Diagnostic Capacity Research Articles

Clinical Evaluation of Xpert Xpress CoV-2/Flu/RSV plus and Alinity m Resp-4-Plex Assay.

The performance of the Xpert Xpress CoV-2/Flu/RSV plus and Alinity m Resp-4-Plex Assays were evaluated using 167 specimens, including 158 human respiratory specimens and 9 external quality assessment program (EQAP) samples. For respiratory specimens, CoV-2/Flu/RSV plus exhibited perfect agreement with the standard-of-care (SOC) methods (Cohen's κ: 1, 100% agreement). The overall positive and negative percent agreement (PPA and NPA) were 100%, with 95% confidence intervals of 96.50 to 100% and 85.70 to 100%, respectively. On the other hand, Resp-4-Plex revealed an almost perfect agreement with the SOC methods (Cohen's κ: 0.92, 97.71% agreement). The overall PPA and NPA were 100% (95.76 to 100%) and 88.46% (70.20 to 96.82%), respectively. For EQAP samples, the results of CoV-2/Flu/RSV plus (9/9) and Resp-4-Plex (4/4) were concordant with the expected results. The experimental limit of detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was the lowest (25 copies/mL for both methods), and that of the respiratory syncytial virus was the highest (400 copies/mL for CoV-2/Flu/RSV plus and 100 copies/mL for Resp-4-Plex). Threshold cycle (Ct) value correlation showed a large positive linear association between CoV-2/Flu/RSV plus and Resp-4-Plex, with R-squared values of 0.92-0.97, and on average, the Ct values of CoV-2/Flu/RSV plus were higher than that of Resp-4-Plex by 1.86-2.78, except for Flu A1 target (-0.66). To conclude, the performance of both assay was comparable to the SOC methods for both upper and lower respiratory specimens. Implementation of these rapid assay may reinforce the diagnostic capacity for the post-pandemic co-circulation of SARS-CoV-2 and other respiratory viruses.

Multiple Behavioral Risk Factors As Assets for Chronic Disease Prevention: Observations From Urban Primary Care Settings in Crete, Greece.

This study aimed to assess the health profile of patient-attendees visiting primary healthcare (PHC) practice settings in the midst of the COVID-19 pandemic and to explore the relationships between multiple behavioral risk factors (MBRFs) and consultation-driven health information. Multiple behavioral risk factors involve a variety of unhealthy behaviors that are associated with an increased prevalence of non-communicable diseases (NCDs). The study design was based on a dataset analysis, afterward exploring the feasibility and diagnostic capacity of respiratory morbidity aspects from a study previously conducted. The study dataset contained information regarding socio-demographic characteristics, health habits, clinical information, and reported comorbidities from 183 primary care patient-attendees. A categorical regression analysis was performed, using as a numeric variable the multiple MBRFs (clustering of 0to four factors) in order to examine relationships with the basic and clinical characteristics of the patient-attendees. Based on this secondary analysis, it was found that the prevalence of MBRFs is quite common among patient-attendees visiting urban PHC facilities. The prevalence of current smoking, sleep deprivation, increased body weight, and medium/high perceived stress levels were 33.9%, 52.5%, 83.1%, and 35.0%, respectively. An increased occurrence of MBRFs might be significantly predicted by the lower age of patient-attendees (b = -0.221, p = 0.05), by the absence of gray hair at an early age (b = -0.144, p = 0.042), by the physical discomfort during activities (b = 0.191, p = 0.017), or by the lower oxygen saturation (b = -0.184, p = 0.004). Diabetes mellitus (25.1%) was the most prevalent condition, followed by bronchial asthma (18.6%) and depression (15.8%). Lower age, absence of premature hair whitening, physical discomfort during activities, and lower oxygen saturation are linked with an increased occurrence of MBRFs, leading to a neglected way of living. Those factors could be used to alert researchers, policymakers, and PHC professionals to act accordingly in order to prevent or early diagnose NCDs.

Amide proton transfer weighted contrast has diagnostic capacity in detecting diabetic foot: an MRI-based case-control study.

