Model Mixture Research Articles

Finite mixtures of matrix variate Poisson-log normal distributions for three-way count data.

Three-way data structures, characterized by three entities, the units, the variables and the occasions, are frequent in biological studies. In RNA sequencing, three-way data structures are obtained when high-throughput transcriptome sequencing data are collected for n genes across p conditions at r occasions. Matrix variate distributions offer a natural way to model three-way data and mixtures of matrix variate distributions can be used to cluster three-way data. Clustering of gene expression data is carried out as means of discovering gene co-expression networks. In this work, a mixture of matrix variate Poisson-log normal distributions is proposed for clustering read counts from RNA sequencing. By considering the matrix variate structure, full information on the conditions and occasions of the RNA sequencing dataset is simultaneously considered, and the number of covariance parameters to be estimated is reduced. We propose three different frameworks for parameter estimation: a Markov chain Monte Carlo-based approach, a variational Gaussian approximation-based approach, and a hybrid approach. Various information criteria are used for model selection. The models are applied to both real and simulated data, and we demonstrate that the proposed approaches can recover the underlying cluster structure in both cases. In simulation studies where the true model parameters are known, our proposed approach shows good parameter recovery. The GitHub R package for this work is available at https://github.com/anjalisilva/mixMVPLN and is released under the open source MIT license.

Long-term trajectories of depressive symptoms and machine learning techniques for fall prediction in older adults: Evidence from the China Health and Retirement Longitudinal Study (CHARLS)

BackgroundFalls are the most common adverse outcome of depression in older adults, yet a accurate risk prediction model for falls stratified by distinct long-term trajectories of depressive symptoms is still lacking. MethodsWe collected the data of 1617 participants from the China Health and Retirement Longitudinal Study register, spanning between 2011 and 2018. The 36 input variables included in the baseline survey were regarded as candidate features. The trajectories of depressive symptoms were classified by the latent class growth model and growth mixture model. Three data balancing technologies and four machine learning algorithms were utilized to develop predictive models for fall classification of depressive prognosis. ResultsDepressive symptom trajectories were divided into four categories, i.e., non-symptoms, new-onset increasing symptoms, slowly decreasing symptoms, and persistent high symptoms. The random forest-TomekLinks model achieved the best performance among the case and incident models with an AUC-ROC of 0.844 and 0.731, respectively. In the chronic model, the gradient boosting decision tree-synthetic minority oversampling technique obtained an AUC-ROC of 0.783. In the three models, the depressive symptom score was the most crucial component. The lung function was a common and significant feature in both the case and the chronic models. ConclusionsThis study suggests that the ideal model has a good chance of identifying older persons with a high risk of falling stratified by long-term trajectories of depressive symptoms. Baseline depressive symptom score, lung function, income, and injury experience are influential factors associated with falls of depression evolution.

Simultaneous Quantification of Fullerenes C60 and C70 in Organic Solvents by Excitation–Emission Matrix Fluorescence Spectroscopy

Excitation–emission matrix (EEM) fluorescence spectroscopy of unmodified (pristine) fullerenes C60 and C70 in benzene, toluene, and n-hexane at room temperature was used for their quantification by their solvent-dependent EEM bands specific to each fullerene. The intensity and parameters of fluorescence depend on both the fullerene and solvent and provide the conditions for the quantification of both fullerenes in their mixtures without separation. The detection limits for C60 in n-hexane and C70 in benzene under the selected conditions are 7 and 2 nmol/L, respectively. The approach was tested for model and real mixtures of fullerenes C60 and C70.

Characterization of PFAS Air Emissions from Thermal Application of Fluoropolymer Dispersions on Fabrics

