ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Oral Ibrexafungerp for Vulvovaginal Candidiasis Treatment: An Analysis of VANISH 303 and VANISH 306

Background: Ibrexafungerp is a novel antifungal treatment for acute vulvovaginal candidiasis (VVC). Using pooled data from two phase three studies (VANISH 303 and 306) in the treatment of acute VVC, this analysis sought to determine the effectiveness of ibrexafungerp in various patient subgroups that may impact outcomes. Materials and Methods: Data from VANISH 303 (NCT03734991) and VANISH 306 (NCT03987620) evaluating ibrexafungerp 300 mg twice daily (BID) for 1 day versus placebo, were pooled and analyzed to determine clinical cure rate, clinical improvement, and mycological cure at the test-of-cure visit (day 11 ± 3) and symptom resolution at the follow-up visit (day 25 ± 4) in the overall population. Patient subgroups analyzed included race, body mass index (BMI), baseline vulvovaginal signs and symptoms (VSS) score, and Candida species. Results: At the test-of-cure visit, patients receiving ibrexafungerp, compared with those who received placebo, had significantly higher rates of clinical cure (56.9% [214/376 patients] vs. 35.7% [65/182 patients]), clinical improvement (68.4% [257/376 patients] vs. 45.1% [82/182 patients]), and mycological cure (54.0% [203/376 patients] vs. 24.2% [44/182 patients]; all p < 0.0001). At the follow-up visit, patients receiving ibrexafungerp had sustained responses with higher symptom resolution rates (66.8% [251/376 patients]) versus placebo (48.4% [88/182 patients]; p < 0.0001). Race, BMI, baseline VSS score (including VSS severity score 13-18), and Candida species infection did not adversely affect clinical cure rates. Safety analysis results were consistent with the individual studies. Conclusions: Ibrexafungerp provides a safe and well-tolerated first-in-class fungicidal, 1-day oral treatment for patients with acute VVC, the first new therapy in >20 years. Clinical Trial Registration Number: NCT03734991.
Using a Serious Game as an Elicitation Tool in Interview Research: Reflections on Methodology

It can be difficult to understand the process of making decisions in health care, because of both the complexity of health care systems and the demands faced by health care professionals. Serious games offer an underexplored opportunity to elicit data about decision-making. In this article, we present and reflect on a methodological case study where we used a serious game as part of a semistructured interview to study the process of decision-making. The game Resilience Challenge presented a patient's journey through a hospital, where a player had to make decisions that influenced patient care. The game was used during interviews with 20 nurses, both in person and remotely. Having a mini debrief with a participant after each game scenario provides to be a helpful technique to understand the participants' decision-making process and elicit tacit knowledge about their work. Serious games show promise as a methodological research tool to elicit the process of decision-making among health care professionals.
Sports, Executive Functions and Academic Performance: A Comparison between Martial Arts, Team Sports, and Sedentary Children

It is well known that curricular physical activity benefits children's executive functions and academic performance. Therefore, this study aimed to determine whether there is an influence of extracurricular sports on executive functions and academic performance. However, it is less known which specific types of the sport better enhance executive functions in children; to investigate this issue, this study compared the performance on executive functions tasks and academic performance in one hundred and two boys and girls with an average age of 11.84 years recruited from Italian schools and gyms (N = 102), who participated in martial arts or team sports or were sedentary children. Executive functions were measured with the tests: Attenzione e Concentrazione, Digit Span test, Tower of London, IOWA Gambling task BVN 5-11, and BVN 12-18. Results demonstrated that children practicing martial arts showed better executive functioning and higher school marks than those involved in team sports or not involved in any sports. Furthermore, participants aged 12 to 15 years old outperformed in cool and hot executive functions tasks and had a better academic performance. Thus, the present findings supported the view that regular practice of extracurricular sports enhances executive functions development and consequently influences academic performance.
Deep Convolutional Neural Networks for pedestrian detection

