TCR-Pred: A new web-application for prediction of epitope and MHC specificity for CDR3 TCR sequences using molecular fragment descriptors.

The search for the relationships between CDR3 TCR sequences and epitopes or MHC types is a challenging task in modern immunology. We propose a new approach to develop the classification models of structure-activity relationships (SAR) using molecular fragment descriptors MNA (Multilevel Neighbourhoods of Atoms) to represent CDR3 TCR sequences and the naïve Bayes classifier algorithm. We have created the freely available TCR-Pred web application (http://way2drug.com/TCR-pred/) to predict the interactions between α chain CDR3 TCR sequences and 116 epitopes or 25 MHC types, as well as the interactions between ß chain CDR3 TCR sequences and 202 epitopes or 28 MHC types. The TCR-Pred web application is based on the data (more 250 000 unique CDR3 TCR sequences) from VDJdb, McPAS-TCR, and IEDB databases and the proposed approach. The average AUC values of the prediction accuracy calculated using a 20-fold cross-validation procedure varies from 0.857 to 0.884. The created web application may be useful in studies related with T-cell profiling based on CDR3 TCR sequences.

Pred-SF: A Precipitation Prediction Model Based on Deep Neural Networks.

How to predict precipitation accurately and efficiently is the key and difficult problem in the field of weather forecasting. At present, we can obtain accurate meteorological data through many high-precision weather sensors and use them to forecast precipitation. However, the common numerical weather forecasting methods and radar echo extrapolation methods have insurmountable defects. Based on some common characteristics of meteorological data, this paper proposes a Pred-SF model for precipitation prediction in target areas. The model focuses on the combination of multiple meteorological modal data to carry out self-cyclic prediction and a step-by-step prediction structure. The model divides the precipitation prediction into two steps. In the first step, the spatial encoding structure and PredRNN-V2 network are used to construct the autoregressive spatio-temporal prediction network for the multi-modal data, and the preliminary predicted value of the multi-modal data is generated frame by frame. In the second step, the spatial information fusion network is used to further extract and fuse the spatial characteristics of the preliminary predicted value and, finally, output the predicted precipitation value of the target region. In this paper, ERA5 multi-meteorological mode data and GPM precipitation measurement data are used for testing to predict the continuous precipitation of a specific area for 4 h. The experimental results show that Pred-SF has strong precipitation prediction ability. Some comparative experiments were also set up for comparison to demonstrate the advantages of the combined prediction method of multi-modal data and the stepwise prediction method of Pred-SF.

CLC-Pred 2.0: A Freely Available Web Application for In Silico Prediction of Human Cell Line Cytotoxicity and Molecular Mechanisms of Action for Druglike Compounds.

In vitro cell-line cytotoxicity is widely used in the experimental studies of potential antineoplastic agents and evaluation of safety in drug discovery. In silico estimation of cytotoxicity against hundreds of tumor cell lines and dozens of normal cell lines considerably reduces the time and costs of drug development and the assessment of new pharmaceutical agent perspectives. In 2018, we developed the first freely available web application (CLC-Pred) for the qualitative prediction of cytotoxicity against 278 tumor and 27 normal cell lines based on structural formulas of 59,882 compounds. Here, we present a new version of this web application: CLC-Pred 2.0. It also employs the PASS (Prediction of Activity Spectra for Substance) approach based on substructural atom centric MNA descriptors and a Bayesian algorithm. CLC-Pred 2.0 provides three types of qualitative prediction: (1) cytotoxicity against 391 tumor and 47 normal human cell lines based on ChEMBL and PubChem data (128,545 structures) with a mean accuracy of prediction (AUC), calculated by the leave-one-out (LOO CV) and the 20-fold cross-validation (20F CV) procedures, of 0.925 and 0.923, respectively; (2) cytotoxicity against an NCI60 tumor cell-line panel based on the Developmental Therapeutics Program's NCI60 data (22,726 structures) with different thresholds of IG50 data (100, 10 and 1 nM) and a mean accuracy of prediction from 0.870 to 0.945 (LOO CV) and from 0.869 to 0.942 (20F CV), respectively; (3) 2170 molecular mechanisms of actions based on ChEMBL and PubChem data (656,011 structures) with a mean accuracy of prediction 0.979 (LOO CV) and 0.978 (20F CV). Therefore, CLC-Pred 2.0 is a significant extension of the capabilities of the initial web application.

