Ultrasound ability in early diagnosis of metatarsal stress fractures.

Stress fractures are common in young and active individuals, associated with aggressive or repetitive physical activity and their early detection is fundamental to optimise patient care, decrease complications and avoid unnecessary exams. Currently, magnetic resonance imaging is the standard of care for detecting these lesions. Recently, ultrasound has been getting an increasing interest for the detection of stress fractures. In this article, we describe a clinical case that involved a second metatarsal stress fracture diagnosed by ultrasound and review the literature regarding the use of ultrasound in the diagnosis of stress fractures, particularly of the metatarsals.

Target product profiles: leprosy diagnostics.

The World Health Organization (WHO) aims to reduce new leprosy cases by 70% by 2030, necessitating advancements in leprosy diagnostics. Here we discuss the development of two WHO's target product profiles for such diagnostics. These profiles define criteria for product use, design, performance, configuration and distribution, with a focus on accessibility and affordability. The first target product profile outlines requirements for tests to confirm diagnosis of leprosy in individuals with clinical signs and symptoms, to guide multidrug treatment initiation. The second target product profile outlines requirements for tests to detect Mycobacterium leprae or M. lepromatosis infection among asymptomatic contacts of leprosy patients, aiding prophylactic interventions and prevention. Statistical modelling was used to assess sensitivity and specificity requirements for these diagnostic tests. The paper highlights challenges in achieving high specificity, given the varying endemicity of M. leprae, and identifying target analytes with robust performance across leprosy phenotypes. We conclude that diagnostics with appropriate product design and performance characteristics are crucial for early detection and preventive intervention, advocating for the transition from leprosy management to prevention.

L'Organisation mondiale de la Santé (OMS) vise à réduire le nombre de nouveaux cas de lèpre de 70% d'ici 2030, ce qui nécessite un meilleur diagnostic de la maladie. Dans le présent document, nous évoquons le développement de deux profils de produit cible établis par l'OMS à cette fin. Ces profils définissent des critères en matière d'utilisation, de conception, de performances, de configuration et de distribution du produit, en accordant une attention particulière à l'accessibilité et à l'abordabilité. Le premier profil de produit cible décrit les exigences pour les tests servant à confirmer le diagnostic de la lèpre chez les individus qui présentent des signes cliniques et des symptômes, afin d'orienter l'instauration d'un traitement à base de plusieurs médicaments. Le second profil de produit cible décrit les exigences pour les tests servant à détecter une infection à Mycobacterium leprae ou M. lepromatosis parmi les contacts asymptomatiques de patients lépreux, ce qui contribue à l'adoption de mesures prophylactiques et à la prévention. Nous avons eu recours à une modélisation statistique pour évaluer les exigences de sensibilité et de spécificité de ces tests diagnostiques. Cet article met en évidence les obstacles à l'atteinte d'un niveau élevé de spécificité en raison de l'endémicité variable de M. leprae, et à l'identification d'analytes cibles offrant de bons résultats chez les phénotypes lépreux. Nous concluons qu'un diagnostic reposant sur des caractéristiques de performance et de conception appropriées est essentiel pour détecter rapidement la maladie et intervenir en amont, et nous plaidons pour une prévention plutôt qu'une gestion de la lèpre.

La Organización Mundial de la Salud (OMS) pretende reducir los nuevos casos de lepra en un 70% para 2030, lo que requiere avances en el diagnóstico de la lepra. Aquí se analiza el desarrollo de dos perfiles de productos objetivo de la OMS para este tipo de diagnósticos. Estos perfiles definen los criterios de uso, diseño, rendimiento, configuración y distribución de los productos, centrándose en su accesibilidad y asequibilidad. El primer perfil de producto objetivo describe los requisitos de las pruebas para confirmar el diagnóstico de la lepra en personas con signos y síntomas clínicos, con el fin de orientar el inicio del tratamiento con múltiples fármacos. El segundo perfil de producto objetivo describe los requisitos de las pruebas para detectar la infección por Mycobacterium leprae o M. lepromatosis entre los contactos asintomáticos de los pacientes con lepra, para facilitar las intervenciones profilácticas y la prevención. Se utilizaron modelos estadísticos para evaluar los requisitos de sensibilidad y especificidad de estas pruebas diagnósticas. El artículo destaca las dificultades para lograr una alta especificidad, dada la diferente endemicidad de M. leprae, y para identificar analitos diana con un rendimiento sólido en todos los fenotipos de lepra. Concluimos que los diagnósticos con un diseño de producto y unas características de rendimiento adecuados son fundamentales para la detección precoz y la intervención preventiva, lo que favorece la transición del manejo de la lepra a la prevención.

