Unveiling the Unexpected: Why Doctors Should Look beyond the Lungs when Predicting COVID-19 Mortality.

INTRODUCTION: The main objective of this study was to identify the best combination of admission day parameters for predicting COVID-19 mortality in hospitalized patients. Furthermore, we sought to compare the predictive capacity of pulmonary parameters to that of renal parameters for mortality from COVID-19. METHODS: In this retrospective study, all patients admitted to a tertiary hospital between September 1st, 2020, and December 31st, 2020, who were clinically symptomatic and tested positive for COVID-19, were included. We gathered extensive data on patient admissions, including laboratory results, comorbidities, chest X-ray (CXR) images, and SpO2 levels, to determine their role in predicting mortality. Experienced radiologists evaluated the CXR images and assigned a score from 0 to 18 based on the severity of COVID-19 pneumonia. Further, we categorized patients into two independent groups based on their renal function using the RIFLE and KDIGO criteria to define the acute kidney injury (AKI) and chronic kidney disease (CKD) groups. The first group ("AKI&CKD") was subdivided into six subgroups: normal renal function (A); CKD grade 2+3a (B); AKI-DROP (C); CKD grade 3b (D); AKI-RISE (E); and grade 4 + 5 CKD (F). The second group was based only on estimated glomerular filtration rate (eGFR) at the admission, and thus it was divided into four grades: grade 1, grade 2+3a, grade 3b, and grade 4 + 5. RESULTS: The cohort comprised 619 patients. Patients who died during hospitalization had a significantly higher mean radiological score compared to those who survived, with a p value <0.01. Moreover, we observed that the risk for mortality was significantly increased as renal function deteriorated, as evidenced by the AKI&CKD and eGFR groups (p < 0.001 for each group). Regarding mortality prediction, the area under the curve (AUC) for renal parameters (AKI&CKD group, eGFR group, and age) was found to be superior to that of pulmonary parameters (age, radiological score, SpO2, CRP, and D-dimer) with an AUC of 0.8068 versus 0.7667. However, when renal and pulmonary parameters were combined, the AUC increased to 0.8813. Optimal parameter combinations for predicting mortality from COVID-19 were identified for three medical settings: Emergency Medical Service (EMS), the Emergency Department, and the Internal Medicine Floor. The AUC for these settings was 0.7874, 0.8614, and 0.8813, respectively. CONCLUSIONS: Our study demonstrated that selected renal parameters are superior to pulmonary parameters in predicting COVID-19 mortality for patients requiring hospitalization. When combining both renal and pulmonary factors, the predictive ability of mortality significantly improved. Additionally, we identified the optimal combination of factors for mortality prediction in three distinct settings: EMS, Emergency Department, and Internal Medicine Floor.

The searching for agents for Alzheimer's disease treatment via the system of self-consistent models.

Robust quantitative structure-activity relationships (QSARs) for hBACE-1 inhibitors (pIC50) for a large database (n = 1706) are established. New statistical criteria of the predictive potential of models are suggested and tested. These criteria are the index of ideality of correlation (IIC) and the correlation intensity index (CII). The system of self-consistent models is a new approach to validate the predictive potential of QSAR-models. The statistical quality of models obtained using the CORAL software (http://www.insilico.eu/coral) for the validation sets is characterized by the average determination coefficient R2v= 0.923, and RMSE = 0.345. Three new promising molecular structures which can become inhibitors hBACE-1 are suggested.

The sequence of amino acids as the basis for the model of biological activity of peptides.

The algorithm of building up a model for the biological activity of peptides as a mathematical function of a sequence of amino acids is suggested. The general scheme is the following: The total set of available data is distributed into the active training set, passive training set, calibration set, and validation set. The training (both active and passive) and calibration sets are a system of generation of a model of biological activity where each amino acid obtains special correlation weight. The numerical data on the correlation weights calculated by the Monte Carlo method using the CORAL software (http://www.insilico.eu/coral). The target function aimed to give the best result for the calibration set (not for the training set). The final checkup of the model is carried out with data on the validation set (peptides, which are not visible during the creation of the model). Described computational experiments confirm the ability of the approach to be a tool for the design of predictive models for the biological activity of peptides (expressed by pIC50).