ABSTRACT During thermal processes utilized in affixing fluoropolymer coatings to fibers and fabrics, coating components are vaporized. It is suspected that per- and polyfluoroalkyl substances (PFAS) from the dispersions may undergo chemical transformations at the temperatures used, leading to emitted PFAS thermal byproducts. It is important to characterize these emissions to support evaluation of resulting environmental and health impacts. In this study, a bench-scale system was built to simulate this industrial process via thermal application of dispersions to fiberglass utilizing relevant temperatures and residence times in sequential drying, baking, and sintering steps. Experiments were performed with two commercially available dispersions and a simple model mixture containing a single PFAS species (6:2 fluorotelomer alcohol [6:2 FTOH]). Vapor-phase emissions were sampled and characterized by several off-line and real-time mass spectrometry techniques for targeted and non-targeted PFAS. Results indicate multiple PFAS thermal transformation products and multiple non-halogenated organic species were emitted from the exit of the high temperature third (sintering) furnace when 6:2 FTOH was the only PFAS present in the aqueous mixture. This finding supports the hypothesis that temperatures typical of these industrial furnaces may also induce chemical transformations within the fluorinated air emissions. Experiments using the two commercial fluoropolymer dispersions indicate air emissions of part-per-million by volume (ppmv) concentrations of heptafluoropropyl-1,2,2,2-tetrafluoroethyl ether (Fluoroether E1), as well as other PFAS at operationally relevant temperatures. We suspect that E1 is a direct thermal decomposition product (via decarboxylation) of 2,3,3,3-tetrafluoro-2-(heptafluoropropoxy)propanoic acid (commonly referred to as HFPO-DA) present in the dispersions. Other thermal decomposition products, including the monomer, tetrafluoroethene, may originate from the PFAS used to stabilize the dispersion or from the polymer particles in suspension. This study represents the first researcher-built coating application simulator to report nontargeted PFAS emission characterization, real-time analyses, and the quantification of 30 volatile target PFAS.

Fouling of Polyalkylmethylsiloxane Composite Membranes during Pervaporation Separation of ABE-Fermentation Mixtures

Production of bio-alcohols is one of the approaches used in the development of alternative energy. Pervaporation is a promising option for the separation of bio-alcohols from the fermentation mixture. A serious problem in the process of continuous extraction of biobutanol from the fermentation broth is the contamination of the membrane, which leads to a decrease in its permeability over time. In this work, the transport properties of composite membranes based on polyheptylmethylsiloxane (PHeptMS), polydecylmethylsiloxane (PDecMS), and a commercial membrane MDK-3 were studied during separation of a real ABE-fermentation broth in vacuum pervaporation mode. The study was performed before and after continuous contact of the membranes with the fermentation broth for one month. Visually and by scanning electron spectroscopy, the presence of membrane surface residue and its effect on the wettability of the membrane selective layer by the components of the ABE broth were determined. The sediment composition was evaluated by energy dispersive analysis and infrared spectroscopy. According to the pervaporation separation of the ABE-broth using PHeptMS, PDecMS, and MDK-3 membranes, the butanol flux was 0.029, 0.012, and 0.054 kg/(m2·h), respectively. The butanol-water partition factor was 41, 22, and 13 for PHeptMS, PDecMS, and MDK-3, respectively. After one month of incubation of the membranes in ABE-fermentation broth during the separation of the model mixture, a decrease of 10 and 5% in permeate flux and separation factor, respectively, was observed for all membranes. Temperature dependences (30–60 °C) of permeate flux, permeability, and selectivity were obtained for the membranes after clogging. The most promising in terms of minimal negative changes as a result of fouling was demonstrated by the PHeptMS membrane. For it, the clogging dynamics during separation of the real fermentation broth for 216 h were investigated. Two characteristic steps of decrease in transport and separation properties were observed, after 28 and 150 h of the experiment. After 216 h of experiment, a 1.28-fold decrease in total flux through the membrane, a 9% decrease in butanol permeability, and a 10% decrease in n-butanol selectivity were found for PHeptMS.

Mass Transfer through Vapor–Liquid Interfaces Studied by Non-Stationary Molecular Dynamics Simulations

