Miracle-dz/UniFAR-FTUs huggingface dataset:
The Correlation Among COVID-19 Vaccine Acceptance, the Ability to Detect Fake News, and e-Health Literacy

Background: The coronavirus disease 2019 (COVID-19) pandemic has seen a rise in the spread of misleading and deceptive information, leading to a negative impact on the acceptance of the COVID-19 vaccine and public opinion. To address this issue, the importance of public e-Health literacy cannot be overstated. It empowers individuals to effectively utilize information technology and combat the dissemination of inaccurate narratives. Objective: This study aimed to investigate the relationship between the ability to identify disingenuous news, electronic health literacy, and the inclination to receive the COVID-19 immunization. Methods: In this descriptive-analytical cross-sectional study conducted during summer 2021 in Isfahan, Iran, 522 individuals older than age 18 years, seeking medical attention at health centers, were surveyed. The participants were selected through a meticulous multistage cluster sampling process from the pool of individuals referred to these health centers. Along with demographic information, data collection instruments included the standard e-Health literacy questionnaire and a researcher-developed questionnaire designed to identify misinformation. The collected questionnaires were entered into SPSS 24 for statistical analysis, which included the Kruskal-Wallis test, the Chi-square test, the Spearman test, and logistic regression models. Key Results: The study findings revealed a statistically significant relationship between acceptance of the COVID-19 vaccine and the ability to identify deceptive news. An increase of one unit in the score for recognizing misinformation led to a 24% and 32% reduction in vaccine hesitancy and the intention to remain unvaccinated, respectively. Furthermore, a significant correlation was found between the intention to receive the vaccine and e-Health literacy, where an increase of one unit in e-Health literacy score corresponded to a 6% decrease in the intention to remain unvaccinated. Additionally, the study found a notable association between the ability to detect false and misleading information and e-Health literacy. Each additional point in e-Health literacy was associated with a 0.33% increase in the capacity to identify fake news (Spearman's Rho = 0.333, p < .001). Conclusion: The study outcomes demonstrate a positive correlation between the COVID-19 vaccine acceptance, the ability to identify counterfeit news, and proficiency in electronic health literacy. These findings provide a strong foundation for policymakers and health care practitioners to develop and implement strategies that counter the dissemination of spurious and deceitful information related to COVID-19 and COVID-19 immunization. [HLRP: Health Literacy Research and Practice. 2023;7(3):e130–e138. ]
Anisotropic mean shift clustering using distance metric learning

Mean shift is a non-parametric mode seeking procedure widely used in many computer vision problems. Mean shift clustering in particular is a well studied and established algorithm, which has many merits over the classic k-means clustering algorithm. These algorithms repeatedly calculate distance between data points to compute mean shift vector and cluster mean respectively using some distance function. In most of the cases, Euclidean distance function is used which weighs every dimension equally in the input space and thus often fails to capture the semantics of the data. To alleviate this problem, a general form of distance metric based on Mahalanobis distance is used that can be learned using the training data. Distance metric learning has received a lot of attention in recent years and has proven to be very successful in various problem domains. By learning a Mahalanobis distance metric, the input space is transformed such that, similar points get closer to each other and dissimilar points move further apart. A lot of research has been done on learning a global metric and integrating it with k-means algorithm, but there have been very few eﬀorts of integrating metric learning with mean shift clustering. This work focuses on developing a uniﬁed framework for improving mean shift clustering by using global and local metric learning. We use a recently proposed Sparse Compositional Metric Learning (SCML) framework and integrate it with mean shift clustering to investigate the aﬀect of using local metrics over a global metric. We also perform kernelization in the proposed framework that can handle datasets with non-linear decision boundaries. To establish the eﬀectiveness of our approach, we performed experiments on 6 datasets of varying diﬃculty.
Haematological parameters of laying hens under different group size