To evaluate the role of foot muscle amide proton transfer weighted (APTw) contrast and tissue rest perfusion in quantifying diabetic foot (DF) infection and its correlation with blood parameters. With approval from an ethical review board, this study included 40 diabetes mellitus (DM) patients with DF and 31 DM patients without DF or other lower extremity arterial disease. All subjects underwent MRI, which included foot sagittal APTw and coronal arterial spin labeling (ASL) imaging. The normalized MTRasym (3.5 ppm) and the ratio of blood flow (rBF) in rest status of the affected side lesions to the non-affected contralateral side were determined. The inter-group differences of these variables were evaluated. Furthermore, the association between normalized MTRasym (3.5 ppm), rBF, and blood parameters [fasting blood glucose (FBG), glycosylated hemoglobin content, C-reactive protein, neutrophil percentage, and white blood cell count] was explored. Using an ROC curve, the diagnostic capacity of normalized MTRasym (3.5 ppm), BF, and blood biochemical markers in differentiating with or without DF in DM was assessed. In the DF group, MTRasym (3.5 ppm) and BF in lesion and normalized MTRasym (3.5 ppm) were higher than those in the control group (p < 0.05). In addition, correlations were identified between normalized MTRasym (3.5 ppm) and blood parameters, such as C-reactive protein, glycosylated hemoglobin content, FBG, neutrophil ratio, and white blood cell (p < 0.001). Meanwhile, association between BF in lesion and blood parameters, such as C-reactive protein, neutrophil percentage, and FBG (p < 0.01). AUC of normalized MTRasym (3.5 ppm) in identifying with/without DF in patients with DM is 0.986 (95% CI, 0.918-1.00) with the sensitivity of 97.22% and the specificity of 100%. Normalized MTRasym (3.5 ppm) and the BF in lesion may be treated as a safer and more convenient new indicator to evaluate the tissue infection without using a contrast agent, which may be useful in monitoring and preoperatively assessing DF patients with renal insufficiency.

Gut Microbiota Signatures with Potential Clinical Usefulness in Colorectal and Non-Small Cell Lung Cancers.

The application of bacterial metagenomic analysis as a biomarker for cancer detection is emerging. Our aim was to discover gut microbiota signatures with potential utility in the diagnosis of colorectal cancer (CRC) and non-small cell lung cancer (NSCLC). A prospective study was performed on a total of 77 fecal samples from CRC and NSCLC patients and controls. DNA from stool was analyzed for bacterial genomic sequencing using the Ion Torrent™ technology. Bioinformatic analysis was performed using the QIIME2 pipeline. We applied logistic regression to adjust for differences attributable to sex, age, and body mass index, and the diagnostic accuracy of our gut signatures was compared with other previously published results. The feces of patients affected by different tumor types, such as CRC and NSCLC, showed a differential intestinal microbiota profile. After adjusting for confounders, Parvimonas (OR = 53.3), Gemella (OR = 6.01), Eisenbergiella (OR = 5.35), Peptostreptococcus (OR = 9.42), Lactobacillus (OR = 6.72), Salmonella (OR = 5.44), and Fusobacterium (OR = 78.9) remained significantly associated with the risk of CRC. Two genera from the Ruminococcaceae family, DTU089 (OR = 20.1) and an uncharacterized genus (OR = 160.1), were associated with the risk of NSCLC. Our two panels had better diagnostic capacity for CRC (AUC = 0.840) and NSLC (AUC = 0.747) compared to the application of two other published panels to our population. Thus, we propose a gut bacteria panel for each cancer type and show its potential application in cancer diagnosis.

Accelerating targeted mosquito control efforts through mobile West Nile virus detection.