A Multi-Modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease Using Three Paradigms With Various Task Difficulties.

Alzheimer's Disease (AD) accounts for the majority of dementia, and Mild Cognitive Impairment (MCI) is the early stage of AD. Early and accurate diagnosis of dementia plays a vital role in more targeted treatments and effectively halting disease progression. However, the clinical diagnosis of dementia requires various examinations, which are expensive and require a high level of expertise from the doctor. In this paper, we proposed a classification method based on multi-modal data including Electroencephalogram (EEG), eye tracking and behavioral data for early diagnosis of AD and MCI. Paradigms with various task difficulties were used to identify different severity of dementia: eye movement task and resting-state EEG tasks were used to detect AD, while eye movement task and delayed match-to-sample task were used to detect MCI. Besides, the effects of different features were compared and suitable EEG channels were selected for the detection. Furthermore, we proposed a data augmentation method to enlarge the dataset, designed an extra ERPNet feature extract layer to extract multi-modal features and used domain-adversarial neural network to improve the performance of MCI diagnosis. We achieved an average accuracy of 88.81% for MCI diagnosis and 100% for AD diagnosis. The results of this paper suggest that our classification method can provide a feasible and affordable way to diagnose dementia.

Chest X-ray evaluation using machine learning to support the early diagnosis of pulmonary TB.

TB is a public health problem, and its diagnosis can be challenging. Among imaging methods, chest X-ray (CXR) is the leading choice for assessing pulmonary TB (PTB). Recent advancements in the field of artificial intelligence have stimulated studies evaluating the performance of machine learning (ML) for medical diagnosis. This study validated a new original Brazilian tool, the XmarTB, applied to CXR images to support the early diagnosis of PTB.An ML model was trained on 3,800 normal images, 3,800 abnormal CXRs without PTB and 1,376 with PTB manifestations from the publicly available TBX11K database.The binary classification can distinguish between normal and abnormal CXR with a sensitivity of 99.4% and specificity of 99.4%. The XmarTB tool had a sensitivity of 98.1% and a specificity of 99.7% in detecting TB cases among CXRs with abnormal CXRs; sensitivity was 96.7% and specificity 98.7% in detecting TB cases among all samples.This diagnostic tool can accurately and automatically detect abnormal CXRs and satisfactorily differentiate PTB from other pulmonary diseases. This tool holds significant promise in aiding the proactive detection of TB cases, providing rapid and accurate support for early diagnosis..

Quantitative Neuromuscular Monitoring Permits Early Diagnosis of Abnormal Butyrylcholinestrase: Two Case Studies Demonstrating Prevention of Awareness from Premature Awakening.

Administration of succinylcholine to patients with a variant in the butyrylcholinesterase (BChE) gene increases the risk of anesthesia emergence prior to recovery from neuromuscular blockade (NMB). Application of quantitative neuromuscular monitoring (NMM) can identify residual NMB. We present two patients with abnormal BChE gene variants. In the first case, quantitative monitoring was applied too late to prevent awareness, but allowed diagnosis and prevented admission to the intensive care unit. In the second case, monitoring was applied prior to NMB, which enabled early diagnosis and prevented premature awakening from anesthesia. These cases illustrate the importance of quantitative NMM, even in short cases and with short-acting depolarizing agents such as succinylcholine. The clinical implications of this report include a more consistent use of NMM to identify and manage patients with undiagnosed abnormal BChE and to prevent premature anesthesia emergence.

Early detection of emerging viral variants through analysis of community structure of coordinated substitution networks.

The emergence of viral variants with altered phenotypes is a public health challenge underscoring the need for advanced evolutionary forecasting methods. Given extensive epistatic interactions within viral genomes and known viral evolutionary history, efficient genomic surveillance necessitates early detection of emerging viral haplotypes rather than commonly targeted single mutations. Haplotype inference, however, is a significantly more challenging problem precluding the use of traditional approaches. Here, using SARS-CoV-2 evolutionary dynamics as a case study, we show that emerging haplotypes with altered transmissibility can be linked to dense communities in coordinated substitution networks, which become discernible significantly earlier than the haplotypes become prevalent. From these insights, we develop a computational framework for inference of viral variants and validate it by successful early detection of known SARS-CoV-2 strains. Our methodology offers greater scalability than phylogenetic lineage tracing and can be applied to any rapidly evolving pathogen with adequate genomic surveillance data.