In silico identification of prolyl hydroxylase inhibitor by per-residue energy decomposition-based pharmacophore approach.

Hypoxia is an effective preconditioning stimulus and many cellular responses to hypoxia are mediated through a transcription control complex termed the hypoxia-inducible factor (HIF). The stability and activation of HIF are governed by HIF prolyl-4-hydroxylases 2 (PHD2). Hence, the development of a small molecule inhibitor for prolyl hydroxylase has been suggested as a potentially useful therapeutic strategy for the treatment of oxidative/ischemic stress conditions. Thus, to unveil a novel human PHD2 inhibitor, a custom-based virtual screening was carried out to identify the potential inhibitors against PHD2 based on; (1) the per-residue energy decomposition (PRED)-based pharmacophore model, (2) molecular docking, and (3) MD approaches. The PRED analysis was performed to identify the common interaction pattern of HIF fragment (5L9B) and crystallized ligand (4JZR) to develop a relevant accurate allosteric pharmacophore model. The custom pharmacophore model (AAARR) was developed and further used to screen multiple databases. The docking was performed as a secondary strategy for screening the pharmacophore hits. Furthermore, the docked complexes were screened by molecular dynamics (MD) simulation and molecular mechanics/generalized Born surface area (MM-GBSA) based binding free energy calculations to determine the binding energy of the inhibitors and to identify crucial interaction energy fingerprint. One hit has demonstrated good binding free energy and a better binding affinity for PHD2 compared to the other four selected ligands. Thus, the results obtained from pharmacophore, docking, and MD simulations depicted that linker length and metal binding in the scaffold could be effectively used as a potent inhibitor toward human PHD2 in AD therapeutics.

ANOX: A robust computational model for predicting the antioxidant proteins based on multiple features.

As an indispensable component of various living organisms, the antioxidant proteins have been studied for anti-aging and prevention of various diseases, such as altitude sickness, coronary heart disease, and even cancer. However, the traditional experimental methods for identifying the antioxidant proteins are very expensive and time-consuming. Thus, to address the challenge, a new predictor, named ANOX, was developed in this study. Multiple features, such as frequency matrix features (FRE), amino acid and dipeptide composition (AADP), evolutionary difference formula features (EEDP), k-separated bigrams (KSB), and PSI-PRED secondary structure (PRED), were extracted to generate the original feature space. To find the optimized feature subset, the Max-Relevance-Max-Distance (MRMD) algorithm was implemented for feature ranking and our model received the best performance with the top 1170 features. Rigorous tests were performed to evaluate the performance of ANOX, and the results showed that ANOX achieved a major improvement in the prediction accuracy of the antioxidant proteins (AUC:0.930 and 0.935 using 5-fold cross-validation or the jackknife test) compared to the state-of-the-art predictor AOPs-SVM (AUC:0.869 and 0.885). The dataset used in this study and the source code of ANOX are all available at https://github.com/NWAFU-LiuLab/ANOX.

Vitamin D intake, serum 25-hydroxy vitamin D and pulmonary function in paediatric patients with cystic fibrosis: a longitudinal approach.

Pancreatic-insufficient children with cystic fibrosis (CF) receive age-group-specific vitamin D supplementation according to international CF nutritional guidelines. The potential advantageous immunomodulatory effect of serum 25-hydroxy vitamin D (25(OH)D) on pulmonary function (PF) is yet to be established and is complicated by CF-related vitamin D malabsorption. We aimed to assess whether current recommendations are optimal for preventing deficiencies and whether higher serum 25(OH)D levels have long-term beneficial effects on PF. We examined the longitudinal relationship between vitamin D intake, serum 25(OH)D and PF in 190 CF children during a 4-year follow-up period. We found a significant relationship between total vitamin D intake and serum 25(OH)D (ß = 0·02; 95 % CI 0·01, 0·03; P = 0·000). However, serum 25(OH)D decreased with increasing body weight (ß = -0·79; 95 % CI -1·28, -0·29; P = 0·002). Furthermore, we observed a significant relationship between serum 25(OH)D and forced expiratory volume in 1 s (ß = 0·056; 95 % CI 0·01, 0·102; P = 0·018) and forced vital capacity (ß = 0·045; 95 % CI 0·008, 0·082; P = 0·017). In the present large study sample, vitamin D intake is associated with serum 25(OH)D levels, and adequate serum 25(OH)D levels may contribute to the preservation of PF in children with CF. Furthermore, to maintain adequate levels of serum 25(OH)D, vitamin D supplementation should increase with increasing body weight. Adjustments of the international CF nutritional guidelines, in which vitamin D supplementation increases with increasing weight, should be considered.

