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Numerous studies have shown that an improper diet in parents has a negative impact on offspring's health. Furthermore, the negative effects of trans fatty acids (TFA) in maternal diets on fertility and health and their impact on future generations have been documented. However, there is limited research on the negative effects of TFA in paternal diets on male children. The current work used qRT-PCR to investigate the effects of trans fatty acids and vitamin E in the paternal diet on the expression pattern of androgen signaling pathway genes such as STAR, CYP11a1, HSD3B, SRD5a2, and SCARB1 in offspring testes. In this experiment, parental rats were randomly separated into four groups, each with ten father rats, and were fed for eight weeks (60 days) as follows. 1: Standard diet group plus liquid sunflower oil (control). 2: Standard diet group containing trans fatty acids (CTH). 3: The regular diet group received 2.5 times the recommended quantity of vitamin E supplement. 4: Standard diet group with vitamin E and trans fatty acid supplementation (ETH). The testis tissue samples from 35 offspring were then used. Following RNA extraction from tissues and cDNA synthesis, quantitative real-time PCR was used to evaluate the expression levels of androgen signaling pathway genes such as STAR, CYP11A1, HSD3B, SCARB1, and SRD5A2. Our findings showed that the expression of CYP11A1 was considerably reduced in the progeny of paternal rats given ETH compared to the CTH group. The expression levels of the STAR gene were significantly lower in the progeny of paternal rats administered TFA, ETH, and vitamin E compared to the controls. Although the CTH group had lower SCARB1 expression than the other groups, the difference was not statistically significant. Paternal vitamin E consumption substantially affected SRD5A2 expression when compared to offspring of paternal rats fed vitamin E + trans fatty acid or those fed a conventional diet containing trans fatty acid. Furthermore, the vitamin E group showed a statistically significant increase in HSD3B expression compared to the other groups. Bioinformatics analyses, such as protein-protein interaction networks and gene ontology term enrichment, revealed that these genes play roles in lipid biosynthesis, hormone metabolism, male sex differentiation, reproductive development, and steroid biosynthesis. Our data indicate that paternal trans fatty acid consumption influences the expression of particular androgen signaling pathway genes in offspring testis, with vitamin E potentially mitigating some of these effects., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest in this study., (Copyright © 2024. Published by Elsevier Inc.)

Background and Objective: Micro-ultrasound (MUS) uses a high-frequency transducer with superior resolution to conventional ultrasound, which may differentiate prostate cancer from normal tissue and thereby allow targeted biopsy. Preliminary evidence has shown comparable sensitivity to magnetic resonance imaging (MRI), but consistency between users has yet to be described. Our objective was to assess agreement of MUS interpretation across multiple readers., Methods: After institutional review board approval, we prospectively collected MUS images for 57 patients referred for prostate biopsy after multiparametric MRI from 2022 to 2023. MUS images were interpreted by six urologists at four institutions with varying experience (range 2-6 yr). Readers were blinded to MRI results and clinical data. The primary outcome was reader agreement on the locations of suspicious lesions, measured in terms of Light's κ and positive percent agreement (PPA). Reader sensitivity for identification of grade group (GG) ≥2 prostate cancer was a secondary outcome., Key Findings and Limitations: Analysis revealed a κ value of 0.30 (95% confidence interval [CI] 0.21-0.39). PPA was 33% (95% CI 25-42%). The mean patient-level sensitivity for GG ≥2 cancer was 0.66 ± 0.05 overall and 0.87 ± 0.09 when cases with anterior lesions were excluded. Readers were 12 times more likely to detect higher-grade cancers (GG ≥3), with higher levels of agreement for this subgroup (κ 0.41, PPA 45%). Key limitations include the inability to prospectively biopsy reader-delineated targets and the inability of readers to perform live transducer maneuvers., Conclusions and Clinical Implications: Inter-reader agreement on the location of suspicious lesions on MUS is lower than rates previously reported for MRI. MUS sensitivity for cancer in the anterior gland is lacking., Patient Summary: The ability to find cancer on imaging scans can vary between doctors. We found that there was frequent disagreement on the location of prostate cancer when doctors were using a new high-resolution scan method called micro-ultrasound. This suggests that the performance of micro-ultrasound is not yet consistent enough to replace MRI (magnetic resonance imaging) for diagnosis of prostate cancer., (© 2024 The Author(s).)

