Diagnostics

7 pages, 832 KiB

Open AccessBrief Report

Performance of Whole Blood Stimulation Assays for the Quantification of SARS-CoV-2 Specific T-Cell Response: A Cross-Sectional Study

by Federica Bergami, Francesca Arena, Eleonora Francesca Pattonieri, Marilena Gregorini, Federica Meloni, Massimo Abelli, Elena Ticozzelli, Giorgia Testa, Daniele Lilleri, Irene Cassaniti and Fausto Baldanti

Diagnostics 2022, 12(6), 1509; https://doi.org/10.3390/diagnostics12061509 - 20 Jun 2022

Cited by 3 | Viewed by 2335

Abstract

Since the identification of the new severe acute respiratory syndrome virus 2 (SARS-CoV-2), a huge effort in terms of diagnostic strategies has been deployed. To date, serological assays represent a valuable tool for the identification of recovered COVID-19 patients and for the monitoring [...] Read more.

Since the identification of the new severe acute respiratory syndrome virus 2 (SARS-CoV-2), a huge effort in terms of diagnostic strategies has been deployed. To date, serological assays represent a valuable tool for the identification of recovered COVID-19 patients and for the monitoring of immune response elicited by vaccination. However, the role of T-cell response should be better clarified and simple and easy to perform assays should be routinely introduced. The main aim of this study was to compare a home-made assay for whole blood stimulation with a standardized ELISpot assay design in our laboratory for the assessment of spike-specific T-cell response in vaccinated subjects. Even if a good correlation between the assays was reported, a higher percentage of responder subjects was reported for immunocompromised subjects with ELISpot assay (56%) than home-made whole blood stimulation assay (33%). Additionally, three commercial assays were compared with our home-made assay, reporting a good agreement in terms of both positive and negative results. Full article

(This article belongs to the Section Diagnostic Microbiology and Infectious Disease)

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24 pages, 428 KiB

Open AccessReview

False Negative Results in Cervical Cancer Screening—Risks, Reasons and Implications for Clinical Practice and Public Health

by Anna Macios and Andrzej Nowakowski

Diagnostics 2022, 12(6), 1508; https://doi.org/10.3390/diagnostics12061508 - 20 Jun 2022

Cited by 8 | Viewed by 4914

Abstract

False negative (FN) results in cervical cancer (CC) screening pose serious risks to women. We present a comprehensive literature review on the risks and reasons of obtaining the FN results of primary CC screening tests and triage methods and discuss their clinical and [...] Read more.

False negative (FN) results in cervical cancer (CC) screening pose serious risks to women. We present a comprehensive literature review on the risks and reasons of obtaining the FN results of primary CC screening tests and triage methods and discuss their clinical and public health impact and implications. Misinterpretation or true lack of abnormalities on a slide are the reasons of FN results in cytology and p16/Ki-67 dual-staining. For high-risk human papillomavirus (HPV) molecular tests, those include: truly non-HPV-associated tumors, lesions driven by low-risk HPV types, and clearance of HPV genetic material before sampling. Imprecise disease threshold definition lead to FN results in visual inspection with acetic acid. Lesions with a discrete colposcopic appearance are a source of FN in colposcopic procedures. For FAM19A4 and hsa-miR124-2 genes methylation, those may originate from borderline methylation levels. Histological misinterpretation, sampling, and laboratory errors also play a role in all types of CC screening, as well as reproducibility issue, especially in methods based on human-eye evaluation. Primary HPV-based screening combined with high quality-assured immunocytochemical and molecular triage methods seem to be an optimal approach. Colposcopy with histological evaluation remains the gold standard for diagnosis but requires quality protocols and assurance measures. Full article

(This article belongs to the Section Pathology and Molecular Diagnostics)

23 pages, 1670 KiB

Open AccessReview

Hodgkin Lymphoma: Biology and Differential Diagnostic Problem

by Taishi Takahara, Akira Satou, Toyonori Tsuzuki and Shigeo Nakamura

Diagnostics 2022, 12(6), 1507; https://doi.org/10.3390/diagnostics12061507 - 20 Jun 2022

Cited by 4 | Viewed by 4988

Abstract

Hodgkin lymphomas (HLs) are lymphoid neoplasms that are morphologically defined as being composed of dysplastic cells, namely, Hodgkin and Reed–Sternberg cells, in a reactive inflammatory background. The biological nature of HLs has long been unclear; however, our understanding of HL-related genetics and tumor [...] Read more.

Hodgkin lymphomas (HLs) are lymphoid neoplasms that are morphologically defined as being composed of dysplastic cells, namely, Hodgkin and Reed–Sternberg cells, in a reactive inflammatory background. The biological nature of HLs has long been unclear; however, our understanding of HL-related genetics and tumor microenvironment interactions is rapidly expanding. For example, cell surface overexpression of programmed cell death 1 ligand 1 (CD274/PD-L1) is now considered a defining feature of an HL subset, and targeting such immune checkpoint molecules is a promising therapeutic option. Still, HLs comprise multiple disease subtypes, and some HL features may overlap with its morphological mimics, posing challenging diagnostic and therapeutic problems. In this review, we summarize the recent advances in understanding the biology of HLs, and discuss approaches to differentiating HL and its mimics. Full article

(This article belongs to the Special Issue Diagnostic Pathology of Lymphomas and Lymphoproliferative Disorders)

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10 pages, 431 KiB

Open AccessArticle

Cone Beam Computerized Tomographic Analysis of Collum Angle of the Maxillary Central Incisors in Different Types of Malocclusion: Comparative Assessment in Saudi, Jordan and Egypt Subpopulation

by Rakhi Issrani, Namdeo Prabhu, Sunitha Siddanna, Sarah Hatab Alanazi, Mohammad Khursheed Alam, Manay Srinivas Munisekhar, May Othman Hamza, Reham Fawzi Dawood Shabanah and Rasha Saleh Ali Gadh

Diagnostics 2022, 12(6), 1506; https://doi.org/10.3390/diagnostics12061506 - 20 Jun 2022

Cited by 2 | Viewed by 2088

Abstract

Background: The collum angle (CA) is an extremely significant for patients who are undergoing orthodontic, dental implant restoration, prosthodontic and periodontic treatments. Aim and Objectives: To determine and compare the mean CA for maxillary central incisor in different types of malocclusion utilizing 3D [...] Read more.

Background: The collum angle (CA) is an extremely significant for patients who are undergoing orthodontic, dental implant restoration, prosthodontic and periodontic treatments. Aim and Objectives: To determine and compare the mean CA for maxillary central incisor in different types of malocclusion utilizing 3D Cone Beam Computerized Tomography (CBCT) images. The additional objectives were to determine and compare the mean CA for maxillary central incisor based upon the demographic characteristics among Saudi, Jordan and Egypt subpopulation and to test for significant differences in the CA of maxillary central incisor with different molar malocclusions. Methodology: A total of 400 CBCT images were included from the radiology archive at the College of Dentistry, Jouf University (Sakaka, Saudi Arabia). The CBCT images were divided into four groups based upon molar classifications. The selected records were used for the measurement of CA of maxillary central incisor using the measurement tool built into 3D:OnDemand software. Statistical analysis was done using independent t test and ANOVA to examine the differences between gender and races. Results: The mean CA for Class II div 2 exhibited significantly higher crown-root variation as compared other groups (p < 0.0001). Males sample showed greater value of CA for each group as compared to the females and this difference was statistically significant for all the groups other than for Class I (p < 0.05). The post hoc pairwise comparisons between the races showed statistically insignificant findings (p > 0.05). Significant difference was found on pairwise comparisons among different malocclusion groups other than for group Class I/Class II div 1 (p < 0.05). Conclusion: The CA of Class II div 2 group was the greatest as compared to other malocclusion groups. Males sample showed greater value of CA for each group as compared to the females and this difference was statistically significant for all the groups other than for Class I. Statistically insignificant difference was noted for the mean CA among different races whereas significant difference was found on pairwise comparisons among different malocclusion groups other than for group Class I/Class II div 1. Full article

