Individual Parameters Research Articles

The Effects of Various Cognitive Tasks Including Working Memory, Visuospatial, and Executive Function on Postural Control in Patients With Anterior Cruciate Ligament Injury.

Anterior cruciate ligament (ACL) rupture can impair balance performance, particularly during cognitive motor dual-tasks. This study aimed to determine the effects of various modalities of cognitive load (working memory, and visuospatial and executive function) on postural control parameters in individuals with ACL injury. Twenty-seven ACL-injured and 27 healthy participants were evaluated doing different cognitive tasks (silent backward counting, Benton's judgment of line orientation, and Stroop color-word test) while standing on a rigid surface or a foam. Each task was repeated three times and then averaged. Center of pressure variables used to measure postural performance included sway area and sway velocity in anterior-posterior and medial-lateral directions. Cognitive performance was also assessed by calculating errors and the score of cognitive tasks. A mixed model analysis of variance for center of pressure parameters indicated that patients had more sways than the healthy group. The interaction of group by postural difficulty by cognitive tasks was statistically significant for cognitive errors (p < .01), and patients with ACL injury indicated more cognitive errors compared to healthy controls while standing on the foam. The main effect of cognitive task was statistically significant for all postural parameters, representing reduced postural sways in both groups with all cognitive tasks. However, ACL-injured patients showed more cognitive errors in difficult postural conditions, suggesting that individuals with ACL injury may prioritize postural control over cognitive task accuracy and adopt the posture-first strategy to maintain balance under dual-task conditions.

Effects of a low, medium, and high-intensity aquatic physiotherapy protocol on functional and biochemical parameters in individuals with knee osteoarthritis: protocol for a crossover randomized controlled trial

Background Knee osteoarthritis affects the performance of daily activities, independence, and quality of life. The etiopathogenesis of this condition considers the mechanisms of activation of metalloproteinase and reactive oxygen species production pathways. Metalloproteinases-3 (MMP-3) and Glutathione Peroxidase (GPx) may be responsible for cartilage destruction. Aquatic physiotherapy promotes a positive impact on the clinical picture of osteoarthritis, and this study presents an intervention protocol that aims to evaluate the effects of a single session of different aquatic physiotherapy modalities on the biochemical and functional behavior of patients with knee osteoarthritis. Methods This will be a crossover randomized controlled trial in which 15 individuals will be submitted to three aquatic physiotherapy modalities with a minimum 15-day wash-out period in patients over 50 years old and diagnosed with OA in at least one knee, presence of pain and at least one functional dysfunction for at least 6 months, absence of physical limitation that prevents the exercise protocol from being performed, Kellgren and Lawrence ranking between I and IV, walk independently and without auxiliary device. Variations in the concentrations of MMP-3 and GPx in peripheral blood, pain, edema, and flexibility resulting from the three aquatic physiotherapeutic interventions will be evaluated both pre- and immediate post-intervention. The reference group will be submitted to the same aquatic physiotherapy protocols, however, only the biochemical parameters and the self-perception questionnaires will be evaluated. Registration ClinicalTrials.gov (NCT05610696, 18/01/2023).

Changes in salivary oxytocin in response to biologically-relevant events in farm animals: method optimization and usefulness as a biomarker.

Although best known for its established role in mediating parturition and lactation, the highly-conserved neuropeptide hormone oxytocin also mediates a range of social and stress-buffering processes across mammalian species. Measurements of peripheral oxytocin in plasma have long been considered the gold standard, but there is increasing interest in developing methods to detect oxytocin non-invasively in saliva. Here we present an analytical and biological validation of a novel method to measure salivary oxytocin (sOXT) in an under-studied research group: farm animals. Given their similarities with humans in physiology and brain, methods that can identify valued social contexts and social relationships for farm animals and investigate their function have implications for clinical research as well as for animal welfare science. However, current methods to measure sOXT vary greatly in terms of sample collection, pre-measurement processing and measurement and more rigorous standardization and validation of methods is critical to determine the utility of sOXT as a biomarker of salient social events and related emotions. We optimized a method for extracting sOXT in pigs and horses and measured sOXT in extracted samples using a commercially available enzyme-immunoassay. Extracted samples were within acceptable ranges for precision (CVs < 15.2%), parallelism and recovery (94%-99%) in both species. Salivary oxytocin increased in samples collected during birth in pigs (Friedmans, p = 0.02) and horses (Wilcoxon, p = 0.02). Salivary oxytocin tended to decrease in sows after a 90-min separation from their piglets (Wilcoxon, p = 0.08). We conclude that sOXT can be reliably linked to physiological events that are mediated by the oxytocinergic system in farm animals, but that more research is needed to determine whether sOXT is a reliable trait marker for more general oxytocin system activation in response to salient social events. Future research should characterize how individual attributes and salivary parameters influence sOXT measurement and should emphasize reporting of analytical and biological validations to increase acceptance of non-invasive methods.