The antiviral activity and cytotoxicity of 15 natural phenolic compounds with previously demonstrated antifungal activity.

The present study attempted to evaluate the carcinogenicity of natural phenolic compounds with previously demonstrated antifungal activity, using a computational structure-cytotoxicity approach, namely the quantum structure cytotoxicity relationship model. The cytotoxicity of 15 phenolic compounds with antiviral activity 96 h after treatment was studied using the AdmetSAR computational program. Per the EPA classification, four of the investigated compounds would be included in the second cytotoxicity category, four in the third category, and six showed no toxicity, rendering the studied natural phenolic compounds much less toxic to aquatic life than synthetic pesticides, the organophosphorus compounds, which mostly fall into the first and second categories of toxicity.

Medium optimization for pyrroloquinoline quinone (PQQ) production by Methylobacillus sp. zju323 using response surface methodology and artificial neural network-genetic algorithm.

Methylobacillus sp. zju323 was adopted to improve the biosynthesis of pyrroloquinoline quinone (PQQ) by systematic optimization of the fermentation medium. The Plackett-Burman design was implemented to screen for the key medium components for the PQQ production. CoCl2 · 6H2O, ρ-amino benzoic acid, and MgSO4 · 7H2O were found capable of enhancing the PQQ production most significantly. A five-level three-factor central composite design was used to investigate the direct and interactive effects of these variables. Both response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA) were used to predict the PQQ production and to optimize the medium composition. The results showed that the medium optimized by ANN-GA was better than that by RSM in maximizing PQQ production and the experimental PQQ concentration in the ANN-GA-optimized medium was improved by 44.3% compared with that in the unoptimized medium. Further study showed that this ANN-GA-optimized medium was also effective in improving PQQ production by fed-batch mode, reaching the highest PQQ accumulation of 232.0 mg/L, which was about 47.6% increase relative to that in the original medium. The present work provided an optimized medium and developed a fed-batch strategy which might be potentially applicable in industrial PQQ production.

BC CLC-Pred: a freely available web-application for quantitative and qualitative predictions of substance cytotoxicity in relation to human breast cancer cell lines.

In silico prediction of cell line cytotoxicity considerably decreases time and financial costs during drug development of new antineoplastic agents. (Q)SAR models for the prediction of drug-like compound cytotoxicity in relation to nine breast cancer cell lines (T47D, ZR-75-1, MX1, Hs-578T, MCF7-DOX, MCF7, Bcap37, MCF7R, BT-20) were created by GUSAR software based on the data from ChEMBL database (v. 30). The separate datasets related with IC50 and IG50 values were used for the creation of (Q)SAR models for each cell line. Based on leave-one-out and 5F CV procedures, 24 reasonable (Q)SAR models were selected for the creation of a freely available web-application (BC CLC-Pred: https://www.way2drug.com/bc/) to predict substance cytotoxicity in relation to human breast cancer cell lines. The mean accuracies of prediction r2, RMSE, Balance Accuracy for the selected (Q)SAR models calculated by 5F CV were 0.599, 0.679 and 0.875, respectively. As a result, BC CLC-Pred provides simultaneous quantitative and qualitative predictions of IC50 and IG50 values for most of the nine breast cancer cell lines, which may be helpful in selecting promising compounds and optimizing lead compounds during the development of new antineoplastic agents against breast cancer.

AKIMLpred: An interpretable machine learning model for predicting acute kidney injury within seven days in critically ill patients based on a prospective cohort study.