Molecular dynamics (MD) simulations are highly attractive for studying the influence of interfacial effects, such as the enrichment of components, on the mass transfer through the interface. In a recent work, we have presented a steady-state MD simulation method for investigating this phenomenon and tested it using model mixtures with and without interfacial enrichment. The present study extends this work by introducing a non-stationary MD simulation method. A rectangular simulation box that contains a mixture of two components 1 + 2 with a vapor phase in the middle and two liquid phases on both sides is used. Starting from a vapor-liquid equilibrium state, a non-stationary molar flux of component 2 is induced by inserting particles of component 2 into the center of the vapor phase in a pulse-like manner. During the isothermal relaxation process, particles of component 2 pass through the vapor phase, cross the vapor-liquid interface, and enter the liquid phase. The system thereby relaxes into a new vapor-liquid equilibrium state. During the relaxation process, spatially resolved responses for the component densities, fluxes, and pressure are sampled. To reduce the noise and provide measures for the uncertainty of the observables, a set of replicas of simulations is carried out. The new simulation method was applied to study mass transfer in two binary Lennard-Jones mixtures: one that exhibits a strong enrichment of the low-boiling component 2 at the vapor-liquid interface and one that shows no enrichment. Even though both mixtures have similar transport coefficients in the bulk phases, the results for mass transfer differ significantly, indicating that the interfacial enrichment influences the mass transfer.

Nanofiltration Mixed Matrix Membranes from Cellulose Modified with Zn-Based Metal–Organic Frameworks for the Enhanced Water Treatment from Heavy Metal Ions

Nowadays, nanofiltration is actively used for water softening and disinfection, pre-treatment, nitrate, and color removal, in particular, for heavy metal ions removal from wastewater. In this regard, new, effective materials are required. In the present work, novel sustainable porous membranes from cellulose acetate (CA) and supported membranes consisting of CA porous substrate with a thin dense selective layer from carboxymethyl cellulose (CMC) modified with first-time synthesized Zn-based metal-organic frameworks (Zn(SEB), Zn(BDC)Si, Zn(BIM)) were developed to increase the efficiency of nanofiltration for the removal of heavy metal ions. Zn-based MOFs were characterized by sorption measurements, X-ray diffraction (XRD), and scanning electron microscopy (SEM). The obtained membranes were studied by the spectroscopic (FTIR), standard porosimetry and microscopic (SEM and AFM) methods, and contact angle measurement. The CA porous support was compared with other, prepared in the present work, porous substrates from poly(m-phenylene isophthalamide) and polyacrylonitrile. Membrane performance was tested in the nanofiltration of the model and real mixtures containing heavy metal ions. The improvement of the transport properties of the developed membranes was achieved through Zn-based MOF modification due to their porous structure, hydrophilic properties, and different particle shapes.

The Role of the Anion in Imidazolium-Based Ionic Liquids for Fuel and Terpenes Processing.

The potentialities of methylimidazolium-based ionic liquids (ILs) as solvents were evaluated for some relevant separation problems-terpene fractionation and fuel processing-studying selectivities, capacities, and solvent performance indices. The activity coefficients at infinite dilution of the solute (1) in the IL (3), γ13∞, of 52 organic solutes were measured by inverse gas chromatography over a temperature range of 333.2-453.2 K. The selected ILs are 1-butyl-3-methylimidazolium hexafluorophosphate, [C4mim][PF6], and the equimolar mixture of [C4mim][PF6] and 1-butyl-3-methylimidazolium chloride, [C4mim]Cl. Generally, low polar solutes follow γ1,C4mimCl∞ > γ1,C4mimPF6+C4mimCl∞ > γ1,C4mimPF6∞ while the opposite behavior is observed for alcohols and water. For citrus essential oil deterpenation, the results suggest that cations with long alkyl chains, such as C12mim+, promote capacity, while selectivity depends on the solute polarity. Promising results were obtained for the separation of several model mixtures relevant to fuel industries using the equimolar mixture of [C4mim][PF6] and [C4mim]Cl. This work demonstrates the importance of tailoring the polarity of the solvents, suggesting the use of ILs with mixed anions as alternative solvents for the removal of aliphatic hydrocarbons and contaminants from fuels.

Detection and quantification of white and black sesame as potential allergenic ingredients in processed foods: A comparative gene marker study

White and black sesame seeds are widely used as technological ingredients in processed foods, but they are also considered as important allergenic foods. This work aims at exploiting several molecular markers for developing highly sensitive/specific real-time PCR methods for sesame detection/quantification in complex matrices. Three allergen-encoding genes and 4 multicopy regions of sesame were extensively evaluated, from which two, targeting CO6b1 and ITS sequences, provided the best sensitivity and specificity. Both markers allowed the successful development of real-time PCR approaches using white/black sesame seeds and raw/processed model mixtures of sesame-containing biscuits. The ITS region (0.02 pg of DNA and 1.0 mg/kg) provided higher sensitivity for sesame detection than the CO6b1 gene (2 pg of DNA and 50 mg/kg). Both methods presented optimal performance parameters for sesame detection, even after processing, but only CO6b1 gene showed precise and accurate determinations of sesame in blind mixtures.

