The analog methods used in the clinical assessment of the patient’s chronological age are subjective and characterized by low accuracy. When using those methods, there is a noticeable discrepancy between the chronological age and the age estimated based on relevant scientific studies. Innovations in the field of information technology are increasingly used in medicine, with particular emphasis on artificial intelligence methods. The paper presents research aimed at developing a new, effective methodology for the assessment of the chronological age using modern IT methods. In this paper, a study was conducted to determine the features of pantomographic images that support the determination of metric age, and neural models were produced to support the process of identifying the age of children and adolescents. The whole conducted work was a new methodology of metric age assessment. The result of the conducted study is a set of 21 original indicators necessary for the assessment of the chronological age with the use of computer image analysis and neural modelling, as well as three non-linear models of radial basis function networks (RBF), whose accuracy ranges from 96 to 99%. The result of the research are three neural models that determine the chronological age. The first part of this paper reviews the literature beginning with issues related to dental and metric age assessment. The commonly used methods are referred to and their advantages and disadvantages indicated. The following paragraphs present the possibility of using computer and neuronal image analysis methods in medicine and indicate the area of using artificial intelligence methods in dentistry. Then the material and research methods are described, a set of 21 original indicators is presented, which were the basis of the research and further analysis. Moreover, the three best generated network models that accomplish the task of metric age assessment are presented. The whole was crowned with a discussion and conclusion.Metric age assessment is particularly useful for doctors to plan and evaluate the results of treatment, in anthropology and forensic medicine to determine the metric age of human remains, and to determine the age of children in the case of international adoptions or in individuals illegally staying in a given country.The analog methods used in the clinical assessment of the patient’s chronological age, based on the development of his or her dentition, are subjective and characterized by low accuracy. These methods are not reliable because there are noticeable discrepancies between the chronological age and the predicted age determined using relevant scientifically developed tables, charts, and atlases ,16,17,18Methods of assessing the dental age are divided into clinical methods, comparing the time of eruption of specific tooth groups and pantomographic methods, assessing the mineralization process of tooth buds.Clinical methods, which assess the presence of tooth groups or individual teeth in a patient, allow to determine the dental age during a routine dental check-up. These methods are considered by dentists to be easy to use, relatively fast and non-invasive. However, the main drawbacks of these methods include inaccuracy, due to the difficulty of determining whether a tooth in the process of eruption should be counted among teeth that have already reached the occlusal plane, and the presence of factors disturbing the time of eruption .Clinical methods of assessing dental age determine the age on the basis of tables and growth charts, from which the average time of eruption is determined. The most frequently quoted methods come from 1928 (Matiegka and Lukasova method) and 1968 (Charzewski and Panek method) . Due to Pantomographic methods of dental age assessment are methods based on the assessment of mineralization of tooth buds. They are used for more precise assessment of dental age than that using tables and growth charts. Many systems have been developed which differ in the number and type of evaluated teeth and the number of individual stages of teeth development ,7,8,9.Most methods of assessing dental age are based on the teeth of the mandible. This is due to the fact that the tooth buds develop earlier in the mandible. The exceptions are the third molars, which are formed first in the maxilla ,10. AddiAmong the many methods of assessing the dental age ,12,13,14Another commonly used method is the Schour and Massler method, sometimes referred to as the atlas method, in which, based on the developmental pattern of deciduous and permanent teeth, 21 stages are distinguished and all teeth are assessed simultaneously .In 1944, the Ubelaker method was developed, which, similarly to the Schour and Massler method, distinguishes 21 stages of development and is currently used worldwide in patients between 5 months and 35 years of age .The latest analog method of assessing dental age is the London Atlas, which was developed in 2009 and presents 31 stages of development and eruption of teeth from 30 weeks in utero to 23 years of age. The data for the Atlas were obtained from 72 prenatal and 104 postnatal skeletal remains of children and young adults of known age-at-death. These data were supplemented by the analysis of archival radiological pictures of living individuals. The median stage for each age category was used to prepare the Atlas ,18.It should be noted that medical science is increasingly using innovations in the field of information technology, with particular emphasis on artificial intelligence methods, including neural modelling. These methods help to improve the effectiveness of treatment and accuracy of diagnosis of medical conditions ,20.The use of artificial neural networks in the processing of medical information and images helps to generalize the data contained even in X-rays with noise ,22, thusNeural modelling is becoming increasingly popular in the biological and medical community . It can In dentistry, the Seok-Ki and Tae-Woo team  developeIt should be noted that the early 2021 papers touch on age determination for children, adolescents, and adults using artificial neural networks. The paper by Mauer and team  presentsThe study of age in relation to tooth deterioration was conducted by . In a stThe analysis of the literature in terms of the assessment of the chronological age, artificial intelligence methods, including the use of neural modelling methods, has shown that so far no highly effective and accurate methodology has been developed and published, which uses modern computer systems and tools to determine the chronological age on the basis of digital pantomographic images. Currently used analog methods are rather imprecise and subjective, which has created the necessity to search for new, more effective techniques and methods of determining the chronological age.Therefore, work has been carried out in determining the features of pantomographic images that support the determination of metric age, and neural models were produced to support the process of identifying the age of children and adolescents. The whole conducted work was a new methodology of metric age assessment from pantomographic images.The source of the analyzed data was the database of patients of the University Centre of Dentistry and Specialist Medicine in Poznań, Poland. The research material consisted of 619 digital pantomographic images of children and adolescents aged 4 to 15 (from 48 to 144 months). At the age of 4 to 15, the development of teeth is best visible. The research group included 296 photos of girls and 323 photos of boys. All cases were verified and the photos that presented developmental abnormalities and disorders such as: systemic diseases, facial lesions, facial and dental malformations or dental hard tissues and pulp diseases were excluded. The Bioethics Committee of the Medical University of Poznań considered that the research carried out does not have the characteristics of a medical experiment and therefore agreed to carry out the relevant work.In order to carry out the work and to solve the scientific problem, actions were taken that included the following steps:Acquisition of research material-pantomographic images of children and adolescents aged 4 to 15 (from 48 to 144 months);Verification and exclusion of abnormal cases, e.g., with developmental disorders;Preparation of a database of selected digital pantomographic images in MS Excel;Determination of patients’ age at the moment of picture taking-expressed in months;Determination of a set of tooth and bone parameters;Collection of tooth and bone parameters using ImageJ software;Combining the collected tooth and bone measurements with the patient base using MS Excel;Definition of a set of indicators-values of proportions of measured tooth and bone parameters;Processing of data;Preparation of a teaching set for neural modelling;Neural modelling in STATISTICA 7.1;Verification of the produced models;Determination of a set of indicators and parameters relevant for the assessment of chronological age;Development of a methodology for the assessment of chronological age;The pantomographic photos used in the research process were taken with the Duerr Dental-VistaPano S Ceph camera. The standard camera of this type is equipped with an X-ray head with 0.5 mm focus and a digital sensor, Cls-CMOS matrix. In addition, this model of the camera enables shooting in S-PAN technology, which improves the sharpness and saturation of mineralized structures. The camera records digital images in DICOM 3.0 format, which is supported by DBSWIN -specialiThe process of generating a neural model was carried out using STATISTICA 7.1. software in the neural modeling module, which allows to create, validate, and test artificial neural network models. In this module it is also possible to perform a sensitivity analysis of variables of the developed models .The neural modelling process was carried out in the STATISTICA 7.1. suite. The generation of neural models was performed in three stages, in the module “Neural Networks”. In the first stage the “Automatic designer” function was used. It tested 20 networks of each type; the program was to keep the top 10 networks of each type. The criterion for selecting the networks remembered for further analysis was to maintain the balance between the error and the diversity of networks. During this stage, PNN , GRNN , RBF , and three- and four-layer MLP (Multilayer Perceptron) networks were tested. After the analysis, it turned out that none of the produced models were characterized by favorable coefficients determining the quality of operation. The quality of the network is understood as the difference between the initial value of the network  and the set value . The higher the value for the training set is and the closer it is to 1, the higher the network quality . HoweverAutomatic network designer is a feature of the STATISTICA package that allows you to define assumptions for selected, generated neural models. Owing to