Clinical Diagnostic Systems Research Articles

Detection of Urea in Human Blood using Graphene-Based Amperometric Biosensor

Urea being an essential clinical analyte, its early and easy detection would be helpful in preventing various diseases. In this study, a graphene-based screen-printed enzymatic biosensor was fabricated for the blood urea detection by amperometric method. The electrode surface was modified with urease and glutamate dehydrogenase (Urs-GLDH) enzymes for sensitive and selective detection of urea with a wide detection range. The diffusion coefficient and the electron transfer rate constant of the screen-printed graphene electrode (SPGE) was found to be 8.53 × 10−7 cm2 s−1 and 7.58 × 10−5 cm s−1, respectively. The proposed graphene-based enzymatic urea biosensor can detect urea ranging from 2.5 to 30 mM with a sensitivity of 10.59 μA·mM−1·cm−2. The reliability of this enzymatic biosensor was confirmed by measuring urea in human blood and the results showed excellent agreement with the clinical testing method. Hence, the graphene-based biosensor described herein could provide rapid, sensitive, and selective detection of urea with low-cost and simple fabrication. In conclusion, this urea detection approach could be a promising method for developing feasible low-cost clinical diagnostic systems.

Computerized diagnostic decision support systems- a comparative performance study of Isabel Pro vs. ChatGPT4.

Validate the diagnostic accuracy of the Artificial Intelligence Large Language Model ChatGPT4 by comparing diagnosis lists produced by ChatGPT4 to Isabel Pro. This study used 201 cases, comparing ChatGPT4 to Isabel Pro. Systems inputs were identical. Mean Reciprocal Rank (MRR) compares the correct diagnosis's rank between systems. Isabel Pro ranks by the frequency with which the symptoms appear in the reference dataset. The mechanism ChatGPT4 uses to rank the diagnoses is unknown. A Wilcoxon Signed Rank Sum test failed to reject the null hypothesis. Both systems produced comprehensive differential diagnosis lists. Isabel Pro's list appears immediately upon submission, while ChatGPT4 takes several minutes. Isabel Pro produced 175 (87.1 %) correct diagnoses and ChatGPT4 165 (82.1 %). The MRR for ChatGPT4 was 0.428 (rank 2.31), and Isabel Pro was 0.389 (rank 2.57), an average rank of three for each. ChatGPT4 outperformed on Recall at Rank 1, 5, and 10, with Isabel Pro outperforming at 20, 30, and 40. The Wilcoxon Signed Rank Sum Test confirmed that the sample size was inadequate to conclude that the systems are equivalent. ChatGPT4 fabricated citations and DOIs, producing 145 correct references (87.9 %) but only 52 correct DOIs (31.5 %). This study validates the promise of Clinical Diagnostic Decision Support Systems, including the Large Language Model form of artificial intelligence (AI). Until the issue of hallucination of references and, perhaps diagnoses, is resolved in favor of absolute accuracy, clinicians will make cautious use of Large Language Model systems in diagnosis, if at all.

AAPM Task Group 334: A guidance document to using radiotherapy immobilization devices and accessories in an MR environment.

Use of magnetic resonance (MR) imaging in radiation therapy has increased substantially in recent years as more radiotherapy centers are having MR simulators installed, requesting more time on clinical diagnostic MR systems, or even treating with combination MR linear accelerator (MR-linac) systems. With this increased use, to ensure the most accurate integration of images into radiotherapy (RT), RT immobilization devices and accessories must be able to be used safely in the MR environment and produce minimal perturbations. The determination of the safety profile and considerations often falls to the medical physicist or other support staff members who at a minimum should be a Level 2 personnel as per the ACR. The purpose of this guidance document will be to help guide the user in making determinations on MR Safety labeling (i.e., MR Safe, Conditional, or Unsafe) including standard testing, and verification of image quality, when using RT immobilization devices and accessories in an MR environment.

Diagnostic Accuracy of Clinical Diagnostic Scoring Systems for Childhood Tuberculosis: A Systematic Review and Meta-analysis.

