Increasingly stricter rules on pollutant emissions and greater awareness of air quality by consumers are pushing the transport sector towards sustainable mobility. In this historical context, electric cars have been considered the most valid alternative to traditional internal combustion engine cars, thanks to their low polluting potential, with high growth prospects in the coming years. This growth is an important element for companies operating in the electricity sector, since the spread of electric cars is necessarily accompanied by an increasing need of electric charging points, which may impact the electricity distribution network. In this work we proposed a novel application of machine learning methods for the estimation of factors which could impact the distribution of the circulating fleet of electric cars in Italy. We first collected a new dataset from public repository to evaluate the most relevant features impacting the electric cars market. The collected datasets are completely new, and were collected starting from the identification of the main variables that were potentially responsible for the spread of electric cars. Subsequently we distributed a novel designed survey to further investigate such factors on a population sample. Using machine learning models, we could disentangle potentially new interesting information concerning the Italian scenario. We analysed it, in fact, according to different geographical Italian dimensions  and with the final identification of those potential factors that could play a fundamental role in the success and distribution of electric cars mobility. Code and data are available at:  In the latest years, great attention has been devoted to the problem of climate change and an effort has been put towards the decrease of gas emissions in the atmosphere, especially due to the still excessive presence of internal combustion engine cars. Therefore, the electrification of the road transport sector is considered as a necessary component towards obtaining the so-called “urban decarbonization”.The diffusion of electric cars in Italy has started, but still some issues are present, and represent an obstacle to its wide spread. Nowadays, hybrid technology is the most followed by users, particularly due to socioeconomic factors such as cost of investment and range anxiety.Most likely, the solution that could really represent a clear step towards an ever greener mobility is to aim at the diffusion and use of fuel cell vehicles, as highlighted in previous studies .However, the current Italian scenario is still far from achieving the necessary objectives for decarbonization. So far, in fact, Italy has shown a much lower electric vehicle (EV) adoption rate than other European countries.In  the authClosely related to the need to recharge the vehicle battery is the driving range factor. This represents, in fact, one of the main characteristics for an electric car, and at the same time one of the most limiting factors for its adoption by consumers. Previous analyses showed that very relevant factors, contributing to the propensity of buying an electric car (ranging from 28% to 68%) over a petrol one, are jointly represented by improvements in the fast charging network, driving range and financial incentives . HoweverIn  the authSuch prediction is in fact of extreme interest for italian electric companies, which deal with the phase of energy distribution to the final consumer, whether it is a private individual or a company, through the distribution network. As a result, all electrical appliances that are daily powered by electricity, receive energy through the distribution network. These certainly include charging points for electric car batteries, both private and public. In particular, the road coverage of charging stations is an essential element for the spread of electric mobility and although it is still considered insufficient, it is destined to grow considerably in the years to come. In this regard, it is important to refer to the estimates contained in the italian PNRR , better known as Recovery Plan, which represents the Italian program of reforms and investments for the achievement of a series of objectives among which emerges the one related to ecological transition. According to the forecasts contained in the PNRR, in order to achieve the objectives of decarbonization of the mobility sector, a circulating fleet of about 6 millions electric cars is estimated by 2030 for which about 31.500 public fast charging points are needed [For this purpose, we initially identify from the existing literature some of the main factors influencing the diffusion of electric mobility:The economic incentives: the Italian “Ecobonus” is the measure promoted by the Ministry of Economic Development that offers grants for the purchase of low-emissions vehicles. This incentive was introduced in 2019 with a funding of 60 millions of euros and was then renewed for both 2020 and 2021 with much higher funding of 262 and 283 million respectively [ectively . This reThe recharging infrastructure: as already mentioned above, the charging structure for electric cars is the dominant factor, especially considering large cities, where the availability of private parking spaces or garages is more limited, and the capillarity of public charging points becomes decisive for the spread of purely electric vehicles. The network of charging infrastructure for electric vehicles in Italy follows growth trends that are comparable with those of electric cars. Despite the continuous growth, however, it is necessary to highlight the need for a widespread network of high-power recharges, the so-called fast and ultra-fast (from 50 kW upwards), which allow a recharge to 80% even in less than 10 minutes. Currently, more than 70% of public recharges have a power of around 22 kW , while high-power recharges will have to be quickly installed on the main routes and in urban areas paying particular attention to the choice of installation locations  [s, etc.) .The price: Italian citizens still perceive the limit on the purchase price as binding. If on the one hand this is linked to the low propensity of people to evaluate the Total Cost of Ownership (TCO) of the goods they buy, on the other hand there is no doubt that the purchase prices still do not allow, despite the incentives, access to this type of solution to citizens belonging to the lowest income brackets. In particular, the battery pack weighs 40% on the cost of the vehicle, but the birth of new production poles, especially in Europe, is expected to bring the price below the threshold of 100 $/kWh within 2025, identified by several authoritative subjects as the target that allows to electric cars’ price to be competitive with the one of ICE vehicles.Considering all of the aforementioned factors, in this paper we present an analysis of electric mobility, through which the main factors influencing its diffusion have been identified. In this way we could tackle the research question of estimating the distribution of the fleet of electric cars circulating on the Italian national territory, identifying the possible features that may have a fundamental role in their diffusion. This objective was pursued by applying machine learning techniques. Machine learning, in fact, has been proved to be nowadays successful in a wide variety of different fields –21 from To study the phenomenon in depth, the research was deployed in such a way that we could perform modelling at different scales to give a general Italian overview of the scenario. A first analysis was carried out on an Italian regional scale, which was then followed by an Italian provincial one. In both scenarios we compared four supervised machine learning approaches. The performances showed to be quite comparable among the machine learning approaches implemented, with a slighter better performance obtained through the use of the Extreme Gradient Boosting Regressor (XGBR).Both from the national and the regional case, it emerged that among the features that played a more central role stand out those that are directly connected to the main drivers that influence the spread of electric mobility, such as the price and the presence of charging points.Subsequently we further refined the research, through the creation of a novel dataset obtained from a survey distributed in a sample of the population. These data were obtained through the creation and diffusion of a survey. Also in this case, the same machine learning models used in previous analyses were applied. The results obtained further confirmed the hypotheses made regarding the main factors of diffusion of electric mobility, thus allowing to make a direct comparison on the three different scales considered for the analysis.The paper presents several novel contributions. To the best of our knowledge this is the first time that a machine learning approach is used for the purpose of studying electric cars in the Italian scenario. Moreover the dataset used in the study is novel, and collected with the purpose of further investigating the possible factors affecting the electric cars Italian mobility. Furthermore the survey proposed is newly designed, and it allowed to have a three dimensional analysis of the electric cars scenario in Italy, which is, to the best of our knowledge, the first time this is done in the literature.The paper is organized as follows. In Section 2 we present a comprehensive overview of the dataset collected, showing the public repository we used to collect it, as well as a description of the survey modelling. In Section 3 we describe the machine learning approaches used in the study, while in Section 4 we thoroughly describe the experimental setting designed. In Section 5 we draw conclusions and future perspective of the study proposed.The several features that constitute the datasets were crawled separately and downloaded from online available databases, mainly from ISTAT  which rehttps://github.com/GiovannaMariaDimitri/A-machine-learning-approach-to-analyse-and-predict-the-electric-cars-scenario-the-Italian-caseWe released the data and the code for our project at: Regional and provincial datasetsThe construction of regional and provincial datasets took place in three steps:Variables identification: among the identified factors which may influence the diffusion of electric mobility, the most commonly reported ones were the presence of charging infrastructure, the price of electric cars and consumers’ sensitivity towards a more sustainable mobility. Therefore, the research focused on finding all of those variables that could be identified with the factors or that could somehow be related to them. In addition to those already mentioned, we identified further elements such as the growth of renewable energy sources, more precisely photovoltaic panels, and the presence of increasingly electric company fleets. Although sometimes they are not treated by the existing bibliography, photovoltaic panels could contribute to the growth of electric cars since they would allow consumers to recharge batteries at no-cost by using the energy produced by the panels.Data gathering: once the variables to use for the composition of the datasets were identified, it was necessary to search and download the data relating to each of them from online data sources. Initially, given the higher availability, the data were downloaded on a regional scale, and only later the field of research was restricted also at the provincial level. These were mainly collected from the database of the National Institute of Statistics, better known as ISTAT [as ISTAT , 25.Data aggregation: finally, once the data for all the variables had been downloaded, they were aggregated to obtain a single dataset. Obviously, parallel to the aggregation phase, a series of data transformations and corrections were carried out, mainly due to the fact that these were available in different formats. These procedures, as well as all the subsequent analysis that led to the creation of predictive models, was carried out entirely using the Google Colaboratory service .The regional and provincial variables are summarized in The data presented are divided into macro category, and for each macro category we report the name of the feature, the type of feature (if floating point or integer). A green tick highlights if the relevant feature is present for the regional or provincial case or both.The first column of Survey datasetIn this subsection we describe the process that led us to the creation of the dataset obtained from the survey we designed and distributed. The process can be discretized in three steps.Survey creation: similarly, to what was done in the research phase for the creation of regional