Image registration can map the ground truth extent of prostate cancer from histopathology images onto MRI, facilitating the development of machine learning methods for early prostate cancer detection. Here, we present RAdiology PatHology Image Alignment (RAPHIA), an end-to-end pipeline for efficient and accurate registration of MRI and histopathology images. RAPHIA automates several time-consuming manual steps in existing approaches including prostate segmentation, estimation of the rotation angle and horizontal flipping in histopathology images, and estimation of MRI-histopathology slice correspondences. By utilizing deep learning registration networks, RAPHIA substantially reduces computational time. Furthermore, RAPHIA obviates the need for a multimodal image similarity metric by transferring histopathology image representations to MRI image representations and vice versa. With the assistance of RAPHIA, novice users achieved expert-level performance, and their mean error in estimating histopathology rotation angle was reduced by 51% (12 degrees vs 8 degrees), their mean accuracy of estimating histopathology flipping was increased by 5% (95.3% vs 100%), and their mean error in estimating MRI-histopathology slice correspondences was reduced by 45% (1.12 slices vs 0.62 slices). When compared to a recent conventional registration approach and a deep learning registration approach, RAPHIA achieved better mapping of histopathology cancer labels, with an improved mean Dice coefficient of cancer regions outlined on MRI and the deformed histopathology (0.44 vs 0.48 vs 0.50), and a reduced mean per-case processing time (51 vs 11 vs 4.5 min). The improved performance by RAPHIA allows efficient processing of large datasets for the development of machine learning models for prostate cancer detection on MRI. Our code is publicly available at: https://github.com/pimed/RAPHIA., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mirabela Rusu is a paid consultant for Roche, the conflict is unrelated to this research. Also, Mirabela Rusu has research grants from Phillips Healthcare. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Tissue preservation strategies have been increasingly used for the management of localized prostate cancer. Focal ablation using ultrasound-guided high-intensity focused ultrasound (HIFU) has demonstrated promising short and medium-term oncological outcomes. Advancements in HIFU therapy such as the introduction of tissue change monitoring (TCM) aim to further improve treatment efficacy., Objective: To evaluate the association between intraoperative TCM during HIFU focal therapy for localized prostate cancer and oncological outcomes 12 mo afterward., Design, Setting, and Participants: Seventy consecutive men at a single institution with prostate cancer were prospectively enrolled. Men with prior treatment, metastases, or pelvic radiation were excluded to obtain a final cohort of 55 men., Intervention: All men underwent HIFU focal therapy followed by magnetic resonance (MR)-fusion biopsy 12 mo later. Tissue change was quantified intraoperatively by measuring the backscatter of ultrasound waves during ablation., Outcome Measurements and Statistical Analysis: Gleason grade group (GG) ≥2 cancer on postablation biopsy was the primary outcome. Secondary outcomes included GG ≥1 cancer, Prostate Imaging Reporting and Data System (PI-RADS) scores ≥3, and evidence of tissue destruction on post-treatment magnetic resonance imaging (MRI). A Student's t - test analysis was performed to evaluate the mean TCM scores and efficacy of ablation measured by histopathology. Multivariate logistic regression was also performed to identify the odds of residual cancer for each unit increase in the TCM score., Results and Limitations: A lower mean TCM score within the region of the tumor (0.70 vs 0.97, p = 0.02) was associated with the presence of persistent GG ≥2 cancer after HIFU treatment. Adjusting for initial prostate-specific antigen, PI-RADS score, Gleason GG, positive cores, and age, each incremental increase of TCM was associated with an 89% reduction in the odds (odds ratio: 0.11, confidence interval: 0.01-0.97) of having residual GG ≥2 cancer on postablation biopsy. Men with higher mean TCM scores (0.99 vs 0.72, p = 0.02) at the time of treatment were less likely to have abnormal MRI (PI-RADS ≥3) at 12 mo postoperatively. Cases with high TCM scores also had greater tissue destruction measured on MRI and fewer visible lesions on postablation MRI., Conclusions: Tissue change measured using TCM values during focal HIFU of the prostate was associated with histopathology and radiological outcomes 12 mo after the procedure., Patient Summary: In this report, we looked at how well ultrasound changes of the prostate during focal high-intensity focused ultrasound (HIFU) therapy for the treatment of prostate cancer predict patient outcomes. We found that greater tissue change measured by the HIFU device was associated with less residual cancer at 1 yr. This tool should be used to ensure optimal ablation of the cancer and may improve focal therapy outcomes in the future., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Magnetic resonance imaging (MRI) underestimation of prostate cancer extent complicates the definition of focal treatment margins., Objective: To validate focal treatment margins produced by an artificial intelligence (AI) model., Design Setting and Participants: Testing was conducted retrospectively in an independent dataset of 50 consecutive patients who had radical prostatectomy for intermediate-risk cancer. An AI deep learning model incorporated multimodal imaging and biopsy data to produce three-dimensional cancer estimation maps and margins. AI margins were compared with conventional MRI regions of interest (ROIs), 10-mm margins around ROIs, and hemigland margins. The AI model also furnished predictions of negative surgical margin probability, which were assessed for accuracy., Outcome Measurements and Statistical Analysis: Comparing AI with conventional margins, sensitivity was evaluated using Wilcoxon signed-rank tests and negative margin rates using chi-square tests. Predicted versus observed negative margin probability was assessed using linear regression. Clinically significant prostate cancer (International Society of Urological Pathology grade ≥2) delineated on whole-mount histopathology served as ground truth., Results and Limitations: The mean sensitivity for cancer-bearing voxels was higher for AI margins (97%) than for conventional ROIs (37%, p  < 0.001), 10-mm ROI margins (93%, p  = 0.24), and hemigland margins (94%, p  < 0.001). For index lesions, AI margins were more often negative (90%) than conventional ROIs (0%, p  < 0.001), 10-mm ROI margins (82%, p  = 0.24), and hemigland margins (66%, p  = 0.004). Predicted and observed negative margin probabilities were strongly correlated (R 2  = 0.98, median error = 4%). Limitations include a validation dataset derived from a single institution's prostatectomy population., Conclusions: The AI model was accurate and effective in an independent test set. This approach could improve and standardize treatment margin definition, potentially reducing cancer recurrence rates. Furthermore, an accurate assessment of negative margin probability could facilitate informed decision-making for patients and physicians., Patient Summary: Artificial intelligence was used to predict the extent of tumors in surgically removed prostate specimens. It predicted tumor margins more accurately than conventional methods., (© 2023 The Author(s).)