Diagnosis of childhood tuberculosis (TB) poses several challenges. Therefore, point-based scoring systems and diagnostic algorithms have been developed to improve the diagnostic yields in this population. However, there are no updated systematic reviews of the existing childhood TB scoring systems and algorithms. Hence, we systematically reviewed the diagnostic accuracy of the childhood TB diagnostic scoring systems and algorithms. We systematically searched PubMed, CINAHL, Embase, Scopus, and Google Scholar databases for relevant articles published until 30 March 2023. QUADAS-2 was used to assess their study quality. Diagnostic accuracy measures (ie, sensitivity, specificity, diagnostic odds ratio, positive and negative likelihood ratios) were pooled using a random-effects model. We included 15 eligible studies, with a total of 7327 study participants aged <15 years, with 10 evaluations of childhood TB diagnostic scoring systems and algorithms. Among these algorithms and scoring systems, only 3 were evaluated more than once. These were the Keith Edwards scoring system with 5 studies (sensitivity, 81.9%; specificity, 81.2%), Kenneth Jones criteria with 3 studies (sensitivity, 80.1%; specificity, 45.7%), and the Ministry of Health-Brazil algorithm with 3 studies (sensitivity, 79.9%; specificity, 73.2%). We recommend using the Keith Edwards scoring system because of its high sensitivity and specificity. Further research is necessary to assess the effectiveness of scoring systems and algorithms in identifying TB in children with HIV and malnutrition.

Operational definitions and measurement of externalizing behavior problems: An integrative review including research models and clinical diagnostic systems.

Measurement of externalizing disorders such as antisocial disorders, attention-deficit/hyperactivity disorder or borderline disorder have relevant implications for the daily lives of people with these disorders. While the Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International Classification of Diseases (ICD) have provided the diagnostic framework for decades, recent dimensional frameworks question the categorical approach of psychopathology, inherent in traditional nosotaxies. Tests and instruments develop under the DSM or ICD framework preferentially adopt this categorical approach, providing diagnostic labels. In contrast, dimensional measurement instruments provide an individualized profile for the domains that comprise the externalizing spectrum, but are less widely used in practice. Current paper aims to review the operational definitions of externalizing disorders defined under these different frameworks, revise the different measurement alternatives existing, and provide an integrative operational definition. First, an analysis of the operational definition of externalizing disorders among the DSM/ICD diagnostic systems and the recent Hierarchical Taxonomy of Psychopathology (HiTOP) model is carried out. Then, in order to analyze the coverage of operational definitions found, a description of measurement instruments among each conceptualization is provided. Three phases in the development of the ICD and DSM diagnosis systems can be observed with direct implications for measurement. ICD and DSM versions have progressively introduced systematicity, providing more detailed descriptions of diagnostic criteria and categories that ease the measurement instrument development. However, it is questioned whether the DSM/ICD systems adequately modelize externalizing disorders, and therefore their measurement. More recent theoretical approaches, such as the HiTOP model seek to overcome some of the criticism raised towards the classification systems. Nevertheless, several issues concerning this model raise mesasurement challenges. A revision of the instruments underneath each approach shows incomplete coverage of externalizing disorders among the existing instruments. Efforts to bring nosotaxies together with other theoretical models of psychopathology and personality are still needed. The integrative operational definition of externalizing disorders provided may help to gather clinical practice and research.

Diagnostic accuracy of clinical diagnostic scoring systems for childhood tuberculosis: a systematic review and meta-analysis protocol

IntroductionDiagnosis of childhood tuberculosis (TB) poses several challenges. Therefore, clinical signs and symptoms, radiological studies, laboratory examinations, point-based scoring systems or diagnostic algorithms have been developed to improve diagnostic yields...

DC: 0-5 system in clinical assessment with specialty pediatric populations.

Early childhood mental health (ECMH) programs provide an opportunity to provide specialized mental health services to vulnerable young children and connect them with necessary evidence-based early intervention. However, there is a paucity of descriptive and explorative studies of the clinic protocols in the literature. Even within published work, there is a lack of standardization in clinical models and diagnostic systems limiting comparison and extrapolation. This paper describes how the DC: 0-5 framework guides the development of the model for an ECMH clinic embedded in the context of academic pediatrics. It also highlights the opportunity the DC 0-5 presents for developing the standardized protocols and a mechanism for standardized data collection in clinical settings. The paper demonstrates the utility of using the DC 0-5 in protocol development, assessment and data collection the mental health assessments of 87 children ages 0-6 were reviewed to gather information on history, presenting problems, parent-child relationship, and mental health diagnoses. This paper and associated data underscore the utility and necessity of ECMH clinics while identifying challenges in the field.

