Miracle-dz/UniFAR-FTUs huggingface dataset:
Desensitization in the Era of Precision Medicine: Moving from the Bench to Bedside.

Patients with antibodies to human leukocyte antigens (HLA) wait longer for transplant and are at increased risk of adverse outcomes. For more than a decade, drug therapy approaches have been tested to modulate the immune system in order to prevent or reduce donor-specific antibody (DSA) levels. Despite some studies reporting success in facilitating transplant, many patients do not respond or experience DSA rebound, highlighting the diversity of the individual patient's immune response. While advances in immunomodulatory therapies have resulted in escalating efforts to successfully treat highly sensitized patients, further insight into the human immune system has uncovered its enormous complexity and diversity calling for a personalized approach. Yet, even defining the sensitized transplant candidate can be troublesome and much remains to be understood about the interaction between an individual's immune system as a whole, and their response to our current desensitization strategies. The shift toward a personalized approach calls for a re-evaluation of what we know, and what remains to be determined; a process that will require iterative translational approaches. This review will focus on new insights into how the interaction between immune risk assessment, the patient's immunological history, and the clinical context can be reconciled to develop a precision-based approach to pre transplant management.
Predicting Risky Program Source Files

Change in source codes is an essential and routine activity of software development and maintenance. It has been observed that this activity might result in faults that might harm the use of the software. Therefore, it is always useful for software managers and programmers that before making any changes in source files, they should know the degree of risk associated with changing source files. In this paper, we present our approach to identify source files, which are risky or at least sensitive to new changes. We defined a set of metrics to compute the degree of risk associated to a source file. To validate our approach, an experiment has been performed by using Mozilla project's data. The experimental results show that the source files having higher risk values are more risky when applying the next change and thus should be tested more thoroughly.
Risk prediction scores for mortality, cerebrovascular, and heart disease amongst Chinese people with type 2 diabetes.

CONTEXT Risk scores for cardiovascular and mortality outcomes have not been commonly applied in Chinese populations. OBJECTIVE To develop and externally validate a set of parsimonious risk scores (HKU-SG) to predict the risk of mortality, cerebrovascular disease and ischemic heart disease among Chinese people with type 2 diabetes; and compare HKU-SG risk scores to other existing ones. DESIGN Retrospective population-based cohorts drawn from Hong Kong Hospital Authority health records from 2006 to 2014 for development and Singapore Ministry of Health records from 2008 to 2016 for validation. Separate 5-year risk scores were derived using Cox proportional hazards models for each outcome. SETTING Study participants were adults with type 2 diabetes aged 20 years or over, consisting of 678,750 participants from Hong Kong and 386,425 participants from Singapore. MAIN OUTCOME Measures Performance was evaluated by discrimination (Harrell's c-index), and calibration plots comparing predicted against observed risks. RESULTS All models had fair external discrimination. Among the risk scores for the diabetes population, ethnic-specific risk scores (HKU-SG and JADE) performed better than UKPDS and RECODe models. External validation of the HKU-SG risk scores for mortality, cerebrovascular disease and ischemic heart disease had corresponding c-indices of 0.778, 0.695, and 0.644. The HKU-SG models appeared well calibrated on visual plots, with predicted risks closely matching observed risks. CONCLUSIONS The HKU-SG risk scores were developed and externally validated in two large Chinese population-based cohorts. The parsimonious use of clinical predictors compared to previous risk scores could allow wider implementation of risk estimation in diverse Chinese settings.
Disrupting the TRIB3-SQSTM1 interaction reduces liver fibrosis by restoring autophagy and suppressing exosome-mediated HSC activation

