This study aims to investigate hard and soft tissue asymmetry in skeletal Class III patients to elucidate how soft tissue thickness alters overall asymmetry and whether menton deviation is correlated with bilateral differences in hard and soft tissue prominence and soft tissue thickness. The cone-beam computed tomography data of 50 skeletal Class III adults were divided based on menton deviation into symmetric (n = 25; deviation ≤ 2.0 mm) and asymmetric (n = 25; deviation > 2.0 mm) groups. Forty-four corresponding hard and soft tissue points were identified. Bilateral hard and soft tissue prominence and soft tissue thickness were compared using paired t-tests. The correlations between bilateral differences in these variables and menton deviation were examined using Pearson’s correlation analysis. In the symmetric group, no significant bilateral differences in soft and hard tissue prominence and soft tissue thickness were observed. In the asymmetric group, both hard and soft tissue prominence were significantly greater on the deviated side than the non-deviated side at most of the points; however, no significant differences in soft tissue thickness were detected except at point 9 (ST9/ST’9, p = 0.011). The difference of hard and soft tissue prominence at point 8 (H8/H’8 and S8/S’8) was positively correlated with menton deviation, whereas the soft tissue thickness at point 5 (ST5/ST’5) and point 9 (ST9/ST’9) was negatively correlated with menton deviation (p = 0.05). Soft tissue thickness does not affect overall asymmetry in the presence of underlying hard tissue asymmetry. Soft tissue thickness at the centre of the ramus may be correlated with the degree of menton deviation in patients with asymmetry; however, this correlation needs to be confirmed by further studies. Full article

Background: Endometriosis is a common inflammatory disease characterized by the presence of endometrial cells outside of the uterine cavity. Endometriosis affects 10% of women of reproductive age and significantly reduces their quality of life as a result of chronic pelvic pain and infertility. Biologic mechanisms, including persistent inflammation, immune dysfunction, and epigenetic modifications, have been proposed as the pathogenesis of endometriosis. In addition, endometriosis can potentially be associated with an increased risk of pelvic inflammatory disease (PID). Changes in the vaginal microbiota associated with bacterial vaginosis (BV) result in PID or a severe form of abscess formation, tubo-ovarian abscess (TOA). This review aims to summarize the pathophysiology of endometriosis and PID and to discuss whether endometriosis may predispose to PID and vice versa. Methods: Papers published between 2000 and 2022 in the PubMed and Google Scholar databases were included. Results: Available evidence supports that women with endometriosis are at increased risk of comorbid PID and vice versa, supporting that endometriosis and PID are likely to coexist. There is a bidirectional relationship between endometriosis and PID that shares a similar pathophysiology, which includes the distorted anatomy favorable to bacteria proliferation, hemorrhage from endometriotic lesions, alterations to the reproductive tract microbiome, and impaired immune response modulated by aberrant epigenetic processes. However, whether endometriosis predisposes to PID or vice versa has not been identified. Conclusions: This review summarizes our current understanding of the pathogenesis of endometriosis and PID and discusses the similarities between them. Full article

This study aimed to compare the rapid bedside quantitative assessment of C-reactive protein (CRP) in saliva to serum CRP to predict blood culture-positive sepsis in neonates. The research was carried out over eight months at Fernandez Hospital in India (February 2021–September 2021). The study included 74 randomly selected neonates with clinical symptoms or risk factors of neonatal sepsis requiring blood culture evaluation. SpotSense rapid CRP test was conducted to estimate salivary CRP. In analysis, the area under the curve (AUC) on the receiver operating characteristics (ROC) curve was used. The study population’s mean gestational age and median birth weight were 34.1 weeks (SD: ±4.8) and 2370 g (IQR: 1067–3182). The AUC on ROC curve analysis for predicting culture-positive sepsis was 0.72 (95% CI: 0.58 to 0.86, p-value: 0.002) for serum CRP and 0.83 (95% CI: 0.70 to 0.97, p-value: <0.0001) for salivary CRP. The Pearson correlation coefficient between salivary and serum CRP was moderate (r = 0.352, p-value: 0.002). Salivary CRP cut-off scores were comparable to serum CRP in terms of sensitivity, specificity, PPV, NPV, and accuracy in predicting culture-positive sepsis. The rapid bedside assessment of salivary CRP appears to be an easy and promising non-invasive tool in culture-positive sepsis prediction. Full article