Application of machine learning combined with population pharmacokinetics to improve individual prediction of vancomycin clearance in simulated adult patients.

Vancomycin, a glycopeptide antimicrobial drug. PPK has problems such as difficulty in accurately reflecting inter-individual differences, and the PPK model may not be accurate enough to predict individual pharmacokinetic parameters. Therefore, the aim of this study is to investigate whether the application of machine learning combined with the PPK method can improve the prediction of vancomycin CL in adult Chinese patients. In the first step, a vancomycin CL prediction model for Chinese adult patients is given by PPK and Hamilton Monte Carlo sampling is used to obtain the reference CL of 1,000 patients; the second step is to obtain the final prediction model by machine learning using an appropriate model for the predictive factor and the reference CL; and the third step is to randomly select, in the simulated data, a total of 250 patients for prediction effect evaluation. XGBoost model is selected as final machine learning model. More than four-fifths of the subjects' predictive values regarding vancomycin CL are improved by machine learning combined with PPK. Machine learning combined with PPK models is more stable in performance than the PPK method alone for predicting models. The first combination of PPK and machine learning for predictive modeling of vancomycin clearance in adult patients. It provides a reference for clinical pharmacists or clinicians to optimize the initial dosage given to ensure the effectiveness and safety of drug therapy for each patient.

Correlations of Corneal Endothelial Morphology and Corneal Thickness With Anterior Segment Parameters in Healthy Individuals.

The purpose of this study was to investigate the associations between central corneal endothelial cell density (ECD), endothelial morphology, and corneal thickness (central corneal thickness) with the anterior chamber depth, corneal volume (CV), white-to-white (WTW) distance, mean anterior chamber angle (CAmean), and gender in healthy individuals. This observational study included 136 healthy volunteers. The ECD, coefficient of variation of cell area, and hexagonal cell appearance ratio (%Hex) were measured by means of specular microscopy. The central corneal thickness, anterior chamber depth, CV, WTW distance, and the angle width of 12 points were taken by the Pentacam HR Scheimpflug anterior segment imaging. The arithmetical mean of the 12 points was considered as the CAmean. We used mixed effect linear regression model for the statistical analysis of the data. ECD was positively correlated with CV (P = 0.028), while after adjusting for age, it was negatively correlated with age (P < 0.001). Coefficient of variation of cell area was positively correlated with CAmean (P = 0.036), while after adjusting for age, it was positively correlated with age (P < 0.001) and CAmean (P = 0.005). Hex was negatively correlated with WTW (P = 0.023) and CAmean (P = 0.025), and after adjusting for age, this correlation remained the same (P = 0.029 when correlated with WTW and P = 0.035 with CAmean). There were significant changes in the morphology of the corneal endothelial cells in eyes with wider anterior chamber angle. Greater pleomorphism and polymegethism of the corneal endothelium was observed in healthy patients with wider CAmean. Deepening of the anterior chamber as myopia progresses could render the corneal endothelium more fragile and susceptible to mechanical stress, which is an area worthy of further study.

Nlmixr2 Versus NONMEM: An Evaluation of Maximum A Posteriori Bayesian Estimates Following External Evaluation of Gentamicin and Tobramycin Population Pharmacokinetic Models.

