ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Transfer Recurrent Feature Learning for Endomicroscopy Image Recognition

Probe-based confocal laser endomicroscopy (pCLE) is an emerging tool for epithelial cancer diagnosis, which enables in-vivo microscopic imaging during endoscopic procedures and facilitates the development of automatic recognition algorithms to identify the status of tissues. In this paper, we propose a transfer recurrent feature learning framework for classification tasks on pCLE videos. At the first stage, the discriminative feature of single pCLE frame is learned via generative adversarial networks based on both pCLE and histology modalities. At the second stage, we use recurrent neural networks to handle the varying length and irregular shape of pCLE mosaics taking the frame-based features as input. The experiments on real pCLE data sets demonstrate that our approach outperforms, with statistical significance, state-of-the-art approaches. A binary classification accuracy of 84.1% has been achieved.
Dual Attention Multi-Instance Deep Learning for Alzheimer's Disease Diagnosis With Structural MRI

Structural magnetic resonance imaging (sMRI) is widely used for the brain neurological disease diagnosis, which could reflect the variations of brain. However, due to the local brain atrophy, only a few regions in sMRI scans have obvious structural changes, which are highly correlative with pathological features. Hence, the key challenge of sMRI-based brain disease diagnosis is to enhance the identification of discriminative features. To address this issue, we propose a dual attention multi-instance deep learning network (DA-MIDL) for the early diagnosis of Alzheimer's disease (AD) and its prodromal stage mild cognitive impairment (MCI). Specifically, DA-MIDL consists of three primary components: 1) the Patch-Nets with spatial attention blocks for extracting discriminative features within each sMRI patch whilst enhancing the features of abnormally changed micro-structures in the cerebrum, 2) an attention multi-instance learning (MIL) pooling operation for balancing the relative contribution of each patch and yield a global different weighted representation for the whole brain structure, and 3) an attention-aware global classifier for further learning the integral features and making the AD-related classification decisions. Our proposed DA-MIDL model is evaluated on the baseline sMRI scans of 1689 subjects from two independent datasets (i.e., ADNI and AIBL). The experimental results show that our DA-MIDL model can identify discriminative pathological locations and achieve better classification performance in terms of accuracy and generalizability, compared with several state-of-the-art methods.
HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation

Real-time six degrees-of-freedom (6D) object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we propose an effective and lightweight model, namely high-resolution 6D pose estimation network (HRPose). We adopt the efficient and small HRNetV2-W18 as a feature extractor to reduce computational burdens while generating accurate 6D poses. With only 33% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Moreover, by transferring knowledge from a large model to our proposed HRPose through output and feature-similarity distillations, the performance of our HRPose is improved in effectiveness and efficiency. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods.
[Second opinions and reference pathology]

Second opinions in pathology require expert knowledge for diagnosis in difficult cases and in Germany have a tradition that has grown into a network between primarily diagnostic care institutions and university-associated institutes of pathology that are active in research. The term reference pathology is not a designation for a class of institutes for pathology but rather indicates a function within prospective clinical trials with defined endpoints and a central pathology, which derives expert knowledge from this function. The thus generated collection of samples and expertise enable diagnostic support in difficult cases. Furthermore, research based on this tissue material might lead to the establishment of novel biomarkers and methods, which when transferred back for local application will enhance the overall diagnostic validity of pathological tissue analysis. Thus, for all institutes participating in the network second opinion and reference pathology provide considerable benefit.
Characterization and shelf stability of sweetened condensed milk formulated with different sucrose substitutes during storage