(This article belongs to the Special Issue Advances in Orthodontic Diagnosis and Treatment)

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14 pages, 1545 KiB

Open AccessArticle

COVID-19 Underreporting in Brazil among Patients with Severe Acute Respiratory Syndrome during the Pandemic: An Ecological Study

by Tainá Momesso Lima, Camila Vantini Capasso Palamim, Vitória Franchini Melani, Matheus Ferreira Mendes, Letícia Rojina Pereira and Fernando Augusto Lima Marson

Diagnostics 2022, 12(6), 1505; https://doi.org/10.3390/diagnostics12061505 - 20 Jun 2022

Cited by 8 | Viewed by 2051

Abstract

Underreporting of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection is a global problem and might hamper Coronavirus Disease (COVID-19) epidemiological control. Taking this into consideration, we estimated possible SARS-CoV-2 infection underreporting in Brazil among patients with severe acute respiratory syndrome (SARS). An [...] Read more.

Underreporting of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection is a global problem and might hamper Coronavirus Disease (COVID-19) epidemiological control. Taking this into consideration, we estimated possible SARS-CoV-2 infection underreporting in Brazil among patients with severe acute respiratory syndrome (SARS). An ecological study using a descriptive analysis of the SARS report was carried out based on data supplied by the Influenza Epidemiological Surveillance Information (SIVEP)-Flu (in Brazilian Portuguese, Sistema de Vigilância Epidemiológica da Gripe) in the period between January 2015 and March 2021. The number of SARS cases and related deaths after infection by SARS-CoV-2 or Influenzae was described. The estimation of underreporting was evaluated considering the relative increase in the number of cases with undefined etiological agent comparing 2020 to 2015–2019; and descriptive analysis was carried out including data from January–March/2021. In our data, SARS-CoV-2 infection and the presence of SARS with undefined etiological agent were associated with the higher number of cases and deaths from SARS in 2020/2021. SARS upsurge was six times over that expected in 2020, according to SARS seasonality in previous years (2015–2019). The lowest possible underdiagnosis rate was observed in the age group < 2 y.o. and individuals over 30 y.o., with ~50%; while in the age groups 10–19 and 20–29 y.o., the rates were 200–250% and 100%, respectively. For the remaining age groups (2–5 and 5–9 y.o.) underreporting was over 550%, except for female individuals in the age group 2–5 y.o., in which a ~500% rate was found. Our study described that the SARS-CoV-2 infection underreporting rate in Brazil in SARS patients is alarming and presents different indices, mainly associated with the patients’ age groups. Our results, mainly the underreporting index according to sex and age, should be evaluated with caution. Full article

(This article belongs to the Special Issue SARS-CoV-2 Identification by Diagnostic/Medical Laboratory: Methods, Statistics, Interesting Cases)

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23 pages, 1561 KiB

Open AccessArticle

Predicting Visual Acuity in Patients Treated for AMD

by Beatrice-Andreea Marginean, Adrian Groza, George Muntean and Simona Delia Nicoara

Diagnostics 2022, 12(6), 1504; https://doi.org/10.3390/diagnostics12061504 - 20 Jun 2022

Cited by 1 | Viewed by 2487

Abstract

The leading diagnostic tool in modern ophthalmology, Optical Coherence Tomography (OCT), is not yet able to establish the evolution of retinal diseases. Our task is to forecast the progression of retinal diseases by means of machine learning technologies. The aim is to help [...] Read more.

The leading diagnostic tool in modern ophthalmology, Optical Coherence Tomography (OCT), is not yet able to establish the evolution of retinal diseases. Our task is to forecast the progression of retinal diseases by means of machine learning technologies. The aim is to help the ophthalmologist to determine when early treatment is needed in order to prevent severe vision impairment or even blindness. The acquired data are made up of sequences of visits from multiple patients with age-related macular degeneration (AMD), which, if not treated at the appropriate time, may result in irreversible blindness. The dataset contains 94 patients with AMD and there are 161 eyes included with more than one medical examination. We used various techniques from machine learning (linear regression, gradient boosting, random forest and extremely randomised trees, bidirectional recurrent neural network, LSTM network, GRU network) to handle technical challenges such as how to learn from small-sized time series, how to handle different time intervals between visits, and how to learn from different numbers of visits for each patient (1–5 visits). For predicting the visual acuity, we performed several experiments with different features. First, by considering only previous measured visual acuity, the best accuracy of 0.96 was obtained based on a linear regression. Second, by considering numerical OCT features such as previous thickness and volume values in all retinal zones, the LSTM network reached the highest score (

R^{2} = 0.99

). Third, by considering the fundus scan images represented as embeddings obtained from the convolutional autoencoder, the accuracy was increased for all algorithms. The best forecasting results for visual acuity depend on the number of visits and features used for predictions, i.e., 0.99 for LSTM based on three visits (monthly resampled series) based on numerical OCT values, fundus images, and previous visual acuities. Full article

(This article belongs to the Special Issue Artificial Intelligence in Clinical Medical Imaging Analysis)

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18 pages, 2167 KiB

Open AccessReview

COVID-19 Diagnosis: A Comprehensive Review of the RT-qPCR Method for Detection of SARS-CoV-2

by Debashis Dutta, Sarah Naiyer, Sabanaz Mansuri, Neeraj Soni, Vandana Singh, Khalid Hussain Bhat, Nishant Singh, Gunjan Arora and M. Shahid Mansuri

Diagnostics 2022, 12(6), 1503; https://doi.org/10.3390/diagnostics12061503 - 20 Jun 2022

Cited by 48 | Viewed by 9649

Abstract

The world is grappling with the coronavirus disease 2019 (COVID-19) pandemic, the causative agent of which is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 symptoms are similar to the common cold, including fever, sore throat, cough, muscle and chest pain, brain fog, [...] Read more.

The world is grappling with the coronavirus disease 2019 (COVID-19) pandemic, the causative agent of which is severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 symptoms are similar to the common cold, including fever, sore throat, cough, muscle and chest pain, brain fog, dyspnoea, anosmia, ageusia, and headache. The manifestation of the disease can vary from being asymptomatic to severe life-threatening conditions warranting hospitalization and ventilation support. Furthermore, the emergence of mutecated variants of concern (VOCs) is paramount to the devastating effect of the pandemic. This highly contagious virus and its emergent variants challenge the available advanced viral diagnostic methods for high-accuracy testing with faster result yields. This review is to shed light on the natural history, pathology, molecular biology, and efficient diagnostic methods of COVID-19, detecting SARS-CoV-2 in collected samples. We reviewed the gold standard RT-qPCR method for COVID-19 diagnosis to confer a better understanding and application to combat the COVID-19 pandemic. This comprehensive review may further develop awareness about the management of the COVID-19 pandemic. Full article

(This article belongs to the Collection Virus Diagnostic Methods and Techniques: Learning from the COVID-19 Global Outbreak)

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14 pages, 7500 KiB

Open AccessArticle

MCRS1 Expression Regulates Tumor Activity and Affects Survival Probability of Patients with Gastric Cancer

by Liang-Han Wang, Chih-Chun Chang, Chiao-Yin Cheng, Yao-Jen Liang, Dee Pei, Jen-Tang Sun and Yen-Lin Chen

Diagnostics 2022, 12(6), 1502; https://doi.org/10.3390/diagnostics12061502 - 20 Jun 2022

Viewed by 2174

Abstract

Gastric cancer is the fifth most common cancer worldwide and the third most common cause of cancer-related deaths. Surgery remains the first-choice treatment. Chemotherapy is considered in the middle and advanced stages, but has limited success. Microspherule protein 1 (MCRS1, also known as [...] Read more.

