Psychological maltreatment, meaning-centered coping, psychological flexibility, and suicide cognitions: A moderated mediation model.

BACKGROUND: Mental health problems are currently diversifying, increasing the possibility of suicide. Exposure to psychological maltreatment is one of the factors that increases suicidal cognitions. In addition, psychological flexibility and meaning-centered approaches may be effective in coping with suicidal cognitions. OBJECTIVE: A hypothesized model was tested to examine the relationships among psychological maltreatment, suicidal cognitions, psychological flexibility, and meaning-centered coping. METHOD: Data were collected from a sample of 652 participants. Mediation and moderation analyses were conducted to examine the mediating and moderating role of psychological flexibility and meaning-centered coping in the relationship between childhood psychological maltreatment and suicide cognitions among Turkish adults. RESULTS: The results of the study indicated significant negative and positive relationships between psychological maltreatment, suicide cognitions, psychological flexibility, and meaning-centered coping. The findings suggest that psychological flexibility and meaning-centered coping have a serial mediating effect on the relationship between psychological maltreatment and suicide cognitions. Furthermore, according to the study results, psychological flexibility plays a moderating role both between psychological maltreatment and suicidal cognitions and between meaning-centered coping and suicidal cognitions. CONCLUSIONS: The study suggests that psychological maltreatment can be considered as a risk factor and meaning-centered coping and psychological flexibility as protective factors in suicide prevention studies.

FFPE++: Improving the quality of formalin-fixed paraffin-embedded tissue imaging via contrastive unpaired image-to-image translation.

Formalin-fixation and paraffin-embedding (FFPE) is a technique for preparing and preserving tissue specimens that has been utilized in histopathology since the late 19th century. This process is further complicated by FFPE preparation steps such as fixation, processing, embedding, microtomy, staining, and coverslipping, which often results in artifacts due to the complex histological and cytological characteristics of a tissue specimen. The term "artifacts" includes, but is not limited to, staining inconsistencies, tissue folds, chattering, pen marks, blurring, air bubbles, and contamination. The presence of artifacts may interfere with pathological diagnosis in disease detection, subtyping, grading, and choice of therapy. In this study, we propose FFPE++, an unpaired image-to-image translation method based on contrastive learning with a mixed channel-spatial attention module and self-regularization loss that drastically corrects the aforementioned artifacts in FFPE tissue sections. Turing tests were performed by 10 board-certified pathologists with more than 10 years of experience. These tests which were performed for ovarian carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, and papillary thyroid carcinoma, demonstrate the clear superiority of the proposed method in many clinical aspects compared with standard FFPE images. Based on the qualitative experiments and feedback from the Turing tests, we believe that FFPE++ can contribute to substantial diagnostic and prognostic accuracy in clinical pathology in the future and can also improve the performance of AI tools in digital pathology. The code and dataset are publicly available at https://github.com/DeepMIALab/FFPEPlus.

The effects of breast reduction with superomedial and inferior pedicle techniques on radiological breast imaging.

BACKGROUND: Several breast reduction techniques have been introduced, and the reliability of these techniques has been demonstrated in clinical practice. However, it is still controversial how patients should be evaluated radiologically both preoperative and postoperative. This study aims to compare the radiological findings seen following reduction mammoplasty with two different techniques (inferior pedicle and superomedial pedicle), in connection with the surgical steps. METHODS: Medical records of 141 patients and a total of 278 breasts who underwent breast reduction with the diagnosis of macromastia were retrospectively analyzed. Demographic and operative data such as age, type of pedicle, preoperative and postoperative nipple-areola complex (NAC) position, and NAC transfer distance were recorded. Radiological evaluation was performed by two radiologists experienced in breast imaging by reinterpreting preoperative and postoperative mammography images. RESULTS: The rate of postoperative structural distortion (p < 0.001), thickened areola (p = 0.011), and retroareolar fibrotic band (p < 0.001) were observed to be significantly higher in the superomedial group. The risk of fat necrosis increases as the NAC transfer distance increases and a value of >9.5 cm in the NAC transfer distance can be considered as the cutoff value in terms of fat necrosis development, especially in those using superomedial pedicle technique. CONCLUSION: Surgical technique-specific benign radiological changes occur following reduction mammoplasty. However, these changes do not significantly affect the Breast imaging, reporting, and data system category. The localization of fat necrosis differs depending on the surgical technique, and the risk of fat necrosis increases as the NAC transfer distance increases, especially in those who have undergone superomedial pedicle breast reduction surgery.

