Over the past few years, studies have increasingly focused on the development of mobile apps as complementary tools to existing traditional pharmacovigilance surveillance systems for improving and facilitating adverse drug reaction (ADR) reporting.In this research, we evaluated the potentiality of a new mobile app (vaxEffect@UniMiB) to perform longitudinal studies, while preserving the anonymity of the respondents. We applied the app to monitor the ADRs during the COVID-19 vaccination campaign in a sample of the Italian population.We administered vaxEffect@UniMiB to a convenience sample of academic subjects vaccinated at the Milano-Bicocca University hub for COVID-19 during the Italian national vaccination campaign. vaxEffect@UniMiB was developed for both Android and iOS devices. The mobile app asks users to send their medical history and, upon every vaccine administration, their vaccination data and the ADRs that occurred within 7 days postvaccination, making it possible to follow the ADR dynamics for each respondent. The app sends data over the web to an application server. The server, along with receiving all user data, saves the data in a SQL database server and reminds patients to submit vaccine and ADR data by push notifications sent to the mobile app through Firebase Cloud Messaging (FCM). On initial startup of the app, a unique user identifier (UUID) was generated for each respondent, so its anonymity was completely ensured, while enabling longitudinal studies.A total of 3712 people were vaccinated during the first vaccination wave. A total of 2733 (73.6%) respondents between the ages of 19 and 80 years, coming from the University of Milano-Bicocca (UniMiB) and the Politecnico of Milan (PoliMi), participated in the survey. Overall, we collected information about vaccination and ADRs to the first vaccine dose for 2226 subjects (60.0% of the first dose vaccinated), to the second dose for 1610 subjects (43.4% of the second dose vaccinated), and, in a nonsponsored fashion, to the third dose for 169 individuals (4.6%).vaxEffect@UniMiB was revealed to be the first attempt in performing longitudinal studies to monitor the same subject over time in terms of the reported ADRs after each vaccine administration, while guaranteeing complete anonymity of the subject. A series of aspects contributed to the positive involvement from people in using this app to report their ADRs to vaccination: ease of use, availability from multiple platforms, anonymity of all survey participants and protection of the submitted data, and the health care workers’ support. Pharmacovigilance is defined by the World Health Organization (WHO) as “the science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other drug-related problem” . MonitorWe performed a literature analysis to review the currently adopted methods for ADR reporting. In particular, we searched PubMed for papers published in the past 10 years using the following query: “adverse drug reactions” OR “adverse drug reaction reporting” OR “adverse drug reaction reporting systems” . The considered papers included clinical trials, meta-analyses, randomized controlled trials, and reviews. This search identified 131 studies. By excluding papers that, after a full text or abstract screening, seemed too descriptive and generic or focused on just presenting the incidence or prevalence of ADRs, the literature survey performed was limited to 28 eligible papers. We found 9 other papers that met the considered criteria. We grouped the studies based on the system used to conduct ADR surveillance.Of the total 39 papers found, 7 (17.9%) . This waSubstantial delays may occur between occurrence and discovery of ADRs, because reports received by government agencies are often released with a delay of months or even years due to the period required for proper assessment of ADR data. Further limitations of traditional ADR surveillance relate to the fact that ADR reporting is voluntary, and several studies have shown that as many as 90% of serious ADRs end up being unreported ,14. MoreIn response to these limitations, ADR surveillance is also conducted by applying a participant-centered active approach that, contrary to passive reporting, involves an intentional searching for ADRs via continuous and organized contact with health care providers or other relevant reporting sources, such as hospitals, laboratories, and patients ,12.At the same time, active surveillance requires more resources for a positive outcome. Of the 39 papers, in 16 (41%), active systems falling under this category used digital strategies to facilitate and encourage ADR surveillance, which means the adoption of electronic technology for capturing and processing data.Common participant-centered active surveillance methods are conducted using health diary reporting cards , SMS texThanks to the spread of internet use, our literature survey suggested increasing usage of web-based platforms to promote ADR reporting. A large proportion of the papers found 16/39, 41%) adopted interactive web-based ADR-reporting tools -33 also , 41% adoThe results of the European Union's Innovative Medicines Initiative Web–Recognising Adverse Drug Reactions (WEB-RADR) project, reported in a recent paper , suggestOver the past few years, studies have increasingly focused on the development and usage of mobile apps as additional tools to pharmacovigilance for improving and facilitating reporting of ADRs, as reported by Defer et al , Ahn et In view of these findings, we searched for available mobile apps with the ability to allow ADR reporting for medical devices, drugs, or vaccines, without searching for apps that are specific for a particular case of study.Among the tools we found, the MedWatcher app  is a webAnother available tool is presented by Ahn et al , where tMy eReport France  is a freThe WEB-RADR project developed 3 mobile apps suitable for patients, caregivers, and health care professionals not only to report their ADRs but also to receive up-to-date information and news alerts on selected medicines, check the number of received reports for a particular drug, and view previous ADR reports submitted through the app. These 3 apps, which are free to use for everyone on iOS and Android devices, are the free Yellow Card smartphone app  developeIn the context of the WEB-RADR project, the international WEB-RADR app has been launched under the name Med Safety . SimilarIn the following cases, no specific details about the app design and features, other