this function, the user can test models and select the optimal number of neurons for each topology. The program selects the values of the neural weights of individual networks and, depending on the settings, performs the task by selecting different networks with different topologies with information about their quality characteristics.The remaining parameters of the program operation remained unchanged. However, also at this stage, the quality of performance of the generated networks was not satisfactory. Next, in the third stage of neural modeling, the “User network designer” function was used. Using this function, the type of network was determined: network performing generalized regression (RBF) and the number of hidden neurons, depending on the modelled number of input variables: 23 for the set containing cases of women and men, or 22 variables—for women and men separately. Entropy was assumed to be a function of the network classification error. Networks generated at this stage were subjected to an optimization-tuning, which consisted of neuronal reduction in the input layer of the network, reduction of hidden neurons, etc. Probabilistic Neural Network is one of the so-called Bayesian networks used to solve classification problems. PNNs use kernel approximation to estimate the likelihood function of learning classes. During network learning, it is important to determine the smoothing factor, which is user-defined and has a significant impact on the network learning process. This coefficient determines the degree of overlap in the sequence of learning instances fed to the network inputs. The probabilistic network returns an estimated probability value as a result. The advantage of this type of network is fast learning process, whereas the disadvantages include very complex structure.Generalized Regression Neural Network resemble PNN type networks in their operation. They are used to solve only regression problems. These networks are characterized by a specific structure: the first layer of the network contains radial neurons, the second a weighted average of outputs from the previous layer, while the output layer has the task of normalizing the calculated values. This process consists in dividing the received signal values with the average calculated in the hidden layer and therefore the hidden layer contains one additional neuron.Radial Basis Function networks are characterized by a single hidden layer containing radial neurons. The algorithm of these networks includes a Gaussian response surface, which is a strongly nonlinear function. Thus, using a single hidden layer, it is possible to model a function of arbitrary shape. To get the right result in RBF networks, linear neurons are used in the output layer with a linear activation function. RBF network learns much faster than MLP networks.One of the most popular ANN  topologies is the Multi Layer Perceptron. Its concept was developed in 1986 by Rumelhart and McClelland. It consists of multiple layered neurons. Each computes a weighted sum of its inputs, which is the argument of the output value activation function. The multilayer perceptron is a one-way structure whose main advantage is that it can model functions of any complexity.Indicator X01 a ratio bIndicator X02 b ratio bIndicator X03 c ratio bIndicator X04 d ratio bIndicator X05 e ratio bIndicator X06 f ratio bIndicator X07 g ratio bIndicator X08 h ratio bIndicator X09 i ratio bIndicator X10 j ratio bIndicator X11 k ratio bIndicator X12 l ratio bIndicator X13 m ratio bIndicator X14 n ratio bIndicator X15 o ratio bIndicator X16 p ratio bIndicator X17 q ratio bIndicator X18 r ratio bIndicator X19 s ratio bIndicator X20 t ratio bIndicator X21 u ratio bIn the conducted research, an original, authored, and developed set of 21 indicators was determined, i.e., distinctive tooth and bone parameters, which were developed in the form of mathematical proportions:The measurements were made manually with using ImageJ software.Various network topologies  were tested during the research. RBF networks were characterized by the best quality indicators, therefore the three best models determining the chronological age were obtained: 1. women and men, 2. women, 3. men. The results of the research are three neural models that determine the chronological age. Three possibilities were tested on different groups to identify which model is the most accurate and to determine which variables are characteristic of gender groups and which variables affect the accuracy of the modelling result.In the process of neural modelling for 619 cases of women and men, 23 input variables were involved . The output variable was “MONTHS”, the remaining variables were input variables.The sex “SEX” was a categorized variable. Months are a continuous variable, defining the number of months from birth to the date of the study. Variables from X01 to X21 were continuous in nature and marked the calculated indicators characterized above.In the process of neural modelling for 296 female cases 22 variables were involved (the gender variable was eliminated). The output variable was “MONTHS”, the other variables were input variables.In the process of neural modelling for 323 cases of men 22 variables were involved (the gender variable was eliminated). The output variable was “MONTHS”, the remaining variables were input variables.The division into learning, validation, and testing set was carried out at random for each data collection. The learning set takes part in the process of generating the network, the validation set takes part in tuning the model, while the test set is excluded from the process of neural modelling—these are raw, previously unused data to check and evaluate the artificial neural network. As a standard, the data set is divided into a 2:1:1 ratio .Neural model RBF 22:22-22-1:1  Table 1Table 1, Neural model RBF 13:13-1-1:1  Table 3Table 3, Neural model RBF 18:18-1-1:1  Table 5Table 5 dThe result of the conducted research is the development of a new, original methodology for determining the chronological age of children between the ages of 4 and 15 (from 48 to 144 months). The paper identifies and presents an original set of 21 tooth and bone parameters, indicators, owing to which, on the basis of digital pantomographic images, using neural modelling methods, it is possible to accurately and effectively assess the chronological age. The accuracy of the proposed method of chronological age assessment is 99.7%.During the study, three optimal neural models were developed to determine the chronological age: female and male RBF 22:22-15-1:1 with test set quality of 0.9974 and test set error of 0.0365, female only RBF 13:13-1-1:1 with test set quality of 0.9631 and test set error of 0.0336, and male only RBF 18:18-1-1:1 with test set quality of 0.9993, and test set error of 0.0398. The measure of error is RMSE (Root Mean Square Error). A summary of the models is presented in the To work properly, each model needs appropriate input variables and indicators that have been defined in the sensitivity analysis process. For the model RBF 22:22-15-1:1 these are the indicators: X04; X02; X15; X18; X11; X12; X20; X14; X03; X16; X08; X19; X17; X10; SEX; X06; X01; X07; X09; X21; X05; X13. For model RBF 13:13-1-1:1, these are the indicators: X07; X14; X08; X12; X19; X20; X10; X13; X03; X01; X18; X06; X05. For the model RBF 18:18-1-1:1, these are the indicators: X18; X20; X16; X10; X03; X19; X14; X01; X07; X09; X05; X06; X17; X13; X08; PLEC; X12; X11.Variables relevant for each of the analyzed models are X01; X03; X05; X06; X07; X08; X10; X12; X13; X14; X18; X19. A detailed analysis of the models produced is shown in The newly developed methodology is based on: taking a digital pantomographic picture; making measurements of fixed sections on digital pantomographic pictures (measurements with ImageJ software), exporting a set of these measurements to a spreadsheet, e.g., MS Excel, and calculating the original indicators-proportions-proposed in the course of the conducted tests; subsequently, the calculated values should be transformed into a *.csv file and entered into the neural model used to determine the chronological age.The novelty of the developed method lies in the use of a pantomogarphy photograph and the use of a proprietary set of developed indicators. Currently, there is no scientific or commercial method that allows to estimate metric age in this way, using neural modeling methods.Comparing the conducted study with other works ,47,48,49A limitation in the application of the developed method and technique is the use of 2D images . It should also be emphasized that the models produced are dedicated to children and adolescents aged 48 to 144 months. The presented method is simpler than others cited in this work.Mention should be made of the research of the Bunyarit  team, whThe paper by Galibourg and team  uses theIn Pan and team’s work , a modifWallraff’s team  obtainedMost recent work on metric age assessment is based on traditional, atlas-based methods ,62. TherThe next stage of metric age estimation work should be related to the application of deep learning methods or using convolutional networks.It is possible to develop a new methodology for the assessment of chronological age on the basis of digital pantomographic images with the determination of a new set of tooth and bone parameters.It is possible to develop a neural model, which will facilitate the assessment of chronological age.The optimal model for assessing the chronological age of women and men is RBF 22:22-15-1:1 with 99% test set quality and 3.7% RMSE set error.The optimal model for assessing the metric age of women is RBF 13:13-1-1:1 with 96% test set quality and 3.4% RMSE set error.The optimal model for assessing the metric age of men is RBF 18:18-1-1:1 with 99% test set quality and 4.0.% RMSE set error.This newly developed methodology is an effective and innovative tool in the assessment of the chronological age of Polish children and adolescents aged between the ages of 4 and 15 (from 48 to 144 months).This newly developed methodology may serve as an algorithm for implementation in a computer application, which will automatically determine the chronological age of children and adolescents between the ages of 4 and 15 (from 48 to 144 months). The conducted research allowed to formulate the following conclusions:The developed model works only for pantomographic images of children and adolescents aged 4 to 15 years. The models are dedicated to this age group and to the developed indicators. On the other hand, the measurements obtained during the study are dimensionless—they have a charter of proportions which allows universalizing the use of images from different pantomographic cameras.The next recommended step in the research will be to use deep learning methods and compare the network characteristics with the new models, and to conduct a sensitivity analysis of the variables needed to determine metric age using deep learning methods.