Pedestrian detection is a popular research topic due to its paramount importance for a number of applications, especially in the fields of automotive, surveillance and robotics. Despite the significant improvements, pedestrian detection is still an open challenge that calls for more and more accurate algorithms. In the last few years, deep learning and in particular Convolutional Neural Networks emerged as the state of the art in terms of accuracy for a number of computer vision tasks such as image classification, object detection and segmentation, often outperforming the previous gold standards by a large margin. In this paper, we propose a pedestrian detection system based on deep learning, adapting a general-purpose convolutional network to the task at hand. By thoroughly analyzing and optimizing each step of the detection pipeline we propose an architecture that outperforms traditional methods, achieving a task accuracy close to that of state-of-the-art approaches, while requiring a low computational time. Finally, we tested the system on an NVIDIA Jetson TK1, a 192-core platform that is envisioned to be a forerunner computational brain of future self-driving cars. (C) 2016 Elsevier B.V. All rights reserved.
CO2 capture with potassium carbonate solutions: A state-of-the-art review

The potassium carbonate (PC) solution is an important chemical solvent to reduce CO2 emissions due to its advantages of low cost, little toxicity, ease of regeneration, slow corrosiveness, low degradation, and its high stability as well as CO2 absorption capacity. As a result, the PC process has been applied in more than 700 plants worldwide for CO2 and hydrogen sulphide removal from streams like ammonia synthesis gas, crude hydrogen, natural gas, and town gas. This paper provides a state-of-the-art review on the research works on CO2 capture using the PC solution. The studies related to the PC solution comprise three main areas: process, thermodynamics, and kinetics. Important experimental studies as well as modeling and simulation studies are reviewed. Future research directions on CO2 absorption by aqueous PC solution are highlighted and discussed. (C) 2015 Elsevier Ltd. All rights reserved.
SLIM-ADC: Spin-based Logic-In-Memory Analog to Digital Converter leveraging SHE-enabled Domain Wall Motion devices

This paper devises a novel Analog to Digital Converter (ADC) framework for energy-aware acquisition of analog signals with Logic-in-Memory capabilities. The beyond-CMOS hardware architecture has been designed to minimize the overall cost of signal acquisition. Spin-Hall Effect driven Domain Wall Motion (SHE-DWM) devices are utilized to realize the proposed framework called Spin-based Logic-In-Memory ADC (SLIM-ADC). Our simulation results indicate that the proposed SLIM-ADC offers similar to 200 fJ energy consumption on average for each analog conversion or logic operation with up to 1 GHz speed. Furthermore, our results indicate that the proposed SLIM-ADC outperforms other state of the art spin-based ADC designs by offering similar to 5.45 mW improved power dissipation on average. Additionally, a Majority Gate (MG)-based Full-Adder (MG-FA) is implemented using the proposed SLIM-ADC. Our results show that the proposed MG-FA offers similar to 2.9-fold reduced power dissipation on average and similar to 1.7-fold reduced delay on average compared to the state of the art Full-Adder designs reported herein.
A "signal-on" photoelectrochemical sensor for human epidermal growth factor receptor 2 detection based on Y6/CdS organic-inorganic heterojunction

A "signal-on" photoelectrochemical (PEC) immunosensor was successfully constructed for determination of human epidermal growth factor receptor 2 (HER2) based on organic-inorganic heterojunction Y6/CdS as photoactive material. Compared with single organic semiconductor, Y6, Y6/CdS exhibited higher photoelectric conversion efficiency due to the formation of heterojunction. In the presence of HER2, sandwich immune structure was formed between HER2 aptamer and anti-HER2 antibody (Ab) by specific recognition. The polydopamine (PDA) nanoparticles were used for signal amplification to enhance photocurrent intensity as PDA can act as electron donor to eliminate holes and promote electron-hole pairs separation. The developed PEC sensor displayed a wide detection range of 5-1000 pg mL-1 and a low detection limit of 2.2 pg mL-1 for HER2 (S/N = 3). The sensor was successfully used for the detection of HER2 in serum with recoveries between 94.8 and 104% and relative standard deviations (RSDs) in the range of 1.2-4.3%. Furthermore, the designed immunosensor possessed good stability, selectivity, and reproducibility, which can find potential clinical applications for disease diagnosis. A "signal-on" photoelectrochemical sensor was reported for human epidermal growth factor receptor 2 detection based on Y6/CdS organic-inorganic heterojunction.
Emerging Evidence on Coronary Heart Disease Screening in Kidney and Liver Transplantation Candidates: A Scientific Statement From the American Heart Association: Endorsed by the American Society of Transplantation

