ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
How should we address the inevitable harms from non-negligent variant reclassification in predictive genetic testing?

The process of interpreting genetic variants, in which experts use all available evidence to determine whether an identified variant is associated with a current or future disease, is both scientific and nevertheless subjective. In this paper, we summarize the existing evidence that any given variant could be reclassified and that such a reclassification could lead to harm. Furthermore, the racial gap in genetic databases could lead to a higher likelihood of harm for non-white patients. We also review recent legal analyses indicating it is unlikely that an individual who sues for restitution would be successful, especially in the absence of evidence of lab negligence. We then propose a compensation program for medical genetic tests to ensure that individuals who experience demonstrable harm due to a variant reclassification can be made whole financially. We conclude by discussing outstanding questions that must be answered for such a program to be feasible.
Implications of the changing epidemiology of inflammatory bowel disease in a changing world

The epidemiology of inflammatory bowel disease (IBD) has undergone considerable shifts since its emergence in the Western world over a century ago, especially in the last few decades, with increasing global burden of disease. IBD incidence continues to rise in developed countries in all age groups which is contributing to compounding prevalence. Further, IBD incidence is rising sharply in Asia and other recently developed and developing countries. In this review, we discuss the implications of changing trends of IBD epidemiology. First, changing patterns provide insights into IBD causes, as they occur concurrent with shifts in the environment, cultures, and attitudes. Understanding the impact of the environment on IBD risk can help towards prediction and prevention strategies. Second, we must prepare healthcare systems for the rising burden of IBD and address it at various levels towards improving outcomes and health, overall.
Antibacterial activity of essential oils extracted from the unique Chinese spices cassia bark, bay fruits and cloves

Spices are widely used in daily life such as diet and have certain activity. Especially in China, spices have been mainly used as condiments for thousands of years in order to improve the sensory quality of food; in addition, they and their derivatives can also be used as preservatives. In this study, three spices with unique Chinese characteristics widely used were selected: cassia bark (bark of Cinnamomum camphora Presl), bay fruits (Laurus nobilis), and cloves (Syzygiumaromaticum). The main components and antibacterial ability of these three spices were analyzed by simulated extraction method. Through headspace solid-phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS) analysis, it was determined that the main active compounds in the essential oils of cassia bark, bay fruits and cloves were cinnamaldehyde (78.11%), cinnamaldehyde (61.78%) and eugenol (75.23%), respectively. The agar plate diffusion test and the simulated food culture medium experiment confirmed that the essential oils extracted from the three flavors have antibacterial effects on Listeria monocytogenes, Listeria innocua, Listeria welshimeri, Listeria ivanovii, Listeria grayi and Vibrio parahaemolyticus. The antibacterial activity of different strains has different optimal extraction conditions. Generally speaking, cinnamon essential oil has the strongest antibacterial activity, while laurel fruit has the lowest antibacterial activity. The study proved the antibacterial activity of these three Chinese-specific spices and provided some new ideas and methods for the subsequent research and preparation of natural food additives and food antibacterial agents.
TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution

Image Super Resolution is a potential approach that can improve the image quality of low-resolution optical sensors, leading to improved performance in various industrial applications. It is important to emphasize that most state-of-the-art super resolution algorithms often use a single channel of input data for training and inference. However, this practice ignores the fact that the cost of acquiring high-resolution images in various spectral domains can differ a lot from one another. In this paper, we attempt to exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). We propose a dual stream Transformer-based super resolution approach that uses the visible image as a guide to super-resolve another spectral band image. To this end, we introduce Transformer in Transformer network for Guidance super resolution, named TnTViT-G, an efficient and effective method that extracts the features of input images via different streams and fuses them together at various stages. In addition, unlike other guidance super resolution approaches, TnTViT-G is not limited to a fixed upsample size and it can generate super-resolved images of any size. Extensive experiments on various datasets show that the proposed model outperforms other state-of-the-art super resolution approaches. TnTViT-G surpasses state-of-the-art methods by up to $0.19 \sim 2.3dB $ , while it is memory efficient.
Association between history of miscarriage and autism spectrum disorder

