ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Effect of curcumin on lipid profile, fibrosis, and apoptosis in liver tissue in abemaciclib-administered rats

Abemaciclib (ABEM) is an important antitumor agent for breast cancer treatment. However, the side-effects of ABEM are unclear in the liver. This study investigated the protective effect of curcumin (CURC) on liver damage caused by ABEM. The rats were divided into five groups with eight animals in each group; Control, DMSO (150 µL for per rats), CURC, 30 mg/kg/day), ABE (26 mg/kg/day), and ABE + CURC (26 mg/kg/day ABE, 30 mg/kg/day) groups. Injections were administered daily for 28 days. The levels of AST, LDH, HDL, LDL, triglyceride, and total cholesterol in serum, and hepatic tissue fibrosis, caspase-3, Bax, and TNF-α expression were higher in the ABE group compared to the control group (p < 0.05). Also, these parameters in the ABEM + CURC group were lower than in the ABE group (p < 0.05). The results showed that ABE administration could cause liver damage and increase fibrosis in the liver. In addition, it was shown that co-administration of CURC with ABE could suppress the levels of AST, LDH, HDL, LDL, triglyceride, and total cholesterol in serum, and fibrosis, caspase-3, Bax, and TNF-α expressions in the liver. These data are the first in the literature. Therefore, the administration of CURC following ABE may be a therapeutic agent in preventing liver damage.
Spatial Multiomics of Lipids, N-Glycans, and Tryptic Peptides on a Single FFPE Tissue Section

Mass spectrometry imaging (MSI) is an emerging technology that is capable of mapping various biomolecules within their native spatial context, and performing spatial multiomics on formalin-fixed paraffin-embedded (FFPE) tissues may further increase the molecular characterization of pathological states. Here we present a novel workflow which enables the sequential MSI of lipids, N-glycans, and tryptic peptides on a single FFPE tissue section and highlight the enhanced molecular characterization that is offered by combining the multiple spatial omics data sets. In murine brain and clear cell renal cell carcinoma (ccRCC) tissue, the three molecular levels provided complementary information and characterized different histological regions. Moreover, when the spatial omics data was integrated, the different histopathological regions of the ccRCC tissue could be better discriminated with respect to the imaging data set of any single omics class. Taken together, these promising findings demonstrate the capability to more comprehensively map the molecular complexity within pathological tissue.
Deep and shallow fast embedded capsule networks: going faster with capsules

Capsule Networks (CapsNets) is a great approach for understanding data in the field of computer vision. CapsNets allow a deeper understanding of images compared to the traditional Convolutional Neural Networks. The first test for CapsNet was in digits recognition on the 'MNIST' dataset, where it successfully achieved high accuracy. CapsNets are reliable at deciphering overlapping digits. Deep Capsule Networks achieved state-of-the-art accuracy in CIFAR10 which isn't achieved by shallow capsule networks. Despite all these accomplishments, Deep Capsule Networks are very slow due to the 'Dynamic Routing' algorithm. In this paper, Fast Embedded Capsule Network and Deep Fast Embedded Capsule Network are introduced, representing novel capsule network architectures that uses 1D convolution based dynamic routing with a fast element-wise multiplication transformation process. These architectures not only compete with the state-of-the-art methods in terms of accuracy in the capsule domain, but also excels in terms of speed, and reduced complexity. This is shown by the 58% reduction in the number of trainable parameters and 64% reduction in the average epoch time in the training process. Experimental results shows excellent and verified properties.
Kalman-Based Real-Time Functional Decomposition for the Spectral Calibration in Swept Source Optical Coherence Tomography

