Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of Dietary Patterns with Weight Status and Metabolic Risk Factors among Children and Adolescents

Unhealthy dietary patterns are associated with obesity in children and adolescents. However, few studies have investigated the relationships between dietary patterns and obesity-related metabolic disorders among Asians. We identified dietary patterns in children and adolescents and examined the associations between these patterns and obesity, insulin resistance, and metabolic syndrome in South Korea. This study is a cross-sectional design. We used baseline data from an intervention study of 435 Korean children and adolescents aged 6–17 years. Insulin resistance was assessed as HOMA-IR ≥ 2.6. Metabolic syndrome was diagnosed by cardiovascular disease risk factor clustering. Dietary intakes were estimated using 3-day food records. Factor analysis was used to obtain dietary patterns, and we examined the associations between dietary patterns and obesity-related markers adjusted for potential covariates. Three dietary patterns were identified as fast food and soda (FFS), white rice and kimchi (WRK), and oil and seasoned vegetable (OSV) patterns. Compared with participants in the lower intake of FFS pattern, those in the top intake were associated with a higher waist circumference (WC) (β = 1.55), insulin level (β = 1.25), and body mass index (BMI) (β = 0.53) and it was positively associated with HOMA-IR ≥ 2.6 (OR = 2.11; 95% CI: 1.227–3.638) (p < 0.05). WRK pattern was associated with lower weight and higher HDL cholesterol, and the OSV pattern was associated with a lower weight, WC, and insulin level (p < 0.05). The FFS pattern showed a positive relation with WC, serum insulin, and BMI, and the other two dietary patterns indicated a preventive effect of those parameters. The FFS pattern was associated with significantly elevated insulin resistance among children and adolescents.
Monitoring compliance of CITES lion bone exports from South Africa

From 2008 to 2018, South Africa permitted the export of captive-bred African lion (Panthera leo) skeletons to Southeast Asia under CITES Appendix II. Legal exports rose from approximately 50 individuals in 2008 to a maximum of 1,771 skeletons in 2016, and has led to ongoing concerns over possible laundering of non-lion, multiple-source and wild-sourced bones. South Africa is required under its obligations to CITES to employ mechanisms for monitoring and reporting trade, and to limit the potential for illegal trade and laundering of lion and other large felid bones. Monitoring tools for legal trade are critical to compliance with CITES. Here we evaluate the CITES-compliance procedure implemented by South Africa for export of lion bones and identify six essential general points for consideration in the implementation of animal export quota compliance protocols. We provide specific insight into the South African lion bone export monitoring system through: i) outlining the protocols followed; ii) assessing the utility of cranial morphology to identify species; iii) evaluating skeleton consignment weight as a monitoring tool; and iv) presenting molecular (DNA) species assignment and pairwise-comparative sample matching of individuals. We describe irregularities and illicit behaviour detected in the 2017 and 2018 lion bone quotas. Notably, we report that the compliance procedure successfully identified and prevented the attempted laundering of a tiger (P. tigris) skeleton in 2018. We emphasise the utility of mixed-method protocols for the monitoring of compliance in CITES Appendix II export quota systems.
THE NATURE OF THE SLOWLY EXCHANGING PROTONS OF RIBONUCLEASE

The nuclear magnetic resonance spectra of ribonuclease in D/sub 2/O solutions between pD 2.8 and 4.5 showed exchange of NH protons with time. 24 plus or minus 5 protons exchanged much more slowly than the others. Guanidinated ribonuclease was shown to contain 54 plus or minus 7 hydrogens exchanging at a comparable rate. When both proteins were heated to 72 deg exchange of all the NH protons was noted. It was suggested that the very slowly exchanging protons are attached to the guanidinium groups of both proteins. (auth)
Adherence to French and ESGO Quality Indicators in Ovarian Cancer Surgery: An Ad-Hoc Analysis from the Prospective Multicentric CURSOC Study