EFFECT OF TRIFLUORIODOMETHANE ADDITIVES ON THE IGNITION OF MULTICOMPONENT COMBUSTIBLE MIXTURES BEHIND SHOCKWAVES

The problems of improving the safety of various industrial facilities dealing with combustible mixtures are constantly in the center of increased attention. The introduction of chemically active additives into the gas mixture is considered as one of the most effective ways to prevent ignition and explosion. One of the most promising highly effective and, at the same time, environmentally friendly additives is trifluoroiodomethane CF3I. Recently, the effect of CF3I on the ignition of acetylene and methane has been studied; however, combustible gas mixtures encountered in practice are often far from pure gases in composition and contain impurities that can significantly affect the ignition kinetics. In this work, an experimental study of the effect of CF3I on ignition delays behind shock waves of complex combustible mixtures, including mixtures of hydrogen and carbon monoxide (model mixture “synthesis gas”) as well as mixtures of methane with acetylene additives (model mixture “mine gas”) has been carried out. As a result of the experiments, it is shown that in mixtures simulating synthesis gas, CF3I additives lead to a considerable inhibition of ignition, while in “mine gas,” this effect turned out to be insignificant.

A new method for determination of lignocellulose crystallinity from XRD data using NMR calibration

A new method was proposed which allowed more accurate crystallinity results for lignocellulose comparing to conventional calculation models. The method is based on the estimation of the changing rate of the XRD intensity I = f(2θ) over the entire range of scattering angles. Pure crystalline and amorphous cellulose were used as calibration samples, since cellulose crystallinity can be easily determined by 13C CP/MAS NMR. The method was successfully applied to three types of plant biomass model mixtures, such as a mixture of birch xylem with micro crystalline cellulose, a mixture of birch xylem with alkaline sulfate lignin, and a mixture of alkaline sulfate lignin with microcrystalline cellulose. The obtained crystallinity depended linearly on the content of microcrystalline cellulose in the sample. The proposed equation can be used to calculate the crystallinity degree of lignocelluloses, as well as any lignocellulose materials.

Mapping study of papillary thyroid carcinoma in China: Predicting EQ-5D-5L utility values from FACT-H&N.

To develop a mapping algorithm that can be used to predict EQ-5D-5L health utility scores from FACT-H&N and obtain health utility parameters for Chinese patients with papillary thyroid carcinoma (PTC), which can be used for cost-utility analysis in health economic. A total of 1,050 patients with PTC from a tertiary hospital in China were included, and they completed FACT-H&N and EQ-5D-5L. Four mapping algorithms of direct mapping functions were used to derive the models: Ordinary least squares (OLS), Tobit model (Tobit), Two-part model (TPM), and Beta mixture regression model (Beta). The goodness-of-fit of models was assessed by the mean absolute error (MAE), root mean square error (RMSE), Akaike information criteria (AIC), Bayesian information criteria (BIC), and absolute error (AE). A fivefold cross-validation method was used to test the stability of the models. The mean utility value of the EQ-5D-5L was 0.870 ± 0.094. The mean EQ-VAS score was 76.5 ± 13.0. The Beta mixture regression model mapping FACT-H&N to EQ-5D-5L achieved the best performance [fivefold cross-validation MAE = 0.04612, RMSE = 0.06829, AIC = -2480.538, BIC = -2381.137, AE > 0.05 (%) = 32.48, AE > 0.1 (%) = 8.95]. The independent variables in this model were Physical Well-Being (PWB), Emotional Well-Being (EWB), Head & Neck Cancer Subscale (HNCS) scores and its square term and interaction term scores. This study calculated the health utility score of Chinese patients with PTC. The reported algorithms can be used to map the FACT-H&N into the EQ-5D-5L, which can be applied in the cost-utility related study of patients with PTC.