Different mosquito control strategies have been implemented to mitigate or prevent mosquito-related public health situations. Modern mosquito control largely relies on multiple approaches, including targeted, specific treatments. Given this, it is becoming increasingly important to supplement these activities with rapid and mobile diagnostic capacities for mosquito-borne diseases. We aimed to create and test the applicability of a rapid diagnostic system for West Nile virus that can be used under field conditions. In this pilot study, various types of adult mosquito traps were applied within the regular mosquito monitoring activity framework for mosquito control. Then, the captured specimens were used for the detection of West Nile virus RNA under field conditions with a portable qRT-PCR approach within 3-4h. Then, positive samples were subjected to confirmatory RT-PCR or NGS sequencing in the laboratory to obtain genome information of the virus. We implemented phylogenetic analysis to characterize circulating strains. A total of 356 mosquito individuals representing 7 species were processed in 54 pools, each containing up to 20 individuals. These pools were tested for the presence of West Nile virus, and two pools tested positive, containing specimens from the Culex pipiens and Anopheles atroparvus mosquito species. As a result of subsequent sequencing, we present the complete genome of West Nile virus and Bagaza virus. The rapid identification of infected mosquitoes is the most important component of quick response adulticide or larvicide treatments to prevent human cases. The conceptual framework of real-time surveillance can be optimized for other pathogens and situations not only in relation to West Nile virus. We present an early warning system for mosquito-borne diseases and demonstrate its application to aid rapid-response mosquito control actions.

Exploring the Role of Serum Osteonectin and Hsp27 in Pediatric MAFLD Diagnosis and Cardiometabolic Health.

Childhood obesity is one of the major challenges of public health policies. The problem of fatty liver in childhood, known as MAFLD (metabolic dysfunction-associated fatty liver disease), is of particular interest as the gold standard diagnosis technique is invasive (liver biopsy). Hence, efforts are made to discover more specific biomarkers for the MAFLD signature. Therefore, the aim of the study was to evaluate Osteonectin and Hsp27 as biomarkers for MAFLD diagnosis and to assess their links with auxological and biochemical profiles of overweight and obese pediatric subjects. A cross-sectional study in which we (re)analyzed data from the MR PONy cohort comprising 71 pediatric subjects. Auxological data, liver ultrasonography and biochemical serum profile were recorded. Lipid-derived indices and body composition indices were calculated. Nevertheless, serum Osteonectin and Hsp27 levels were assessed using an ELISA approach. MAFLD prevalence was 40.8%. Higher Osteonectin levels were noted in MAFLD subjects versus non-MAFLD subjects and in dyslipidemic children regardless of their liver function status. Lipid-derived indices had good diagnostic capacity for MAFLD. We confirm Osteonectin as a MAFLD diagnosis biomarker in children. Also, lipid-derived indices are useful as metabolic-associated organ impairment markers in children even before the onset of obesity.

Testing for SARS-CoV-2: lessons learned and current use cases.

SUMMARYThe emergence and worldwide dissemination of SARS-CoV-2 required both urgent development of new diagnostic tests and expansion of diagnostic testing capacity on an unprecedented scale. The rapid evolution of technologies that allowed testing to move out of traditional laboratories and into point-of-care testing centers and the home transformed the diagnostic landscape. Four years later, with the end of the formal public health emergency but continued global circulation of the virus, it is important to take a fresh look at available SARS-CoV-2 testing technologies and consider how they should be used going forward. This review considers current use case scenarios for SARS-CoV-2 antigen, nucleic acid amplification, and immunologic tests, incorporating the latest evidence for analytical/clinical performance characteristics and advantages/limitations for each test type to inform current debates about how tests should or should not be used.

Engagement of private health care facilities in TB management in Lusaka district of Zambia: lessons learned and achievements

