Diagnostics

Background: Inspired by the molecular classification of endometrial carcinoma (EC) proposed by The Cancer Genome Atlas Research Network (TCGA), we investigated tumor-infiltrating CD8-positive T-cell as well as DNA mismatch repair (MMR) protein and p53 protein expression, and we developed a new classification system for ECs to predict patients’ prognosis using immunohistochemical methods. Methods: The study included 128 patients with ECs who underwent surgery. Paraffin-embedded tissue sections of the tumor were stained using antibodies against MMR protein, p53, and CD8. Cases were stratified into four classes by a sequential algorithm. An immunohistochemical classification system for ECs (ICEC) was created, including HCD8, MMR-D, LCD8, and p53 LCD8. Results: In ICEC, 16 cases (12.5%), 27 cases (21.09%), 67 cases (52.34%), and 18 cases (14.06%) belonged to HCD8, MMR-D, LCD8, and p53 LCD8, respectively. ICEC did not show any correlation with clinical stage, lymphovascular space invasion, or lymph node metastasis. However, the p53 LCD8 class contained a significantly higher proportion of G3 ECs and serous carcinoma (p < 0.0001). ICEC showed prognostic significance in overall survival (OS) (p < 0.0001) and disease-free survival (DFS) (p < 0.0001). The class of p53 LCD8 showed the worst prognosis among the classes. Conclusions: ICEC classification is useful in predicting the prognosis of ECs. Full article

Microbiota composition might play a role in the pathophysiology and course of sepsis, and understanding its dynamics is of clinical interest. Invasive meningococcal disease (IMD) is an important cause of community-acquired serious infection, and there is no information regarding microbiota composition in children with meningococcemia. In this study, we aimed to evaluate the intestinal and nasopharyngeal microbiota composition of children with IMD. Materials and Methods: In this prospective, multi-center study, 10 children with meningococcemia and 10 age-matched healthy controls were included. Nasopharyngeal and fecal samples were obtained at admission to the intensive care unit and on the tenth day of their hospital stay. The V3 and V4 regions of the 16S rRNA gene were amplified following the 16S Metagenomic Sequencing Library Preparation. Results: Regarding the alpha diversity on the day of admission and on the tenth day at the PICU, the Shannon index was significantly lower in the IMD group compared to the control group (p = 0.002 at admission and p = 0.001, on the tenth day of PICU). A statistical difference in the stool samples was found between the IMD group at Day 0 vs. the controls in the results of the Bray–Curtis and Jaccard analyses (p = 0.005 and p = 0.001, respectively). There were differences in the intestinal microbiota composition between the children with IMD at admission and Day 10 and the healthy controls. Regarding the nasopharyngeal microbiota analysis, in the children with IMD at admission, at the genus level, Neisseria was significantly more abundant compared to the healthy children (p < 0.001). In the children with IMD at Day 10, genera Moraxella and Neisseria were decreased compared to the healthy children. In the children with IMD on Day 0, for paired samples, Moraxella, Neisseria, and Haemophilus were significantly more abundant compared to the children with IMD at Day 10. In the children with IMD at Day 10, the Moraxella and Neisseria genera were decreased, and 20 different genera were more abundant compared to Day 0. Conclusions: We first found alterations in the intestinal and nasopharyngeal microbiota composition in the children with IMD. The infection itself or the other care interventions also caused changes to the microbiota composition during the follow-up period. Understanding the interaction of microbiota with pathogens, e.g., N. meningitidis, could give us the opportunity to understand the disease’s dynamics. Full article

