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Primary brainstem haemorrhage (PBH) is characterized by acute onset, rapid deterioration, many complications, and poor prognosis. Its treatment has been controversial. This study aimed to explore the clinical risk factors of postoperative survival and neurological function recovery of stereotactic aspiration in the treatment of PBH. The clinical data of 65 patients with severe brainstem haemorrhage from February 2019 to February 2020 in the First Hospital of Hebei Medical University were reviewed. All patients were treated with stereotactic haematoma aspiration. We determined the survival status of patients at 30 days after the operation and the recovery of neurological function at 90 days. The modified Rankin Scale score (mRS) was used to assess the survival status. The 30-day mortality rate was 23.1% (15 patients). The proportion of patients with good neurological recovery at 90 days after the operation was 32.3% (21 patients). According to the multivariate logistic regression analysis, the haematoma classification was an independent risk factor for postoperative survival (OR = 0.197, 95% CI : 0.016–0.385, p = 0.046) and recovery of neurological function 90 days after surgery (OR = 0.019, 95% CI : 0.001–0.267, p = 0.003). The haematoma classification is an independent risk factor for 30-day mortality and recovery of neurological function 90 days after surgery. Massive and basal-tegmental haematomas were associated with higher mortality. The prognosis of patients with unilateral and bilateral tegmental haematoma was better than that of patients with other haematoma types. [ABSTRACT FROM AUTHOR]

This paper investigates model validation under a variety of different data scenarios and clarifies how different validation metrics may be appropriate for different scenarios. In the presence of multiple uncertainty sources, model validation metrics that compare the distributions of model prediction and observation are considered. Both ensemble validation and point-by-point approaches are discussed, and it is shown how applying the model reliability metric point-by-point enables the separation of contributions from aleatory and epistemic uncertainty sources. After individual validation assessments are made at different input conditions, it may be desirable to obtain an overall measure of model validity across the entire domain. This paper proposes an integration approach that assigns weights to the validation results according to the relevance of each validation test condition to the overall intended use of the model in prediction. Since uncertainty propagation for probabilistic validation is often unaffordable for complex computational models, surrogate models are often used; this paper proposes an approach to account for the additional uncertainty introduced in validation by the uncertain fit of the surrogate model. The proposed methods are demonstrated with a microelectromechanical system (MEMS) example. [ABSTRACT FROM AUTHOR]

Abstract: This paper develops new insights into quantitative methods for the validation of computational model prediction. Four types of methods are investigated, namely classical and Bayesian hypothesis testing, a reliability-based method, and an area metric-based method. Traditional Bayesian hypothesis testing is extended based on interval hypotheses on distribution parameters and equality hypotheses on probability distributions, in order to validate models with deterministic/stochastic output for given inputs. Formulations and implementation details are outlined for both equality and interval hypotheses. Two types of validation experiments are considered—fully characterized (all the model/experimental inputs are measured and reported as point values) and partially characterized (some of the model/experimental inputs are not measured or are reported as intervals). Bayesian hypothesis testing can minimize the risk in model selection by properly choosing the model acceptance threshold, and its results can be used in model averaging to avoid Type I /II errors. It is shown that Bayesian interval hypothesis testing, the reliability-based method, and the area metric-based method can account for the existence of directional bias, where the mean predictions of a numerical model may be consistently below or above the corresponding experimental observations. It is also found that under some specific conditions, the Bayes factor metric in Bayesian equality hypothesis testing and the reliability-based metric can both be mathematically related to the p-value metric in classical hypothesis testing. Numerical studies are conducted to apply the above validation methods to gas damping prediction for radio frequency (RF) micro-electro-mechanical-system (MEMS) switches. The model of interest is a general polynomial chaos (gPC) surrogate model constructed based on expensive runs of a physics-based simulation model, and validation data are collected from fully characterized experiments. [Copyright &y& Elsevier]

Abstract: Polysorbate 80 coated temozolomide-loaded PLGA-based superparamagnetic nanoparticles (P80-TMZ/SPIO-NPs) were successfully synthesized and characterized as drug carriers and diagnosis agent for malignant brain glioma. The mean size of P80-TMZ/SPIO-NPs was 220nm with narrow hydrodynamic particle size distribution. The superparamagnetic characteristic of P80-TMZ/SPIO-NPs was proved by vibration simple magnetometer. P80-TMZ/SPIO-NPs exhibited high drug loading and encapsulation efficiency as well as good sustained drug release performance for 15 days. MTT assay demonstrated the antiproliferative effect of P80-TMZ/SPIO-NPs for C6 glioma cells. Significant cellular uptake of P80-TMZ/SPIO-NPs was evaluated in C6 glioma cells by fluorescence microscopy, Prussian blue staining, and atomic absorption spectrophotometer (AAS) for qualitative and quantitative study, respectively. MRI scanning analyses in vitro indicated that P80-TMZ/SPIO-NPs could be used as a good MRI contrast agent. Polysorbate 80 coated temozolomide-loaded PLGA-based superparamagnetic nanoparticles could be able to promise a multifunctional theragnostic carrier of brain cancer. [Copyright &y& Elsevier]

