INTRODUCTION: Aim of this study is to validate some constitutive models by assessing their capabilities in describing and predicting uniaxial and biaxial behavior of porcine aortic tissue. METHODS: 14 samples from porcine aortas were used to perform 2 uniaxial and 5 biaxial tensile tests. Transversal strains were furthermore stored for uniaxial data. The experimental data were fitted by four constitutive models: Holzapfel-Gasser-Ogden model (HGO), model based on generalized structure tensor (GST), Four-Fiber-Family model (FFF) and Microfiber model. Fitting was performed to uniaxial and biaxial data sets separately and descriptive capabilities of the models were compared. Their predictive capabilities were assessed in two ways. Firstly each model was fitted to biaxial data and its accuracy (in term of R2 and NRMSE) in prediction of both uniaxial responses was evaluated. Then this procedure was performed conversely: each model was fitted to both uniaxial tests and its accuracy in prediction of 5 biaxial responses was observed. RESULTS: Descriptive capabilities of all models were excellent. In predicting uniaxial response from biaxial data, microfiber model was the most accurate while the other models showed also reasonable accuracy. Microfiber and FFF models were capable to reasonably predict biaxial responses from uniaxial data while HGO and GST models failed completely in this task. CONCLUSIONS: HGO and GST models are not capable to predict biaxial arterial wall behavior while FFF model is the most robust of the investigated constitutive models. Knowledge of transversal strains in uniaxial tests improves robustness of constitutive models.

• Klíčová slova
• Constitutive modeling, Arterial biomechanics, Mechanical testing, Uniaxial – biaxial tension,
• MeSH
• aorta thoracica * MeSH
• biologické modely * MeSH
• biomechanika MeSH
• mechanické jevy * MeSH
• pevnost v tahu MeSH
• prasata MeSH
• testování materiálů MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

PRIMARY OBJECTIVE: To assess predisposing and precipitating risk factors and create a predictive model for post-stroke delirium. RESEARCH DESIGN: A prospective observational study in a cohort of consecutive patients with ischemic stroke or intracerebral haematoma admitted within 24 hours of stroke onset. METHODS: Patients were assessed daily for delirium during the first week by means of DSM-IV criteria and risk factors were recorded. RESULTS: One hundred patients completed a 7-day evaluation (47 women and 53 men, median age 77 years). An episode of delirium was detected in 43 patients (43%). Using multivariate logistic regression, a predictive statistical model was developed that utilized independent risk factors: age (OR = 1.08; 95% CI = 1.02-1.15); intracerebral haemorrhage (OR = 6.11; 95% CI = 1.62-22.98), lesion volume > 40 ccm (OR = 3.99; 95% CI = 1.29-12.39) and either elevated gamma-glytamyl transferase (OR = 4.88; 95% CI = 1.45-16.35) and elevated serum bilirubin (OR = 3.70; 95% CI = 1.32-10.38) or maximum sequential organ failure assessment score >2 (OR = 3.33; 95% CI = 1.06-10.45) with acceptable sensitivity and specificity (69.0% and 80.7%). In ischemic strokes, total anterior circulation infarctions were more frequently associated with delirium (73.3% developed delirium) compared with the remainder of the groups combined (p = 0.004; OR = 6.66; 95% CI = 1.85-24.01). CONCLUSION: Higher age, metabolic disturbances, intracerebral haemorrhage and larger ischemic hemispheric strokes increase the risk of post-stroke delirium.

• MeSH
• bilirubin krev   MeSH
• C-reaktivní protein metabolismus   MeSH
• časové faktory MeSH
• cerebrální krvácení krev   komplikace   patofyziologie   MeSH
• cévní mozková příhoda krev   komplikace   patofyziologie   MeSH
• delirium krev   etiologie   patofyziologie   MeSH
• Diagnostický a statistický manuál mentálních poruch MeSH
• gama-glutamyltransferasa krev   MeSH
• lidé MeSH
• míra přežití MeSH
• prediktivní hodnota testů MeSH
• prognóza MeSH
• prospektivní studie MeSH
• rizikové faktory MeSH
• senioři MeSH
• teoretické modely MeSH
• věkové faktory MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• bilirubin MeSH
• C-reaktivní protein MeSH
• gama-glutamyltransferasa MeSH