Gastric cancer is the fifth most common cancer worldwide and the third most common cause of cancer-related deaths. Surgery remains the first-choice treatment. Chemotherapy is considered in the middle and advanced stages, but has limited success. Microspherule protein 1 (MCRS1, also known as MSP58) is a protein originally identified in the nucleus and cytoplasm that is involved in the cell cycle. High expression of MCRS1 increases tumor growth, invasiveness, and metastasis. The mechanistic relationships between MCSR1 and proliferation, apoptosis, angiogenesis, and epithelial–mesenchymal transition (EMT) remain to be elucidated. We clarified these relationships using immunostaining of tumor tissues and normal tissues from patients with gastric cancer. High MCRS1 expression in gastric cancer positively correlated with Ki-67, Caspase3, CD31, Fibronectin, pAKT, and pAMPK. The hazard ratio of high MCRS1 expression was 2.44 times that of low MCRS1 expression, negatively impacting patient survival. Full article

(This article belongs to the Special Issue Advances in the Detection and Screening of Gastric Cancer)

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15 pages, 7468 KiB

Open AccessArticle

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients

by Camilla Risoli, Marco Nicolò, Davide Colombi, Marco Moia, Fausto Rapacioli, Pietro Anselmi, Emanuele Michieletti, Roberta Ambrosini, Marco Di Terlizzi, Luigi Grazioli, Cristian Colmo, Angelo Di Naro, Matteo Pio Natale, Alessandro Tombolesi, Altin Adraman, Domenico Tuttolomondo, Cosimo Costantino, Elisa Vetti and Chiara Martini

Diagnostics 2022, 12(6), 1501; https://doi.org/10.3390/diagnostics12061501 - 20 Jun 2022

Cited by 13 | Viewed by 3300

Abstract

Background: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients [...] Read more.

Background: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. Methods: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. Results: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73–0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90–0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer “LungCTAnalyzer” and the median of the visual score (0.75 with a CI 0.67–82 and with a median value of 22% of disease extension for the software and 25% for the visual values). Conclusions: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters. Full article

(This article belongs to the Special Issue Advances in Cardiopulmonary Imaging)

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13 pages, 1219 KiB

Open AccessArticle

Cardiac Rehabilitation and Mortality Risk Reduction in Peripheral Artery Disease at 6-Month Outcome

by Razvan Anghel, Cristina Andreea Adam, Ovidiu Mitu, Dragos Traian Marius Marcu, Viviana Onofrei, Mihai Roca, Alexandru Dan Costache, Radu Stefan Miftode, Grigore Tinica and Florin Mitu

Diagnostics 2022, 12(6), 1500; https://doi.org/10.3390/diagnostics12061500 - 20 Jun 2022

Cited by 5 | Viewed by 2476

Abstract

The management of patients with peripheral artery disease (PAD) is integrative and multidisciplinary, in which cardiac rehabilitation (CR) plays a prognostic role in terms of functional status, quality of life, and long-term impact on morbidity and mortality. We conducted a prospective cohort study [...] Read more.

The management of patients with peripheral artery disease (PAD) is integrative and multidisciplinary, in which cardiac rehabilitation (CR) plays a prognostic role in terms of functional status, quality of life, and long-term impact on morbidity and mortality. We conducted a prospective cohort study on 97 patients with PAD admitted to a single tertiary referral center. Based on a prognostic index developed to stratify long-term mortality risk in PAD patients, we divided the cohort into two groups: low and low-intermediate risk group (45 cases) and high-intermediate and high risk group (52 cases). We analyzed demographics, clinical parameters, and paraclinical parameters in the two groups, as well as factors associated with cardiological reassessment prior to the established deadline of 6 months. Obesity (p = 0.048), renal dysfunction (p < 0.001), dyslipidemia (p < 0.001), tobacco use (p = 0.048), and diabetes mellitus (p < 0.001) are comorbidities with long-term prognostic value. Low-density lipoprotein cholesterol (p = 0.002), triglycerides (p = 0.032), fasting glucose (p = 0.011), peak oxygen uptake (p = 0.005), pain-free walking distance (p = 0.011), maximum walking time (p < 0.001), and maximum walking distance (p = 0.002) influence the outcome of PAD patients by being factors associated with clinical improvement at the 6-month follow-up. PAD patients benefit from enrollment in CR programs, improvement of clinical signs, lipid and carbohydrate profile, and weight loss and maintenance of blood pressure profile within normal limits, as well as increased exercise capacity being therapeutic targets. Full article

(This article belongs to the Special Issue Clinical Advances in Diagnostics & Management of Cardiovascular Diseases)

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9 pages, 1575 KiB

Open AccessArticle

Optics and Utility of Low-Cost Smartphone-Based Portable Digital Fundus Camera System for Screening of Retinal Diseases

by K. V. Chalam, Joud Chamchikh and Suzie Gasparian

Diagnostics 2022, 12(6), 1499; https://doi.org/10.3390/diagnostics12061499 - 20 Jun 2022

Cited by 10 | Viewed by 4019

Abstract

Purpose: To describe optical principles and utility of inexpensive, portable, non-contact digital smartphone-based camera for the acquisition of fundus photographs for the evaluation of retinal disorders. Methods: The digital camera has a high-quality glass 25 D condensing lens attached to a 21.4-megapixel smartphone [...] Read more.

Purpose: To describe optical principles and utility of inexpensive, portable, non-contact digital smartphone-based camera for the acquisition of fundus photographs for the evaluation of retinal disorders. Methods: The digital camera has a high-quality glass 25 D condensing lens attached to a 21.4-megapixel smartphone camera. The white-emitting LED light of the smartphone at low illumination levels is used to visualize the fundus and limit source reflection. The camera captures a high-definition fundus (5344 × 4016) image on a complementary metal oxide semiconductor (CMO) with an area of 6.3 mm × 4.5 mm. The auto-acquisition mode of the device facilitates the quick capture of the image from continuous video streaming in a fraction of a second. Results: This new smartphone-based camera provides high-resolution digital images of the retina (50° telescopic view) in patients at a fraction of the cost (USD 1000) of established, non-transportable, office-based fundus photography systems. Conclusions: The portable user-friendly smartphone-based digital camera is a useful alternative for the acquisition of fundus photographs and provides a tool for screening retinal diseases in various clinical settings such as primary care clinics or emergency rooms. The ease of acquisition of photographs from a continuously streaming video of fundus obviates the need for a skilled photographer. Full article

(This article belongs to the Section Biomedical Optics)

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10 pages, 4319 KiB

Open AccessArticle

Monitoring Protein Denaturation of Egg White Using Passive Microwave Radiometry (MWR)

by Igor Goryanin, Lev Ovchinnikov, Sergey Vesnin and Yuri Ivanov

Diagnostics 2022, 12(6), 1498; https://doi.org/10.3390/diagnostics12061498 - 19 Jun 2022

Cited by 1 | Viewed by 2266

Abstract

Passive microwave radiometry (MWR) is a measurement technique based on the detection of passive radiation in the microwave spectrum of different objects. When in equilibrium, this radiation is known to be proportional to the thermodynamic temperature of an emitting body. We hypothesize that [...] Read more.