Personalized stability monitoring based on body postures of construction workers working at heights

Fall from heights accidents are one of the most frequent causes of death in the construction industry. Many fall-from-height accidents are related to stability issues and stability is critical for construction workers working at heights. Thus, stability monitoring of workers is important for proactive accident prevention. However, workers’ stability is highly personalized due to differences in workers’ physical characteristics and habits. Little attention has been paid to the personalized posture-based stability analysis of workers working at heights. This study proposes a personalized stability monitoring framework based on workers' body posture patterns when working at heights. The proposed method includes two main components: posture recognition and stability monitoring. First, OpenPose is used to extract the coordinates of workers’ posture key points from video clips. Two posture features, duration and count, are extracted to reflect workload and frequency. Secondly, a two-stage stability monitoring framework, including instability detection and instability evaluation, is designed based on the Gaussian model and Gaussian mixture model. Finally, the proposed framework is validated by on-site construction videos of workers working at height. The validation results showed the accuracy of 84.38% and the precision of 81.25% in identifying the stable status of a subject. The robustness of the personalised monitoring method was validated through comparisons with five other workers. The study provides a practical reference for active safety monitoring for workers with high fall-from-height risk. It also helps to extend personalized and adaptive behaviour-based safety training for construction workers.

Molybdenum-Containing Catalysts Based on Porous Aromatic Frameworks as Catalysts of Oxidation of Sulfur-Containing Compounds

New molybdenum-containing catalysts based on PAF-30 mesoporous carbon material for oxidation of sulfur-containing compounds (SCs) in a model fuel were synthesized. The PAF-30 support was modified with functional groups containing a positively charged nitrogen atom with various substituents. The modified supports were studied by the methods of low-temperature nitrogen adsorption/desorption, IR spectroscopy, and elemental analysis. The major factors affecting the oxidation were considered: reaction temperature and time, oxidant amount, catalyst dosage, and kind of sulfur-containing substrate. For the Мо/PAF-30-NEt3 catalyst, optimum conditions were found for oxidation of various classes of SCs in model mixtures: H2O2 : S molar ratio 6 : 1, 60°С, 60 min. The Мо/PAF-30-NEt3 catalyst operates in dibenzothiophene (DBT) oxidation during five cycles without appreciable activity loss.

Automated desorption electrospray ionization two-dimensional tandem mass spectrometry: Instrumentation and software for high throughput analysis

Two-dimensional tandem mass spectrometry (2D-MS/MS) provides rapid unsupervised access to all product ions of all ionized precursor ions. In this study, desorption electrospray ionization is used to create ion populations from complex mixtures prior to the use of 2D MS/MS to characterize each sample in an array. Automated high-throughput examination of each sample in the array is achieved by integrating movement of the sample array with automated acquisition of data from each sample through a serial handshake procedure. Position control is achieved using an Arduino microprocessor, a RAMPS control board, two stepper motors, and a custom MATLAB graphical user interface. Data processing improvements to the 2D-MS/MS workflow included optimizing the contrast of the intensity distribution of individual precursor to product ion transitions in local regions of the 2D spectrum to enhance visual discrimination of peaks from the background. This study demonstrates the combination of several advanced capabilities for practical mass spectrometry including high throughput experiments on unmodified samples with unconstrained sample acquisition and fully automated instrumental control and data manipulation. These capabilities are demonstrated with several model mixtures spotted on a PTFE-coated substrate, where the total analysis time was 64 s. Details of the system and discussion of the system performance are provided.

Effects of Wax Components and the Cooling Rate on Crystal Morphology and Mechanical Properties of Wax–Oil Mixtures

Crystal morphologies of different binary and ternary “model mixtures” were investigated using different cooling conditions (with cooling rates from 0.1 to more than 300 °C min–1). Needle-like crystals tend to form in binary mixtures under fast cooling, while uncommon spherulitic crystals were observed in ternary mixtures under slow cooling. These differences in crystal morphology lead to distinct mechanical behavior of the “model mixtures”. As shown by cone penetration tests, mixtures with spherulitic crystals exhibit a lower mechanical strength than those with densely arranged needle-like crystals. In addition, nanoindentation tests demonstrated that Young’s modulus and plasticity limit of mixtures are higher upon faster cooling. Lipstick-shaped cast samples were studied, in which cooling rates are much higher at the surface than at the center. A gradual change in crystal morphology was observed from surface to center, correlating to the hard surface and the soft center as revealed by nanoindentation tests. This work illustrates the relationship between process thermal control, microstructure morphology, and mechanical properties for wax–oil mixtures, paving the way to the monitoring of sensorial properties by control of mixture composition and process control.

