Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling ethnic differences in the distribution of insulin resistance via Bayesian nonparametric processes: an application to the SABRE cohort study

Abstract We analyse data from the Southall And Brent REvisited (SABRE) tri-ethnic study, where measurements of metabolic and anthropometric variables have been recorded. In particular, we focus on modelling the distribution of insulin resistance which is strongly associated with the development of type 2 diabetes. We propose the use of a Bayesian nonparametric prior to model the distribution of Homeostasis Model Assessment insulin resistance, as it allows for data-driven clustering of the observations. Anthropometric variables and metabolites concentrations are included as covariates in a regression framework. This strategy highlights the presence of sub-populations in the data, characterised by different levels of risk of developing type 2 diabetes across ethnicities. Posterior inference is performed through Markov Chains Monte Carlo (MCMC) methods.
Development of a Thymoquinone Polymeric Anticancer Nanomedicine through Optimization of Polymer Molecular Weight and Nanoparticle Architecture

Thymoquinone (TQ) is a water-insoluble natural compound isolated from Nigella sativa that has demonstrated promising chemotherapeutic activity. The purpose of this study was to develop a polymeric nanoscale formulation for TQ to circumvent its delivery challenges. TQ-encapsulated nanoparticles (NPs) were fabricated using methoxy poly(ethylene glycol)-b-poly(ε-caprolactone) (mPEG-PCL) copolymers by the nanoprecipitation technique. Formulation variables included PCL chain length and NP architecture (matrix-type nanospheres or reservoir-type nanocapsules). The formulations were characterized in terms of their particle size, polydispersity index (PDI), drug loading efficiency, and drug release. An optimized TQ NP formulation in the form of oil-filled nanocapsules (F2-NC) was obtained with a mean hydrodynamic diameter of 117 nm, PDI of 0.16, about 60% loading efficiency, and sustained in vitro drug release. The formulation was then tested in cultured human cancer cell lines to verify its antiproliferative efficacy as a potential anticancer nanomedicine. A pilot pharmacokinetic study was also carried out in healthy mice to evaluate the oral bioavailability of the optimized formulation, which revealed a significant increase in the maximum plasma concentration (Cmax) and a 1.3-fold increase in bioavailability compared to free TQ. Our findings demonstrate that the versatility of polymeric NPs can be effectively applied to design a nanoscale delivery platform for TQ that can overcome its biopharmaceutical limitations.
Current Pharmaceutical Biotechnology

DOI: 10.2174/1874471013999200820165734 Abstract: The present review describes the methodological aspects and prospects of the production of Positron Emission Tomography (PET) radiometals in a liquid target using low-medium energy medical cyclotrons. The main objective of this review is to delineate and discuss the critical factors involved in the liquid target production of radiometals, including type of salt solution, solution composition, beam energy, beam current, the effect of irradiation duration (length of irradiation) and challenges posed by in-target chemistry in relation with irradiation parameters. We also summarize the optimal parameters for the production of various radiometals in liquid targets.
Genome-Wide Association Study and Pathway Analysis for Heterophil/Lymphocyte (H/L) Ratio in Chicken

Disease control and prevention have been critical factors in the dramatic growth of the poultry industry. Disease resistance in chickens can be improved through genetic selection for immunocompetence. The heterophil/lymphocyte ratio (H/L) in the blood reflects the immune system status of chickens. Our objective was to conduct a genome-wide association study (GWAS) and pathway analysis to identify possible biological mechanisms involved in H/L traits. In this study, GWAS for H/L was performed in 1317 Cobb broilers to identify significant single-nucleotide polymorphisms (SNPs) associated with H/L. Eight SNPs (p < 1/8068) reached a significant level of association. The significant SNP on GGA 19 (chicken chromosome 19) was in the gene for complement C1q binding protein (C1QBP). The wild-type and mutant individuals showed significant differences in H/L at five identified SNPs (p < 0.05). According to the results of pathway analysis, nine associated pathways (p < 0.05) were identified. By combining GWAS with pathway analysis, we found that all SNPs after QC explained 12.4% of the phenotypic variation in H/L, and 52 SNPs associated with H/L explained as much as 9.7% of the phenotypic variation in H/L. Our findings contribute to understanding of the genetic regulation of H/L and provide theoretical support.
NSGA-II Algorithm For The Reallocation Problem In Land Consolidation

To solve problems encountered in real life, we sometimes need optimization algorithms. Some of these problems have single objective, while others have multiple objectives. If there is a single objective, the problem is defined as a single-objective optimization problem and if there are more than one objective it is called multi-objective optimization problem. Today, lands are fragmented and scattered. This makes agriculture difficult and costly. To prevent these problems, Land Consolidation (LC) studies are being carried out. The reallocation stage, which is part of LC, can be defined as a multi objective optimization problem. In this study, one of the multi objective optimization techniques, NSGA-II algorithm, is applied to the reallocation problem. The results are comparable with the studies in the literature.
Effects of Green Brazilian Propolis Alcohol Extract on Nociceptive Pain Models in Rats