ABSTRACT Impaired macroautophagy/autophagy is involved in the pathogenesis of hepatic fibrosis. However, how aberrant autophagy promotes fibrosis is far from understood. Here, we aimed to define a previously unrevealed pro-fibrotic mechanism for the stress protein TRIB3 (tribbles pseudokinase 3)-mediated autophagy dysfunction. Human fibrotic liver tissues were obtained from patients with cirrhosis who underwent an open surgical repair process. The functional implications of TRIB3 were evaluated in mouse models of hepatic fibrosis induced by bile duct ligation (BDL) or thioacetamide (TAA) injection. Human fibrotic liver tissues expressed higher levels of TRIB3 and selective autophagic receptor SQSTM1/p62 (sequestosome 1) than nonfibrotic tissues and the elevated expression of TRIB3 and SQSTM1 was positively correlated in the fibrotic tissues. Silencing Trib3 protected against experimentally induced hepatic fibrosis, accompanied by restored autophagy activity in fibrotic liver tissues. Furthermore, TRIB3 interacted with SQSTM1 and hindered its binding to MAP1LC3/LC3, which caused the accumulation of SQSTM1 aggregates and obstructed autophagic flux. The TRIB3-mediated autophagy impairment not only suppressed autophagic degradation of late endosomes but also promoted hepatocellular secretion of INHBA/Activin A-enriched exosomes which caused migration, proliferation and activation of hepatic stellate cells (HSCs), the effector cells of liver fibrosis. Disrupting the TRIB3-SQSTM1 interaction with a specific helical peptide exerted potent protective effects against hepatic fibrosis by restoring autophagic flux in hepatocytes and HSCs. Together, stress-elevated TRIB3 expression promotes hepatic fibrosis by interacting with SQSTM1 and interfering with its functions in liver-parenchymal cells and activating HSCs. Targeting this interaction is a promising strategy for treating fibroproliferative liver diseases. Abbreviations: 3-MA: 3-methyladenine; AAV: adeno-associated virus; ACTA2/α-SMA: actin, alpha 2, smooth muscle, aorta; BDL: bile duct ligation; BECN1/Beclin 1: beclin 1, autophagy related; CHX: cycloheximide; CQ: chloroquine; Edu: 5-ethynyl-2-deoxyuridine; ESCRT: endosomal sorting complexes required for transport; HSC: hepatic stellate cell; ILV: intralumenal vesicle; LAMP1: lysosomal-associated membrane protein 1; MAP1LC3/LC3: microtubule-associated protein 1 light chain 3; MVB: multivesicular body; PIK3C3: phosphatidylinositol 3-kinase, catalytic subunit type 3; PPI: protein-protein interaction; SQSTM1/p62: sequestosome 1; TAA: thioacetamide; TEM: transmission electron microscopy; TGFB1/TGFβ1: transforming growth factor, beta 1; TLR2: toll-like receptor 2; TRIB3: tribbles pseudokinase 3
Ameliorative Effect of Beta vulgaris Root Extract on Chlorpyrifos-Induced Oxidative Stress, Inflammation and Liver Injury in Rats

Exposure to organophosphorus insecticides causes several health problems to animals and humans. Red beetroot (RBR) is rich in antioxidant ingredients and possesses a promising hepatoprotective activity. This study evaluated the potential of RBR extract to prevent chlorpyrifos (CPF)-induced liver injury, with an emphasis on oxidative stress, inflammation and apoptosis. Rats received 10 mg/kg CPF and were treated with 300 mg/kg RBR extract for 28 days. CPF caused liver injury evidenced by elevated serum levels of serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) and bilirubin, along with several histological alterations. Hepatic lipid peroxidation (LPO) and nitric oxide (NO) levels, as well as inducible nitric oxide synthase (iNOS) and pro-inflammatory cytokines were increased in CPF-intoxicated rats. RBR prevented CPF-induced histological alterations, and ameliorated liver function, LPO, NO, iNOS and pro-inflammatory cytokines. RBR boosted glutathione and antioxidant enzymes, and increased Nrf2 expression. In addition, RBR diminished Bax and caspase-3, and increased Bcl-2 expression. In conclusion, RBR prevented CPF-induced liver injury via attenuation of oxidative stress, inflammation and apoptosis. RBR enhanced antioxidant defenses, suggesting that it could be used as a potential therapeutic intervention to minimize CPF hepatotoxicity.
First mitochondrial genomes of five hoverfly species of the genus Eristalinus (Diptera: Syrphidae).