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.

Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g., speckle and shadowing) in ultrasound images limit the performance of automated prostate segmentation techniques and generalizing these methods to new image domains is inherently difficult. In this study, we address these challenges by introducing a novel 2.5D deep neural network for prostate segmentation on ultrasound images. Our approach addresses the limitations of transfer learning and finetuning methods (i.e., drop in performance on the original training data when the model weights are updated) by combining a supervised domain adaptation technique and a knowledge distillation loss. The knowledge distillation loss allows the preservation of previously learned knowledge and reduces the performance drop after model finetuning on new datasets. Furthermore, our approach relies on an attention module that considers model feature positioning information to improve the segmentation accuracy. We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions. We analyzed the performance of our method on three large datasets encompassing 2067 subjects from three different institutions. Our method achieved an average Dice Similarity Coefficient (Dice) of 94.0±0.03 and Hausdorff Distance (HD95) of 2.28 mm in an independent set of subjects from the first institution. Moreover, our model generalized well in the studies from the other two institutions (Dice: 91.0±0.03; HD95: 3.7 mm and Dice: 82.0±0.03; HD95: 7.1 mm). We introduced an approach that successfully segmented the prostate on ultrasound images in a multi-center study, suggesting its clinical potential to facilitate the accurate fusion of ultrasound and MRI images to drive biopsy and image-guided treatments., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care., Competing Interests: The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: MR has research grants from GE Healthcare and Philips Healthcare., (© The Author(s), 2022.)

Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, compared with the other sequences LGE CMR images with gold standard labels are particularly limited. This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019. The challenge offered a data set of paired MS-CMR images, including auxiliary CMR sequences as well as LGE CMR, from 45 patients who underwent cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark existing ones for LGE CMR segmentation focusing on myocardial wall of the left ventricle and blood cavity of the two ventricles. In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the ventricle segmentation of LGE CMR. Nine representative works were selected for evaluation and comparisons, among which three methods are unsupervised domain adaptation (UDA) methods and the other six are supervised. The results showed that the average performance of the nine methods was comparable to the inter-observer variations. Particularly, the top-ranking algorithms from both the supervised and UDA methods could generate reliable and robust segmentation results. The success of these methods was mainly attributed to the inclusion of the auxiliary sequences from the MS-CMR images, which provide important label information for the training of deep neural networks. The challenge continues as an ongoing resource, and the gold standard segmentation as well as the MS-CMR images of both the training and test data are available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mscmrseg/)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

An important role has been recently reported for bacterial biofilm in the pathophysiology of chronic diseases, such as chronic rhinosinusitis (CRS). CRS, affecting sinonasal mucosa, is a persistent inflammatory condition with a high prevalence around the world. Although the exact pathological mechanism of this disease has not been elicited yet, biofilm formation is known to lead to a more significant symptom burden and major objective clinical indicators. The high prevalence of multidrug-resistant bacteria has severely restricted the application of antibiotics in recent years. Furthermore, systemic antibiotic therapy, on top of its insufficient concentration to eradicate bacteria in the sinonasal biofilm, often causes toxicity, antibiotic resistance, and an effect on the natural microbiota, in patients. Thus, coming up with alternative therapeutic options instead of systemic antibiotic therapy is emphasized in the treatment of bacterial biofilm in CRS patients. The use of topical antibiotic therapy and antibiotic eluting sinus stents that induce higher antibiotic concentration, and decrease side effects could be helpful. Besides, recent research recognized that various natural products, nitric oxide, and bacteriophage therapy, in addition to the hindered biofilm formation, could degrade the established bacterial biofilm. However, despite these improvements, new antibacterial agents and CRS biofilm interactions are complicated and need extensive research. Finally, most studies were performed in vitro, and more preclinical animal models and human studies are required to confirm the collected data. The present review is specifically discussing potential therapeutic strategies for the treatment of bacterial biofilm in CRS patients., (© 2021 Society for Applied Microbiology.)

Deep learning models are sensitive to domain shift phenomena. A model trained on images from one domain cannot generalise well when tested on images from a different domain, despite capturing similar anatomical structures. It is mainly because the data distribution between the two domains is different. Moreover, creating annotation for every new modality is a tedious and time-consuming task, which also suffers from high inter- and intra- observer variability. Unsupervised domain adaptation (UDA) methods intend to reduce the gap between source and target domains by leveraging source domain labelled data to generate labels for the target domain. However, current state-of-the-art (SOTA) UDA methods demonstrate degraded performance when there is insufficient data in source and target domains. In this paper, we present a novel UDA method for multi-modal cardiac image segmentation. The proposed method is based on adversarial learning and adapts network features between source and target domain in different spaces. The paper introduces an end-to-end framework that integrates: a) entropy minimization, b) output feature space alignment and c) a novel point-cloud shape adaptation based on the latent features learned by the segmentation model. We validated our method on two cardiac datasets by adapting from the annotated source domain, bSSFP-MRI (balanced Steady-State Free Procession-MRI), to the unannotated target domain, LGE-MRI (Late-gadolinium enhance-MRI), for the multi-sequence dataset; and from MRI (source) to CT (target) for the cross-modality dataset. The results highlighted that by enforcing adversarial learning in different parts of the network, the proposed method delivered promising performance, compared to other SOTA methods.

Quantitative assessment of cardiac left ventricle (LV) morphology is essential to assess cardiac function and improve the diagnosis of different cardiovascular diseases. In current clinical practice, LV quantification depends on the measurement of myocardial shape indices, which is usually achieved by manual contouring of the endo- and epicardial. However, this process subjected to inter and intra-observer variability, and it is a time-consuming and tedious task. In this article, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardiac LV morphology, regional-wall thickness (RWT), and additionally detecting the cardiac phase cycle (systole and diastole) for a given 3D Cine-magnetic resonance (MR) image sequence. We first segment cardiac LVs using an encoder-decoder network and then introduce a multitask framework to regress 11 LV indices and classify the cardiac phase, as parallel tasks during model optimization. The proposed deep learning model is based on the 3D spatio-temporal convolutions, which extract spatial and temporal features from MR images. We demonstrate the efficacy of the proposed method using cine-MR sequences of 145 subjects and comparing the performance with other state-of-the-art quantification methods. The proposed method obtained high prediction accuracy, with an average mean absolute error (MAE) of 129 mm 2 , 1.23 mm, 1.76 mm, Pearson correlation coefficient (PCC) of 96.4%, 87.2%, and 97.5% for LV and myocardium (Myo) cavity regions, 6 RWTs, 3 LV dimensions, and an error rate of 9.0% for phase classification. The experimental results highlight the robustness of the proposed method, despite varying degrees of cardiac morphology, image appearance, and low contrast in the cardiac MR sequences.