Simple Summary French and European Quality Indicators have been developed for the management of ovarian cancer. In this study, we aimed to assess the ovarian cancer care distribution in France according to the volume of patients treated per hospital, and to evaluate the adherence of different centers to the quality indicators. We found that the majority of ovarian cancer patients were treated in hospitals that did not reach recommended cut-off values in terms of volume, and it is known that surgical care in low-volume hospitals is associated with worse outcome. Only 44% of high-volume centers met all the quality indicator criteria. Therefore, access to high-volume ovarian cancer providers accomplishing all the recommended institutional quality indicators is restricted to a minority of patients in France. It is mandatory that national authorities work both to improve the centralization of ovarian cancer management and to incorporate quality assurance programs into certified centers. Abstract Background: Quality Indicators for ovarian cancer (OC) have been developed by the European Society of Gynaecological Oncology (ESGO) and by the French National Cancer Institute (Institut National du Cancer, INCa). The aim of the study was to characterize OC care distribution in France by case-volume and to prospectively evaluate the adherence of high-volume institutions to INCa/ESGO quality indicators. Methods: The cost-utility of radical surgery in ovarian cancer (CURSOC) trial is a prospective, multicenter, comparative and non-randomized study that includes patients with stage IIIC-IV epithelial OC treated in nine French health care tertiary institutions. Adherence to institutional quality indicators were anonymously assessed by an independent committee. OC care distribution in France were provided by the nationwide database of hospital procedures. Results: More than half of patients are treated in low-volume institutions. Among the nine high-volume centers participating in the study, four (44.4%) met all institutional INCa/ESGO quality indicators. The other five (55.6%) did not fulfil one of the quality indicator criteria. Conclusions: Access to high-volume OC providers in France is restricted to a minority of patients, and yet half of the referral institutions included in this study failed to meet all recommended institutional quality indicators. It is mandatory that national authorities work both to improve OC centralization and to incorporate quality assurance programs into certified centers.
Glycan Epitopes on 201B7 Human-Induced Pluripotent Stem Cells Using R-10G and R-17F Marker Antibodies

We developed two human-induced pluripotent stem cell (hiPSC)/human embryonic stem cell (hESC)-specific glycan-recognizing mouse antibodies, R-10G and R-17F, using the Tic (JCRB1331) hiPSC line as an antigen. R-10G recognizes a low-sulfate keratan sulfate, and R-17F recognizes lacto-N-fucopentaose-1. To evaluate the general characteristics of stem cell glycans, we investigated the hiPSC line 201B7 (HPS0063), a prototype iPSC line. Using an R-10G affinity column, an R-10G-binding protein was isolated from 201B7 cells. The protein yielded a single but very broad band from 480 to 1236 kDa by blue native gel electrophoresis. After trypsin digestion, the protein was identified as podocalyxin by liquid chromatography/mass spectrometry. According to Western blotting, the protein reacted with R-10G and R-17F. The R-10G-positive band was resistant to digestion with glycan-degrading enzymes, including peptide N-glycanase, but the intensity of the band was decreased significantly by digestion with keratanase, keratanase II, and endo-β-galactosidase, suggesting the R-10G epitope to be a keratan sulfate. These results suggest that keratan sulfate-type epitopes are shared by hiPSCs. However, the keratan sulfate from 201B7 cells contained a polylactosamine disaccharide unit (Galβ1-4GlcNAc) at a significant frequency, whereas that from Tic cells consisted mostly of keratan sulfate disaccharide units (Galβ1-4GlcNAc(6S)). In addition, the abundance of the R-10G epitope was significantly lower in 201B7 cells than in Tic cells.
A Novel Infection Protocol in Zebrafish Embryo to Assess Pseudomonas aeruginosa Virulence and Validate Efficacy of a Quorum Sensing Inhibitor In Vivo