and provincial datasets, also in this case we started from the main factors that play a fundamental role in the spread of electric mobility. However differently from before, the initial objective was not to search for the variables connected to the main factors, but to create the questions to include in the survey which are related to them. Moreover, differently from the previous case, we could also add further questions to compare the features identified as important at the previous step with respect to the previous case in which only online available data could be used. In particular, for example, we added questions investigating aspects such as economic incentives and fuel economy, both of which had not been deepened in previous analysis due to the unavailability of data. In conclusion, a total of 44 questions were formulated and circulated through the obtained survey. In Survey administration: in order to reach as many respondents as possible, the method of administration chosen for the diffusion of the survey was the online one. More precisely, the used tool is Google Forms, which is part of the free and web-based Google Docs Editor suite offered by Google [y Google . The surDataset creation: once the response collection period was over, the relevant data was downloaded in CSV  format directly from Google Forms. Subsequently, the data were uploaded to Google Colaboratory, as was also the case with regional and provincial analyses.https://en.unisi.it/research/ethics-committee-research-human-and-social-sciences-careus). The committee stated that given the questions of which the survey was composed of, which do not imply the presence of sensible and reserved information, the CAREUS did not need to express any ethical approval concerning the research hereby presented. This is also reported in the ethical statement section of the paper. Informed consent was obtained verbally by the distributor of the survey and a privacy statement included in the beginning of the survey. No minors were included in the survey study.One of the main objectives of this study is to compare the analyses carried out on different Italian scales in order to highlight the similarities and differences and paying particular attention to which were the features most significant in the various machine learning models. Consultation for what concerns the ethical approval was requested to the CAREUS ethics committee of the University of Siena , also known as the predictors, and the single output variable (y), which is also called the response variable. Therefore, a linear regression aims at finding a relationship among one or more predictors, which are used to compute the value of the response variable. More specifically, the output variable (y) can be computed from a linear combination of the input variables (x) [bles (x) . For thiy is the dependent variable, xi is the i-th independent variables, m is the number of predictors, βi the polynomial coefficients of xi and ε the residual. Assuming X as the predictors matrix and Y, β, ε as the vector of the dependent variable, the coefficient vector and the residuals vector, the standard least-square error method can be used to compute the coefficient vector β, and the formula is presented in y) related to new input values (x) [Once the regression coefficients have been computed, lues (x) .Ridge Regression: it is one of the best alternatives to the standard linear regression method in the case of multicollinearity. The phenomenon of multicollinearity occurs whenever two or more predictors in a regression model are moderately or highly correlated. In this case, the explanatory variables are dependent on each other. The main consequence is the fact that small changes in the input data can highly affect the estimates of regression coefficients and this can be translated into a model’s loss of robustness [bustness . The ridλ is to reduce the values of the estimated coefficients. In this way, all variables that do not play an important role in estimation, have coefficients close to zero, but never completely zero [The purpose of the parameter ely zero .Decision Tree Regression: in decision tree modelling, an empirical tree represents a segmentation of the data that is created by applying a series of simple rules [le rules . The treSince in the case of our study, the target variables of the different analyses are continuous, therefore the algorithm is a decision tree regressor. The construction of a regression tree is based on binary recursive partitioning, which is an iterative process that splits the data into partitions. More precisely, starting from the root node, the algorithm breaks the data using every possible binary split and then the best partition is selected. The quality of a partition is measured using the reduction of variance, therefore the best split is the one that minimizes the variance . The splExtreme Gradient Boosting Regressor: it is a specific application of the Gradient Boosting method, which uses more accurate approximation to find the best tree model. More precisely, the Gradient Boosting method produce a predictive model in the form of an ensemble of weak prediction models, typically decision trees [on trees . Generalon trees .The regional dataset consists of 33 features, which represent the variables that have been obtained by downloading the data from the ISTAT databases as described in Section 2. Depending on the availability of the data, it was possible to construct a total of five datasets, one corresponding to each year from 2015 to 2019 (including both extreme years). As we will see, not all features will be used to define the predictive models. In fact, only half of them, more precisely 15, will be useful for estimating the percentage of electric cars in the region.To perform a features selection step, in order to identify the variables that are most important for the definition of the models, the correlation matrices among features collected in our novel regional dataset, were computed. For each of the features we computed the average of the correlations of the five years considered. In this way we took into account a unique correlation value with the target variable. The reason for this choice is due to the fact that in this way we could consider a single set of features valid for each of the five datasets. The selection of the variables considered most relevant for the creation of the predictive models has been made through the definition of a minimum absolute correlation’s threshold equal to 0,5. The choice of this threshold was made in an iterative way. Initially, the models were defined using all the features of the dataset, thus placing the threshold equal to zero. Subsequently, the value of the correlation threshold was increased in such a way as to exclude part of the variables from the model and the performance was compared with the previous models. This procedure was repeated iteratively until the value of 0.5 was reached, which maximizes the performance of the model while avoiding an excessive reduction of the dimensionality of the dataset.Some comments concerning the results obtained in this pre-processing step. It is not surprising that among the variables that have absolute value correlations equal or higher than 0,5, it is possible to detect those that are closely related to the main factors that influence the spread of electric cars. Among these, the noteworthy ones are:GDP Per Capita (PIL): which can be associated with the price factor, assuming that in the richer regions the propensity to spend more on the purchase of electric cars is higher.Polluting potential and PM10 exceedances: which can be associated with the sustainability factor. In fact, the percentage of electric cars is higher in regions where the polluting potential is lower and where there has been a higher number of times when the limit threshold of PM10 particulate has been exceeded. Consequently, it can be assumed that policies are being adopted in these regions in order to raise the awareness of air quality safeguards and to accelerate the adoption of electric cars.Number of charging points: which is the main factor in the spread of electric cars. It is therefore not surprising that there is a direct proportionality between the number of charging points and that of electric cars.Number of photovoltaic panels: which, although it is not frequently mentioned in the bibliography related to electric mobility, is actually a potentially relevant factor in favor of an increase in circulating electric cars. In fact, in addition to the thematic of sustainability, it can also be linked to the maintenance cost of an electric vehicle. If a potential user has a private charging station, he/she could recharge the battery of his car at no cost, thanks to the energy produced by the photovoltaic panel. Obviously, this is a clear element in favor of the adoption of an electric vehicle.All the considerations made so far are also confirmed in the scatter plots shown in Each of the machine learning models presented in section 4 was applied for each yearly collected dataset. Performances were then evaluated comparing predicted and real values, for the subsequent year.The metrics we used to evaluate individual models  were:Root Mean Squared Error (RMSE): it represents the square root of the second sample moment of the differences between predicted values and observed values or the quadratic mean of these differences. Assuming that y the original value, for n samples, the formula is the one shown by Eq S1 in S.3 Section of The coefficient of determination (R2): it is an index that measures the link between the variability of the data and the correctness of the statistical model used. The most generic formulation is shown in Eq S2 in S.3 Section of Using the just described metrics, it was possible to evaluate and compare the four different regression models applied to the various datasets.Extreme Gradient Boosting Regressor (XGB), albeit slightly if compared to the Decision Tree Regressor (DT). In fact, by looking at the values of the metrics, we can see that the coefficient of determination and the RMSE are both 0.1 higher and lower in the case of XGBR.Looking at the values shown in We repeated the experiments described so far for the analysis of the Italian provinces. Also in this case the main objective was to identify which are the variables that have a greater relevance for the purposes of the model. We first obtained the correlation matrix to highlight the value of the correlation that the individual features have with the target variable for each dataset (for each year).It is possible to notice that both at regional and provincial level, the variables indicating the percentage of companies present in the territory, divided by size, are highly correlated with the target variable.The reason why these variables have been included in the construction of the various datasets, must be identified in the fact that they can be associated with the company’s car fleets. Given the absence of company fleet data on online databases, we hypothesized that a greater territorial concentration of large companies means that these companies are more likely to have company fleets that include electric cars. Analyzing the correlation values, this assumption seems to be confirmed. In fact, it is noted that where the concentration of very small companies is greater, the percentage of electric cars is lower. We can further notice this, observing the trend highlighted in By iteratively evaluating the performance of the forecasting models as the minimum correlation threshold for variables changes, a value equal 0,3 to which performance is optimized has been reached. Therefore, the selected features for the definition of the models are shown in We subsequently applied the regressive models, similarly to what has been seen for the analysis on a regional scale and using the same types of metrics for the evaluation. The results obtained are as shown in R2 score is 0.88 and the RMSE is 0.06. In conclusion, the graphical visualization of the models’ performance is shown in Looking at the values, even in this case, the performance of the 2015 dataset is significantly lower if compared with those of the other datasets. Performances are quite comparable among the 4 models proposed. However, among them, the XGBR model turns out to be the best predictive model, for which the coefficient of determination is maximized and the RMSE is minimized. More precisely, the As a further step of our analysis, we decided to conduct a survey to go down in the analysis hierarchy of our study. Starting from the regions, in fact, we then zoomed into the provinces, to eventually conduct a survey that considered a sample of the Italian