Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the online presence of residency programs to ameliorate COVID-19’s impact on residency applications

ABSTRACT Objectives: The coronavirus disease 2019 (COVID-19) pandemic has created several challenges for residency programs and prospective interns alike during the upcoming application cycle, including the cancellation of away sub-internships and in-person interviews. Given prior research documenting that applicants’ application and ranking decisions are significantly influenced by residency webpages, a potential solution to the loss of in-person experiences during the pandemic is the expansion of residency programs’ online presence through their program websites, provision of virtual grand rounds and pseudo-away rotations, and enhancement of virtual interviews. This study seeks to summarize the existing literature on these areas and provide concrete suggestions for improving programs’ virtual presence.Methods: The authors summarize earlier literature querying the content of program websites across 14 medical specialties, which documented significant gaps in the content of interest to applicants.Results: Among 14 analyzed specialties, the majority of programs had a functional website (>90%), with the exception of interventional radiology (73.9%). However, significant gaps in content were documented, with the percentage of content variables contained on websites ranging from 33.3% to 70.5% (median = 47.0%, interquartile range = 37.8-52.6%). Program websites were also limited by underrepresentation of content most valued by applicants as well as potential areas of inaccurate or outdated information.Conclusions: There are several interventions programs can undertake to address existing gaps in online presence. During an application cycle facing unprecedented resource strain, bolstering the online presence of programs may facilitate an improved fit between programs and future residents.
Age and discharge modified Rankin score are associated with 90-Day functional outcome after basilar artery occlusion treated with endovascular therapy

Introduction Prognostic factors for functional outcome after basilar artery occlusion (BAO) treated with modern endovascular therapy (EVT) are sparse. We investigated the association between clinical characteristics, readily available imaging variables, and outcome in BAO patients treated with EVT. Methods Retrospective analysis from a large healthcare system’s prospectively collected code stroke registry of acute BAO patients treated with EVT between January 2017–January 2020. The primary outcome measure was a favorable 90-day modified Rankin score (mRS) of 0-2. Results 65 patients (median age 67 years, 57% male, median NIHSS 16) met the study inclusion criteria. Thrombolysis in Cerebral Infarction (TICI) 2 b-3 revascularization was achieved in 57/65 patients (88%) with a median time to revascularization of 445 minutes [IQR 302-840]. Ninety-day good outcome was seen in 35% (23/65) of patients. In a univariate analysis, age, history of ischemic stroke, baseline NIHSS, BAO site, and discharge mRS were associated with significant differences between the good and poor outcome groups. A multivariable logistic regression analysis demonstrated an independent association with 90-day good outcome and younger age (per 1-year, OR 0.79, 95% CI 0.64, 0.98) and good discharge mRS (0-2) (OR > 999.99, 95% CI 13.26, > 999.99). Conclusions Patients presenting with an acute BAO treated with modern EVT have a good 90-day outcome in over one-third of cases. Age and discharge mRS are independently associated with good 90-day outcome. Additional studies may focus on factors that can enhance discharge function after BAO, a novel prognostic indicator for favorable 90-day outcome in our study.
Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100

Machine learning-based feature selection can identify parameters with higher performance in outcome prediction. BACKGROUND AND PURPOSE: Traditional statistical models and pretreatment scoring systems have been used to predict the outcome for acute ischemic stroke patients (AIS). Our aim was to select the most relevant features in terms of outcome prediction on the basis of machine learning algorithms for patients with acute ischemic stroke and to compare the performance between multiple models and the Stroke Prognostication Using Age and National Institutes of Health Stroke Scale (SPAN-100) index model. MATERIALS AND METHODS: A retrospective multicenter cohort of 1431 patients with acute ischemic stroke was subdivided into recanalized and nonrecanalized patients. Extreme Gradient Boosting machine learning models were built to predict the mRS score at 90 days using clinical, imaging, combined, and best-performing features. Feature selection was performed using the relative weight and frequency of occurrence in the models. The model with the best performance was compared with the SPAN-100 index model using area under the receiver operating curve analysis. RESULTS: In 3 groups of patients, the baseline NIHSS was the most significant predictor of outcome among all the parameters, with relative weights of 0.36∼0.69; ischemic core volume on CTP ranked as the most important imaging biomarker with relative weights of 0.29∼0.47. The model with the best-performing features had a better performance than the other machine learning models. The area under the curve of the model with the best-performing features was higher than SPAN-100 model and reached statistical significance for the total (P < .05) and the nonrecanalized patients (P < .001). CONCLUSIONS: Machine learning–based feature selection can identify parameters with higher performance in outcome prediction. Machine learning models with the best-performing features, especially advanced CTP data, had superior performance of the recovery outcome prediction for patients with stroke at admission in comparison with SPAN-100.
Larval Gnathostomes and Zoonotic Trematode Metacercariae in Fish from a Local Market in Yangon City, Myanmar

