ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
The critical role of quercetin in autophagy and apoptosis in HeLa cells

In recent years, the effects of quercetin on autophagy and apoptosis of cancer cells have been widely reported, while effects on HeLa cells are still unclear. Here, HeLa cells were subjected to quercetin treatment, and then proliferation, apoptosis, and autophagy were evaluated using MTT, flow cytometry, and MDC staining, respectively. The LC3-I/II, Beclin 1, active caspase-3, and S6K1 phosphorylation were detected using Western blot assay. The ultrastructure of HeLa was observed via transmission electron microscope (TEM). Our findings showed that quercetin can dose-dependently inhibit the growth of HeLa cells. The MDC fluorescence was enhanced with increased concentration of quercetin and hit a plateau at 50 μmol/l. Western blot assay revealed that LC3-I/II ratio, Beclin 1, and active caspase-3 protein were enforced in a dose-dependent method. However, the phosphorylation of S6K1 gradually decreased, concomitant with an increase of autophagy. In addition, TEM revealed that the number of autophagic vacuoles was peaked at 50 μmol/l of quercetin. Besides, interference of autophagy with 3-MA led to proliferation inhibition and increased apoptosis in HeLa cells, accompanied by the decreased LC3-I/II conversion and the increased active caspase-3. In conclusion, quercetin can inhibit HeLa cell proliferation and induce protective autophagy at low concentrations; thus, 3-MA plus quercetin would suppress autophagy and effectively increased apoptosis.
Anti-PD-1 and PD-L1 therapy for bladder cancer: what is on the horizon?

Oncologic therapeutics has evolved enormously as we entered the 21st century. Unfortunately, the treatment of advanced urothelial cancer has remained unchanged over the last two decades despite a better understanding of the genetic alterations in bladder cancer. Pathways such as the PI3K/AKT3/mTOR and FGFR have been implicated in urothelial bladder cancer. However, targeted therapies have not shown proven benefit yet and are still considered investigational. Recently, researchers have been successful in manipulating the systemic immune response to mount antitumor effects in melanoma, lung cancer and lymphoma. Historically, intravesical Bacillus Calmette-Guérin immunotherapy has been highly active in nonmuscle invasive bladder cancer. Early data suggest that immune checkpoint inhibitors will soon prove to be another cornerstone in the treatment armamentarium of advanced bladder cancer.
Design parameters of an omnidirectional planar microstrip antenna

The design parameters of all onmidirectional planar microstrip antenna art examined. The impedance bandwidth and radiation efficiency increase as the element width is increased. Increasing the element width can cause a lower-order mode to increase in frequency and degrade the omnidirectional pattern and sidelobes, and thus creates a beam scan from broadside. This beam scan and pattern degradation call he eliminated with narrower elements or by moving the position of the shorting pins at the expense of impedance bandwidth. (c) 2005 Wiley Periodicals, Inc.
Determinants of Survival of HIV Patients Receiving Dolutegravir: A Prospective Cohort Study in Conflict-Affected Bunia, Democratic Republic of Congo

This study aims to determine the factors influencing HIV-related mortality in settings experiencing continuous armed conflict atrocities. In such settings, people living with HIV (PLHIV), and the partners of those affected may encounter specific difficulties regarding adherence to antiretroviral therapy (ART), and retention in HIV prevention, treatment, and care programs. Between July 2019 and July 2021, we conducted an observational prospective cohort study of 468 PLHIV patients treated with Dolutegravir at all the ART facilities in Bunia. The probability of death being the primary outcome, as a function of time of inclusion in the cohort, was determined using Kaplan-Meier plots. We used the log-rank test to compare survival curves and Cox proportional hazard modeling to determine mortality predictors from the baseline to 31 July 2021 (endpoint). The total number of person-months (p-m) was 3435, with a death rate of 6.70 per 1000 p-m. Compared with the 35-year-old reference group, older patients had a higher mortality risk. ART-naive participants at the time of enrollment had a higher mortality risk than those already using ART. Patients with a high baseline viral load (>= 1000 copies/mL) had a higher mortality risk compared with the reference group (adjusted hazard ratio = 6.04; 95% CI: 1.78-20.43). One-fourth of deaths in the cohort were direct victims of armed conflict, with an estimated excess death of 35.6%. Improving baseline viral load monitoring, starting ART early in individuals with high baseline viral loads, the proper tailoring of ART regimens and optimizing long-term ART, and care to manage non-AIDS-related chronic complications are recommended actions to reduce mortality. Not least, fostering women's inclusion, justice, peace, and security in conflict zones is critical in preventing premature deaths in the general population as well as among PLHIV.
CRISPR/Cas9: A revolutionary genome editing tool for human cancers treatment