Diagnostic Accuracy of Imaging Modalities on Occult Groin Hernias According to Hernia type and a Surgeon-centered Individualized Groin Hernia Management Algorithm.

BACKGROUND: The purpose of this study is to evaluate the diagnostic performance of imaging techniques for the identification of groin hernias based on the type of hernia and to demonstrate the efficacy of an algorithm based on the surgeon and surgical technique. MATERIALS AND METHODS: Medical records of 561 patients who were operated on for groin hernia were retrospectively analyzed. A total of 102 patients who had both pelvic computed tomography (CT) and ultrasonography (USG) recordings preoperatively and underwent transabdominal preperitoneal repair were included in the study. RESULTS: A contralateral asymptomatic occult groin hernia was detected in 25.5% of all patients. The overall sensitivity of USG and CT on contralateral asymptomatic occult hernia was 42.3% and 65.4%, respectively. The sensitivity of USG according to the contralateral occult hernia type was 66.7%, 35.7%, 33.3%, and 50% for direct, indirect, femoral, and pantaloon hernias, respectively. The sensitivity of CT according to the contralateral occult hernia type was 0%, 57.1%, 100%, and 100% for direct, indirect, femoral, and pantaloon hernias, respectively. CONCLUSIONS: The handicap created by the variability in the diagnostic sensitivity of imaging modalities can be overcome with the choice of transabdominal preperitoneal repair in the surgical technique.

A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded.

Histological artefacts in cryosectioned tissue can hinder rapid diagnostic assessments during surgery. Formalin-fixed and paraffin-embedded (FFPE) tissue provides higher quality slides, but the process for obtaining them is laborious (typically lasting 12-48 h) and hence unsuitable for intra-operative use. Here we report the development and performance of a deep-learning model that improves the quality of cryosectioned whole-slide images by transforming them into the style of whole-slide FFPE tissue within minutes. The model consists of a generative adversarial network incorporating an attention mechanism that rectifies cryosection artefacts and a self-regularization constraint between the cryosectioned and FFPE images for the preservation of clinically relevant features. Transformed FFPE-style images of gliomas and of non-small-cell lung cancers from a dataset independent from that used to train the model improved the rates of accurate tumour subtyping by pathologists.

Shame and guilt activations: Associations and sociodemographic differences among ploysubstance abusers.

Objectives: Shame and guilt in polysubstance abusers are still understudied despite their significance in substance use disorders (SUD). The goal of the current study is to develop a better understanding of how shame and guilt interact among polysubstance abusers who are receiving residential treatment. Methods: The sample of two hundred four males with SUD admitted to five rehabilitation centers from two cities in Pakistan participated in this study. For comparison, 215 age-matched healthy individuals were recruited (control). All participants reported their scores on the state shame and guilt scale (SSGS) and demographic form. A cross-sectional study design was adopted. Results: The group with SUD reported greater activations on SGSS (r = 0.79, p < 0.001) as compared to healthy (control) individuals (r = 0.48, p < 0.001). Further, multivariate analysis indicated that people with SUD who were of older age, unemployed, living in a nuclear family system, with a higher level of education, and low income, experienced higher levels of shame and guilt. Multinomial logistic regression analysis revealed that people with SUD in the age group 41-60 years (OR 5.2, 95%CI 2.4-6.8), unemployed (OR 4.4, 95%CI 3.2-4.7), nuclear family system (OR 5.9, 95%CI 4.5-6.4) and low monthly income group (OR 5.4, 95%CI 3.5-5.8) had a significantly high risk of shame and guilt than the control group. Conclusion: Findings of the current study indicate an association between shame and guilt activation and SUD. These results suggest that polysubstance users may benefit from therapeutic interventions to avoid a generalization of shame and guilt toward their substance use. Reducing shame and guilt should be considered a priority in treating adults with multiple SUD.