A survey was performed to investigate the infection status of zoonotic helminth larvae in fish from a local market of North Dagon District in Yangon City, Myanmar. A total of 486 fish in 13 species were collected 8 times from December 2015 to December 2019. All fish were transported under ice to a laboratory in Korea and examined for helminth larvae using artificial digestion method. Larval gnathostomes and metacercariae of more than 8 zoonotic trematode species, i.e., Opisthorchis viverrini, Haplorchis taichui, H. pumilio, H. yokogawai, Centrocestus spp. , Stellantchasmus falcatus, Pygidiopsis cambodiensis, and Procerovum sp. , were detected. Larval gnathostomes were found in 58 (16.0%) out of 362 fish of 6 species, with mean intensity of 2.8 per fish infected. Metacercariae of O. viverrini were detected in 10 (2.9%) out of 349 fish of 5 species, with mean intensity of 16.9 per fish infected. Metacercarial prevalences of 4 intestinal flukes, H. taichui, H. pumilio, H. yokogawai, and Centrocestus spp. , were 16.8%, 26.0%, 12.5%, and 15.0% in the positive fish species, respectively, and mean metacercarial intensity was 63.3, 26.8, 86.2, and 8.7 per fish infected. Metacercariae of S. falcatus and P. cambodiensis were detected only from the mullet, Chelon macrolepis. Metacercariae of Procerovum sp. were found in Channa striata and Anabas testudineus. Collectively, it was confirmed that the fish were infected with gnathostome larvae and metacercariae of O. viverrini and intestinal flukes in Yangon City, Myanmar.
3D multi-scale deep convolutional neural networks for pulmonary nodule detection

With the rapid development of big data and artificial intelligence technology, computer-aided pulmonary nodule detection based on deep learning has achieved some successes. However, the sizes of pulmonary nodules vary greatly, and the pulmonary nodules have visual similarity with structures such as blood vessels and shadows around pulmonary nodules, which make the quick and accurate detection of pulmonary nodules in CT image still a challenging task. In this paper, we propose two kinds of 3D multi-scale deep convolution neural networks for nodule candidate detection and false positive reduction respectively. Among them, the nodule candidate detection network consists of two parts: 1) the backbone network part Res2SENet, which is used to extract multi-scale feature information of pulmonary nodules, it is composed of the multi-scale Res2Net modules of multiple available receptive fields at a granular level and the squeeze-and-excitation units; 2) the detection part, which uses a region proposal network structure to determine region candidates, and introduces context enhancement module and spatial attention module to improve detection performance. The false positive reduction network, also composed of the multi-scale Res2Net modules and the squeeze-and-excitation units, can further classify the nodule candidates generated by the nodule candidate detection network and screen out the ground truth positive nodules. Finally, the prediction probability generated by the nodule candidate detection network is weighted average with the prediction probability generated by the false positive reduction network to obtain the final results. The experimental results on the publicly available LUNA16 dataset showed that the proposed method has a superior ability to detect pulmonary nodules in CT images.
Predicting drug-metagenome interactions: Variation in the microbial β-glucuronidase level in the human gut metagenomes