This paper presents a real-time functional decomposition adaptive algorithm for the optimal sampling of the interferometric signal in Swept-Source Optical Coherence Tomography imaging systems, which completely eliminates the input signal dependent nonlinearities that are problematic in current state-of-the-art OCT realizations that use interpolation and resampling. The proposed adaptive calibration algorithm uses the Kalman approach to estimate the wavenumber index parameter k from the Mach-Zender Interferometer signal which is then applied to an adaptive level crossing sampler to generate a sampling clock that k-linearizes the data on real-time during the sampling process. Such a system implements an artifact-free realization of the technology removing the need for classical interpolation and resampling. The new real-time linearization scheme has the additional capability of increasing the imaging acquisition speed by 10X while providing robustness to noise, properties that are demonstrated through mathematical analysis and simulation results throughout the paper.
On Establishing Edge Adaptive Grid for Bilevel Image Data Hiding

We propose, in this paper, a novel edge-adaptive data hiding method for authenticating binary host images. Through establishing a dense edge-adaptive grid (EAG) along the object contours, we use a simple binary image to show that EAG more efficiently selects good data carrying pixel locations (DCPL) associated with "l-shaped" patterns than block-based methods. Our method employs a dynamic system structure with the redesigned fundamental content adaptive processes (CAP) switch to iteratively trace new contour segments and to search for new DCPLs. By maintaining and updating a location status map, a protective mechanism is proposed to preserve the context of each CAP and their corresponding outcomes. We prove that our method is robust against the interferences caused by close-by contours, image noises, and invariantly selects the same sequence of DCPLs for an arbitrary binary host image and its various marked versions. Comparison shows that our method achieves a good tradeoff between large payload and minimal visual distortion as compared with several classic prior arts for diverse types of binary host images. Moreover, our method well supports state-of-the-art hybrid authentication that integrates data hiding and modern cryptographic techniques.
Tissue Sealants for Facial Rhytidectomy: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background: The aging face can be surgically treated with a face-lift (rhytidectomy); however, bleeding and hematoma are complications that surgeons seek to prevent. Objective: To compare the drainage volume and rate of hematoma in studies of rhytidectomy among those having tissue sealants and those without. Methods: This systematic review and meta-analysis was prospectively registered in PROSPERO (CRD42022325404). We included randomized controlled trials (RCTs) that the enrolled participants undergoing rhytidectomy and used tissue sealants as the intervention. We calculated the mean and standard deviation for the drainage volume; risk ratios (RRs) were used for hematoma incidents. Results: Seven RCTs were included. The drainage volume was significantly lower in the tissue sealant group than in the control group (mean difference [MD]: -11.01, confidence interval [95% CI]: -18.39 to -3.63, p < 0.00001). As for hematomas, the incidence was also lower in the tissue sealant group (RR: 0.29, 95% CI: 0.08-0.99, p = 0.05). Conclusion: This study suggests that tissue sealants can be effective in reducing drainage volume and hematoma in face-lift; however, autologous and homologous tissue sealants can be further compared in future RCTs.
Heterologous expression of Arabidopsis pattern recognition receptor RLP23 increases broad-spectrum resistance in poplar to fungal pathogens

The pattern recognition receptor AtRLP23 from Arabidopsis thaliana recognizes the epitopes (nlp24s) of necrosis and ethylene-inducing peptide 1-like proteins (NLPs) and triggers pattern-triggered immunity (PTI). Here, we established methods for studying the early events of PTI in the hybrid poplar cultivar Shanxin (Populus davidiana × Populus bolleana) in response to the flagellin epitope. We confirmed that wild-type Shanxin cannot generate PTI responses on nlp24 treatment. Four NLP homologues were characterized from two common fungal pathogens of Shanxin, namely Marssonina brunnea f. sp. monogermtubi (MbMo) and Elsinoë australis (Ea), which cause black leaf spot and anthracnose disease, respectively, and the nlp24s of three of them could be responded to by Nicotiana benthamiana leaves expressing AtRLP23. We then created AtRLP23 transgenic Shanxin lines and confirmed that the heterologous expression of AtRLP23 conferred on transgenic Shanxin the ability to respond to one nlp24 of each fungal pathogen. Consistently, infection assays with MbMo or Ea showed obviously lower levels of disease symptoms and significantly inhibited the growth of fungi on the transgenic poplar compared with that in wild-type poplar. Overall, our results indicated that the heterologous expression of AtRLP23 allowed transgenic Shanxin to generate a PTI response to nlp24s, resulting in increased broad-spectrum fungal disease resistance.
Giant ventral hernia following left ventricular assist device bridge to heart transplantation