The objective of this project is to compare the results of the same study carried out on NONMEM and nlmixr2. This analysis consists of evaluating previously published population pharmacokinetic models of gentamicin and tobramycin in our population of interest with sparse concentrations. A literature review was performed to determine the gentamicin and tobramycin models in critically ill adult patients. In parallel, gentamicin and tobramycin dosing data, information on the treatment, the patient, and the bacteria were collected retrospectively in 2 Quebec establishments. The external evaluations were previously performed using NONMEM Version 7.5. Model equations were rewritten with R, and external evaluations were performed using nlmixr2. Predictive performance was assessed based on the estimation of bias and imprecision of the prediction error for maximum a posteriori (MAP) Bayesian PK parameter and observed concentrations. Comparison between nlmixr2 and NONMEM was performed on 4 gentamicin and 3 tobramycin population pharmacokinetic models. Compared to NONMEM, for gentamicin and tobramycin clearance and central volume of distribution, nlmixr2 produced individual pharmacokinetic parameters with bias values ranging from -32.5% to 5.67% and imprecision values ranging from 6.33% to 32.5%. Despite these differences, population bias and imprecision for sparse concentrations were low and ranged from 0% to 5.3% and 0.2% to 6.5%, respectively. The external evaluations performed with both software packages resulted in the same interpretation in terms of population predictive performance for all 7 models. Nlmxir2 showed comparable predictive performance with NONMEM with sparse concentrations that are, at most, sampled twice within a single dose administration (peak and trough).

A Lightning Classification Method Based on Convolutional Encoding Features

At present, for business lightning positioning systems, the classification of lightning discharge types is mostly based on lightning pulse signal features, and there is still a lot of room for improvement. We propose a lightning discharge classification method based on convolutional encoding features. This method utilizes convolutional neural networks to extract encoding features, and uses random forests to classify the extracted encoding features, achieving high accuracy discrimination for various lightning discharge events. Compared with traditional multi-parameter-based methods, the new method proposed in this paper has the ability to identify multiple lightning discharge events and does not require precise detailed feature engineering to extract individual pulse parameters. The accuracy of this method for identifying lightning discharge types in intra-cloud flash (IC), cloud-to-ground flash (CG), and narrow bipolar events (NBEs) is 97%, which is higher than that of multi-parameter methods. Moreover, our method can complete the classification task of lightning signals at a faster speed. Under the same conditions, the new method only requires 28.2 µs to identify one pulse, while deep learning-based methods require 300 µs. This method has faster recognition speed and higher accuracy in identifying multiple discharge types, which can better meet the needs of real-time business positioning.

A Model for Simulating the Upward Flow of a Viscous Fluid in a Fracture Network

Fluid migration in a fracture network plays an important role in the oil accumulation mechanism and hence is key to oil exploration. In this study, we build a model by combining one-dimensional (1D) Navier–Stokes equations, linear elastic equations, and energy equations, and validate the model by reproducing the thickness profile of a fluid-driven crack measured in an experiment. We employ this model to simulate the upward flow of viscous fluid in a single fracture during hydrocarbon migration. The simulation suggests that the parameters of both the fluid and the surrounding rock matrix, as well as the boundary condition imposed on the fracture outlet, affect the upward flow in the fracture. We then extend our model from the single fracture to the bifurcated fracture and the fracture network by maintaining homogeneous pressure and mass conservation at the connection of the channels. We find that the increase in network complexity leads to an increase in the inlet pressure gradient and inlet speed, and a decrease in the outlet pressure gradient and outlet speed. The effective area where the fluid is driven upward from the inlet to the outlet is offset toward the inlet. More importantly, the main novelty of our model is that it allows us to evaluate the effect of inconsistencies in individual branch parameters, such as matrix stiffness, permeability, temperature, and boundary conditions, on the overall upward flow of viscous fluid. Our results suggest that the heterogeneity enforces the greater impact on the closer branches.

Preparation and Immunochemical Characterization of a Water-Soluble Gluten Peptide Fraction for Improving the Diagnosis of Celiac Disease.