Passive microwave radiometry (MWR) is a measurement technique based on the detection of passive radiation in the microwave spectrum of different objects. When in equilibrium, this radiation is known to be proportional to the thermodynamic temperature of an emitting body. We hypothesize that living systems feature other mechanisms of emission that are based on protein unfolding and water rotational transitions. To understand the nature of these emissions, microwave radiometry was used in several in vitro experiments. In our study, we performed pilot measurements of microwave emissions from egg whites during denaturation induced by ethanol. Egg whites comprise 10% proteins, such as albumins, mucoproteins, and globulins. We observed a novel phenomenon: microwave emissions changed without a corresponding change in the water’s thermodynamic temperature. We also found striking differences between microwave emissions and thermodynamic temperature kinetics. Therefore, we hypothesize that these two processes are unrelated, contrary to what was thought before. It is known that some pathologies such as stroke or brain trauma feature increased microwave emissions. We hypothesize that this phenomenon originates from protein denaturation and is not related to the thermodynamic temperature. As such, our findings could explain the reason for the increase in microwave emissions after trauma and post mortem for the first time. These findings could be used for the development of novel diagnostics methods. The MWR method is inexpensive and does not require fluorescent or radioactive labels. It can be used in different areas of basic and applied pharmaceutical research, including in kinetics studies in biomedicine. Full article

(This article belongs to the Section Point-of-Care Diagnostics and Devices)

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7 pages, 6233 KiB

Open AccessInteresting Images

A Case of Hepatic Immunoglobulin G4-Related Disease Presenting as an Inflammatory Pseudotumor and Sclerosing Cholangitis

by Se Young Jang, Young Seok Han, Sang Yub Lee, Ja Ryung Han, Young Oh Kweon, Won Young Tak, Soo Young Park, Yu Rim Lee, Hun Kyu Ryeom, Jung Guen Cha, Jihoon Hong and Yoo Na Kang

Diagnostics 2022, 12(6), 1497; https://doi.org/10.3390/diagnostics12061497 - 19 Jun 2022

Cited by 3 | Viewed by 1919

Abstract

An inflammatory pseudotumor is a benign disease characterized by tumor-like lesions consisting of inflammatory cells including plasma cells and fibrous tissue. Recently, some inflammatory pseudotumor cases proved to be a form of Immunoglobulin G4-related disease (IgG4-RD). This novel clinical entity, recognized as a [...] Read more.

An inflammatory pseudotumor is a benign disease characterized by tumor-like lesions consisting of inflammatory cells including plasma cells and fibrous tissue. Recently, some inflammatory pseudotumor cases proved to be a form of Immunoglobulin G4-related disease (IgG4-RD). This novel clinical entity, recognized as a fibroinflammatory condition, is characterized by lymphoplasmacytic infiltration with a predominance of IgG4-positive plasma cells, storiform fibrosis, and often elevated serum IgG4 concentrations. We report a case of IgG4-RD in the form of an inflammatory pseudotumor in the liver with combined sclerosing cholangitis. We recommend that for diagnosing IgG4-RD accurately, it is important to obtain adequate tissue samples and follow-up the lesion in clinical practice. Full article

(This article belongs to the Special Issue Advancements on Diagnostic and Management of Liver Disease)

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22 pages, 11590 KiB

Open AccessArticle

Explainable Machine Learning-Based Risk Prediction Model for In-Hospital Mortality after Continuous Renal Replacement Therapy Initiation

by Pei-Shan Hung, Pei-Ru Lin, Hsin-Hui Hsu, Yi-Chen Huang, Shin-Hwar Wu and Chew-Teng Kor

Diagnostics 2022, 12(6), 1496; https://doi.org/10.3390/diagnostics12061496 - 19 Jun 2022

Cited by 8 | Viewed by 2677

Abstract

In this study, we established an explainable and personalized risk prediction model for in-hospital mortality after continuous renal replacement therapy (CRRT) initiation. This retrospective cohort study was conducted at Changhua Christian Hospital (CCH). A total of 2932 consecutive intensive care unit patients receiving [...] Read more.

In this study, we established an explainable and personalized risk prediction model for in-hospital mortality after continuous renal replacement therapy (CRRT) initiation. This retrospective cohort study was conducted at Changhua Christian Hospital (CCH). A total of 2932 consecutive intensive care unit patients receiving CRRT between 1 January 2010, and 30 April 2021, were identified from the CCH Clinical Research Database and were included in this study. The recursive feature elimination method with 10-fold cross-validation was used and repeated five times to select the optimal subset of features for the development of machine learning (ML) models to predict in-hospital mortality after CRRT initiation. An explainable approach based on ML and the SHapley Additive exPlanation (SHAP) and a local explanation method were used to evaluate the risk of in-hospital mortality and help clinicians understand the results of ML models. The extreme gradient boosting and gradient boosting machine models exhibited a higher discrimination ability (area under curve [AUC] = 0.806, 95% CI = 0.770–0.843 and AUC = 0.823, 95% CI = 0.788–0.858, respectively). The SHAP model revealed that the Acute Physiology and Chronic Health Evaluation II score, albumin level, and the timing of CRRT initiation were the most crucial features, followed by age, potassium and creatinine levels, SPO2, mean arterial pressure, international normalized ratio, and vasopressor support use. ML models combined with SHAP and local interpretation can provide the visual interpretation of individual risk predictions, which can help clinicians understand the effect of critical features and make informed decisions for preventing in-hospital deaths. Full article

(This article belongs to the Special Issue Diagnostic Modalities in Critical Care)

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17 pages, 740 KiB

Open AccessArticle

Diagnostic Coding Intensity among a Pneumonia Inpatient Cohort Using a Risk-Adjustment Model and Claims Data: A U.S. Population-Based Study

by Ruchi Mishra, Himadri Verma, Venkata Bhargavi Aynala, Paul R. Arredondo, John Martin, Michael Korvink and Laura H. Gunn

Diagnostics 2022, 12(6), 1495; https://doi.org/10.3390/diagnostics12061495 - 19 Jun 2022

Cited by 5 | Viewed by 2406

Abstract

Hospital payments depend on the Medicare Severity Diagnosis-Related Group’s estimated cost and the set of diagnoses identified during inpatient stays. However, over-coding and under-coding diagnoses can occur for different reasons, leading to financial and clinical consequences. We provide a novel approach to measure [...] Read more.