The relevance of this study lies in its potential implications for poultry farming practices and animal welfare. Understanding the impact of group size on the blood parameters of hens can provide valuable insights for optimising their health and well‐being in farming environments. The aim of the study was an analysis of haematological parameters of laying hens under the influence group size with the same stoking density in analogous cages by design. Hens were kept for 34 weeks in cages of multilevel batteries at different group sizes: I group—93 birds, II group—52 birds, III group—17 birds and IV group—9 birds. The study analysed blood samples from laying hens at 18 and 52 weeks of age. Results showed that reducing the group size from 93 to 52 birds led to a haematological status within the physiological norm, characterized by increased heterophils and decreased monocyte levels. Whereas, the decrease in group size to 17 birds was accompanied by a decrease in the hens' blood content of thrombocytes, an increase of heterophile, a decrease of monocytes and lymphocytes within the physiological norm, as well as an increase in the heterophiles to lymphocytes ratio.
Above-ground biomass estimates based on active and passive microwave sensor imagery in low-biomass savanna ecosystems

Abstract. Although many studies exist on the estimation and monitoring of above-ground biomass (AGB) of forest ecosystems by methods of remote sensing, very little research has been carried out for ecosystems of low primary production, such as grasslands, steppes, or savannas. Our study intends to approach this gap and investigates the correlation between space-borne radar information and AGB at the scale of 10 tons per hectare and below. Additionally, we introduce the integration of passive brightness temperature as an additional covariate for biomass estimation, based on the hypothesis that it contains information complementary to microwave backscatter of the active sensors. Our findings show that large-scale estimates of AGB can be conducted for grasslands and savannas at high accuracy (R2 up to 0.52). Additionally, we found that the integration of passive radar can increase the quality of AGB estimates in terms of explained variance for selected cases. We hope that these indications are a starting point for more integrated approaches toward biomass estimations based on Earth observation methods.
Influence of the vessel wall geometry on the wall-induced migration of red blood cells

The geometry of the blood vessel wall plays a regulatory role on the motion of red blood cells (RBCs). The overall topography of the vessel wall depends on many features, among which the endothelial lining of the endothelial surface layer (ESL) is an important one. The endothelial lining of vessel walls presents a large surface area for exchanging materials between blood and tissues. The ESL plays a critical role in regulating vascular permeability, hindering leukocyte adhesion as well as inhibiting coagulation during inflammation. Changes in the ESL structure are believed to cause vascular hyperpermeability and entrap immune cells during sepsis, which could significantly alter the vessel wall geometry and disturb interactions between RBCs and the vessel wall, including the wall-induced migration of RBCs and the thickening of a cell-free layer. To investigate the influence of the vessel wall geometry particularly changed by the ESL under various pathological conditions, such as sepsis, on the motion of RBCs, we developed two models to represent the ESL using the immersed boundary method in two dimensions. In particular, we used simulations to study how the lift force and drag force on a RBC near the vessel wall vary with different wall thickness, spatial variation, and permeability associated with changes in the vessel wall geometry. We find that the spatial variation of the wall has a significant effect on the wall-induced migration of the RBC for a high permeability, and that the wall-induced migration is significantly inhibited as the vessel diameter is increased.
Psychosocial outcomes after varying risk management strategies in women at increased familial breast cancer risk: a mixed methods study of patient and partner outcomes