population. The number of respondents to the survey was 300, each of whom answered the questions that compose the survey. The final survey dataset is therefore composed of 300 rows, each of which corresponds to a user and 44 features, which represent the information collected through the survey questions. In the rest of this section we will describe the main results obtained. In S1 Section in For what concerns the answers related to the factors that may or may not favor the adoption of an electric vehicle, The presence of photovoltaic panels, as already stated above, could in fact represent an element in favor of the purchase of an electric car, since it would allow the user to recharge the battery of his car using the energy produced in a sustainable way, therefore at no cost. Regarding this concept, we noticed how only 14% of respondents currently own photovoltaic panels. Despite this, the role that this aspect has in relation to electric mobility is evident. In fact, thanks to the presence of photovoltaics, the propensity to purchase goes from 43% (in the absence of photovoltaics) to almost 80% (with possession of photovoltaics). These values were obtained by adding the percentages for replies to questions "4" and "5".Always connected to battery charging, almost three-quarters of respondents have private parking for their car and assuming they own an electric car more than half (58.6%) would be inclined to invest in the installation of a private charging station. These percentages are illustrated in One of the main objectives of the survey is to investigate the weight that the main factors that can influence the spread of electric cars have. In this regard, users were asked to quantify the influence they have on them. As can be seen from Before moving on to the machine learning and modelling description of the analysis, the main statistical data relating to those who responded to owning at least one electric car are reported in By looking at sustainability factor could be included among those that were decisive for the choice, since the totality of EV owners provided a grade equal to or greater than 3.The incentives, for which only one respondent believes that they were not important.Similar reasoning applies to price factor.Conversely, only a quarter of respondents provide a medium-low grade for the fuel.Most users are also satisfied with the savings on the cost of autonomy factor are more widely distributed, there is clear confirmation from EVs owners that the availability of charging points is still an obstacle, since more than half attribute the maximum weight to this factor. This last consideration is confirmed in the answers given to the additional questions.Finally, while the weights given to the vehicle’s current buying propensity and the future buying propensity. In particular, "future propensity" means the purchase intentions that users will have starting from 2025, the year in which the experts estimate that the production costs of batteries will fall to such an extent as to make electric cars competitive with "traditional" ones in terms of price [For this analysis, two different target variables were considered: the of price .As it can be seen from the two graphs in current and future propensity to purchase of users. The same regressive models seen in the case of regional and provincial analysis were applied: Linear Regression, Ridge Regressor, Decision Tree Regressor and Extreme Gradient Boosting Regressor. However, it should be noted that in this case, unlike the previous ones, the numerical variable is discrete, not continuous. In fact, it can take as values 0,1 and 2, while the prediction made through one of the previous models returns continuous values in output. For this reason, the strategy used was to transform the continuous values obtained from the predictions into the discrete values assumed by the target variable according to special approximation criteria. In this way, it was then possible to use, as evaluation criteria, those that are generally used in the case of classification problems such as: accuracy, precision and recall score.We therefore proceeded defining predictive models able to estimate the current propensity.In order to assess model performances we implemented a K-Fold Cross Validation (CV) procedure. Based on this kind of technique, the original dataset is randomly partitioned into K equal sized subsamples. Of the K subsamples, a single subsample is retained as the validation data for testing the model, and the remaining K-1 subsamples are used as training data. In particular for the purpose of this study we implemented a 3-Fold CV procedure. If compared with the performance of the models in case of the regional and provincial analysis, the difference between the LR and XGBR is negligible. The first one has an accuracy value of 0.52 that is slightly higher than the 0.5 accuracy of the XGBR.future propensity, the main results are reported in For what concerns the prediction of The value of the accuracy is on average considerably higher than in the previous case. If for the prediction of the current propensity the accuracy value did not go beyond 0.52, in this case it reaches a maximum value of 0.8. This is due to the models that manage to estimate almost perfectly those who show a high propensity to buy and this increases the accuracy of the model, since these represent the greatest part of observations .In addition to this, the good performance of the models in the case of the estimation of future propensity confirms what has already been seen in the case of regional and provincial analysis. As it has already been shown in The XGBR model performed slightly worse than the LR model, only for the case of the current propensity prediction. Overall the 4 models showed robustness also in the cross validation performances and showed to be quite robust in the various cases considered.In our paper we described a novel three dimensional view of the Italian electric cars scenario. We summarize it graphically in We can now proceed in the analysis of the common features which were found to be significant across the models.Extreme Gradient Boosting Regressor gave the best results to be the best model, it is therefore possible to view the importance that each of the features has had in the definition of the model itself. The aspect that is really interesting is that in all three cases it is possible to identify some features that are the same or that at least refer to the same concept. The variables can be summarized per category as: number of charging points, the sustainability factors, and the availability of photovoltaic panels.Starting from the fact that in all three cases the We can further analyze each of the these features:PRICE: with the exception of the provincial dataset, for which, due to the lack of data on online databases, it was not possible to obtain information on the average purchasing power of citizens, the price factor plays an important role in the defined predictive models. It has been included in the regional dataset in the form of Pro-Capite GDP (Gross Domestic Product), that is the average purchasing power of citizens, and represents the first variable in order of importance. It is not surprising that the regions with the highest percentages of circulating electric cars are those with the greatest purchasing power, a factor that confirms the weight of the price in the spread of these vehicles. In the case of the survey, on the other hand, the price factor was treated more explicitly, directly asking users to assign a weight to it. As already shown in Section 5.3, respondents confirmed the high importance of this factor.RECHARGING POINTS: regarding the regional and provincial analysis, it has already been highlighted above that the number of charging points is positively correlated with the number of electric cars. This in fact represents the main factor that weighs on the spread of electric cars and this has also been confirmed by the analysis of the data collected through the survey. In fact, the variable pdr_futuri indicates the propensity of users to buy an electric vehicle in the future when there will be greater road coverage of charging stations. About 80% were inclined, which also justifies the importance of the variable, thus confirming that this factor still represents an obstacle.SUBSTAINABILITY: the sustainability factor was explicitly covered in the case of the survey, where about 70% assigned it the maximum scores. Instead, in the case of the regional and provincial analysis, this factor has been linked to the variable indicating the number of exceedances of the limit threshold of PM10 dust (number of excendacees of PM10) and to the polluting potential (polluting factor). In fact, the data shows that the spread of electric cars is directly proportional to the first variable and inversely proportional to the second.PHOTOVOLTAIC: despite being in the low part of the ranking in order of importance, the feature that indicates the number of photovoltaic panels present in the region or province (number of photovoltaic panels) has a good correlation with the percentage of electric cars present in that region or province, as mentioned in Sections 4.1 and 4.2. The presence of a photovoltaic panel, as already expressed above, potentially plays a non-negligible role in convincing a consumer to buy an electric car. In fact, it allows users to recharge the battery of their car without incurring any cost, thanks to the fact that the energy used is the one produced through the photovoltaic panel. If in the case of the regional and provincial analysis this could only be a hypothesis, in the case of the survey analysis it has been confirmed. In fact, the percentage of respondents who would be inclined to buy an electric car goes from 43% to 78.4% if they have a photovoltaic panel in their home. In addition, about 59% of them would also be willing to install a private charging station to take full advantage of the recharge at no cost.The description of the factors just exposed allows to have a clearer idea of what was the main objective of the study: to identify and understand the role of the main factors that influence the spread of electric mobility. The analyses carried out allow us to face the same study, but with three different points of view: the regional, the provincial and the individual one. Despite the necessary differences that do not allow to overlap the considerations made in the three cases, the just described concepts have allowed us to define what were the common factors for all three analyses, although with different forms and entities.Predicting the electric vehicle scenario in Italy is an aspect of key importance for future electric network , 36. ThiIn our work we implemented a machine learning pipeline to define a model capable of estimating the distribution of electric cars with high accuracy. The novelties of this study are manifold:we constructed a novel dataset crawling from public sources; we proceeded with the construction of such novel dataset after a thorough search in the literature in which we could identify the possible drivers in terms of electric mobility diffusion. Starting from them, the data were downloaded separately from the online databases and they were subsequently aggregated in forming two types of new datasets on which to implement the models. More precisely, a first analysis was carried out on a regional scale and later also on a provincial scale. For both, the target variable consists of the regional or provincial percentage of circulating electric cars. In both cases, the approach used to test the models was the same. In fact, they were trained at time t and then tested at time t+1, through the comparison between the predicted and real values. For this reason, in the construction phase of the datasets, data relating to five different years, from 2015 to 2019, were downloaded and aggregated.We designed and distributed a survey to test a sample of the population regarding electric mobility; The survey was novel and completely designed for the purpose of our study, showing the capability of testing the presence of similar factors identified both in the national, regional and local Italian dimensionality tested. Narrowing the field of research, the idea behind the survey was to find further confirmation regarding the main diffusion factors of electric mobility, already covered in previous analyses. In addition, it was possible to investigate those for which no data were available, such as economic incentives. By applying the same predictive models, the current and future buying propensity of users were estimated.We applied machine learning models and identification of important features for electric