UC-NfNet: Deep learning-enabled assessment of ulcerative colitis from colonoscopy images.

Ulcerative colitis (UC) belongs to the inflammatory bowel disease (IBD) family, which is mainly caused by inflammation of the tissue in the colon and rectum. The severity of this infection can radically affect the patient's overall well-being. Although there is no definitive treatment for this disease, diagnosis of the severity of the disease through colonoscopy imaging and the use of personalized treatment can prevent progression to more malignant stages. Inter- and intra-observer variability combined with the complex nature of UC infection makes medical assessment cumbersome. Diagnosis and treatment of UC can be made more accurate and robust if disease severity can be determined in a standardized and automated manner. Therefore, the development of a computerized tool that can be integrated into the clinical decision-making process of UC classification is of great importance. In this work, we present an automated UC classification method, UC-NfNet, complemented by a synthetic data generation pipeline aimed at classifying colonoscopy UC images. We show that our model quantitatively outperforms state-of-the-art classification models such as ConViT, Inception-v4, NFNets, ResNets and Swin Transformer. In an independent reader study of five gastroenterologists, the average agreement between the UC-NfNet and individual gastroenterologists was higher than the agreement between individual gastroenterologists. This robust evaluation of the proposed AI system paves the way for clinical trials of AI-assisted UC classification. The code and dataset are publicly available at https://github.com/DeepMIALab/UC-NfNet.

A hypothetic model for examining the relationship between happiness, forgiveness, emotional reactivity and emotional security.

The ultimate goal of life is happiness, according to Plato. Perhaps the most critical questions in the life of human beings have been on happiness and processes that affect happiness. The present study was planned during the COVID-19 pandemic; perhaps human beings are most needed for happiness. The original hypothetical model and the findings constitute the powerful and different aspects of the present study. This study determined a hypothetical model to examine the relationships among happiness, forgiveness, emotional reactivity, and emotional security. The participant group of the study consists of a total of 916 individuals from Turkey, 617 women, and 299 men. The age scale of the participants is between 18-25. Participants completed the Heartland Forgiveness Scale, the Emotional Security Scale, the Emotional Reactivity Scale, and the Oxford Happiness Scale. Mediation analysis was conducted using Hayes' (2017) process macro. According to the proposed model in the study, emotional reactivity mediates the relationship between forgiveness and happiness. As the individual's forgiveness increases, their emotional reactivity decreases, and as the emotional reactivity decreases, the individual's level of happiness increases.

Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies.

Endomyocardial biopsy (EMB) screening represents the standard of care for detecting allograft rejections after heart transplant. Manual interpretation of EMBs is affected by substantial interobserver and intraobserver variability, which often leads to inappropriate treatment with immunosuppressive drugs, unnecessary follow-up biopsies and poor transplant outcomes. Here we present a deep learning-based artificial intelligence (AI) system for automated assessment of gigapixel whole-slide images obtained from EMBs, which simultaneously addresses detection, subtyping and grading of allograft rejection. To assess model performance, we curated a large dataset from the United States, as well as independent test cohorts from Turkey and Switzerland, which includes large-scale variability across populations, sample preparations and slide scanning instrumentation. The model detects allograft rejection with an area under the receiver operating characteristic curve (AUC) of 0.962; assesses the cellular and antibody-mediated rejection type with AUCs of 0.958 and 0.874, respectively; detects Quilty B lesions, benign mimics of rejection, with an AUC of 0.939; and differentiates between low-grade and high-grade rejections with an AUC of 0.833. In a human reader study, the AI system showed non-inferior performance to conventional assessment and reduced interobserver variability and assessment time. This robust evaluation of cardiac allograft rejection paves the way for clinical trials to establish the efficacy of AI-assisted EMB assessment and its potential for improving heart transplant outcomes.