Cancer is a genetic disease stemming from genetic and epigenetic mutations and is the second most common cause of death across the globe. Clustered regularly interspaced short palindromic repeats (CRISPR) is an emerging gene-editing tool, acting as a defense system in bacteria and archaea. CRISPR/Cas9 technology holds immense potential in cancer diagnosis and treatment and has been utilized to develop cancer disease models such as medulloblastoma and glioblastoma mice models. In diagnostics, CRISPR can be used to quickly and efficiently detect genes involved in various cancer development, proliferation, metastasis, and drug resistance. CRISPR/Cas9 mediated cancer immunotherapy is a well-known treatment option after surgery, chemotherapy, and radiation therapy. It has marked a turning point in cancer treatment. However, despite its advantages and tremendous potential, there are many challenges such as off-target effects, editing efficiency of CRISPR/Cas9, efficient delivery of CRISPR/Cas9 components into the target cells and tissues, and low efficiency of HDR, which are some of the main issues and need further research and development for completely clinical application of this novel gene editing tool. Here, we present a CRISPR/Cas9 mediated cancer treatment method, its role and applications in various cancer treatments, its challenges, and possible solution to counter these challenges.
MR-Nucleomics: The study of pathological cellular processes with multinuclear magnetic resonance spectroscopy and imaging in vivo

Clinical medicine has experienced a rapid development in recent decades, during which therapies targeting specific cellular signaling pathways, or specific cell surface receptors, have been increasingly adopted. While these developments in clinical medicine call for improved precision in diagnosis and treatment monitoring, modern medical imaging methods are restricted mainly to anatomical imaging, lagging behind the requirements of precision medicine. Although positron emission tomography and single photon emission computed tomography have been used clinically for studies of metabolism, their applications have been limited by the exposure risk to ionizing radiation, the subsequent limitation in repeated and longitudinal studies, and the incapability in assessing downstream metabolism. Magnetic resonance spectroscopy (MRS) or spectroscopic imaging (MRSI) are, in theory, capable of assessing molecular activities in vivo, although they are often limited by sensitivity. Here, we review some recent developments in MRS and MRSI of multiple nuclei that have potential as molecular imaging tools in the clinic.
Learning and generalising object extraction skill for contact-rich disassembly tasks: an introductory study

Remanufacturing automation must be designed to be flexible and robust enough to overcome the uncertainties, conditions of the products, and complexities in the planning and operation of the processes. Machine learning methods, in particular reinforcement learning, are presented as techniques to learn, improve, and generalise the automation of many robotic manipulation tasks (most of them related to grasping, picking, or assembly). However, not much has been exploited in remanufacturing, in particular in disassembly tasks. This work presents the state of the art of contact-rich disassembly using reinforcement learning algorithms and a study about the generalisation of object extraction skills when applied to contact-rich disassembly tasks. The generalisation capabilities of two state-of-the-art reinforcement learning agents (trained in simulation) are tested and evaluated in simulation, and real world while perform a disassembly task. Results show that at least one of the agents can generalise the contact-rich extraction skill. Besides, this work identifies key concepts and gaps for the reinforcement learning algorithms' research and application on disassembly tasks.
GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention

We propose GourmetNet, a single-pass, end-to-end trainable network for food segmentation that achieves state-of-the-art performance. Food segmentation is an important problem as the first step for nutrition monitoring, food volume and calorie estimation. Our novel architecture incorporates both channel attention and spatial attention information in an expanded multi-scale feature representation using our advanced Waterfall Atrous Spatial Pooling module. GourmetNet refines the feature extraction process by merging features from multiple levels of the backbone through the two attention modules. The refined features are processed with the advanced multi-scale waterfall module that combines the benefits of cascade filtering and pyramid representations without requiring a separate decoder or post-processing. Our experiments on two food datasets show that GourmetNet significantly outperforms existing current state-of-the-art methods.
Listen and Look: Audio-Visual Matching Assisted Speech Source Separation

Source permutation, i.e., assigning separated signal snippets to wrong sources over time, is a major issue in the state-of-the-art speaker-independent speech source separation methods. In addition to auditory cues, humans also leverage visual cues to solve this problem at cocktail parties: matching lip movements with voice fluctuations helps humans to better pay attention to the speaker of interest. In this letter, we propose an audio-visual matching network to learn the correspondence between voice fluctuations and lip movements. We then propose a framework to apply this network to address the source permutation problem and improve over audio-only speech separation methods. The modular design of this frame work makes it easy to apply the matching network to any audio-only speech separation method. Experiments on two-talker mixtures show that the proposed approach significantly improves the separation quality over the state-of-the-art audio-only method. This improvement is especially pronounced on mixtures that the audio-only method fails, in which the speakers often have similar voice characteristics.
Robust Deep Learning for IC Test Problems