Automatic classification of MSA subtypes using Whole-brain gray matter function and Structure-Based radiomics approach

BackgroundThis study aims to develop a radiomics method based on the function and structure of whole-brain gray matter to accurately classify multiple system atrophy with predominant Parkinsonism (MSA-P) or predominant cerebellar ataxia (MSA-C). MethodsWe enrolled 30 MSA-C and 41 MSA-P cases for the internal cohort and 11 MSA-C and 10 MSA-P cases for the external test cohort. We extracted 7,308 features, including gray matter volume (GMV), mean amplitude of low-frequency fluctuation (mALFF), mean regional homogeneity (mReHo), degree of centrality (DC), voxel-mirrored homotopic connectivity (VMHC), and resting-state functional connectivity (RSFC) from 3D-T1 and Rs-fMR data. Feature selection was conducted with t-test and least absolute shrinkage and selection operator (Lasso). Classification was performed using the support vector machine with linear and RBF kernel (SVM-linear/SVM-RBF), random forest and logistic regression. Model performance was assessed via receiver operating characteristic (ROC) curve and compared with DeLong's test. ResultsFeature selection resulted in 12 features, including 1 ALFF, 1 DC and 10 RSFC. All the classifiers showed remarkable classification performance, especially the RF model which exhibited AUC values of 0.91 and 0.80 in the validation and test datasets, respectively. The brain functional activity and connectivity in the cerebellum, orbitofrontal lobe and limbic system were important features to distinguish MSA subtypes with the same disease severity and duration. ConclusionRadiomics approach has the potential to support clinical diagnostic systems and to achieve high classification accuracy for distinguishing between MSA-C and MSA-P patients at the individual level.

A 3D-Printed Standardized Modular Microfluidic System for Droplet Generation

Droplet-based microfluidics has a variety of applications, such as material synthesis and single-cell analysis. In this paper, we propose a modular microfluidic system using projection micro-stereolithography three-dimensional (3D) printing technology for droplet generation. All modules are designed using a standard cubic structure with a specific leakage-free connection interface. Versatile droplets, including single droplets, alternating droplets, merged droplets, and Janus particles, have been successfully produced. The droplet size and the generation rate can be flexibly controlled by adjusting the flow rates. The influence of the flow rate fraction between the discrete phase and the continuous phase over the generation of the alternating and merged droplets is discussed. Furthermore, the 'UV curing' module can be employed to solidify the generated droplets to avoid coalescence and fix the status of the Janus particles. The proposed modular droplet generators are promising candidates for various chemical and biological applications, such as single-cell incubation, screening of protein crystallization conditions, synthesis of nanoparticles, and gene delivery. In addition, we envision that more functional modules, e.g., valve, microreactor, and detection modules, could be developed, and the 3D standardized modular microfluidics could be further applied to other complex systems, i.e., concentration gradient generators and clinical diagnostic systems.

Introducing Audio Podcasts into a Practical Laboratory Course for Pharmacy Students as a Novel Tool for Performance Assessment.

The use of digital tools can positively impact higher education for both scholars and faculty. In recent years, it has become apparent that podcasts are a suitable medium for use in teaching. They are provided almost exclusively by lecturers for students, with students passively listening to them rather than actively participating in their production. However, this could also be valuable for students. Therefore, this pilot study investigated the extent to which the creation of a podcast would be accepted by students as a method for capturing pharmacy students’ understanding of the learning content. The evaluation was performed as part of the “Clinical Chemistry” practical course, which was attended by third-year pharmacy students in groups of three. After passing the station dealing with practical clinical chemistry relevant diagnostic systems, the groups were asked to produce an educational podcast covering the essential content on the topics of urine test strips or pulse oximetry, respectively. Student attitudes toward the adoption of podcasts as a tool for performance assessment were determined with an anonymous and voluntary survey. The respondents reported that they had fun creating the podcast, which enabled them to look at the instructional content from a different perspective. Competencies such as social and communication skills and media literacy as well as self-organized and self-directed learning were also promoted. However, the students assumed that the tool is not ideally suited for dealing with extensive topics. Nonetheless, the students clearly support the continued creation of podcasts as a performance assessment tool. In addition, they suggest integrating podcasts into other courses within the pharmacy curriculum. This may also be related to the infrequent use of novel technologies, such as podcasts, in their education thus far.

