST-ERATO Yamauchi Materials Space-Tectonics Project(JPMJER2003)+1 种基金the funding from the Queensland government through the Advance Queensland Fellowship Program(AQIRF043-2020-CV)supported by the National Health and Medical Research Council(NHMRC,1195451).
文摘Sweat contains numerous vital biomarkers such as metabolites,electrolytes,proteins,nucleic acids and antigens that reflect hydration status,exhaustion,nutrition,and physiological changes.Conventional healthcare diagnosis relies on disease diagnostics in sophisticated centralized laboratories with invasive sample collection(e.g.,chemical analyses,plasma separation via centrifugation,tissue biopsy,etc.).Cutting-edge point-of-care diagnostics for sweat biomarker analysis allow for non-invasive monitoring of physiologically related biomarkers in sweat and real-time health status tracking.Moreover,using advanced nanoarchitectures,including nanostructured platforms and nanoparticles,can enhance the specificity,sensitivity,wearability and widen the sensing modality of sweat biosensors.Herein,we comprehensively review the secretory mechanisms,clinical uses of sweat biomarkers,and the design,principle,and latest technologies of sweat biosensors.With an emphasis on cutting-edge technologies for sweat biomarker analysis,this review chronicles the issues associated with the current sweat biomarkers analysis of sweat biomarkers and provides insights into strategies for enhancing the translation of such biosensors into routine clinical practice.
基金the Special Project of State Key Laboratory of Dampness Syndrome of Chinese Medicine:Study on Criteria for Diagnosis of Dampness Syndrome of Idiopathic Membranous Nephropathy,Cohort Study on Pathogenesis and Material Basis of Dampness Syndrome of Idiopathic Membranous Nephropathy,Randomized Controlled Clinical Study of Sanqi Qushi Granule in Treatment of Membranous Nephropathy(No.SZ2021ZZ02,SZ2021ZZ09 and SZ2021ZZ36)the 2020 Guangdong Provincial Science and Technology Innovation Strategy Special Fund:Guangdong-Hong Kong-Macao Joint Lab(No.2020B1212030006)+2 种基金the Natural Science Foundation of Guangdong Province:Study on the Mechanism of Sanqi Qushi Prescription Delaying Podocellular Senescence in Membranous Nephropathy based on Cyclic Guanosine Monophosphate-Adenosine Monophosphate Synthase-Stimulator of Interferon Genes-Nuclear Factor Kappa-B Signaling Pathway(No.2022A1515011628)the Guangzhou Science and Technology Plan Project:to Explore the Mechanism of Treating Membranous Nephropathy from the Perspective of Regulating Amino Acid Metabolism Disorder(No.2023A03J0746)Special Funding for Scientific and Technological Research on Traditional Chinese Medicine,Guangdong Provincial Hospital of Chinese Medicine:a Multimodular Machine Learning Prediction Model based on Pathological Image-transcriptomics and Traditional Chinese Medicine Syndromes was Used to Investigate the Prognostic Correlation of Long non-coding RNA Molecules in Nephropathy and the Intervention Mechanism of Sanqi Qushi Formula,to Investigate the Pathogenesis and Microbiological Mechanism of Dampness Syndrome of Membranous Nephropathy based on the Microecological Changes of Tongue Coating(No.YN2023MB02,YN2023MB10)。
文摘OBJECTIVE:To reach consensus on the diagnostic criteria of syndrome of dampness obstruction in idiopathic membranous nephropathy(IMN)patients by literature research and expert investigation(interviews and a Delphi method).METHODS:Our study was consistent with T/CACM 1336-2020.We searched the monographs and references published in the past 40 years(1983-2022),and established the diagnostic criteria pool of waterdampness syndrome and dampness-turbidity syndrome in Traditional Chinese Medicine(TCM)based on literature by using frequency statistics and correlation analysis.Expert investigation(interview method and two rounds of Delphi method)was used to form the diagnostic criteria of water-dampness syndrome and dampnessturbidity syndrome of idiopathic membranous nephropathy.Clinical diagnostic test research was carried out,and compared with“Diagnostic Criteria for dampness syndrome”(T/CACM 1454-2023)to evaluate the authenticity,reliability and clinical application value of the standard.RESULTS:A total of 122 relevant guides,standards,monographs and documents were included through searching books and Chinese databases.Four experts were interviewed and two rounds of delphi method(75 experts nationwide)were carried out.The experts'opinions are relatively concentrated and the differences are small.Based on the weight of each index,the diagnostic criteria indexes of water-dampness syndrome and dampness-turbidity syndrome were selected.After discussion by the core group members,the diagnostic model of"necessary symptoms and optional symptoms"was established,and the final diagnostic criteria of waterdampness syndrome and dampness-turbidity syndrome were established.One hundred and ninety-one inpatients and outpatients of Guangdong Provincial Hospital of Chinese Medicine from January 2021 to February 2023 were included in Diagnostic test study.There was no statistical difference in gender,age and course of disease(P>0.05).The sensitivity and specificity of the trial standard were 90.34%and 73.33%respectively,while the sensitivity and specificity of T/CACM 1454-2023 were 99.43%and 6.67%,respectively.CONCLUSIONS:The consensus-based diagnostic criteria for IMN can be widely incorporated in TCM.A further clinical study will be conducted to analyze the diagnosis value and cut-off score of our IMN criteria.