vehicles scenario prediction. The four machine learning models used were robust across the three dimensional datasets to further understand if similar factors were observable at a national, regional and sampled population scale. From the analysis on the local dimension we identified the same factors that can influence the diffusion of electric mobility in all three analyses, despite the different research scales. Both in the regional and provincial analysis, among the highlighted variables, all those that were linked to the main factors of diffusion of electric mobility were included, thus confirming the hypotheses made in the research phase. A further confirmation came from the analysis of the data collected through the survey, where the variables related to the factors again played a central role. More precisely, the price, the presence of charging infrastructure, the sustainability and the presence of photovoltaic panels emerged among those highlighted. All these factors had already been introduced in Section 1, based on the research done in the existing literature. The performed analyses have therefore made it possible to verify the hypotheses made in the research phase, finding the actual contribution that these factors have for the spread of electric mobility according to three different scales: regional, provincial and personal.All of this focusing on the Italian scenario, which, to the best of our knowledge, has not been previously analyzed as in this present thesis.This study could certainly be extended to include new and interesting studies in the field. Novel approaches, for example are going towards the identification of machine learning models for solving practical traffic problems, which could be enlarged to include specific studies for electric cars , extendiMoreover the present study could be improved through the progressive updating of estimates based on new available data. It should be pointed out that the spread of electric mobility is an extremely recent phenomenon and for this reason the availability of data related to it is currently very limited. In addition, factors such as price, the presence of charging infrastructure and economic incentives could have a relevance that is limited mainly to the initial phase of deployment. In fact, as soon as the prices of the battery pack are lowered, the cost of the electric car will also decrease, and the incentives will certainly have less relevance.Moreover, it is expected that in view of the estimates made by the PNRR, charging infrastructure will also have such a diffusion that it no longer represents an obstacle. On the basis of these considerations, it should be noted that this study therefore represents a starting point for the analysis of the phenomenon of Italian electric mobility, and it can be updated, focusing mainly on long-term factors, such as sustainability, which is certainly destined to cover an increasingly central role in coming years.https://en.unisi.it/research/ethics-committee-research-human-and-social-sciences-careus). The committee stated that given the questions of which the survey was composed of, which do not imply the presence of sensible and reserved information, the CAREUS did not need to express any ethical approval concerning the research hereby presented. Informed consent was obtained verbally by the distributor of the survey and a privacy statement included in the beginning of the survey. No minors were included in the survey study.Consultation for what concerns the ethical approval was requested to the CAREUS ethics committee of the University of Siena (S1 File(DOCX)Click here for additional data file.


The effect of fish oil-containing lipid emulsions on preventing parenteral nutrition-associated cholestasis (PNAC) in very low birth weight (VLBW) infants is not known. Thus, we conducted a meta-analysis to identify any prevention effect.I2 < 50%), and a random-effects model was used when the heterogeneity was significant (I2 > 50%).PubMed, EMBASE, and CENTRAL were searched up to 26 January 2021 for studies related to the preventive effect of fish oil-containing lipid emulsions and fish oil-free lipid emulsions on cholestasis in VLBW infants. Revman 5.3 was used to synthesize the results. A fixed-effect model was used to summarize the data when the heterogeneity was non-significant  and pooled RR = 0.47, 95% CI 0.30–0.73, P = 0.0008; and for patients with duration less than 14 days revealed I2 = 0% (P = 0.72) and pooled RR = 1.14, 95% CI 0.39–3.35, P = 0.81. The synthesis for patients with birth weight more than 1000 g revealed I2 = 0% (P = 0.41) and pooled RR = 0.55, 95% CI 0.26–1.18, P = 0.12; and for patients with birth weight below 1000 g revealed I2 = 44% (P = 0.11) and pooled RR = 0.53, 95% CI 0.33–0.85, P = 0.009.Of 728 articles, 11 randomized controlled trials met the inclusion criteria. The meta-analysis indicated that fish oil-containing lipid emulsion reduced the occurrence of PNAC significantly with risk ratio (RR) = 0.53, 95% confidence interval (CI) 0.36–0.80, The fish oil-containing lipid emulsion can reduce the occurrence of PNAC in VLBW infants based on the available original randomized controlled trial studies, especially for patients with parenteral nutrition duration exceeding 14 days and extremely low birth weight infants. Future studies should be performed before a definitive conclusion can be established. Infants born with birth weight less than 1500 g are defined as very low birth weight (VLBW) infants. Among them, infants born with birth weight less than 1000 g are defined as extremely low birth weight (ELBW) infants. With the establishment of neonatal intensive care units and the development of medical technology, including mechanical ventilators and artificial pulmonary surfactant utilization, the survival rate of VLBW infants has increased in recent years, and is currently 80.6–93.8% according to different regions [Usually, enteral nutrition is not feasible immediately after birth for VLBW infants, because they are susceptible to respiratory distress syndrome, hypotension, temperature instability, and gut immaturity. Thus, parenteral nutrition (PN) should be initiated as soon as possible after birth of VLBW infants to avoid growth failure in postnatal life . HoweverThe mechanism of PNAC is not established and seems to be multifactorial. Some evidence suggests that high content of ω-6 polyunsaturated long-chain fatty acids in soybean oil may exert pro-inflammatory effects and accumulation of phytosterols can reduce bile flow, both of which are linked to development of PNAC , 13. An Previous meta-analysis demonstrated that fish oil-containing lipid emulsion can prevent PNAC in neonates . HoweverWe performed the meta-analysis according to PRISMA  guidelines. The study was registered on INPLASY. The registration number is INPLASY2021110046. The protocol was available on the INPLASY website.We searched PubMed, EMBASE, and CENTRAL up to 26 January 2021 using the search terms “very low birth weight infant? OR VLBW OR extremely low birth weight infant? OR ELBW OR low birth weight infant? OR preterm infant? OR prematurity OR premature?” and “fish oils OR fatty acids, omega-3 OR eicosapentaenoic acid OR docosahexaenoic acids OR fish-oil lipid emulsion? OR (fish? and oil?) OR (fish? adj2 lipid?) OR (fish? adj2 emulsion?) OR (fish? adj2 fat?) OR (omega-3 adj2 fatty acid?) OR eicosapentaenoic acid? OR icosapentaenoic acid? OR docosahexaenoic acid? OR SMOF OR SMOF-LE OR SMOFlipid OR FMOS OR FMOS lipid OR Omegaven OR lipidem” and “ OR (soy? adj2 lipid?) OR (soy? adj2 emulsion?) OR (soy? adj2 fat?) OR (omega-6 adj2 fatty acid?) OR plant oil? OR vegetable oil? OR olive oil? OR coconut oil? OR intralipid OR lipofundin OR clinoleic OR linoleic acid OR arachidonic acid)”. We also searched clinical trials and ISRCTN to get more initial information and identify unpublished data. No restrictions were placed on the searches for language, population, or publication year.The inclusion criteria follow: (1) patients were neonates with birth weight less than 1500 g or gestational age less than 32 weeks with mean/median birth weight less than 1500 g in the whole group; (2) the intervention was fish oil-containing lipid emulsions compared to fish oil-free lipid emulsions administrated by PN; (3) the PN began within 24 hours after birth and lasted at least 1 week; (4) the observation outcome included the number of patients with cholestasis; and (5) study design was a randomized controlled trial (RCT).The exclusion criteria follow: (1) patients with metabolic disorders, congenital infection, congenital abnormality, severe sepsis, or post-surgery; (2) birth weight or gestational age was not clear; (3) studies lacked initial clinical data, such as reviews, meta-analyses, comments, and editorials; and (4) observational studies.Two authors extracted the data independently from the full-text articles of all included studies. The disagreements were resolved by discussion or by consulting a third assessor. The following data were extracted: first author, publication year, study location, study design, birth weight of participants, cholestasis definition, sample size, intervention (type of lipid emulsion), PN duration, and the number of patients with cholestasis.Two authors assessed the quality of the included studies using the Cochrane Risk of Bias tool for RCTs. This tool contains seven domains: sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), selective reporting (reporting bias), and any other bias.I2 measurement, with I2 > 50% indicating significant heterogeneity. A fixed-effect model was used to summarize the data when I2 < 50% and a random-effect model was used when I2 > 50%. The subgroup analysis was performed to seek the source of heterogeneity. A sensitivity analysis was performed to evaluate the robustness of the conclusion by removing each study sequentially and examining the effect of removing each study on the pooled RR results. A funnel plot was constructed to detect reporting bias.Review Manager 5.3 was used to analyze the results. Because the outcome was a dichotomous value, a risk ratio (RR) and the corresponding 95% confidence interval (CI) were calculated. Heterogeneity was assessed using the A total of 728 articles were retrieved, 119 of which were duplicated. Of the 609 articles, 550 were excluded on the basis of the title or abstract. Of the remaining 59 studies, 26 had inconformity of birth weight or gestational week or outcome indicator, 14 were reviews or comments which lacked initial data, one used a duplicated cohort, seven were observational trials, and 11 RCTs fulfilled the inclusion criteria and were used for the meta-analysis , a fixed-effect was used to synthesize the data. The result indicated that fish oil-containing lipid emulsion significantly reduced the PNAC occurrence with RR = 0.53, 95% CI 0.36–0.80, P = 0.002  and pooled RR = 0.47, 95% CI 0.30–0.73, P = 0.0008; and patients with PN duration less than 14 days had I2 = 0% (P = 0.72) and pooled RR = 1.14, 95% CI 0.39–3.35, P = 0.81. The heterogeneity analysis for patients with birth weight more than 1000 g revealed I2 = 0% (P = 0.41) and pooled RR = 0.55, 95% CI 0.26–1.18, P = 0.12; and patients with birth weight less than 1000 g had I2 = 44% (P = 0.11) and pooled RR = 0.53, 95% CI 0.33–0.85, P = 0.009. Thus, the heterogeneity may come from studies of PN duration of more than 2 weeks and studies of median birth weight less than 1000 g  in VLBW infants compared to fish oil-free lipid emulsions. For patients with PN duration exceeding 14 days, the preventive effect was significant, with an average reduction of 53% , while it was non-significant for patients with PN duration less than 14 days (P = 0.81). The preventive effect was also remarkable in patients with birth weight less than 1000 g , with an average reduction of 47% , but non-significant for those with birth weight exceeding 1000 g (P = 0.12). The meta-analysis had low to median heterogeneity. The subgroup analysis showed that the duration of lipid emulsion utilization and birth weight may be the sources of heterogeneity. The sensitivity analysis indicated that the study of Pawlik et al. [The meta-analysis result revealed that fish oil-containing lipid emulsion significantly reduced the occurrence of PNAC by an average of 47% . Infants with PN duration exceeding 14 days and ELBW benefited more. The meta-analysis results supply new evidence for clinical workers to choose more suitable lipid emulsions for VLBW infants, especially ELBW infants. Future studies, focusing on the preventive effect of fish oil-containing lipid emulsion on total PN and comparing different single components and fish oil concentrations, will give a more robust conclusion.