ABCA1 gene associated with smoking. In the alveolar macrophages of smokers, the expression levels of ABCA1 were lower than in non-smokers. At the same time, in most of the airway epithelial datasets, gene expression did not show any difference between the groups of smokers and non-smokers. In addition, it was shown that the expression of ABCA1 in the epithelial cells of the trachea and large bronchi is higher than in small bronchi. Conclusions: The conducted bioinformatic analysis showed that smoking can influence the expression of the ABCA1 gene, thereby modulating lipid transport processes in macrophages, which are part of the mechanisms of inflammation development.Smoking is a key modifiable risk factor for developing the chronic obstructive pulmonary disease (COPD). When smoking, many processes, including the reverse transport of cholesterol mediated by the ATP binding cassette transporter A1 (ABCA1) protein are disrupted in the lungs. Changes in the cholesterol content in the lipid rafts of plasma membranes can modulate the function of transmembrane proteins localized in them. It is believed that this mechanism participates in increasing the inflammation in COPD. Methods: Bioinformatic analysis of datasets from Gene Expression Omnibus (GEO) was carried out. Gene expression data from datasets of alveolar macrophages and the epithelium of the respiratory tract in smokers and COPD patients compared with non-smokers were used for the analysis. To evaluate differentially expressed genes, bioinformatic analysis was performed in comparison groups using the limma package in R (v. 4.0.2), and the GEO2R and Phantasus tools (v. 1.11.0). Results: The conducted bioinformatic analysis showed changes in the expression of the  According to rough estimates, more than a billion people smoke in the world ,2,3. SmoAccording to modern concepts, the pathogenesis of COPD is based on inflammation in the bronchi, in which many cells are involved. Macrophages play an important role in the development and progression of COPD ,11,12. TIt is known that smoking disrupts the transport of lipids and lipid-like molecules, including cholesterol in lung cells, which may be one of the links in a complex chain of processes underlying the development and progression of COPD ,22,23,24ABCC7 gene ) are the cause of cystic fibrosis.ABCA1 belongs to a large group of ATP-binding (ABC) transporters that facilitate the movement of a wide range of substrates through cell membranes. There are 48 ABC transporters in humans, which are divided into 7 subfamilies (ABCA-ABCG) based on structural characteristics. At the moment, the role of only a few ABC transporters in lung function and the development of their diseases is well known. For example, ABCA3 is involved in the formation of a surfactant, and mutations of the The ABCA subfamily in humans includes 12 proteins that are well known for their participation in lipid transport. ABCA1 is one of the most well-studied representatives of the ABCA subfamily. ABCA1 is expressed in various cells of many organs and it participates in the export of cholesterol and phospholipids from the cell to extracellular acceptors, thereby regulating the lipid homeostasis of cells ,28. Due ABCA1 gene—transporter in smoking and COPD using bioinformatics analysis methods. To do this, we use the available capabilities of modern developments that ensure the availability of biological data for repeated analysis. Similar approaches are widely used in research, including for the analysis of the differential expression of ABC transporter gene profiles in the epithelium of the respiratory tract  and bronchi of the 4–6th order [n = 20] obtained in healthy non-smokers and the epithelium of the distal respiratory tract (bronchi of the 10th–12th order) obtained in 44 healthy non-smokers and 36 healthy smokers were included in the analysis from the GSE64614 set . The datData on gene expression in samples of the tracheal epithelium, bronchi of the 2nd–3rd order, and bronchi of the 10–12th order, selected by fiber-optic bronchoscopy in 124 people  were included in the analysis from the GSE11906 set . The datData on gene expression in the bronchial epithelium of the 10th–12th order obtained in 63 healthy non-smokers, 72 healthy smokers, and 22 patients with COPD were included in the analysis from the GSE11784 set ,42. The The datasets selected for analysis were obtained from different studies and differed in data normalization methods. GSE13896, GSE4498, GSE76324, GSE18385, GSE64614, GSE11906, GSE11784, used the MAS5 normalization method, where GSE130928 used the Robust Multi-array Average (RMA) method. The sets were analyzed in accordance with the methods of obtaining and normalizing data that were used in the original study. Data from different sets were analyzed independently of each other and were not combined for analysis.ABCA1 gene, comparison groups were formed: smokers, healthy individuals, and patients with COPD , the location in the respiratory tract from which samples were obtained  and pre-processed gene expression data. To analyze the data on the expression of the ith COPD . Table 2In addition to the smoking status and the presence of COPD, data on smoking intensity (the pack-years index) were also taken into account. Other available categorical data from the sets were not analyzed in this study.ABCA1 gene in each of the sets in the comparison groups was carried out using GEO2R, Phantasus (v. 1.11.0), and the limma package in R (v. 4.0.2).In this study, the analysis of the differential expression of the http://www.ncbi.nlm.nih.gov/geo/geo2r/) is an interactive web tool for the analysis to compare gene expression levels in groups in a dataset of GEO. Phantasus (https://artyomovlab.wustl.edu/phantasus/)—a web application for visual and interactive gene expression analysis.GEO2  was used, implemented in GEO2R, and using the limma package and the p. adjust function in R (v. 4.0.2) [p values satisfying the condition < 0.05 at FDR ≤ 0.1 were taken as statistically significant.Using these tools, data on the differential expression of the . 4.0.2) . All p vABCA1 gene expression levels in the comparison groups in each of the data sets was carried out using the Phantasus tool (v. 1.11.0) [Visualization of the  1.11.0) . The datThe conducted bioinformatic analysis of the data sets of alveolar macrophages (GSE13896 and GSE130928) showed that smoking alters the expression of the ABCA1 gene. A statistically significant decrease in the expression levels of ABCA1 in the alveolar macrophages of smokers compared with non-smokers is determined . No diffThe obtained results correspond to the available data that cigarette smoke suppresses the RCT in alveolar macrophages, mediated by the ABCA1 transporter. Alveolar macrophages are important participants in inflammation in COPD ,46. ThesThe analysis of ABCA1 gene expression in the epithelium of the respiratory tract in smokers showed contradictory results. No statistically significant changes in the expression of ABCA1 were found in the sets GSE4498, GSE11906, GSE64614, GSE76324, and GSE18385, whereas in the set GSE11784 the expression of the ABCA1 gene was upregulated in smokers (more than 10 pack-years) .It was also found that both smokers and non-smokers have upregulated expression of ABCA1 in the epithelial cells of the large bronchi (generation 2nd–4th) than small (generation 10th–12th) ones .In general, the obtained results indicate that cigarette smoking, which is a modifiable risk factor for the development of COPD, is associated with differentiated patterns of the ABCA1 gene expression.We conducted a bioinformatic analysis of the ABCA1 gene expression in alveolar macrophages and airway epithelium in smokers, non-smokers, and COPD patients from GEO datasets and showed that the levels of gene expression in the alveolar macrophages of smokers are lower than in non-smokers. These data may indicate the effect of smoking on the expression of ABCA1.Our experimental project included the analysis of publicly available databases obtained from alveolar macrophages  and respiratory tract epithelium  to determine the differential expression of the ABCA1 gene associated with smoking. Bioinformatic analysis of gene expression data placed in publicly available databases is widely used in research both for evaluating differentially expressed genes and for their functional analysis.Using tools for online analysis, we formed comparison groups: smokers, healthy non-smokers, and patients with COPD. Statistically significant differences in gene expression levels were taken into account, which was corrected in accordance with the algorithm of Benjamini & Hochberg.In a previous study using similar tools, the authors analyzed the differential expression of ABC transporter genes in the epithelium of the respiratory tract during smoking, in patients with COPD and bronchial asthma . We analLiterature data suggest that smoking has a significant effect on the expression and function of ABCA1 in the respiratory tract. The data obtained in recent years have expanded our understanding of the function of the ABCA1 protein . This reThe expression of ABCA1 is influenced by many factors. The transporter has a complex transcriptional and post-transcriptional regulation, which involves both cholesterol metabolites and humoral inflammatory factors ,56,57,58The choice of alveolar macrophages for analysis is due to the multifaceted role of ABCA1 in the function of these cells. It is known that lung macrophages act as the first line of immune defense of the lungs. These cells are heterogeneous in their origin and functions. They play an important role in the pathogenesis of COPD and their number increases significantly in the lungs with COPD. ABCA1 is involved in providing several functions of macrophages associated with inflammation ,60. The An important mechanism that ensures the participation of ABCA1 in