Characterizing the gut microbiota in terms of their capacity to interfere with drug metabolism is necessary to achieve drug efficacy and safety. Although examples of drug-microbiome interactions are well-documented, little has been reported about a computational pipeline for systematically identifying and characterizing bacterial enzymes that process particular classes of drugs. The goal of our study is to develop a computational approach that compiles drugs whose metabolism may be influenced by a particular class of microbial enzymes and that quantifies the variability in the collective level of those enzymes among individuals. The present paper describes this approach, with microbial β-glucuronidases as an example, which break down drug-glucuronide conjugates and reactivate the drugs or their metabolites. We identified 100 medications that may be metabolized by β-glucuronidases from the gut microbiome. These medications included morphine, estrogen, ibuprofen, midazolam, and their structural analogues. The analysis of metagenomic data available through the Sequence Read Archive (SRA) showed that the level of β-glucuronidase in the gut metagenomes was higher in males than in females, which provides a potential explanation for the sex-based differences in efficacy and toxicity for several drugs, reported in previous studies. Our analysis also showed that infant gut metagenomes at birth and 12 months of age have higher levels of β-glucuronidase than the metagenomes of their mothers and the implication of this observed variability was discussed in the context of breastfeeding as well as infant hyperbilirubinemia. Overall, despite important limitations discussed in this paper, our analysis provided useful insights on the role of the human gut metagenome in the variability in drug response among individuals. Importantly, this approach exploits drug and metagenome data available in public databases as well as open-source cheminformatics and bioinformatics tools to predict drug-metagenome interactions.
ORGANIC AMENDMENTS AND PHOSPHORUS DYNAMICS: I. PHOSPHORUS CHEMISTRY AND SORPTION

The influence of the amount and kinds of organic and inorganic amendments on phosphorus (P) sorption characteristics was determined on two soils from Oregon, Jory (Xeric Haplohumult) and Tolo (Typic Vitrandept), and three soils from Rwanda, Mata (Sombrihumult), Kibeho (Paleudult), and Kinigi (Typic
FNDC-1-mediated mitophagy and ATFS-1 coordinate to protect against hypoxia-reoxygenation

ABSTRACT Mitochondrial quality control (MQC) balances organelle adaptation and elimination, and mechanistic crosstalk between the underlying molecular processes affects subsequent stress outcomes. FUNDC1 (FUN14 domain containing 1) is a mammalian mitophagy receptor that responds to hypoxia-reoxygenation (HR) stress. Here, we provide evidence that FNDC-1 is the C. elegans ortholog of FUNDC1, and that its loss protects against injury in a worm model of HR. This protection depends upon ATFS-1, a transcription factor that is central to the mitochondrial unfolded protein response (UPRmt). Global mRNA and metabolite profiling suggest that atfs-1-dependent stress responses and metabolic remodeling occur in response to the loss of fndc-1. These data support a role for FNDC-1 in non-hypoxic MQC, and further suggest that these changes are prophylactic in relation to subsequent HR. Our results highlight functional coordination between mitochondrial adaptation and elimination that organizes stress responses and metabolic rewiring to protect against HR injury. Abbreviations: AL: autolysosome; AP: autophagosome; FUNDC1: FUN14 domain containing 1; HR: hypoxia-reperfusion; IR: ischemia-reperfusion; lof: loss of function; MQC: mitochondrial quality control; PCA: principle component analysis; PPP: pentonse phosphate pathway; proK (proteinase K);UPRmt: mitochondrial unfolded protein response; RNAi: RNA interference.
Commentary on Furr-Holden et al. 2020: As opioid overdose deaths accelerate among Black Americans, COVID-19 widens inequities-a critical need to invest in community-based approaches.