The diagnosis of celiac disease (CD) is complex and requires a multi-step procedure (symptoms, serology, duodenal biopsy, effect of a gluten-free diet, and optional genetic). The aim of the study was to contribute to the improvement of CD diagnosis by preparing a water-soluble gluten peptide fraction (called Solgluten) and by selecting gluten-specific enzyme-linked immunosorbent assays (ELISA) for the detection of gluten immunogenic gluten peptides (GIPs) in urine and blood serum spiked with Solgluten. Food-grade Solgluten was prepared by the extraction of a peptic digest of vital gluten with water, centrifugation, and freeze-drying. The process was relatively easy, repeatable, and cheap. The content of gliadin-derived GIPs was 491 mg/g. Solgluten was used as antigenic material to compare two competitive ELISA kits (R7021 and K3012) and two sandwich ELISA kits (M2114 and R7041) in their quality regarding the quantitation of GIPs in urine and blood serum. The quality parameters were the reactivity, sensitivity, coefficients of variation and determination, and curve shape. The evaluation of the kits showed a number of discrepancies in individual quality parameters measured in urine and serum. Due to the lowest limit of quantitation and the highest coefficient of determination, M2114 may be the first choice, while R7021 appeared to be less suitable because of the high coefficients of variation and unfavorable curve progression. The results set the stage for improving CD diagnosis by supplementing conventional blood tests with oral provocation with Solgluten and subsequent ELISA measurement of GIPs that could support the no-biopsy approach and by better assessing the effect of a gluten-free diet by monitoring adherence to the diet by measuring GIPs in urine and blood.

Effect of Cumulative Spirulina Intake on Broiler Meat Quality, Nutritional and Health-Related Attributes.

This work aimed to assess how different cumulative levels of Spirulina (Arthrospira platensis) intake influence individual broiler meat quality parameters, nutritional value and health-related traits. The data analysed showed varying cumulative Spirulina intake levels, ranging from 3.46 to 521 g/bird, with large changes in meat traits. The key findings indicate that Spirulina intake significantly enhances meat colour, primarily due to its rich carotenoid content. However, this enhancement shows a saturation effect at higher intake levels, where additional Spirulina does not further improve the colour. Regarding the meat nutritional profile, Spirulina increases beneficial n - 3 polyunsaturated fatty acids and reduces lipid oxidation. These effects on meat, however, are not linear and become more complex at higher microalga intake levels. Regarding meat sensory attributes, moderate Spirulina levels positively influence flavour and texture. Still, higher levels may lead to changes not universally preferred by meat consumers, highlighting the need for balanced Spirulina inclusion in diets. Optimal Spirulina cumulative intake levels must be identified to balance meat's nutritional benefits with consumer preferences. Additionally, ensuring Spirulina's purity and adherence to regulatory standards is essential for consumer safety and market access. These findings provide valuable insights for poultry nutritionists and the food industry, emphasising the necessity of a balanced approach to Spirulina's incorporation in poultry diets.

Pharmacokinetic and Pharmacodynamic Modeling Analysis of Delpazolid (LCB01-0371) in Adult Patients with Pulmonary Tuberculosis.

Delpazolid (LCB01-0371) is a novel oxazolidinone derivative with a good safety profile for treating gram-positive pathogenic infections such as Mycobacterium abscessus, a highly pathogenic drug-resistant Mycobacterium. In this study, we evaluated the pharmacokinetics (PK) and pharmacodynamics (PD) of delpazolid after 14 days of multiple oral administration, using data from adult patients with pulmonary tuberculosis. 800 mg once a day, 400 mg twice a day, 800 mg twice a day, and 1200 mg once a day delpazolid for 14 days were tested in 63 patients with pulmonary tuberculosis. For PK blood collection, inpatient and outpatient scheduling were separately implemented. Plasma concentrations of delpazolid were measured at visits 2, 4, 6, and 8 in outpatients, and four sparse blood samples were measured in inpatients. PD models were sequentially fitted using individual PK parameter estimates obtained from PK compartmental models. For PK modeling, 180 plasma concentrations of delpazolid from 56 patients were included. A two-compartment mixed first- and zero-order absorption model best described the time course of plasma concentration. For the PD model, 448 bacterial titer data from 60 patients were used. The time course of bacterial titers (log10 CFU/mL) was described by a model that consists of the growth and killing rate of bacteria with the sigmoid Emax model. The PK-PD simulation suggested that the bacterial titers are the lowest on the 800 mg bid regimen among the four, consistent with observed data, as all regimens substantially decrease. In the dose-response relationship, the effectiveness of delpazolid was suggested.