Groove pancreatitis (GP) is an uncommon appearance of pancreatitis represented by fibrous inflammation and a pseudo-tumor in the area over the head of the pancreas. The underlying etiology is unidentified but is firmly associated with alcohol abuse. We report the case of a 45-year-old male patient with chronic alcohol abuse who was admitted to our hospital with upper abdominal pain radiating to the back and weight loss. Laboratory data were within normal limits, except for the level of carbohydrate antigen (CA) 19-9. An abdominal ultrasound and computed tomography (CT) scan revealed swelling of the pancreatic head and duodenal wall thickening with luminal narrowing. We performed an endoscopic ultrasound (EUS) with fine needle aspiration (FNA) from the markedly thickened duodenal wall and the groove area, which revealed only inflammatory changes. The patient improved and was discharged. The principal objective in managing GP is to exclude a diagnosis of malignancy, whilst a conservative approach might be more acceptable for patients instead of extensive surgery. Full article

Purpose: The detection of where an organ starts and where it ends is achievable and, since this information can be delivered in real time, it could be quite important for several reasons. For one, by having the practical knowledge of the Wireless Endoscopic Capsule (WEC) transition through an organ’s domain, we are able to align and control the endoscopic operation with any other possible protocol, i.e., delivering some form of treatment on the spot. Another is having greater anatomical topography information per session, therefore treating the individual in detail (not “in general”). Even the fact that by gathering more accurate information for a patient by merely implementing clever software procedures is a task worth exploiting, since the problems we have to overcome in real-time processing of the capsule findings (i.e., wireless transfer of images to another unit that will apply the necessary real time computations) are still challenging. This study proposes a computer-aided detection (CAD) tool, a CNN algorithm deployed to run on field programmable gate array (FPGA), able to automatically track the capsule transitions through the entrance (gate) of esophagus, stomach, small intestine and colon, in real time. The input data are the wireless transmitted image shots of the capsule’s camera (while the endoscopy capsule is operating). Methods: We developed and evaluated three distinct multiclass classification CNNs, trained on the same dataset of total 5520 images extracted by 99 capsule videos (total 1380 frames from each organ of interest). The proposed CNNs differ in size and number of convolution filters. The confusion matrix is obtained by training each classifier and evaluating the trained model on an independent test dataset comprising 496 images extracted by 39 capsule videos, 124 from each GI organ. The test dataset was further evaluated by one endoscopist, and his findings were compared with CNN-based results. The statistically significant of predictions between the four classes of each model and the comparison between the three distinct models is evaluated by calculating the p-values and chi-square test for multi class. The comparison between the three models is carried out by calculating the macro average F1 score and Mattheus correlation coefficient (MCC). The quality of the best CNN model is estimated by calculations of sensitivity and specificity. Results: Our experimental results of independent validation demonstrate that the best of our developed models addressed this topological problem by exhibiting an overall sensitivity (96.55%) and specificity of (94.73%) in the esophagus, (81.08% sensitivity and 96.55% specificity) in the stomach, (89.65% sensitivity and 97.89% specificity) in the small intestine and (100% sensitivity and 98.94% specificity) in the colon. The average macro accuracy is 95.56%, the average macro sensitivity is 91.82%. Full article

Refined hybrid convolutional neural networks are proposed in this work for classifying brain tumor classes based on MRI scans. A dataset of 2880 T1-weighted contrast-enhanced MRI brain scans are used. The dataset contains three main classes of brain tumors: gliomas, meningiomas, and pituitary tumors, as well as a class of no tumors. Firstly, two pre-trained, fine-tuned convolutional neural networks, GoogleNet and AlexNet, were used for classification process, with validation and classification accuracy being 91.5% and 90.21%, respectively. Then, to improving the performance of the fine-tuning AlexNet, two hybrid networks (AlexNet-SVM and AlexNet-KNN) were applied. These hybrid networks achieved 96.9% and 98.6% validation and accuracy, respectively. Thus, the hybrid network AlexNet-KNN was shown to be able to apply the classification process of the present data with high accuracy. After exporting these networks, a selected dataset was employed for testing process, yielding accuracies of 88%, 85%, 95%, and 97% for the fine-tuned GoogleNet, the fine-tuned AlexNet, AlexNet-SVM, and AlexNet-KNN, respectively. The proposed system would help for automatic detection and classification of the brain tumor from the MRI scans and safe the time for the clinical diagnosis. Full article