This case-control study was designed to examine the association between different types of miscarriage history and autism spectrum disorder (ASD), and determine whether the number of miscarriage history affects the risk of ASD. All of 2274 children with ASD and 1086 healthy controls were recruited. Sociodemographic and prenatal, perinatal, and neonatal characteristics were compared between the two groups. Multivariable logistic regression analyses were applied to investigate association between miscarriage history and ASD. Stratified analyses based on sex and types of miscarriages were similarly performed. History of miscarriage was potential risk factors for ASD ([aOR] = 2.919; 95% [CI] = 2.327-3.517). Stratified analyses revealed that induced ([aOR] = 2.763, 95% [CI] = 2.259-3.379) and spontaneous miscarriage history ([aOR] = 3.341, 95% [CI] = 1.939-4.820) were associated with high risk of ASD, respectively. A sex-biased ratio in the risk of ASD was observed between females ([aOR] = 3.049, 95% [CI] = 2.153-4.137) and males ([aOR] = 2.538, 95% [CI] = 1.978-3.251). Stratified analysis of induced miscarriage history revealed that only iatrogenic miscarriage history was associated with an increased risk ASD ([aOR] = 2.843, 95% [CI] = 1.534-4.268). Also, multiple spontaneous miscarriage histories ([aOR] = 1.836, 95% [CI] = 1.252-2.693) were associated with higher autism risk than one spontaneous miscarriages history ([aOR] = 3.016, 95% [CI] = 1.894-4.174). In conclusion, miscarriage history is related to an increased risk for ASD in offspring, which is affected by the types of miscarriage and sex of the fetus.
Surgery of the skull base as a new section in Acta Otorhinolaryngologica Italica

La chirurgia della base del cranio in una nuova sezione di Acta Otorhinolaryngologica Italica.
Generalized Zero-Shot Chest X-Ray Diagnosis Through Trait-Guided Multi-View Semantic Embedding With Self-Training

Zero-shot learning (ZSL) is one of the most promising avenues of annotation-efficient machine learning. In the era of deep learning, ZSL techniques have achieved unprecedented success. However, the developments of ZSL methods have taken place mostly for natural images. ZSL for medical images has remained largely unexplored. We design a novel strategy for generalized zero-shot diagnosis of chest radiographs. In doing so, we leverage the potential of multi-view semantic embedding, a useful yet less-explored direction for ZSL. Our design also incorporates a self-training phase to tackle the problem of noisy labels alongside improving the performance for classes not seen during training. Through rigorous experiments, we show that our model trained on one dataset can produce consistent performance across test datasets from different sources including those with very different quality. Comparisons with a number of state-of-the-art techniques show the superiority of the proposed method for generalized zero-shot chest x-ray diagnosis.
Construction of a p-n heterojunction based on magnetic Mn0.6Zn0.4Fe2O4 and ZnIn2S4 to improve photocatalytic performance

To improve the photocatalytic performance of Mn0.6Zn0.4Fe2O4 (MZFO) and ZnIn2S4 (ZIS) for organic pollutants, the p-n MZFO@ZIS heterojunctions with different weight percentage (10 ~ 40%) of MZFO are constructed from spent batteries and added indium ion via a green bioleaching and hydrothermal method. Structural, optical, and photocatalytic properties for the heterojunctions are investigated systematically by XRD, FT-IR, SEM-EDX, TEM, BET, VB-XPS, UV-vis DRS, PL, etc. The results confirm that p-n junction significantly improves the visible light adsorption and the separation efficiency of photogenerated carriers. Specifically, MZFO-25%@ZIS shows the highest photodegradation performance toward Congo red (CR), and its reactive kinetic constant is about 9.6, 7.8, and 7.0 times higher than that of P25 TiO2, MZFO, and ZIS, respectively, and MZFO-25%@ZIS still possesses a high reusability and simple magnetic separation after 5 cycles of reuse. The radical trapping experiments and electronic paramagnetic resonance (EPR) tests show that ·O2-, ·OH, and h+ are the most important active substance for degrading CR. The pathways for the CR degradation are further proposed based on the intermediate analysis. DFT + U calculations confirm that the high charge density of Zn-O, Fe-O, and Zn-S bonds in the MZFO and ZIS molecules provides the electrons for the sufficient production of free radicals. This work also provides a novel high value-added strategy for the green utilization of spent batteries.
Digital Twin for Urban Planning in the Green Deal Era: A State of the Art and Future Perspectives