The opportunistic human pathogen Pseudomonas aeruginosa is responsible for a variety of acute infections and is a major cause of mortality in chronically infected cystic fibrosis patients. Due to increased resistance to antibiotics, new therapeutic strategies against P. aeruginosa are urgently needed. In this context, we aimed to develop a simple vertebrate animal model to rapidly assess in vivo drug efficacy against P. aeruginosa. Zebrafish are increasingly considered for modeling human infections caused by bacterial pathogens, which are commonly microinjected in embryos. In the present study, we established a novel protocol for zebrafish infection by P. aeruginosa based on bath immersion in 96-well plates of tail-injured embryos. The immersion method, followed by a 48-hour survey of embryo viability, was first validated to assess the virulence of P. aeruginosa wild-type PAO1 and a known attenuated mutant. We then validated its relevance for antipseudomonal drug testing by first using a clinically used antibiotic, ciprofloxacin. Secondly, we used a novel quorum sensing (QS) inhibitory molecule, N-(2-pyrimidyl)butanamide (C11), the activity of which had been validated in vitro but not previously tested in any animal model. A significant protective effect of C11 was observed on infected embryos, supporting the ability of C11 to attenuate in vivo P. aeruginosa pathogenicity. In conclusion, we present here a new and reliable method to compare the virulence of P. aeruginosa strains in vivo and to rapidly assess the efficacy of clinically relevant drugs against P. aeruginosa, including new antivirulence compounds.
Titanium and Protein Adsorption: An Overview of Mechanisms and Effects of Surface Features

Titanium and its alloys, specially Ti6Al4V, are among the most employed materials in orthopedic and dental implants. Cells response and osseointegration of implant devices are strongly dependent on the body–biomaterial interface zone. This interface is mainly defined by proteins: They adsorb immediately after implantation from blood and biological fluids, forming a layer on implant surfaces. Therefore, it is of utmost importance to understand which features of biomaterials surfaces influence formation of the protein layer and how to guide it. In this paper, relevant literature of the last 15 years about protein adsorption on titanium-based materials is reviewed. How the surface characteristics affect protein adsorption is investigated, aiming to provide an as comprehensive a picture as possible of adsorption mechanisms and type of chemical bonding with the surface, as well as of the characterization techniques effectively applied to model and real implant surfaces. Surface free energy, charge, microroughness, and hydroxylation degree have been found to be the main surface parameters to affect the amount of adsorbed proteins. On the other hand, the conformation of adsorbed proteins is mainly dictated by the protein structure, surface topography at the nano-scale, and exposed functional groups. Protein adsorption on titanium surfaces still needs further clarification, in particular concerning adsorption from complex protein solutions. In addition, characterization techniques to investigate and compare the different aspects of protein adsorption on different surfaces (in terms of roughness and chemistry) shall be developed.
Highly Porous Carbons Synthesized from Tannic Acid via a Combined Mechanochemical Salt-Templating and Mild Activation Strategy

Highly porous activated carbons were synthesized via the mechanochemical salt-templating method using both sustainable precursors and sustainable chemical activators. Tannic acid is a polyphenolic compound derived from biomass, which, together with urea, can serve as a low-cost, environmentally friendly precursor for the preparation of efficient N-doped carbons. The use of various organic and inorganic salts as activating agents afforded carbons with diverse structural and physicochemical characteristics, e.g., their specific surface areas ranged from 1190 m2·g−1 to 3060 m2·g−1. Coupling the salt-templating method and chemical activation with potassium oxalate appeared to be an efficient strategy for the synthesis of a highly porous carbon with a specific surface area of 3060 m2·g−1, a large total pore volume of 3.07 cm3·g−1 and high H2 and CO2 adsorption capacities of 13.2 mmol·g−1 at −196 °C and 4.7 mmol·g−1 at 0 °C, respectively. The most microporous carbon from the series exhibited a CO2 uptake capacity as high as 6.4 mmol·g−1 at 1 bar and 0 °C. Moreover, these samples showed exceptionally high thermal stability. Such activated carbons obtained from readily available sustainable precursors and activators are attractive for several applications in adsorption and catalysis.
A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm Optimization