BackgroundGlobally, at least 3 million TB patients are missed every year. In Zambia, the TB treatment coverage increased from 66% in 2020 to 92% in 2022. Involvement of all levels of health care service delivery is critical to finding all the missing TB patients.MethodsA survey was undertaken in 15 private facilities in Lusaka district of Zambia using a structured tool administered by project team and a district health team member. Data collected during the survey was analysed and results were used to determine the type of TB services that were offered as well as barriers and enablers to TB service provision. This was followed by a set of interventions that included; training and mentorship on active case finding and systematic TB screening, increased diagnostic capacity, provision of national recording and reporting tools and provision of TB medication through linkage with the National TB program (NTP). We report findings from the baseline survey and changes in presumptive TB identification and notification following interventions.ResultsMajor barriers to TB service delivery were the high cost of TB diagnostic testing and treatment in facilities where services were not supported by the National TB program; the mean cost was 33 (SD 33) and 93 (SD 148) for GeneXpert testing and a full course of treatment respectively. Pre-intervention, presumptive TB identification appeared to increase monthly by 4 (P = 0.000, CI=[3.00–5.00]). The monthly trends of presumptive TB identification during the intervention period increased by 5.32 (P = 0.000, [CI 4.31–6.33. Pre-intervention, the notification of TB appeared to decrease every month by -4.0 (P = 0.114, CI=[-9.00-0.10]) followed by an immediate increase in notifications of 13.94 TB patients (P = 0.001, CI [6.51, 21.36] in the first month on intervention. The monthly trends of notification during the intervention period changed by 0.34 (P = 0.000 [CI 0.19–0.48]). Private facility contribution to TB notification increased from 3 to 7%.ConclusionEngagement and inclusion of private health facilities in TB service provision through a systems strengthening approach can increase contribution to TB notification by private health facilities.

Different diagnostic strategies combining prostate health index and magnetic resonance imaging for predicting prostate cancer: A multicentre study

ObjectiveTo explore how prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) should be used in concert to improve diagnostic capacity for clinically significant prostate cancers (CsCaP) in patients with prostate-specific antigen (PSA) between 4 and 20 ng/ml. MethodsAbout 426 patients fulfilling the inclusion criteria were included in this study. Univariable and multivariable logistic analyses were performed to analyze the association between the clinical indicators and CaP/CsCaP. We used the Delong test to compare the differences in the area under the curve (AUC) values of four models for CaP and CsCaP. Decision curve analysis (DCA) and calibration plots were used to assess predictive performance. We compared clinical outcomes of different diagnostic strategies constructed using different combinations of the models by the chi-square test and the McNemar test. ResultsThe AUC of PHI-MRI (a risk prediction model based on PHI and mpMRI) was 0.859, which was significantly higher than those of PHI (AUC = 0.792, P < 0.001) and mpMRI (AUC = 0.797, P < 0.001). PHI-MRI had a higher net benefit on DCA for predicting CaP and CsCaP in comparison to PHI and mpMRI. Adding the PHI-MRI in diagnostic strategies for CsCaP, such as use PHI-MRI alone or sequential use of PHI followed by PHI-MRI, could reduce the number of biopsies by approximately 20% compared to use PHI followed by mpMRI (256 vs 316, 257 vs 316, respectively). ConclusionsThe PHI-MRI model was superior to PHI and MRI alone. It may reduce the number of biopsies and ensure the detection rate of CsCaP under an appropriate sensitivity at the cost of an increased number of MRI scans.

Instituting coronavirus disease 2019 testing: opportunities and challenges of molecular laboratory diagnosis in a Southern Nigerian teaching hospital.

The coronavirus disease 2019 pandemic emphasised the importance of laboratory preparedness, including molecular diagnostic capacity, in the control of infectious disease outbreaks. This article reflects on diagnostic capacity-building opportunities presented by the pandemic, the challenges experienced along the way and the lessons learned from the perspective of a university teaching hospital in Southern Nigeria. We advocate for these lessons to inform strategic planning for laboratory preparedness at subnational, national and continental levels.