The World Health Organization (WHO) has identified that diabetes mellitus (DM) is one of the most prevalent disease worldwide. Individuals with DM have a higher risk of mortality, and it is crucial to prioritize the treatment of foot ulcers, which is a significant complication associated with the disease, as they lead to the development of plantar ulcers, which results in the need to amputate part of the foot or leg. People with diabetes are at risk of experiencing various complications, such as heart disease, eye problems, kidney dysfunction, nerve damage, skin issues, foot ulcers, and dental diseases. Unawareness of the risk associated with diabetic foot ulcers (DFU) is a significant contributing factor to the mortality of diabetic patients. Evolving technological advancements such as deep learning techniques can be used to predict the symptoms of diabetic foot ulcers as early as possible, which helps to provide effective treatment to DM patients. This research introduces a methodology for analyzing images of foot ulcers in diabetic patients, focusing on feature extraction and classification. The dataset used in this study was collected from historical medical records and foot images of patients with diabetes, who commonly experience foot ulcers as a major complication. The dataset was pre-processed and segmented, and features were extracted using a deep recurrent neural network (DRNN). Image and numerical/text data were extracted separately, and the normal and abnormal diabetes ranges were identified. Foot images of patients with abnormal diabetes ranges were separated and classified using a pre-trained fast convolutional neural network (PFCNN) with U++net. The classification procedure involves the analysis of foot ulcers to predict their pathogenesis. To assess the effectiveness of the proposed technique, the study presented simulation results, including a confusion matrix and receiver operating characteristic curve. These results specifically focused on predicting two classes: normal and abnormal diabetes foot ulcerations. The analysis yielded various parameters, including accuracy, precision, recall curve, and area under the curve. The main goal of the study was to introduce an novel technique for assessing the risk of foot ulceration development in patients with diabetes, leveraging the analysis of foot ulcer images. The researchers collected a dataset of foot images and medical data from historical records of patients with diabetes and pre-processed and segmented the data. They then used a deep recurrent neural network to extract features from the segmented data and identified normal and abnormal diabetes ranges based on numerical and text data. Foot images of patients with abnormal diabetes ranges were classified using a pre-trained fast convolutional neural network with U++net to examine foot ulcers and forecast the development of the risk of diabetic foot ulcers (DFU). The study assessed the accuracy of the proposed technique as 99.32% by simulating results for feature extraction and the classification of diabetic foot ulcers. A comparison was made between this proposed technique and existing approaches. Full article

In kidney transplantation, a biopsy is currently the gold standard for monitoring the transplanted organ. However, this is far from an ideal screening method given its invasive nature and the discomfort it can cause the patient. Large-scale studies in renal transplantation show that approximately 1% of biopsies generate major complications, with a risk of macroscopic hematuria greater than 3.5%. It would not be until 2011 that a method to detect donor-derived cell-free DNA (dd-cfDNA) employing digital PCR was devised based on analyzing the differences in SNPs between the donor and recipient. In addition, since the initial validation studies were carried out at the specific moments in which rejection was suspected, there is still not a good understanding of how dd-cfDNA levels naturally evolve post-transplant. In addition, various factors, both in the recipient and the donor, can influence dd-cfDNA levels and cause increases in the levels of dd-cfDNA themselves without suspicion of rejection. All that glitters in this technology is not gold; therefore, in this article, we discuss the current state of clinical studies, the benefits, and disadvantages. Full article

Chronic Kidney Disease (CKD) represents a considerable global health challenge, emphasizing the need for precise and prompt prediction of disease progression to enable early intervention and enhance patient outcomes. As per this study, we introduce an innovative fusion deep learning model that combines a Graph Neural Network (GNN) and a tabular data model for predicting CKD progression by capitalizing on the strengths of both graph-structured and tabular data representations. The GNN model processes graph-structured data, uncovering intricate relationships between patients and their medical conditions, while the tabular data model adeptly manages patient-specific features within a conventional data format. An extensive comparison of the fusion model, GNN model, tabular data model, and a baseline model was conducted utilizing various evaluation metrics, encompassing accuracy, precision, recall, and F1-score. The fusion model exhibited outstanding performance across all metrics, underlining its augmented capacity for predicting CKD progression. The GNN model’s performance closely trailed the fusion model, accentuating the advantages of integrating graph-structured data into the prediction process. Hyperparameter optimization was performed using grid search, ensuring a fair comparison among the models. The fusion model displayed consistent performance across diverse data splits, demonstrating its adaptability to dataset variations and resilience against noise and outliers. In conclusion, the proposed fusion deep learning model, which amalgamates the capabilities of both the GNN model and the tabular data model, substantially surpasses the individual models and the baseline model in predicting CKD progression. This pioneering approach provides a more precise and dependable method for early detection and management of CKD, highlighting its potential to advance the domain of precision medicine and elevate patient care. Full article