Differential evolution with proration-based mutation strategy and multi-segment mixed parameter setting for numerical optimization

In this paper, a new differential evolution (DE) algorithm is presented for solving global optimization problems by guiding the individuals adaptively to explore different decision regions and setting the control parameters properly for individuals. To consolidate algorithm efficiency and preserve population diversity, we first introduce a proration-based mutation strategy. This strategy dynamically selects promising individuals from population as guiders and proportionately allocates the individuals following them based on their fitness values and unsuccessful updates. Besides, we design a multi-segment mixed parameter setting to provide suitable parameters for individuals, considering feedback information from the population, individual requirements, and parameters' interaction. Additionally, we devise an adaptive population maintaining mechanism to refresh the population by replacing individuals with higher unsuccessful updates and removing those with poor fitness values. Unlike previous DE versions, our new algorithm dynamically and appropriately explores promising areas, refines parameter suitability, and accelerates convergence by updating and removing unpromising individuals. This enhances the algorithm search efficiency and achieves a good balance between exploration and exploitation. Finally, we evaluate the proposed algorithm's performance by comparing it with 17 typical or state-of-the-art algorithms on IEEE CEC2014 and CEC2017 test suites. Experimental results indicate that the proposed algorithm is a more promising optimizer.

In vitro PK/PD modeling of tyrosine kinase inhibitors in non-small cell lung cancer cell lines.

Tyrosine kinase inhibitors (TKIs) are routinely prescribed for the treatment of non-small cell lung cancer (NSCLC). As with all medications, patients can experience adverse events due to TKIs. Unfortunately, the relationship between many TKIs and the occurrence of certain adverse events remains unclear. There are limited invivo studies which focus on TKIs and their effects on different regulation pathways. Many invitro studies, however, that investigate the effects of TKIs observe additional changes, such as changes in gene activations or protein expressions. These studies could potentially help to gain greater understanding of the mechanisms for TKI induced adverse events. However, in order to utilize these pathways in a pharmacokinetic/pharmacodynamic (PK/PD) framework, an invitro PK/PD model needs to be developed, in order to characterize the effects of TKIs in NSCLC cell lines. Through the use of ordinary differential equations, cell viability data and nonlinear mixed effects modeling, an invitro TKI PK/PD model was developed with estimated PK and PD parameter values for the TKIs alectinib, crizotinib, erlotinib, and gefitinib. The relative standard errors for the population parameters are all less than 25%. The inclusion of random effects enabled the model to predict individual parameter values which provided a closer fit to the observed response. It is hoped that this model can be extended to include invitro data of certain pathways that may potentially be linked with adverse events and provide a better understanding of TKI-induced adverse events.

The diagnostic efficiency of integration of 2HG MRS and IVIM versus individual parameters for predicting IDH mutation status in gliomas in clinical scenarios: A retrospective study.

Currently, there remains a scarcity of established preoperative tests to accurately predict the isocitrate dehydrogenase (IDH) mutation status in clinical scenarios, with limited research has explored the potential synergistic diagnostic performance among metabolite, perfusion, and diffusion parameters. To address this issue, we aimed to develop an imaging protocol that integrated 2-hydroxyglutarate (2HG) magnetic resonance spectroscopy (MRS) and intravoxel incoherent motion (IVIM) by comprehensively assessing metabolic, cellular, and angiogenic changes caused by IDH mutations, and explored the diagnostic efficiency of this imaging protocol for predicting IDH mutation status in clinical scenarios. Patients who met the inclusion criteria were categorized into two groups: IDH-wild type (IDH-WT) group and IDH-mutant (IDH-MT) group. Subsequently, we quantified the 2HG concentration, the relative apparent diffusion coefficient (rADC), the relative true diffusion coefficient value (rD), the relative pseudo-diffusion coefficient (rD*) and the relative perfusion fraction value (rf). Intergroup differences were estimated using t-test and Mann-Whitney U test. Finally, we performed receiver operating characteristic (ROC) curve and DeLong's test to evaluate and compare the diagnostic performance of individual parameters and their combinations. 64 patients (female, 21; male, 43; age, 47.0 ± 13.7years) were enrolled. Compared with IDH-WT gliomas, IDH-MT gliomas had higher 2HG concentration, rADC and rD (P < 0.001), and lower rD* (P = 0.013). The ROC curve demonstrated that 2HG + rD + rD* exhibited the highest areas under curve (AUC) value (0.967, 95%CI 0.889-0.996) for discriminating IDH mutation status. Compared with each individual parameter, the predictive efficiency of 2HG + rADC + rD* and 2HG + rD + rD* shows a statistically significant enhancement (DeLong's test: P < 0.05). The integration of 2HG MRS and IVIM significantly improves the diagnostic efficiency for predicting IDH mutation status in clinical scenarios.