BACKGROUND: Early recognition and timely intervention for AKI in critically ill patients were crucial to reduce morbidity and mortality. This study aimed to use biomarkers to construct a optimal machine learning model for early prediction of AKI in critically ill patients within seven days. METHODS: The prospective cohort study enrolled 929 patients altogether who were admitted in ICU including 680 patients in training set (Jiefang Campus) and 249 patients in external testing set (Binjiang Campus). After performing strict inclusion and exclusion criteria, 421 patients were selected in training set for constructing predictive model and 167 patients were selected in external testing for evaluating the predictive performance of resulting model. Urine and blood samples were collected for kidney injury associated biomarkers detection. Baseline clinical information and laboratory data of the study participants were collected. We determined the average prediction efficiency of six machine learning models through 10-fold cross validation. RESULTS: In total, 78 variables were collected when admission in ICU and 43 variables were statistically significant between AKI and non-AKI cohort. Then, 35 variables were selected as independent features for AKI by univariate logistic regression. Spearman correlation analysis was used to remove two highly correlated variables. Three ranking methods were used to explore the influence of 33 variables for further determining the best combination of variables. The gini importance ranking method was found to be applicable for variables filtering. The predictive performance of AKIMLpred which constructed by the XGBoost algorithm was the best among six machine learning models. When the AKIMLpred included the nine features (NGAL, IGFBP7, sCysC, CAF22, KIM-1, NT-proBNP, IL-6, IL-18 and L-FABP) with the highest influence ranking, its model had the best prediction performance, with an AUC of 0.881 and an accuracy of 0.815 in training set, similarly, with an AUC of 0.889 and an accuracy of 0.846 in validation set. Moreover, the performace was slightly outperformed in testing set with an AUC of 0.902 and an accuracy of 0.846. The SHAP algorithm was used to interpret the prediction results of AKIMLpred. The web-calculator of AKIMLpred was shown for predicting AKI with more convenient(https://www.xsmartanalysis.com/model/list/predict/model/html?mid=8065&symbol=11gk693982SU6AE1ms21). AKIMLpred was better than the optimal model built with only routine tests for predicting AKI in critically ill patients within 7 days. CONCLUSION: The model AKIMLpred constructed by the XGBoost algorithm with selecting the nine most influential biomarkers in the gini importance ranking method had the best performance in predicting AKI in critically ill patients within 7 days. This data-driven predictive model will help clinicians to make quick and accurate diagnosis.

The effect of liver transplantation on fatigue in patients with primary biliary cirrhosis: a prospective study.

BACKGROUND & AIMS: The role of liver transplantation (LT) for the relief of fatigue in patients with primary biliary cirrhosis (PBC) is unclear, and while many centers exclude fatigue as an indication for transplantation, there have been no studies to prospectively evaluate the impact of LT on fatigue. We aimed at assessing the severity of fatigue in LT candidates with PBC and the impact of LT on fatigue. METHODS: In a prospective, longitudinal study, we used the PBC-40 questionnaire in 49 adult patients with PBC at listing and at 6, 12, and 24 months after LT and in two sex- and age-matched cohorts of community controls and non-transplanted PBC patients. Correlation analysis was used to assess the relationship between liver function and fatigue. ANOVA was used to compare the variation of fatigue score before and after LT. RESULTS: There was no correlation between MELD and fatigue before LT (r(2)=0.01). Overall, the fatigue score after LT was substantially lower than before LT, falling from 40.7 ± 11.4 pre-transplant to 27.7 ± 9.5, 28.7 ± 10.1, 26.2 ± 10.1 (p<0.0001) at 6, 12, and 24 months after LT, respectively. The same improvement of fatigue was observed in both low-MELD (<17) and high-MELD (≥ 17) patients. Improvement in fatigue was also evident in the comparison with a "non-transplant PBC" control group (31.1 ± 11.6, p=0.03). However, 44% of the total cohort, and 47% of those with low-MELD, for whom the probability of dying of LT may be higher than that of dying without LT, had moderate to severe fatigue (defined as a fatigue score ≥ 29) at two years after LT. Moreover, fatigue scores at two years were higher in the transplant PBC cohort compared to a cohort of community controls (17.8 ± 5.9, p<0.0001). CONCLUSIONS: Liver transplantation is associated with improvement in fatigue in patients with PBC. However, a substantial proportion of patients continue to suffer from significant fatigue after two years. Whether the improvement is enough to justify organ allocation in patients with fatigue alone, without liver failure, is still an open issue. Certainly, in the era of organ shortage, with many patients dying waiting for a graft, this may not represent the optimal use of donated deceased organs.

Identification of intelligence-related proteins through a robust two-layer predictor.