Distinguishing between inflammatory and fibrotic lesions drastically influences treatment decision-making regarding Crohn’s disease. However, it is challenging to distinguish these two phenotypes before surgery. This study investigates the diagnostic yield of shear-wave elastography and computed tomography enterography to distinguish intestinal phenotypes in Crohn’s disease. Thirty-seven patients (mean age, 29.51 ± 11.52; 31 men) were evaluated with average value of shear-wave elastography (Emean) and computed tomography enterography (CTE) scores. The results demonstrated that a positive correlation between the Emean and fibrosis (Spearman’s r = 0.653, p = 0.000). The cut-off value for fibrotic lesions was 21.30 KPa (AUC: 0.877, sensitivity: 88.90%, specificity: 89.50%, 95% CI:0.755~0.999, p = 0.000). The CTE score showed a positive correlation with inflammation (Spearman’s r = 0.479, p = 0.003), and a 4.5-point grading system was the optimal cut-off value for inflammatory lesions (AUC: 0.766, sensitivity: 73.70%, specificity: 77.80%, 95% CI: 0.596~0.936, p = 0.006). Combining these two metrics improved the diagnostic performance and specificity (AUC: 0.918, specificity: 94.70%, 95% CI: 0.806~1.000, p = 0.000). In conclusion, shear-wave elastography can be used to help detect fibrotic lesions and the computed tomography enterography score emerged as a feasible predictor of inflammatory lesions. The combination of these two imaging techniques is proposed to distinguish intestinal predominant phenotypes. Full article

Background: The neutrophil/lymphocyte ratio (NLR) at baseline has been demonstrated to correlate with higher stages of disease and to be a prognostic factor in numerous cancers. However, its function as a prognostic factor for mycosis fungoides (MF) has not been yet clarified. Objective: Our work aimed to assess the association of the NLR with different stages of MF and to outline whether higher values of this marker are related to a more aggressive MF. Methods: We retrospectively calculated the NLRs in 302 MF patients at the moment of diagnosis. The NLR was obtained using the complete blood count values. Results: The median NLR among patients with early stage disease (low-grade IA-IB-IIA) was 1.88, while the median NLR for patients with high-grade MF (IIB-IIIA-IIIB) was 2.64. Statistical analysis showed positive associations of advanced MF stages with NLRs higher than 2.3. Conclusions: Our analysis demonstrates that the NLR represents a cheap and easily available parameter functioning as a marker for advanced MF. This might guide physicians in recognizing patients with advanced stages of disease requiring a strict follow-up or an early treatment. Full article

Advances in computer technology and image processing now allow us to obtain from angiographic images a large variety of information on coronary physiology without the use of a guide-wire as a diagnostic information equivalent to FFR and iFR but also information allowing for the performance of a real virtual percutaneous coronary intervention (PCI) and finally the ability to obtain information to optimize the results of PCI. With specific software, it is now possible to have a real upgrading of invasive coronary angiography. In this review, we present the different advances in this field and discuss the future perspectives offered by this technology. Full article

Invasive Ductal Carcinoma Breast Cancer (IDC-BC) is the most common type of cancer and its asymptomatic nature has led to an increased mortality rate globally. Advancements in artificial intelligence and machine learning have revolutionized the medical field with the development of AI-enabled computer-aided diagnosis (CAD) systems, which help in determining diseases at an early stage. CAD systems assist pathologists in their decision-making process to produce more reliable outcomes in order to treat patients well. In this work, the potential of pre-trained convolutional neural networks (CNNs) (i.e., EfficientNetV2L, ResNet152V2, DenseNet201), singly or as an ensemble, was thoroughly explored. The performances of these models were evaluated for IDC-BC grade classification using the DataBiox dataset. Data augmentation was used to avoid the issues of data scarcity and data imbalances. The performance of the best model was compared to three different balanced datasets of Databiox (i.e., 1200, 1400, and 1600 images) to determine the implications of this data augmentation. Furthermore, the effects of the number of epochs were analysed to ensure the coherency of the most optimal model. The experimental results analysis revealed that the proposed ensemble model outperformed the existing state-of-the-art techniques in relation to classifying the IDC-BC grades of the Databiox dataset. The proposed ensemble model of the CNNs achieved a 94% classification accuracy and attained a significant area under the ROC curves for grades 1, 2, and 3, i.e., 96%, 94%, and 96%, respectively. Full article