SelfVIO: Self-supervised deep monocular Visual-Inertial Odometry and depth estimation.

In the last decade, numerous supervised deep learning approaches have been proposed for visual-inertial odometry (VIO) and depth map estimation, which require large amounts of labelled data. To overcome the data limitation, self-supervised learning has emerged as a promising alternative that exploits constraints such as geometric and photometric consistency in the scene. In this study, we present a novel self-supervised deep learning-based VIO and depth map recovery approach (SelfVIO) using adversarial training and self-adaptive visual-inertial sensor fusion. SelfVIO learns the joint estimation of 6 degrees-of-freedom (6-DoF) ego-motion and a depth map of the scene from unlabelled monocular RGB image sequences and inertial measurement unit (IMU) readings. The proposed approach is able to perform VIO without requiring IMU intrinsic parameters and/or extrinsic calibration between IMU and the camera. We provide comprehensive quantitative and qualitative evaluations of the proposed framework and compare its performance with state-of-the-art VIO, VO, and visual simultaneous localization and mapping (VSLAM) approaches on the KITTI, EuRoC and Cityscapes datasets. Detailed comparisons prove that SelfVIO outperforms state-of-the-art VIO approaches in terms of pose estimation and depth recovery, making it a promising approach among existing methods in the literature.

A rare cause of acute anal pain: strangulated hemorrhoids in a perianal fistula.

BACKGROUND: Hemorrhoidal disease is characterized by painless rectal bleeding and palpable swelling in the anus and very common in the society. In the presence of pain, it is called a complicated hemorrhoidal disease including conditions, such as thrombosed hemorrhoids, strangulation of the internal hemorrhoid, or accompanying anal fissure. Edema that develops as a result of impaired venous return is accepted as the main source of pathology in the development of strangulated internal hemorrhoidal disease, which is one of these complicated conditions. CASE PRESENTATION: This case report shows that strangulated hemorrhoidal disease can also develop due to a mechanical cause as a result of incarceration of the hemorrhoid into the accompanying perianal fistula tract. KEY WORDS: Anorectal pain, Hemorrhoidal disease, Strangulated internal hemorrhoidal, Perianal fistula.

Transoral endoscopic parathyroidectomy vestibular approach (TOEPVA) for primary hyperparathyroidism: Turkey's experience.

AIM: Parathyroid surgery has witnessed a significant evolution with the introduction of more efficacious preoperative localization imaging techniques and the use of rapid intraoperative parathormone assays. Parathyroid surgery can now be performed with the minimum of invasion. Through the adaptation of the transoral endoscopic thyroidectomy vestibular approach (TOETVA), the technique has now been adopted for parathyroid surgery, known as the transoral endoscopic parathyroidectomy vestibular approach (TOEPVA). We present here the initial experiences of 11 centers carrying out TOEPVA surgery in Turkey. MATERIALS AND METHODS: Participating in the study were 11 centers, all of which were tertiary care institutions carrying out endocrine surgery. A retrospective review was made of 35 primary hyperparathyroidism patients who underwent the TOEPVA procedure between July 2017 and January 2020. RESULTS: Of the total 35 patients, 32 patients underwent the TOEPVA procedure successfully. All patients but one were female, and the mean age was 47.2 (20-73) years. According to localization studies, 18 of the lesions were lower left, 12 were lower right, 3 were upper right and 2 were upper left. The mean operative time was 116 (30-225) min, and three cases were converted to an open procedure. Simultaneous thyroidectomy was performed in seven cases. The average PTH level dropped to normal within 20 min. after the resection in all cases. The complication rate was 19% (ecchymosis, subcutaneous emphysema, nasal bleeding, surgical site infection and seroma). There were neither recurrent nerve palsies, nor mental nerve root or branch injuries. The average hospital stay was 1 day. No persistence was documented on follow up. CONCLUSION: TOEPVA is a "hidden scar" parathyroidectomy procedure that can be safely performed on parathyroid adenomas, in cases that have scar-related concerns. Having its own procedure-related complications, the procedure provides satisfactory objective results, particularly in centers experienced in endoscopic and endocrine surgery.