Recent Advances in Biosensors for Diagnosis of Autoimmune Diseases.

Over the last decade, autoimmune diseases (ADs) have undergone a significant increase because of genetic and/or environmental factors; therefore, their simple and fast diagnosis is of high importance. The conventional diagnostic techniques for ADs require tedious sample preparation, sophisticated instruments, a dedicated laboratory, and qualified personnel. For these reasons, biosensors could represent a useful alternative to these methods. Biosensors are considered to be promising tools that can be used in clinical analysis for an early diagnosis due to their high sensitivity, simplicity, low cost, possible miniaturization (POCT), and potential ability for real-time analysis. In this review, recently developed biosensors for the detection of autoimmune disease biomarkers are discussed. In the first part, we focus on the main AD biomarkers and the current methods of their detection. Then, we discuss the principles and different types of biosensors. Finally, we overview the characteristics of biosensors based on different bioreceptors reported in the literature.

Predicting early Alzheimer's with blood biomarkers and clinical features.

Alzheimer's disease (AD) is an incurable neurodegenerative disorder that leads to dementia. This study employs explainable machine learning models to detect dementia cases using blood gene expression, single nucleotide polymorphisms (SNPs), and clinical data from Alzheimer's Disease Neuroimaging Initiative (ADNI). Analyzing 623 ADNI participants, we found that the Support Vector Machine classifier with Mutual Information (MI) feature selection, trained on all three data modalities, achieved exceptional performance (accuracy = 0.95, AUC = 0.94). When using gene expression and SNP data separately, we achieved very good performance (AUC = 0.65, AUC = 0.63, respectively). Using SHapley Additive exPlanations (SHAP), we identified significant features, potentially serving as AD biomarkers. Notably, genetic-based biomarkers linked to axon myelination and synaptic vesicle membrane formation could aid early AD detection. In summary, this genetic-based biomarker approach, integrating machine learning and SHAP, shows promise for precise AD diagnosis, biomarker discovery, and offers novel insights for understanding and treating the disease. This approach addresses the challenges of accurate AD diagnosis, which is crucial given the complexities associated with the disease and the need for non-invasive diagnostic methods.

HCC-Check: A Novel Diagnostic Tool for Early Detection of Hepatocellular Carcinoma Based on Cytokeratin-1 and Epithelial Membrane Antigen: A Cross-Sectional Study.

Background: Hepatocellular carcinoma is frequently diagnosed in advanced stages, leading to a poorer prognosis. Therefore, early diagnosis and identification of biomarkers may significantly improve outcomes. Methods: This cross-sectional study enrolled 486 participants distributed among 3 groups: F1 to F3 = 184, F4 = 183, and hepatocellular carcinoma = 119. Liver fibrosis staging was performed using FibroScan, while imaging features were used for hepatocellular carcinoma detection. Epithelial membrane antigen and cytokeratin-1 levels in serum were quantified through Western blot and ELISA, respectively. Results: Patients diagnosed with hepatocellular carcinoma exhibited significantly elevated levels of epithelial membrane antigen and cytokeratin-1 compared to non-hepatocellular carcinoma patients, with a highly significant statistical difference (P < .0001). Epithelial membrane antigen demonstrated diagnostic performance with an area under the curve of 0.75, a sensitivity of 69.0%, and a specificity of 68.5%. Cytokeratin-1 for the identification of hepatocellular carcinoma showed a sensitivity of 79.0% and a specificity of 81.4%, resulting in an area under the curve of 0.87. The developed HCC-Check, which incorporates epithelial membrane antigen, cytokeratin-1, albumin, and alpha-fetoprotein, displayed a higher area under the curve of 0.95 to identify hepatocellular carcinoma, with a sensitivity of 89.8% and a specificity of 83.9%. Notably, HCC-Check values exceeding 2.57 substantially increased the likelihood of hepatocellular carcinoma, with an estimated odds ratio of 50.65, indicating a higher susceptibility to hepatocellular carcinoma development than those with lower values. The HCC-Check diagnostic test exhibited high precision in identifying patients with hepatocellular carcinoma, particularly those with small tumor sizes (<5 cm) and a single nodule, as reflected in area under the curve values of 0.92 and 0.85, respectively. HCC-Check was then applied to the validation study to test its accuracy and reproducibility, showing superior area under the curves for identifying different stages of hepatocellular carcinoma. These outcomes underscore the effectiveness of the test in the early detection of hepatocellular carcinoma. Conclusion: The HCC-Check test presents a highly accurate diagnostic method for detecting hepatocellular carcinoma in its early stages.