The study of intestinal permeability is gaining growing interest due to its relevance in the onset and progression of several gastrointestinal and non-gastrointestinal diseases. Though the involvement of impaired intestinal permeability in the pathophysiology of such diseases is recognized, there is currently a need to identify non-invasive biomarkers or tools that are able to accurately detect alterations in intestinal barrier integrity. On the one hand, promising results have been reported for novel in vivo methods based on paracellular probes, i.e., methods that can directly assess paracellular permeability and, on the other hand, on fecal and circulating biomarkers able to indirectly assess epithelial barrier integrity and functionality. In this review, we aimed to summarize the current knowledge on the intestinal barrier and epithelial transport pathways and to provide an overview of the methods already available or currently under investigation for the measurement of intestinal permeability. Full article

Staphylococcus aureus bacteremia (SAB) is a severe infection frequently associated with significant morbidity and mortality. Recent studies have shown that SAB mortality has decreased during the last decades. However, about 25% of patients suffering from the disease will ultimately die. Hence, there is an urgent need for more timely and efficient treatment of patients with SAB. The aim of the present study was to retrospectively evaluate a cohort of SAB patients hospitalized in a tertiary hospital and to identify factors independently associated with mortality. All 256 SAB patients hospitalized from January 2005 to December 2021 in the University Hospital of Heraklion, Greece, were evaluated. Their median age was 72 years, while 101 (39.5%) were female. Most SAB patients were cared for in medical wards (80.5%). The infection was community-acquired in 49.5%. Among all strains 37.9% were methicillin-resistant S. aureus (MRSA), however, definite treatment with an antistaphylococcal penicillin was given only in 22% of patients. Only 14.4% of patients had a repeat blood culture after the initiation of antimicrobial treatment. Infective endocarditis was present in 8%. In-hospital mortality has reached 15.9%. Female gender, older age, higher McCabe score, previous antimicrobial use, presence of a central venous catheter, neutropenia, severe sepsis, septic shock, and MRSA SAB were positively associated with in-hospital mortality, while monomicrobial bacteremia was negatively associated. The multivariate logistic regression model identified only severe sepsis (p = 0.05, odds ratio = 12.294) and septic shock (p = 0.007, odds ratio 57.18) to be independently positively associated with in-hospital mortality. The evaluation revealed high rates of inappropriate empirical antimicrobial treatment and non-adherence to guidelines, as shown, by the lack of repeat blood cultures. These data underline the urgent need for interventions with antimicrobial stewardship, increased involvement of infectious diseases physicians, educational sessions, and creation and implementation of local guidelines for improvement of the necessary steps for timely and efficient SAB treatment. Optimization of diagnostic techniques is needed to overcome challenges such as heteroresistance that may affect treatment. Clinicians should be aware of the factors associated with mortality in patients with SAB to identify those who are at a higher risk and optimize medical management. Full article