The COVID-19 pandemic has exacerbated racial inequities in fatal opioid overdoses affecting Black Americans. To reduce overdose rates among Black Americans, funding and policy efforts should prioritize local strategies that build community trust, such as grassroots organizations already engaged in outreach, advocacy, and harm reduction services. in overdose deaths. Recently obtained numbers from St. Louis City and County Medical Examiners demonstrate expected COVID-19-related increases in opioid-involved
Minimal transmission of SARS-CoV-2 from paediatric COVID-19 cases in primary schools, Norway, August to November 2020

An intense debate on school closures to control the COVID-19 pandemic is ongoing in Europe. We prospectively examined transmission of SARS-CoV-2 from confirmed paediatric cases in Norwegian primary schools between August and November 2020. All in-school contacts were systematically tested twice during their quarantine period. With preventive measures implemented in schools, we found minimal child-to-child (0.9%, 2/234) and child-to-adult (1.7%, 1/58) transmission, supporting that under 14 year olds are not the drivers of SARS-CoV-2 transmission.
Multifocal Skull Base Defects With Associated Spontaneous Cerebrospinal Fluid Leaks.

OBJECTIVE Spontaneous cerebrospinal fluid (CSF) leaks are thought to arise due to elevated intracranial pressure which is distributed across the skull base, potentially predisposing the development of multifocal CSF leaks. The aims of this study are to evaluate the characteristics of this population at presentation and surgical outcomes. STUDY DESIGN Retrospective case series. SETTING Tertiary referral center. PATIENTS Patients 18 years or older with spontaneous CSF leak diagnosis were eligible for study inclusion. Multifocal spontaneous CSF leak was defined as leaks originating from more than one skull base defect at anatomically distinct subsites. INTERVENTION None. MAIN OUTCOME MEASURE Clinical presentation and surgical outcome. RESULTS Two hundred ninety-three patients with diagnosis of spontaneous CSF leak were identified. Of these, 11 (3.8%) were characterized as having multifocal spontaneous CSF leaks. Mean body mass index was significantly higher in multifocal CSF leak patients (31.5 ± 8.5 vs 46.2 ± 9.9, p < 0.0001). There was also higher prevalence of women (100% vs 63.8%, p = 0.012) and African-Americans (63.6% vs 23.4%, p = 0.003). The overall success rate of CSF leak repair was 95%. Fifty percent of patients developed headaches or blurry vision after surgical repair, and two patients required ventriculoperitoneal shunting. CONCLUSIONS Multifocal spontaneous CSF leaks are rare and occurred in 3.8% of patients with spontaneous CSF. These patients were morbidly obese and more likely to be female and African American. Surgical repair conferred excellent outcomes. However, these patients have high risk of developing symptoms suggestive of elevated intracranial pressure postoperatively and should be counseled accordingly.
Experiences and Strategies Influencing Older Adults to Continue Playing Walking Football.

Adults aged 55+ years are most likely to be inactive, despite research suggesting that older adults experience multiple benefits when participating in physical activity and sport. Limited research focuses on long-term continuation of sport participation in this population, especially in "adapted sports" like walking football. This study explored the experiences of walking football maintenance in 55- to 75-year-old players. Semistructured interviews were conducted, with 17 older adults maintaining walking football play over 6 months. The inductive analysis revealed five higher-order themes representing maintenance influences and two higher-order themes relating to maintenance mechanisms (i.e., the conscious process by which players maintain). Influences when maintaining walking football included individual- and culture-level influences (e.g., perceived benefits of maintenance and ability acceptance). Maintenance mechanisms included cognitions and behaviors (e.g., scheduling sessions and redefining physical activity expectations). Findings highlight novel implications for policy and practice, which are important to consider when delivering walking football to older adults.
lncRNA NEAT1 ameliorates LPS-induced inflammation in MG63 cells by activating autophagy and suppressing the NLRP3 inflammasome