Hospital payments depend on the Medicare Severity Diagnosis-Related Group’s estimated cost and the set of diagnoses identified during inpatient stays. However, over-coding and under-coding diagnoses can occur for different reasons, leading to financial and clinical consequences. We provide a novel approach to measure diagnostic coding intensity, built on commonly available administrative claims data, and demonstrated through a 2019 pneumonia acute inpatient cohort (N = 182,666). A Poisson additive model (PAM) is proposed to model risk-adjusted additional coded diagnoses. Excess coding intensity per patient visit was estimated as the difference between the observed and PAM-based expected counts of secondary diagnoses upon risk adjustment by patient-level characteristics. Incidence rate ratios were extracted for patient-level characteristics and further adjustments were explored by facility-level characteristics to account for facility and geographical differences. Facility-level factors contribute substantially to explain the remaining variability in excess diagnostic coding, even upon adjusting for patient-level risk factors. This approach can provide hospitals and stakeholders with a tool to identify outlying facilities that may experience substantial differences in processes and procedures compared to peers or general industry standards. The approach does not rely on the availability of clinical information or disease-specific markers, is generalizable to other patient cohorts, and can be expanded to use other sources of information, when available. Full article

(This article belongs to the Special Issue Advances in the Diagnosis of Pneumonia)

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37 pages, 73340 KiB

Open AccessReview

Role of Echocardiography in the Diagnosis and Interventional Management of Atrial Septal Defects

by P. Syamasundar Rao

Diagnostics 2022, 12(6), 1494; https://doi.org/10.3390/diagnostics12061494 - 18 Jun 2022

Cited by 6 | Viewed by 9325

Abstract

This review centers on the usefulness of echo-Doppler studies in the diagnosis of ostium secundum atrial septal defects (ASDs) and in their management, both in children and adults. Transthoracic echocardiography can easily identify the secundum ASDs and also differentiate secundum ASDs from other [...] Read more.

This review centers on the usefulness of echo-Doppler studies in the diagnosis of ostium secundum atrial septal defects (ASDs) and in their management, both in children and adults. Transthoracic echocardiography can easily identify the secundum ASDs and also differentiate secundum ASDs from other kinds of ASDs, such as ostium primum ASD, sinus venosus ASD and coronary sinus ASD, as well as patent foramen ovale. Preliminary selection of patients for device occlusion can be made by transthoracic echocardiograms while final selection is based on transesophageal (TEE) or intracardiac (ICE) echocardiographic studies with optional balloon sizing of ASDs. TEE and ICE are extremely valuable in guiding device implantation and in evaluating the position of the device following its implantation. Echo-Doppler evaluation during follow-up is also useful in documenting improvements in ventricular size and function, in assessing the device position, in detecting residual shunts, and in identifying rare device-related complications. Examples of echo images under each section are presented. The reasons why echo-Doppler is very valuable in diagnosing and managing ASDs are extensively discussed. Full article

(This article belongs to the Special Issue Diagnosis and Management of Congenital Heart Disease)

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21 pages, 32993 KiB

Open AccessArticle

MixPatch: A New Method for Training Histopathology Image Classifiers

by Youngjin Park, Mujin Kim, Murtaza Ashraf, Young Sin Ko and Mun Yong Yi

Diagnostics 2022, 12(6), 1493; https://doi.org/10.3390/diagnostics12061493 - 18 Jun 2022

Cited by 3 | Viewed by 3010

Abstract

CNN-based image processing has been actively applied to histopathological analysis to detect and classify cancerous tumors automatically. However, CNN-based classifiers generally predict a label with overconfidence, which becomes a serious problem in the medical domain. The objective of this study is to propose [...] Read more.

CNN-based image processing has been actively applied to histopathological analysis to detect and classify cancerous tumors automatically. However, CNN-based classifiers generally predict a label with overconfidence, which becomes a serious problem in the medical domain. The objective of this study is to propose a new training method, called MixPatch, designed to improve a CNN-based classifier by specifically addressing the prediction uncertainty problem and examine its effectiveness in improving diagnosis performance in the context of histopathological image analysis. MixPatch generates and uses a new sub-training dataset, which consists of mixed-patches and their predefined ground-truth labels, for every single mini-batch. Mixed-patches are generated using a small size of clean patches confirmed by pathologists while their ground-truth labels are defined using a proportion-based soft labeling method. Our results obtained using a large histopathological image dataset shows that the proposed method performs better and alleviates overconfidence more effectively than any other method examined in the study. More specifically, our model showed 97.06% accuracy, an increase of 1.6% to 12.18%, while achieving 0.76% of expected calibration error, a decrease of 0.6% to 6.3%, over the other models. By specifically considering the mixed-region variation characteristics of histopathology images, MixPatch augments the extant mixed image methods for medical image analysis in which prediction uncertainty is a crucial issue. The proposed method provides a new way to systematically alleviate the overconfidence problem of CNN-based classifiers and improve their prediction accuracy, contributing toward more calibrated and reliable histopathology image analysis. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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11 pages, 287 KiB

Open AccessReview

The Continuum of Invasive Techniques for the Assessment of Intermediate Coronary Lesions

by Nicoleta-Monica Popa-Fotea, Alexandru Scafa-Udriste and Maria Dorobantu

Diagnostics 2022, 12(6), 1492; https://doi.org/10.3390/diagnostics12061492 - 18 Jun 2022

Viewed by 2006

Abstract

Ischemic heart disease is one of the most important causes of mortality and morbidity worldwide. Revascularization of coronary stenosis inducing ischemia, either by percutaneous or surgical intervention, significantly reduces major adverse cardiovascular events and improves quality of life. However, in cases of intermediate [...] Read more.

Ischemic heart disease is one of the most important causes of mortality and morbidity worldwide. Revascularization of coronary stenosis inducing ischemia, either by percutaneous or surgical intervention, significantly reduces major adverse cardiovascular events and improves quality of life. However, in cases of intermediate lesions, classified by a diameter stenosis between 50 and 90% by European guidelines and 40–70% in American counterparts with no clear evidence of ischemia, the indication of revascularization and impact is determined using various methods that altogether comprehensively evaluate the lesions. This review will discuss the various techniques to assess intermediate stenoses, highlighting indications and advantages, but also drawbacks. Fractional flow rate (FFR) and instantaneous wave-free ratio (iFR) are the gold standard for the functional evaluation of intermediate lesions, but there are clinical circumstances in which these pressure-wire-derived indices are not accurate. Complementary invasive investigations, mainly intravascular ultrasound and/or optical coherence tomography, offer parameters that can be correlated with FFR/iFR and additional insights into the morphology of the plaque guiding the eventual percutaneous intervention in terms of length and size of stents, thus improving the outcomes of the procedure. The development of artificial intelligence and machine learning with advanced algorithms of prediction will offer multiple scenarios for treatment, allowing real-time selection of the best strategy for revascularization. Full article

(This article belongs to the Section Pathology and Molecular Diagnostics)

21 pages, 3238 KiB

Open AccessReview

Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature

by Nathan Blake, Riana Gaifulina, Lewis D. Griffin, Ian M. Bell and Geraint M. H. Thomas

Diagnostics 2022, 12(6), 1491; https://doi.org/10.3390/diagnostics12061491 - 17 Jun 2022

Cited by 24 | Viewed by 4361

Abstract

Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this [...] Read more.

Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models. Full article

(This article belongs to the Special Issue Advances of Raman Spectroscopy in Medical Applications)

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23 pages, 1559 KiB

Open AccessSystematic Review

Systematic Review on Post-Mortem Protein Alterations: Analysis of Experimental Models and Evaluation of Potential Biomarkers of Time of Death

by Matteo Antonio Sacco, Fabrizio Cordasco, Carmen Scalise, Pietrantonio Ricci and Isabella Aquila

Diagnostics 2022, 12(6), 1490; https://doi.org/10.3390/diagnostics12061490 - 17 Jun 2022

Cited by 11 | Viewed by 2695

Abstract

Estimating the post-mortem interval (PMI) is a very complex issue due to numerous variables that may affect the calculation. Several authors have investigated the quantitative and qualitative variations of protein expression on post-mortem biological samples in certain time intervals, both in animals and [...] Read more.