Patient and Clinician Perspectives: To Create a Better Future for Chronic Kidney Disease, We Need to Talk About Our Kidneys.

Chronic kidney disease (CKD) affects more than one in ten people worldwide. However, results from the REVEAL-CKD study suggest that it is often not diagnosed. Many patients are therefore unaware that they have CKD, putting them at increased risk of disease progression and complications. Empowering patients with knowledge about CKD will allow them to become active participants in their own care, driving improvements in diagnosis rates and changing patient outcomes for the better. In this article, we provide patient and clinician perspectives on the importance of early CKD diagnosis and management. We present an overview of the tests commonly used to diagnose CKD in clinical practice, as well as actionable suggestions for patients, clinicians, and health policymakers that could help improve disease detection and treatment. Navdeep Tangri, a nephrologist and epidemiologist at the University of Manitoba, and Jane DeMeis, a patient living with chronic kidney disease, discuss how results from the REVEAL-CKD study highlight the need for change to improve management of chronic kidney disease. Video Abstract (MP4 141866 KB).

Efforts to screen kids for type 1 diabetes multiply.

Blood tests can detect the disease process early, avoiding complications and aiding treatment.

Development and validation a nomogram prediction model for early diagnosis of bloodstream infections in the intensive care unit.

Purpose: This study aims to develop and validate a nomogram for predicting the risk of bloodstream infections (BSI) in critically ill patients based on their admission status to the Intensive Care Unit (ICU). Patients and methods: Patients' data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (training set), the Beijing Friendship Hospital (BFH) database (validation set) and the eICU Collaborative Research Database (eICU-CRD) (validation set). Univariate logistic regression analyses were used to analyze the influencing factors, and lasso regression was used to select the predictive factors. Model performance was assessed using area under receiver operating characteristic curve (AUROC) and Presented as a Nomogram. Various aspects of the established predictive nomogram were evaluated, including discrimination, calibration, and clinical utility. Results: The model dataset consisted of 14930 patients (1444 BSI patients) from the MIMIC-IV database, divided into the training and internal validation datasets in a 7:3 ratio. The eICU dataset included 2100 patients (100 with BSI) as the eICU validation dataset, and the BFH dataset included 419 patients (21 with BSI) as the BFH validation dataset. The nomogram was constructed based on Glasgow Coma Scale (GCS), sepsis related organ failure assessment (SOFA) score, temperature, heart rate, respiratory rate, white blood cell (WBC), red width of distribution (RDW), renal replacement therapy and presence of liver disease on their admission status to the ICU. The AUROCs were 0.83 (CI 95%:0.81-0.84) in the training dataset, 0.88 (CI 95%:0.88-0.96) in the BFH validation dataset, and 0.75 (95%CI 0.70-0.79) in the eICU validation dataset. The clinical effect curve and decision curve showed that most areas of the decision curve of this model were greater than 0, indicating that this model has a certain clinical effectiveness. Conclusion: The nomogram developed in this study provides a valuable tool for clinicians and nurses to assess individual risk, enabling them to identify patients at a high risk of bloodstream infections in the ICU.

NMR and MS reveal characteristic metabolome atlas and optimize esophageal squamous cell carcinoma early detection.

Metabolic changes precede malignant histology. However, it remains unclear whether detectable characteristic metabolome exists in esophageal squamous cell carcinoma (ESCC) tissues and biofluids for early diagnosis. Here, we conduct NMR- and MS-based metabolomics on 1,153 matched ESCC tissues, normal mucosae, pre- and one-week post-operative sera and urines from 560 participants across three hospitals, with machine learning and WGCNA. Aberrations in 'alanine, aspartate and glutamate metabolism' proved to be prevalent throughout the ESCC evolution, consistently identified by NMR and MS, and reflected in 16 serum and 10 urine metabolic signatures in both discovery and validation sets. NMR-based simplified panels of any five serum or urine metabolites outperform clinical serological tumor markers (AUC = 0.984 and 0.930, respectively), and are effective in distinguishing early-stage ESCC in test set (serum accuracy = 0.994, urine accuracy = 0.879). Collectively, NMR-based biofluid screening can reveal characteristic metabolic events of ESCC and be feasible for early detection (ChiCTR2300073613).