Deep Learning–Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome

Congenital long QT syndrome (LQTS) is associated with syncope, ventricular arrhythmias, and sudden death. Half of patients with LQTS have a normal or borderline-normal QT interval despite LQTS often being detected by QT prolongation on resting electrocardiography (ECG). To develop a deep learning-based neural network for identification of LQTS and differentiation of genotypes (LQTS1 and LQTS2) using 12-lead ECG. This diagnostic accuracy study used ECGs from patients with suspected inherited arrhythmia enrolled in the Hearts in Rhythm Organization Registry (HiRO) from August 2012 to December 2021. The internal dataset was derived at 2 sites and an external validation dataset at 4 sites within the HiRO Registry; an additional cross-sectional validation dataset was from the Montreal Heart Institute. The cohort with LQTS included probands and relatives with pathogenic or likely pathogenic variants in KCNQ1 or KCNH2 genes with normal or prolonged corrected QT (QTc) intervals. Convolutional neural network (CNN) discrimination between LQTS1, LQTS2, and negative genetic test results. The main outcomes were area under the curve (AUC), F1 scores, and sensitivity for detecting LQTS and differentiating genotypes using a CNN method compared with QTc-based detection. A total of 4521 ECGs from 990 patients (mean [SD] age, 42 [18] years; 589 [59.5%] female) were analyzed. External validation within the national registry (101 patients) demonstrated the CNN's high diagnostic capacity for LQTS detection (AUC, 0.93; 95% CI, 0.89-0.96) and genotype differentiation (AUC, 0.91; 95% CI, 0.86-0.96). This surpassed expert-measured QTc intervals in detecting LQTS (F1 score, 0.84 [95% CI, 0.78-0.90] vs 0.22 [95% CI, 0.13-0.31]; sensitivity, 0.90 [95% CI, 0.86-0.94] vs 0.36 [95% CI, 0.23-0.47]), including in patients with normal or borderline QTc intervals (F1 score, 0.70 [95% CI, 0.40-1.00]; sensitivity, 0.78 [95% CI, 0.53-0.95]). In further validation in a cross-sectional cohort (406 patients) of high-risk patients and genotype-negative controls, the CNN detected LQTS with an AUC of 0.81 (95% CI, 0.80-0.85), which was better than QTc interval-based detection (AUC, 0.74; 95% CI, 0.69-0.78). The deep learning model improved detection of congenital LQTS from resting ECGs and allowed for differentiation between the 2 most common genetic subtypes. Broader validation over an unselected general population may support application of this model to patients with suspected LQTS.

Self-administered at-home lung ultrasound with remote guidance in patients without clinical training

BackgroundAccess to timely and accurate diagnostic imaging is essential for high-quality healthcare. Point-of-care ultrasound has been shown to be accessible and effective in many aspects of healthcare, including assessing changes in lung pathology. However, few studies have examined self-administered at-home lung ultrasound (SAAH-LUS), in particular performed by non-clinical patients (NCPs).Research questionAre NCPs able to perform SAAH-LUS using remote teleguidance and produce interpretable images?Study designPatients were enrolled to the study in a mix of in-person and virtual recruitment, and shipped a smartphone as well as a point of care ultrasound device. Tele-guidance was provided by a remote physician using software integrated with the point of care ultrasound device, allowing real-time remote visualization and guidance of a patient scanning their own chest. A post-intervention survey was conducted to assess patient satisfaction, feasibility, and acceptability of SAAH-LUS. Two POCUS expert reviewers reviewed the scans for interpretability, and inter-rater agreement between the two reviewers was also computed.ResultsEighteen patients successfully underwent 7–14 days of daily telemedicine in parallel to daily SAAH-LUS. Across 1339 scans obtained from ten different lung zones, the average proportion of interpretability was 96% with a chance-corrected agreement, or Cohen’s kappa, reported as κ = 0.67 (significant agreement). 100% of NCPs surveyed found SAAH-LUS to be a positive experience, particularly for its ease of operation and ability to increase access to healthcare services.InterpretationThis study demonstrates that NCPs can obtain interpretable LUS images at home, highlighting the potential for SAAH-LUS to increase diagnostic capacity, particularly for rural and remote regions where complex imaging and healthcare providers are difficult to obtain.Trial registration The clinical trials has been registered (clinicaltrials.gov). Registration number: NCT04967729

Multimodal Imaging in Oncology: Challenges and Future Directions

Multimodal imaging, a vital tool in oncology, combines various imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) to offer valuable insights for tumor detection, diagnosis, staging, and treatment planning. Despite its advantages, challenges persist in image registration, fusion, optimization of algorithms, hardware advancements, and the integration of artificial intelligence (AI) and computer vision techniques. This paper delves into the current state of multimodal imaging in oncology, addresses the challenges faced, explores potential solutions, and underscores future developments to augment diagnostic and therapeutic capacities, including the application of AI, computer vision, and big data analytics to enhance the efficiency and effectiveness of multimodal imaging in cancer diagnosis and treatment. Additionally, it highlights how AI-powered algorithms can automate image analysis, assist in tumor segmentation, and provide predictive insights, ultimately improving the precision and speed of diagnosis and treatment planning in oncology.

