The ability to detect SARS-CoV-2 in the upper respiratory tract ceases after 2 to 3 weeks post-symptom-onset in most patients. In contrast, SARS-CoV-2 can be detected in the stool of some patients ...for greater than 4 weeks, suggesting that stool may hold utility as an additional source for diagnosis. We validated the Cepheid Xpert Xpress SARS-CoV-2 and Hologic Panther Fusion real-time RT-PCR assays for detection of viral RNA in stool specimens and compared performance. We utilized remnant stool specimens (
= 79) from 77 patients with gastrointestinal symptoms. Forty-eight patients had PCR-confirmed COVID-19, and 29 either were nasopharyngeal/oropharyngeal PCR negative or presented for reasons unrelated to COVID-19 and were not tested. Positive percent agreement between the Cepheid and Hologic assays was 93% (95% confidence interval CI: 81.1% to 98.2%), and negative percent agreement was 96% (95% CI: 89% to 0.99%). Four discrepant specimens (Cepheid positive only,
= 2; Hologic positive only,
= 2) exhibited average cycle threshold (
) values of >37 for the targets detected. Of the 48 patients with PCR-confirmed COVID-19, 23 were positive by both assays (47.9%). For the negative patient group, 2/29 were positive by both assays (6.9%). The two stool PCR-positive, nasopharyngeal/oropharyngeal PCR-negative patients were SARS-CoV-2 IgG positive. Our results demonstrate acceptable agreement between two commercially available molecular assays and support the use of stool PCR to confirm diagnosis when SARS-CoV-2 is undetectable in the upper respiratory tract.
In 2004, we started an intergroup randomized trial of adjuvant imatinib versus no further therapy after R0-R1 surgery in localized, high/intermediate-risk gastrointestinal stromal tumors (GIST) ...patients. Interim analysis results were published in 2015 upon recommendation from an independent data review committee. We report the final outcome of the study.
This was a randomized, open-label, multicenter phase III trial carried out at 112 hospitals in 12 countries. Patients were randomized to 2 years of imatinib, 400 mg daily, or no further therapy after surgery. The primary endpoint was imatinib failure-free survival (IFFS), while relapse-free survival (RFS), relapse-free interval (RFI), overall survival (OS) and toxicity were secondary endpoints. Adjusting for the interim analyses, results on IFFS were assessed on a 4.3% significance level; for the other endpoints, 5% was used.
Nine hundred and eight patients were randomized between January 2005 and October 2008: 454 to imatinib and 454 to observation; 835 patients were eligible. With a median follow-up of 9.1 years, 5 (10)-year IFFS was 87% (75%) in the imatinib arm versus 83% (74%) in the control arm hazard ratio (HR) = 0.87, 95.7% confidence interval (CI) (0.65; 1.15), P = 0.31; RFS was 70% versus 63% at 5 years and 63% versus 61% at 10 years, HR = 0.71, 95% CI (0.57; 0.89), P = 0.002; OS was 93% versus 92% at 5 years and 80% versus 78% at 10 years HR = 0.88, 95% CI (0.65; 1.21), P = 0.43. Among 526 patients with high-risk GIST by local pathology, 10-year IFFS and RFS were 69% versus 61%, and 48% versus 43%, respectively.
With 9.1 years of follow-up, a trend toward better long-term IFFS in imatinib-treated patients was observed in the high-risk subgroup. Although the difference was not statistically significant and the surrogacy value of such an endpoint is not validated, this may be seen as supporting the results reported by the Scandinavian/German trial, showing a sustained small but significant long-term OS benefit in high-risk GIST patients treated with 3 years of adjuvant imatinib.
•Adjuvant imatinib for 2 years significantly improved RFS, with a trend towards a better imatinib failure free survival.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
For coral reef organisms with bipartite lifecycles, the ontogenetic shift from the pelagic larval stage to the benthic environment is often associated with high mortality that may be influenced by ...the local environment as well as individual traits that alter vulnerability to predation. Habitat variability such as food availability and competition for resources can influence traits such as growth and size, ultimately affecting mortality rates as well as the strength or direction of trait-mediated mortality. In this study, we examined subregional patterns of early life-history traits (ELHTs) of a model coral reef fish (bicolor damselfish:
Stegastes partitus
) in environmentally and oceanographically distinct regions of the Florida Keys, USA: the relatively more productive lower Keys (LK) and the more oligotrophic upper Keys (UK). Fish arrived to reef habitats with similar larval ELHTs (larval growth, pelagic larval duration, settlement size) but experienced higher mortality in the LK. Despite variability in mortality rates, patterns of selective mortality were similar between the UK and LK. For juvenile fish, growth during the first 4 days post-settlement was significantly faster in the LK compared to the UK, potentially linked to higher productivity and food availability. Results of this study indicate that environmental variability in settlement habitat at subregional spatial scales can affect post-settlement growth and survival of young fish soon after they transition to the demersal juvenile stage in coral reef environments.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
During a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. Machine learning models have been proposed to accurately predict COVID-19 ...disease severity. Previous studies have typically tested only one machine learning algorithm and limited performance evaluation to area under the curve analysis. To obtain the best results possible, it may be important to test different machine learning algorithms to find the best prediction model.