Ventral hernias following left ventricular assist device (LVAD) placement are rare. With the improvement in technology, and miniaturization of devices associated with intrapericardial placement, these complications have largely been abolished. The mere presence of a large ventral hernia should not exclude recipients from being candidates for orthotopic heart transplantation.
Low Complexity Generic VLSI Architecture Design Methodology for Nth Root and Nth Power Computations

In this paper, we propose a low complexity architecture design methodology for fixed point root and power computations. The state of the art approaches perform the root and power computations based on the natural logarithm-exponential relation using Hyperbolic COordinate Rotation DIgital Computer (CORDIC). In this paper, any root and power computations have been performed using binary logarithm-binary inverse logarithm relation. The designs are modeled using VHDL for fixed point numbers and synthesized under the TSMC 40 -nm CMOS technology @ 1 GHz frequency. The synthesis results shows that the proposed N-th root computation saves 19.38% on chip area and 15.86% power consumption when compared with the state of the art architecture for root computation without compromising the computational accuracy. Similarly, the proposed N-th power computation saves 38% on chip area, 35.67% power consumption when compared with the state of the art power computation with out loss in accuracy. The proposed root and power computation designs save 8 clock cycle latency when compared with the state of the art implementations.
Enhancing online english language and literature classrooms: effective and practical teaching strategies

This paper examines key effective and practical online strategies for teaching and learning literature subjects in online education. The present study also explores the leading concepts and principles of e- teaching strategies and extends to address the way to adopt literary lesson plans to various types of college students, determine subject descriptions, class activities, assignments, assessments, and expectations with students, and build rapport and communication with the students. This article discusses how to enjoy appropriate online media, channels, platforms, and e-pedagogical tools to help the learners and support the college curriculum in literary subjects e.g. poetry, fiction, and drama. The results show that online pedagogues portray and guide interactive literary texts and skills to the students, educators, and course designers and can facilitate instructional methods in learning the English language and literature online.
BioWolf: A Sub-10-mW 8-Channel Advanced Brain-Computer Interface Platform With a Nine-Core Processor and BLE Connectivity

Advancements in digital signal processing (DSP) and machine learning techniques have boosted the popularity of brain-computer interfaces (BCIs), where electroencephalography is a widely accepted method to enable intuitive human-machine interaction. Nevertheless, the evolution of such interfaces is currently hampered by the unavailability of embedded platforms capable of delivering the required computational power at high energy efficiency and allowing for a small and unobtrusive form factor. To fill this gap, we developed BioWolf, a highly wearable (40mm 20 mm 2 mm) BCI platform based on Mr. Wolf, a parallel ultra low power system-on-chip featuring nine RISC-V cores with DSP-oriented instruction set extensions. BioWolf also integrates a commercial 8-channel medical-grade analog-to-digital converter, and an ARM-Cortex M4 microcontroller unit (MCU) with bluetooth low-energy connectivity. To demonstrate the capabilities of the system, we implemented and tested a BCI featuring canonical correlation analysis (CCA) of steady-state visual evoked potentials. The system achieves an average information transfer rate of 1.46 b/s (aligned with the state-of-the-art of bench-top systems). Thanks to the reduced power envelope of the digital computational platform, which consumes less than the analog front-end, the total power budget is just 6.31mW, providing up to 38h operation (65mAh battery). To our knowledge, our design is the first to explore the significant energy boost of a parallel MCU with respect to single-core MCUs for CCA-based BCI.
High-Power Lensless THz Imaging of Hidden Objects