Strengthening laboratories in response to outbreaks in humanitarian emergencies and conflict settings: Results, challenges and lessons from expanding PCR diagnostic capacities for COVID-19 testing in Yemen.

When the COVID-19 pandemic was declared, Yemen, a country facing years of conflict had only one laboratory with PCR testing capacity. In this article, we describe the outcome of the implementation of molecular based diagnostics platform in Yemen and highlight the key milestones the country went through to increase access to testing for its populations residing in a geographically vast and politically divided country. A retrospective assessment of COVID-19 laboratory response activities was done detailing the needs assessment process, timelines, geographical coverage, and outcomes of the activities. Laboratory data was analyzed to construct the geographical locations of COVID-19 testing laboratories and the numbers of tests performed in each facility to highlight the demands of testing for travelers. Finally, we discuss the impact these activities had in enabling the movement of people across international borders for economic gains and in delivery of critical humanitarian aid. PCR testing capacities in Yemen significantly improved, from one laboratory in Sanaa in April 2020 to 18 facilities across the country by June 2022. In addition, the number of functional Real-Time PCR thermocyclers increased from one to 32, the PCR tests output per day improved from 192 to 6144 tests per day. Results from analysis of laboratory data showed there were four peaks of COVID-19 in Yemen as October 2022. The majority of laboratory tests were performed for travelers than for medical or public health reasons. Demand for laboratory testing in Yemen was generally low and waned over time as the perceived risk of COVID-19 declined, in parallel with rollout of the COVID-19 vaccines. The successful expansion of laboratory testing capacity was instrumental in the control and management of COVID-19 cases and critical in the implementation of public response strategies, including restrictions on gathering. Laboratory testing also facilitated the movement of humanitarian agencies and delivery of aid and enabled hundreds of thousands of Yemeni nationals to travel internationally. By virtue of these outcomes, the impact of laboratory strengthening activities was thus felt in the health sector and beyond.

A diverse spectrum of mycoses histologically diagnosed in Ghana: Insights from a 10-year retrospective study.

In Ghana, most laboratory diagnoses of severe mycoses are based on histopathology findings due to inadequate availability of serology, culture, and molecular tests. The aim of this study was to evaluate the spectrum of mycoses diagnosed in Ghana. We retrospectively reviewed reports from 2012 to 2021 from three major pathology laboratories in Ghana to identify reports indicating the presence of fungal elements and diagnosis of a mycosis, then extracted demographic, clinical history, site of infection, stain(s), used and diagnosed mycosis details. Over the 10-year period, 107 cases were found. No apparent increasing and decreasing trend in the number of cases per year or in a period was observed. The age range of affected patients was from 4 to 86 years. Special stains for fungi were only used in 22 of 107 (20.6%) of cases. The most frequently affected site was the sino-nasal area (34%). Mycosis type was determined for 58 (54.2%) cases, comprising aspergillosis (21), candidiasis (14), dermatophytosis (6), mucormycosis (3), two cases each of chromoblastomycosis, histoplasmosis, eumycetoma, entomophthoromycosis, sporotrichosis, and Malassezia infection and a single case each of cryptococcosis and deep onychomycosis. Of the 53 (49.5%) cases with presumptive diagnosis data, only seven (13.2%) had a pre-biopsy suspicion of mycosis. There is a wide spectrum of mycoses in Ghana, including endemic mycoses not previously reported. Improving the use of special fungal stains could increase yield and mycoses identification. Laboratory diagnostic capacity needs enhancement to complement histopathology investigations with serology, culture, and molecular methods.

Urinary Post-Translationally Modified Fetuin-A (uPTM-FetA) in Chronic Kidney Disease Patients with and without Diabetic Kidney Disease.