Coronary heart disease is an important source of mortality and morbidity among kidney transplantation and liver transplantation candidates and recipients and is driven by traditional and nontraditional risk factors related to end-stage organ disease. In this scientific statement, we review evidence from the past decade related to coronary heart disease screening and management for kidney and liver transplantation candidates. Coronary heart disease screening in asymptomatic kidney and liver transplantation candidates has not been demonstrated to improve outcomes but is common in practice. Risk stratification algorithms based on the presence or absence of clinical risk factors and physical performance have been proposed, but a high proportion of candidates still meet criteria for screening tests. We suggest new approaches to pretransplantation evaluation grounded on the presence or absence of known coronary heart disease and cardiac symptoms and emphasize multidisciplinary engagement, including involvement of a dedicated cardiologist. Noninvasive functional screening methods such as stress echocardiography and myocardial perfusion scintigraphy have limited accuracy, and newer noninvasive modalities, especially cardiac computed tomography-based tests, are promising alternatives. Emerging evidence such as results of the 2020 International Study of Comparative Health Effectiveness With Medical and Invasive Approaches-Chronic Kidney Disease trial emphasizes the vital importance of guideline-directed medical therapy in managing diagnosed coronary heart disease and further questions the value of revascularization among asymptomatic kidney transplantation candidates. Optimizing strategies to disseminate and implement best practices for medical management in the broader end-stage organ disease population should be prioritized to improve cardiovascular outcomes in these populations.
HDSR-Flor: A Robust End-to-End System to Solve the Handwritten Digit String Recognition Problem in Real Complex Scenarios

Automatic handwriting recognition systems are of interest for academic research fields and for commercial applications. Recent advances in deep learning techniques have shown dramatic improvement in relation to classic computer vision problems, especially in Handwritten Text Recognition (HTR). However, several approaches try to solve the problem of deep learning applied to Handwritten Digit String Recognition (HDSR), where it has to deal with the low number of trainable data, while learning to ignore any writing symbol around the digits (noise). In this context, we present a new optical model architecture (Gated-CNN-BGRU), based on HTR workflow, applied to HDSR. The International Conference on Frontiers of Handwriting Recognition (ICFHR) 2014 competition on HDSR were used as baselines to evaluate the effectiveness of our proposal, whose metrics, datasets and recognition methods were adopted for fair comparison. Furthermore, we also use a private dataset (Brazilian Bank Check - Courtesy Amount Recognition), and 11 different approaches from the state-of-the-art in HDSR, as well as 2 optical models from the state-of-the-art in HTR. Finally, the proposed optical model demonstrated robustness even with low data volume (126 trainable data, for example), surpassing the results of existing methods with an average precision of 96.50%, which is equivalent to an average percentage of improvement of 3.74 points compared to the state-of-the-art in HDSR. In addition, the result stands out in the competition's CVL HDS set, where the proposed optical model achieved a precision of 93.54%, while the best result so far had been from Beijing group (from the competition itself), with 85.29%.
Protective role of vitamin D status against COVID-19: a mini-review