Dietary Inflammatory Index of Obese Individuals with Obstructive Sleep Apnea: A Descriptive Study.

The objective of the present study was to describe the dietary inflammatory profile and its correlations with sleep parameters of obese individuals with obstructive sleep apnea (OSA). Forty individuals underwent nocturnal polysomnography, anthropometric measurements, body composition by plethysmography, assessment of food consumption by three-day food records, and blood collection for the lipid, glycemic and hormonal profile. Food consumption data were evaluated semiquantitatively, quantitalively assessment, and calculation of the dietary inflammatory index (DII) was perfomed. The results demonstrated a predominantly proinflammatory dietary profile. The participants showed a low intake of fruit and vegetables. Additionally, a low consumption of fiber, magnesium, vitamin D, and vitamin E was observed, although there was an adequate distribution of macronutrients. In conclusion, although the inflammatory profile did not correlate with OSA, the study showed a directly proportional relationship between adequate dietary patterns and better sleep quality.

Analysis and prediction of erosion behavior of epoxy composites using statistical and machine learning techniques

This work reports on the application of different machine learning (ML) techniques and statistical methods to analyze and predict the erosion wear performance of ramie fiber-reinforced epoxy composites. These composites, fabricated by conventional hand layup technique, are subjected to high-temperature erosion wear following a test schedule based on the design-of-experiments. The experimental database is used to determine the relative significance and contribution of individual parameter on the erosion wear rate using the analysis of variance. It suggests the impact velocity as the most significant factor contributing 81.2%, followed by impingement angle (13.56%), erodent temperature (0.06%) and stand-off distance (0.05%) to the wear rate. The data generated from experimentation are further processed to predict the erosion performance of the composites with a ML approach following four different algorithms such as support vector machine, decision tree, random forest and gradient boosting machine (GBM). The feature scores for different parameters in the above models are also evaluated to ascertain the contribution of each parameter, which consequently revealed that impact velocity and impingement angle with respective mean feature scores of 0.7538 and 0.1824 have maximum influence on the erosion rate. The performances of four different ML models are compared on the basis of their coefficient of determination (R2 value). It is found that the GBM outperforms other models with a R2 value as high as 0.9543 and has thus emerged as the best-performing prediction model. The possible mechanisms causing the wear loss are identified using electron microscopy.

Quality Management in Pharmaceutical Development of Biotechnology-Derived Medicinal Products: Implementation and Evaluation

SCIENTIFIC RELEVANCE. One of the challenges associated with the development of medicines lies in creating a quality management system (QMS) and tools to assess its performance.AIM. The study aimed to propose a QMS model for the development of biotechnology-derived medicinal products and a performance evaluation tool for this QMS universally applicable in the research centre of the company regardless of the specific activities of its individual divisions.MATERIALS AND METHODS. The study analysed internal audit outcomes, noncompliance responses, and the document flow of the research centre. Parameter values for quality index (QI) calculations were entered into validated Microsoft Excel spreadsheets. Data analysis and visualisation involved using the Microsoft Power Business Intelligence (BI) business analytics platform (mainly, the Power BI Desktop and Power BI Service components).RESULTS. The QMS was implemented, and the authors proposed their QMS performance evaluation tool universally applicable to all the divisions of the research centre. CONCLUSIONS. The authors proposed their QMS model for the development of biotechnology-derived medicinal products and the QI tool for collecting digital data, carrying out standardised monitoring, and tracking QMS status changes. The QI tool is universal for all company departments regardless of their requirements, easy to use, and customisable by adding individual company-specific quality parameters. This makes the QI tool applicable not only to drug development departments but also to other research units.

Validation of Population Pharmacokinetic Models for Clozapine Dosage Prediction.

