-
Bonner Bekker posted an update 6 months ago
costs and improved the prediction efficiency. Compared with the state-of-the-art calculation method CROSS, RPRes has significantly improved performance.
Delirium is a common disorder among hospitalized older patients and results in increased morbidity and mortality. The prevention of delirium is still challenging in older patient care. The role of antipsychotics in delirium prevention has been limited. Therefore, we conducted a trial to investigate the efficacy of quetiapine use to prevent delirium in hospitalized older medical patients.
This study was a randomized double-blind controlled trial conducted at Ramathibodi Hospital, Bangkok, Thailand. Patients aged ≥65 years hospitalized in the internal medicine service were randomized to quetiapine 12.5 mg or placebo once daily at bedtime for a maximum 7-day duration. The primary end point was delirium incidence. Secondary end points were delirium duration, length of hospital stay, ICU admission, rehospitalization and mortality within 30 and 90 days.
A total of 122 patients were enrolled in the study. Eight (6.6%) left the trial before receiving the first dose of the intervention, whereas 114 (93.4%) were etrospectively registered with the Thai clinical trials registry (TCTR) at clinicaltrials.in.th (TCTR20190927001) on September 26, 2019.
This trial was retrospectively registered with the Thai clinical trials registry (TCTR) at clinicaltrials.in.th (TCTR20190927001) on September 26, 2019.
Protein post-translational modification (PTM) is a key issue to investigate the mechanism of protein’s function. With the rapid development of proteomics technology, a large amount of protein sequence data has been generated, which highlights the importance of the in-depth study and analysis of PTMs in proteins.
We proposed a new multi-classification machine learning pipeline MultiLyGAN to identity seven types of lysine modified sites. Using eight different sequential and five structural construction methods, 1497 valid features were remained after the filtering by Pearson correlation coefficient. To solve the data imbalance problem, Conditional Generative Adversarial Network (CGAN) and Conditional Wasserstein Generative Adversarial Network (CWGAN), two influential deep generative methods were leveraged and compared to generate new samples for the types with fewer samples. Finally, random forest algorithm was utilized to predict seven categories.
In the tenfold cross-validation, accuracy (Acc) and Matthews correlation coefficient (MCC) were 0.8589 and 0.8376, respectively. In the independent test, Acc and MCC were 0.8549 and 0.8330, respectively. learn more The results indicated that CWGAN better solved the existing data imbalance and stabilized the training error. Alternatively, an accumulated feature importance analysis reported that CKSAAP, PWM and structural features were the three most important feature-encoding schemes. MultiLyGAN can be found at https//github.com/Lab-Xu/MultiLyGAN .
The CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.
The CWGAN greatly improved the predictive performance in all experiments. Features derived from CKSAAP, PWM and structure schemes are the most informative and had the greatest contribution to the prediction of PTM.
Coronavirus disease 2019 (COVID-19) is a pandemic infection with substantial risk of death, especially in elderly persons. Information about the prognostic significance of functional status in older patients with COVID-19 is scarce.
Demographic, clinical, laboratory and short-term mortality data were collected of 186 consecutive patients aged ≥ 65 years hospitalized with COVID-19. The data were compared between 4 study groups (1) age 65-79 years without severe functional dependency; (2) age ≥ 80 years without severe functional dependency; (3) age 65-79 years with severe functional dependency; and (4) age ≥ 80 years with severe functional dependency. Multivariate logistic regressions were performed to evaluate the variables that were most significantly associated with mortality in the entire sample.
Statistically significant differences were observed between the groups in the proportions of males (p = 0.007); of patients with diabetes mellitus (p = 0.025), cerebrovascular disease (p < 0.001), renal fa remained one of the variables most significantly associated with mortality (OR 10.42, 95 % CI 3.27-33.24 and p < 0.001).
Among patients with COVID-19, the association of severe functional dependency with mortality is stronger among those aged ≥ 80 years than aged 65-79 years. Assessment of functional status may contribute to decision making for care of older inpatients with COVID-19.
Among patients with COVID-19, the association of severe functional dependency with mortality is stronger among those aged ≥ 80 years than aged 65-79 years. Assessment of functional status may contribute to decision making for care of older inpatients with COVID-19.
To address the need for easy and reliable species classification in plant genetic resources collections, we assessed the potential of five classifiers (Random Forest, Neighbour-Joining, 1-Nearest Neighbour, a conservative variety of 3-Nearest Neighbours and Naive Bayes) We investigated the effects of the number of accessions per species and misclassification rate on classification success, and validated theirs generic value results with three complete datasets.
We found the conservative variety of 3-Nearest Neighbours to be the most reliable classifier when varying species representation and misclassification rate. Through the analysis of the three complete datasets, this finding showed generic value. Additionally, we present various options for marker selection for classification taks such as these.
Large-scale genomic data are increasingly being produced for genetic resources collections. These data are useful to address species classification issues regarding crop wild relatives, and improve genebank documentation.
Please follow & like us :)
All content contained on CatsWannaBeCats.Com, unless otherwise acknowledged,is the property of CatsWannaBeCats.Com and subject to copyright.