In the present study, α-amylase from Streptomyces griseus TBG19NRA1 was amplified, cloned and successfully expressed in E. coli BL21/DE3. Sequence analysis of S. griseus α-amylase (SGAmy) revealed the presence of four domains (A, B, C and E). Alpha-amylases with E domain (also known as carbohydrate binding module 20 (CBM20)) are capable of degrading raw starch and this property holds great potential for application in starch processing industries. Though α-amylase is a well-studied and characterized enzyme, there is no experimental structure available for this four domain-containing α-amylases. To gain more insight about SGAmy structure and function, homology modelling was performed using a multi-template method. The template α-amylase from Pseudoalteromonas haloplanktis (PDB ID 1AQH) and E domain of Cyclodextrin glucanotransferase from Bacillus circulans (PDB ID 1CGY) was found to have significant similarity with the complete target sequence of SGAmy. Therefore, homology model for SGAmy was generated from the crystal structure of 1AQH and 1CGY and the resulting structure was subjected to 10 ns molecular dynamics (MD) simulation. Remarkably, CBM20 domain of SGAmy showed greater flexibility in MD simulation than other three domains. This observation is highly rational as this part of SGAmy is strongly implicated in substrate (raw starch) binding. Thus, conformational plasticity at CBM20 is functionally beneficial.Communicated by Ramaswamy H. Sarma.BACKGROUND/OBJECTIVE Data regarding delirium in patients presenting with infections of the central nervous system, such as meningitis and/or encephalitis (ME), are scarce. We aimed to determine the frequency and early predictors of delirium in the acute phase of ME. METHODS We assessed clinical, radiologic, and laboratory data of patients with ME at a Swiss academic medical center from 2011 to 2017. The highest Intensive Care Delirium Screening Checklist (ICDSC) score was assessed within 24 hours around lumbar puncture. Multivariable logistic regression was performed to identify predictors of delirium (ICDSC ≥4). RESULTS Among 330 patients with ME, infectious pathogens were identified in 41%. An ICDSC >1 was found in 28% with and 19% without identified infectious pathogens. Delirium was diagnosed in 18% with and 14% without infectious pathogens and significantly associated with prolonged in-hospital treatment and mechanical ventilation, more frequent administration of neuroleptics and anesthetics (in 96% with delirium vs 35% without), complications, and less recovery to premorbid functional baseline. Low serum albumin at presentation was the only independent predictor of delirium (area under the receiver-operating curve [AUROC] = 0.792) in patients with pathogens. In patients with infections, the AUROC was smallest for encephalitis (AUROC = 0.641) and larger for patients with meningeal infections (meningitis AUROC = 0.807; meningoencephalitis AUROC = 0.896). CONCLUSIONS Delirium in the context of ME is seen in almost every fifth patient and linked to prolonged treatment, complications, and incomplete recovery. Among clinical, radiologic, and laboratory parameters, the good calibration and discrimination of low albumin serum concentrations for the prediction of delirium in patients with ME seem promising, especially if meninges are affected.Background Chronic pain is a complex condition frequently encountered in nursing practice, resulting in negative multidimensional effects on the individual and healthcare system. Increasingly, people with chronic pain are turning to Complementary and Alternative Medicine (CAM) to manage their pain.Objectives To explore the relationship between healthcare access, unmet healthcare needs, and practitioner-based Complementary and Alternative Medicine use in adults with chronic pain.Design A secondary analysis of 1688 individuals ≥18 years old self-reporting chronic pain from Cycle 9 of the Canadian National Population Health Survey.Methods Multivariate logistic regression and descriptive statistics.Results When controlling for demographics and health status indicators, the presence of unmet healthcare needs was found to predict CAM use (p  less then  0.001; OR 2.02; CI [1.45, 2.81]), along with sex, education, income, employment, and restriction of activities.Conclusion People may be using CAM due to shortcomings of the conventional healthcare system, with implications for policymakers and healthcare professions to develop more integrative strategies to improve chronic pain management.Impact statement Having unmet healthcare needs is associated with two-fold increased odds of using Complementary and Alternative Medicine in Canadian adults with chronic pain.Background. Accurate prediction of clinical impairment in upper-extremity motor function following therapy in chronic stroke patients is a difficult task for clinicians but is key in prescribing appropriate therapeutic strategies. Machine learning is a highly promising avenue with which to improve prediction accuracy in clinical practice. Objectives. The objective was to evaluate the performance of 5 machine learning methods in predicting postintervention upper-extremity motor impairment in chronic stroke patients using demographic, clinical, neurophysiological, and imaging input variables. Methods. A total of 102 patients (female 31%, age 61 ± 11 years) were included. The upper-extremity Fugl-Meyer Assessment (UE-FMA) was used to assess motor impairment of the upper limb before and after intervention. Elastic net (EN), support vector machines, artificial neural networks, classification and regression trees, and random forest were used to predict postintervention UE-FMA. The performances of methods were compared using cross-validated R2.  https://www.selleckchem.com/products/deg-77.html  Results. EN performed significantly better than other methods in predicting postintervention UE-FMA using demographic and baseline clinical data (median REN2=0.91,RRF2=0.88,RANN2=0.83,RSVM2=0.79,RCART2=0.70; P less then .05). Preintervention UE-FMA and the difference in motor threshold (MT) between the affected and unaffected hemispheres were the strongest predictors. The difference in MT had greater importance than the absence or presence of a motor-evoked potential (MEP) in the affected hemisphere. Conclusion. Machine learning methods may enable clinicians to accurately predict a chronic stroke patient's postintervention UE-FMA. Interhemispheric difference in the MT is an important predictor of chronic stroke patients' response to therapy and, therefore, could be included in prospective studies.