Bacteroides spp. was found specifically in CS birth, while decreased bifidobacteria and increased Bacilli were common in CS birth and exposure to IP antibiotics in vaginal delivery. The abundances of numerous taxa differed between the birth modes among cephalosporin-exposed infants. Penicillin had a milder impact on the infant gut microbiota than cephalosporin. CS birth and maternal IP antibiotics had both specific and overlapping effects on infants’ gut microbiota development. The resulting deviations in the gut microbiota are associated with increased defecation rate, flatulence, perceived stomach pain, and intensity of crying in infancy.Birth mode and maternal intrapartum (IP) antibiotics affect infants’ gut microbiota development, but their relative contribution to absolute bacterial abundances and infant health has not been studied. We compared the effects of Cesarean section (CS) delivery and IP antibiotics on infant gut microbiota development and well-being over the first year. We focused on 92 healthy infants born between gestational weeks 37–42 vaginally without antibiotics (N = 26), with IP penicillin (N = 13) or cephalosporin (N = 7) or by CS with IP cephalosporin (N = 33) or other antibiotics (N = 13). Composition and temporal development analysis of the gut microbiota concentrated on 5 time points during the first year of life using 16S rRNA gene amplicon sequencing, integrated with qPCR to obtain absolute abundance estimates. A mediation analysis was carried out to identify taxa linked to gastrointestinal function and discomfort , and birth interventions. Based on absolute abundance estimates, the depletion of  Emerging evidence suggests that IP antibiotics affect the infant’s microbiota development over a period of several months.10–12CS birth is not the only intervention influencing the neonate’s early colonization. Intrapartum (IP) antibiotic prophylaxis is routinely administered to mothers undergoing CS to reduce the risk of post-cesarean maternal infection. In VDs, approximately 25% of mothers receive antibiotics to prevent neonatal group B Streptococcus (GBS) infection.14 and the effects of birth interventions on absolute bacterial profiles are unknown.15 In addition, existing studies addressing the effects of IP antibiotics on child health have largely focused on neonatal GBS infection.17 Recently, also their relationship to the atopic triad18 and body mass index19 up to 5 y age has been investigated.Previous studies utilizing next-generation sequencing (NGS) have characterized the infant gut microbiota using relative bacterial abundance. However, quantitative analyses indicate that early microbiota development entails major quantitative changes in total bacterial load as well as in individual taxa,20 This approach enables us to bypass the compositionality problem arising from relative abundances,21 which may mask true community dynamics,14 and lead to high false discovery rates.22 The bacterial abundances were further associated with parallelly collected data on gastrointestinal (GI) function, crying, and general health. We controlled for potential confounding factors using a carefully selected cohort, aiming to disentangle the effects of CS birth and IP antibiotics from potentially collinear effects.To understand the impact of birth interventions on the gut microbiota in relation to health in early infancy, we followed the development of gut microbiota in 144 healthy infants over the first year of life, focusing on 92 infants from whom quantitative PCR- (qPCR) and NGS-based quantitative microbiota profiling data for absolute abundance estimation was available.23 cohort and Jorvi cohort born by VD (N = 96) or CS (N = 48) were sampled during the first year of life for gut microbiota analysis. After the preprocessing and quality control of the 16S rRNA gene amplicon sequences, an average of 4.9 samples (range 1–7) was approved for analysis per infant .One hundred forty-four infants from the Finnish Health and Early Life Microbiota (HELMi)r infant . Absolutr infant : (1) VD IP antibiotics and doses varied among the delivery modes, with cefuroxime being the most common antibiotic used in CS and penicillin in VD . Out of P > .05). Overall, the bacterial load increased with infant age in all groups, especially during the first weeks .Birth mode or antibiotic exposure did not influence the total bacterial abundance quantified by qPCR at any given sampling time . By 6 months (24 in P > .05).The overall developmental trajectories of the microbiota differed between the study groups categorized by delivery mode and antibiotic type  in the ohs 24 in , all buths 24 in . By 12 mhs 24 in , the dif14 we focus on the absolute abundance data hereafter. We first compared abundances of bacterial families and genera of the exposure groups to the reference (VD without antibiotics), adjusting for breastfeeding, probiotics, and the introduction of solids when relevant. The results observed at the family level  and in VD-pen .In the first 12 weeks, the absolute abundance of Coriobacteriaceae (Actinobacteria) followed a pattern similar to that of Bacteroidaceae, being mainly associated with birth mode. The absolute abundance of Bifidobacteriaceae was strongly and negatively affected by CS birth and by both cephalosporin and penicillin at 4 weeks .–6 weeks . The absP < .05, . Comparivice versa (including only VD infants with different IP exposures) . Several bacterial families differed in absolute abundance between the VD-cep and CS-cep groups. In the CS-cep group, we observed enrichment of Verrucomicrobiaceae , Bifidobacteriaceae , Streptomycetaceae , Lactobacillaceae , Bacillaceae , Peptostreptococcaceae , Lachnospiraceae , Clostridiaceae , and Christensenellaceae . In contrast, Bacteroidaceae was reduced in CS-cep in comparison to VD-cep during weeks 4–12 .We compared the birth modes while controlling for IP antibiotic exposure, including only cephalosporin-exposed CS and VD, to understand the effect of birth mode independently of antibiotic treatment, and Verrucomicrobiaceae and Rikenellaceae , Bifidobacteriaceae and Lactobacillaceae , Coriobacteriaceae , Peptostreptococcaceae , Christensenellaceae , Bacillaceae  in the VD-pen group compared to VD-cep. Members of Clostridiaceae were consistently reduced in VD-pen in comparison to VD-cep at 4 weeks . Again, families significantly increased in the absolute comparisons were masked in the relative data comparisons .In terms of the effects of antibiotic types in the VD infants, increased absolute abundances were found in P > .1) nor did the cumulative number of antibiotic courses by 12 months (P = .81) nor infant weights at birth or at 12 months . All exposure groups showed increased GI symptoms mainly during the first 12 weeks compared to the reference group VD. There were significant differences in defecation rate, crying intensity, parent-perceived stomach pain intensity, and flatulence between the study groups . Infants’ overall health as reported by the parents did not differ between the study groups at any sampling age (ANOVA: y groups . We asseP = .009). A model using absolute bacterial abundances explained 24% of the variation in crying intensity  and CS (P < .0001). The bacterial composition explained 28% of the variation in defecation rate. The increased defecation rate in CS-born and cephalosporin-exposed infants compared to the controls appeared attributable to the decreased abundances of Bacteroides and uncultured Veillonellaceae  was significant and positively associated with crying intensity  and cephalosporin exposure (P = .01) were independently associated with increased defecation rate. The gut microbiota composition was associated with the defecation rate, explaining 51% of the variation. The effects of CS birth and cephalosporin appeared to be mediated by the decreased abundances of Bifidobacterium and Finegoldia (P = .005). The association between cephalosporin exposure and stomach pain was mediated by the reduced abundance of Collinsella and the increased abundances of Escherichia and Streptococcus in the CS-born and cep-exposed infants (including VD-cep)  . Gut micP = .02). The gut microbiota explained 46% of variation in stomach pain, low absolute abundance of Alistipes, and high abundance of Roseburia being linked with both CS birth and stomach pain (P = .05) and cephalosporin exposure (including CS-cep and VD-cep) (P = .02) were associated with increased flatulence intensity. The gut microbiota explained 23% of the variation in flatulence intensity at 12 weeks. The association of CS birth with cephalosporin and flatulence appeared mediated by the reduced abundance of Bacteroides and Alistipes . Gut microbiota composition explained 54% of the variation in stomach pain intensity, with Paludibacter mediating the association with penicillin exposure , compared to taxa with high copy numbers, such as Clostridiales (mean 5.5 copies28), as observed in this study. Quantitative microbiota profiling captures inter-personal temporal variability and ecosystem maturation in the gut microbiota better than relative microbiota profiling.29 This inter-personal variability and distinct developmental trajectories of several bacterial taxa captured only in absolute abundance were reflected in the mediation analysis, where some significant associations were not consistent throughout all sampling points.To the best of our knowledge, this is the first study using quantitative microbiota profiling for community-wide absolute abundance estimates of infants’ gut bacteria,30 and a faster age-driven increase in bacterial quantities in the stools of CS-born infants.31Although the common belief is that CS birth causes microbial deprivation, the total bacterial loads did not differ between CS-born, antibiotic-exposed, and non-exposed VD infants 4 weeks onwards in our study. We observed a clear age-driven increase in the bacterial loads in all study groups. The possibly transient difference in total microbial load before 4 weeks of age could not be evaluated in the present study due to the