This paper provides a state of the art of contemporary Digital Twins (DTs) projects for urban planning at an international level. The contribution investigates the evolution of the DT concept and contextualises this tool within the scientific-cultural debate, highlighting the interconnection between global policies and local needs/wishes. Specifically, six case studies of DTs are compared, illustrating their application, content, technological infrastructure, and priority results. The projects presented provide an overview of the existing DT typologies, focusing on the evaluative/prefigurative use and the limits/potential of the tool in light of the socio-health, climate, and environmental crises. Reflections on DT reveal, on the one hand, its potential role in supporting decision-making and participatory processes and, on the other, the potential utopian trend of data-driven planning encouraged by public-private investments in the smart city/twin city sector. In conclusion, the study underlines the innovative role of DT as a cutting-edge scientific format in the disciplinary framework but highlights that the practical use of the tool is still in an experimental research-action phase. From this theoretical-critical review, it is possible to hypothesise new research paths to implement the realism and application potential of DTs for urban planning and urban governance.
Pictorial review: radiological diagnosis of anastomotic leakage with water-soluble contrast enema after anterior resection of the rectum

For patients who have undergone colorectal surgery, anastomotic leakage is a serious and challenging complication with a variable rate ranging between 1.8% and 19.2%. Postoperative anastomotic leaks after colorectal surgery can have severe consequences for patients, particularly ones who present with few or no symptoms. Computed tomography and/or water-soluble contrast enema (WSE) are the most frequently utilized imaging methods to identify and diagnose anastomotic leaks early. WSE is a safe and complication-free procedure that allows to identify the presence of otherwise unrecognized anastomotic leaks, both in asymptomatic and symptomatic patients. Fluoroscopic rectal examination using a water-soluble contrast agent for postoperative patients is never an easy examination to perform since it requires careful preparation, skill, and knowledge. Four morphological types of anastomotic dispersion have been described: "saccular type", "horny type", "serpentine type" and "dendritic type". Among 4 types of leakage, dendritic and serpentine types are more frequently followed by clinical symptoms and none of the dendritic type resolves spontaneously. On the other hand, the saccular and horny types have a better prognosis after healing of the loss and subsequent restoration of the ostomy as they consist of a cavity that provides a sort of physical barrier to the spread of inflammation. The aim of this pictorial essay was to illustrate the spectrum of imaging findings of morphological types of radiologic leakages on WCE in patients with colorectal surgical anastomosis. We have also tried to provide tips and tools to enable identification of radiological leakages on retrograde WCE, particularly of the smallest leaks which can be more easily missed.
PdS Quantum Dots as a Hole Attractor Encapsulated into the MOF@Cd0.5Zn0.5S Heterostructure for Boosting Photocatalytic Hydrogen Evolution under Visible Light