Chronic Rhinosinusitis (CRS) is a multifactorial disease, and different etiologies like metabolism and immunity disorders, bacterial superantigens, biofilms, and fungal allergens are known to develop this disease, especially the CRS with nasal polyps. Alternaria alternata (Alternaria) is one of the most prevalent airborne fungal species in the nasal discharge, which might have vigorous immunologic activities in nasal epithelial cells and play an essential role in the pathogenesis of CRS. Moreover, the interaction between this fungus and the innate and adaptive immune systems leads to the development of chronic inflammation. This inflammation may consequently instigate the CRS and nasal polyposis. The attenuation of surfactant protein synthesis or intracellular reserves and mucus hypersecretion could prevent the clearance of Alternaria from sinuses and may be correlated with colonization and re-infection of airborne fungi. Furthermore, higher expression of cathelicidin, thymic stromal lymphopoietin, toll-like receptors, and T helper 2-dominant immune responses can result in an IgE-mediated pathway activation and eosinophils degranulation. Moreover, higher local Alternaria-specific IgE was shown to be correlated with eosinophilic cationic proteins and might relate to nasal polyps. However, the role of genetic and environmental factors affecting CRS and nasal polyposis is not well studied. Likewise, further animal and clinical studies are required to better understand the role of Alternaria in CRS disease. The current article reviews the recent findings around the Alternaria-induced CRS and nasal polyposis., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Multi-Drug Resistant (MDR) uropathogenic bacteria have increased in number in recent years and the development of new treatment options for the corresponding infections has become a major challenge in the field of medicine. In this respect, recent studies have proposed bacteriophage (phage) therapy as a potential alternative against MDR Urinary Tract Infections (UTI) because the resistance mechanism of phages differs from that of antibiotics and few side effects have been reported for them. Escherichia coli, Klebsiella pneumoniae, and Proteus mirabilis are the most common uropathogenic bacteria against which phage therapy has been used. Phages, in addition to lysing bacterial pathogens, can prevent the formation of biofilms. Besides, by inducing or producing polysaccharide depolymerase, phages can easily penetrate into deeper layers of the biofilm and degrade it. Notably, phage therapy has shown good results in inhibiting multiple-species biofilm and this may be an efficient weapon against catheter-associated UTI. However, the narrow range of hosts limits the use of phage therapy. Therefore, the use of phage cocktail and combination therapy can form a highly attractive strategy. However, despite the positive use of these treatments, various studies have reported phage-resistant strains, indicating that phage-host interactions are more complicated and need further research. Furthermore, these investigations are limited and further clinical trials are required to make this treatment widely available for human use. This review highlights phage therapy in the context of treating UTIs and the specific considerations for this application.

Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address this, we organized the 2018 Left Atrium Segmentation Challenge using 154 3D LGE-MRIs, currently the world's largest atrial LGE-MRI dataset, and associated labels of the left atrium segmented by three medical experts, ultimately attracting the participation of 27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show that the top method achieved a Dice score of 93.2% and a mean surface to surface distance of 0.7 mm, significantly outperforming prior state-of-the-art. Particularly, our analysis demonstrated that double sequentially used CNNs, in which a first CNN is used for automatic region-of-interest localization and a subsequent CNN is used for refined regional segmentation, achieved superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging community., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interests., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Cardiac magnetic resonance (CMR) imaging is used widely for morphological assessment and diagnosis of various cardiovascular diseases. Deep learning approaches based on 3D fully convolutional networks (FCNs), have improved state-of-the-art segmentation performance in CMR images. However, previous methods have employed several pre-processing steps and have focused primarily on segmenting low-resolutions images. A crucial step in any automatic segmentation approach is to first localize the cardiac structure of interest within the MRI volume, to reduce false positives and computational complexity. In this paper, we propose two strategies for localizing and segmenting the heart ventricles and myocardium, termed multi-stage and end-to-end, using a 3D convolutional neural network. Our method consists of an encoder-decoder network that is first trained to predict a coarse localized density map of the target structure at a low resolution. Subsequently, a second similar network employs this coarse density map to crop the image at a higher resolution, and consequently, segment the target structure. For the latter, the same two-stage architecture is trained end-to-end. The 3D U-Net with some architectural changes (referred to as 3D DR-UNet) was used as the base architecture in this framework for both the multi-stage and end-to-end strategies. Moreover, we investigate whether the incorporation of coarse features improves the segmentation. We evaluate the two proposed segmentation strategies on two cardiac MRI datasets, namely, the Automatic Cardiac Segmentation Challenge (ACDC) STACOM 2017, and Left Atrium Segmentation Challenge (LASC) STACOM 2018. Extensive experiments and comparisons with other state-of-the-art methods indicate that the proposed multi-stage framework consistently outperforms the rest in terms of several segmentation metrics. The experimental results highlight the robustness of the proposed approach, and its ability to generate accurate high-resolution segmentations, despite the presence of varying degrees of pathology-induced changes to cardiac morphology and image appearance, low contrast, and noise in the CMR volumes.

We investigated whether the integration of machine learning (ML) into MRI interpretation can provide accurate decision rules for the management of suspicious breast masses. A total of 173 consecutive patients with suspicious breast masses upon complementary assessment (BI-RADS IV/V: n = 100/76) received standardized breast MRI prior to histological verification. MRI findings were independently assessed by two observers (R1/R2: 5 years of experience/no experience in breast MRI) using six (semi-)quantitative imaging parameters. Interobserver variability was studied by ICC (intraclass correlation coefficient). A polynomial kernel function support vector machine was trained to differentiate between benign and malignant lesions based on the six imaging parameters and patient age. Ten-fold cross-validation was applied to prevent overfitting. Overall diagnostic accuracy and decision rules (rule-out criteria) to accurately exclude malignancy were evaluated. Results were integrated into a web application and published online. Malignant lesions were present in 107 patients (60.8%). Imaging features showed excellent interobserver variability (ICC: 0.81-0.98) with variable diagnostic accuracy (AUC: 0.65-0.82). Overall performance of the ML algorithm was high (AUC = 90.1%; BI-RADS IV: AUC = 91.6%). The ML algorithm provided decision rules to accurately rule-out malignancy with a false negative rate <1% in 31.3% of the BI-RADS IV cases. Thus, integration of ML into MRI interpretation can provide objective and accurate decision rules for the management of suspicious breast masses, and could help to reduce the number of potentially unnecessary biopsies., Competing Interests: The authors of this manuscript declare relationships with the following companies: Michael Uder is on the speakers’ bureau for Bracco, Medtronic, Siemens and Bayer Schering. Rolf Janka is on the speakers’ bureau for Bracco. Tobias Bäuerle is on the speakers’ bureau for Bracco and Boehringer Ingelheim. For the remaining authors no potential conflicts of interest were declared. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: Neuromyelitis optica spectrum disease (NMOSD) is a severe autoimmune demyelinating disorder of the central nervous system that throughout epidemiological data, it has not been completely determined. The aim of this study was to assess characteristics of NMOSD patients in Isfahan as one of the most prevalent cities for multiple sclerosis in Iran., Materials and Methods: Forty-five patients diagnosed as neuromyelitis optica (NMO) disease through 5 years enrolled in this study. Demographics and characteristics of disease such as Expanded Disability Status Scale (EDSS) score, disease duration, clinical symptoms, laboratory data, and magnetic resonance imaging findings (including T1, T2, and flair protocols) were recorded. NMO-immunoglobulin G serology assay was done in all of the patients by ELISA test., Results: Female to male ratio was 5.4:1. The mean age of disease onset was 29.8 ± 11.2 years. NMO antibody was positive in 24.4% of patients. The presenting symptoms were optic neuritis (55.5%), transverse myelitis (40%), and brainstem symptoms (4.5%). The interval between the first and second attack was 19.28 ± 31.27 months (range: 1 month to 17 years). The mean EDSS score of the patients was 2.8 ± 2.25. Frequency of long-extending cervical plaque was higher among men than women (85.7% vs. 57.9%)., Conclusion: Based on this study, the mean age of NMOSD onset among Isfahan population was considerably lower than other studies, and there was higher frequency of long-extending cervical lesion among male patients which needs more consideration in further studies., Competing Interests: There are no conflicts of interest.