The potential of pulsed THz radiation for time-of-flight imaging applications is well recognized. However, advances in this field are currently severely limited by the low average power of ultrafast THz sources. Typically, this results in impractically long acquisition times and a loss in resolution and contrast. These limitations make imaging of the objects in real-life scenarios impossible. Here, conclusively, the potential of state-of-the-art high-average power THz time-domain spectrometer (TDS), driven by a 100-W class, one-box ultrafast oscillator for imaging applications is shown by demonstrating lensless THz imaging in reflection mode of a dielectric sample with low reflectivity. Images obtained with our home-built 20-mW average power THz-TDS system show a significant contrast enhancement compared to a state-of-the-art commercial THz-TDS with less than 200 mu W of average power. Our unique setup even allows us to obtain images of such an object with high-contrast hidden inside a medium-density fiberboard (MDF) box. This opens the door to THz time-of-flight imaging of concealed objects of unknown shape and orientation in various real-life scenarios which were so far impossible to realize.
Automated Skin Lesion Segmentation Via an Adaptive Dual Attention Module

We present a convolutional neural network (CNN) equipped with a novel and efficient adaptive dual attention module (ADAM) for automated skin lesion segmentation from dermoscopic images, which is an essential yet challenging step for the development of a computer-assisted skin disease diagnosis system. The proposed ADAM has three compelling characteristics. First, we integrate two global context modeling mechanisms into the ADAM, one aiming at capturing the boundary continuity of skin lesion by global average pooling while the other dealing with the shape irregularity by pixel-wise correlation. In this regard, our network, thanks to the proposed ADAM, is capable of extracting more comprehensive and discriminative features for recognizing the boundary of skin lesions. Second, the proposed ADAM supports multi-scale resolution fusion, and hence can capture multi-scale features to further improve the segmentation accuracy. Third, as we harness a spatial information weighting method in the proposed network, our method can reduce a lot of redundancies compared with traditional CNNs. The proposed network is implemented based on a dual encoder architecture, which is able to enlarge the receptive field without greatly increasing the network parameters. In addition, we assign different dilation rates to different ADAMs so that it can adaptively capture distinguishing features according to the size of a lesion. We extensively evaluate the proposed method on both ISBI2017 and ISIC2018 datasets and the experimental results demonstrate that, without using network ensemble schemes, our method is capable of achieving better segmentation performance than state-of-the-art deep learning models, particularly those equipped with attention mechanisms.
Factors Associated with Antiretroviral Therapy Toxicity Out-Comes in Patients with and without Hypertension

Background: Negative effects of antiretroviral therapy (ART) drugs on HIV/AIDS patients are one of the major health issues in the therapeutic treatment of this communicable disease. This holds particularly for people living with HIV (PLHIV) who might have a non-communicable disease (like hypertension), which also requires a lifetime treatment. In this study, we investigated the association between hypertension and other possible factors on ART toxicity markers in patients with hypertension, compared to those without hypertension. Methods: This retrospective longitudinal study reviewed chronic patient files of 525 patients (of which 222 were hypertensive) who satisfied the inclusion criteria and were on ART at a hospital in central Eswatini. Specific levels of estimated glomerular filtration (eGFR), alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were used as drug toxicity markers. To analyze the longitudinal data between the exposure of interest and outcome variables, a Generalized Estimated Equation method was employed. Results: Participants with hypertension had decreased eGFR compared to those without hypertension (beta = -2.22; p-value = 0.03). There was no significant association between ALT, AST and hypertension (p-value = 0.34 and 0.20, respectively). Factors that were found to have a significant association with ART toxicity markers included age, sex, ART duration, hypertension treatment and time of study. The eGFR was found to be significantly increasing over the study period (p-value < 0.001) for all participants. The significance was consistent in both hypertensive and non-hypertensive participants independently (p-value = 0.002 and <0.001, respectively). The overall trends of ALT and AST over time were also significant (p-value = 0.003 and <0.001, respectively). Conclusions: Patients with hypertension had decreased eGFR, and there was a significant association of eGFR with time of the study. Special attention, therefore, to monitor calamities which are indicated by a decrease of eGFR (like renal impairment) should be given in PLHIV on ART with hypertension, especially more so if they were on ART for longer time.
Improved Zn bioavailability by its enhanced colocalization and speciation with S in wheat grain tissues after N addition