INTRODUCTION: This study aimed to establish efficient, cost-effective, and early predictive models for adverse pregnancy outcomes based on the combinations of a minimum number of miRNA biomarkers, whose altered expression was observed in specific pregnancy-related complications and selected maternal clinical characteristics. METHODS: This retrospective study included singleton pregnancies with gestational hypertension (GH, n = 83), preeclampsia (PE, n = 66), HELLP syndrome (n = 14), fetal growth restriction (FGR, n = 82), small for gestational age (SGA, n = 37), gestational diabetes mellitus (GDM, n = 121), preterm birth in the absence of other complications (n = 106), late miscarriage (n = 34), stillbirth (n = 24), and 80 normal term pregnancies. MiRNA gene expression profiling was performed on the whole peripheral venous blood samples collected between 10 and 13 weeks of gestation using real-time reverse transcription polymerase chain reaction (RT-PCR). RESULTS: Most pregnancies with adverse outcomes were identified using the proposed approach (the combinations of selected miRNAs and appropriate maternal clinical characteristics) (GH, 69.88%; PE, 83.33%; HELLP, 92.86%; FGR, 73.17%; SGA, 81.08%; GDM on therapy, 89.47%; and late miscarriage, 84.85%). In the case of stillbirth, no addition of maternal clinical characteristics to the predictive model was necessary because a high detection rate was achieved by a combination of miRNA biomarkers only [91.67% cases at 10.0% false positive rate (FPR)]. CONCLUSION: The proposed models based on the combinations of selected cardiovascular disease-associated miRNAs and maternal clinical variables have a high predictive potential for identifying women at increased risk of adverse pregnancy outcomes; this can be incorporated into routine first-trimester screening programs. Preventive programs can be initiated based on these models to lower cardiovascular risk and prevent the development of metabolic/cardiovascular/cerebrovascular diseases because timely implementation of beneficial lifestyle strategies may reverse the dysregulation of miRNAs maintaining and controlling the cardiovascular system.

• Klíčová slova
• cardiovascular risk, first-trimester screening, miRNA, predictive models, preventive program, risk factors,
• Publikační typ
• časopisecké články MeSH

Spondylotic cervical cord compression detected by imaging methods is a prerequisite for the clinical diagnosis of spondylotic cervical myelopathy (SCM). Little is known about the spontaneous course and prognosis of clinically "silent" presymptomatic spondylotic cervical cord compression (P-SCCC). The aim of the present study was to update a previously published model predictive for the development of clinically symptomatic SCM, and to assess the early and late risks of this event in a larger cohort of P-SCCC subjects. A group of 199 patients (94 women, 105 men, median age 51 years) with magnetic resonance signs of spondylotic cervical cord compression, but without clear clinical signs of myelopathy, was followed prospectively for at least 2 years (range 2-12 years). Various demographic, clinical, imaging, and electrophysiological parameters were correlated with the time for the development of symptomatic SCM. Clinical evidence of the first signs and symptoms of SCM within the follow-up period was found in 45 patients (22.6%). The 25th percentile time to clinically manifested myelopathy was 48.4 months, and symptomatic SCM developed within 12 months in 16 patients (35.5%). The presence of symptomatic cervical radiculopathy and electrophysiological abnormalities of cervical cord dysfunction detected by somatosensory or motor-evoked potentials were associated with time-to-SCM development and early development (< or =12 months) of SCM, while MRI hyperintensity predicted later (>12 months) progression to symptomatic SCM. The multivariate predictive model based on these variables correctly predicted early progression into SCM in 81.4% of the cases. In conclusion, electrophysiological abnormalities of cervical cord dysfunction together with clinical signs of cervical radiculopathy and MRI hyperintensity are useful predictors of early progression into symptomatic SCM in patients with P-SCCC. Electrophysiological evaluation of cervical cord dysfunction in patients with cervical radiculopathy or back pain is valuable. Meticulous follow-up is justified in high-risk P-SCCC cases.