An outbreak of pneumonia caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is called COVID-19 and has led to a pandemic worldwide. It is reasonable to investigate and control factors affecting disease severity and mortality. The relation between vitamin D and viral pneumonia has been previously reported. Vitamin D deficiency is common and may increase hospital admission and mortality rate in patients with COVID-19. This mini-review examines the pathways that show the association between vitamin D and COVID-19. On the other hand, it deals with the available evidence related to the relationship between vitamin D deficiency and the effect of vitamin D supplementation on the prevalence, severity, and mortality of COVID-19. Also, we described the pathophysiology of the organs' involvement in COVID-19 and the effect of vitamin D on these outcomes. Vitamin D strengthens the innate and adaptive immune system, modulates immune responses, prevents lung and cardiovascular system damage, and reduces thrombotic events. Vitamin D exerts these effects in several pathways. Vitamin D prevents virus entry and replication by maintaining the integrity of the body's physical barrier. Vitamin D reduces the damage to vital organs and thrombotic events by increasing the level of Angiotensin-converting enzyme 2 (ACE2), nitric oxide, and antioxidants or by reducing inflammatory cytokines and free radicals. Sufficient vitamin D may be reduced morbidity and mortality due to COVID-19. However, this issue should be investigated and confirmed by further research in the future.
Actions Do Not Always Speak Louder Than Words

Financial (dis)incentives (e.g., bonuses, taxes) and social incentives (e.g., public praise) have typically been proposed as methods to encourage greater cooperation for the benefit of all. However, when cooperation requires exertion of effort, such interventions might not always be effective. While incentives tend to be highly motivating when choosing to exert effort, evidence suggests that they have less of an effect on behavior during effort execution. The aim of this exploratory study was to incorporate these insights into empirical investigation of the effects of social incentives on cooperative effort. To this end, we modified a public goods game task to require effort contributions to a common good. Crucial manipulation involved incorporating social incentives into this task and linking them to (a) choices that people made or (b) effortful actions they exerted. Our findings suggest, in line with recent effort-based decision-making models, that social incentives have a stronger effect on cooperative effort when they are linked to choices that people make, rather than the actual effort they exert. This study demonstrates potential benefits of eliciting a priori declarations of cooperative effort tied to social incentives to encourage greater effort for the benefit of all.
Lightweight convolutional neural networks for player detection and classification

Vision-based player detection and classification are important in sports applications. Accuracy, efficiency, and low memory consumption are desirable for real-time tasks such as intelligent broadcasts and event classification. In this paper, we present a convolutional neural network (CNN) that satisfies all these requirements. The network contains a three-branch proposal network and a four-cascade classification network. Our method first trains these cascaded networks from labeled image patches. Then, we efficiently apply the network to a whole image by using a dilation strategy in testing. We conducted experiments on soccer, basketball, ice hockey and pedestrian datasets. Experimental results demonstrate that our method can accurately detect players under challenging conditions. Compared with CNNs that are adapted from general object detection networks such as Faster-RCNN, our approach achieves state-of-the-art accuracy on three types of games (basketball, soccer and ice hockey) with 1000 x fewer parameters. The generality of our method is also demonstrated on a standard pedestrian detection dataset in which our method achieves competitive performance compared with state-of-the-art methods.
Instrument platforms for thin-layer chromatography

High performance column and thin-layer chromatography are both instrumental techniques but differ in that column chromatography requires a fully integrated instrument platform with high pressure capability while for thin-layer chromatography separate devices are used for each unit operation, usually at or close to atmospheric pressure, and afford higher flexibility supporting on-line or off-line operation. The unit operations of thin-layer chromatography are defined as sample application, development and evaluation with derivatization as an optional step. The diversity of equipment for each operation contributes to the flexibility of analysis by thin-layer chromatography and supports manual, semi-automated or full-automation of the separation process. Instrument platforms are more than a convenience as they affect performance, repeatability, sample detectability, and time management. The current trend in thin-layer chromatography is to make the unit operations independent of the user so that analysts can perform other tasks while each step is performed. In addition, in thin-layer chromatography it is general practice to separate several samples simultaneously, and instrument platforms are required to accommodate this feature. In this article, we review contemporary instrumentation employed in thin-layer chromatography for sample application, development, derivatization, photodocumentation, densitometric evaluation, and hyphenation with spectroscopic detectors with an emphasis on the variety and performance of commercially available systems. Some suggestions for best practices and avoidance of common mistakes are included.
RESEARCH ON ART DESIGN BASED ON THE CONCEPT OF LOW CARBON ECONOMY