inflammation is the transporter-mediated regulation of the cholesterol content in the lipid rafts of plasma membranes ,63. LipiA decrease in the expression of ABCA1 and its functional activity during smoking leads to a decrease in RCT and intracellular accumulation of cholesterol, which contributes to the activation of inflammation through several mechanisms ,61,67. CThus, a decrease in the expression of ABCA1 caused by smoking can lead to the inflammatory activation of macrophages. The data obtained in this study indicate that smoking is associated with a decrease in the expression of ABCA1 in alveolar macrophages.Cholesterol transport is not the only function of ABCA1, since it is involved in the movement of other lipids that are, for example, part of a surfactant . This roThe analysis of the expression of ABCA1 in the epithelium of large and small airways showed that there are variations in the results depending on which data set is being studied. In most datasets , there were no statistically significant differences in the expression of ABCA1 in the comparison groups, whereas, in the GSE11784 set, the expression of the ABCA1 gene was increased in smokers. When interpreting these data, differences in the expression of ABCA1 in different types of bronchial epithelial cells should be taken into account . HoweverPrevious studies have already shown a violation of ABCA1 expression in smoking and COPD ,60,72. TIt should be noted that the present study has some limitations due to the fact that the data sets contain a small number of patients; there is not enough information about the patients taking medications that can affect lipid metabolism. However, these limitations, typical for bioinformatic analysis, may be useful for planning further experimental research.In this regard, it is interesting to further study the role of the ABCA1 transporter in different cells of the respiratory tract, as well as in cells with different functional activity. Bioinformatic analysis is a useful tool that can be used to analyze data to obtain new information on gene expression, as well as when planning experimental studies.ABCA1 gene, thereby modulating lipid transport processes in macrophages and epithelium of the respiratory tract, which are part of the mechanisms of inflammation development.Thus, the conducted bioinformatic analysis showed that smoking can influence the expression of the


The aim of the study was to estimate the accuracy of standardized uptake values of 18F-fluorodeoxyglucose (18F-FDG) in lung lesions during positron emission tomography combined with computed tomography (PET/CT) imaging, based on phantom studies performed for different PET/CT scanners.18F-FDG data was performed for 86 patients newly diagnosed with the lung lesions: malignant tumors (n=37), benign tumors and inflammatory diseases (n=49). The criteria for inclusion in the study were developed considering the recommendations of the Fleischner Society (2017). The characteristics of the lesions on CT met the following requirements: a round shape or close to it; total size of 8 to 30 mm; solid or subsolid structure (with the exception of lesion with ground-glass opacity); a solid part size of ≥8 mm. All the patients had no signs of pleurisy, lymphadenopathy, or cancer history. PET/CT imaging with 18F-FDG was performed with three scanners: Discovery 690 , Biograph mCT 128 , and Biograph mCT 40 (Siemens); the preparation of patients prior to the scan was standardized. To determine the reference accumulation of a radiopharmaceutical in the pathological lesion, four scans of a specialized NEMA IEC PET Body Phantom Set (USA) were performed for each scanner. For each unit, the recovery coefficients (RCs) of radioactivity, maximum and recovered (corrected) standardized uptake values (SUVs) were determined. Statistical relationship between the size of lesions, SUVmax and SUVcorrect was evaluated. Data processing was performed using MedCalc v. 19.2.0 software.The analysis of the PET/CT with During the phantom study, the underestimation of the radioactivity was determined in the spheres with the diameters of 10 and 13 mm, overestimation was observed in the sphere with the diameter of 28 mm. Both underestimation and overestimation of radioactivity were determined for the spheres with a diameter of 17 and 22 mm.max differed from the reference values for 85 patients (98.8%). The underestimation of these values was found for 63 patients (73.2%) due to the partial volume effect. The greatest underestimation was observed for the patients with 8 mm diameter lesions. Depending on the scanner, the underestimation of the SUVmax in these patients reached up to 54–73%. For 9 patients (25%) with malignant tumors of 9–12 mm, the utility of RC made it possible to avoid false negative results. For the lesions with a diameter of 30 mm, an overestimation of SUVmax up to 22% was determined due to the negative influence of the reconstruction algorithms.SUVmax in lung lesions, which ensures reproducibility, increase in the information content of the method, as well as the comparability of the results of PET/CT with 18F-FDG obtained on the different models of PET/CT units with different technological characteristics.The use of RC eliminates the influence of the partial volume effect and reconstruction methods on the accuracy of estimating the SUV The accuracy of the evaluation of the radiopharmaceutical accumulation depends on the features of the detecting system of the scanner, radionuclide used, scanning protocol, data reconstruction algorithm, size of a pathological lesion, reconstruction and processing methods, etc . Accordict (PVE) , 6.max. This entails the underestimation of the biological aggressiveness of the tumor and, as a consequence, an increase in the number of false negative results.The PVE concept combines two related phenomena that negatively affect both the qualitative characteristics of the images and the semi-quantitative values obtained during positron emission tomography combined with computed tomography (PET/CT) –7. The fIn other countries, one of the ways to correct PVE is the use of the recovery coefficient (RC), which is determined by scanning a specialized phantom of the NEMA IEC PET Body Phantom Set (USA), recommended by the National Electrical Manufacturers Association (NEMA) –10. ThisThe aim of this study was to estimate the accuracy of the evaluation of the standardized uptake values of 18F-fluorodeoxyglucose (18F-FDG) in lung lesions when performing positron emission tomography combined with computed tomography (PET/CT), based on phantom studies performed for different PET/CT scanners.18F solution. The appearance of the phantom and its components is shown in Preparation of a dedicated NEMA IEC PET Body Phantom Set for PET/CT scanning consisted of filling the main volume and spheres with a 18F activity concentration in the main volume of the phantom was lower than that in the spheres. The prepared phantom was scanned four times on each of the tested scanners: a Discovery 690 , a Biograph mCT 128 , and a Biograph mCT 40 (Siemens) using the clinical protocols used for patients. Before each new scan, 18F was added to the total volume of the phantom to create a different ratio of the sphere–main volume. The analysis of the phantom images consisted of measuring radioactivity in the main volume of the phantom and in the spheres, as well as calculating the ratios of sphere–main volume. The radioactivity values in the main volume of the phantom, spheres, as well as the ratio of sphere–main volume for four scans, are presented in st scan for the selected devices was 10.3; at the 2nd — 7.0; at the 3rd — 3.3; at the 4th — 1.7.The phantom spheres with diameters of 10, 13, 17, 22, and 28 mm were used to simulate lung lesions, the main volume of the phantom used as analogue of anatomical structures adjacent to the focus. The The parameters of the scan protocols and reconstruction, depending on the used PET/CT unit, are presented in 18F was measured using a verified Curiementor 4 dose calibrator  with a relative 5% error in activity measuring. For each sphere, the volumes of interest were determined using automatic delineation in order to measure the maximum value of activity concentration. An example of delineation and measuring the maximum value of activity concentration in the spheres is shown in The radioactivity of To evaluate the reproduction of radioactivity in the lung lesions on the PET images, the RC was calculated by the formulaAimage is the activity concentration in the sphere measured on PET images (kBq/ml); Ainject is the activity concentration injected into the sphere measured during preparation of the phantom for scanning (kBq/ml).where The RC values obtained for the four scans were averaged. The averaged RCs were interpolated for the unknown (intermediate) sizes of the lung lesions.18F-FDG data was carried out in 86 patients with solitary newly diagnosed lung lesions: in 37 patients with malignant tumors (MT), in 49 patients with benign tumors (BT) and inflammatory diseases (ID). The study was performed in accordance with the Declaration of Helsinki (2013) and approved by the Ethics Committee of the A.M. Granov Russian Research Center for Radiology and Surgical Technologies of the Ministry of Health of the Russian Federation.The analysis of the PET/CT with The criteria for inclusion in the study were formed on the basis of the recommendations by the Fleischner Society . The fol18F-FDG was performed at the A.M. Granov Russian Research Center for Radiology and Surgical Technologies in the Department of Positron Emission Tomography on the Discovery 690, Biograph mCT 128, and Biograph mCT 40. The distribution of the patients into groups depended on the morphological diagnosis and the scanners are presented in PET/CT with 18F-FDG with the activity of 110 MBq per unit of the patient’s body surface area. The scan protocol consisted