low bacterial biomass samples. Previous studies have reported lower bacterial loads in CS-born infants,33 recently shown to be restored by maternal FMT.34 Most studies on infant microbiota in relation to birth mode have neglected the fact that practically all CS births involve exposure to IP antibiotics, rendering it impossible to isolate the effects of IP from the birth route.35 The administration of IP antibiotics is known to alter the development of VD infants’ gut microbiota.36 Our results comparing the effect of birth mode under the exposure to the same antibiotic, cephalosporin, confirmed that birth mode per se influences the infant gut microbiota. These results are corroborated by two recent randomized trials that documented a lack of clear differences in the gut microbiota of CS-born infants exposed versus unexposed to IP antibiotic.38 In conclusion, our study and earlier data indicate that the mode of birth is a major factor affecting neonatal colonization, in line with CS, which eliminates the contact between the infant and maternal gut bacteria, preventing their transmission at birth.39Studies relying on relative abundance have consistently documented an altered bacterial signature in the gut microbiota of CS-born infants,Bacteroidaceae, Coriobacteriaceae, and Burkholderiaceae were reduced by CS birth but not by IP antibiotics during first 6 weeks, while Ruminococcaceae, Porphyromonadaceae, Rikenellaceae, and Pasteurellaceae abundances were negatively affected by both CS birth and antibiotics. Bifidobacteriaceae was most strongly affected by antibiotic exposure. Our observations recapitulate and extend the results of recent studies.41 Using absolute abundance data, we found the effect of IP antibiotics in VD infants to peak at 12 weeks with some differences that persist up to 12 months. Previous studies using relative abundance data have reported that the effect of birth-related antibiotics typically subsides after 3 months;42 however, most of the studies have not investigated the effect beyond this age.12 A recent prospective study of 100 VD infants reported that the effects of IP antibiotics overruled those of postnatal antibiotics on infant microbiota and persisted until the age of 1 y.43In contrast, in VD infants the IP antibiotic plays a major role. We showed that especially the impact of cephalosporin exposure during VD was largely similar to the effect of CS delivery on the infant gut microbiota. However, we identified several distinct effects of birth mode and antibiotic exposure. Namely, the absolute abundances of 44 In fact, the majority of the bacterial taxa affected by IP antibiotics, except bifidobacteria, was increased in absolute abundance during the first month. This suggests that the major driver of microbial changes after exposure to IP antibiotics is not the antibiotic itself but the antibiotic-induced reduction in bifidobacteria. Since the direct effects of IP antibiotics on infants are short (from hours to a couple of days), the lasting effects likely reflect the consequences of fundamental alterations in ecosystem assembly.45 The pattern is somewhat different after CS birth, where diverse members of the microbiota, including Bacteroides, bifidobacteria, and members of Clostridiales and Negativicutes, were likely directly reduced due to lower mother–infant microbiota transmission rates during CS birth.6Importantly, several bacterial taxa were increased in absolute abundance in the CS-born and in the antibiotic-exposed infants. For example, IP antibiotic exposure in VD and CS births both resulted in an increase in streptococci, as well as staphylococci and enterococci. This is likely due to reduced colonization resistance resulting from the depletion of bifidobacteria, which have anti-streptococcal activity.46 In our study, CS-born and VD IP antibiotic-exposed infants experienced more parent-reported abdominal discomfort and higher intensity of crying compared to VD infants with no IP antibiotic exposure, while their general health and growth during the first year was comparable. Previously, analogous mediation analysis has been used e.g. to study the role of infant gut microbiota in ethnicity-associated development of food sensitization.47 Colic symptoms have previously been linked with the reduced relative abundance of bifidobacteria,49 taxon also found in our associations, and to an altered balance between H2-producing and -utilizing taxa.50 Overall, the models based on absolute bacterial abundances explained 23–54% of the infants’ GI symptom variation, which is remarkable considering that the origin and underlying reasons of functional GI symptoms in infants often remain unclear.51To our knowledge, our study is the first to evaluate the influence of birth mode and associated maternal antibiotic administration in parallel on the infant gut microbiota and functional GI symptoms. The CS birth and postnatal antibiotics are known risk factors for functional GI disorders in pre- and full-term infants.Bacteroides and bifidobacteria, associated with infant well-being in the current study, are the major human milk oligosaccharide (HMO)-degraders and thus keystone taxa in the breastfed infant gut,52–54 so their depletion will have profound impact on metabolic processes, pH, and other conditions55 in the infant gut.56 Such changes likely affect gut physiology and function and may favor bacteria that are normally less abundant in the infant gut, such as members of Clostridia, which in the present study were associated with increased defecation rates, flatulence, and stomach pain.18 Overall, our results on health associations are preliminary and derived from a relatively small study, and hence need validation in future cohorts.9 and contributes to weighing the benefits and potential harms of antibiotic use in mothers and infants.Together, CS and antibiotic exposure at birth affect a substantial proportion of infants worldwide. Our data suggest that their effects on early colonization of the gut microbiota may have reduced the well-being of infants during the first months of life, but reassuringly their overall health was comparable to that of non-exposed infants. This study is an important primer for filling the knowledge gaps that underlie the lack of consensus for the choice between universal or risk-based screening for IP antibiotic use23 (NCT03996304) were included in the study. In both cohorts, pregnant women with singleton gestation were recruited from the general population in Southern Finland. Healthy babies, born in gestational weeks 37–42, without known congenital defects and exceeding the birth weight of 2.5 kg were included in the study (except for one baby with a birth weight of 2.4 kg). At least one parent had to be Finnish speaking to be able to answer the questionnaires.Infants specifically recruited for this study  and additional 83 infants recruited for the HELMi cohort20 representing 26 infants in the reference group VD, 7 in VD-cep, 13 in VD pen, 33 in CS-cep, and 13 in CS-other group.For the Jorvi cohort, 31 mothers delivered with (VD N = 26/31) and 37 without IP antibiotics . Infant fecal samples were collected on days 1 and 2, at 1, 4, and 12 weeks, and 6 and 12 months. From the HELMi cohort, 83 infants were selected based on their birth mode, IP antibiotics, and the availability of sequencing data from infant’s fecal samples at the age of 3 (considered as week 4 sample), 6, and 12 weeks, and 6, 9, and 12 months. From the combined cohort, five infants were excluded as they received post-natal antibiotics prior to the first sampling point, and two infants were excluded for low sequence quality, leaving a total of 144 infants. Additionally, all samples following a post-natal antibiotic treatment were excluded from the analysis as well as individual samples with low sequence quality . We divided the remaining infants into five study groups based on birth mode and IP antibiotics: VD reference group without IP antibiotics (N = 58), CS-cep (N = 34), VD-cep (N = 13), VD-pen (N = 25), and CS-other (N = 14) . The CS Both cohort studies were approved by the ethical committee of the Hospital District of Helsinki and Uusimaa and performed in accordance with the principles of the Helsinki Declaration. Parents signed an informed consent at enrollment.i.e., the mode of delivery and the usage, type, and dosing of maternal IP antibiotics were collected from hospital records for both cohorts.Parents collected fecal samples from the infants and stored them at −20°C. The samples were transported frozen to the study center within 6 months for −80°C storage. Data on feeding regime, probiotic and antibiotic uses, and child health and growth were collected with questionnaires (HELMi) or via phone interviews from the parents at the time of stool sampling (Jorvi). Data on exposures, 23 We used data from weekly to monthly repeating questionnaires using the questionnaire closest to each fecal sampling. The variables included were related to crying intensity estimated by a 0–100 mm visual analog scale (VAS), collected until 6 months and GI function  until 9 months. We also compared the frequency of infections estimated by the number of postnatal antibiotic courses by the age of 12 months. Infant growth was measured in postnatal care visits and transformed into age-dependent WHO z-scores.57Infant health and well-being were addressed using questionnaire data available for the HELMi cohort.58 and KingFisherTM Flex-automated purification system (ThermoFisher Scientific). The fecal DNA was extracted from 250 to 340 mg of fecal material that was suspended in 0.5–1 ml of sterile ice-cold PBS, and 175–250 μl of fecal suspensions was combined with 235–250 μl of RBB lysis buffer , 50 mM EDTA, 4% SDS) in a bead-beating tube from the Ambion MagMAX™ Total Nucleic Acid Isolation Kit (Life Technologies). After repeated bead-beating, 200 μl of the supernatant was used for DNA extraction with a KingFisherTM Flex automated purification system (ThermoFisher Scientific) using a MagMAXTM Pathogen High Vol. DNA was quantified using