• MeSH
• dospělí MeSH
• elektrodiagnostika metody   MeSH
• evokované potenciály fyziologie   MeSH
• kohortové studie MeSH
• komprese míchy diagnóza   patofyziologie   MeSH
• krční obratle patologie   patofyziologie   MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie MeSH
• mícha patofyziologie   MeSH
• modely neurologické * MeSH
• nervové dráhy patofyziologie   MeSH
• osteofytóza páteře diagnóza   patofyziologie   MeSH
• prediktivní hodnota testů MeSH
• prognóza MeSH
• progrese nemoci MeSH
• prospektivní studie MeSH
• radikulopatie diagnóza   patofyziologie   MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Current standard treatments for metastatic colorectal cancer (CRC) are based on combination regimens with one of the two chemotherapeutic drugs, irinotecan or oxaliplatin. However, drug resistance frequently limits the clinical efficacy of these therapies. In order to gain new insights into mechanisms associated with chemoresistance, and departing from three distinct CRC cell models, we generated a panel of human colorectal cancer cell lines with acquired resistance to either oxaliplatin or irinotecan. We characterized the resistant cell line variants with regards to their drug resistance profile and transcriptome, and matched our results with datasets generated from relevant clinical material to derive putative resistance biomarkers. We found that the chemoresistant cell line variants had distinctive irinotecan- or oxaliplatin-specific resistance profiles, with non-reciprocal cross-resistance. Furthermore, we could identify several new, as well as some previously described, drug resistance-associated genes for each resistant cell line variant. Each chemoresistant cell line variant acquired a unique set of changes that may represent distinct functional subtypes of chemotherapy resistance. In addition, and given the potential implications for selection of subsequent treatment, we also performed an exploratory analysis, in relevant patient cohorts, of the predictive value of each of the specific genes identified in our cellular models.

• Klíčová slova
• Cell line models, Colorectal cancer, Irinotecan, Oxaliplatin, Resistance,
• MeSH
• biologické modely * MeSH
• chemorezistence * MeSH
• irinotekan MeSH
• kamptothecin analogy a deriváty   farmakologie   MeSH
• kolorektální nádory * farmakoterapie   genetika   metabolismus   MeSH
• lidé MeSH
• nádorové buněčné linie MeSH
• organoplatinové sloučeniny farmakologie   MeSH
• oxaliplatin MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• irinotekan MeSH
• kamptothecin MeSH
• organoplatinové sloučeniny MeSH
• oxaliplatin MeSH

Here, we report the data visualization, analysis and modeling for a large set of 4830 SN 2 reactions the rate constant of which (logk) was measured at different experimental conditions (solvent, temperature). The reactions were encoded by one single molecular graph - Condensed Graph of Reactions, which allowed us to use conventional chemoinformatics techniques developed for individual molecules. Thus, Matched Reaction Pairs approach was suggested and used for the analyses of substituents effects on the substrates and nucleophiles reactivity. The data were visualized with the help of the Generative Topographic Mapping approach. Consensus Support Vector Regression (SVR) model for the rate constant was prepared. Unbiased estimation of the model's performance was made in cross-validation on reactions measured on unique structural transformations. The model's performance in cross-validation (RMSE=0.61 logk units) and on the external test set (RMSE=0.80) is close to the noise in data. Performances of the local models obtained for selected subsets of reactions proceeding in particular solvents or with particular type of nucleophiles were similar to that of the model built on the entire set. Finally, four different definitions of model's applicability domains for reactions were examined.