Zinc bioavailability with the presence of other elements in wheat grains might be affected by fertilizers. A long-term field experiment was conducted to examine effects of N fertilizer on Zn bioavailability in wheat grain tissues, with changes in the concentrations, distribution, and speciation of Zn as well as P and sulfur S via synchrotron-based technology. Results showed that addition of N fertilizer was associated with changes in Zn concentrations and distributions in grain tissues, especially in the crease region and endosperm. Simultaneously, N addition enhanced Zn-S colocalization in the crease region and endosperm and lowered the P/Zn ratio and Zn-P colocalization. Addition of N fertilizer with P increased Zn-cysteine (9.2%) and decreased Zn-phytate (47.3%) in the crease region, leading to potentially higher grain Zn bioavailability. Thus, addition of N fertilizer improved concentrations and bioavailability of Zn, by coordinating the relationships among Zn, P and S within wheat grains.
The immune response in the CNS in Theiler's virus induced demyelinating disease switches from an early adaptive response to a chronic innate-like response

Theiler's murine encephalomyelitis virus-induced demyelinating disease (TMEV-IDD) is an important model of the progressive disability caused by irreversible CNS tissue injury, and provides an example of how a CNS pathogen can cause inflammation, demyelination, and neuronal damage. We were interested in which molecules, especially inflammatory mediators, might be upregulated in the CNS throughout TMEV-IDD. We quantitated by a real-time RT-PCR multi-gene system the expression of a pathway-focused panel of genes at 30 and 165 days post infection, characterizing both the early inflammatory and the late neurodegenerative stages of TMEV-IDD. Also, we measured 32 cytokines/chemokines by multiplex Luminex analysis in CSF specimens from early and late TMEV-IDD as well as sham-treated mice. Results indicate that, in the later stage of TMEV-IDD, activation of the innate immune response is most prominent: TLRs, type I IFN response genes, and innate immunity-associated cytokines were highly expressed in late TMEV-IDD compared to sham (p ≤ 0.0001) and early TMEV-IDD (p < 0.05). Conversely, several molecular mediators of adaptive immune response were highly expressed in early TMEV-IDD (all p ≤ 0.001). Protein detection in the CSF was broadly concordant with mRNA abundance of the corresponding gene measured by real-time RT-PCR in the spinal cord, since several cytokines/chemokines were increased in the CSF of TMEV-IDD mice. Results show a clear shift from adaptive to innate immunity from early to late TMEV-IDD, indicating that adaptive and innate immune pathways are likely involved in the development and progression of the disease to different extents. CSF provides an optimal source of biomarkers of CNS neuroinflammation.
Digital Health Solutions to Reduce the Burden of Atherosclerotic Cardiovascular Disease Proposed by the CARRIER Consortium

Digital health is a promising tool to support people with an elevated risk for atherosclerotic cardiovascular disease (ASCVD) and patients with an established disease to improve cardiovascular outcomes. Many digital health initiatives have been developed and employed. However, barriers to their large-scale implementation have remained. This paper focuses on these barriers and presents solutions as proposed by the Dutch CARRIER (ie, Coronary ARtery disease: Risk estimations and Interventions for prevention and EaRly detection) consortium. We will focus in 4 sections on the following: (1) the development process of an eHealth solution that will include design thinking and cocreation with relevant stakeholders; (2) the modeling approach for two clinical prediction models (CPMs) to identify people at risk of developing ASCVD and to guide interventions; (3) description of a federated data infrastructure to train the CPMs and to provide the eHealth solution with relevant data; and (4) discussion of an ethical and legal framework for responsible data handling in health care. The Dutch CARRIER consortium consists of a collaboration between experts in the fields of eHealth development, ASCVD, public health, big data, as well as ethics and law. The consortium focuses on reducing the burden of ASCVD. We believe the future of health care is data driven and supported by digital health. Therefore, we hope that our research will not only facilitate CARRIER consortium but may also facilitate other future health care initiatives.