Medical practitioner's adoption of intelligent clinical diagnostic decision support systems: A mixed-methods study

Artificial intelligence-based clinical diagnostic decision support systems promise transformational improvements in doctors’ efficiency and accuracy. Nevertheless, low adoption rates suggest that this innovation could fail without adequate uptake. This study uses a mixed-methods approach to develop and test a model based on theories of Unified Theory of Acceptance and Use of Technology, status quo bias, and technology trust. The results show that performance expectancy, effort expectancy, social influence, initial trust, and resistance to change predict intention to use. Further, inertia, perceived threat, and risks (medico-legal and performance) determine resistance to change. Measures for alleviating resistance and improving adoption are proposed.

Clinical Assessment of the Medial Longitudinal Arch in Children: Rater Agreement and Relationship to Objective Foot Arch Measurements

The purpose of this study was to (1) investigate the agreement among clinical experts regarding the classification of the medial longitudinal arch in children and to (2) examine the relationship between the expert ratings and the static and dynamic foot arch measurements. Eleven foot experts were asked to identify foot arches of seventy-four children (mean ± SD age of 9.0 ± 1.81 years) using three categories (low, normal, and high arch). Additionally, dynamic footprint data of all children were captured with a pedobarographic platform. For static arch measurements, a specially constructed caliper was used to assess heel-to-toe length and dorsum height. Agreement among raters was determined using the kappa statistic. For comparisons between clinical rating and diagnostic systems, receiver operating characteristic (ROC) curves and ordinal logistic regression were calculated. The combined kappa score for all three arch types (0.422, 32.8%) shows borderline moderate agreement between raters. Area under the curves (AUC) values of the ROC curves ranging from 0.48 to 0.68 show poor relationship between quantitative and qualitative arch height measurements. The highest agreement was found for the dynamic arch index and foot experts’ ratings for low arched vs non-low arched feet (AUC = 0.68) and normal vs low arched feet (AUC = 0.67). The results of this study showed borderline moderate agreement among clinical experts in the clinical assessment of the medial longitudinal plantar arch in children. Also, only a poor relationship between the clinical foot arch assessments and the static and dynamic foot measurements was revealed. Further research is necessary to establish an accurate and consistent diagnostic system for the measurement of the medial longitudinal arch in children.

A Radiomics Approach to Predicting Parkinson's Disease by Incorporating Whole-Brain Functional Activity and Gray Matter Structure.

Parkinson’s disease (PD) is a progressive, chronic, and neurodegenerative disorder that is primarily diagnosed by clinical examinations and magnetic resonance imaging (MRI). In this study, we proposed a machine learning based radiomics method to predict PD. Fifty healthy controls (HC) along with 70 PD patients underwent resting-state magnetic resonance imaging (rs-fMRI). For all subjects, we extracted five types of 6664 features, including mean amplitude of low-frequency fluctuation (mALFF), mean regional homogeneity (mReHo), resting-state functional connectivity (RSFC), voxel-mirrored homotopic connectivity (VMHC) and gray matter (GM) volume. After conducting dimension reduction utilizing Least absolute shrinkage and selection operator (LASSO), fifty-three radiomic features including 46 RSFCs, 1 mALFF, 3 mReHos, 1 VMHC, 2 GM volumes and 1 clinical factor were retained. The selected features also indicated the most discriminative regions for PD. We further conducted model fitting procedure for classifying subjects in the training set employing random forest and support volume machine (SVM) to evaluate the performance of the two methods. After cross-validation, both methods achieved 100% accuracy and area under curve (AUC) for distinguishing between PD and HC in the training set. In the testing set, SVM performed better than random forest with the accuracy, true positive rate (TPR) and AUC being 85%, 1 and 0.97, respectively. These findings demonstrate the radiomics technique has the potential to support radiological diagnosis and to achieve high classification accuracy for clinical diagnostic systems for patients with PD.