The mechanisms of inflammation in bone and joint tissue are complex and involve long non-coding RNAs (lncRNAs), which play an important role in this process. The aim of the present study was to screen out differentially expressed genes in human osteoblasts stimulated by inflammation, and to further explore the mechanisms underlying inflammatory responses and the functional activity of human osteoblasts through bioinformatics methods and in vitro experiments. For this purpose, MG63 cells were stimulated with various concentrations of lipopolysaccharide (LPS) for different periods of time to construct an optimal inflammatory model and RNA sequencing was then performed on these cells. The levels of nuclear enriched abundant transcript 1 (NEAT1), various inflammatory factors, Nod-like receptor protein 3 (NLRP3) protein and osteogenesis-related proteins, as well as the levels of cell apoptosis- and cell cycle-related markers were measured in MG63 cells stimulated with LPS, transfected with NEAT1 overexpression plasmid and treated with bexarotene by western blot analysis, RT-qPCR, immunofluorescence, FISH, TEM and flow cytometry. There were 427 differentially expressed genes in the LPS-stimulated MG63 cells, in which NEAT1 was significantly downregulated. LPS upregulated the expression of inflammatory cytokines and NLRP3, inhibited the expression of autophagy-related and osteogenesis-related proteins, promoted apoptosis and altered the cell cycle, which was partially inhibited by NEAT1 overexpression and promoted by bexarotene. LPS stimulated inflammation in the MG63 cells and inhibited the retinoid X receptor (RXR)-α to downregulate the expression of NEAT1 and decrease levels of autophagy, which promoted the activation of NLRP3 and the release of inflammatory factors, and impaired the functional activity of osteoblasts, thus promoting the development of inflammation.
A Developmental Eye Tracking Investigation of Cued Task Switching Performance

Children perform worse than adults on tests of cognitive flexibility, which is a component of executive function. To assess what aspects of a cognitive flexibility task (cued switching) children have difficulty with, investigators tested where eye gaze diverged over age. Eye‐tracking was used as a proxy for attention during the preparatory period of each trial in 48 children ages 8–16 years and 51 adults ages 18–27 years. Children fixated more often and longer on the cued rule, and made more saccades between rule and response options. Behavioral performance correlated with gaze location and saccades. Mid‐adolescents were similar to adults, supporting the slow maturation of cognitive flexibility. Lower preparatory control and associated lower cognitive flexibility task performance in development may particularly relate to rule processing.
Forecasting COVID-19 confirmed cases, deaths and recoveries: Revisiting established time series modeling through novel applications for the USA and Italy

The novel coronavirus (COVID-19) is an emergent disease that initially had no historical data to guide scientists on predicting/ forecasting its global or national impact over time. The ability to predict the progress of this pandemic has been crucial for decision making aimed at fighting this pandemic and controlling its spread. In this work we considered four different statistical/time series models that are readily available from the ‘forecast’ package in R. We performed novel applications with these models, forecasting the number of infected cases (confirmed cases and similarly the number of deaths and recovery) along with the corresponding 90% prediction interval to estimate uncertainty around pointwise forecasts. Since the future may not repeat the past for this pandemic, no prediction model is certain. However, any prediction tool with acceptable prediction performance (or prediction error) could still be very useful for public-health planning to handle spread of the pandemic, and could policy decision-making and facilitate transition to normality. These four models were applied to publicly available data of the COVID-19 pandemic for both the USA and Italy. We observed that all models reasonably predicted the future numbers of confirmed cases, deaths, and recoveries of COVID-19. However, for the majority of the analyses, the time series model with autoregressive integrated moving average (ARIMA) and cubic smoothing spline models both had smaller prediction errors and narrower prediction intervals, compared to the Holt and Trigonometric Exponential smoothing state space model with Box-Cox transformation (TBATS) models. Therefore, the former two models were preferable to the latter models. Given similarities in performance of the models in the USA and Italy, the corresponding prediction tools can be applied to other countries grappling with the COVID-19 pandemic, and to any pandemics that can occur in future.
Application of the distributed activation energy model to biomass and biomass constituents devolatilization

In this study, an investigation about the thermal behavior of four different woods was carried out. The distributed activation energy model was applied to study the effect of heating rate on the reaction of single solids. Results obtained were used in the curve prediction of fraction of mass remaining and rate of mass loss vs. temperature at more realistic heating rates. The possible calculation of biomass samples behavior in pyrolysis conditions as the summation of their constituents, lignin, cellulose, and hemi-cellulose is also explored. All the samples show a weak interaction between the constituents which produce slight differences between experimental and calculated behavior. However, differences between experimental and calculated data lower than 2% offer a robust test of the applicability of the model on kinetic studies of a wide range of biomass samples, heating rates, data input format and equipment layout. © 2009 American Institute of Chemical Engineers AIChE J, 2009