Numerous machine learning (ML), and more recently, deep-learning (DL)-based approaches, have been proposed to tackle scalability issues in electronic design automation, including those in integrated circuit (IC) test. This article examines state-of-the-art DL for IC test and highlights two critical unaddressed challenges. The first challenge involves identifying fit-for-purpose statistical metrics to train and evaluate ML model performance and usefulness in IC test. Our work shows that current metrics do not reflect how well ML models have learned to generalize and perform in the domain-specific context. From this insight, we propose and evaluate alternative metrics that better capture a model's likely usefulness in the IC test problem. The second challenge is to choose an appropriate input abstraction so as to enable an ML model to learn robust and reliable features. We investigate how well DL for IC test techniques generalize by exploring their robustness to perturbations that alter a netlist's structure but do not alter its functionality. This article provides insights into challenges via empirical evaluation of the state-of-the-art and offers guidance for future work.
E2BNet: MAC-free yet accurate 2-level binarized neural network accelerator for embedded systems

Deep neural networks are widely used in computer vision, pattern recognition, and speech recognition and achieve high accuracy at the cost of remarkable computation. High computational complexity and memory accesses of such networks create a big challenge for using them in resource-limited and low-power embedded systems. Several binary neural networks have been proposed that exploit only 1-bit values for both weights and activations. Binary neural networks substitute complex multiply-accumulation operations with bitwise logic operations to reduce computations and memory usage. However, these quantized neural networks suffer from accuracy loss, especially in big datasets. In this paper, we introduce a quantized neural network with 2-bit weights and activations that is more accurate compared to the state-of-the-art quantized neural networks, and also the accuracy is close to the full precision neural networks. Moreover, we propose E2BNet, an efficient MAC-free hardware architecture that increases power efficiency and throughput/W about 3.6 x and 1.5 x , respectively, compared to the state-of-the-art quantized neural networks. E2BNet processes more than 500 images/s on the ImageNet dataset that not only meet real-time requirements of images/video processing but also can be deployed on high frame rate video applications.
LPCVD silicon nitride uniformity improvement using adaptive real-time temperature control

Art effective approach to improve silicon nitride thickness uniformity has been demonstrated on a batch LPCVD furnace platform. Implementation of adapthe real time temperature control provides accurate, real-time estimation of substrate temperature profiles that enables model-based optimization of Process temperature. optimization of a 200-nm silicon nitride deposition yielded long-term, overall nitride thickness uniformity of 0.79% 1sigma over a seven-week period, compared to 1-24% for an equivalent PID-tuned process. Three sequential silicon nitride deposition iterations were implemented in the proem recipe to enable increased temperature ramp rates, for more efficient optimization of within wafer uniformity The optimized process requalified quickly after major and minor equipment maintenance, and is suitable for use in a manufacturing environment The ART-optimized temperature ramp intervals used in this study are comparable to temperature deltas often used to offset dichlorosilane depletion effects encountered in some large-batch vertical furnace depositions. SIMS depth profiling of ART-optimized silicon nitride does reveal small oxygen and chlorine peaks, indicating slight interface formation between deposition steps.
SAGE: Steering the Adversarial Generation of Examples With Accelerations

To generate image adversarial examples, state-of-the-art black-box attacks usually require thousands of queries. However, massive queries will introduce additional costs and exposure risks in the real world. Towards improving the attack efficiency, we carefully design an acceleration framework SAGE for existing black-box methods, which is composed of sLocator (initial point optimization) and sRudder (search process optimization). The core idea of SAGE in terms of 1) saliency map can guide the perturbations towards the most adversarial direction and 2) exploiting bounding box (bbox) to capture those salient pixels in the black-box attack. Meanwhile, we provide a series of observations and experiments that demonstrate bbox holds model invariance and process invariance. We extensively evaluate SAGE on four state-of-the-art black-box attacks involving three popular datasets (MNIST, CIFAR10, and ImageNet). The results show that SAGE could present fundamental improvements even against robust models that use adversarial training. Specifically, SAGE could reduce > 20% of queries and improve the success rate of attacks to 95%similar to 100%. Compared with the other acceleration framework, SAGE fulfills the more significant effect in a flexible, stable, and low-overhead manner. Moreover, our practical evaluation (Google Cloud Vision API) shows SAGE can be applied to real-world scenarios.
Moving Toward Intelligence: Detecting Symbols on 5G Systems Through Deep Echo State Network