In this study, we advance a robust methodology for identifying specific intelligence-related proteins across phyla. Our approach exploits a support vector machine-based classifier capable of predicting intelligence-related proteins based on a pool of meaningful protein features. For the sake of illustration of our proposed general method, we develop a novel computational two-layer predictor, Intell_Pred, to predict query sequences (proteins or transcripts) as intelligence-related or non-intelligence-related proteins or transcripts, subsequently classifying the former sequences into learning and memory-related classes. Based on a five-fold cross-validation and independent blind test, Intell_Pred obtained an average accuracy of 87.48 and 88.89, respectively. Our findings revealed that a score >0.75 (during prediction by Intell_Pred) is a well-grounded choice for predicting intelligence-related candidate proteins in most organisms across biological kingdoms. In particular, we assessed seismonastic movements and associate learning in plants and evaluated the proteins involved using Intell_Pred. Proteins related to seismonastic movement and associate learning showed high percentages of similarities with intelligence-related proteins. Our findings lead us to believe that Intell_Pred can help identify the intelligence-related proteins and their classes using a given protein/transcript sequence.

Influences of Two FEV1 Reference Equations (GLI-2012 and GIRH-2017) on Airflow Limitation Classification Among COPD Patients.

Objective: To explore the clinical effects of different forced expiratory volume in 1s (FEV1) reference equations on chronic obstructive pulmonary disease (COPD) airflow limitation (AFL) classification. Methods: We conducted a COPD screening program for residents over 40 years old from 2019 to 2021. All residents received the COPD screening questionnaire (COPD-SQ) and spirometry. Postbronchodilator FEV1/FVC (forced vital capacity) <0.7 was used as the diagnostic criterion of COPD and two reference equations of FEV1 predicted values were used for AFL severity classification: the European Respiratory Society Global Lung Function Initiative reference equation in 2012 (GLI-2012) and the Guangzhou Institute of Respiratory Health reference equation in 2017 (GIRH-2017). Clinical characteristics of patients in GOLD (Global Initiative for Chronic Obstructive Pulmonary Disease) 1-4 grades classified by the two reference equations were compared. Results: Among 3524 participants, 659 subjects obtained a COPD-SQ score of 16 or more and 743 participants were found to have AFL. The COPD-SQ showed high sensitivity (59%) and specificity (91%) in primary COPD screening. Great differences in COPD severity classification were found when applying the two equations (p < 0.001). Compared with GIRH-2017, patients with AFL classified by GLI-2012 equations were significantly severer. The relationship between symptom scores, acute exacerbation (AE) history distributions and COPD severities classified by the two equations showed a consistent trend of positive but weak correlation. Group A, B, C and D existed in all GOLD 1 to 3 COPD patients, but in GOLD 4, only Groups B and D existed. However, no clear significant differences were found in symptoms, AE risk assessments, risk factors exposure and even the combined ABCD grouping under the two equations. Conclusion: There were significant differences in COPD AFL severity classification with GLI-2012 and GIRH-2017 FEV1 reference equations. But these severity estimation differences did not affect symptoms, AE risk assessments and ABCD grouping of patients at all GOLD grades.

DHU-Pred: accurate prediction of dihydrouridine sites using position and composition variant features on diverse classifiers.

Background: Dihydrouridine (D) is a modified transfer RNA post-transcriptional modification (PTM) that occurs abundantly in bacteria, eukaryotes, and archaea. The D modification assists in the stability and conformational flexibility of tRNA. The D modification is also responsible for pulmonary carcinogenesis in humans. Objective: For the detection of D sites, mass spectrometry and site-directed mutagenesis have been developed. However, both are labor-intensive and time-consuming methods. The availability of sequence data has provided the opportunity to build computational models for enhancing the identification of D sites. Based on the sequence data, the DHU-Pred model was proposed in this study to find possible D sites. Methodology: The model was built by employing comprehensive machine learning and feature extraction approaches. It was then validated using in-demand evaluation metrics and rigorous experimentation and testing approaches. Results: The DHU-Pred revealed an accuracy score of 96.9%, which was considerably higher compared to the existing D site predictors. Availability and Implementation: A user-friendly web server for the proposed model was also developed and is freely available for the researchers.

Pedunculopontine Nucleus--Rapid Eye Movement Sleep--Electroencephalogram--Desynchronization (PRED) Axis in the Evolution of Epilepsy: A Novel Concept.

Epilepsy is one of the commonest and oldest neurological diseases in the history of mankind, the exact pathophysiology of the evolution of which still remains elusive. The intimate and intriguing relation between epilepsy and sleep has been known for a long time. Rapid eye movement sleep (REMS) is well documented to exert potent antiepileptic action in human epilepsies and the underlying mechanism of which is largely based on its property to induce widespread electroencephalogram (EEG)-desynchronization. The pedunculopontine nucleus (PPN) owing to its property to enhance REMS has recently been under study for its potential role in intractable epilepsy (IE) and has been proposed as a novel deep brain stimulation target in IE. This brief paper unfolds the existing role of PPN, REMS, and EEG-desynchronization (PRED) in the evolution of epilepsy in an axial manner, the realization and comprehension of which is likely to open new avenues for further understanding of epileptogenesis, improved treatment of epilepsy and reducing the risk of IE.