Four subfamilies are recognized in the family Syrphidae (Insecta: Diptera), also known as hoverflies or flower flies: Microdontinae, Syrphinae, Eristalinae and Pipizinae (Mengual et al. 2015; Young et al. 2016). Yet, the subfamily Eristalinae appears paraphyletic in relation with the three other subfamilies (Hippa and Ståhls 2005; Skevington and Yeates 2000; Ståhls et al. 2003). A widespread genus within the Eristalinae is Eristalinus Rondani, 1845. It is naturally present in all biogeographic regions except in the Neotropics, although a few records indicate that it was introduced in Chile (Thompson 1999). Species of this genus are large hoverflies (4-16 mm), and most of them are imperfect bee mimics with punctate (spotted) and/or fasciate (striped) eyes. They occur in a wide variety of habitats including open grasslands, shrub lands, river valleys, forest margins, wetlands, river banks, lake shores and even urban areas. Larvae can be found in small temporary waterbodies. The genus comprises approximately 75 species worldwide, of which 54 occur in the Afrotropical Region. It is divided into five subgenera: Eristalinus Rondani, 1845 (including Lathyrophthalmus Mik, 1897), Eristalodes Mik, 1897, Helophilina Becker, 1923, Merodonoides Curran, 1931 and Oreristalis Séguy, 1951. So far, the phylogenetic relationships among the Eristalinus species of the Afrotropical Region remain unknown. A preliminary phylogenetic study on Eristalinus species from the Afrotropics using thre mitochondrial (cytochrome c oxidase subunit 1, cytochrome b and 12S rRNA) and two nuclear (18S and 28S rRNA) gene fragments showed low support for many nodes in the phylogenetic trees (De Smet et al. unpublished results) prompting the usage of additional DNA markers to resolve species and subgenus relationships within Eristalinus. Phylogenetic studies using whole mitochondrial genomes (mitogenomes) have shown the potential to tackle phylogenetic issues at varying taxonomic levels (e.g. Cameron 2013; Cameron et al. 2007, 2009; Ma et al. 2012; Nelson et al. 2012; Yong et al. 2015). The objectives of the current study are to assemble the mitogenomes of five Afrotropical Eristalinus species (belonging to two subgenera), infer their phylogenetic relationships and measure the informativeness of each mitochondrial protein coding gene (PCG) and rRNA gene for the resolution of phylogenetic relationships within Eristalinus.
Applying Deep Neural Network Analysis to High-Content Image-Based Assays

The etiological underpinnings of many CNS disorders are not well understood. This is likely due to the fact that individual diseases aggregate numerous pathological subtypes, each associated with a complex landscape of genetic risk factors. To overcome these challenges, researchers are integrating novel data types from numerous patients, including imaging studies capturing broadly applicable features from patient-derived materials. These datasets, when combined with machine learning, potentially hold the power to elucidate the subtle patterns that stratify patients by shared pathology. In this study, we interrogated whether high-content imaging of primary skin fibroblasts, using the Cell Painting method, could reveal disease-relevant information among patients. First, we showed that technical features such as batch/plate type, plate, and location within a plate lead to detectable nuisance signals, as revealed by a pre-trained deep neural network and analysis with deep image embeddings. Using a plate design and image acquisition strategy that accounts for these variables, we performed a pilot study with 12 healthy controls and 12 subjects affected by the severe genetic neurological disorder spinal muscular atrophy (SMA), and evaluated whether a convolutional neural network (CNN) generated using a subset of the cells could distinguish disease states on cells from the remaining unseen control–SMA pair. Our results indicate that these two populations could effectively be differentiated from one another and that model selectivity is insensitive to batch/plate type. One caveat is that the samples were also largely separated by source. These findings lay a foundation for how to conduct future studies exploring diseases with more complex genetic contributions and unknown subtypes.
Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory

Background Unplanned readmission of a hospitalized patient is an extremely undesirable outcome as the patient may have been exposed to additional risks. The rates of unplanned readmission are, therefore, regarded as an important performance indicator for the medical quality of a hospital and healthcare system. Identifying high-risk patients likely to suffer from readmission before release benefits both the patients and the medical providers. The emergence of machine learning to detect hidden patterns in complex, multi-dimensional datasets provides unparalleled opportunities to develop efficient discharge decision-making support system for physicians. Methods and Findings We used supervised machine learning approaches for ICU readmission prediction. We used machine learning methods on comprehensive, longitudinal clinical data from the MIMIC-III to predict the ICU readmission of patients within 30 days of their discharge. We have utilized recent machine learning techniques such as Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), by this we have been able incorporate the multivariate features of EHRs and capture sudden fluctuations in chart event features (e.g. glucose and heart rate) that are significant in time series with temporal dependencies, which cannot be properly captured by traditional static models, but can be captured by our proposed deep neural network based model. We incorporate multiple types of features including chart events, demographic, and ICD9 embeddings. Our machine learning models identifies ICU readmissions at a higher sensitivity rate (0.742) and an improved Area Under the Curve (0.791) compared with traditional methods. We also illustrate the importance of each portion of the features and different combinations of the models to verify the effectiveness of the proposed model. Conclusion Our manuscript highlights the ability of machine learning models to improve our ICU decision making accuracy, and is a real-world example of precision medicine in hospitals. These data-driven results enable clinicians to make assisted decisions within their patient cohorts. This knowledge could have immediate implications for hospitals by improving the detection of possible readmission. We anticipate that machine learning models will improve patient counseling, hospital administration, allocation of healthcare resources and ultimately individualized clinical care.
Architecture of the Bacterial Flagellar Distal Rod and Hook of Salmonella

The bacterial flagellum is a large molecular complex composed of thousands of protein subunits for motility. The filamentous part of the flagellum, which is called the axial structure, consists of the filament, the hook, and the rods, with other minor components—the cap protein and the hook associated proteins. They share a common basic architecture of subunit arrangement, but each part shows quite distinct mechanical properties to achieve its specific function. The distal rod and the hook are helical assemblies of a single protein, FlgG and FlgE, respectively. They show a significant sequence similarity but have distinct mechanical characteristics. The rod is a rigid, straight cylinder, whereas the hook is a curved tube with high bending flexibility. Here, we report a structural model of the rod constructed by using the crystal structure of a core fragment of FlgG with a density map obtained previously by electron cryomicroscopy. Our structural model suggests that a segment called L-stretch plays a key role in achieving the distinct mechanical properties of the rod using a structurally similar component protein to that of the hook.
Bias-adjusted Kaplan–Meier survival curves for marginal treatment effect in observational studies

ABSTRACT For time-to-event outcomes, the Kaplan–Meier estimator is commonly used to estimate survival functions of treatment groups and to compute marginal treatment effects, such as the difference in survival rates between treatments at a landmark time. The derived estimates of the marginal treatment effect are uniformly consistent under general conditions when data are from randomized clinical trials. For data from observational studies, however, these statistical quantities are often biased due to treatment-selection bias. Propensity score-based methods estimate the survival function by adjusting for the disparity of propensity scores between treatment groups. Unfortunately, misspecification of the regression model can lead to biased estimates. Using an empirical likelihood (EL) method in which the moments of the covariate distribution of treatment groups are constrained to equality, we obtain consistent estimates of the survival functions and the marginal treatment effect. Equating moments of the covariate distribution between treatment groups simulate the covariate distribution that would have been obtained if the patients had been randomized to these treatment groups. We establish the consistency and the asymptotic limiting distribution of the proposed EL estimators. We demonstrate that the proposed estimator is robust to model misspecification. Simulation is used to study the finite sample properties of the proposed estimator. The proposed estimator is applied to a lung cancer observational study to compare two surgical procedures in treating early-stage lung cancer patients.
Community, household, and individual correlates of sleep behaviors in Guyanese female youth

Chronic sleep loss among youth is a worldwide health problem. Since research on its predictors is often limited to high‐income countries, it is unclear if these findings are generalizable to all populations. To address this gap from an ecological perspective, we evaluated the associations between adolescents' sleep behaviors and several factors at the community, household, and individual levels in a middle‐income country.
Impact of Different Sensory Stimuli on Presence in Credible Virtual Environments