Pain is one of the most common symptoms encountered in the medical practice. None of the management procedures used currently offer a complete relief for patients suffering from nociceptive pain. New treatment strategies for pain management are needed. Propolis has been used in traditional medicine to relieve various types of pain. The aim of the current study was to investigate the potential effects of the green Brazilian propolis alcohol extract in vivo on the nociceptive and inflammatory pain models in rats. Rats were distributed into three random groups (n = 6); Group I: control group received normal saline intraperitoneally (i.p.); Group II: treated with green Brazilian propolis alcohol extract (P50 mg/kg i.p.); Group III: treated with P100 mg/kg i.p. After sixty minutes, 50 μL of 5% formalin was injected subcutaneously into the dorsal surface of the right hind paw. The nociceptive response was identified by counting the number of flinches of the injected paw. The number of flinches was counted for the period of 0–5 min (early phase; neurogenic) and 10–60 min (late phase; inflammatory). Thermal hyperalgesia was assessed using three-paw withdrawal latency measurement with ten minutes intervals using a planter analgesic meter. Abdominal writhe (contraction) was induced by i.p. injection of acetic acid (1 mL of 2%). The results showed that green Brazilian propolis alcohol extract caused a significant inhibition of acetic acid-induced pain and significantly increased the pain threshold against infrared and formalin tests. The promising antinociceptive and anti-inflammatory properties of propolis and/or its active constituents as natural compounds in the present study indicates that it merits further studies in pain.
Multifunctional biodegradable polymer/clay nanocomposites with antibacterial properties in drug delivery systems.

PURPOSE The aim of this study was to investigate the possibility of intercalation of gentamicin and neomycin in montmorillonite (MMT) nanofillers, as well as to study the in vitro antimicrobial properties of nanocomposite films containing a small amount of thus obtained nanofillers. METHODS The polylactide matrix (PLA) nanocomposite films with drug-intercalated montmorillonite fillers were obtained by casting after intercalation of drugs in aqueous solutions. The efficiency of intercalation has been confirmed by X-ray diffraction (XRD) and Zeta potential measurements. The materials were studied for surface wettability, roughness and mechanical properties during 6 weeks of incubation in phosphate buffer saline, and their bactericidal activity was tested against Escherichia coli bacteria before and after 6 weeks of incubation in distilled water at 37 °C. The presence of antibiotics during the incubation was monitored by conductivity and pH measurements. RESULTS The results indicate that nanocomposite polylactide films with montmorillonite filler intercalated with gentamicin and neomycin tend to degrade faster that their counterparts with non-intercalated fillers, which affects their mechanical properties. However, drug intercalation provided an antibacterial activity, which was confirmed by the presence of zones inhibiting the growth of Gram-negative bacteria for both antibiotics. It was also confirmed that the interaction of antibiotics with clay and polymer matrix did not adversely affect this bactericidal effect. CONCLUSIONS Montmorillonite can be successfully intercalated with both gentamicin and neomycin, and then used as active filler for polylactide films having very good antibacterial properties, therefore their use in biomedical applications can be significantly expanded.
Genome‐wide pathogenesis interpretation using a heat diffusion‐based systems genetics method and implications for gene function annotation

Genetics is best dedicated to interpreting pathogenesis and revealing gene functions. The past decade has witnessed unprecedented progress in genetics, particularly in genome‐wide identification of disorder variants through Genome‐Wide Association Studies (GWAS) and Phenome‐Wide Association Studies (PheWAS). However, it is still a great challenge to use GWAS/PheWAS‐derived data to elucidate pathogenesis.
CONCUR: quick and robust calculation of codon usage from ribosome profiling data

Abstract Summary CONCUR is a standalone tool for codon usage analysis in ribosome profiling experiments. CONCUR uses the aligned reads in BAM format to estimate codon counts at the ribosome E-, P- and A-sites and at flanking positions. Availability and implementation CONCUR is written in Perl and is freely available at https://github.com/susbo/concur. Supplementary information Supplementary data are available at Bioinformatics online.
Spectral dynamic causal modelling of resting-state fMRI: an exploratory study relating effective brain connectivity in the default mode network to genetics