The aim of the study was to evaluate particular polymerase chain reaction primers targeting selected representative genes and the influence of a preincubation step in a selective broth on the sensitivity of group B Streptococcus (GBS) detection by nucleic acid amplification techniques (NAAT). Research samples were vaginal and rectal swabs collected in duplicate from 97 pregnant women. They were used for enrichment broth culture-based diagnostics, bacterial DNA isolation, and amplification, using primers based on species-specific 16S rRNA, atr and cfb genes. To assess the sensitivity of GBS detection, additional isolation of samples preincubated in Todd-Hewitt broth with colistin and nalidixic acid was performed and then subjected to amplification again. The introduction of the preincubation step increased the sensitivity of GBS detection by about 33–63%. Moreover, NAAT made it possible to identify GBS DNA in an additional six samples that were negative in culture. The highest number of true positive results compared to the culture was obtained with the atr gene primers, as compared to cfb and 16S rRNA primers. Isolation of bacterial DNA after preincubation in enrichment broth significantly increases the sensitivity of NAAT-based methods applied for the detection of GBS from vaginal and rectal swabs. In the case of the cfb gene, the use of an additional gene to ensure the appropriate results should be considered. Full article

Programmed cell death ligand-1 (PD-L1) binds PD-1 on CD⁸⁺ lymphocytes, inhibiting their cytotoxic action. Its aberrant expression by head and neck squamous cell carcinoma (HNSCC) cells leads to immune escape. Pembrolizumab and nivolumab, two humanized monoclonal antibodies against PD-1, have been approved in HNSCC treatment, but ~60% of patients with recurrent or metastatic HNSCC fail to respond to immunotherapy and only 20 to 30% of treated patients have long-term benefits. The purpose of this review is to analyze all the fragmentary evidence present in the literature to identify what future diagnostic markers could be useful for predicting, together with PD-L1 CPS, the response to immunotherapy and its durability. We searched PubMed, Embase, and the Cochrane Register of Controlled Trials and we summarize the evidence collected in this review. We confirmed that PD-L1 CPS is a predictor of response to immunotherapy, but it should be measured across multiple biopsies and repeatedly over time. PD-L2, IFN-γ, EGFR, VEGF, TGF–β, TMB, blood TMB, CD73, TILs, alternative splicing, tumor microenvironment, and some macroscopic and radiological features are promising predictors worthy of further studies. Studies comparing predictors appear to give greater potency to TMB and CXCR9. Full article

A wide range of histological as well as clinical properties are exhibited by B-cell non-Hodgkin’s lymphomas. These properties could make the diagnostics process complicated. The diagnosis of lymphomas at an initial stage is essential because early remedial actions taken against destructive subtypes are commonly deliberated as successful and restorative. Therefore, better protective action is needed to improve the condition of those patients who are extensively affected by cancer when diagnosed for the first time. The development of new and efficient methods for early detection of cancer has become crucial nowadays. Biomarkers are urgently needed for diagnosing B-cell non-Hodgkin’s lymphoma and assessing the severity of the disease and its prognosis. New possibilities are now open for diagnosing cancer with the help of metabolomics. The study of all the metabolites synthesised in the human body is called “metabolomics.” A patient’s phenotype is directly linked with metabolomics, which can help in providing some clinically beneficial biomarkers and is applied in the diagnostics of B-cell non-Hodgkin’s lymphoma. In cancer research, it can analyse the cancerous metabolome to identify the metabolic biomarkers. This review provides an understanding of B-cell non-Hodgkin’s lymphoma metabolism and its applications in medical diagnostics. A description of the workflow based on metabolomics is also provided, along with the benefits and drawbacks of various techniques. The use of predictive metabolic biomarkers for the diagnosis and prognosis of B-cell non-Hodgkin’s lymphoma is also explored. Thus, we can say that abnormalities related to metabolic processes can occur in a vast range of B-cell non-Hodgkin’s lymphomas. The metabolic biomarkers could only be discovered and identified as innovative therapeutic objects if we explored and researched them. In the near future, the innovations involving metabolomics could prove fruitful for predicting outcomes and bringing out novel remedial approaches. Full article