Phenotype of genetically confirmed Silver-Russell syndrome beyond childhood

BackgroundSilver-Russell syndrome is an imprinting disorder that restricts growth, resulting in short adult stature that may be ameliorated by treatment. Approximately 50% of patients have loss of methylation of the...

An Accurate and Fast Cardio-Views Classification System Based on Fused Deep Features and LSTM

Echocardiography is an ultrasound-based imaging modality that helps the physician to visualize heart chambers and valves motion activity. Recently, deep learning plays an important role in several clinical computer-assisted diagnostic systems. There is a real need to employ deep learning methodologies to increase such systems. In this paper, we proposed a deep learning system to classify several echocardiography views and identify its physiological location. Firstly, the spatial CNN features are extracted from each frame in the echo-motion. Secondly, we proposed novel temporal features based on neutrosophic sets. The neutrosophic temporal motion features are extracted from echo-motion activity. To extract the deep CNN features, we activated a pre-trained deep ResNet model. Then, both spatial and neutrosophic temporal CNN features were fused based on features concatenation technique. Finally, the fused CNN features were fed into deep long short-term memory network to classify echo-cardio views and identify their location. During our experiments, we employed a public echocardiography dataset that consisted of 432 videos for eight cardio-views. We have investigated several pre-trained network activation performance. ResNet architecture activation achieved the best accuracy score among several pre-trained networks. The Proposed system based on fused spatial neutrosophic temporal deep features achieved 96.3% accuracy and 95.75% sensitivity. For the classification of cardio-views location, the proposed system achieved 99.1% accuracy. The proposed system achieved more accuracy than previous deep learning methods with a significant decrease in the training time cost. The experimental results showed promising results for our proposed approach.

Concave wall-based mixing chambers and convex wall-based constriction channel micromixers

ABSTRACT Micromixers with concave wall-based mixing chambers and convex wall-based constriction channel are studied. The effect of shape and aspect ratio of the obstacle on mixing performance was investigated for the Reynolds number in the range of 0.1–75 using Computational Fluid Dynamics (CFD). The micromixers showed interesting mixing dynamics and flow features in respect of two flow regimes identified for the present study. The first flow regime is for the Reynolds number in the range 0.1 < Re < 5 wherein diffusion-dominated mixing was observed and the second flow regime is for the Reynolds number in the range 5 < Re ≤ 75 wherein convection-dominated mixing was observed. The simulation results reveal that the rectangular obstacle-based micromixer gives mixing index MI ≥ 0.90 at the Re ≥ 30 in all cases of aspect ratio and it is highest compared to triangular obstacle-based and teardrop obstacle-based micromixers. Further, in respect of pressure drop, teardrop obstacle-based micromixer showed less pressure drop. Both mixing index and pressure drop increase with increase in aspect ratio of the obstacle. The proposed micromixers are simple in design which make them best candidates for environmental and clinical analysis or diagnostic systems.

Classification of Unmedicated Bipolar Disorder Using Whole-Brain Functional Activity and Connectivity: A Radiomics Analysis.

The aim of this study was to develop and validate a method of disease classification for bipolar disorder (BD) by functional activity and connectivity using radiomics analysis. Ninety patients with unmedicated BD II as well as 117 healthy controls underwent resting-state functional magnetic resonance imaging (rs-fMRI). A total of 4 types of 7018 features were extracted after preprocessing, including mean regional homogeneity (mReHo), mean amplitude of low-frequency fluctuation (mALFF), resting-state functional connectivity (RSFC), and voxel-mirrored homotopic connectivity (VMHC). Then, predictive features were selected by Mann-Whitney U test and removing variables with a high correlation. Least absolute shrinkage and selection operator (LASSO) method was further used to select features. At last, support vector machine (SVM) model was used to estimate the state of each subject based on the selected features after LASSO. Sixty-five features including 54 RSFCs, 7 mALFFs, 1 mReHo, and 3 VMHCs were selected. The accuracy and area under curve (AUC) of the SVM model built based on the 65 features is 87.3% and 0.919 in the training dataset, respectively, and the accuracy and AUC of this model validated in the validation dataset is 80.5% and 0.838, respectively. These findings demonstrate a valid radiomics approach by rs-fMRI can identify BD individuals from healthy controls with a high classification accuracy, providing the potential adjunctive approach to clinical diagnostic systems.