Sweetened condensed milk (SCM) is a value-added milk product with extended shelf life and high levels of nutrition. The high level of sucrose may lead to health problems. Many studies have focused on the reduction of sucrose but seldomly on different combination of sugar substitutes. This study aims to find an ideal sucrose substitution through physiochemical, microbiological and sensory properties of SCM under different storage times. The results demonstrated that substitution with 20% trehalose, 5% lactulose and 15% erythritol resulted in similar sensory and color as control group. The volatile flavor analysis showed that substitution with 30% trehalose, 5% lactulose and 5% polyols was the most similar and hexanoic acid was the symbolistic flavor. Sucrose replacement increased the antibacterial effect and Staphylococcus, Penicillium, Apiotrichum and Candida were widely present. Substitution with 30% trehalose, 5% lactulose and 5% polyols resulted in the most similar water activity, texture, aroma and microbial diversity.
Automatic Segmentation and Quantitative Analysis of the Articular Cartilages From Magnetic Resonance Images of the Knee

In this paper, we present a segmentation scheme that automatically and accurately segments all the cartilages from magnetic resonance (MR) images of nonpathological knees. Our scheme involves the automatic segmentation of the bones using a three-dimensional active shape model, the extraction of the expected bone-cartilage interface (BCI), and cartilage segmentation from the BCI using a deformable model that utilizes localization, patient specific tissue estimation and a model of the thickness variation. The accuracy of this scheme was experimentally validated using leave one out experiments on a database of fat suppressed spoiled gradient recall MR images. The scheme was compared to three state of the art approaches, tissue classification, a modified semi-automatic watershed algorithm and nonrigid registration (B-spline based free form deformation). Our scheme obtained an average Dice similarity coefficient (DSC) of (0.83, 0.83, 0.85) for the (patellar, tibial, femoral) cartilages, while (0.82, 0.81, 0.86) was obtained with a tissue classifier and (0.73, 0.79, 0.76) was obtained with nonrigid registration. The average DSC obtained for all the cartilages using a semi-automatic watershed algorithm (0.90) was slightly higher than our approach (0.89), however unlike this approach we segment each cartilage as a separate object. The effectiveness of our approach for quantitative analysis was evaluated using volume and thickness measures with a median volume difference error of (5.92, 4.65, 5.69) and absolute Laplacian thickness difference of (0.13, 0.24, 0.12) mm.
Building Extraction from Remote Sensing Images with Sparse Token Transformers

Deep learning methods have achieved considerable progress in remote sensing image building extraction. Most building extraction methods are based on Convolutional Neural Networks (CNN). Recently, vision transformers have provided a better perspective for modeling long-range context in images, but usually suffer from high computational complexity and memory usage. In this paper, we explored the potential of using transformers for efficient building extraction. We design an efficient dual-pathway transformer structure that learns the long-term dependency of tokens in both their spatial and channel dimensions and achieves state-of-the-art accuracy on benchmark building extraction datasets. Since single buildings in remote sensing images usually only occupy a very small part of the image pixels, we represent buildings as a set of "sparse " feature vectors in their feature space by introducing a new module called "sparse token sampler ". With such a design, the computational complexity in transformers can be greatly reduced over an order of magnitude. We refer to our method as Sparse Token Transformers (STT). Experiments conducted on the Wuhan University Aerial Building Dataset (WHU) and the Inria Aerial Image Labeling Dataset (INRIA) suggest the effectiveness and efficiency of our method. Compared with some widely used segmentation methods and some state-of-the-art building extraction methods, STT has achieved the best performance with low time cost.
Insights into the mapping of green synthesis conditions for ZnO nanoparticles and their toxicokinetics

Research on ZnO nanoparticles (NPs) has broad medical applications. However, the green synthesis of ZnO NPs involves a wide range of properties requiring optimization. ZnO NPs show toxicity at lower doses. This toxicity is a function of NP properties and pharmacokinetics. Moreover, NP toxicity and pharmacokinetics are affected by the species type and age of the animals tested. Physiologically based pharmacokinetic (PBPK) modeling offers a mechanistic platform to scrutinize the colligative effect of the interplay between these factors, which reduces the need for in vivo studies. This review provides a guide to choosing green synthesis conditions that result in minimal toxicity using a mechanistic tool, namely PBPK modeling.
Identification of antigens recognized by salivary IgA using microbial protein microarrays