Herein, a new photocatalyst PdS@UiOS@CZS is successfully synthesized, where thiol-functionalized UiO-66 (UiOS), a metal-organic framework (MOF) material, is used as a host to encapsulate PdS quantum dots (QDs) in its cages, and Cd0.5Zn0.5S (CZS) solid solution nanoparticles (NPs) are anchored on its outer surface. The resultant PdS@UiOS@CZS with an optimal ratio between components displays an excellent photocatalytic H2 evolution rate of 46.1 mmol h-1 g-1 under visible light irradiation (420∼780 nm), which is 512.0, 9.2, and 5.9 times that of pure UiOS, CZS, and UiOS@CZS, respectively. The reason for the significantly enhanced performance is that the encapsulated PdS QDs strongly attract the photogenerated holes into the pores of UiOS, while the photogenerated electrons are effectively migrated to CZS due to the heterojunction effect, thereby effectively suppressing the recombination of charge carriers for further high-efficiency hydrogen production. This work provides an idea for developing efficient photocatalysts induced by hole attraction.
ResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection

Diabetic retinopathy, an eye disease commonly afflicting diabetic patients, can result in loss of vision if prompt detection and treatment are not done in the early stages. Once the symptoms are identified, the severity level of the disease needs to be classified for prescribing the right medicine. This study proposes a deep learning-based approach, for the classification and grading of diabetic retinopathy images. The proposed approach uses the feature map of ResNet-50 and passes it to Random Forest for classification. The proposed approach is compared with five state-of-the-art approaches using two category Messidor-2 and five category EyePACS datasets. These two categories on the Messidor-2 dataset include 'No Referable Diabetic Macular Edema Grade (DME)' and 'Referable DME' while five categories consist of 'Proliferative diabetic retinopathy', 'Severe', 'Moderate', 'Mild', and 'No diabetic retinopathy'. The results show that the proposed approach outperforms compared approaches and achieves an accuracy of 96% and 75.09% for these datasets, respectively. The proposed approach outperforms six existing state-of-the-art architectures, namely ResNet-50, VGG-19, Inception-v3, MobileNet, Xception, and VGG16.
Appearance Learning for Image-Based Motion Estimation in Tomography

In tomographic imaging, anatomical structures are reconstructed by applying a pseudo- inverse forward model to acquired signals. Geometric information within this process is usually depending on the system setting only, i.e., the scanner position or readout direction. Patient motion therefore corrupts the geometry alignment in the reconstruction process resulting in motion artifacts. We propose an appearance learning approach recognizing the structures of rigid motion independently from the scanned object. To this end, we train a siamese triplet network to predict the reprojection error (RPE) for the complete acquisition as well as an approximate distribution of the RPE along the single views from the reconstructed volume in a multi-task learning approach. The RPE measures the motion-induced geometric deviations independent of the object based on virtual marker positions, which are available during training. We train our network using 27 patients and deploy a 21-4-2 split for training, validation and testing. In average, we achieve a residual mean RPE of 0.013 mm with an inter-patient standard deviation of 0.022 mm. This is twice the accuracy compared to previously published results. In a motion estimation benchmark the proposed approach achieves superior results in comparison with two state-of-the-art measures in nine out of twelve experiments. The clinical applicability of the proposed method is demonstrated on a motion-affected clinical dataset.
Angular-Based Preprocessing for Image Denoising

There is not a large research on how to use color information for improving results in image denoising. Currently, most of the methods modify the color space from standard red green blue (sRGB) to an opponent-like one as better results are obtained, but out of this conversion, color is mostly ignored in the image denoising pipelines. In this letter, we propose a color decomposition to preprocess an image before applying a typical denoising. Our decomposition consists in obtaining a set of images in the spherical coordinate system, each of them with the origin of the spherical transformation in a different color value. These color values, that we call color centers, are defined so as to be far away from the dominant colors of the image. Once in the spherical coordinate system, we perform a mild denoising operation with some state-of-the-art method in the angular components. Then, we convert these images back to sRGB, and wemerge them depending on the distance between the color of each pixel and the color centers. Finally, we denoise the preprocessed image with the same state-of-the-art method used in our preprocessing. Experiments show that our method outperforms the results of directly applying the denoising method on the input image for different state-of-the-art denoising methods.
Validity assessment of Michigan's proposed qPCR threshold value for rapid water-quality monitoring of E. coli contamination