of a topogram, helical CT scan without contrast enhancement for attenuation correction, and PET scan.The 60.5% of patients were scanned on a Discovery 690 scanner, 12.8% on a Biograph mCT 128 scanner, and 26.7% on a Biograph mCT 40 scanner. The preparation of patients prior to the scan was standardized. The study was limited to scanning one anatomical region — the thoracic region — and started 70–90 min after intravenous administration of max normalized to lean (muscle) body mass (SUL) were considered to be diagnostically significant levels of radiopharmaceutical uptake in the lung lesions. SUVmax (SUL) calculation was performed by the software package automatically according to the formulaThe post-processing data analysis consisted of visual evaluation of CT, PET, and hybrid images, as well as performing a semi-quantitative analysis. The measurement of SUVs was performed by automatic delineation of the volume of interest (VOI) in a specialized program on an AW 4.7 workstation . The SUVAVOI is the radioactivity in the volume of interest (MBq/ml); Ainject is the total injected activity to the patient, corrected for lean (muscle) body mass (MBq/kg); LBM — lean body mass.where correct) were calculated by the formulaThe corrected maximum SUVs . Applying the methods of descriptive statistics, the median and 95% confidence interval (CI) were calculated. The statistical significance of the differences between the values was calculated using the Wilcoxon test. The critical level of statistical significance of the null statistical hypothesis was taken equal to 0.05. Spearman’s correlation coefficient (ρ) was calculated to study the relationship between the variables. The qualitative characteristic of the relationship between the studied variables was assessed using the Chaddock scale . For visual data representation, the box and whiskers plots were used.18F solution on three scanners, are presented in The RCs, averaged for dilutions and interpolated for unknown (intermediate) lesion sizes in the lungs, for four scans of the NEMA IEC PET Body Phantom Set with different max was found . No correlation was found between the size of the lesions and SUVcorrect . The SUVmax and SUVcorrect are shown in In 37 patients with MT, the sizes of the detected lung lesions on the CT images ranged from 8 to 30 mm . A direct correlation between the lesion size and SUVth and 75th percentiles, and medians with 95% CI. The outliers were considered to be the values lying in the ranges exceeding the height of the box from its upper and lower boundaries by 1.5 times.The values in the box plots show the minimum and maximum values, 25max in patients with MT had a wide range (from 0.6 to 27.5), differed among themselves significantly (p˂0.0001); the median was determined at the level of 1.9 (95% CI 1.5–2.4). After applying the RC, the corrected levels of radiopharmaceutical accumulation were determined within the range of 1.1–26.2, the median SUVcorrect was 2.6 (95% CI 2.1–3.8). No statistically significant differences were found (p=0.0024) between SUVmax and SUVcorrect.max, a direct high correlation was found between these characteristics . No correlation was found between the lesion size and SUVcorrect . The SUVmax and SUVcorrect recorded in the patients with BT and ID are shown in In 49 patients with BT and ID the size of the detected lesions on the CT images varied from 8 to 29 mm, the median was 16.0 (95% CI 14.0–18.8). When comparing the size of the lesion and the SUVth and 75th percentiles, and medians with 95% CI. The outlying values were considered thoseThe values in the box plots show the minimum and maximum values, 25max in the patients with BT and ID were determined in the range of 0.3–6.7; the median was 1.5 (95% CI 1.2–1.8). After applying the RC, the corrected levels of radiopharmaceutical accumulation ranged from 0.5 to 5.9; the median was 1.8 (95% CI 1.5–2.1). The comparison of SUVmax and SUVcorrect showed statistically significant differences (p=0.0006).max was recorded in the lesions from 10 to 20 mm. The SUVmax overestimation was observed in 25.6% of the patients with larger lesions (20–28 mm). Only in one case, the measured level of radiopharmaceutical uptake in the tumor corresponded to the reference value in a patient with MT scanned on the PET/CT Discovery 690. According to the phantom studies, the RC on this scanner equals 1 when the focus size is 26 mm.max nearly leads to obtaining false negative results in 9 MT patients (32.1%) with the lung lesions ranged from 9 to 12 mm. In all these cases, due to the low level of radiopharmaceutical uptake in the MT, the changes in the lung were erroneously interpreted as an inflammatory process. lying in the ranges exceeding the height of the box from its upper and lower boundaries by 1.5 times. Applying RC in these patients ensured objective assessment of the radiopharmaceutical accumulation levels and, as a consequence, correctly suggested the nature of the tumor , total lesion glycolysis (TLG), average ratio of the tumor-to-background radioactivity (TBR), standardized uptake peak value determined within the VOI of the fixed size (SUVpeak), etc. [max, which is determined by the semiquantitative method [Currently, PET/CT is one of the principal methods of diagnostics and evaluation of the treatment effectiveness of various oncological diseases. The given technology is characterized by substantially high visualizing capabilities and, at the same time, is a qualitative method that allows for measuring various biochemical processes ongoing in the human body. To solve clinical problems with the utility of PET/CT as quantitative criteria, different indicators are used: SUVk), etc. –18. The e method .max, as well as other quantitative values, depends on several factors: a glucose level in the patient’s blood plasma, duration of radiopharmaceutical accumulation, parameters of the scan protocol, data reconstruction algorithms, accuracy of volume of interest determination and delineation method, intensity of radiopharmaceutical accumulation in the adjoining tissues, heterogeneity of a lesion structure, etc [SUV displays the intensity of radiopharmaceutical accumulation in the target volume of interest and depends on the volume in which the given activity is spread . There aure, etc –7.max relative to the true levels of radiopharmaceutical accumulation. Because of PVE, the lesions with the same level of radiopharmaceutical accumulation, but differing size and histological origin, are displayed on PET images with different brightness. As a result, tumors of a malignant nature may be erroneously regarded as less aggressive, and this leads to an increase in the number of false negative results at PET/CT with 18F-FDG.Another important factor that affects both the qualitative and quantitative characteristics of PET images is PVE –7. This max underestimation from 54 to 73% for different scanners. At the same time, in 9 patients having MT with the size of the pathological lung lesions ranged from 9 to 12 mm, the use of RC avoids false negative results. In all these cases, the MT was not clearly visible on the background of an intact lung, and the SUVmax for the lesion did not exceed 1.0.Today, the most famous and easily reproducible method for PVE correction is the use of RC, which is determined on the basis of the phantom studies , 21. Acc18F-FDG imaging, physiological radiopharmaceutical pathological accumulation is observed in the mediastinum, liver, left ventricular myocardium, and ribs. According to these studies, RC may be ineffective for the tumors located close to at least two anatomical structures with different radiopharmaceutical accumulation.According to the literature , 15, 21,The severity of the PVE also depends on the shape of the tumor. It is generally assumed that PVE is more pronounced for larger the surface area of the pathological lesions , 22. Accmax overestimation — by 22%. According to the literature [max in the lesion of a larger size can also be eliminated with the RC. Correction of SUVs in these cases can prevent obtaining false positive results.On the other hand, the pathological lesions with the diameter of 30 mm showed a marked SUVterature , 23, 24,max and the sizes of the lung lesions was confirmed by the results of numerous studies [correct was not determined for all morphological diagnoses and scanners. It evidenced that RC eliminated a negative impact of PVE on SUVmax in the small lesions and also showed the influence of the reconstruction methods on the larger lesions.Direct correlation between SUV studies –28. In t18F-FDG [In 2010, the EANM launched a accreditation programme  of the medical facilities performing PET/CT with 18F-FDG , 28–31. Today, professional societies as the American College of Radiology Imaging Network (ACRIN), the Radiologic Society of North America’s Quantitative Imaging Biomarker Alliance, American Association of Physicists in Medicine are also actively working to promote the concept of harmonization of PET/CT . In our max in the lung lesions, which allows for increasing the information of the PET/CT with 18F-FDG method and ensures the reproducibility and comparability of the results obtained on the scanners of different models with different technological characteristics.The use of the recovery coefficient neutralizes the PVE and impact of the reconstruction methods on the accuracy of SUV