Secretory IgA plays an important role in the mucosal immune system for protection against pathogens. However, the antigens recognized by these antibodies have only been partially studied. We comprehensively investigated the antigens bound by salivary IgA in healthy adults using microbial protein microarrays. This confirmed that saliva contained IgA antibodies that bind to a variety of pathogenic microorganisms, including spike proteins of severe acute respiratory syndrome coronavirus 2, severe acute respiratory syndrome coronavirus, Middle East respiratory syndrome coronavirus, and other human coronavirus species. Also, many subtypes and strains of influenza virus were bound, regardless of the seasonal or vaccine strains. Salivary IgA also bound many serogroups and serovars of Escherichia coli and Salmonella. Taken together, these findings suggest that salivary IgA, which exhibits broad reactivity, is likely an essential element of the mucosal immune system at the forefront of defense against infection.
Prenatal heroin exposure alters brain morphology and connectivity in adolescent mice

The United States is experiencing a dramatic increase in maternal opioid misuse and, consequently, the number of individuals exposed to opioids in utero. Prenatal opioid exposure has both acute and long-lasting effects on health and wellbeing. Effects on the brain, often identified at school age, manifest as cognitive impairment, attention deficit, and reduced scholastic achievement. The neurobiological basis for these effects is poorly understood. Here, we examine how in utero exposure to heroin affects brain development into early adolescence in a mouse model. Pregnant C57BL/6J mice received escalating doses of heroin twice daily on gestational days 4-18. The brains of offspring were assessed on postnatal day 28 using 9.4 T diffusion MRI of postmortem specimens at 36 μm resolution. Whole-brain volumes and the volumes of 166 bilateral regions were compared between heroin-exposed and control offspring. We identified a reduction in whole-brain volume in heroin-exposed offspring and heroin-associated volume changes in 29 regions after standardizing for whole-brain volume. Regions with bilaterally reduced standardized volumes in heroin-exposed offspring relative to controls include the ectorhinal and insular cortices. Regions with bilaterally increased standardized volumes in heroin-exposed offspring relative to controls include the periaqueductal gray, septal region, striatum, and hypothalamus. Leveraging microscopic resolution diffusion tensor imaging and precise regional parcellation, we generated whole-brain structural MRI diffusion connectomes. Using a dimension reduction approach with multivariate analysis of variance to assess group differences in the connectome, we found that in utero heroin exposure altered structure-based connectivity of the left septal region and the region that acts as a hub for limbic regulatory actions. Consistent with clinical evidence, our findings suggest that prenatal opioid exposure may have effects on brain morphology, connectivity, and, consequently, function that persist into adolescence. This work expands our understanding of the risks associated with opioid misuse during pregnancy and identifies biomarkers that may facilitate diagnosis and treatment.
Rapidly Photocurable Solid-State Poly(ionic liquid) Ionogels For Thermally Robust and Flexible Electrochromic Devices

Formation of ionogels through in situ polymerization can effectively improve electrolyte processability; however, the curing process has been slow and oxygen-sensitive. Considering the low oxygen solubility of poly(ionic liquid)s (PILs), in situ polymerized ionogels are designed to realize excellent electrolytes. Herein, two in situ polymerized ionogels (PIL A & PIL B) are formulated, and they can be rapidly photocured within a minute. The ionogels are highly transparent, stretchable, and exhibit excellent physicochemical stability, including thermal, electrochemical, and air stability, allowing them to perform in various conditions. Benefitting from these properties, two high-performance electrochromic devices (ECDs) are assembled, with iron-centered coordination polymer (FeCP) and tungsten oxide (P-WO3 ) electrochromic materials, achieving high color contrast (45.2% and 56.4%), fast response time (1.5/1.9 and 1.7/6.4 s), and excellent cycling endurance (>90% retention over 3000 cycles). Attributed to the thermal robustness of the ionogels, the ECDs can also be operated over a wide temperature range (-20 to 100 °C). With the use of deformable substrates (e.g., ultrathin ITO glass), curved electrochromic eye protector and flexible electrochromic displays are realized, highlighting their potential use in futuristic wearables.
A case of left foot drop as initial symptom of granulomatosis with polyangiitis: Triggered by COVID-19 disease?