The Value of Combined Detection of Serum BNP, Cardiac Troponin-I and Dynamic Electrocardiogram in Early Clinical Diagnosis and Prognosis of Patients with Acute Myocardial Infarction.

BACKGROUND: Acute myocardial infarction (AMI) is a prevalent cardiovascular disease resulting from myocardial ischemia and necrosis due to coronary artery occlusion. AMI is characterized by a sudden onset and high mortality, underscoring the significance of early diagnosis and treatment for improving patient prognosis. This study endeavors to assess the utility of a combined assessment involving serum brain natriuretic peptide (BNP), cardiac troponin-I (cTnI), and dynamic electrocardiogram (ECG) in the early clinical diagnosis and prognosis prediction of AMI. METHODS: This paper constitutes a retrospective study. All enrolled patients underwent dynamic ECG examination. The study compared the serum levels of BNP and cTnI, along with pertinent dynamic ECG parameters [turbulence slope (TS) and standard deviation (SDNN) of the 24-hour interval between normal atrial depolarization and ventricular depolarization (R-R)], between the observation group (AMI patients) and the control group (patients with unstable angina (UA)). To evaluate the early diagnostic potential of AMI, we utilized receiver operating characteristic (ROC) curves to analyze serum BNP, cTnI, dynamic ECG, and their combined utility. Furthermore, a follow-up period of 6 months was conducted for AMI patients to record major adverse cardiovascular events (MACE). RESULTS: In the observation group, the serum levels of BNP and cTnI were significantly higher than those in the control group (p < 0.001), while dynamic ECG parameters, specifically TS and SDNN, were significantly lower in the observation group compared to the control group (p < 0.001). The results obtained from the ROC curve analysis revealed that the area under the curve (AUC) for BNP, cTnI, dynamic ECG, and their combination in early AMI diagnosis were 0.838, 0.887, 0.874, and 0.974, respectively. The 95% confidence intervals (CI) were 0.781~0.884, 0.836~0.926, 0.822~0.915, and 0.942~0.991, respectively. Sensitivity values were 64.29%, 82.14%, 91.07%, and 88.39%, and specificity values were 91.00%, 88.00%, 70.00%, and 98.00%, respectively. Significantly, the combination of all three markers demonstrated superior efficacy in early AMI diagnosis compared to any single index (p < 0.05). During the 6-month follow-up of 112 AMI patients, 22 experienced MACE. The MACE group exhibited notably higher serum BNP and cTnI levels compared to the non-MACE group. Additionally, dynamic electrocardiogram parameters TS and SDNN demonstrated a significant decrease (p < 0.05) in the MACE group. CONCLUSIONS: The combined assessment of serum BNP, cTnI, and dynamic electrocardiogram enhances the early clinical diagnostic potential for AMI and holds value in assessing the prognosis of AMI patients.

Early diagnosis of aspiration pneumonia by gastrointestinal ultrasound combined with lung ultrasound.

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[Value of cardiodynamicsgram in early diagnosis of patients with acute coronary syndrome].