Introduction Female carriers of BRCA1/2 genes have an increased lifetime risk of breast cancer. Options for managing risk include imaging surveillance or risk-reducing surgery (RRS). This mixed methods study aimed to identify factors affecting risk-management decisions and the psychosocial outcomes of these decisions for high-risk women and their partners. Methods Semi-structured qualitative interviews were performed with women at high breast cancer risk who had faced these choices. Partners were also interviewed. Analysis used a framework approach. A bespoke questionnaire was developed to quantify and explore associations. Results A total of 32 women were interviewed. Of these, 27 had partners of whom 7 (26%) agreed to be interviewed. Four main themes arose: perception of risk and impact of increased risk; risk-management strategy decision-making; impact of risk-management strategy; support needs and partner relationship issues. The questionnaire response rate was 36/157 (23%). Decision satisfaction was high in both surveillance and RRS groups. Relationship changes were common but not universal. Common causes of distress following RRS included adverse body image changes. Both groups experienced generalised and cancer-specific anxiety. Drivers for surgery included having children, deaths of close family from breast cancer and higher levels of cancer anxiety. Conclusions Levels of psychosocial and decision satisfaction were high for women choosing both RRS and surveillance but, for a minority, risk-reducing measures result in long-term psychosocial morbidity. Efforts to recognise women at increased risk of psychological morbidity may allow targeted support.
IDH2 mutations in acute myeloid leukemia

Abstract Advances in the treatment of acute myeloid leukemia (AML) over the last 40 years have been limited. With an improved understanding of the pathophysiology of the disease, the advent of new treatment options has enriched the armamentarium of the physician to combat the disease. Mutations of the isocitrate dehydrogenase (IDHs) genes are common in AML and occur in 20-30% of cases. These mutations lead to DNA hypermethylation, aberrant gene expression, cell proliferation, and abnormal differentiation. Targeting mutant IDH, either as monotherapy or in combination with hypomethylating agents (HMAs) or BCL-2 inhibitors, has opened new avenues of therapy for these patients. This review will outline the function of IDHs and focus on the biological effects of IDH2 mutations in AML, their prognosis and treatment options.
Feasibility and acceptability of peer-delivered interventions using mHealth for PrEP services among adolescent girls and young women in DREAMS program in Botswana

ABSTRACT Background Adolescent girls and young women accounted for 25% of all new HIV infections despite representing only 10% of the population in Sub Saharan Africa. PEPFAR has launched the Determined, Resilient, Empowered, AIDS-free, Mentored, and Safe (DREAMS) initiative, a comprehensive HIV prevention program including PrEP services. Among adolescent girls and young women, PrEP adherence is currently sub-optimal. Tailored strategies for adolescent girls and young women to improve access and use of PrEP delivery are urgently needed to maximise its potential. Recommended interventions include peer-delivered interventions using mobile technology. However, data on the feasibility and acceptability of this approach is limited for SSA. Objectives We assessed the feasibility and perceived acceptability of providing mHealth peer-delivered interventions to support PrEP services among adolescent girls and young women in Botswana. Methods This cross-sectional study included HIV-negative women aged 18–24 years old seeking health services at DREAMS-supported facilities. Participants completed a survey assessing the feasibility and perceived acceptability of the mHealth peer-delivered interventions, which included the Acceptability of Intervention Measure (AIM). Descriptive analyses were performed. Results A total of 131 participated in the study. Overall, 89% owned a mobile phone (feasibility). There was no difference in cell phone ownership between participants from rural and urban settings. Among participants, 85% reported interest in participating in a mHealth peer-delivered intervention if it was available to them. Regarding perceived acceptability for mHealthpeer support groups for PrEP, the average score on the AIM was 3.8 out of 5 (SD = 0.8). Conclusion mHealthpeer-delivered interventions appear to be feasible and perceived acceptable among adolescent girls and young women in Botswana. This modality should be incorporated into PEPFAR’s programmatic toolkit of implementation strategies to improve PrEP services.
Assessing ChatGPT Responses to Common Patient Questions Regarding Total Hip Arthroplasty