Multiple factors can affect presence in virtual environments, such as the number of human senses engaged in a given experience or the extent to which the virtual experience is credible. The purpose of the present work is to study how the inclusion of credible multisensory stimuli affects the sense of presence, namely, through the use of wind, passive haptics, vibration, and scent. Our sample consisted of 37 participants (27 men and 10 women) whose ages ranged from 17 to 44 years old and were mostly students. The participants were divided randomly into 3 groups: Control Scenario (visual and auditory – N = 12), Passive Haptic Scenario (visual, auditory, and passive haptic – N = 13) and Multisensory Scenario (visual, auditory, wind, passive haptic, vibration, and scent – N = 12). The results indicated a significant increase in the involvement subscale when all multisensory stimuli were delivered. We found a trend where the use of passive haptics by itself has a positive impact on presence, which should be the subject of further work.
Cumulative gadodiamide administration leads to brain gadolinium deposition in early MS

Objective Frequent administration of gadolinium-based contrast agents in multiple sclerosis (MS) may increase signal intensity (SI) unenhanced T1-weighted imaging MRI throughout the brain. We evaluated the association between lifetime cumulative doses of gadodiamide administration and increased SI within the dentate nucleus (DN), globus pallidus (GP), and thalamus in patients with early MS. Methods A total of 203 patients with MS (107 with baseline and follow-up MRI assessments) and 262 age- and sex-matched controls were included in this retrospective, longitudinal, 3T MRI-reader-blinded study. Patients with MS had disease duration <2 years at baseline and received exclusively gadodiamide at all MRI time points. SI ratio (SIR) to pons and CSF of lateral ventricle volume (CSF-LVV) were assessed. Analysis of covariance and correlation analyses, adjusted for age, sex, and region of interest volume, were used. Results The mean follow-up time was 55.4 months, and the mean number of gadolinium-based contrast agents administrations was 9.2. At follow-up, 49.3% of patients with MS and no controls showed DN T1 hyperintensity (p < 0.001). The mean SIR of DN (p < 0.001) and of GP (p = 0.005) to pons and the mean SIR of DN, GP, and thalamus to CSF-LVV were higher in patients with MS compared to controls (p < 0.001). SIR of DN to pons was associated with number of gadodiamide doses (p < 0.001). No associations between SIR of DN, GP, and thalamus and clinical and MRI outcomes of disease severity were detected over the follow-up. Conclusions DN, GP, and thalamus gadolinium deposition in early MS is associated with lifetime cumulative gadodiamide administration without clinical or radiologic correlates of more aggressive disease.
A cell–cell interaction format for selection of high-affinity antibodies to membrane proteins

Significance Antibodies are an important class of drugs. We developed a simple, easy-to-use method for antibody drug discovery targeting membrane proteins including gated ion channels and G protein-coupled receptors. While phage or yeast display provides a convenient tool for antibody screening, the development of therapeutic antibodies against membrane proteins still encounters bottlenecks, because it is usually difficult to purify membrane proteins in native conformation for selecting functional antibodies. To address this challenge, we designed a whole-cell screening platform based on yeast–mammalian cell interaction, which bypasses the step of membrane protein purification and enables the direct selection of antibodies against “difficult-to-purify membrane proteins” in native state. Generating and improving antibodies and peptides that bind specifically to membrane protein targets such as ion channels and G protein-coupled receptors (GPCRs) can be challenging using established selection methods. Current strategies are often limited by difficulties in the presentation of the antigen or the efficiency of the selection process. Here, we report a method for obtaining antibodies specific for whole cell membrane-associated antigens which combines a cell–cell interaction format based on yeast display technology with fluorescence-activated cell sorting of dual fluorescent complexes. Using this method, we were able to direct the affinity maturation of an antagonist antibody specific for the proton-gated ion channel ASIC1a and showed that both the affinity and potency were improved. We were also able to use this method to do kinetic selections to generate clones with better dissociation profiles. In addition, this method was employed successfully to handle the difficult problem of selecting antibodies specific to a GPCR target, the mu-opioid receptor.