OBJECTIVE: To explore the value of cardiodynamicsgram (CDG) obtained from electrocardiogram (ECG) data by radial basis functionradial basis function (RBF) neural network in early diagnosis of patients with acute coronary syndrome (ACS). METHODS: Retrospective analysis method was used. Patients with chest pain as the main initial symptom in the emergency department of Baoan District People's Hospital of Shenzhen from October 2021 to September 2022 were enrolled. Baseline data were collected, including gender, age, smoking history, family history of coronary heart disease and history of hypertension, diabetes, hyperlipidemia, and atherosclerosis. The first 12-lead ECG was recorded after admission to the emergency department, and electrocardiodynamics analysis was performed to generate CDG. Receiver operator characteristic curve (ROC curve) was plotted to analyze the value of CDG and ECG in the early diagnosis of ACS and non-ST segment elevation ACS (NSTE-ACS). Sensitivity, specificity, area under the ROC curve (AUC), and 95% confidence interval (95%CI) were calculated. CDG and coronary angiography results of 3 patients with ACS with normal ECG were observed and analyzed. Non-ACS patients with normal ECG but positive CDG were followed for 30 days for adverse cardiovascular events. RESULTS: A total of 384 patients with chest pain were included, including 169 patients with ACS and 215 patients without ACS. The proportion of male (87.0% vs. 53.0%), smoking history (37.9% vs. 12.1%), hypertension (46.2% vs. 22.3%), diabetes (24.3% vs. 7.9%), hyperlipidemia (55.0% vs. 14.0%) and history of atherosclerosis (22.5% vs. 2.3%) in ACS group were significantly higher than those in non-ACS group (all P < 0.05). The ROC curve showed that the AUC of CDG diagnosis of ACS was higher than that of ECG [AUC (95%CI): 0.88 (0.66-0.76) vs. 0.71 (0.84-0.92)], the sensitivity was 92.8%, 78.6%, and the specificity was 83.3%, 64.2%, respectively. The AUC of CDG diagnosis of NSTE-ACS was higher than that of ECG [AUC (95%CI): 0.85 (0.80-0.90) vs. 0.63 (0.56-0.69)], the sensitivity was 87.1%, 61.3%, and the specificity was 83.3%, 64.2%, respectively. CDG of 3 patients with ACS with normal ECG showed disordered state, and coronary angiography showed ≥70% stenosis of major coronary branches. Of 215 non-ACS patients, 20 had a normal ECG but positive CDG, and 3 developed ST segment elevation myocardial infarction (STEMI) within 30 days, and 2 developed unstable angina (UA) within 30 days. CONCLUSIONS: CDG has high value in early diagnosis of ACS patients and is expected to become an important means of early diagnosis of ACS in emergency.

Clinical Presentation and Diagnosis of Drug Reaction with Eosinophilia and Systemic Symptoms (DReSS) in Children: A Scoping Review.

Effective treatment of drug reactions with eosinophilia and systemic symptoms (DReSS) requires early diagnosis and close monitoring. Diagnosing DReSS is especially challenging in children due to a low incidence rate, heterogeneous clinical presentation, and a lack of (pediatric) diagnostic criteria and clinical practice guidelines. We performed a scoping review, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to summarize the clinical presentation and diagnostic process of DReSS in children (aged 0-18 years). Data from 644 individuals showed that DReSS manifests differently in children compared to adults. Children have a higher number of organs involved, including higher rates of cardiac and respiratory involvement compared to adults. Children < 6 years of age appear more prone to develop neurologic symptoms. Conversely, eosinophilia, edema, and kidney involvement are less frequently observed in children. Anti-seizure medications are by far the most common causative drug class, but the range of implicated drugs increases as children get older. This study highlights that children with DReSS not only differ from adults but also that differences exist between children of different ages. As such, there is a need to establish pediatric-specific diagnostic criteria. These efforts will promote earlier diagnosis of DReSS and likely lead to improved clinical care offered to children and their families.

Evaluation of Individual Variation of d-Amino Acids in Human Plasma by a Two-Dimensional LC-MS/MS System and Application to the Early Diagnosis of Chronic Kidney Disease.

For the discovery of sensitive biomarkers of kidney function focusing on chiral amino acids, a multiple heart-cutting two-dimensional (2D) liquid chromatography-mass spectrometry (LC-MS)/MS system has been designed/developed. As the target analytes, alanine (Ala), aspartic acid, glutamic acid (Glu), leucine (Leu), lysine, methionine, phenylalanine (Phe), proline (Pro), serine (Ser), and valine were selected considering the presence of their d-forms in mammals. The 2D LC-MS/MS system consisted of the nonenantioselective reversed-phase separation of the target amino acids, the separations of the d- and l-enantiomers, and detection using MS/MS. Using the method, the plasma chiral amino acids, precolumn derivatized with 4-fluoro-7-nitro-2,1,3-benzoxadiazole, were isolated from other intrinsic substances, then determined without losing sensitivity by the fully automated whole-peak volume transfer operation from first to second dimension. In all of the tested plasma samples obtained from five healthy individuals and 15 patients with chronic kidney disease (CKD), the target chiral amino acids were determined without interference. In healthy individuals, the levels of all the tested d-amino acids were regulated in the low ranges. In contrast, the % d values of Glu, Leu, and Phe significantly increased with the progress of kidney dysfunction, besides the previously reported values of d-Ala, Pro, and Ser. Concerning Phe, the significant increase of the % d values (p < 0.05) was reported for the first time even in the mild CKD group compared to those of the healthy group; d-Phe might be a more sensitive marker than the previously reported d-forms. These results demonstrated the potential of these d-forms as the sensitive biomarkers of kidney function for the early diagnosis of CKD.