Background: Multiple sclerosis may have profound emotional consequences. The relation between psychological and physical factors could lead patients toward unforeseen disease. This study focuses on multiple sclerosis (MS) disease duration on personality type A and B in relation to individuals' behaviors., Materials and Methods: This descriptive-analytical study was conducted in Isfahan Alzahra hospital in 2013. Three hundred MS patients and 100 healthy individuals were determined. The distributed questionnaires related to MS patients and considering the descriptive statistics such as demographic variables. Data were analyzed by SPSS software (version 18) based on Chi-square test and independent T-test., Results: Disease duration varied between 1 to 38 years: 30% (1-4 years), 38% (5-10 years), 20% (10-20 years), and 12% (more than 20 years). Significant relationship was observed between disease duration and tendency to type A (higher stress). This relation was positive and significant in Relapsing Remitting MS patients; but negative correlation was seen in Secondary Progressive MS patients. These patients tended to type B (lower stress) when disease duration increased., Conclusions: Individuals with disease duration of one year and less than one year tend to type A personality, while patients with increment of disease duration have tendency to type B.

Background: Midkine (MK) is a heparin-binding growth factor with promoting effects in inflammatory responses through enhancing leukocytes migration., Objective: To study the correlation between MK serum levels and concentration of inflammatory cytokines in Multiple Sclerosis (MS) patients., Methods: We evaluated the MK level and its relationship with inflammatory cytokines (IL-17 and IL-23) and anti-inflammatory ones (IL-10 and TGF-β) in multiple sclerosis (MS) patients. The serum concentrations of MK and cytokines were assessed by ELISA in 32 MS patients in comparison with 32 healthy subjects., Results: Our data showed that the MK concentration in MS patients is lower than healthy controls (341.15 ± 40.71 Pg/ml vs. 620.15 ± 98.61 Pg/ml, respectively, p=0.015). We also observed a significant decrease in IL-10, IL-23, and TGF-β cytokine levels in MS patients. There was a significant correlation between MK and IL-23 concentrations in our study (r = +0.829, p≤ 0.001)., Conclusion: These results confirm a role for MK in inflammatory reactions in MS.

Introduction: Studies showed that lack of interest in the field of study in the 1(st) year of residency could create stress and then causing psychological problems like anxiety. The purpose of this study was the evaluation of relationship between interest to the specialized field of study and the level of medical residents' anxiety in 2010., Materials and Methods: This study was a cross-sectional study. The statistical population of this study was the medical residents (1(st)-4(th) year) from the Universities of Medical Sciences of Isfahan, Gilan, Sanandaj and Kashan (370 residents). They were selected by stratified sampling method proportional to size and were included in this study. Data was collected by using the researcher-made questionnaire of demographic characteristics, the questionnaire about the field of study selection and Zung anxiety self-assessment standard questionnaire. The findings were analyzed by using the SPSS statistical software version 16, descriptive and analytical tests (t-test, one-way ANOVA and Pearson). The significance level was considered as P ≤ 0.05., Results: The results showed that more than 92% of the surveyed residents did not have anxiety and were in the normal group. There was a significant correlation between the specialized field of interest and anxiety scores of the residents (P < 0.05). In particular, the following cases had a direct effect on interest rates and anxiety in residents: the amount of time for visiting and patient care, job stress, time for personal affairs, the ability to predict the agenda and job security., Conclusions: The relationship between various factors and anxiety level was corroborated that the residents' interest and success in studying was the result of their choices and management plans to eliminate barriers for the selection of the field and the favorite city by the officials and planners.