Background and Objectives: A novel post-translational modification (PTM) fragment derived from the cleavage of Fetuin-A (PTM-FetA) has recently emerged as a sensitive biomarker for kidney damage in diabetic patients, but evidence in other chronic renal diseases is lacking. In this pilot study, we aimed at evaluating the clinical significance of urinary PTM-FetA (uPTM-FetA) in a mixed cohort of patients with non-advanced chronic kidney disease (CKD) secondary to diabetic kidney disease (DKD) or other causes. Materials and Methods: We enrolled 47 adult patients with CKD (mean CKD-Epi 40.10 ± 16.5 mL/min/1.73 m2) due to DKD (n = 34) or other etiology (n = 13). uPTM-FetA was measured in the urine using a commercially available ELISA kit. Fifteen healthy individuals served as controls. Results: Collectively, all CKD patients displayed remarkably higher levels of uPTM-FetA than controls (0.84 [0.10-1.15] vs. 29.68 [2.50-55.16] ng/mL p = 0.0005), but values were lower in non-DKD than in DKD patients (1.66 [0.09-4.19] vs. 13.9 [0.01-45.02] ng/mL; p = 0.01). uPTM-FetA showed a great diagnostic capacity at ROC analyses to identify the presence of CKD (AUC 0.776; p < 0.001) and, within CKD patients, to discriminate the diabetic and non-diabetic etiology (AUC 0.673; p = 0.02). At multivariate correlation analyses, proteinuria (β = 0.442; p = 0.02) and BMI (β = -0.334; p = 0.04) were the sole independent predictors of uPTM-FetA in this study population. Conclusions: uPTM-FetA could be a novel sensitive biomarker at the crossroad of chronic renal damage and metabolic dysfunction. Additionally, this biomarker could also represent a non-invasive, complementary tool for discriminating among different CKD etiologies (DKD vs. non-DKD) in difficult cases or when renal biopsy is not available.

PREDICTING CARDIAC ISSUES FROM ECHOCARDIOGRAMS: A LITERATURE REVIEW USING DEEP LEARNING AND MACHINE LEARNING TECHNIQUES

Cardiovascular disease (CVD) has a substantial impact on overall health, well-being, and life-expectancy. Echocardiography is a widely used imaging technique in cardiovascular medicine, utilizing various medical imaging technology to visualize heart chambers and valve’s motion activity. In order to diagnose and treat complicated cardiovascular problems, it takes high-resolution images of the heart and its surroundings. However, it has limitations such as long procedure times, multiple measurement values, complex analyses, individualized assessments, operator subjectivity, and wide observation ranges. This makes it challenging for sonographers to accurately detect and diagnose heart diseases. In recent days, Deep Learning (DL) is increasingly used in clinical computer-assisted systems for disease detection, feature segmentation, functional evaluation, and diagnosis. It is an alternate technique for accurate detection and treatment of cardiovascular disorders; it improves the diagnostic capacities of echocardiography by identifying pathological conditions, extracting anatomically significant data, measuring cardio-motion, and calculating echo image quality. This paper presents a detailed review of various DL frameworks developed to analyse different cardiac views using echocardiography for improving the prediction and diagnosis of CVD. At the outset, a variety of echocardiography systems linked with DL-based segmentation and classification are reviewed briefly. Afterwards, a comparison research is carried out to gain insight into the shortcomings of those algorithms and provide a fresh approach to improve the accuracy of cardiac view categorization in echocardiography systems.

Radiological usual interstitial pneumonia pattern is associated with two-year mortality in patients with idiopathic pulmonary fibrosis