Abstract We conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer’s disease and mild cognitive impairment. We develop an analysis of longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data obtained from a sample of 111 subjects with a total of 319 rs-fMRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. A Dynamic Causal Model (DCM) is fit to the rs-fMRI scans to estimate effective brain connectivity within the DMN and related to a set of single nucleotide polymorphisms (SNPs) contained in an empirical disease-constrained set which is obtained out-of-sample from 663 ADNI subjects having only genome-wide data. We relate longitudinal effective brain connectivity estimated using spectral DCM to SNPs using both linear mixed effect (LME) models as well as function-on-scalar regression (FSR). In both cases we implement a parametric bootstrap for testing SNP coefficients and make comparisons with p-values obtained from asymptotic null distributions. In both networks at an initial q-value threshold of 0.1 no effects are found. We report on exploratory patterns of associations with relatively high ranks that exhibit stability to the differing assumptions made by both FSR and LME.
Clinical Predictors Influencing the Length of Stay in Emergency Department Patients Presenting with Acute Heart Failure

Background and objectives: Acute heart failure is a common problem encountered in the emergency department (ED). More than 80% of the patients with the condition subsequently require lengthy and repeated hospitalization. In a setting with limited in-patient capacity, the patient flow is often obstructed. Appropriate disposition decisions must be made by emergency physicians to deliver effective care and alleviate ED overcrowding. This study aimed to explore clinical predictors influencing the length of stay (LOS) in patients with acute heart failure who present to the ED. Materials and Methods: We conducted prognostic factor research with a retrospective cohort design. Medical records of patients with acute heart failure who presented to the ED of Ramathibodi Hospital from January to December 2015 were assessed for eligibility. Thirteen potential clinical predictors were selected as candidates for statistical modeling based on previous reports. Multivariable Poisson regression was used to estimate the difference in LOS between patients with and without potential predictors. Results: A total of 207 patients were included in the analysis. Most patients were male with a mean age of 74.2 ± 12.5 years. The median LOS was 54.6 h (Interquartile range 17.5, 149.3 h). From the multivariable analysis, four clinical characteristics were identified as independent predictors with an increase in LOS. These were patients with New York Heart Association (NYHA) functional class III/IV (+72.9 h, 95%Confidence interval (CI) 23.9, 121.8, p = 0.004), respiratory rate >24 per minute (+80.7 h, 95%CI 28.0, 133.3, p = 0.003), hemoglobin level <10 mg/dL (+60.4 h, 95%CI 8.6, 112.3, p = 0.022), and serum albumin <3.5 g/dL (+52.8 h, 95%CI 3.6, 102.0, p = 0.035). Conclusions: Poor NYHA functional class, tachypnea, anemia, and hypoalbuminemia are significant clinical predictors of patients with acute heart failure who required longer LOS.
CNN Training with Twenty Samples for Crack Detection via Data Augmentation

The excellent generalization ability of deep learning methods, e.g., convolutional neural networks (CNNs), depends on a large amount of training data, which is difficult to obtain in industrial practices. Data augmentation is regarded commonly as an effective strategy to address this problem. In this paper, we attempt to construct a crack detector based on CNN with twenty images via a two-stage data augmentation method. In detail, nine data augmentation methods are compared for crack detection in the model training, respectively. As a result, the rotation method outperforms these methods for augmentation, and by an in-depth exploration of the rotation method, the performance of the detector is further improved. Furthermore, data augmentation is also applied in the inference process to improve the recall of trained models. The identical object has more chances to be detected in the series of augmented images. This trick is essentially a performance–resource trade-off. For more improvement with limited resources, the greedy algorithm is adopted for searching a better combination of data augmentation. The results show that the crack detectors trained on the small dataset are significantly improved via the proposed two-stage data augmentation. Specifically, using 20 images for training, recall in detecting the cracks achieves 96% and Fext(0.8), which is a variant of F-score for crack detection, achieves 91.18%.
When Vectorwise Meets Hyper, Pipeline Breakers Become the Moderator

In today’s database systems, there is an ongoing debate on which processing model is the best. While the vectorized processing model shows better performance for memory-bound operations and queries, the compiled processing model is beneﬁcial for compute-intensive ones. However, a huge drawback of compiled query engines is their initial query compilation time, which especially sets them back in case of short query runtime, because compilation times overshadow the short query execution times. Hence, the ﬁrst approaches aim to avoid compilation time by interpreting the code that is to be compiled. However, naturally, interpreted operators optimized for compiled execution cannot compete with pure vectorized operators that were designed for an interpreted execution. Hence, the intuitive approach to hide compilation time is to start query execution in a vectorized engine, and upon ﬁnished query compilation, switch to the compiled engine. In this paper, we investigate how to switch from a vectorized to a compiled execution engine by using diﬀerent natural transition points (i.e., pipeline breakers) in query execution. Our investigations show that this procedure eﬀectively hides compilation times and gives a speedup of 3x when comparing separate query runtimes of compiled execution.
Constipation Predominant Irritable Bowel Syndrome and Functional Constipation Are Not Discrete Disorders: A Machine Learning Approach.