Edge detection is a fundamental image analysis task, as it provides insight on the content of an image. There are weaknesses in some of the edge detectors developed until now, such as disconnected edges, the impossibility to detect branching edges, or the need for a ground truth that is not always accessible. Therefore, a specialized detector that is optimized for the image particularities can help improve edge detection performance. In this paper, we apply transfer learning to optimize cellular automata (CA) rules for edge detection using particle swarm optimization (PSO). Cellular automata provide fast computation, while rule optimization provides adaptability to the properties of the target images. We use transfer learning from synthetic to medical images because expert-annotated medical data is typically difficult to obtain. We show that our method is tunable for medical images with different properties, and we show that, for more difficult edge detection tasks, batch optimization can be used to boost the quality of the edges. Our method is suitable for the identification of structures, such as cardiac cavities on medical images, and could be used as a component of an automatic radiology decision support tool.
Burnout Determinants among Nurses Working in Palliative Care during the Coronavirus Disease 2019 Pandemic

Nurses working in palliative care are at risk of burnout. The Copenhagen Burnout Inventory was used to determine burnout levels of nurses working in the Portuguese national network of palliative care. We evaluated the contribution of personal, work, and COVID-19 variables in three burnout subclasses: personal, work, and patient-related. A cross-sectional, exploratory, and quantitative design was employed and participants were sampled using convenience and snowball technique. An online survey was conducted and 153 nurses participated in our study. Socio-demographic characterization was conducted, levels of burnout and determinants were explored through multiple linear regression models for its three dimensions. High levels of personal, working, and patient burnout were present in 71 (46%), 68 (44%), and 33 nurses (22%), respectively. Most of them agreed that COVID-19 had an impact on their activities. Significant personal and work related burnout factors found were specialization in palliative care, self-perceived health status, unit type, weekly hours of work, and allocation to COVID-19 units. Gender was found to be a significant factor in patient-related burnout. There is a high level of burnout among nurses working in the Portuguese national network of palliative care. Measures for identification and mitigation of burnout are necessary to protect health care professionals.
Synthesis and Characterization of Silver–Strontium (Ag-Sr)-Doped Mesoporous Bioactive Glass Nanoparticles

Biomedical implants are the need of this era due to the increase in number of accidents and follow-up surgeries. Different types of bone diseases such as osteoarthritis, osteomalacia, bone cancer, etc., are increasing globally. Mesoporous bioactive glass nanoparticles (MBGNs) are used in biomedical devices due to their osteointegration and bioactive properties. In this study, silver (Ag)- and strontium (Sr)-doped mesoporous bioactive glass nanoparticles (Ag-Sr MBGNs) were prepared by a modified Stöber process. In this method, Ag+ and Sr2+ were co-substituted in pure MBGNs to harvest the antibacterial properties of Ag ions, as well as pro-osteogenic potential of Sr2 ions. The effect of the two-ion concentration on morphology, surface charge, composition, antibacterial ability, and in-vitro bioactivity was studied. Scanning electron microscopy (SEM), X-Ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR) confirmed the doping of Sr and Ag in MBGNs. SEM and EDX analysis confirmed the spherical morphology and typical composition of MBGNs, respectively. The Ag-Sr MBGNs showed a strong antibacterial effect against Staphylococcus carnosus and Escherichia coli bacteria determined via turbidity and disc diffusion method. Moreover, the synthesized Ag-Sr MBGNs develop apatite-like crystals upon immersion in simulated body fluid (SBF), which suggested that the addition of Sr improved in vitro bioactivity. The Ag-Sr MBGNs synthesized in this study can be used for the preparation of scaffolds or as a filler material in the composite coatings for bone tissue engineering.
The Relationship between the Gut Microbiome and Metformin as a Key for Treating Type 2 Diabetes Mellitus