Intellectual functioning in Silver-Russell syndrome: First study in adults

Silver-Russell syndrome (SRS) is a rare genetic disorder (estimated incidence 1/30,000 to 100,000 live births). So far, only a few studies have focused on the cognitive profile of individuals with SRS, and these were conducted some time ago, concentrated on pediatric cohorts, and included patients who had been diagnosed using a variety of clinical diagnostic systems. There has yet to be any research on the intellectual functioning of adults with SRS. This study sought to establish the intelligence, strengths and weaknesses within intellectual profile of adults with SRS, compared with normative data. Ten individuals with 11p15 epimutation aged 18–39 years completed the Wechsler Adult Intelligence Scale-Fourth Edition. Measures of interest included participants’ intelligence (Full Scale Intelligence Quotient [FSIQ]) and four domains of cognitive functioning: verbal comprehension, perceptual reasoning, working memory and processing speed. Discrepancy scores were calculated, and descriptive statistical and linear correlations were used to investigate factors associated with IQ outcome. Clinical and medical information such as rehabilitation, and perceived difficulties in daily life were collected by interviews and questionnaires. Results showed that the mean FSIQ score was in the average range (M = 95.40, SD = 18.55) and they performed best on verbal comprehension. Frequent daily difficulties were reported by patients and/or their families: learning disabilities and low self-esteem were perceived by 60% of adults. Early intervention and multidisciplinary care from childhood to adulthood are important in SRS for care potential medical, cognitive and psychosocial problems. This is the first study to document the intellectual functioning of adults with SRS.

Investigation of the source-detector separation in near infrared spectroscopy for healthy and clinical applications.

Understanding near infrared light propagation in tissue is vital for designing next generation optical brain imaging devices. Monte Carlo (MC) simulations provide a controlled mechanism to characterize and evaluate contributions of diverse near infrared spectroscopy (NIRS) sensor configurations and parameters. In this study, we developed a multilayer adult digital head model under both healthy and clinical settings and assessed light-tissue interaction through MC simulations in terms of partial differential pathlength, mean total optical pathlength, diffuse reflectance, detector light intensity and spatial sensitivity profile of optical measurements. The model incorporated four layers: scalp, skull, cerebrospinal-fluid and cerebral cortex with and without a customizable lesion for modeling hematoma of different sizes and depths. The effect of source-detector separation (SDS) on optical measurements' sensitivity to brain tissue was investigated. Results from 1330 separate simulations [(4 lesion volumes × 4 lesion depths for clinical +3 healthy settings) × 7 SDS × 10 simulation = 1330)] each with 100 million photons indicated that selection of SDS is critical to acquire optimal measurements from the brain and recommended SDS to be 25 to 35 mm depending on the wavelengths to obtain optical monitoring of the adult brain function. The findings here can guide the design of future NIRS probes for functional neuroimaging and clinical diagnostic systems.

CLINICAL DIAGNOSTICS AND TREATMENT TACTICS OF ACUTE TONSILLOFARINGITIS IN CHILDREN

Abstract. In children suffering from acute tonsillopharyngitis estimated by the Centor Score (Modified/McIsaac) as the total score less than 3, with specificity 90,9% and 93,9% respectively, sensitivity 20,0% and 12,5% respectively, with predictable value of positive and negative results on the level of 50,0%, was indicative of non-streptococcal nature of the disease. Express-diagnostics of streptococcal infection by means of “Streptatest” determined false-negative results of the test in every second child, which requires evidence by means of bacteriological inoculation. Considering insufficient diagnostic informative value of clinical diagnostic systems and quick express-tests in detection of non-streptococcal tonsillopharyngitis and concerning streptococcal acute tonsillopharyngitis the constellation-diagnostic system is suggested to be applied which is a component of the algorithm of treatment of the disease.