Peritoneal carcinosis is a condition characterized by the spread of cancer cells to the peritoneum, which is the thin membrane that lines the abdominal cavity. It is a serious condition that can result from many different types of cancer, including ovarian, colon, stomach, pancreatic, and appendix cancer. The diagnosis and quantification of lesions in peritoneal carcinosis are critical in the management of patients with the condition, and imaging plays a central role in this process. Radiologists play a vital role in the multidisciplinary management of patients with peritoneal carcinosis. They need to have a thorough understanding of the pathophysiology of the condition, the underlying neoplasms, and the typical imaging findings. In addition, they need to be aware of the differential diagnoses and the advantages and disadvantages of the various imaging methods available. Imaging plays a central role in the diagnosis and quantification of lesions, and radiologists play a critical role in this process. Ultrasound, computed tomography, magnetic resonance, and PET/CT scans are used to diagnose peritoneal carcinosis. Each imaging procedure has advantages and disadvantages, and particular imaging techniques are recommended based on patient conditions. Our aim is to provide knowledge to radiologists regarding appropriate techniques, imaging findings, differential diagnoses, and treatment options. With the advent of AI in oncology, the future of precision medicine appears promising, and the interconnection between structured reporting and AI is likely to improve diagnostic accuracy and treatment outcomes for patients with peritoneal carcinosis. Full article

Due to the rarity and complexity of treatment for Ewing sarcoma and rhabdomyosarcoma, studies demonstrate improved patient outcomes when managed by a multidisciplinary team at high-volume centres (HVCs). Our study explores the difference in outcomes of Ewing sarcoma and rhabdomyosarcoma patients based on the centre of initial consultation in British Columbia, Canada. This retrospective study assessed adults diagnosed with Ewing sarcoma and rhabdomyosarcoma between 1 January 2000 and 31 December 2020 undergoing curative intent therapy in one of five cancer centres across the province. Seventy-seven patients were included, 46 seen at HVCs and 31 at low-volume centres (LVCs). Patients at HVCs were younger (32.1 vs. 40.8 years, p = 0.020) and more likely to receive curative intent radiation (88% vs. 67%, p = 0.047). The time from diagnosis to first chemotherapy was 24 days shorter at HVCs (26 vs. 50 days, p = 0.120). There was no significant difference in overall survival by treatment centre (HR 0.850, 95% CI 0.448–1.614). Variations in care exist amongst patients treated at HVCs vs. LVCs, which may reflect differences in access to resources, clinical specialists, and varying practice patterns across centres. This study can be used to inform decisions regarding triaging and centralization of Ewing sarcoma and rhabdomyosarcoma patient treatment. Full article

The WHO recently declared that COVID-19 no longer constitutes a public health emergency of international concern; however, lessons learned through the pandemic should not be left behind. Lung ultrasound was largely utilized as a diagnostic tool thanks to its feasibility, easy application, and the possibility to reduce the source of infection for health personnel. Lung ultrasound scores consist of grading systems used to guide diagnosis and medical decisions, owning a good prognostic value. In the emergency context of the pandemic, several lung ultrasound scores emerged either as new scores or as modifications of pre-existing ones. Our aim is to clarify the key aspects of lung ultrasound and lung ultrasound scores to standardize their clinical use in a non-pandemic context. The authors searched on PubMed for articles related to “COVID-19”, “ultrasound”, and “Score” until 5 May 2023; other keywords were “thoracic”, “lung”, “echography”, and “diaphragm”. A narrative summary of the results was made. Lung ultrasound scores are demonstrated to be an important tool for triage, prediction of severity, and aid in medical decisions. Ultimately, the existence of numerous scores leads to a lack of clarity, confusion, and an absence of standardization. Full article

Deep learning, with continuous development, has achieved relatively good results in the field of left atrial segmentation, and numerous semi-supervised methods in this field have been implemented based on consistency regularization to obtain high-performance 3D models by training. However, most semi-supervised methods focus on inter-model consistency and ignore inter-model discrepancy. Therefore, we designed an improved double-teacher framework with discrepancy information. Herein, one teacher learns 2D information, another learns both 2D and 3D information, and the two models jointly guide the student model for learning. Simultaneously, we extract the isomorphic/heterogeneous discrepancy information between the predictions of the student and teacher model to optimize the whole framework. Unlike other semi-supervised methods based on 3D models, ours only uses 3D information to assist 2D models, and does not have a fully 3D model, thus addressing the large memory consumption and limited training data of 3D models to some extent. Our approach shows excellent performance on the left atrium (LA) dataset, similar to that of the best performing 3D semi-supervised methods available, compared to existing techniques. Full article