文摘This study investigates the variability in cancer diagnosis across different tissues and organs, with a focus on the role of diagnostic methods such as Hematoxylin and Eosin (H&E) staining and immunohistochemistry (IHC). The predominance of female breast cancer (30%) aligns with global trends, underscoring the need for robust diagnostic protocols, particularly in developing regions. Other prevalent cancers, including skin, stomach, and cervix uteri, reflect a mix of environmental, genetic, and infectious factors. The underrepresentation of gallbladder and thyroid cancers (<1%) suggests potential underdiagnosis or lower prevalence. Age distribution data indicate peak cancer incidence in individuals aged 31 - 45 years, with gender-specific cancers like breast and cervical cancer predominantly affecting females (63.4%). The analysis also highlights significant diagnostic gaps, as 61.2% of cases did not undergo IHC testing due to resource constraints, leading to potential biases in cancer prevalence and diagnostic accuracy. The study emphasizes the complementary role of IHC in confirming ambiguous H&E findings, with strong alignment observed when both methods were used. However, the absence of IHC in many cases limits the robustness of conclusions, suggesting the need for increased access to IHC testing. The findings advocate for integrating IHC into routine diagnostics, expanding diagnostic capabilities, and improving sample sizes to ensure more reliable and comprehensive cancer data.
文摘Peritoneal tuberculosis is the most common digestive location of tuberculosis. Its diagnosis is often based on a combination of clinical and biological arguments, and confirmed by bacteriology which is rarely available. In Congo there is little published data on this entity. Objectives: To describe the epidemiological, diagnostic, and progression characteristics of peritoneal tuberculosis at the university hospital center in Brazzaville. Patients and Methods: This study is a descriptive and retrospective analysis conducted from January 1, 2015, to December 31, 2021, in the Gastroenterology and Internal Medicine department of the CHU of Brazzaville. It included all patients hospitalized during this period with a confirmed diagnosis of peritoneal tuberculosis, encompassing 54 records that met the inclusion criteria. Results: Out of the study period, 54 records that fulfilled the inclusion criteria were analyzed. The annual incidence of peritoneal tuberculosis was 7.7 patients, with a prevalence of 1.4%, showing a male predominance of 61% and an average age of 39.93 ± 14.62 years. The primary symptoms were abdominal bloating and abdominal pain, present in 100% and 74% of cases, respectively. The clinical presentation was primarily characterized by febrile ascites observed in all patients. HIV co-infection was noted in 29.6% of cases. Anemia was present in 79.6% of patients, and an elevated sedimentation rate was observed in 74% of cases. The tuberculin skin test returned positive in 50% of cases. The ascitic fluid was exudative, rich in proteins and white blood cells (exceeding 1000/mm3, predominantly lymphocytes) in the majority of cases (100%, 83.3%, 83.3%, respectively). The diagnosis was deemed highly probable based on the clinical and paraclinical signs and the favorable response to treatment in 79.6% of cases. There were instances of pleural involvement (33.3%) and lymph node involvement (pulmonary 22.2% and lymph node 16.6%). Treatment outcomes were favorable in 37% of cases, with a mortality rate of 9%. Conclusion: Peritoneal tuberculosis is prevalent in Brazzaville, predominantly affecting young males. The diagnosis relies chiefly on a combination of clinical, paraclinical, and progression indicators.
文摘Objective To qualitatively assess the diagnostic performance of dynamic contrast enhancement(DCE),diffusionweighted imaging(DWI),and T2-weighted imaging(T2WI),alone or in combination,in the evaluation of breast cancer.Methods We retrospectively reviewed the records of 394 consecutive patients with pathologically confirmed breast lesions who had undergone 3-T magnetic resonance imaging(MRI).The morphological characteristics of breast lesions were evaluated using DCE,DWI,and T2WI based on BI-RADS lexicon descriptors by trained radiologists.Patients were categorized into mass and non-mass groups based on MRI characteristics of the lesions,and the differences between benign and malignant lesions in each group were compared.Clinical prediction models for breast cancer diagnosis were constructed using logistic regression analysis.Diagnostic efficacies were compared using the area under the receiver operating characteristic curve(AUC)and DeLong test.Results For mass-like lesions,all the morphological parameters significantly differentiated benign and malignant lesions on consensus DCE,DWI,and T2WI(P<0.05).The combined method(DCE+DWI+T2WI)had a higher AUC(0.865)than any of the individual modality(DCE:0.786;DWI:0.793;T2WI:0.809)(P<0.05).For non-mass-like lesions,DWI signal intensity was a significant predictor of malignancy(P=0.036),but the model using DWI alone had a low AUC(0.669).Conclusion Morphological assessment using the combination of DCE,DWI,and T2WI provides better diagnostic value in differentiating benign and malignant breast mass-like lesions than assessment with only one of the modalities.