Abstract: Theragnostics polymer nanoparticles (NPs) loaded simultaneously with anticancer drug docetaxel (Dtxl) and superparamagnetic iron oxide (SPIO) nanocrystals were developed for both cancer therapy and ultrasensitive MRI. These multifunctional polymer vesicles were formed by carboxy-terminated poly(lactic-co-glycolic) acid using a single emulsion evaporation method. The active tumor-targeting single chain prostate stem cell antigen antibodies (scAbPSCA) were conjugated on the surface of polymer vesicles by using functional poly(ethylene glycol). The diameter of NPs was about 147 nm and the SPIO and drug encapsulation efficacy was 23% and 6.02%, respectively. Vibration simple magnetometer and X-ray diffraction proved that the superparamagnetic behavior of SPIO was not changed during NPs formation and modification. The NPs exhibited a triphasic drug release pattern in vitro over 30 days. Enhanced cellular uptake ability and antiproliferative effect of the targeted NPs in prostate cancer PC3 cell line by using the confocal laser scanning microscopy and cytotoxicity assay were observed. Moreover, the Prussian blue staining and the MRI assay in vitro demonstrated that the NPs have a high SPIO clustering effect. Therefore, these stable and tumor-targeting polymer NPs could be promising multifunctional vesicles for simultaneous targeting imaging, drug delivery and real time monitoring of therapeutic effect. [Copyright &y& Elsevier]

Abstract: Accurate characterization and prediction of loading, while properly accounting for uncertainty, are essential for probabilistic fatigue damage prognosis. Three different techniques – rainflow counting, the Markov chain method, and autoregressive moving average (ARMA) modeling – are investigated for stochastic characterization and reconstruction of the fatigue load history. The ARMA method is extended in this paper by introducing random coefficients and probabilistic weights, to account for the uncertainty in the selection of the model, inherent variability in loading, and uncertainty due to sparse data. A continuous model updating approach based on real-time monitoring data is developed and applied to all the three techniques mentioned above. The relation between prediction accuracy and updating interval is evaluated quantitatively. A quantitative model validation approach using Bayesian hypothesis testing is proposed to assess the confidence in load prediction from all the three methods. [Copyright &y& Elsevier]

Abstract: This paper presents a methodology for uncertainty quantification and model validation in fatigue crack growth analysis. Several models – finite element model, crack growth model, surrogate model, etc. – are connected through a Bayes network that aids in model calibration, uncertainty quantification, and model validation. Three types of uncertainty are included in both uncertainty quantification and model validation: (1) natural variability in loading and material properties; (2) data uncertainty due to measurement errors, sparse data, and different inspection results (crack not detected, crack detected but size not measured, and crack detected with size measurement); and (3) modeling uncertainty and errors during crack growth analysis, numerical approximations, and finite element discretization. Global sensitivity analysis is used to quantify the contribution of each source of uncertainty to the overall prediction uncertainty and to identify the important parameters that need to be calibrated. Bayesian hypothesis testing is used for model validation and the Bayes factor metric is used to quantify the confidence in the model prediction. The proposed methodology is illustrated using a numerical example of surface cracking in a cylindrical component. [Copyright &y& Elsevier]