In this study, we aimed to use automated machine learning (autoML) to train various machine learning algorithms. We selected the model that best predicted patients' chances of surviving a SARS-CoV-2 infection. In addition, we identified which variables (ie, vital signs, biomarkers, comorbidities, etc) were the most influential in generating an accurate model.
Data were retrospectively collected from all patients who tested positive for COVID-19 at our institution between March 1 and July 3, 2020. We collected 48 variables from each patient within 36 hours before or after the index time (ie, real-time polymerase chain reaction positivity). Patients were followed for 30 days or until death. Patients' data were used to build 20 machine learning models with various algorithms via autoML. The performance of machine learning models was measured by analyzing the area under the precision-recall curve (AUPCR). Subsequently, we established model interpretability via Shapley additive explanation and partial dependence plots to identify and rank variables that drove model predictions. Afterward, we conducted dimensionality reduction to extract the 10 most influential variables. AutoML models were retrained by only using these 10 variables, and the output models were evaluated against the model that used 48 variables.
Data from 4313 patients were used to develop the models. The best model that was generated by using autoML and 48 variables was the stacked ensemble model (AUPRC=0.807). The two best independent models were the gradient boost machine and extreme gradient boost models, which had an AUPRC of 0.803 and 0.793, respectively. The deep learning model (AUPRC=0.73) was substantially inferior to the other models. The 10 most influential variables for generating high-performing models were systolic and diastolic blood pressure, age, pulse oximetry level, blood urea nitrogen level, lactate dehydrogenase level, D-dimer level, troponin level, respiratory rate, and Charlson comorbidity score. After the autoML models were retrained with these 10 variables, the stacked ensemble model still had the best performance (AUPRC=0.791).
We used autoML to develop high-performing models that predicted the survival of patients with COVID-19. In addition, we identified important variables that correlated with mortality. This is proof of concept that autoML is an efficient, effective, and informative method for generating machine learning-based clinical decision support tools.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
We present the transient source detection efficiencies of the Palomar Transient Factory (PTF), parameterizing the number of transients that PTF found versus the number of similar transients that ...occurred over the same period in the survey search area but were missed. PTF was an optical sky survey carried out with the Palomar 48 inch telescope over 2009-2012, observing more than 8000 square degrees of sky with cadences of between one and five days, locating around 50,000 non-moving transient sources, and spectroscopically confirming around 1900 supernovae. We assess the effectiveness with which PTF detected transient sources, by inserting million artificial point sources into real PTF data. We then study the efficiency with which the PTF real-time pipeline recovered these sources as a function of the source magnitude, host galaxy surface brightness, and various observing conditions (using proxies for seeing, sky brightness, and transparency). The product of this study is a multi-dimensional recovery efficiency grid appropriate for the range of observing conditions that PTF experienced and that can then be used for studies of the rates, environments, and luminosity functions of different transient types using detailed Monte Carlo simulations. We illustrate the technique using the observationally well-understood class of type Ia supernovae.
•A new collision scheme is developed for variable-weight DSMC simulations.•The scheme is aimed at improving simulation of low-probability processes.•Use of the scheme is shown to drastically reduce ...noise in ionization computations.•Possible future extensions to the scheme are proposed.
We propose a new scheme for simulation of collisions with multiple possible outcomes in variable-weight DSMC computations. The scheme is applied to a 0-D ionization rate coefficient computation, and 1-D electrical breakdown simulation. We show that the scheme offers a significant (up to an order of magnitude) improvement in the level of stochastic noise over the usual acceptance-rejection algorithm, even when controlling for the slight additional computational costs. The benefits and performance of the scheme are analyzed in detail, and possible extensions are proposed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We search Dark Energy Survey (DES) Year 3 imaging for galaxy-galaxy strong gravitational lenses using convolutional neural networks, extending previous work with new training sets and covering a ...wider range of redshifts and colors. We train two neural networks using images of simulated lenses, then use them to score postage-stamp images of 7.9 million sources from DES chosen to have plausible lens colors based on simulations. We examine 1175 of the highest-scored candidates and identify 152 probable or definite lenses. Examining an additional 20,000 images with lower scores, we identify a further 247 probable or definite candidates. After including 86 candidates discovered in earlier searches using neural networks and 26 candidates discovered through visual inspection of blue-near-red objects in the DES catalog, we present a catalog of 511 lens candidates.