Due to the nonlinear distortion caused by radio-frequency (RF) components in the transceiver, detecting transmitted symbols for multiple-input and multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems can be challenging and resource consuming. In this work, we introduce a Deep Echo State Network (DESN) to serve as the symbol detector for 5G communication networks. Our DESN employs memristive synapses as the dynamic reservoir layer to accelerate the learning algorithm and computation. By cascading multiple dynamic reservoir layers in a hierarchical processing structure, our DESN processes received signal in both spatial and temporal domains. The resulting hybrid memristor-CMOS co-design provides the nonlinear computation required by the reservoir layer while significantly reduces the power consumption. From the benchmark on nonlinear system prediction, our DESN exhibits 10.31 X reduction on the prediction error compared to state-of-the-art neural network designs. Moreover, our DESN records a bit error rate (BER) of 5.76x10(-2) on the high-speed transmitted symbol detection task for MIMO-OFDM systems, yielding 47.73% more precise than state-of-the-art techniques in the literate for 5G communication networks.
Propagation of Lamb waves in a metal plate with an abrupt change in thickness using Peridynamics and laser Doppler velocimetry

Plate-like structures can be characterized by a variety of abrupt geometric changes affecting the Lamb wave propagation, similarly to damage occurring in service. Therefore, a deep knowledge of phenomena involved in the interaction between guide waves and discontinuities is required. For this purpose, an experimental investigation is carried out considering an isotropic plate where an abrupt thickness change is present. The fundamental modes excitation is operated by a piezoelectric transducer while the signal sensing in multiple locations, also across the discontinuity, is performed by a scanning laser Doppler vibrometer. The investigation reveals mode conversion and highlights how the effects on the wave propagation depend upon the discontinuity geometrical characteristics. A peridynamics-based model representing the examined problem is also defined and its effectiveness to simulate the observed phenomena is proven.
Sex-specific newborn screening for X-linked adrenoleukodystrophy

Males with X-linked adrenoleukodystrophy (ALD) are at high risk for developing adrenal insufficiency and/or progressive leukodystrophy (cerebral ALD) at an early age. Pathogenic variants in ABCD1 result in elevated levels of very long-chain fatty acids (VLCFA), including C26:0-lysophosphatidylcholine (C26:0-LPC). Newborn screening for ALD enables prospective monitoring and timely therapeutic intervention, thereby preventing irreversible damage and saving lives. The Dutch Health Council recommended to screen only male newborns for ALD without identifying untreatable conditions associated with elevated C26:0-LPC, like Zellweger spectrum disorders and single peroxisomal enzyme defects. Here, we present the results of the SCAN (Screening for ALD in the Netherlands) study which is the first sex-specific newborn screening program worldwide. Males with ALD are identified based on elevated C26:0-LPC levels, the presence of one X-chromosome and a variant in ABCD1, in heel prick dried bloodspots. Screening of 71 208 newborns resulted in the identification of four boys with ALD who, following referral to the pediatric neurologist and confirmation of the diagnosis, enrolled in a long-term follow-up program. The results of this pilot show the feasibility of employing a boys-only screening algorithm that identifies males with ALD without identifying untreatable conditions. This approach will be of interest to countries that are considering ALD newborn screening but are reluctant to identify girls with ALD because for girls there is no direct health benefit. We also analyzed whether gestational age, sex, birth weight and age at heel prick blood sampling affect C26:0-LPC concentrations and demonstrate that these covariates have a minimal effect.
Probabilistic Sparse Matching for Robust 3D/3D Fusion in Minimally Invasive Surgery

Classical surgery is being overtaken by minimally invasive and transcatheter procedures. As there is no direct view or access to the affected anatomy, advanced imaging techniques such as 3D C-arm computed tomography (CT) and C-arm fluoroscopy are routinely used in clinical practice for intraoperative guidance. However, due to constraints regarding acquisition time and device configuration, intraoperative modalities have limited soft tissue image quality and reliable assessment of the cardiac anatomy typically requires contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a probabilistic sparse matching approach to fuse high-quality preoperative CT images and nongated, noncontrast intraoperative C-arm CT images by utilizing robust machine learning and numerical optimization techniques. Thus, high-quality patient-specific models can be extracted from the preoperative CT and mapped to the intraoperative imaging environment to guide minimally invasive procedures. Extensive quantitative experiments on 95 clinical datasets demonstrate that our model-based fusion approach has an average execution time of 1.56 s, while the accuracy of 5.48 mm between the anchor anatomy in both images lies within expert user confidence intervals. In direct comparison with image-to-image registration based on an open-source state-of-the-art medical imaging library and a recently proposed quasi-global, knowledge-driven multi-modal fusion approach for thoracic-abdominal images, our model-based method exhibits superior performance in terms of registration accuracy and robustness with respect to both target anatomy and anchor anatomy alignment errors.