Abstract: A probabilistic methodology is proposed in this paper to estimate the equivalent initial flaw size (EIFS) distribution accounting for various sources of variability, uncertainty and error, for mechanical components with complicated geometry and multi-axial variable amplitude loading conditions. A Bayesian approach is used to calibrate the distribution of EIFS, where the likelihood function is constructed from model-based fatigue crack growth analysis and inspection results. The variability, uncertainties and errors in the above procedures are quantified, and the distribution of EIFS is calibrated by explicitly accounting for the various sources of uncertainty. Three types of uncertainty are considered: (1) natural variability in loading and material properties; (2) data uncertainty, due to crack detection uncertainty, measurement errors, and sparse data; (3) modeling uncertainty and errors during crack growth analysis, numerical approximations, and finite element discretization. A Monte Carlo simulation-based approach is developed for uncertainty quantification in the crack growth analysis and for constructing the likelihood function of EIFS. The proposed methodology is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents several efficient statistical inference techniques to calibrate the equivalent initial flaw size (EIFS) of fatigue cracks for mechanical components with complicated geometry and multi-axial, variable amplitude loading. Finite element analysis is used to address the complicated geometry and calculate the stress intensity factors. Multi-modal stress intensity factors due to multi-axial loading are combined to calculate an equivalent stress intensity factor using a characteristic plane approach. During cycle-by-cycle integration of the crack growth law, a Gaussian process surrogate model is used to replace the expensive finite element analysis, resulting in rapid computation. Experimental data (crack size after a particular number of loading cycles) and statistical methods are used to calibrate the EIFS. The methods of least squares and maximum likelihood method are extended to evaluate the entire probability distribution of EIFS. Bayesian techniques are also implemented for this purpose. A fast numerical integration technique is developed as an efficient alternative to the expensive Markov Chain Monte Carlo sampling approach in the Bayesian analysis. An application problem of cracking in a cylindrical structure is used to illustrate the proposed methods. [Copyright &y& Elsevier]

Abstract: Folate (FOL) mediated poly–lactide-co-glycolide–polyethylene glycol nanoparticles (FOL–PEG–PLGA NPs) bearing paclitaxel (PTX) were prepared for the effective delivery of drug to endometrial carcinoma. The average size, zeta potential and encapsulation efficiency of FOL-targeted NPs were found to be around 220nm, −30.43mV and 95.6%. Cellular uptake was observed. The accumulation of FOL-targeted NPs depends on dual effects of passive and active targeting. The FOL-targeted PTX NPs showed a greater cytotoxicity against HEC-1A cancer cells in vitro and in vivo, which might be induced by apoptosis. H&E staining did not showed apparent tissue damage to liver and kidney of the mice after injecting NPs intravenously. These results suggest that the novel FOL–PEG–PLGA NPs could be a potential delivery system with excellent therapeutic efficacy for targeting the drugs to cancer cells. [Copyright &y& Elsevier]

Urinary trichloroacetic acid (TCAA) and baseline blood trihalomethanes (THMs) have been measured as biomarkers of exposure to drinking water disinfection by-products (DBPs) that have been associated with increased risk of cancers and adverse reproductive outcomes. This study aimed to identify predictors of urinary TCAA and baseline blood THMs among men in China. Urine samples, blood samples, and information on socio-demographic factors and water-use activities were collected from 2216 men who participated in a cross-sectional study of exposure to drinking water DBPs and reproductive health during 2011 to 2012. Urinary TCAA and baseline blood THMs including chloroform (TCM), bromodichloromethane (BDCM), dibromochloromethane (DBCM), and bromoform (TBM) were analyzed. Multivariable linear regression was used to evaluate predictors of urinary TCAA and baseline blood THM concentrations. Tap water consumption was significantly associated with creatinine-adjusted urinary TCAA concentration (β = 0.23 µg/g creatinine per log10 unit; 95% CI: 0.12, 0.35). Men with surface water source had 0.13 (95% CI: 0.00, 0.27) higher mean creatinine-adjusted urinary TCAA concentrations than those with ground water source. Smoking was associated with lower concentration of creatinine-adjusted urinary TCAA. Age was significantly associated with baseline blood Br-THM (sum of BDCM, DBCM, and TBM) concentration (β = 0.01 ng/L per unit; 95% CI: 0.00, 0.02). Increased household income was associated with decreased concentrations of baseline blood BDCM and Br-THMs. Our results suggest that tap water consumption, water source, smoking, age, and household income as the primary determinants of exposure to drinking water DBPs should be considered in exposure assessment. [ABSTRACT FROM AUTHOR]

Abstract: Diglycidyl ether of 4,4′-dihydroxybiphenol (BPDGE) is a liquid crystalline epoxy. The biphenyl epoxy (diglycidyl ether of 3,3′,5,5′-tetramethyl-4,4′-biphenyl, TMBPDGE) has found great applications in plastic encapsulated semiconductor packaging. Phenol novolac (PN) was used as curing agent. The reaction kinetics of BPDGE/PN and TMBPDGE/PN systems in the presence of triphenylphosphine (TPP) were characterized by an isoconversional method under dynamic conditions using differential scanning calorimetry (DSC) measurements. The results showed that the curing of epoxy resins involves different reaction stages and the values of activation energy are dependent on the degree of conversion. The effects of curing temperature on their phase structure have been investigated with polarized optical microscopy and Wide-angle X-ray diffraction. With proper curing process, BPDGE showed a nematic phase when cured with PN. [Copyright &y& Elsevier]