Estimating the post-mortem interval (PMI) is a very complex issue due to numerous variables that may affect the calculation. Several authors have investigated the quantitative and qualitative variations of protein expression on post-mortem biological samples in certain time intervals, both in animals and in humans. However, the literature data are very numerous and often inhomogeneous, with different models, tissues and proteins evaluated, such that the practical application of these methods is limited to date. The aim of this paper was to offer an organic view of the state of the art about post-mortem protein alterations for the calculation of PMI through the analysis of the various experimental models proposed. The purpose was to investigate the validity of some proteins as “molecular clocks” candidates, focusing on the evidence obtained in the early, intermediate and late post-mortem interval. This study demonstrates how the study of post-mortem protein alterations may be useful for estimating the PMI, although there are still technical limits, especially in the experimental models performed on humans. We suggest a protocol to homogenize the study of future experimental models, with a view to the next concrete application of these methods also at the crime scene. Full article

(This article belongs to the Section Pathology and Molecular Diagnostics)

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37 pages, 2410 KiB

Open AccessReview

A Survey on Deep Learning for Precision Oncology

by Ching-Wei Wang, Muhammad-Adil Khalil and Nabila Puspita Firdi

Diagnostics 2022, 12(6), 1489; https://doi.org/10.3390/diagnostics12061489 - 17 Jun 2022

Cited by 7 | Viewed by 3620

Abstract

Precision oncology, which ensures optimized cancer treatment tailored to the unique biology of a patient’s disease, has rapidly developed and is of great clinical importance. Deep learning has become the main method for precision oncology. This paper summarizes the recent deep-learning approaches relevant [...] Read more.

Precision oncology, which ensures optimized cancer treatment tailored to the unique biology of a patient’s disease, has rapidly developed and is of great clinical importance. Deep learning has become the main method for precision oncology. This paper summarizes the recent deep-learning approaches relevant to precision oncology and reviews over 150 articles within the last six years. First, we survey the deep-learning approaches categorized by various precision oncology tasks, including the estimation of dose distribution for treatment planning, survival analysis and risk estimation after treatment, prediction of treatment response, and patient selection for treatment planning. Secondly, we provide an overview of the studies per anatomical area, including the brain, bladder, breast, bone, cervix, esophagus, gastric, head and neck, kidneys, liver, lung, pancreas, pelvis, prostate, and rectum. Finally, we highlight the challenges and discuss potential solutions for future research directions. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

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12 pages, 710 KiB

Open AccessArticle

The Impact of SARS-CoV-2 Pandemic on Patients Undergoing Radiation Therapy for Advanced Cervical Cancer at a Romanian Academic Center: A Four-Year Retrospective Analysis

by Alin Popescu, Stelian Pantea, Daniela Radu, Adrian Gluhovschi, Catalin Dumitru, George Dahma, Adelina Geanina Mocanu, Radu Neamtu, Sorin Dema, Codruta Victoria Tigmeanu, Mirela Loredana Grigoras, Silvius Alexandru Pescariu, Hazzaa Aabed and Marius Craina

Diagnostics 2022, 12(6), 1488; https://doi.org/10.3390/diagnostics12061488 - 17 Jun 2022

Viewed by 1950

Abstract

Background and Objectives: Throughout the COVID-19 pandemic, health systems worldwide adapted to support COVID-19 patients while continuing to provide assistance to patients with other potentially fatal illnesses. While patients with cancer may be at an elevated risk of severe COVID-19-related complications, their [...] Read more.

Background and Objectives: Throughout the COVID-19 pandemic, health systems worldwide adapted to support COVID-19 patients while continuing to provide assistance to patients with other potentially fatal illnesses. While patients with cancer may be at an elevated risk of severe COVID-19-related complications, their oncologic therapies generally cannot be postponed indefinitely without a negative effect on outcomes. Taking this into account, a thorough examination of the therapy management of various cancers is necessary, such as cervical cancer. Therefore, we aimed to develop a retrospective cohort study to measure the impact of the COVID-19 pandemic on the delivery of cancer care services for women diagnosed with cervical cancer staged IB2-IVA, necessitating chemo- and radiotherapy in Romania, as well as determine the difference in cervical cancer staging between the pandemic and pre-pandemic period. Materials and Methods: Using a multicentric hospital database, we designed a retrospective study to compare the last 24 months of the pre-pandemic period to the first 24 months of the SARS-CoV-2 pandemic to evaluate the variation in the proportion of women diagnosed with cervical cancer and the percentage of inoperable cases requiring chemotherapy and radiotherapy, as well as to detail their clinical presentation and other findings. Results: We observed that the likelihood of cervical cancer patients requiring radiation therapy at a later stage than before the pandemic increased by about 20% during the COVID-19 pandemic. Patients at an advanced FIGO stage of cervical cancer had a 3.39 higher likelihood of disease progression after radiotherapy (CI [2.06–4.21], p-value < 0.001), followed by tumor size at diagnosis with a hazard ratio (HR) of 3.12 (CI [2.24–4.00], p-value < 0.001). The factors related to the COVID-19 pandemic, postponed treatment and missed appointments, were also identified as significant risk factors for cervical cancer progression (HR = 2.51 and HR = 2.24, respectively). Conclusions We predict that there will be a considerable rise in cervical cancer cases over the next several years based on existing data and that expanding screening and treatment capacity will attenuate this with a minimal increase in morbidity and fatality. Full article

(This article belongs to the Special Issue Diagnosis and Management of Gynecological Cancers)

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7 pages, 251 KiB

Open AccessArticle

Performance Evaluation of the VITEK2 and Sensititre Systems to Determine Colistin Resistance and MIC for Acinetobacter baumannii

by Hae-Sun Chung, Soo-Kyung Kim, Chorong Hahm and Miae Lee

Diagnostics 2022, 12(6), 1487; https://doi.org/10.3390/diagnostics12061487 - 17 Jun 2022

Cited by 4 | Viewed by 2181

Abstract

Performances of the colistin antimicrobial susceptibility testing (AST) systems of Acinetobacter baumannii vary depending on the manufacturer, and data on colistin-resistant A. baumannii are limited. We evaluated the VITEK2 and Sensititre systems to determine colistin resistance and minimum inhibitory concentration (MIC) for A. [...] Read more.

Performances of the colistin antimicrobial susceptibility testing (AST) systems of Acinetobacter baumannii vary depending on the manufacturer, and data on colistin-resistant A. baumannii are limited. We evaluated the VITEK2 and Sensititre systems to determine colistin resistance and minimum inhibitory concentration (MIC) for A. baumannii isolated from a clinical microbiology laboratory. A total of 213 clinical A. baumannii isolates were tested, including 81 colistin-resistant A. baumannii. ASTs were performed using the VITEK2 and Sensititre systems according to the manufacturer’s instructions. Reference MICs for colistin were determined using the manual broth microdilution method (BMD). The results of the two AST methods were compared with the BMD results. VITEK2 and Sensititre systems showed category agreements of 95.3% and 99.1%, respectively. VITEK2 had a relatively high very major error (VME) rate (9.9%). Sensititre reported higher MICs than the reference method for the susceptible isolates and showed low essential agreement. In conclusion, the automated systems investigated in this study showed good category agreements for colistin AST of A. baumannii. However, VITEK2 had a high VME rate, and Sensititre had differences in MIC results. Colistin AST remains a challenging task in the clinical laboratory. Full article

(This article belongs to the Section Diagnostic Microbiology and Infectious Disease)

13 pages, 995 KiB

Open AccessArticle

Triglyceride and Glucose Index Predicts Acute Coronary Syndrome in Patients with Antineutrophil Cytoplasmic Antibody-Associated Vasculitis

by Pil Gyu Park, Jung Yoon Pyo, Sung Soo Ahn, Jason Jungsik Song, Yong-Beom Park, Ji Hye Huh and Sang-Won Lee

Diagnostics 2022, 12(6), 1486; https://doi.org/10.3390/diagnostics12061486 - 17 Jun 2022

Cited by 3 | Viewed by 2101

Abstract

This study investigated whether the triglyceride (TG) glucose (TyG) index at diagnosis could predict acute coronary syndrome (ACS) in patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). The medical records of 152 AAV were reviewed. Clinical and laboratory data were collected. The TyG [...] Read more.

