Integration of transcriptomics and metabolomics data can provide detailed information for better understanding the molecular mechanisms underlying salt tolerance in rice. In the present study, we report a comprehensive analysis of the transcriptome and metabolome of rice overexpressing the OsDRAP1 gene, which encodes an ERF transcription factor and was previously identified to be conferring drought tolerance. Phenotypic analysis showed that OsDRAP1 overexpression (OE) improved salt tolerance by increasing the survival rate under salt stress. OsDRAP1 affected the physiological indices such as superoxide dismutase (SOD), catalase (CAT) and malondialdehyde (MDA) to enhance redox homeostasis and membrane stability in response to salt stress. Higher basal expression of OsDRAP1 resulted in differential expression of genes that potentially function in intrinsic salt tolerance. A core set of genes with distinct functions in transcriptional regulation, organelle gene expression and ion transport were substantially up-regulated in the OE line in response to salt stress, implying their important role in OsDRAP1-mediated salt tolerance. Correspondingly, metabolome profiling detected a number of differentially metabolites in the OE line relative to the wild type under salt stress. These metabolites, including amino acids (proline, valine), organic acids (glyceric acid, phosphoenolpyruvic acid and ascorbic acid) and many secondary metabolites, accumulated to higher levels in the OE line, demonstrating their role in salt tolerance. Integration of transcriptome and metabolome analysis highlights the crucial role of amino acids and carbohydrate metabolism pathways in OsDRAP1-mediated salt tolerance.Monitoring the pain intensity in critically ill patients is crucial because intense pain can cause adverse events, including poor survival rates; however, continuous pain evaluation is difficult. Vital signs have traditionally been considered ineffective in pain assessment; nevertheless, the use of machine learning may automate pain assessment using vital signs. This retrospective observational study was performed at a university hospital in Sendai, Japan. Objective pain assessments were performed in eligible patients using the Critical-Care Pain Observation Tool (CPOT). Three machine-learning methods-random forest (RF), support vector machine (SVM), and logistic regression (LR)-were employed to predict pain using parameters, such as vital signs, age group, and sedation levels. Prediction accuracy was calculated as the harmonic mean of sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Furthermore, 117,190 CPOT assessments were performed in 11,507 eligible patients (median age 65 years; 58.0% males). We found that pain prediction was possible with all three machine-learning methods. RF demonstrated the highest AUROC for the test data (RF 0.853, SVM 0.823, and LR 0.787). With this method, pain can be objectively, continuously, and semi-automatically evaluated in critically ill patients.Data on chronic postsurgical pain (CPSP) after otorhinolaryngological surgery are sparse. Adult in-patients treated in 2017 were included into the prospective PAIN OUT registry. Patients' pain on the first postoperative day (D1), after six months (M6) and 12 months (M12) were evaluated. Determining factor for CPSP was an average pain intensity ≥ 3 (numeric rating scale 0-10) at M6. Risk factors associated with CPSP were evaluated by univariate and multivariate analyses. 10% of 191 included patients (60% male, median age 52 years; maximal pain at D1 3.5 ± 2.7), had CPSP. Average pain at M6 was 0.1 ± 0.5 for patients without CPSP and 4.2 ± 1.2 with CPSP. Average pain with CPSP still was 3.7 ± 1.1 at M12. Higher ASA status (Odds ratio [OR] = 4.052; 95% confidence interval [CI] = 1.453-11.189; p = 0.007), and higher minimal pain at D1 (OR = 1.721; CI = 1.189-2.492; p = 0.004) were independent predictors of CPSP at M6. Minimal pain at D1 (OR = 1.443; CI = 1.008-2.064; p = 0.045) and maximal pain at M6 (OR = 1.665; CI = 1.340-2.069; p  less then  0.001) were independent predictors for CPSP at M12. CPSP is an important issue after otorhinolaryngological surgery.  https://www.selleckchem.com/products/deferoxamine-mesylate.html  Better instrument for perioperative assessment should be defined to identify patients at risk for CPSP.Early and accurate diagnosis is critical in reducing the morbidity and mortality associated with malaria. Microscopy (MI) is the current diagnostic gold standard in the field; however, it requires expert personnel, is time-consuming, and has limited sensitivity. Although rapid diagnostic tests for antigen detection (RDTs) are an alternative to diagnosis, they also have limited sensitivity and produce false positive results in detecting recent past infection. The automated hematology analyzer XN-31 prototype (XN-31p) (Sysmex Corporation, Kobe, Japan) is able to identify plasmodium-infected erythrocytes, count parasitemia and perform complete blood-cell counts within one minute. The performance of the XN-31p in diagnosing malaria was evaluated and compared with real-time polymerase chain reaction (qPCR), MI and RDT in an endemic area of Colombia where Plasmodium falciparum and Plasmodium vivax are present. Acute febrile patients were enrolled from July 2018 to April 2019 in Quibdó, Colombia. Malaria diagnoses w and an average difference of - 3096 parasites/µL when compared with thick-smear MI and an ICC of 0.98 (95% CI 0.97-0.98) and an average difference of - 0.0013% when compared with thin-smear MI. The XN-31p offers a rapid and accurate alternative method for diagnosing malaria in clinical laboratories in areas where P. falciparum and P. vivax cocirculate.While prolactinoma patients have high bone turnover, current data are inconclusive when it comes to determining whether correction of hyperprolactinemia and associated hypogandism improves osteodensitometric data in men and women over the long term. In a large cohort of including 40 men and 60 women, we studied the long-term impact of prolactinoma treatment on bone mineral density (BMD) in men versus women, assessed adverse effects of a primary surgical or medical approach, and evaluated data for risk factors for impaired BMD at last follow-up using multivariate regression analyses. Median duration of follow-up was 79 months (range 13-408 months). Our data indicate that the prevalence of impaired BMD remained significantly higher in men (37%) than in women (7%, p  less then  0.001), despite the fact that hyperprolactinemia and hypogonadism are under control in the majority of men. We found that persistent hyperprolactinemia and male sex were independent risk factors for long-term bone impairment. Currently, osteoporosis prevention and treatment focus primarily on women, yet special attention to bone loss in men with prolactinomas is advised.