Critical micelle concentration and partition coefficient of mixed micelles: Analysis of ternary systems based on Markov chain model and simple mixture model

When two or more surfactants are mixed, the mixed system exhibits improved performance compared with a single surfactant system in solution. The Markov chain model can analyze the critical micelle concentration (CMC) of mixed micelles, yielding results similar to those of a simple mixture model, which is typically referred to as the “regular solution theory”. In this study, two hypotheses were tested: (1) the Markov chain model for ternary systems can be simplified by approximating the association constant of surfactants i and j as Kij = Kji and (2) the quasi-simple ternary mixture model, that is, a simple mixture model analogous to the Markov chain model, helps interpret the interaction parameter of the simple mixture model that can describe the partition coefficient of the binary mixed micelle. Equations were derived for (1) the Markov chain model for ternary systems by assuming Kij = Kji and (2) the interaction parameter of the simple mixture model that can describe the partition coefficient of the binary mixed micelle. The models were compared with the experiment data, and the derived equations described the data of the CMC and partition coefficient well.

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H-13C HSQC-Based Metabolomics.

Two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with one-dimensional (1D) 1H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D 1H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D 1H-13C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδo) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδo for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D 1H-13C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.

Assessment of lignin and extractive content based models used in estimation of higher heating value of lignocellulosics: Use of model mixtures

Cellulose, lignin and extractive material are mixed in certain proportions by having isolated from lignocellulosic materials, such as Zeyrek stem, hazelnut shell and Scotch pine, respectively. Their higher heating values (HHVs) are determined by using a bomb calorimeter system. Estimated HHVs are calculated by applying these mixture ratios to some Multiple (Non)-Linear Regression (M(N)LR) and Artificial Neural Network (ANN) models from the literature. MLR3 model is developed from the data of this study and this model reveals the highest R2 (0.974), lowest MAPE (0.012) and RMSE (0.278) values. The closest estimation accuracy to the MLR3 model is obtained from MLR2 (R2:0.972, MAPE:0.066 and RMSE:1.714) in the comparative analysis. MNLR and ANN equations containing quadratic terms are found to show deviations up to 132.6% (ANN3). It is attributed to the lower size and poor homogeneity of the individual group of samples from which model equations are developed.

STUDY AND SELECTION OF A MODEL SOLUTION CONTAINING ANTIBIOTICS TO DEVELOP AN EAR DROP TO TREAT OTITIS MEDIA

Introduction. Otitis media is an infection of the middle ear, it happens in the space behind the eardrum which contains the tiny vibrating bones of the ear. it divided into acute otitis media (AOM), suppurative otitis media (SOM) and otitis media with effusion (OME). Otitis is the most common specific disease in children, children aged 0–12 registered in 62 practices (1 March 2019–29 February 2020) and (1 March 2020–28 February 2021) were includ-ed, and secondly, the problem of treating acute otitis media in of adults remains one of the most urgent problems of modern otolaryngology, treat-ment may begin with clear up pain and sometimes, antibiotics are used to clear the infection. The aim of the study – development of a therapeutic solution containing an antibiotic from fluoroquinolones group for the treatment of acute otitis media. Material and methods. Viscometer VPZH-2 0.39 EKROS, RUSSIA, Milwaukee MC120 PRO pH Monitor, Electronic stirrer. Analytical scales Sartogosm CE224-S. Preparation of HEC solutions, preparation of HPMC solutions. polymer solutions prepared according to USP standards. Results. The physical characteristics of the obtained mixtures were studied and the compositions of the model mixtures are shown. The solution, which contains benzyl alcohol, ofloxacin as active ingredients and HPMC with 70% ethanol as excipients, is fully meets the requirements of ear drops. Conclusions. Solution, which contains benzyl alcohol, ofloxacin as active ingredients and HPMC with 70% ethanol as excipients, has suitable viscosity and pH, which are fully meets the requirements of ear drops.