MutTMPredictor: Robust and accurate cascade XGBoost classifier for prediction of mutations in transmembrane proteins.

Transmembrane proteins have critical biological functions and play a role in a multitude of cellular processes including cell signaling, transport of molecules and ions across membranes. Approximately 60% of transmembrane proteins are considered as drug targets. Missense mutations in such proteins can lead to many diverse diseases and disorders, such as neurodegenerative diseases and cystic fibrosis. However, there are limited studies on mutations in transmembrane proteins. In this work, we first design a new feature encoding method, termed weight attenuation position-specific scoring matrix (WAPSSM), which builds upon the protein evolutionary information. Then, we propose a new mutation prediction algorithm (cascade XGBoost) by leveraging the idea learned from consensus predictors and gcForest. Multi-level experiments illustrate the effectiveness of WAPSSM and cascade XGBoost algorithms. Finally, based on WAPSSM and other three types of features, in combination with the cascade XGBoost algorithm, we develop a new transmembrane protein mutation predictor, named MutTMPredictor. We benchmark the performance of MutTMPredictor against several existing predictors on seven datasets. On the 546 mutations dataset, MutTMPredictor achieves the accuracy (ACC) of 0.9661 and the Matthew's Correlation Coefficient (MCC) of 0.8950. While on the 67,584 dataset, MutTMPredictor achieves an MCC of 0.7523 and area under curve (AUC) of 0.8746, which are 0.1625 and 0.0801 respectively higher than those of the existing best predictor (fathmm). Besides, MutTMPredictor also outperforms two specific predictors on the Pred-MutHTP datasets. The results suggest that MutTMPredictor can be used as an effective method for predicting and prioritizing missense mutations in transmembrane proteins. The MutTMPredictor webserver and datasets are freely accessible at http://csbio.njust.edu.cn/bioinf/muttmpredictor/ for academic use.

Non-fasting bioelectrical impedance analysis in cystic fibrosis: Implications for clinical practice and research.

BACKGROUND: Nutritional status affects pulmonary function in cystic fibrosis (CF) patients and can be monitored by using bioelectrical impedance analysis (BIA). BIA measurements are commonly performed in the fasting state, which is burdensome for patients. We investigated whether fasting is necessary for clinical practice and research. METHODS: Fat free mass (FFM) and fat mass (FM) were determined in adult CF patients (n = 84) by whole body single frequency BIA (Bodystat 500) in a fasting and non-fasting state. Fasting and non-fasting BIA outcomes were compared with Bland-Altman plots. Pulmonary function was expressed as Forced Expiratory Volume at 1 s percentage predicted (FEV1%pred). Comparability of the associations between fasting and non-fasting body composition measurements with FEV1%pred was assessed by multiple linear regression. RESULTS: Fasting FFM, its index (FFMI), and phase angle were significantly lower than non-fasting estimates (-0.23 kg, p = 0.006, -0.07 kg/m2, p = 0.002, -0.10°, p = 0.000, respectively). Fasting FM and its index (FMI) were significantly higher than non-fasting estimates (0.22 kg, p = 0.008) 0.32%, p = 0.005, and 0.07 kg/m2, (p = 0.005). Differences between fasting and non-fasting FFM and FM were <1 kg in 86% of the patients. FFMI percentile estimates remained similar in 83% of the patients when measured after nutritional intake. Fasting and non-fasting FFMI showed similar associations with FEV1%pred (ß: 4.3%, 95% CL: 0.98, 7.70 and ß: 4.6%, 95% CI: 1.22, 8.00, respectively). CONCLUSION: Differences between fasting and non-fasting FFM and FM were not clinically relevant, and associations with pulmonary function remained similar. Therefore, BIA measurements can be performed in a non-fasting state.

Antimicrobial Activity of Nitrogen-Containing 5-Alpha-androstane Derivatives: In Silico and Experimental Studies.