Quanti-iT™ Pico Green dsDNA Assay (Invitrogen).Bacterial DNA was extracted from the fecal samples using a previously described bead beating method,59 respectively. The two sequencing platforms give comparable results . Due to the low DNA and/or read yields, for 161 samples the V3-V4 library preparation protocol was modified with one or more of the following modifications: additional DNA concentration with ethanol precipitation, 2-step index PCR, increased input of template DNA (5 vs 1 ng/rx), and more PCR cycles (45 vs 27).The 16S rRNA gene amplicon sequencing was performed using Illumina MiSeq and HiSeq platforms for V3-V4 or V3 region as previously described,20 Bacterial DNA was quantified by amplifying 0.5 ng aliquots of each DNA extract, with universal bacterial primers 331 F/797R60 targeting the 16S rRNA gene. Standard curves ranging from 102 to 107 copies were constructed using the full-length amplicons of 16S rRNA gene of Bifidobacterium bifidum to convert the threshold cycle (Ct) values into 16S rRNA gene copy numbers per g of feces.The numeration of total bacteria was performed in triplicate as previously described.61 which relies on USEARCH62 for quality filtering, chimera detection, and taxonomic annotation. Only the forward reads (V3), truncated to 150 bases, were used.63 Reads occurring <10–50 times, depending on run size, were excluded as potentially erroneous. The taxonomic annotation was performed using USEARCH62 by mapping the reads to the SILVA 16S rRNA reference database version 115,64 restricted to gut-associated taxa. Further, potential contaminants in the low-DNA-yield samples were filtered by removing reads appearing in negative controls (PCR or extraction blanks) in corresponding numbers from all samples. The absolute abundances were estimated and 16S rRNA gene copy-number corrected as previously described by dividing the absolute abundances with taxon-specific copy numbers from the rrnDB28 database.20The sequencing reads were processed using R package mare,Alcaligenaceae)65 were excluded, leaving a total of 688 high-quality samples from 144 infants for downstream analyses, and 393 high-quality qPCR quantified samples from 92 infants.After processing, the median number of reads obtained was 26 384 (range 3 to 253 458). Sufficient sequencing coverage was evaluated by comparing observed species richness to read counts  and a cutoff of 2000 reads was chosen for HELMi samples, and 120 for Jorvi samples (to accommodate low-biomass samples from the first week of life). Samples with insufficient coverage  and 69 samples with over 60 000 reads from a single HiSeq run due to high amplification of probable contaminants , generalized least-squares models were used. Only the genera observed in >30% of the samples were analyzed individually. To account for multiple comparisons, only results with false discovery rate (FDR) values <0.1 were considered significant. All models were adjusted for infant probiotic intake at the time of sampling , feeding type, including breastfeeding status  and weeks since the introduction of solids, and the sample treatment history, including possible PCR modifications and sequencing run ID. Principal coordinate analysis was done by log-transforming the data and calculating Pearson correlation-based distances between samples. Analysis was done using the capscale function in the R package vegan. The significance of the group differences at each time point in the first two principal coordinates was analyzed using ANOVA.The statistical analysis was conducted in R version 3.6.3 with the package PathModel function in mare61 was used to identify taxa predicting symptoms and the role of birth interventions related to the predictive taxa using absolute bacterial counts.The health outcome variables were compared between the birth groups at each fecal sampling until 9 months using a negative binomial model adjusted for feeding type and probiotic use. Additionally, the association of antibiotic exposure  and birth mode (VD or CS) with health outcomes was analyzed at each fecal sampling until 9 months using a negative binomial model adjusted for feeding type and probiotic use. To determine if differences in infant well-being were mediated by changes in gut microbiota and the associated genera, the Click here for additional data file.


Conclusions: This study suggests that KMC can be used to improve the stress of preterm infants and their mothers, and that the greater weight gain observed in these preterm infants could contribute to a shorter average hospital stay and lower healthcare expenditure.It is well documented that the stress of separation of mother and baby can lead to short-term physiological instability as well as neurological, sociological or psychological consequences that may last a lifetime. Objective: The goal was to estimate the effect of kangaroo mother care (KMC) on physiological and biochemical parameters of preterm infant stress and maternal stress in neonatal intensive care. Methods: The investigation involved 112 preterm infants. Two groups were compared according to the mean duration of KMC during 12 days of study: the KMC group (mean duration more than 90 min daily) and the control group (less than 90 min). Results: Kangaroo mother care for more than 90 min on average per day in preterm infants is associated 12 days after the intervention with lower mean cortisol levels ( Preterm birth is a major global health problem. Low birth weight is a major contributor to both neonatal and infant mortality . Every yStress activates the hypothalamus–pituitary–adrenocortical (HPA) axis, triggering a complex feedback loop involving the interaction among the hypothalamus, pituitary and adrenal glands, leading to the secretion of the glucocorticoid cortisol, which is considered an indicator of stress as its plasma levels are elevated due to mental and physical stress reactions . VariousThe practice of kangaroo care has recently begun to gain increased attention and importance in hospitals around the world. The World Health Organisation (WHO) defines Kangaroo Mother Care (KMC) as “the care of premature babies carried skin-to-skin with the mother. It is a powerful and easy-to-use method to promote the health and well-being of infants born preterm and full-term” . The defAs mentioned above, preterm birth can be stressful and traumatic for mothers and can have negative consequences for the natural establishment of the mother–infant relationship, affecting the mother’s psychological state, bonding and interactions with her baby . The NICAlthough researchers over the last decade have called for increased care and support for both preterm infants and their mothers in NICUs, including interventions such as kangaroo care ,41, therMoreover, to date, there is no consensus on the duration of kangaroo mother care that is necessary to optimise beneficial effects and stress reduction ,26. TherThis cohort study of preterm infants was conducted on 112 preterm infants with a gestational age of 28–34 weeks. Inclusion criteria: Mothers with stable preterm infants born at 28–34 weeks gestational age and admitted to the NICU of the Torrecárdenas University Hospital (Spain) for care were eligible to participate in the present study. Neonates with major congenital anomalies, perinatal asphyxia and intraventricular haemorrhage grades 3 or 4, periventricular leukomalacia, hydrocephalus, encephalopathy, genetic malformations and chromosomal syndromes and those who had received corticosteroid treatment were excluded. A total of 124 preterm infants were assessed for eligibility. Twelve infants were excluded: 6 had haemodynamic instability, and 6 mothers refused to participate, so the final sample was 112 preterm infants. The mothers of the newborns gave their written consent before participating in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Torrecárdenas University Hospital (PI.DCC/MMC-2019).n = 56) were considered the “kangaroo care group”, and the “control group” was made of up those who received an average of less than 90 min of KMC per day during the 12 days (n = 56) “. The duration of each KMC session was determined by the condition of the infants and the availability of the parents.Recruitment took place from May 2019 to December 2021. Parents were informed of the study by research staff, if eligible, gave consent and completed baseline questionnaires. Included infants started the study from the third day of life, once stabilised, and were studied for the following 12 days, with hospital course data collected daily from the unit’s health record. All preterm infants were able to perform kangaroo care. Those preterm infants whose mothers wanted to perform kangaroo care received a KMC session of at least 90 min per day; this average time was taken as a reference since the WHO  advises The data collection tool was specifically designed prior to the start of the study. The tool incorporates a series of scales for the measurement of outcome variables, physiological and anthropometric parameters and demographic information on the mother’s child . Weight for the gestational age was classified according to Intergrowth-21  z-scoresKangaroo mother care. During the kangaroo session, babies (swaddled only) were placed in an upright position, head turned to one side, legs and arms bent, in direct skin-to-skin contact with the mother, usually with the mother’s breast exposed. Mothers were reclined in a chair with a blanket over the breasts. The duration of the kangaroo session was at least 90 min per day. Whether kangaroo care was performed or not, all preterm infants received routine neonatal care according to the NICU protocol. Parents were able to visit their babies at any time of the day.Cortisol sampling. A blood sample was taken from each infant between 8 and 9 am to minimise the effects of circadian rhythm, with cortisol being analysed from the blood sample taken from routine NICU blood tests to avoid another painful procedure. Cortisol concentrations were quantitatively assessed using an electrochemiluminescence