This study investigated whether the triglyceride (TG) glucose (TyG) index at diagnosis could predict acute coronary syndrome (ACS) in patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV). The medical records of 152 AAV were reviewed. Clinical and laboratory data were collected. The TyG index was calculated by TyG index = Ln (fasting TG (mg/dL) × fasting glucose (mg/dL)/2). The cut-offs of Birmingham vasculitis activity score (BVAS) and the TyG were obtained by the receiver operator characteristic (ROC) curve and the highest tertile (9.011). The mean age was 57.2 years and 32.9% were male. AAV patients with a TyG index ≥ 9.011 exhibited a lower cumulative ACS-free survival rate than those with a TyG index < 9.011. However, a TyG index ≥ 9.011 was not independently associated with ACS in the multivariable Cox analysis. Meanwhile, there might be a close relationship for predicting ACS among the TyG index, metabolic syndrome (MetS), and BVAS. AAV patients with a TyG index ≥ 9.011 exhibited a higher risk for MetS than those with a TyG index < 9.011 (relative risk 2.833). AAV patients with BVAS ≥ 11.5 also exhibited a higher risk for ACS than those with BVAS < 11.5 (relative risk 10.225). Both AAV patients with MetS and those with BVAS ≥11.5 exhibited lower cumulative ACS-free survival rates than those without. The TyG index at AAV diagnosis could estimate the concurrent presence of MetS and predict the occurrence of ACS during follow-up along with high BVAS at diagnosis in patients with AAV. Full article

(This article belongs to the Section Pathology and Molecular Diagnostics)

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15 pages, 1579 KiB

Open AccessArticle

Comparison of Rapid Diagnostic Test, Microscopy, and Polymerase Chain Reaction for the Detection of Plasmodium falciparum Malaria in a Low-Transmission Area, Jazan Region, Southwestern Saudi Arabia

by Aymen M. Madkhali, Ahmad Hassn Ghzwani and Hesham M. Al-Mekhlafi

Diagnostics 2022, 12(6), 1485; https://doi.org/10.3390/diagnostics12061485 - 17 Jun 2022

Cited by 11 | Viewed by 4749

Abstract

This cross-sectional study aimed to assess the performances of a rapid diagnostic test (RDT)—the AllTest Malaria p.f./p.v., microscopy, and nested polymerase chain reaction (PCR) for diagnosing Plasmodium falciparum malaria in 400 febrile patients from a low-transmission region (Jazan) in southwestern Saudi Arabia. Diagnostic [...] Read more.

This cross-sectional study aimed to assess the performances of a rapid diagnostic test (RDT)—the AllTest Malaria p.f./p.v., microscopy, and nested polymerase chain reaction (PCR) for diagnosing Plasmodium falciparum malaria in 400 febrile patients from a low-transmission region (Jazan) in southwestern Saudi Arabia. Diagnostic performance of all three methods was compared using microscopy and nested PCR as reference methods. Overall, 42 (10.5%), 48 (12.0%), and 57 (14.3%) samples were found positive by microscopy, RDT, and PCR, respectively. With PCR as reference method, the RDT showed higher sensitivity (79% vs. 71.9%), similar specificity (99.1% vs. 99.7%), and better NLR (0.20 vs. 0.27) and area under the curve (89.0% vs. 85.8%) than microscopy. The sensitivity of RDT and microscopy decreased as age increased, and false negatives were associated with low parasite density. In addition, the sensitivity of RDT and microscopy was higher in non-Saudi than in Saudi participants. Against microscopy, both RDT and PCR showed high sensitivity (83.3% vs. 97.6%), specificity (96.4% vs. 95.5%), and NPVs (98.0% vs. 99.7%), but reduced PPVs (72.9% vs. 71.9%), respectively. The results showed that the performance of the AllTest Malaria p.f./p.v RDT was better than that of microscopy in diagnosing P. falciparum malaria among febrile patients in the Jazan region when nested PCR was used as the reference. However, further studies are required to assess malaria diagnostic methods among asymptomatic individuals in the region. Full article

(This article belongs to the Special Issue Diagnosis and Management of Malaria)

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28 pages, 9140 KiB

Open AccessArticle

A New Artificial Intelligence-Based Method for Identifying Mycobacterium Tuberculosis in Ziehl–Neelsen Stain on Tissue

by Sabina Zurac, Cristian Mogodici, Teodor Poncu, Mihai Trăscău, Cristiana Popp, Luciana Nichita, Mirela Cioplea, Bogdan Ceachi, Liana Sticlaru, Alexandra Cioroianu, Mihai Busca, Oana Stefan, Irina Tudor, Andrei Voicu, Daliana Stanescu, Petronel Mustatea, Carmen Dumitru and Alexandra Bastian

Diagnostics 2022, 12(6), 1484; https://doi.org/10.3390/diagnostics12061484 - 17 Jun 2022

Cited by 14 | Viewed by 5875

Abstract

Mycobacteria identification is crucial to diagnose tuberculosis. Since the bacillus is very small, finding it in Ziehl–Neelsen (ZN)-stained slides is a long task requiring significant pathologist’s effort. We developed an automated (AI-based) method of identification of mycobacteria. We prepared a training dataset of [...] Read more.

Mycobacteria identification is crucial to diagnose tuberculosis. Since the bacillus is very small, finding it in Ziehl–Neelsen (ZN)-stained slides is a long task requiring significant pathologist’s effort. We developed an automated (AI-based) method of identification of mycobacteria. We prepared a training dataset of over 260,000 positive and over 700,000,000 negative patches annotated on scans of 510 whole slide images (WSI) of ZN-stained slides (110 positive and 400 negative). Several image augmentation techniques coupled with different custom computer vision architectures were used. WSIs automatic analysis was followed by a report indicating areas more likely to present mycobacteria. Our model performs AI-based diagnosis (the final decision of the diagnosis of WSI belongs to the pathologist). The results were validated internally on a dataset of 286,000 patches and tested in pathology laboratory settings on 60 ZN slides (23 positive and 37 negative). We compared the pathologists’ results obtained by separately evaluating slides and WSIs with the results given by a pathologist aided by automatic analysis of WSIs. Our architecture showed 0.977 area under the receiver operating characteristic curve. The clinical test presented 98.33% accuracy, 95.65% sensitivity, and 100% specificity for the AI-assisted method, outperforming any other AI-based proposed methods for AFB detection. Full article

(This article belongs to the Special Issue Artificial Intelligence in Pathological Image Analysis)

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9 pages, 255 KiB

Open AccessArticle

First Results of an External Quality Assessment (EQA) Scheme for Molecular, Serological and Antigenic Diagnostic Test for SARS-CoV-2 Detection in Lombardy Region (Northern Italy), 2020–2022

by Fabio Pasotti, Laura Pellegrinelli, Giuseppa Liga, Manuela Rizzetto, Giovanna Azzarà, Simona Da Molin, Oana Livia Lungu, Silvia Greco, Cristina Galli, Laura Bubba, Elena Pariani, Matteo Corradin, Danilo Cereda and Sabrina Buoro

Diagnostics 2022, 12(6), 1483; https://doi.org/10.3390/diagnostics12061483 - 16 Jun 2022

Cited by 5 | Viewed by 1995

Abstract

For diagnosing SARS-CoV-2 infection and for monitoring its spread, the implementation of external quality assessment (EQA) schemes is mandatory to assess and ensure a standard quality according to national and international guidelines. Here, we present the results of the 2020, 2021, 2022 EQA [...] Read more.