Prevalence and Risk Factors of Osteoporosis in Postmenopausal Women with Type 2 Diabetes Mellitus.

OBJECTIVE: Patients with type 2 diabetes (T2DM) are at increased risk of fractures. The aim of this study is to analyze the prevalence and risk factors of osteoporosis and osteoporosis related fractures in postmenopausal women with T2DM. METHODS: A total of 112 postmenopausal women with T2DM and 171 control nondiabetic women received a standardized questionnaire on osteoporosis risk factors, and were evaluated for bone mineral density (BMD, by using a dual energy X-ray absorptiometry), biochemical markers of bone and glucose metabolism, soluble receptor for advanced glycation end products (sRAGE) and its gene polymorphisms (rs1800625 or rs2070600). RESULTS: In T2DM patients the prevalence of osteoporosis was 25% and low trauma vertebral (Vfx) and non-vertebral fractures were found in 8% and 19% women, respectively. When compared between subjects with and without fractures, there were no significant differences in BMD at any site between the groups, except for distal radius, which was significantly lower in T2DM women with Vfx (p<0.05 vs.non-fractured without osteoporosis). We found no associations between bone and glucose metabolism variables, sRAGE and BMD. No significant differences were observed in sRAGE levels according to their rs1800625, rs 2070600 genotype or fracture prevalence. Serum osteocalcin was significantly lower in T2DM women (p<0.01 vs. controls) and in T2DM women with Vfx (p<0.05) vs. non-fractured without osteoporosis. T2DM women with low daily walking activity (< 2 h daily) had significantly higher serum sclerostin levels (p<0.05 vs. those who were walking > 2 h daily). CONCLUSION: Diabetes-specific parameters as well as RAGE polymorphisms did not associate with BMD or fractures in T2DM postmenopausal women. Lower levels of osteocalcin, namely in those with Vfx and higher sclerostin levels in those with low daily walking activity suggest lower bone remodeling and/or decreased bone quality in T2DM.

High Prevalence of Hypovitaminosis D in Postmenopausal Women with Type 2 Diabetes Mellitus.

The link between vitamin D and type 2 diabetes mellitus (T2DM) is intensively studied. This study aims to define the serum concentration of 25-hydroxyvitamin D (25-OH D) and to investigate the relationship between 25-OH D status, glycated hemoglobin (HbA1c) and body composition in postmenopausal women with T2DM and in non-diabetic controls. In this cross-sectional study, 75 women with T2DM and 32 control subjects were selected. Serum 25-OH D, intact parathyroid hormone (PTH), calcium, fasting glucose and HbA1c, were measured. The mean 25-OH D level was 21.4±11.4 ng/ml (range 4.1-50.7 ng/ml) in diabetic women and 30.3±9.4 ng/ml (range 10.8-54.2 ng/ml) in control group (p<0.001). The prevalence of hypovitaminosis D (<30 ng/ml) was higher in vitamin D3 non-supplemented T2DM women (89% vs. 63% controls); the difference diminished in vitamin D3 (500-1000 IU per day) supplemented subgroups (45% diabetics vs. 42% controls). In T2DM women, 25-OH D levels were not associated to HbA1c, duration of diabetes, fasting glucose and PTH levels, however, 25-OH D levels negatively associated with body mass index (p=0.011), total body fat mass (p=0.005) and total body lean mass (p=0.004). The prevalence of hypovitaminosis D is higher in non-supplemented postmenopausal women with T2DM than in non-diabetic controls (89% vs. 63%). Obesity is a risk factor for vitamin D insufficiency in T2DM postmenopausal women. Further studies evaluating relationships between fat, muscle, bone and vitamin D metabolism in T2DM patients are warranted.

The study of the index of ideality of correlation as a new criterion of predictive potential of QSPR/QSAR-models.