Mycobacterium kansasii infections predominantly manifest in immunocompromised people and are primarily responsible for lung disease and systemic disseminated infection. Osteopathy is a rare consequence of M. kansasii infection. Here, we present imaging data from a 44-year-old immunocompetent Chinese woman diagnosed with multiple bone destruction, particularly of the spine, secondary to M. kansasii pulmonary disease, which is easily misdiagnosed. The patient underwent an emergency operation after experiencing unexpected incomplete paraplegia during hospitalization, indicating an aggravation of bone destruction. Preoperative sputum testing and next-generation sequencing of DNA and RNA of intraoperative samples confirmed the diagnosis of M. kansasii infection. Treatment with anti-tuberculosis therapy and the subsequent patient response supported our diagnosis. Given the rarity of osteopathy secondary to M. kansasii infection in immunocompetent individuals, our case offers some insight into this diagnosis. Full article

Tooth shade determination methods for evaluating the effectiveness of whitening products at home are limited. In this study, an iPhone app for personalized tooth shade determination was developed. While capturing dental photographs in selfie mode before and after whitening, the app can maintain consistent illumination and tooth appearance conditions that affect tooth color measurement. An ambient light sensor was used to standardize the illumination conditions. To maintain consistent tooth appearance conditions determined by appropriately opening the mouth, facial landmark detection, an artificial intelligence technique that estimates key face parts and outlines, was used. The effectiveness of the app in ensuring uniform tooth appearance was investigated through color measurements of the upper incisors of seven participants via photographs captured in succession. The coefficients of variation for incisors L*, a*, and b* were less than 0.0256 (95% CI, 0.0173–0.0338), 0.2748 (0.1596–0.3899), and 0.1053 (0.0078–0.2028), respectively. To examine the feasibility of the app for tooth shade determination, gel whitening after pseudo-staining by coffee and grape juice was performed. Consequently, whitening results were evaluated by monitoring the ∆E_ab color difference values (1.3 unit minimum). Although tooth shade determination remains a relative quantification method, the proposed method can support evidence-based selection of whitening products. Full article

The COVID-19 virus is one of the most devastating illnesses humanity has ever faced. COVID-19 is an infection that is hard to diagnose until it has caused lung damage or blood clots. As a result, it is one of the most insidious diseases due to the lack of knowledge of its symptoms. Artificial intelligence (AI) technologies are being investigated for the early detection of COVID-19 using symptoms and chest X-ray images. Therefore, this work proposes stacking ensemble models using two types of COVID-19 datasets, symptoms and chest X-ray scans, to identify COVID-19. The first proposed model is a stacking ensemble model that is merged from the outputs of pre-trained models in the stacking: multi-layer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). Stacking trains and evaluates the meta-learner as a support vector machine (SVM) to predict the final decision. Two datasets of COVID-19 symptoms are used to compare the first proposed model with MLP, RNN, LSTM, and GRU models. The second proposed model is a stacking ensemble model that is merged from the outputs of pre-trained DL models in the stacking: VGG16, InceptionV3, Resnet50, and DenseNet121; it uses stacking to train and evaluate the meta-learner (SVM) to identify the final prediction. Two datasets of COVID-19 chest X-ray images are used to compare the second proposed model with other DL models. The result has shown that the proposed models achieve the highest performance compared to other models for each dataset. Full article

We present the case of a 54-year-old male, without any significant medical history, who insidiously developed speech disturbances and walking difficulties, accompanied by backward falls. The symptoms progressively worsened over time. The patient was initially diagnosed with Parkinson’s disease; however, he failed to respond to standard therapy with Levodopa. He came to our attention for worsening postural instability and binocular diplopia. A neurological exam was highly suggestive of a Parkinson-plus disease, most likely progressive supranuclear gaze palsy. Brain MRI was performed and revealed moderate midbrain atrophy with the characteristic “hummingbird” and “Mickey mouse” signs. An increased MR parkinsonism index was also noted. Based on all clinical and paraclinical data, a diagnosis of probable progressive supranuclear palsy was established. We review the main imaging features of this disease and their current role in diagnosis. Full article