Background: The contemporary patient has access to numerous resources on common orthopaedic procedures before ever presenting for a clinical evaluation. Recently, artificial intelligence (AI)-driven chatbots have become mainstream, allowing patients to engage with interfaces that supply convincing, human-like responses to prompts. ChatGPT (OpenAI), a recently developed AI-based chat technology, is one such application that has garnered rapid growth in popularity. Given the likelihood that patients may soon call on this technology for preoperative education, we sought to determine whether ChatGPT could appropriately answer frequently asked questions regarding total hip arthroplasty (THA). Methods: Ten frequently asked questions regarding total hip arthroplasty were posed to the chatbot during a conversation thread, with no follow-up questions or repetition. Each response was analyzed for accuracy with use of an evidence-based approach. Responses were rated as “excellent response not requiring clarification,” “satisfactory requiring minimal clarification,” “satisfactory requiring moderate clarification,” or “unsatisfactory requiring substantial clarification. ” Results: Of the responses given by the chatbot, only 1 received an “unsatisfactory” rating; 2 did not require any correction, and the majority required either minimal (4 of 10) or moderate (3 of 10) clarification. Although several responses required nuanced clarification, the chatbot’s responses were generally unbiased and evidence-based, even for controversial topics. Conclusions: The chatbot effectively provided evidence-based responses to questions commonly asked by patients prior to THA. The chatbot presented information in a way that most patients would be able to understand. This resource may serve as a valuable clinical tool for patient education and understanding prior to orthopaedic consultation in the future.
Five Methods for Estimating Angoff Cut Scores with IRT

This article illustrates five different methods for estimating Angoff cut scores using item response theory (IRT) models. These include maximum likelihood (ML), expected a priori (EAP), modal a priori (MAP), and weighted maximum likelihood (WML) estimators, as well as the most commonly used approach based on translating ratings through the test characteristic curve (i.e., the IRT true-score (TS) estimator). The five methods are compared using a simulation study and a real data example. Results indicated that the application of different methods can sometimes lead to different estimated cut scores, and that there can be some key differences in impact data when using the IRT TS estimator compared to other methods. It is suggested that one should carefully think about their choice of methods to estimate ability and cut scores because different methods have distinct features and properties. An important consideration in the application of Bayesian methods relates to the choice of the prior and the potential bias that priors may introduce into estimates.
Community perception of school-based mass drug administration program for soil-transmitted helminths and Schistosomiasis in Ogun State, Nigeria

Background Neglected tropical diseases, such as soil-transmitted helminths and Schistosomiasis, are prevalent in sub-Saharan Africa, particularly Ogun State, Nigeria. School-based mass drug administration program is the primary control intervention, but the coverage and uptake of this intervention have been inadequate. This study aimed to investigate community perceptions of school-based mass drug administration programs for these infections in Ogun State, Nigeria, and identify the barriers to their uptake and coverage. Methodology/Principal findings The study used a qualitative research approach involving focus group discussions and in-depth interviews with community members and stakeholders engaged in neglected tropical disease control programs in Ogun State, Nigeria. A semi-structured questionnaire guided the exploration of ideas, and the data were analyzed using the QRS Nvivo 12 software package. The study found several barriers, such as the influence of parents, lack of sufficient knowledge, and side effects. The study recommended strategies such as improving community sensitization and engagement, drug distribution and performance, and enhancing partner collaboration and coordination to improve the school-based mass drug administration programs. Conclusions/Significance The study revealed correct perceptions of transmission but some misconceptions about disease causation, transmission, and drug safety. Participants expressed a desire for better sensitization campaigns and more assurances of their safety. The study recommends strengthening health education messages and increasing the visibility of on-site medical personnel. The findings have implications for improving the performance of these programs and reducing the burden of intestinal parasitic infections in the community. The study highlights the need for community engagement and education, health system support, and partner collaboration to successfully implement mass drug administration programs.
Determinants of splenic preservation among patients with sickle cell disease in North‐Eastern Nigeria

In patients with sickle cell disease (SCD), the spleen commonly enlarges during early childhood, but undergoes reduction in size and fibrosis from repeated episodes of vaso‐occlusion and infarction. The rate of progression of this process varies markedly among these patients. The aim of current study was to explore clinical and laboratory factors associated with the preservation of the spleen among these patients.
Operational Energy Saving Potential of Thermal Effluent Source Heat Pump System for Greenhouse Heating in Jeju