Deep learning-based robust positioning for all-weather autonomous driving.

Interest in autonomous vehicles (AVs) is growing at a rapid pace due to increased convenience, safety benefits and potential environmental gains. Although several leading AV companies predicted that AVs would be on the road by 2020, they are still limited to relatively small-scale trials. The ability to know their precise location on the map is a challenging prerequisite for safe and reliable AVs due to sensor imperfections under adverse environmental and weather conditions, posing a formidable obstacle to their widespread use. Here we propose a deep learning-based self-supervised approach for ego-motion estimation that is a robust and complementary localization solution under inclement weather conditions. The proposed approach is a geometry-aware method that attentively fuses the rich representation capability of visual sensors and the weather-immune features provided by radars using an attention-based learning technique. Our method predicts reliability masks for the sensor measurements, eliminating the deficiencies in the multimodal data. In various experiments we demonstrate the robust all-weather performance and effective cross-domain generalizability under harsh weather conditions such as rain, fog and snow, as well as day and night conditions. Furthermore, we employ a game-theoretic approach to analyse the interpretability of the model predictions, illustrating the independent and uncorrelated failure modes of the multimodal system. We anticipate our work will bring AVs one step closer to safe and reliable all-weather autonomous driving.

The effect of Hippophae rhamnoides L. extract on acrylamideinduced brain injury in rats.

PURPOSE: Reactive oxygen species (ROS), interleukin-1ß (IL-1ß) and tumor necrosis factor-α (TNF-α) have been shown in the pathogenesis of acrylamide neurotoxicity. Hippophae rhamnoides L. extract (HRE) has a cytoprotective effect by stabilizing the production of ROS, IL-1ß and TNF-α. The objective of the article was to investigate the effect of HRE on acrylamide-induced brain damage in rats biochemically and histopathologically. METHODS: To the HRE+acrylamide only (ACR) group (n=6) of the animals, HRE was administered orally at a dose of 50 mg / kg into the stomach by gavage. The same volume of solvent (olive oil) was administered orally to the ACR (n=6) and healthy (HG) (n=6) groups. One hour after HRE administration, acrylamide was given orally at a dose of 20 mg/kg to HRE+ACR and ACR groups in the same way. This procedure was repeated once a day for 30 days. At the end of this period, brain tissues extracted from animals killed with 50 mg/kg thiopental anesthesia were examined biochemically and histopathologically. RESULTS: It has been shown that HRE prevents the increase of malondialdehyde (MDA), myeloperoxidase (MPO), IL-1ß and TNF-α with acrylamide and the decrease of total glutathione (tGSH) and glutathione reductase (GSHRd) levels in brain tissue. CONCLUSIONS: HRE may be useful in the treatment of acrylamide-induced neurotoxicity.

Graphene and carbon nanotubes interfaced electrochemical nanobiosensors for the detection of SARS-CoV-2 (COVID-19) and other respiratory viral infections: A review.