In Granulomatosis with polyangiitis (GPA), involvement of the peripheral nervous system is frequent but its occurrence as an initial presentation is unusual. This case highlights the importance of this occurrence to permit an early diagnosis. Moreover, GPA started after a coronavirus disease 2019 infection and could have been induced by this.
Unregulated LDL cholesterol uptake is detrimental to breast cancer cells

Tumor uptake of exogenous cholesterol has been associated with the proliferation of various cancers. Previously, we and others have shown that hypercholesterolemia promotes tumor growth and silencing of the LDL receptor (LDLR) in high LDLR-expressing tumors reduces growth. To advance understanding of how LDL uptake promotes tumor growth, LDLR expression was amplified in breast cancer cell lines with endogenously low LDLR expression. Murine (Mvt1) and human (MDA-MB-468) breast cancer cell lines were transduced to overexpress human LDLR (LDLROE). Successful transduction was confirmed by RNA and protein analysis. Fluorescence-labeled LDL uptake was increased in both Mvt1 and MDA-MD-468 LDLROE cells. The expression of the cholesterol-metabolizing genes, ABCA1 and ABCG1, was increased, while HMGCR was decreased in the MDA-MB-468 LDLROE cells. In contrast, Mvt1 LDLROE cells showed no differences in Abca1 and Abcg1 expression and increased Hmgcr expression. Using a Seahorse analyzer, Mvt1 LDLROE cells showed increased respiration (ATP-linked and maximal) relative to controls, while no statistically significant changes in respiration in MDA-MB-468 LDLROE cells were observed. Growth of LDLROE cells was reduced in culture and in hypercholesterolemic mice by two-fold. However, the expression of proliferation-associated markers (Ki67, PCNA and BrdU-label incorporation) was not decreased in the Mvt1 LDLROE tumors and cells. Caspase-3 cleavage, which is associated with apoptosis, was increased in both the Mvt1 and MDA-MB-468 LDLROE cells relative to controls, with the Mvt1 LDLROE cells also showing decreased phosphorylation of p44/42MAPK. Taken together, our work suggests that while additional LDL can promote tumor growth, unregulated and prolonged LDL uptake is detrimental.
Magnetic resonance imaging of rhino-orbito-cerebral mucormycosis: a pictorial review

Rhino-orbito-cerebral mucormycosis is a potentially fatal disease requiring early magnetic resonance imaging (MRI) for disease evaluation and timely detection of intracranial complications. Angio-invasive nature leading to necrosis and infarction is the hallmark of mucormycosis. The disease follows a fulminant course extending from the paranasal sinuses to involve the orbit, deep neck spaces, skull base, facial bones, and intracranial compartment. Loss of vision either due to direct extension into the orbit or optic nerve infarction adds to disease morbidity. Prompt MRI using dedicated sequences can help in assessing the exact disease extent including early osseous and intracranial changes, which aid in precise disease management.
Improving M-SBL for Joint Sparse Recovery Using a Subspace Penalty

A multiple measurement vector problem (MMV) is a generalization of the compressed sensing problem that addresses the recovery of a set of jointly sparse signal vectors. One of the important contributions of this paper is to show that the seemingly least related state-of-the-art MMV joint sparse recovery algorithms-the M-SBL (multiple sparse Bayesian learning) and subspace-based hybrid greedy algorithms-have a very important link. More specifically, we show that replacing the (.) term in the M-SBL by a rank surrogate that exploits the spark reduction property discovered in the subspace-based joint sparse recovery algorithms provides significant improvements. In particular, if we use the Schatten-quasi-norm as the corresponding rank surrogate, the global minimizer of the cost function in the proposed algorithm becomes identical to the true solution as. Furthermore, under regularity conditions, we show that convergence to a local minimizer is guaranteed using an alternating minimization algorithm that has closed form expressions for each of the minimization steps, which are convex. Numerical simulations under a variety of scenarios in terms of SNR and the condition number of the signal amplitude matrix show that the proposed algorithm consistently outperformed the M-SBL and other state-of-the art algorithms.
Interface Trap Density Metrology of State-of-the-Art Undoped Si n-FinFETs