Whole exome sequencing (WES) has become part of the postnatal diagnostic work-up of both pediatric and adult patients with a range of disorders. In the last years, WES is slowly being implemented in the prenatal setting as well, although some hurdles remain, such as quantity and quality of input material, minimizing turn-around times, and ensuring consistent interpretation and reporting of variants. We present the results of 1 year of prenatal WES in a single genetic center. Twenty-eight fetus-parent trios were analyzed, of which seven (25%) showed a pathogenic or likely pathogenic variant that explained the fetal phenotype. Autosomal recessive (4), de novo (2) and dominantly inherited (1) mutations were detected. Prenatal rapid WES allows for a timely decision-making in the current pregnancy, adequate counseling with the possibility of preimplantation or prenatal genetic testing in future pregnancies and screening of the extended family. With a diagnostic yield in selected cases of 25% and a turn-around time under 4 weeks, rapid WES shows promise for becoming part of pregnancy care in fetuses with ultrasound anomalies in whom chromosomal microarray did not uncover the cause. Full article

Artificial intelligence models do not provide information about exactly how the predictions are reached. This lack of transparency is a major drawback. Particularly in medical applications, interest in explainable artificial intelligence (XAI), which helps to develop methods of visualizing, explaining, and analyzing deep learning models, has increased recently. With explainable artificial intelligence, it is possible to understand whether the solutions offered by deep learning techniques are safe. This paper aims to diagnose a fatal disease such as a brain tumor faster and more accurately using XAI methods. In this study, we preferred datasets that are widely used in the literature, such as the four-class kaggle brain tumor dataset (Dataset I) and the three-class figshare brain tumor dataset (Dataset II). To extract features, a pre-trained deep learning model is chosen. DenseNet201 is used as the feature extractor in this case. The proposed automated brain tumor detection model includes five stages. First, training of brain MR images with DenseNet201, the tumor area was segmented with GradCAM. The features were extracted from DenseNet201 trained using the exemplar method. Extracted features were selected with iterative neighborhood component (INCA) feature selector. Finally, the selected features were classified using support vector machine (SVM) with 10-fold cross-validation. An accuracy of 98.65% and 99.97%, were obtained for Datasets I and II, respectively. The proposed model obtained higher performance than the state-of-the-art methods and can be used to aid radiologists in their diagnosis. Full article

To date, cardiotocography (CTG) is the only non-invasive and cost-effective tool available for continuous monitoring of the fetal health. In spite of a marked growth in the automation of the CTG analysis, it still remains a challenging signal processing task. Complex and dynamic patterns of fetal heart are poorly interpreted. Particularly, the precise interpretation of the suspected cases is fairly low by both visual and automated methods. Also, the first and second stage of labor produce very different fetal heart rate (FHR) dynamics. Thus, a robust classification model takes both stages into consideration separately. In this work, the authors propose a machine-learning-based model, which was applied separately to both the stages of labor, using standard classifiers such as SVM, random forest (RF), multi-layer perceptron (MLP), and bagging to classify the CTG. The outcome was validated using the model performance measure, combined performance measure, and the ROC-AUC. Though AUC-ROC was sufficiently high for all the classifiers, the other parameters established a better performance by SVM and RF. For suspicious cases the accuracies of SVM and RF were 97.4% and 98%, respectively, whereas sensitivity was 96.4% and specificity was 98% approximately. In the second stage of labor the accuracies were 90.6% and 89.3% for SVM and RF, respectively. Limits of agreement for 95% between the manual annotation and the outcome of SVM and RF were (−0.05 to 0.01) and (−0.03 to 0.02). Henceforth, the proposed classification model is efficient and can be integrated into the automated decision support system. Full article