Metformin is the first-line pharmacotherapy for treating type 2 diabetes mellitus (T2DM); however, its mechanism of modulating glucose metabolism is elusive. Recent advances have identified the gut as a potential target of metformin. As patients with metabolic disorders exhibit dysbiosis, the gut microbiome has garnered interest as a potential target for metabolic disease. Henceforth, studies have focused on unraveling the relationship of metabolic disorders with the human gut microbiome. According to various metagenome studies, gut dysbiosis is evident in T2DM patients. Besides this, alterations in the gut microbiome were also observed in the metformin-treated T2DM patients compared to the non-treated T2DM patients. Thus, several studies on rodents have suggested potential mechanisms interacting with the gut microbiome, including regulation of glucose metabolism, an increase in short-chain fatty acids, strengthening intestinal permeability against lipopolysaccharides, modulating the immune response, and interaction with bile acids. Furthermore, human studies have demonstrated evidence substantiating the hypotheses based on rodent studies. This review discusses the current knowledge of how metformin modulates T2DM with respect to the gut microbiome and discusses the prospect of harnessing this mechanism in treating T2DM.
Diagnostic Tools for Autism Spectrum Disorders by Gender: Analysis of Current Status and Future Lines

Studies on the prevalence of autism spectrum disorders have shown gender disproportion. In recent years, there has been an increasing interest in this investigation area. There are two main research lines; the first is focused mostly on gender-related biological reasons that could account for low ASD prevalence in women (i.e., related to some protective factors related to hormones or the immune system, among others), and the second research line studies possible diagnostic biases. In the present study, a review of the latter line of research is made based on two main objectives: (a) analysis of possible biases in diagnostic tools and (b) other nonbiological ASD prevalence explained by gender differences. As a result of our theoretical review, we found that the articles reviewed showed contradictory results and possible diagnostic biases, not only in their design but also in their assessment standards. We concluded that specific or complementary diagnostic tools and procedures differentiated by gender should be developed in order to reduce these biases.
Multiple Human Association and Tracking From Egocentric and Complementary Top Views

Crowded scene surveillance can significantly benefit from combining egocentric-view and its complementary top-view cameras. A typical setting is an egocentric-view camera, e.g., a wearable camera on the ground capturing rich local details, and a top-view camera, e.g., a drone-mounted one from high altitude providing a global picture of the scene. To collaboratively analyze such complementary-view videos, an important task is to associate and track multiple people across views and over time, which is challenging and differs from classical human tracking, since we need to not only track multiple subjects in each video, but also identify the same subjects across the two complementary views. This paper formulates it as a constrained mixed integer programming problem, wherein a major challenge is how to effectively measure subjects similarity over time in each video and across two views. Although appearance and motion consistencies well apply to over-time association, they are not good at connecting two highly different complementary views. To this end, we present a spatial distribution based approach to reliable cross-view subject association. We also build a dataset to benchmark this new challenging task. Extensive experiments verify the effectiveness of our method.
Deep-Learning-Based Detection of Cranio-Spinal Differences between Skeletal Classification Using Cephalometric Radiography

The aim of this study was to reveal cranio-spinal differences between skeletal classification using convolutional neural networks (CNNs). Transverse and longitudinal cephalometric images of 832 patients were used for training and testing of CNNs (365 males and 467 females). Labeling was performed such that the jawbone was sufficiently masked, while the parts other than the jawbone were minimally masked. DenseNet was used as the feature extractor. Five random sampling crossvalidations were performed for two datasets. The average and maximum accuracy of the five crossvalidations were 90.43% and 92.54% for test 1 (evaluation of the entire posterior–anterior (PA) and lateral cephalometric images) and 88.17% and 88.70% for test 2 (evaluation of the PA and lateral cephalometric images obscuring the mandible). In this study, we found that even when jawbones of class I (normal mandible), class II (retrognathism), and class III (prognathism) are masked, their identification is possible through deep learning applied only in the cranio-spinal area. This suggests that cranio-spinal differences between each class exist.