Background: Alzheimer's disease (AD) is one of the most important neurodegenerative disorder. Anti-cyclic citrullinated peptide (anti-CCP) may all be involved in the development of vascular disease such as AD. The aim of this study is detection of seropositivity for anti-CCP antibody in AD patients., Methods: In our study, 30 patients with AD and 29 healthy controls (age and-sex matched) were recruited. Homocysteine and anti-CCP was measured by spectrophotometrically and immunoassay., Results: Mean ± SE anti-CCP was higher significantly between AD (13.6 ± 3) and healthy subjects (4.8 ± 0.2) (P = 0.006). In the patients, anti-CCP serum level was in high range (32.1%) of abnormal levels, but there was no significant difference in serum homocysteine in AD patients compared with controls. There is no correlation between anti-CCP and homocysteine levels in AD patients (P = 0.75), but between age and anti-CCP level observed a significantly correlation (P = 0.04)., Conclusions: It needs more studies to clarify confirmation the role of anti-CCP antibody production in AD patients.

Background: University entrance is accompanied by major changes in social relationship, rules, and expectations that lead to psychological disorders in susceptible students. The goal of this research is to study the anxiety rate in Iranian medical residents in 2010-2011., Methods: This study is a cross-sectional, descriptive study. It contains 370 medical residents from the 1(st) year to the 4(th) year of medical universities in Isfahan, Gilan, Zahedan, Sanandaj, and Kashan. The stratified sampling method proportionate to volume of participants is used in this study. The information is collected based on researchers' questioners and Zung self-rating anxiety scale and analyzed with the use of spss software version 16, addition to descriptive and analytic tests (Pearson, one-way analysis of variance, t-test). Meaningful level is regarded as P ≤ 0.05., Results: The study showed that more than 92% of residents participated in the study did not demonstrate anxiety. Among 370 subjects 5.5% presented with mild symptoms of anxiety and no one had symptom of severe anxiety. A meaningful statistical relationship was observed between anxiety and sex, major of study and the city of study (P < 0.05). The results showed a positive meaningful relationship between the number of visits and the score of anxiety. On average the number of night floats were two in 1 week and the number of patient visit was 19 in the past 24 h. A meaningful statistical relationship between anxiety score and number of patient visits was observed., Conclusions: The anxiety rate in medical students in this study compared to the findings of previous studies reveled very low anxiety in medical residents. The low rate of anxiety could be attributed to the sense of job security and the hope for a better future among residents. The high percentage of anxiolytics abuse and absence of anxiety producing factors among residents in addition to inaccurate response to the questionnaire may all contribute to the low rate of anxiety in this study.

Background: Previous studies confirmed that neural gene expression in embryonic stem cells (ESC) could influence by chemical compounds through stimulating apoptotic pathway. We aimed to use ESCs-derived neural cells by embryoid body formation as an in vitro model for determination of neural gene expression changes in groups that treated by sera from Alzheimer's patients and compare with healthy individuals., Materials and Methods: ESC line which was derived from the C57BL/6 mouse strain was used throughout this study. ESC-derived neural cells were treated with serum from Alzheimer's patient and healthy individual. Neural gene expression was assessed in both groups by quantitative real-time polymerase chain reaction analysis. The data was analyzed by SPSS Software (version 18)., Results: Morphologically, the reducing in neurite out-growth was observed in neural cells in group, which treated by serum from Alzheimer's patient, while neurite growth was natural in appearance in control group. Microtubule-associated protein 2 and glial fibrillary acidic protein expression significantly reduced in the Alzheimer's patient group compared with the control group. Nestin expression did not significantly differ among the groups., Conclusion: Neural gene expression could be reduced in serum treated ESC in Alzheimer's patients.

T-B-NK+ severe combined immunodeficiency (SCID) is an autosomal recessive disease that is caused mainly by a defect in the recombination activating genes (RAG). Patients with SCID usually experience life-threatening opportunistic infections in early infancy and complications after vaccination with bacille Calmette-Guérin (BCG). We report a patient of consanguineous parents who was referred to our center with subaxillary lymphadenitis and respiratory distress. Laboratory studies confirmed the diagnosis of T-B-NK+ SCID and molecular studies revealed homozygous mutations in the RAG2 gene. The patient died despite administration of antituberculosis drugs, antibiotics, and intravenous immunoglobulin. Inoculation of live vaccines such as BCG should be postponed in families with a positive history of SCID until screening tests rule out this condition.