We evaluated the antimicrobial activity of thirty-one nitrogen-containing 5-alpha-androstane derivatives in silico using computer program PASS (Prediction of Activity Spectra for Substances) and freely available PASS-based web applications (www.way2drug.com). Antibacterial activity was predicted for 27 out of 31 molecules; antifungal activity was predicted for 25 out of 31 compounds. The results of experiments, which we conducted to study the antimicrobial activity, are in agreement with the predictions. All compounds were found to be active with MIC (Minimum Inhibitory Concentration) and MBC (Minimum Bactericidal Concentration) values in the range of 0.0005-0.6 mg/mL. The activity of all studied 5-alpha-androstane derivatives exceeded or was equal to those of Streptomycin and, except for the 3ß-hydroxy-17α-aza-d-homo-5α-androstane-17-one, all molecules were more active than Ampicillin. Activity against the resistant strains of E. coli, P. aeruginosa, and methicillin-resistant Staphylococcus aureus was also shown in experiments. Antifungal activity was determined with MIC and MFC (Minimum Fungicidal Concentration) values varying from 0.007 to 0.6 mg/mL. Most of the compounds were found to be more potent than the reference drugs Bifonazole and Ketoconazole. According to the results of docking studies, the putative targets for antibacterial and antifungal activity are UDP-N-acetylenolpyruvoylglucosamine reductase and 14-alpha demethylase, respectively. In silico assessments of the acute rodent toxicity and cytotoxicity obtained using GUSAR (General Unrestricted Structure-Activity Relationships) and CLC-Pred (Cell Line Cytotoxicity Predictor) web-services were low for the majority of compounds under study, which contributes to the chances for those compounds to advance in the development.

Very Low Frequency Heart Rate Variability Predicts the Development of Post-Stroke Infections.

Stroke-induced immunodepression is a major risk factor for severe infectious complications in the immediate post-stroke period. We investigated the predictive value of heart rate variability (HRV) to identify patients at risk of post-stroke infection, systemic inflammatory response syndrome, or severe sepsis during the post-acute interval from days 3 to 5 after stroke onset. A prospective, observational monocentric cohort study was conducted in a university hospital stroke unit of patients with ischemic infarction in the territory of the middle cerebral artery without an ongoing infection at admission. Standard HRV indices were processed from Holter ECG. Recording started within the first day after the onset of stroke. Infection (primary endpoint: pneumonia, urinary tract, unknown localization) was assessed between days 3 and 5. The predictive value of HRV adjusted for clinical data was analyzed by logistic regression models and area under the receiver operating characteristic curve (AUC). From 287 eligible patients, data of 89 patients without event before completion of 24-h Holter ECG were appropriate for prediction of infection (34 events). HRV was significantly associated with incident infection even after adjusting for clinical covariates. Very low frequency (VLF) band power adjusted for both, the National Institutes of Health Stroke Scale (NIHSS) at admission and diabetes predicted infection with AUC = 0.80 (cross-validation AUC = 0.74). A model with clinical data (diabetes, NIHSS at admission, involvement of the insular cortex) performed similarly well (AUC = 0.78, cross-validation AUC = 0.71). Very low frequency HRV, an index of integrative autonomic-humoral control, predicts the development of infectious complications in the immediate post-stroke period. However, the additional predictive value of VLF band power over clinical risk factors such as stroke severity and insular involvement was marginal. The continuous HRV monitoring starting immediately after admission might probably increase the predictive performance of VLF band power. That needs to be clarified in further investigations.

In silico identification and evaluation of new Trypanosoma cruzi trypanothione reductase (TcTR) inhibitors obtained from natural products database of the Bahia semi-arid region (NatProDB).

Trypanosoma cruzi Trypanothione Reductase (TcTR) is one of the therapeutic targets studied in the development of new drugs against Chagas' disease. Due to its biodiversity, Brazil has several compounds of natural origin that were not yet properly explored in drug discovery. Therefore, we employed the Virtual Screening against TcTR aiming to discover new inhibitors from the Natural Products Database of the Bahia Semi-Arid region (NatProDB). This database has a wide chemical diversity favoring the discovery of new chemical entities. Subsequently, we analyzed the best docking conformations using self-organizing maps (AuPosSOM) aiming to verify their interaction sites at TcTR. Finally, the Pred-hERG, the Aggregator Advisor, the FAF-DRUGS and the pkCSM results allowed us to evaluate, respectively, the cardiotoxicity, aggregation capacity, presence of false positives (PAINS) and its toxicity. Thus, we selected three molecules that could be tested in in vitro assays in the hope that the computational results reported here would favor the development of new anti-chagasic drugs.