CD4+CD25+Foxp3+ Tregs play a major role in prevention of autoimmune diseases. The suppressive effect of Tregs on effector T cells (Teffs), the cells that can mediate autoimmunity, has been ...extensively studied. However, the in vivo impact of Teff activation on Tregs during autoimmunity has not been explored. In this study, we have shown that CD4+ Teff activation strongly boosts the expansion and suppressive activity of Tregs. This helper function of CD4+ T cells, which we believe to be novel, was observed in the pancreas and draining lymph nodes in mouse recipients of islet-specific Teffs and Tregs. Its physiological impact was assessed in autoimmune diabetes. When islet-specific Teffs were transferred alone, they induced diabetes. Paradoxically, when the same Teffs were cotransferred with islet-specific Tregs, they induced disease protection by boosting Treg expansion and suppressive function. RNA microarray analyses suggested that TNF family members were involved in the Teff-mediated Treg boost. In vivo experiments showed that this Treg boost was partially dependent on TNF but not on IL-2. This feedback regulatory loop between Teffs and Tregs may be critical to preventing or limiting the development of autoimmune diseases.
Neck metastasis is the most important prognostic factor in oral cavity squamous cell carcinomas (SCC). Apart from the T- stage, depth of invasion has been used as a highly predictable factor for ...microscopic neck metastasis, despite the controversy on the exact depth cut off point. Depth of invasion can be determined clinically and radio logically. However, there is no standard tool to determine depth of invasion preoperatively. Although MRI is used widely to stage the head and neck disease, its utility in depth evaluation has not formally been assessed.
To compare preoperative clinical and radiological depth evaluation in oral tongue SCC using the standard pathological depth. To compare clinical and radiological accuracy between superficial (<5 mm) vs. deep invaded tumor (≥5 mm) METHODS: This prospective study used consecutive biopsy-proven oral tongue invasive SCC that presented to the University health network (UHN), Toronto. Clinical examination, radiological scan and appropriate staging were determined preoperatively. Standard pathology reports postoperatively were reviewed to determine the depth of invasion from the tumor specimen.
72 tumour samples were available for analysis and 53 patients were included. For all tumors, both clinical depth (r = 0.779; p < 0.001) and radiographic depth (r =0.907; p <0.001) correlated well with pathological depth, with radiographic depth correlating slightly better. Clinical depth also correlated well with radiographic depth (r = 0.731; p < 0.001). By contrast, for superficial tumors (less than 5 mm on pathological measurement) neither clinical (r = 0.333, p = 0.34) nor radiographic examination (r = - 0.211; p = 0.56) correlated with pathological depth of invasion.
This is the first study evaluating the clinical assessment of tumor thickness in comparison to radiographic interpretation in oral cavity cancer. There are strong correlations between pathological, radiological, and clinical measurements in deep tumors (≥5 mm). In superficial tumors (<5 mm), clinical and radiological examination had low correlation with pathological thickness.
By leveraging tumorgraft (patient-derived xenograft) RNA-sequencing data, we developed an empirical approach, DisHet, to dissect the tumor microenvironment (eTME). We found that 65% of previously ...defined immune signature genes are not abundantly expressed in renal cell carcinoma (RCC) and identified 610 novel immune/stromal transcripts. Using eTME, genomics, pathology, and medical record data involving >1,000 patients, we established an inflamed pan-RCC subtype (IS) enriched for regulatory T cells, natural killer cells, T
1 cells, neutrophils, macrophages, B cells, and CD8
T cells. IS is enriched for aggressive RCCs, including
-deficient clear-cell and type 2 papillary tumors. The IS subtype correlated with systemic manifestations of inflammation such as thrombocytosis and anemia, which are enigmatic predictors of poor prognosis. Furthermore, IS was a strong predictor of poor survival. Our analyses suggest that tumor cells drive the stromal immune response. These data provide a missing link between tumor cells, the TME, and systemic factors.
We undertook a novel empirical approach to dissect the renal cell carcinoma TME by leveraging tumorgrafts. The dissection and downstream analyses uncovered missing links between tumor cells, the TME, systemic manifestations of inflammation, and poor prognosis.
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