Massive thermal effluent energy from power plant is mostly released to the sea, and only a little is used for fishing culture and agriculture in South Korea. The thermal effluent from the power plant can be an efficient heat source of the heat pump system to provide heating energy for the greenhouse, but energy loss and pump power by long distance pipeline installation from a power plant to the greenhouse should be considered. In this paper, an operational energy saving potential of a thermal effluent source heat pump system for the greenhouse heating was investigated. For the estimation of thermal load, three cases of greenhouse were categorized, and the thermal performance and operating energy consumption during the heating season were compared with those of a conventional ground source heat pump (GSHP) system. The model for heat pump system was newly derived to estimate the energy performance of the proposed system, and then detailed simulations for each system under three cases of greenhouse were conducted. The results showed that the operational energy of the proposed system can be saved by 17–20% than that of the conventional GSHP system.
Localization of unlabeled bepirovirsen antisense oligonucleotide in murine tissues using in situ hybridization and CARS imaging

Current methods for detecting unlabeled antisense oligonucleotide (ASO) drugs rely on immunohistochemistry (IHC) and/or conjugated molecules, which lack sufficient sensitivity, specificity, and resolution to fully investigate their biodistribution. Our aim was to demonstrate the qualitative and quantitative distribution of unlabeled bepirovirsen, a clinical stage ASO, in livers and kidneys of dosed mice using novel staining and imaging technologies at subcellular resolution. ASOs were detected in formalin-fixed paraffin-embedded (FFPE) and frozen tissues using an automated chromogenic in situ hybridization (ISH) assay: miRNAscope. This was then combined with immunohistochemical detection of cell lineage markers. ASO distribution in hepatocytes versus nonparenchymal cell lineages was quantified using HALO AI image analysis. To complement this, hyperspectral coherent anti-Stokes Raman scattering (HS-CARS) imaging microscopy was used to specifically detect the unique cellular Raman spectral signatures following ASO treatment. Bepirovirsen was localized primarily in nonparenchymal liver cells and proximal renal tubules. Codetection of ASO with distinct cell lineage markers of liver and kidney populations aided target cell identity facilitating quantification. Positive liver signal was quantified using HALO AI, with 12.9% of the ASO localized to the hepatocytes and 87.1% in nonparenchymal cells. HS-CARS imaging specifically detected ASO fingerprints based on the unique vibrational signatures following unlabeled ASO treatment in a totally nonperturbative manner at subcellular resolution. Together, these novel detection and imaging modalities represent a significant increase in our ability to detect unlabeled ASOs in tissues, demonstrating improved levels of specificity and resolution. These methods help us understand their underlying mechanisms of action and ultimately improve the therapeutic potential of these important drugs for treating globally significant human diseases.
NERONE: The Fast Way to Efficiently Execute Your Deep Learning Algorithm at the Edge

Semantic segmentation and classification are pivotal in many clinical applications, such as radiation dose quantification and surgery planning. While manually labeling images is highly time-consuming, the advent of Deep Learning (DL) has introduced a valuable alternative. Nowadays, DL models inference is run on Graphics Processing Units (GPUs), which are power-hungry devices, and, therefore, are not the most suited solution in constrained environments where Field Programmable Gate Arrays (FPGAs) become an appealing alternative given their remarkable performance per watt ratio. Unfortunately, FPGAs are hard to use for non-experts, and the creation of tools to open their employment to the computer vision community is still limited. For these reasons, we propose NERONE, which allows end users to seamlessly benefit from FPGA acceleration and energy efficiency without modifying their DL development flows. To prove the capability of NERONE to cover different network architectures, we have developed four models, one for each of the chosen datasets (three for segmentation and one for classification), and we deployed them, thanks to NERONE, on three different embedded FPGA-powered boards achieving top average energy efficiency improvements of $3.4\times$ and $1.9\times$ against a mobile and a datacenter GPU devices, respectively.