Acetylcholinesterase (AChE) inhibitors, dihydrofolate reductase inhibitors (DHFR), Toxicity in Tetrahymena pyriformis (TP), Acute Toxicity in fathead minnow (TFat), Water solubility (WS), and Acute Aquatic Toxicity in Daphnia magna (DM) are examined as endpoints to establish quantitative structure - property/activity relationships (QSPRs/QSARs). The Index of Ideality of Correlation (IIC) is a measure of predictive potential. The IIC has been studied in a few recent works. The comparison of models for the six endpoints above confirms that the index can be a useful tool for building up and validation of QSPR/QSAR models. All examined endpoints are important from an ecologic point of view. The diversity of examined endpoints confirms that the IIC is real criterion of the predictive potential of a model.

CORAL Software: Analysis of Impacts of Pharmaceutical Agents Upon Metabolism via the Optimal Descriptors.

BACKGROUNDS: The CORAL software has been developed as a tool to build up quantitative structure- activity relationships (QSAR) for various endpoints. OBJECTIVE: The task of the present work was to estimate and to compare QSAR models for biochemical activity of various therapeutic agents, which are built up by the CORAL software. METHOD: The Monte Carlo technique gives possibility to build up predictive model of an endpoint by means of selection of so-called correlation weights of various molecular features extracted from simplified molecular input-line entry system (SMILES). Descriptors calculated with these weights are basis for building up correlations &quot;structure - endpoint&quot;. RESULTS: Optimal descriptors, which are aimed to predict values of endpoints with apparent influence upon metabolism are crytically compared in aspect of their robustness and heuristic potential. Arguments which are confirming the necessity of reformulation of basics of QSARs are listed: (i) each QSAR model is stochastic experiment. The result of this experiment is defined by distribution into the training set and validation set; (ii) predictive potential of a model should be checked up with a group of different splits; and (iii) only model stochastically stable for a group of splits can be estimated as a reliable tool for the prediction. Examples of the improvement of the models previously suggested are demonstrated. CONCLUSION: The current version of the CORAL software remains a convenient tool to build up predictive models. The Monte Carlo technique involved for the software confirms the principle "QSAR is a random event" is important paradigm for the QSPR/QSAR analyses.

Improved building up a model of toxicity towards Pimephales promelas by the Monte Carlo method.

By optimization of so-called correlation weights of attributes of simplified molecular input-line entry system (SMILES) quantitative structure - activity relationships (QSAR) for toxicity towards Pimephales promelas are established. A new SMILES attribute has been utilized in this work. This attribute is a molecular descriptor, which reflects (i) presence of different kinds of bonds (double, triple, and stereo chemical bonds); (ii) presence of nitrogen, oxygen, sulphur, and phosphorus atoms; and (iii) presence of fluorine, chlorine, bromine, and iodine atoms. The statistical characteristics of the best model are the following: n=226, r2=0.7630, RMSE=0.654 (training set); n=114, r2=0.7024, RMSE=0.766 (calibration set); n=226, r2=0.6292, RMSE=0.870 (validation set). A new criterion to select a preferable split into the training and validation sets are suggested and discussed.

The Utilization of the Monte Carlo Technique for Rational Drug Discovery.

Quantitative structure - activity relationships (QSARs) are built up for three endpoints (i) blood-brain barrier permeability; (ii) butyrylcholinesterase (BChE) inhibitory activity; and (iii) for biological effect of antibacterial drugs. The models are based on utilization of the Monte Carlo technique. The CORAL software available on the Internet has been utilized for the calculations. The principles of validation of models together with principles of selection of potential therapeutic agents are suggested. An original version of the definition for the domain of applicability as well as the mechanistic interpretation of model calculated with the Monte Carlo technique are described. Advantages and disadvantages of the utilized approach are discussed.

Searching therapeutic agents for treatment of Alzheimer disease using the Monte Carlo method.

Quantitative structure - activity relationships (QSARs) for the pIC50 (binding affinity) of gamma-secretase inhibitors can be constructed with the Monte Carlo method using CORAL software (http://www.insilico.eu/coral). The considerable influence of the presence of rings of various types with respect to the above endpoint has been detected. The mechanistic interpretation and the domain of applicability of the QSARs are discussed. Methods to select new potential gamma-secretase inhibitors are suggested.