Michigan's water-quality standards specify that E. coli concentrations at bathing beaches must not exceed 300 E. coli per 100 mL, as determined by the geometric mean of culture-based concentrations in three or more representative samples from a given beach on a given day. Culture-based analysis requires 18⁠-⁠24 h to complete, so results are not available on the day of sampling. This one-day delay is problematic because results cannot be used to prevent recreation at beaches that are unsafe on the sampling day, nor do they reliably indicate whether recreation should be prevented the next day, due to high between-day variability in E. coli concentrations demonstrated by previous studies. By contrast, qPCR-based E. coli concentrations can be obtained in 3-4 h, making same-day beach notification decisions possible. Michigan has proposed a qPCR threshold value (qTV) for E. coli of 1.863 log10 gene copies per reaction as a potential equivalent value to the state standard, based on statistical analysis of a set of state-wide training data from 2016 to 2018. The main purpose of the present study is to assess the validity of the proposed qTV by determining whether the implied qPCR-based beach notification decisions agree well with culture-based decisions on two sets of test data from 2016⁠-⁠2018 (6,564 samples) and 2019-2020 (3,205 samples), and whether performance of the proposed qTV is similar on the test and training data. The results show that performance of Michigan's proposed qTV on both sets of test data was consistently good (e.g., 95% agreement with culture-based beach notification decisions during 2019⁠-⁠2020) and was as good as or better than its performance on the training data set. The false-negative rate for the proposed qTV was 25-29%, meaning that beach notification decisions based on the qTV would be expected to permit recreation on the day of sampling in 25-29% of cases where the beach exceeds the state standard for FIB contamination. This false-negative rate is higher than one would hope to see but is well below the corresponding error rate for culture-based decisions, which permit recreation at beaches that exceed the state standard on the day of sampling in 100% of cases because of the one-day delay in obtaining results. The key advantage of qPCR-based analysis is that it permits a large percentage (71-75%) of unsafe beaches to be identified in time to prevent recreation on the day of sampling.
Oxidative stress in the RVLM mediates sympathetic hyperactivity induced by circadian disruption

Circadian rhythm plays a significant role in maintaining the function of the cardiovascular system. Emerging studies have demonstrated that circadian disruption enhances the risk of cardiovascular diseases by activating the sympathetic nervous system; however, the underlying mechanisms remain unknown. Therefore, this study aimed to clarify the role of oxidative stress in the rostral ventrolateral medulla (RVLM) in sympathetic hyperactivity induced by circadian disruption. Rats were randomly divided into two groups: the normal light and dark (LD) group and the circadian disruption (CD) group. Sympathetic nerve activity of rats was assessed by recording renal sympathetic nerve activity (RSNA) and indirect methods such as plasma level of norepinephrine (NE). The level of oxidative stress in the RVLM was detected by dihydroethidium probes. Moreover, the expression levels of the oxidative stress-related proteins in the RVLM were detected by Western blotting. Circadian disruption significantly increased blood pressure (BP), RSNA, and plasma levels of NE. Compared to the LD group, the CD group exhibited a more significant depressor response to i.v. hexamethonium bromide, a ganglionic blocker. Furthermore, the reactive oxygen species (ROS) production in the RVLM of rats with circadian disruption was significantly increased. In addition, BP and RSNA of rats with circadian disruption exhibited a greater decrease in the effects of microinjection of tempol, a superoxide scavenger, into the RVLM, compared to artificial cerebrospinal fluid (aCSF). Further investigation of the molecular mechanism by Western blotting showed that nuclear factor-erythroid-2-related factor 2 (Nrf2)/heme oxygenase 1 (HO1)/NAD(P)H: quinone oxidoreductase 1 (NQO1) signaling was down-regulated in the RVLM of circadian disruption rats. These data suggest that oxidative stress in the RVLM mediates sympathetic hyperactivity induced by circadian disruption and possibly by down-regulating Nrf2/HO1/NQO1 signaling.