IntroductionThe new diagnostic guidelines for idiopathic pulmonary fibrosis (IPF) did not rule out the possibility of combining the radiological patterns of usual interstitial pneumonia (UIP) and probable UIP, given the similar management and diagnostic capacity. However, the prognostic implications of these patterns have not been fully elucidated, with different studies showing heterogeneous results. We applied the new criteria to a retrospective series of patients with IPF, assessing survival based on radiological patterns, findings, and their extension. MethodsTwo thoracic radiologists reviewed high-resolution computed tomography images taken at diagnosis in 146 patients with IPF, describing the radiological findings and patterns. The association of each radiological finding and radiological patterns with two-year mortality was analysed. ResultsThe two-year mortality rate was 40.2% in IPF patients with an UIP radiological pattern versus 7.1% in those with probable UIP. Compared to the UIP pattern, probable UIP was protective against mortality, even after adjusting for age, sex, pulmonary function, and extent of fibrosis (hazard ratio (HR) 0.23, 95% confidence interval (CI) 0.06–0.99). Receiving antifibrotic treatment was also a protective factor (HR 0.51, 95%CI 0.27–0.98). Honeycombing (HR 3.62, 95%CI 1.27–10.32), an acute exacerbation pattern (HR 4.07, 95%CI 1.84–8.96), and the overall extent of fibrosis (HR 1.04, 95%CI 1.02–1.06) were predictors of mortality. ConclusionsIn our series, two-year mortality was higher in patients with IPF who presented a radiological pattern of UIP versus probable UIP on the initial scan. Honeycombing, an acute exacerbation pattern, and a greater overall extent of fibrosis were also predictors of increased mortality. The prognostic differences between the radiological pattern of UIP and probable UIP in our series would support maintaining them as two differentiated patterns.

Plasma tryptophan pathway metabolites quantified by liquid chromatography-tandem mass spectrometry as biomarkers in neuroendocrine tumor patients.

A good and accessible biomarker is of great clinical value in neuroendocrine tumor (NET) patients, especially considering its frequently indolent nature and long-term follow-up. Plasma chromogranin A (CgA) and 5-hydroxyindoleacetic acid (5-HIAA) are currently used as biomarkers in NET, but their sensitivity and specificity are restricted. 5-HIAA is the main metabolite of serotonin, an important neurotransmitter of the tryptophan pathway. The aim of this study is to estabish a sensitive and accurate method for the quantification of tryptophan pathway metabolites in plasma. We further aimed to evaluate its utility as a clinical tool in NET disease. We obtained plasma samples from NET patients and healthy controls recruited from the University Hospital of North Norway, Tromsø. Samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and eight metabolites of the tryptophan pathway were quantified. We included 130 NET patients (72/130 small intestinal [SI] NET, 35/130 pancreatic NET, 23/130 other origin) and 20 healthy controls. In the SI-NET group, 26/72 patients presented with symptoms of carcinoid syndrome (CS). We found that combining tryptophan metabolites into a serotonin/kynurenine pathway ratio improved diagnostic sensitivity (92.3%) and specificity (100%) in detecting CS patients from healthy controls compared with plasma 5-HIAA alone (sensitivity 84.6%/specificity 100%). Further, a clinical marker based on the combination of plasma serotonin, 5-HIAA, and 5OH-tryptophan, increased diagnostic capacity identifying NET patients with metastasized disease from healthy controls compared with singular plasma 5-HIAA, serotonin, or CgA. In addition, this marker was positive in 61% of curatively operated SI-NET patients compared with only 10% of healthy controls (p < .001). Our results indicate that simultaneous quantification of several tryptophan metabolites in plasma, using LC-MS/MS, may represent a clinically useful diagnostic tool in NET disease.

A Clinical Breathomics Dataset

This study entailed a comprehensive GC‒MS analysis conducted on 121 patient samples to generate a clinical breathomics dataset. Breath molecules, indicative of diverse conditions such as psychological and pathological states and the microbiome, were of particular interest due to their non-invasive nature. The highlighted noninvasive approach for detecting these breath molecules significantly enhances diagnostic and monitoring capacities. This dataset cataloged volatile organic compounds (VOCs) from the breath of individuals with asthma, bronchiectasis, and chronic obstructive pulmonary disease. Uniform and consistent sample collection protocols were strictly adhered to during the accumulation of this extensive dataset, ensuring its reliability. It encapsulates extensive human clinical breath molecule data pertinent to three specific diseases. This consequential clinical breathomics dataset is a crucial resource for researchers and clinicians in identifying and exploring important compounds within the patient’s breath, thereby augmenting future diagnostic and therapeutic initiatives.