The presence of interface states at the MOS interface is a well-known cause of device degradation. This is particularly true for ultrascaled FinFET geometries where the presence of a few traps can strongly influence the device behavior. Typical methods for interface trap density (D-it) measurements are not performed on ultimate devices but on custom-designed structures. We present the first set of methods that allow direct estimation of D-it in state-of-the-art FinFETs, addressing a critical industry need.
Maladaptive Laterality in Cortical Networks Related to Social Communication in Autism Spectrum Disorder

Neuroimaging studies of individuals with autism spectrum disorders (ASDs) consistently find an aberrant pattern of reduced laterality in brain networks that support functions related to social communication and language. However, it is unclear how the underlying functional organization of these brain networks is altered in ASD individuals. We tested four models of reduced laterality in a social communication network in 70 ASD individuals (14 females) and a control group of the same number of tightly matched typically developing (TD) individuals (19 females) using high-quality resting-state fMRI data and a method of measuring patterns of functional laterality across the brain. We found that a functionally defined social communication network exhibited the typical pattern of left laterality in both groups, whereas there was a significant increase in within- relative to across-hemisphere connectivity of homotopic regions in the right hemisphere in ASD individuals. Furthermore, greater within- relative to across-hemisphere connectivity in the left hemisphere was positively correlated with a measure of verbal ability in both groups, whereas greater within- relative to across-hemisphere connectivity in the right hemisphere in ASD, but not TD, individuals was negatively correlated with the same verbal measure. Crucially, these differences in patterns of laterality were not found in two other functional networks and were specifically correlated to a measure of verbal ability but not metrics of other core components of the ASD phenotype. These results suggest that previous reports of reduced laterality in social communication regions in ASD is because of the two hemispheres functioning more independently than seen in TD individuals, with the atypical right-hemisphere network component being maladaptive.SIGNIFICANCE STATEMENT A consistent neuroimaging finding in individuals with ASD is an aberrant pattern of reduced laterality of the brain networks that support functions related to social communication and language. We tested four models of reduced laterality in a social communication network in ASD individuals and a TD control group using high-quality resting-state fMRI data. Our results suggest that reduced laterality of social communication regions in ASD may be because of the two hemispheres functioning more independently than seen in TD individuals, with atypically greater within- than across-hemisphere connectivity in the right hemisphere being maladaptive.
Museum Moving to Inpatients: Le Louvre a l'Hopital

Anxiety and depressive symptoms are common in hospitalized patients. Arts and cultural programs were reported to enhance their quality of life. The Le Louvre a l'hopital study presents a new approach in which the museum moves to the hospital by displaying and discussing artworks with patients interactively. Over one year, four large statues were disposed in the hospital gardens, 30 reprints of large painting were exhibited in the hospital hall, dining rooms, and circulations areas. A total of 83 small-group guided art discussions (90 min) were organized, which 451 patients attended. The 200 small-size reproductions of paintings placed in the patients' rooms were chosen based on their individual preferences. Decreased anxiety after the art sessions was reported by 160 of 201 patients (79.6%). Out of 451 patients, 406 (90%) said the art program had met their expectations, and 372 (82.4%) wished to continue the experience with caregivers (162 paramedics trained for art activity during 66 workshops). In conclusion, moving the museum to the hospital constitutes a valuable way to provide art activities for inpatients in large numbers, which may reduce hospital-related anxiety in many instances.