immunoassay. No painful intervention was performed at least 8 h before sampling.Postpartum depression was assessed using the Edinburgh Postnatal Depression Scale (EPDS) [e (EPDS) . The EPDe (EPDS)  was usede (EPDS) .The sample size needed to conduct the study was calculated using Epi Info 4.2, estimating to detect a mean difference of 1.8, a common standard deviation of 3.3, a confidence level of 95% and a power of 80%. The result shows that a total of 54 participants were needed in each of the groups, for a total size of 108 participants. The study analysed 112 children and their mothers .2) was used to compare qualitative variables. For the comparison of quantitative variable averages, Student’s t-tests and Mann–Whitney U-tests were used prior to the Kolgomorov–Smirnov test to determine normality. Cohen’s d was used to measure effect size. Pearson’s correlation (r) was used to correlate normal data, and Spearman’s correlation coefficient (rs) was used when any of the variables did not follow a normal distribution. A multivariate analysis was performed by binary logistic regression after performing the Hosmer and Lemeshow goodness-of-fit test and using Wald’s backward elimination technique; only the OR (Odds Ratio) with p < 0.05 were left in the model.Data were analysed using the statistical package SPSS  version 27.0 and G*Power 3.1.9.7 . Frequencies and percentages were used for qualitative variables and means and standard deviation for quantitative variables. The Chi-squared test (χp > 0.05).Of the 112 preterm infants who participated in the study, according to their weight for gestational age at admission, 25% were of low weight for gestational age, 72.3% were of adequate weight and 2.7% were of high weight. According to diagnoses at admission, 17.9% had respiratory distress, 8% hyaline membrane disease, 1.8% infectious risk and 0.9% transient tachypnoea of the newborn. A total of 56 preterm infants were in the KMC group and 56 in the control group. Demographic and clinical characteristics of the preterm infants and mothers are presented in 2 concentrations for the intervention and control groups ranged from 21% to 25%, with no statistically significant differences between the two groups. Most participants were maintained on room air or 21% FiO2. Similarly, cortisol levels in children and mothers between the groups were homogeneous. EDPS scores in both groups were also similar on the first day of the study.At baseline (day 3 of life), mean heart rate, respiratory rate, blood pressure and oxygen saturation were homogeneous in all groups. Mean maximum FiOp = 0.02) and mothers (p = 0.002). Significant changes were also observed in weight gain, quantity, type of feeding and number of feedings, parental nutrition need as well as in the Edinburgh Postpartum Depression Scale . The need for parenteral nutrition (TPN) of infants showed a significant difference between KMC (p = 0.003) and OR = 0.19 , which is a 5.26 times higher risk for the need for TPN in the control group infants than in the KMC group.After the intervention days (15th day), mean cortisol levels in the KMC group were significantly lower than those in the control group in both children .p = 0.001) and the mother ; therefore, the longer the kangaroo care time, the lower the cortisol levels, the greater the weight gain in the infants  and the higher the number of feedings due to better tolerance   .p = 0.739), and it was adjusted to a Nagelkerke R-square of 0.12, introducing into the model the variables weeks of gestation, child cortisol, postintervention weight and respiratory frequency and the kangaroo care variable (Yes/No) as the dependent variable. It was observed that increased kangaroo time was a protective factor for cortisol with an OR of 0.88 and that these infants were 3.55 times more likely to gain weight.  compared to the control group on the 15th day of life. This finding suggests that at least one benefit of KMC treatment is dependent on the duration of KMC. These patterns of hormonal activity support the interpretation that increased involvement of KMC during the infant’s stay in the NICU may have contributed to the regulation of the adrenocortical function and that the intervention could mitigate some potentially harmful effects of hypercortisolemia [We found significantly lower cortisol levels in preterm infants in the kangaroo group . Similarly, Neu et al. [p < 0.01). However, Mirnia et al. found no statistically significant differences in salivary cortisol levels between infants receiving 45 min of SSC with their parents and infants receiving incubator care.Results from other studies have supported KMC as being a stress-reducing intervention compared to incubator care. Vittner et al.  found thu et al.  found thRegarding physiological effects, the results of this study show that the heart rate, respiratory rate, systolic and diastolic blood pressure and oxygen saturation of the infants were lower in the KMC group than in the control group, although they were not statistically significant. However, other studies found that the effect on respiratory rate in preterm infants receiving kangaroo care was significant . Similarp < 0.05). Salivary cortisol decreased in both KC groups compared to controls after 7 days (p < 0.05).Overall, kangaroo care appears to be beneficial in terms of the oxygen needs of preterm infants, as the upright position may promote the stabilisation of cardiopulmonary function. El-Farrash et al.  studied The effects of KMC over time are significant in terms of weight gain and improved breastfeeding, data that are consistent with the results of other authors such as Wang et al.  who studIt is of utmost importance to find methods of care that can buffer stress and thus long-term elevated cortisol levels in preterm infants in the NICU. These findings suggest that KMC may have substantial benefits for maternal mental health, results similar to the recent study by Sinha et al.  who estip < 0.001) during skin-to-skin contact, and parent anxiety scores were significantly related to parent cortisol levels. Cong et al. [Unfamiliarity with the NICU environment, the fragile appearance of the preterm infant, disruption in caregiving roles and constant worry that the baby will die are well recognised causes of stress for mothers of preterm infants . The resg et al.  examinedg et al. , concludg et al. ,66, as wg et al. . Mothersg et al. ,34 suggeg et al. .In hospitals around the world, the practice of KMC has recently begun to gain more attention and importance in relation to neurodevelopmentally focused care of preterm infants  and in iWith the sample size of this study, statistically significant results were obtained with positive effects showing that KMC decreases the cortisol levels of infants and mothers; however, research with a larger sample size would be appropriate to improve the population generalisability of these results. Further, the study was limited to 12 days of data collection; a longer duration of the kangaroo method may be able to confirm whether the decrease in and/or stabilisation of physiological stress parameters is progressive over time.In addition, future work should consider collecting data on variables that might influence the results, such as the average number of painful procedures per day experienced by preterm infants or the severity of their illness. Recent studies show that during the first weeks in the NICU, preterm infants experience a daily mean of 22.97 ± 2.30 stressful procedures and a mean of 42.59 ± 15.02 h of cumulative times of chronic/stressful exposure . DespiteThe practice of kangaroo care continues to evolve and progress within facilities caring for mothers and newborns born preterm. Kangaroo practices for preterm infants in the NICU provide physiological stability. In addition, the mother benefits from reduced stress, improved breastfeeding outcomes and more positive attachment behaviour. Kangaroo care benefits the baby by providing a buffer from the physical environment of the NICU, as well as helping to regulate following subsequent environmental disruption. Providing parents with a supportive environment in the NICU can facilitate active involvement in the care of the infant, which is of significant benefit to the developing infant. Neonatal intensive care providers have the ability and responsibility to influence the practice of kangaroo care as well as to value kangaroo care as a beneficial practice. Promoting education and seeking involvement within the healthcare team can help ensure implementation of this evidence-based practice that has the potential to improve health outcomes for preterm infants and their mothers.Our results help health professionals and mothers of preterm infants by showing that at least 90 min of kangaroo care per day during the first two weeks of life improves their infant’s weight gain, decreases their stress and improves their physiological constants, of particular interest in underdeveloped or developing countries. The findings advance the exploration of serum cortisol as a potential marker to assess the responsiveness of preterm infants to stress and improve synchrony in mother–infant interactions.KMC for more than 90 min daily in preterm infants during the first 2 weeks of life statistically significantly decreases preterm and maternal blood cortisol levels and improves weight gain and contributes to improved physiological constants of the infants as well as mitigating maternal postpartum depression. Our results support our hypothesis that the practice of early KMC intervention in the NICU reduces stress in both preterm infants and mothers and promotes the health and wellbeing of preterm infants. The greater the close and direct contact of mothers with preterm infants, the greater the buffering effect is on infant and maternal stress. Further studies are required to assess the optimal duration of KMC to optimise the beneficial effects in preterm infants, as well as in other infants with other medical complexities who may benefit significantly from this intervention.