INTRODUCTION Chronic constipation is classified into 2 main syndromes, irritable bowel syndrome with constipation (IBS-C) and functional constipation (FC), on the assumption that they differ along multiple clinical characteristics and are plausibly of distinct pathophysiology. Our aim was to test this assumption by applying machine learning to a large prospective cohort of comprehensively phenotyped patients with constipation. METHODS Demographics, validated symptom and quality of life questionnaires, clinical examination findings, stool transit, and diagnosis were collected in 768 patients with chronic constipation from a tertiary center. We used machine learning to compare the accuracy of diagnostic models for IBS-C and FC based on single differentiating features such as abdominal pain (a "unisymptomatic" model) vs multiple features encompassing a range of symptoms, examination findings and investigations (a "syndromic" model) to assess the grounds for the syndromic segregation of IBS-C and FC in a statistically formalized way. RESULTS Unisymptomatic models of abdominal pain distinguished between IBS-C and FC cohorts near perfectly (area under the curve 0.97). Syndromic models did not significantly increase diagnostic accuracy (P > 0.15). Furthermore, syndromic models from which abdominal pain was omitted performed at chance-level (area under the curve 0.56). Statistical clustering of clinical characteristics showed no structure relatable to diagnosis, but a syndromic segregation of 18 features differentiating patients by impact of constipation on daily life. DISCUSSION IBS-C and FC differ only about the presence of abdominal pain, arguably a self-fulfilling difference given that abdominal pain inherently distinguishes the 2 in current diagnostic criteria. This suggests that they are not distinct syndromes but a single syndrome varying along one clinical dimension. An alternative syndromic segregation is identified, which needs evaluation in community-based cohorts. These results have implications for patient recruitment into clinical trials, future disease classifications, and management guidelines.
Preventing mental health conditions in adolescents living with HIV: an urgent need for evidence

As adolescents transition from childhood to adulthood, they experience major physical, social and psychological changes, and are at heightened risk for developing mental health conditions and engaging in health‐related risk behaviours. For adolescents living with HIV (ALHIV), these risks may be even more pronounced. Research shows that this population may face additional mental health challenges related to the biological impact of the disease and its treatment, the psychosocial burdens of living with HIV and HIV‐related social and environmental stressors.
Estimating domestic assistance requirements in medico-legal practice: Australian occupational therapy perspective

Abstract Aim Occupational therapists are experts in determining domestic assistance levels. It is a complex multi-dimensional process resulting in different opinion regarding care hours. Method Therapists providing medicolegal reports on domestic care for the courts within Australia were surveyed. Results 58 occupational therapists responded with most aged between 41 to 50 years of age. There was a strong correlation between years of clinical and medicolegal experience (r = 0.730, n = 58, p = 0.000) as well as between years and numbers of medicolegal assessments conducted in 2017 (r = 0.264, n = 58, p = 0.420). Assessment based on occupational analysis interview combined observation of the claimant performing tasks in their home, medical information, and contextual factors such as family expectations. Translation of data to hours of care was based on therapist’s clinical experience of medical diagnosis (x2 6.89, p = 0.009) and claimant’s statements of performance combined with therapist’s clinical experience (x2 = 5.58, p = 0.18). Therapists providing defendant only reports often use their own time to perform housework as a guide. Triangulation data combined confirmation of the person’s statements through observation of performance, the environment in which they lived, and the occupational therapists own clinical reasoning skill. Conclusion Documenting all the factors influencing the occupational therapist’s decision is the key to understanding clinical reasoning. Implication for rehabilitation When estimating domestic care occupational therapists should: •Provide written documentation that clearly explains the assessment methods used, the results and conclusion made regarding domestic care. •Triangulate data including claimant’s statements of performance, confirmation by observation on tasks, medical report information in the context of the claimant’s environmental and contextual factors. •Be aware their own years of experience do not equate to level of expertise.
Cross-Sector Collaboration for a Healthy Living Environment—Which Strategies to Implement, Why, and in Which Context?

Background: Working toward a healthy living environment requires organizations from different policy domains and nongovernment partners involved in public health and the living environment to collaborate across sectors. The aim of this study is to understand how this cross-sector collaboration for a healthy living environment can be achieved. Methods: The realist evaluation approach was used to investigate what strategies can be used in which contexts to achieve cross-sector collaboration. The “Collaborative Adaptive Health Networks” framework was used as a theoretical framework. Seventeen partners of three Dutch projects collaborating for a healthy living environment in different regions were interviewed about their experiences during the initiating phase of their projects. Results: Seven themes for achieving cross-sector collaboration were identified, namely creating a feeling of equivalence, building trust, bridging different perspectives, providing clarity regarding roles and tasks, creating commitment, creating active engagement, and understanding whom to engage and when. For each theme, the strategies that were used, and why, were specified. Conclusion: This study provides new insights in how cross-sector collaboration for a healthy living environment can be achieved in different contexts. Whether the start of a cross-sectoral collaboration is successful is largely influenced by the choice of leadership and the interorganizational relations.