Clozapine is unique in its capacity to ameliorate severe schizophrenia but at high risk of toxicity. A relationship between blood concentration and clinical response and evidence for concentration-response relationships to some adverse effects justify therapeutic drug monitoring of clozapine. However, the relationship between drug dose and blood concentration is quite variable. This variability is, in part, due to inductive and inhibitory interactions varying the activity of cytochrome P450 1A2 (CYP1A2), the principal pathway for clozapine elimination. Several population pharmacokinetic models have been presented to facilitate dose selection and to identify poor adherence in individual patients. These models have faced little testing for validity in independent populations or even for persisting validity in the source population. Therefore, we collected a large population of clozapine-treated patients (127 patients, 1048 timed plasma concentrations) in whom dosing and covariate information could be obtained with high certainty. A population pharmacokinetic model was constructed with data collected in the first 6 weeks from study enrolment (448 plasma concentrations), to estimate covariate influences and to allow alignment with previously published models. The model was tested for its performance in predicting the concentrations observed at later time intervals up to 5 years. The predictive performances of 6 published clozapine population models were then assessed in the entire population. The population pharmacokinetic model based on the first 6 weeks identified significant influences of sex, smoking, and cotreatment with fluvoxamine on clozapine clearance. The model built from the first 6 weeks had acceptable predictive performance in the same patient population up to the first 26 weeks using individual parameters, with a median predictive error (PE) of -0.1% to -15.9% and median absolute PE of 22.9%-27.1%. Predictive performance fell progressively with time after 26 weeks. Bayesian addition of plasma concentration observations within each prediction period improved individual predictions. Three additional observations extended acceptable predictive performance into the second 6 months of therapy. When the published models were tested with the entire data set, median PE ranged from -8% to +35% with a median absolute PE of >39% in all models. Thus, none of the tested models was successful in external validation. Bayesian addition of single patient observations improved individual predictions from all models but still without achieving acceptable performances. We conclude that the relationship between covariates and blood clozapine concentrations differs between populations and that relationships are not stable over time within a population. Current population models for clozapine are not capturing influential covariates.

ROLE OF ADAPTATION TO CHRONIC HYPOXIA IN THE DEVELOPMENT OF DYSIMUNIC DISORDERS IN DIABETIC FOOT ULCER

The purpose of the study is to establish the relationship between the immune-mediated DFU in patients with T2DM and the degree of HIF-2-dependent adaptation to chronic hypoxia. Materials and Methods: We conducted a comparative analysis of patients’ clinical characteristics, laboratory markers of infection and individual parameters of adaptive immunity in patients of the main group with neuropathic and neuroischemic forms of T2DM (subgroup 1, n=51, ulcer area less than 6 cm2; subgroup 2, n=28, ulcer area more than 6 cm2), as well as in patients of the comparison group (n=44) with similar forms of T2DM. It was found that the nature of the response to hypoxia, mediated by HIF-2α, played an important role in regulating the activity of the adaptive immune response and the inflammatory-reparative process in patients with T2DM and destructed foot tissue. Results: The presence of foot wounds in patients with T2DM indicates a disruption of the HIF-2α-dependent adaptive response, which is accompanied by activation of inflammatory mediators and a decrease in IgG-dependent mechanisms. In patients with T2DM, increased HIF-2α expression in combination with increased production of immunoglobulins, is obviously a protective mechanism that prevents defects of foot tissue. Conclusion: The clinical and laboratory use of hypoxia biomarkers is promising for predicting the risk of infectious and destructive complications in patients with T2DM.

Geothermal Energy Extraction Using a Novel Combined Coaxial and U-Shaped Closed-Loop System

In the present study, a novel combined coaxial and U-shaped closed-loop system was proposed to enhance the thermal performance of the U-shaped geothermal system. The proposed geothermal system consists of two separate energy extraction units, called primary and secondary systems. The primary system consists of an insulated U-shaped tube heat exchanger. In order to extract more thermal energy, a coaxial double-pipe heat exchanger was coupled to the primary system as the secondary system. A conjugate heat transfer (CHT) module incorporating a k-ε turbulent model was employed to simulate the heat extraction and fluid flow within the proposed system. Further, the study extended to analyse the effect of various individual parameters on the thermal performance of the proposed closed-loop system. The results depicted that the proposed system can extract up to 42% higher thermal power than that of an insulated U-shaped tube heat exchanger. Further, the coefficient of performance (COP) of the combined system ranges between 1.03 to 1.22 depending upon the operational parameters. Overall, the proposed geothermal system effectively utilized both the heat loss from the hot primary fluid as well as the thermal energy available in the rock surrounding the insulation of U-shaped geothermal heat exchangers. The outcomes of the present study may be productive for researchers and industries interested in the extraction of clean and renewable energy.