Recent COVID-19 pandemic has claimed millions of lives due to lack of a rapid diagnostic tool. Global scientific community is now making joint efforts on developing rapid and accurate diagnostic tools for early detection of viral infections to preventing future outbreaks. Conventional diagnostic methods for virus detection are expensive and time consuming. There is an immediate requirement for a sensitive, reliable, rapid and easy-to-use Point-of-Care (PoC) diagnostic technology. Electrochemical biosensors have the potential to fulfill these requirements, but they are less sensitive for sensing viruses/viral infections. However, sensitivity and performance of these electrochemical platforms can be improved by integrating carbon nanostructure, such as graphene and carbon nanotubes (CNTs). These nanostructures offer excellent electrical property, biocompatibility, chemical stability, mechanical strength and, large surface area that are most desired in developing PoC diagnostic tools for detecting viral infections with speed, sensitivity, and cost-effectiveness. This review summarizes recent advancements made toward integrating graphene/CNTs nanostructures and their surface modifications useful for developing new generation of electrochemical nanobiosensors for detecting viral infections. The review also provides prospects and considerations for extending the graphene/CNTs based electrochemical transducers into portable and wearable PoC tools that can be useful in preventing future outbreaks and pandemics.

On the moment-determinacy of power Lindley distribution and some applications to software metrics.

The Lindley distribution and its numerous generalizations are widely used in statistical and engineering practice. Recently, a power transformation of Lindley distribution, called the power Lindley distribution, has been introduced by M. E. Ghitany et al. who initiated the investigation of its properties and possible applications. In this article, new results on the power Lindley distribution are presented. The focus of this work is on the moment-(in)determinacy of the distribution for various values of the parameters. Afterwards, certain applications are provided to describe data sets of software metrics.

Using of leaching reactant obtained from mill scale in hydrometallurgical copper extraction.

This study, which covered a set of leaching processes at a few stages, investigated the inclusion of iron found in mill scale, which is a waste of the iron-steel industry, in the solution as FeClx=2,3 in the presence of HCl and the conditions of using this solution with an oxidizing character in extraction of metals from chalcopyrite concentrate. Mill scale was treated with HCl, and an FeClx solution was obtained at a 100% Fe solubility and 83.43% Fe3+ conversion rate in the conditions of 60 min, 105 °C, 7 M HCl, and 1/10 solid-liquid ratio. This solution that was obtained was later used in copper extraction from a chalcopyrite concentrate. In the optimum conditions (120 min of leaching time, 105 °C of leaching temperature, 1/25 solid-liquid ratio, 400 rpm stirring speed), 95.04% of the copper was taken into the solution. In the leaching experiment in a medium containing mill scale + chalcopyrite and HCl at the same time, under the optimum conditions (120 min of leaching time, 105 °C of leaching temperature, 7 M HCl concentration, 1 g chalcopyrite concentrate, 1/25 solid-liquid ratio, 5 g mill scale, 400 rpm stirring speed), approximately 96% of copper was taken into the solution.

EndoSLAM dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos.

Deep learning techniques hold promise to develop dense topography reconstruction and pose estimation methods for endoscopic videos. However, currently available datasets do not support effective quantitative benchmarking. In this paper, we introduce a comprehensive endoscopic SLAM dataset consisting of 3D point cloud data for six porcine organs, capsule and standard endoscopy recordings, synthetically generated data as well as clinically in use conventional endoscope recording of the phantom colon with computed tomography(CT) scan ground truth. A Panda robotic arm, two commercially available capsule endoscopes, three conventional endoscopes with different camera properties, two high precision 3D scanners, and a CT scanner were employed to collect data from eight ex-vivo porcine gastrointestinal (GI)-tract organs and a silicone colon phantom model. In total, 35 sub-datasets are provided with 6D pose ground truth for the ex-vivo part: 18 sub-datasets for colon, 12 sub-datasets for stomach, and 5 sub-datasets for small intestine, while four of these contain polyp-mimicking elevations carried out by an expert gastroenterologist. To verify the applicability of this data for use with real clinical systems, we recorded a video sequence with a state-of-the-art colonoscope from a full representation silicon colon phantom. Synthetic capsule endoscopy frames from stomach, colon, and small intestine with both depth and pose annotations are included to facilitate the study of simulation-to-real transfer learning algorithms. Additionally, we propound Endo-SfMLearner, an unsupervised monocular depth and pose estimation method that combines residual networks with a spatial attention module in order to dictate the network to focus on distinguishable and highly textured tissue regions. The proposed approach makes use of a brightness-aware photometric loss to improve the robustness under fast frame-to-frame illumination changes that are commonly seen in endoscopic videos. To exemplify the use-case of the EndoSLAM dataset, the performance of Endo-SfMLearner is extensively compared with the state-of-the-art: SC-SfMLearner, Monodepth2, and SfMLearner. The codes and the link for the dataset are publicly available at https://github.com/CapsuleEndoscope/EndoSLAM. A video demonstrating the experimental setup and procedure is accessible as Supplementary Video 1.