Based on the introduction of the basic principle of deepening, this paper analyses the application of deepening in the selection of roads and art design based on the concept of low carbon economy. First, different information is processed on the basis of the spatial data acquisition and a technical knowledge base is created on the basis of the GIS. Then, the interactive genetic algorithm is used to optimise the highway, which reflects the roads design based on the concept of low carbon economy. Finally, the multiplicative decision model is created to make an intelligent decision in order to achieve the multi-objective coordination and unification of route selection.
Photoacoustic image-guided biomimetic nanoparticles targeting rheumatoid arthritis

The high level of reactive oxygen species (ROS) in the rheumatoid arthritis (RA) microenvironment (RAM) and its persistent inflammatory nature can promote damage to joints, bones, and the synovium. Targeting strategies that integrate effective RAM regulation with imaging-based monitoring could lead to improvements in the diagnosis and treatment of RA. Here, we report the combined use of small interfering RNAs (siRNAsT/I) and Prussian blue nanoparticles (PBNPs) to silence the expression of proinflammatory cytokines TNF-α/IL-6 and scavenge the ROS associated with RAM. To enhance the in vitro and in vivo biological stability, biocompatibility, and targeting capability of the siRNAsT/I and PBNPs, macrophage membrane vesicles were used to prepare biomimetic nanoparticles, M@P-siRNAsT/I. The resulting constructs were found to suppress tumor necrosis factor-α/interleukin-6 expression and overcome the hypoxic nature of RAM, thus alleviating RA-induced joint damage in a mouse model. The M@P-siRNAsT/I of this study could be monitored via near-infrared photoacoustic (PA) imaging. Moreover, multispectral PA imaging without the need for labeling permitted the real-time evaluation of M@P-siRNAsT/I as a putative RA treatment. Clinical microcomputed tomography and histological analysis confirmed the effectiveness of the treatment. We thus suggest that macrophage-biomimetic M@P-siRNAsT/I and their analogs assisted by PA imaging could provide a new strategy for RA diagnosis, treatment, and monitoring.
GA-Based Optimization of Irregular Subarray Layouts for Wideband Phased Arrays Design

The design of phased arrays generating low sidelobes and grating-lobes-free patterns over wide frequency bandwidths is addressed. The array structure is decomposed in subarrays with irregular polyomino tiles whose locations and orientations are optimized by means of a genetic algorithms-based approach. A set of representative results is reported and discussed to give some in-sights on the performance of the proposed approach also in comparison to state-of-the-art solutions.
Marginalizing Sample Consensus

A new method for robust estimation, MAGSAC++, is proposed. It introduces a new model quality (scoring) function that does not make inlier-outlier decisions, and a novel marginalization procedure formulated as an M-estimation with a novel class of M-estimators (a robust kernel) solved by an iteratively re-weighted least squares procedure. Instead of the inlier-outlier threshold, it requires only its loose upper bound which can be chosen from a significantly wider range. Also, we propose a new termination criterion and a technique for selecting a set of inliers in a data-driven manner as a post-processing step after the robust estimation finishes. On a number of publicly available real-world datasets for homography, fundamental matrix fitting and relative pose, MAGSAC++ produces results superior to the state-of-the-art robust methods. It is more geometrically accurate, fails fewer times, and it is often faster. It is shown that MAGSAC++ is significantly less sensitive to the setting of the threshold upper bound than the other state-of-the-art algorithms to the inlier-outlier threshold. Therefore, it is easier to be applied to unseen problems and scenes without acquiring information by hand about the setting of the inlier-outlier threshold. The source code and examples both in C++ and Python are available at https://github.com/danini/magsac.