In wood–polymer composites (WPCs), regardless of the origin of the filler and its dimensions, their significant role in changing the properties of the WPCs’ material was found. Given the above, it is of particular importance to determine the size of the wood filler particles after their production. In addition, it is also important to determine the degree of distribution of the filler in the polymer matrix. The methodology for determining particle size and distribution is complex, even when using image analysis computer systems. This article presents the application and implementation of the multi-stage procedure for determining the size of wood particles and the degree of their distribution in the WPCs by means of image analysis using a numerical calculation program. The procedure, co-authored by the researchers at the Koszalin University of Technology and School of Mechanical and Materials Engineering, is published in the Industrial Crops and Products 2016 Comparing the results obtained for the PP/Lignocel 3-4 and PP/Lignocel C120 composites produced under highly different conditions in the target zone, it was found that the degree of the component distribution in the polymer matrix was significantly influenced by the width of the target gap. In both cases, the best homogeneity of the material and a good distribution of the filler in the polymer matrix was achieved within the parameters that have a mild effect on the material and allow it to stay longer in the plasticizing system, i.e., Ws = 1.0–3.0 mm with simultaneous impact medium to high speed in the range n = 26–40 rpm. The production of wood–polymer composites (WPCs) is currently at the level of over 1.5 million tonnes worldwide (according to the German Nova-Institute). Polymer composites with at least one biological material component have attracted the attention of researchers worldwide. It was found that the properties of the obtained polymer composites depend not only on their composition but also on the size and form of individual components used for their preparation ,4,5,6,7.As a result of their interaction with the elements of the plasticizing system, the size of the particles changes causing, in turn, the changes in the properties of the obtained composite ,12. HencStudies in the literature indicate that the systems that automatically analyze the image have implemented procedures favoring large  or small particles ,15,16. TThe research material consisted of composite tiles  compriseConiferous wood with different granulation: wood flour with the trade name Lignocel C 120 a with diBefore mixing and extrusion, the wood material was dried at the temperature of 105 °C ± 5 °C for 4 h. The surface of the wood material was not modified.s were the variable factors  was made. Images of composite tiles were recorded with a NIKON 5100 digital camera with additional, multi-point LED lighting. Images with a resolution of 300 dpi were obtained. The image processing algorithm uses a two-dimensional analysis of the registered images [Photographic documentation of composite tiles with dimensions of 130 mm × 60 mm (sample area 7800 mms) and rotational speed (n) selected in such a way as to cause extreme phenomena in the disc zone of the screw-disk extruder. The focus was on the parameters causing the occurrence of high and low shear rates, respectively:s = 0.3 mm and n = 26 rpm (γ · = 800 s−1), under conditions of intense short-term shear-mixing interaction.Simultaneous influence of small disc chink widths and medium rotational speeds, i.e., Ws = 1.7 mm and n = 26 rpm (γ · = 400 s−1), under conditions of intense short-term shear-mixing interaction.Simultaneous influence of medium disc chink widths and medium rotational speeds, i.e., Ws = 3.0 mm and n = 26 rpm (γ · = 70 s−1), under mild long-term mixing conditions.Simultaneous influence of large width of the disc chink and medium rotational speeds, i.e., WThis article presents the results of the analysis of the distribution of Lignocel C120 wood flour and Lignocel 3-4 wood shavings in a polypropylene matrix . The analysis was carried out for the parameters of the extrusion process with disc zone chink  = 12, 26, 40 and WThe presented article is a practical application of the method and the model described in the 2016 article. The procedure, co-authored by research workers of the Koszalin University of Technology and the College of Mechanical and Material Engineering, was published in Industrial Crops and Products 2016 [The WPCs manufactured according to the methodology included in chapter 2 of this article were analyzed. In the first stage, the analysis of the WPCs (PP/Lignocel C120 and PP/Lignocel 3-4) produced at different values of shear rate was carried out.−1, many clearly visible large clusters of wood flour accumulating in the form of agglomerates of various sizes were found , some large clusters of wood flour were found  obtained under conditions of short-term, intense shear-mixing interaction, where the shear rate was approximately 800 sre found a. In thitely 33%  of the sre found b. In thibout 10%  of the sace area c. Specifbout 20%  of the s2 were identified, including 225 aggregates with an area of up to 60 mm2, 2 with an area between 60 and 135 mm2 and single ones above 135 mm2. On the other hand, when analyzing the WPC obtained under the conditions of a mild, long-term mixing effect, the presence of aggregates on their surface, as in the previous analyzed case, was found, but with a smaller surface area and more even distribution in the composite  under conditions of short-term, intense shear-mixing interaction, many clearly visible large aggregates of the filler in the form of aggregates of various sizes were found on their surface a. In thiomposite b. In this = 1.0–3.0 mm), and medium and high rotational speeds (in the range of n = 26–40 rpm). Under these conditions, rolling and circumferential distribution of the filler takes place. In the PP/Lignocel 3-4 composites, it has been found that the extent of distribution of the component in the polymer matrix is mainly influenced by the width of the disc chink, but not as pronounced as in the case of the wood flour filler. When comparing the composites obtained in significantly different processing conditions, i.e., low and high shear rate values, it was found that for processing composites filled with wood shavings, the best homogeneity of the material and good distribution of the filler in the polymer matrix is achieved within the parameters that mildly affect the material and allow a longer stay in the plasticizing system, i.e., large width of the disc chink in the range of Ws = 1.0–3.0 mm with the simultaneous influence of medium to high rotational speed in the range of n = 26–40 rpm. Using a large width of the disc gap and a low rotation speed also reduces the longitudinal dimensions of the long wood chips due to their breaking of the transverse dimensions resulting from defibration. This analysis confirms the exploratory research conducted by T. Rydzkowski [Comparing the results obtained for the PP/Lignocel C120 composites produced under extremely different conditions in the target zone, it was found that the degree of the component distribution in the polymer matrix was significantly influenced by the width of the target gap. In the case of a composite with wood flour, in order to achieve the best possible distribution of the filler in the polymer matrix, it should be produced under mild, long-term mixing effects, i.e., with the simultaneous effect of large disc chink widths  and the amount of filler (i)) on the distribution of the filler in the form of wood flour and wood chips in the WPCs  obtained at n = 12 rpm, many clearly visible large agglomerates of the wood flour were found in the polypropylene matrix a. In thio 26 rpm b, a decro 26 rpm c caused o 26 rpm .2 were identified, including 518 aggregates with an area of up to 40 mm2, 2 with an area between 75 and 150 mm2, and single ones above 190 mm2. Percentage of it accounted for 48% of the total area. By increasing the value of the rotational speed to 26 rpm  obtained at a rotational speed of 12 rpm, many clearly visible large aggregates of various sizes were found on their surface a. In thio 26 rpm b, a decro 40 rpm c caused e 90 mm2 .By analyzing the histograms presented in By analyzing the degree of distribution of the fraction in the form of wood flour in a polypropylene matrix (PP/Lignocel C120), the presence of many aggregates of various sizes was found. The possibility of influencing the extrusion settings, that is, the width of the disc gap (Ws) and the rotational speed (n) on the size and the number of aggregates, was found. The simultaneous effect of high rotational speed and small width of the disc chink creates conditions for an intense shear-mixing effect, resulting in many large aggregates. On the other hand, the mild and long-term mixing effect created at a low rotational speed of the screw and a medium or large width of the disc gap allows for the production of a composite with a higher level of filler mixing in the polymer matrix .By analyzing the degree of distribution of the fraction in the form of wood chips in the polymer matrix (PP/Lignocel 3-4), the presence of many aggregates with a different surface area was found. The possibility of influencing the extruder settings, i.e., the width of the disc chink (Ws) and the rotational speed of the screw (n) on the size and number of aggregates, was found. The primary and significant influence in the case of filling in the form of wood chips is the mild effect of the target zone obtained with an average gap width and rotational speed on the processed material. Under such conditions, we obtain a material with a smaller number of aggregates, and above all, with a smaller surface area, which proves good mixing and distribution of the filler, as well as shortening the longitudinal dimensions of their particles, which facilitates the arrangement of chips in the flow direction.The degree of distribution of the wood chip filler in the polymer matrix is influenced by the value of the disc chink, but not as intensely as in the case of the wood flour filler.Based on the conducted analyzes, the applied method of analyzing images of the WPCs presented in the article is the optimal method for composites with low (i < 15% weight) and medium filled (15% weight < i < 50% weight) with a wood fraction in the form of chips and wood chips.