VR-Caps: A Virtual Environment for Capsule Endoscopy.

Current capsule endoscopes and next-generation robotic capsules for diagnosis and treatment of gastrointestinal diseases are complex cyber-physical platforms that must orchestrate complex software and hardware functions. The desired tasks for these systems include visual localization, depth estimation, 3D mapping, disease detection and segmentation, automated navigation, active control, path realization and optional therapeutic modules such as targeted drug delivery and biopsy sampling. Data-driven algorithms promise to enable many advanced functionalities for capsule endoscopes, but real-world data is challenging to obtain. Physically-realistic simulations providing synthetic data have emerged as a solution to the development of data-driven algorithms. In this work, we present a comprehensive simulation platform for capsule endoscopy operations and introduce VR-Caps, a virtual active capsule environment that simulates a range of normal and abnormal tissue conditions (e.g., inflated, dry, wet etc.) and varied organ types, capsule endoscope designs (e.g., mono, stereo, dual and 360∘ camera), and the type, number, strength, and placement of internal and external magnetic sources that enable active locomotion. VR-Caps makes it possible to both independently or jointly develop, optimize, and test medical imaging and analysis software for the current and next-generation endoscopic capsule systems. To validate this approach, we train state-of-the-art deep neural networks to accomplish various medical image analysis tasks using simulated data from VR-Caps and evaluate the performance of these models on real medical data. Results demonstrate the usefulness and effectiveness of the proposed virtual platform in developing algorithms that quantify fractional coverage, camera trajectory, 3D map reconstruction, and disease classification. All of the code, pre-trained weights and created 3D organ models of the virtual environment with detailed instructions how to setup and use the environment are made publicly available at https://github.com/CapsuleEndoscope/VirtualCapsuleEndoscopy and a video demonstration can be seen in the supplementary videos (Video-I).

The effect of Hippophae rhamnoides L. extract on acrylamideinduced brain injury in rats

ABSTRACT Purpose: Reactive oxygen species (ROS), interleukin-1β (IL-1β) and tumor necrosis factor-α (TNF-α) have been shown in the pathogenesis of acrylamide neurotoxicity. Hippophae rhamnoides L. extract (HRE) has a cytoprotective effect by stabilizing the production of ROS, IL-1β and TNF-α. The objective of the article was to investigate the effect of HRE on acrylamide-induced brain damage in rats biochemically and histopathologically. Methods: To the HRE+acrylamide only (ACR) group (n=6) of the animals, HRE was administered orally at a dose of 50 mg / kg into the stomach by gavage. The same volume of solvent (olive oil) was administered orally to the ACR (n=6) and healthy (HG) (n=6) groups. One hour after HRE administration, acrylamide was given orally at a dose of 20 mg/kg to HRE+ACR and ACR groups in the same way. This procedure was repeated once a day for 30 days. At the end of this period, brain tissues extracted from animals killed with 50 mg/kg thiopental anesthesia were examined biochemically and histopathologically. Results: It has been shown that HRE prevents the increase of malondialdehyde (MDA), myeloperoxidase (MPO), IL-1β and TNF-α with acrylamide and the decrease of total glutathione (tGSH) and glutathione reductase (GSHRd) levels in brain tissue. Conclusions: HRE may be useful in the treatment of acrylamide-induced neurotoxicity.