For diagnosing SARS-CoV-2 infection and for monitoring its spread, the implementation of external quality assessment (EQA) schemes is mandatory to assess and ensure a standard quality according to national and international guidelines. Here, we present the results of the 2020, 2021, 2022 EQA schemes in Lombardy region for assessing the quality of the diagnostic laboratories involved in SARS-CoV-2 diagnosis. In the framework of the Quality Assurance Programs (QAPs), the routinely EQA schemes are managed by the regional reference centre for diagnostic laboratories quality (RRC-EQA) of the Lombardy region and are carried out by all the diagnostic laboratories. Three EQA programs were organized: (1) EQA of SARS-CoV-2 nucleic acid detection; (2) EQA of anti-SARS-CoV-2-antibody testing; (3) EQA of SARS-CoV-2 direct antigens detection. The percentage of concordance of 1938 molecular tests carried out within the SARS-CoV-2 nucleic acid detection EQA was 97.7%. The overall concordance of 1875 tests carried out within the anti-SARS-CoV-2 antibody EQA was 93.9% (79.6% for IgM). The overall concordance of 1495 tests carried out within the SARS-CoV-2 direct antigens detection EQA was 85% and it was negatively impacted by the results obtained by the analysis of weak positive samples. In conclusion, the EQA schemes for assessing the accuracy of SARS-CoV-2 diagnosis in the Lombardy region highlighted a suitable reproducibility and reliability of diagnostic assays, despite the heterogeneous landscape of SARS-CoV-2 tests and methods. Laboratory testing based on the detection of viral RNA in respiratory samples can be considered the gold standard for SARS-CoV-2 diagnosis. Full article

(This article belongs to the Section Diagnostic Microbiology and Infectious Disease)

41 pages, 25585 KiB

Open AccessArticle

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

by Jasjit S. Suri, Sushant Agarwal, Gian Luca Chabert, Alessandro Carriero, Alessio Paschè, Pietro S. C. Danna, Luca Saba, Armin Mehmedović, Gavino Faa, Inder M. Singh, Monika Turk, Paramjit S. Chadha, Amer M. Johri, Narendra N. Khanna, Sophie Mavrogeni, John R. Laird, Gyan Pareek, Martin Miner, David W. Sobel, Antonella Balestrieri, Petros P. Sfikakis, George Tsoulfas, Athanasios D. Protogerou, Durga Prasanna Misra, Vikas Agarwal, George D. Kitas, Jagjit S. Teji, Mustafa Al-Maini, Surinder K. Dhanjil, Andrew Nicolaides, Aditya Sharma, Vijay Rathore, Mostafa Fatemi, Azra Alizad, Pudukode R. Krishnan, Ferenc Nagy, Zoltan Ruzsa, Mostafa M. Fouda, Subbaram Naidu, Klaudija Viskovic and Mannudeep K. Kalra Show full author list Hide full author list

Diagnostics 2022, 12(6), 1482; https://doi.org/10.3390/diagnostics12061482 - 16 Jun 2022

Cited by 36 | Viewed by 4002

Abstract

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. [...] Read more.

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

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10 pages, 1393 KiB

Open AccessReview

Assessment of Orbital Compartment Pressure: A Comprehensive Review

by Tim J. Enz and Markus Tschopp

Diagnostics 2022, 12(6), 1481; https://doi.org/10.3390/diagnostics12061481 - 16 Jun 2022

Cited by 1 | Viewed by 2347

Abstract

The orbit is a closed compartment defined by the orbital bones and the orbital septum. Some diseases of the orbit and the optic nerve are associated with an increased orbital compartment pressure (OCP), e.g., retrobulbar hemorrhage or thyroid eye disease. Our aim was [...] Read more.

The orbit is a closed compartment defined by the orbital bones and the orbital septum. Some diseases of the orbit and the optic nerve are associated with an increased orbital compartment pressure (OCP), e.g., retrobulbar hemorrhage or thyroid eye disease. Our aim was to review the literature on the different approaches to assess OCP. Historically, an assessment of the tissue resistance provoked by the retropulsion of the eye bulb was the method of choice for estimating OCP, either by digital palpation or with specifically designed devices. We found a total of 20 articles reporting direct OCP measurement in animals, cadavers and humans. In nine studies, OCP was directly measured in humans, of which five used a minimally invasive approach. Two groups used experimental/custom devices, whilst the others applied commercially available devices commonly used for monitoring the compartment syndromes of the limbs. None of the nine articles on direct OCP measurements in humans reported complications. Today, OCP is mainly estimated using clinical findings considered surrogates, e.g., elevated intraocular pressure or proptosis. These diagnostic markers appear to reliably indicate elevated OCP. However, particularly minimally invasive approaches show promises for direct OCP measurements. In the future, more sophisticated, specifically designed equipment might allow for even better and safer measurements and hence facilitate the diagnosis and monitoring of orbital diseases. Full article

(This article belongs to the Special Issue Eye Diseases: Diagnosis and Management)

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19 pages, 32792 KiB

Open AccessArticle

Unsupervised Learning Based on Multiple Descriptors for WSIs Diagnosis

by Taimoor Shakeel Sheikh, Jee-Yeon Kim, Jaesool Shim and Migyung Cho

Diagnostics 2022, 12(6), 1480; https://doi.org/10.3390/diagnostics12061480 - 16 Jun 2022

Cited by 7 | Viewed by 2200

Abstract

An automatic pathological diagnosis is a challenging task because histopathological images with different cellular heterogeneity representations are sometimes limited. To overcome this, we investigated how the holistic and local appearance features with limited information can be fused to enhance the analysis performance. We [...] Read more.

An automatic pathological diagnosis is a challenging task because histopathological images with different cellular heterogeneity representations are sometimes limited. To overcome this, we investigated how the holistic and local appearance features with limited information can be fused to enhance the analysis performance. We propose an unsupervised deep learning model for whole-slide image diagnosis, which uses stacked autoencoders simultaneously feeding multiple-image descriptors such as the histogram of oriented gradients and local binary patterns along with the original image to fuse the heterogeneous features. The pre-trained latent vectors are extracted from each autoencoder, and these fused feature representations are utilized for classification. We observed that training with additional descriptors helps the model to overcome the limitations of multiple variants and the intricate cellular structure of histopathology data by various experiments. Our model outperforms existing state-of-the-art approaches by achieving the highest accuracies of 87.2 for ICIAR2018, 94.6 for Dartmouth, and other significant metrics for public benchmark datasets. Our model does not rely on a specific set of pre-trained features based on classifiers to achieve high performance. Unsupervised spaces are learned from the number of independent multiple descriptors and can be used with different variants of classifiers to classify cancer diseases from whole-slide images. Furthermore, we found that the proposed model classifies the types of breast and lung cancer similar to the viewpoint of pathologists by visualization. We also designed our whole-slide image processing toolbox to extract and process the patches from whole-slide images. Full article

(This article belongs to the Section Pathology and Molecular Diagnostics)

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Diagnostics, Volume 12, Issue 6 (June 2022) – 211 articles

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