Results show that specific natural ventilation strategies have a relevant impact that is worth considering on the background noise in indoor spaces. In this sense ventilation rates provided by the different configurations varied between 3.7 and 39.8 air changes per hour (ACH) and the acoustic tests show a background noise ranging from 43 to 54 dBA in these scenarios. Consequently, managers and teachers should take into account not only the ACH, but also other collateral impacts on the indoor environmental conditions such as the thermal comfort or the acoustic environment.Indoor environmental conditions can significantly affect occupants’ health and comfort. These conditions are especially important in educational buildings, where students, teachers and staff spend long periods of the day and are vulnerable to these factors. Recently, indoor air quality has been a focus of attention to ensure that disease transmission in these spaces is minimised. In order to increase the knowledge in this field, experimental tests have been carried out to characterise the impact of natural ventilation strategies on indoor air quality and the acoustic environment. This study has evaluated three ventilation scenarios in four different classrooms in buildings of the University of Granada, considering different window and door opening configurations. Ventilation rates were estimated using the CO Since people spend more than 80% of their time in indoor environments , if the The IEQ is essential in educational buildings, which are typically designed for high occupancy for long periods of the day ,6. In paThese circumstances determine that one of the most demanding challenges facing educational building administrators is IAQ managing . An adeq3 and for an exposure period of 2 h, to keep the probability of infection below 1%, a VR of two Air Changes per Hour (ACH) with masks and seven ACH without masks is necessary. This fact has been highlighted by the COVID-19 pandemic. According to the World Health Organization, as of 7 July 2021, there had been 3,997,640 deaths and 184,572,371 confirmed cases of COVID-19 reported globally . TransmiSince students and teachers spend long periods each day in classrooms, these indoor spaces are risk environments for the airborne transmission of SARS-CoV-2 . Consequ2 and other pollutants, they also have an impact on other important indoor variables in indoor environments. One of the most important in teaching‒learning spaces is the indoor acoustic environment, which is influenced by the natural and/or mechanical ventilation strategy selected [However, while these ventilation strategies ensure an optimal concentration of COselected . In receselected ,34 Acousselected ,36. In tselected  analysedselected ,39 propoIn this context, the management of natural ventilation strategies and their impact in the indoor acoustic environment is essential in the teaching‒learning spaces. Poor acoustic environments in classrooms affect learning achievements ,41 as weACH values recommended in current Spanish public policies to prevent the transmission of COVID-19, the aim of this study was to characterise their impact on the variables conditioning IAQ and the indoor acoustic environment. The study assesses the need to define health protocols for ventilation in educational buildings that, in addition to identifying natural ventilation strategies with a VR value as close as possible to the required ACH value, take into account the background noise level. This will therefore ensure the quality of teaching and learning processes while maintaining the required ventilation protocols.In this context, and given the 6 With the aim of characterising the impact of natural ventilation strategies on the variables conditioning IAQ and the indoor acoustic environment, natural ventilation efficiency was checked in three ventilation scenarios with different window and door opening configurations. Background equivalent continuous sound pPressure level (Leq) in dBA was also calculated from sound pressure levels measured in the configuration that provided sufficient VR through natural ventilation according to the current regulatory limit. This value was compared with the background equivalent continuous sound pressure level, measured in the closed doors and windows scenario. This section describes the study area, the data-collection methodology and the sensors used in the process. The study comprises educational buildings from the Fuentenueva Campus of the University of Granada, located in Granada (Spain). The field measurements were conducted between March and April 2021 (spring season) in the Advanced Technical School for Building Engineering (built in 1972) and the Advanced Technical School for Civil Engineering (built in 2000) .Face-to-face teaching was suspended at the University of Granada from October to January in response to COVID-19. The tests were carried out before the adaptation of the teaching spaces to the return to face-to-face teaching activities. For this purpose, ventilation and acoustic measurements were carried out in the newly adapted spaces. Granada is classified as a C3 zone by the Spanish Technical Building code CTE . This zoTwo representative classrooms were selected for each building based on the data provided by the COVID-19 Action Plan developed by the University of Granada . This pl2. The aim of this method is to determine the ACH. In fact, the decay method consists of increasing the CO2 concentration by using a CO2 generation source  [2 concentration under the different configurations is determined. The experimental test ends when the CO2 level approaches 37% of its original peak concentration above the background [2 concentration is measured at known times and the ACH can be estimated using Equation (1):endC is the measured CO2 concentration at the end of the decay curve, endt is the end time of the decay curve, startC is the measured CO2 concentration at the start of the decay curve, startt is the end time of the decay curve and outdoorC is the measured CO2 concentration outside the building.The decay method can be used in unoccupied spaces using a tracer gas such as COdry ice)  in the cdry ice) ,51,52. S2 concentrations over a portion of the decay period, 2 concentration at the start of the test. The estimated ACH is the slope of the regression of ln :(2)Ct=.In order to characterise the indoor acoustic environment in different configurations of natural ventilation strategies, the sound pressure level of the background noise was measured in the different configurations. For this purpose, a two-phase methodology was followed: in the first phase, the background noise was measured in the classroom with all doors and windows closed. Subsequently, in phase two, the background noise was measured with the natural ventilation configuration selected based on the experimental results of the decay method previously obtained  in both phases, resulting in nine measurements in each phase. The locations were selected because they were typical listener positions inside the classroom. The measurements were recorded at least 1.2 m away from the ground, 0.7 m between measurement positions and at 0.5 m. away from any wall, ceiling or ground surface, in compliance with the UNE-ISO 1996-2:2020  recommenThe continuous equivalent sound pressure level (Leq) of each acoustical measurement was calculated as the averaged equivalent-energy of the sound pressure levels from the left and right channels during the measuring time. Based on these measurements, an energy averaging of the acoustic measurement in each configuration was performed with the aim of obtaining a sound-level value (dBA) representative of each configuration.The obtained values were then compared with the limits for the ambient noise level for teaching‒learning spaces recommended by the World Human Organization (WHO)  and ANSI® MX1102 logger was used to measure the CO2 concentrations in the classroom. The instrument has a measurement range from 0 to 5000 ppm . The sensing method is non-dispersive infrared (NDIR) absorption. Regarding the acoustical signals recordings, these were made using a Squadriga I recorder and BHS I headset/microphone unit. The sampling rate of the external microphones was 48 kHz. Maximum sound pressure level of 130 dBSPL and frequency response of 4 Hz to 20 kHz.The HOBO® MX1102 sensors were used during the decay method experimental tests, numbered in 2 to sensor S7-CO2. With regard to the acoustic measurements, they were performed in the locations P1-Ac, P2-Ac and P3-Ac .One of the fundamental requirements established in the COVID-19 Action Plan elaborated by the University of Granada was to establish natural ventilation through open windows and doors, even in adverse weather conditions .For this reason, different scenarios of window and door opening combinations were selected to generate each configuration. Three types of configurations were defined for each of the four selected classrooms . ExperimACH results. Subsequently, the background noise sound pressure levels Leq obtained in two different ventilation scenarios are shown: (1) doors and windows closed; and (2) the natural ventilation configuration that provides the ACH value required .In the next sections, results are presented for the three configuration scenarios of the four classrooms previously described. Firstly, each section shows the data obtained from the experimental tests of the decay method and the average ACH value for the configuration measured at each point. As can be seen in ACH obtained in each configuration, the ACH values obtained are homogeneous. It should be noted that C-1 configuration is the one that provides the highest number of ACH. The ACH values in C-1 varied from 7.4 to 9.4 with a mean of 8.3 ± 0.6 per hour, whereas configuration C-3 shows the lowest ventilation rates, from 4.3 to 5.1 with a mean of 4.6 ± 0.3 per hour. Following the recommendations of the Spanish Ministry of Health [ACH value for all sensors.Based on the values shown in f Health , the recACH value above 6, it was selected in order to evaluate the background noise in this scenario. Hence, the background noise was measured in the following two configurations: (1) windows and door closed and (2) configuration C-1. As shown in Since configuration C-1 provides an The background noise Leq for the C-1 was 54.1 dBA. This value is above the background noise Leq with windows and door closed (41.5 dBA) and the value recommended by WHO (35dBA). Exposure to traffic noise is the main problem in this classroom, since it is located in the east façade of building 1, close to the main street of this district. The traffic noise has a high impact on the background noise of the classroom, since in order to achieve an adequate VR it is necessary to open all windows and the main door.The experimental results obtained in the tests performed in the classroom B1-A2 are shown in this section. This classroom is characterised by the fact that it can only generate natural ventilation through the windows located on its west side and the main door on its east side. In this respect, the different natural ventilation strategies have been analysed, taking into account scenarios with different opening configurations of these windows, the opening of the door and the possibility of opening the corridor windows. The decay methods results for the three configurations are shown in ACH values obtained from each sensor are shown in ACH values are homogeneous between the sensors in each configuration. If the data obtained for each configuration are compared, it is possible to appreciate that the ACH is higher in configuration C-1. The values obtained in both configuration C-2 and configuration C-3 are similar. However, configuration C-2 and C-3 provide ACH values below the recommended ACH limits stated in the legislation related to COVID-19.The ACH higher than the recommendation set by the ministry guidelines (6 ACH), the background Leq was analysed first in the scenario with closed windows and door, and then in the scenario of configuration C-1. The average Leq value obtained in configuration C-1 was 44.5 dBA, 6.4 dBA above the Leq in the configuration of closed windows and door and also above the value recommended by the WHO (35 dBA).In addition, the results obtained from the field acoustic measurement are shown in In contrast to the classroom B1-A1, classroom B1-A2 is orientated towards a green zone in the opposite façade and a corridor. In this case, the dominant source of background noise is the noise generated by the students themselves when interacting with university activities.ACH in each configuration. The data obtained are shown in Classroom B2-A1 is characterised by two doors located at the ends of one of its side walls. This wall is parallel to the side west wall, which contains the only windows in the room. These characteristics have been taken into account in the analysis of the different natural ventilation strategies. The field measurements were used to analyse the ACH value