• Klíčová slova
• Condensed Graph of Reaction, Generative Topographic Mapping, Matched Reaction Pairs, Support Vector Regression, bimolecular nucleophilic substitution reactions, models applicability domain,
• MeSH
• chemické modely * MeSH
• cyklické uhlovodíky chemie   MeSH
• kinetika MeSH
• oxidace-redukce MeSH
• support vector machine * MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• cyklické uhlovodíky MeSH

Co-milling is an effective technique for improving dissolution rate limited absorption characteristics of poorly water-soluble drugs. However, there is a scarcity of models available to forecast the magnitude of dissolution rate improvement caused by co-milling. Therefore, this study endeavoured to quantitatively predict the increase in dissolution by co-milling based on drug properties. Using a biorelevant dissolution setup, a series of 29 structurally diverse and crystalline drugs were screened in co-milled and physically blended mixtures with Polyvinylpyrrolidone K25. Co-Milling Dissolution Ratios after 15 min (COMDR15 min) and 60 min (COMDR60 min) drug release were predicted by variable selection in the framework of a partial least squares (PLS) regression. The model forecasts the COMDR15 min (R2 = 0.82 and Q2 = 0.77) and COMDR60 min (R2 = 0.87 and Q2 = 0.84) with small differences in root mean square errors of training and test sets by selecting four drug properties. Based on three of these selected variables, applicable multiple linear regression equations were developed with a high predictive power of R2 = 0.83 (COMDR15 min) and R2 = 0.84 (COMDR60 min). The most influential predictor variable was the median drug particle size before milling, followed by the calculated drug logD6.5 value, the calculated molecular descriptor Kappa 3 and the apparent solubility of drugs after 24 h dissolution. The study demonstrates the feasibility of forecasting the dissolution rate improvements of poorly water-solube drugs through co-milling. These models can be applied as computational tools to guide formulation in early stage development.

• Klíčová slova
• Ball milling, Co-grinding, Co-milling, Dissolution rate enhancement, In silico modelling, Multiple linear regression, Partial least squares regression,
• MeSH
• léčivé přípravky chemie   MeSH
• metoda nejmenších čtverců MeSH
• počítačová simulace MeSH
• povidon chemie   MeSH
• příprava léků * metody   MeSH
• rozpustnost * MeSH
• uvolňování léčiv * MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• léčivé přípravky MeSH
• povidon MeSH

Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management.

• Klíčová slova
• artificial intelligence, deep learning, heart failure, machine learning, predictive model, systematic review,
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

A long-term goal in evolutionary ecology is to explain the incredible diversity of insect herbivores and patterns of host plant use in speciose groups like tropical Lepidoptera. Here, we used standardized food-web data, multigene phylogenies of both trophic levels and plant chemistry data to model interactions between Lepidoptera larvae (caterpillars) from two lineages (Geometridae and Pyraloidea) and plants in a species-rich lowland rainforest in New Guinea. Model parameters were used to make and test blind predictions for two hectares of an exhaustively sampled forest. For pyraloids, we relied on phylogeny alone and predicted 54% of species-level interactions, translating to 79% of all trophic links for individual insects, by sampling insects from only 15% of local woody plant diversity. The phylogenetic distribution of host-plant associations in polyphagous geometrids was less conserved, reducing accuracy. In a truly quantitative food web, only 40% of pair-wise interactions were described correctly in geometrids. Polyphenol oxidative activity (but not protein precipitation capacity) was important for understanding the occurrence of geometrids (but not pyraloids) across their hosts. When both foliar chemistry and plant phylogeny were included, we predicted geometrid-plant occurrence with 89% concordance. Such models help to test macroevolutionary hypotheses at the community level.

• Klíčová slova
• Geometridae, Papua New Guinea, Pyraloidea, biodiversity, food webs, oxidative activity,
• MeSH
• biologické modely MeSH
• býložravci * MeSH
• deštný prales MeSH
• fylogeneze MeSH
• larva růst a vývoj   fyziologie   MeSH
• listy rostlin chemie   MeSH
• můry růst a vývoj   fyziologie   MeSH
• potravní řetězec * MeSH
• rostliny MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Nová Guinea MeSH

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.

• Klíčová slova
• CORAL software, Environmental risk, Index of ideality of correlation, OECD principles, QSPR/QSAR, Toxicity,
• MeSH
• chemické látky znečišťující vodu toxicita   MeSH
• chemické modely * MeSH
• kvantitativní vztahy mezi strukturou a aktivitou * MeSH
• metoda Monte Carlo MeSH
• monitorování životního prostředí metody   MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• chemické látky znečišťující vodu MeSH