Stroke is a leading cause of disability and mortality, resulting in substantial socio-economic burden for healthcare systems. With advances in artificial intelligence, visual image information can be processed into numerous quantitative features in an objective, repeatable and high-throughput fashion, in a process known as radiomics analysis (RA). Recently, investigators have attempted to apply RA to stroke neuroimaging in the hope of promoting personalized precision medicine. This review aimed to evaluate the role of RA as an adjuvant tool in the prognosis of disability after stroke. We conducted a systematic review following the PRISMA guidelines, searching PubMed and Embase using the keywords: ‘magnetic resonance imaging (MRI)’, ‘radiomics’, and ‘stroke’. The PROBAST tool was used to assess the risk of bias. Radiomics quality score (RQS) was also applied to evaluate the methodological quality of radiomics studies. Of the 150 abstracts returned by electronic literature research, 6 studies fulfilled the inclusion criteria. Five studies evaluated predictive value for different predictive models (PMs). In all studies, the combined PMs consisting of clinical and radiomics features have achieved the best predictive performance compared to PMs based only on clinical or radiomics features, the results varying from an area under the ROC curve (AUC) of 0.80 (95% CI, 0.75–0.86) to an AUC of 0.92 (95% CI, 0.87–0.97). The median RQS of the included studies was 15, reflecting a moderate methodological quality. Assessing the risk of bias using PROBAST, potential high risk of bias in participants selection was identified. Our findings suggest that combined models integrating both clinical and advanced imaging variables seem to better predict the patients’ disability outcome group (favorable outcome: modified Rankin scale (mRS) ≤ 2 and unfavorable outcome: mRS > 2) at three and six months after stroke. Although radiomics studies’ findings are significant in research field, these results should be validated in multiple clinical settings in order to help clinicians to provide individual patients with optimal tailor-made treatment. Full article

Infective endocarditis (IE) is common in patients with corrected congenital heart disease (CHD) with a residual lesion, but is rarely found on surgical patches used to close atrial septal defects (ASDs). This is also reflected in the current guidelines that do not recommend antibiotic therapy for patients with a repaired ASD with no residual shunt six months after closure (percutaneous or surgical). However, the situation could be different in the case of mitral valve endocarditis, which causes leaflet disruption with severe mitral insufficiency and could seed the surgical patch. We present herein a 40-year-old male patient with a past medical history of a complete surgically corrected atrioventricular canal defect performed in childhood who presented with fever, dyspnea and severe abdominal pain. Transthoracic and transesophageal echocardiography (TTE and TEE) revealed vegetation at the level of the mitral valve and the interatrial septum. The CT scan confirmed ASD patch endocarditis and multiple septic emboli, guiding the therapeutic management. An accurate evaluation of cardiac structures should be mandatory when a systemic infection is detected in CHD patients, even if the defects were surgically corrected, because the detection and eradication of such infectious foci as well as a surgical reintervention are particularly difficult to achieve in this subpopulation. Full article

Ultrasound (US) is acoustic energy that interacts with human tissues, thus, producing bioeffects that may be hazardous, especially in sensitive organs (i.e., brain, eye, heart, lung, and digestive tract) and embryos/fetuses. Two basic mechanisms of US interaction with biological systems have been identified: thermal and non-thermal. As a result, thermal and mechanical indexes have been developed to provide a means of assessing the potential for biological effects from exposure to diagnostic US. The main aims of this paper were to describe the models and assumptions used to estimate the “safety” of acoustic outputs and indices and to summarize the current state of knowledge about US-induced effects on living systems deriving from in vitro models and in vivo experiments on animals. This review work has made it possible to highlight the limits associated with the use of the estimated safety values of thermal and mechanical indices relating above all to the use of new US technologies, such as contrast-enhanced ultrasound (CEUS) and acoustic radiation force impulse (ARFI) shear wave elastography (SWE). US for diagnostic and research purposes has been officially declared safe, and no harmful biological effects in humans have yet been demonstrated with new imaging modalities; however, physicians should be adequately informed on the potential risks of biological effects. US exposure, according to the ALARA (As Low As Reasonably Achievable) principle, should be as low as reasonably possible. Full article