obtained from the sensors for each of the configurations. Configuration C-1 provides ACH values far above the other configurations. The range of values obtained depends on the relative location of the sensors within the room. In this case, all configurations provide an ACH above the minimum set by the ministry’s guideline recommendations. However, the decay rate of the CO2 concentration in the case of configuration C-1 is caused by the air currents when opening the windows and doors. While such a high ACH is very safe, a high airflow through the windows may affect the comfort of the users. A configuration such as C-3 is preferable, which provides an ACH higher than the minimum set out in the guidelines, ensuring that ventilation will not impact on the performance and comfort of the students.In addition, the values obtained from the acoustic field measurements are shown in This classroom is orientated to other areas of the university campus and, as well as the B1-A2 classroom, the dominant source of background noise is the sound from university facilities. In this case, it is the only classroom studied in which it is not necessary to open all the windows to achieve the recommended VR (only windows at the end). This is reflected in the result obtained from monitoring the middle position, where the background noise is lower compared to the front and back positions.The architectural characteristics of classroom B2-A2 are similar to those of classroom B2-A1. Both classrooms are located on the second floor of the ETSICCP building, but on opposite sides. Therefore, the only difference is the orientation of the room and its location in the building. In this case, classroom B2-A2 has the windows located on the west side, which is the opposite of the location of the windows in classroom B2-A1. The configurations selected for the natural ventilation strategy tests were the same as in classroom B2-A1 see . The resACH values obtained from the field measurements are shown in ACH above the minimum set in the standard recommendation. However, the ACH values obtained in configuration C-1 are lower than those obtained in room B2-A1 with the same configuration. This is due to the orientation of the room, as the air currents are higher on the west façade of the building where room B2-A1 is located.The Regarding the background noise sound pressure level values, these were measured first with the windows and doors closed, and secondly with configuration C-2 . The average Leq value obtained with configuration C-2 (44.5 dBA) is above that obtained with windows and doors closed (6.4 dBA). This classroom is similar in both geometry and predominant background noise to the classroom analysed in The classrooms selected in this study are representative of the classrooms’ typology in Building 1 (Building Engineering School) and Building 2 (Civil Engineering School) of the University of Granada during return to teaching activity. For this purpose, field measurements were carried out to test three different configurations in the four selected classrooms.ACH value is configuration C-3 (4.6) and the highest average value is configuration C-1 (8.3). In the case of the classroom B1-A2, the configurations providing the lowest and highest average value of ACH are configuration C-3 (3.7) and C-1 (6.1) respectively. With regard to the results obtained from the experimental analysis in Building 2, in the case of classroom B2-A1, configuration C-3 provides the lowest mean ACH value (8.4) and configuration C-1 provides the highest mean ACH value (24.9). In the case of classroom B2-A2, the configuration providing the lowest average ACH value is configuration C-3 (6.1) and the highest average ACH value is configuration C-1 (15.5).The results obtained from the experimental tests in Building 1 showed that, in classroom B1-A1, the configuration that provides the lowest average ACH value is sufficient to guarantee that the space is safe, although there may be variability in the ACH value due to possible variations in environmental conditions.As can be seen, the VR depends on the local and particular conditions of each indoor space. In this context, the configuration chosen among the three analysed in each classroom was the one that meets the minimum ventilation requirements. The configurations selected for classrooms B1-A1, B1-A2, B2-A1 and B2-A2 were configurations C-1 , C-1 , C-3 (only windows at the end opened and main door opened) and C-2  respectively. This decision is based on ensuring that the Once the natural ventilation configuration was selected for each classroom, an acoustic study was carried out to compare the normal classroom scenario (windows and door closed) with the chosen configuration of natural ventilation. As can be seen from the results obtained, since the background noise level should not exceed 35 dBA for good speech intelligibility, none of the classrooms met this acoustic quality recommendation. With regard to the comparison between the scenario of closed doors and windows and the natural ventilation configuration selected, it was identified that the natural ventilation configuration causes an increase of between 6.4 dBA and 12.6 dBA in the background noise level of the classrooms analysed. The background noise is an important factor that affects the acoustic clarity and quality of teaching and learning process .Background noise is closely related to the signal-to-noise ratio (SNR). In this sense, a high level of background noise can cause a low or negative SNR. Therefore, a poor SNR causes, on the one hand, difficulties for students having to understand the message. On the other hand, it also causes a higher vocal effort among teachers, as the speaker’s speech level has to be higher than the background noise level.In fact, background noise becomes a problem that has a major impact on the current situation. Since the classrooms used for the return to campus are larger, and to ensure physical distance between students the distribution of students occupies all rows of seats, many students are in positions far away from the teacher. As a result, the signal‒noise ratio is very low in the rear positions, causing significant effects on reducing word identification and intelligibility.The classroom selection must take into account both the health recommendations and the impact of background noise. Priority should be given to selecting those indoor spaces that: 1) meet the health requirements  and 2) (due to their location and orientation) have a background noise level that does not interfere with the teaching-learning activities.In those cases where it is not possible to meet the criterion stated in the previous point, an adaptation intervention must be carried out . Noise control solutions for natural ventilation openings must ensure the required VR while also ensuring the background noise does not interfere with the performance of students and teachers.The location and orientation of the classroom also influences the impact of the natural ventilation configuration on classroom background noise. This is evident in the results obtained for classroom B1-A1, which is oriented towards a dense traffic area and the background noise level was 54.1 dBA. Therefore, more factors than room size and ventilation strategy should be taken into account when choosing the classroom. The location and orientation of the classroom should be considered in order to reduce the impact of background noise on the teaching‒learning process. Consequently, the practical implications of the findings show that ventilation strategies management in educational buildings should consider the following design and operation guidelines:The limitations presented in the study stem from the effect of indoor and outdoor environmental conditions . Additionally, this study follows the protocols stated by the Spanish Government and University of Granada prevention guidelines. One of this protocols is the IAQ management of both buildings is to ventilate (for at least 1 h before and after each class) by opening all windows. This procedure achieves indoor temperature and relative humidity levels similar to those outside, so the effect of these factors should be taken into account if different conditions would apply.The aim of this study was to analyse the natural ventilation strategies through the configuration of window and door openings, in accordance with the recommendations established in the COVID Action Plan of the University of Granada, which complies with the recommendations while maintaining the maximum degree of comfort for the user. To this end, the impact of these measures on the acoustic environment of the classroom was analysed, so that both students and teaching staff maintain safe levels of protection against the transmission of SARSCOV-2 without affecting their teaching‒learning activities.2/student to 4.77 m2/student. These spaces were selected according to the COVID-19 contingency plan set up at the beginning of the 2020/2021 academic year in each university centre. The natural ventilation configuration that met the required ACH was chosen to assess the impact on background noise inside the classroom. The main results obtained were:ACH levels required to ensure that the indoor spaces meet the guideline recommendations for a safe return to campus.Natural cross-ventilation is an effective strategy to achieve the There are differences in the specific natural ventilation strategy depending on the configuration of classrooms and building orientation. Thus, for the classrooms in building B1 the configuration of all windows opened and main door opened should be selected no matter the type of possible ventilation . On the other hand, in B2 the specific configuration depends on the classroom type, i.e., all windows opened and main door opened in the case of south-orientated classroom, or only windows at the end opened and main door opened in the case of the case of north-orientated classroom achieve better results due to the different orientation of the building. This fact highlights the needs of performing specific studies to select the best strategy to implement natural cross-ventilation.ACH and 8.8 ACH for classrooms B1-A1, B1-A2, B2-A1 and B2-A2, respectively. Therefore, the average ACH value is above 6 ACH in all the selected natural ventilation configurations.The average VR value provided by the selected configuration for each classroom was 8.3 ACH, 6.1 ACH, 8.4 The background noise level is strongly affected by the selected natural ventilation configuration. The background noise levels with the selected natural ventilation configuration were between 43.2 and 54.1 dBA. As can be seen, all classrooms exceed the recommended 35 dBA background noise level limit for background noise in teaching spaces. Consequently, the teaching activity management has to take into account not only the ACH, but also its impact on the indoor environmental conditions such as the acoustic environment. Since a high value of background noise level can interfere with the teaching and learning process and even interfere with the performance of students and teachers, educational building administrators need to consider this issue. In those cases where in order to achieve a natural ventilation strategy that provides the required VR, the background noise level exceeds 35 dbA, building managers should make intervening adaptations .The results obtained show that a correct choice of configuration can satisfy the VR needs while ensuring that the indoor space is safe for the occupants. The measurements were carried out in four different classrooms with an occupancy per area ranging from 2.20 mSince this research proves that the best strategies to achieve a VR value that complies with the standard imply a significant impact in other indoor environmental variables such as indoor noise levels, some actions to improve the indoor acoustic behaviour of classrooms are recommended. For example, the need of electroacoustic support to increase speech intelligibility, improving the acoustic conditioning of classrooms, increasing noise insulation with other classrooms and other common areas, and reinforcing the compliance of outdoor noise levels achieving the acoustic quality criteria prescribed for sensitive acoustic areas such as the educational ones. Therefore, the management, organization and planning for indoor spaces of educational buildings must not only ensure occupants’ safety, but also not influence the performance of teaching activities. Action plans are required that allow buildings’ administrators to achieve adequate natural ventilation strategies and implement effective noise reduction measures in indoor spaces.Finally, future studies should focus on the environmental conditions of natural ventilation with occupancy in the classrooms, in order to evaluate not only the objective variables of the IEQ factors, but also the subjective variables associated with the perception and comfort of occupants with regard to the window and door opening configurations established.