than the chosen platform and the declared ability to allow ADR reporting, are available: the CANVAS app  and the Although the development of mobile apps to report ADRs is continuing to progress, providing valuable knowledge about drug safety, one feature that is not present in any of the reviewed apps in our literature analysis is the ability to perform longitudinal analysis in an anonymous way, a key point to foster citizenship engagement.Although having valuable features, all the currently available mobile apps lack the ability to collect individual subjects’ ADR time courses anonymously. Monitoring the same subjects over time, while guaranteeing complete privacy and anonymity of the submitted information, can be a valuable addition to pharmacovigilance because it can be used to gather longitudinal safety data of a drug. Consequently, the temporal investigation of the same patient can help detect, within a specific clinical context, any temporal patterns or ADR combinations.On these grounds, we developed, for Android and iOS devices, a new, free app called vaxEffect@UniMiB, tested its capability to enable longitudinal studies by analyzing per subject time series, and evaluated its potential in fostering spontaneous citizenship participation in an ADR data collection campaign. In this work, we specifically presented app features by evaluating the reporting rate of ADRs in a sample of Italian academic subjects vaccinated for COVID-19.During the COVID-19 health emergency, the University of Milano-Bicocca (UniMiB) participated in a vaccination campaign deploying a hub. The hub administered the first dose between March 5 and 29, 2021, and the second dose between May 31 and June 11, 2021. A third dose was administered at the national level, but the authorities decided to not involve the hub. The campaign involved professors, researchers, adjunct professors, postdocs, PhD students, and the technical administrative staff of UniMiB or of the Politecnico of Milan (PoliMi). Due to the deputed authority emergency plan, the UniMiB hub invited all UniMiB employees and only a third of PoliMi ones, the others being distributed to different hubs.Ethical approval was obtained from the Ethical Committee of UniMiB (protocol no. 0041302/21).After receiving ethical approval, vaxEffect@UniMiB was made available on March 24, 2021, from Google Play Store and on March 29, 2021, from Apple App Store. It was advertised to subjects invited to the vaccination campaign at the UniMiB hub.We started to collect reports sent to vaxEffect@UniMiB, starting from January 1, 2021. Given the health emergency, it was not possible to plan the different phases of the study. To highlight the uncertainties of the situation, we report that the UniMiB hub was not aware at the end of the first dose administration whether the second dose would have been administered by the hub itself. When the third dose was administered at the national level, the hub was not involved, and we decided to test the flexibility of the app by keeping it active to allow respondents to use it on a spontaneous basis. The app was deactivated on February 9, 2022.At each vaccination wave, the physicians involved in the vaccination campaign explained to attendees the importance of reporting ADRs and delivered a brochure explaining the aim of the research, as well as the guarantee of anonymization of the digital tool. Moreover, corresponding to the beginning of the first 2 vaccine doses’ administration campaign, an informative mail was sent to UniMiB employees.Our system consists of a mobile app, a web server, and a Firebase Cloud Messaging (FCM) server (Google Inc), as shown in The application server is responsible for receiving all the user data, to store them in a SQL database server, and to remind the patients to submit vaccine and ADR data. The reminders are sent to the patients via push notifications sent to the mobile app by the application server through FCM.The complete data exchange between the mobile app and the application server is described in The framework for recording the responses provided by the participants is a one-way client/server architecture. We decided to opt for this type of structure to ensure the safety of the respondents. This way, it was not necessary to store the answers to the questionnaires on the terminal used by the respondents, minimizing the possibility of illegitimate acquisition of information in the event of loss, theft, or fraudulent use of the device.Furthermore, to ensure the anonymity of the respondents, while preserving the possibility of analyzing the time series relating to the same individual, we decided to generate a unique user identifier (UUID) using an FCM  token soThe mobile app was developed for both Android and iOS devices using the FCM software development kit (SDK). We specifically used Android Studio (Java language) and XCode (Swift language) for, respectively, the Android and iOS versions. Our app sends data over the web to an application server using an application programming interface (API) gateway. The data are sent using the https protocol and are validated by the application server.On initial startup of our mobile app, a splash screen is displayed A, and atOn subsequent startup or after completing the medical history questionnaire, the patient is asked for vaccination data A and, 7 The application server was developed in Python. Its main purpose is to collect data from the mobile app and store them in the database. The application server is also responsible for generating the UUID, to pair it with the FCM token, and to send push notification requests to the FCM server. Indeed, according to the data provided by the patient, after 7 days from the date of vaccination, the application server sends a push notification request to the FCM server in order to inform the patient that the ADR data need to be collected. The notification messages delivered to the mobile app from the FCM server open when the patient taps on the notification.In addition to demographic data, the medical history questionnaire that respondents had to complete required them to indicate whether they had been diagnosed with one or more chronic diseases among those suggested by the app: dyslipidemia, hypertension, autoimmune diseases, asthma, cardiovascular diseases, tumor, liver diseases, diabetes, bronchitis, or kidney disease.In addition, vaxEffect@UniMiB asks users to register the occurrence of specific ADRs among those reported as a COVID-19 vaccine side effect by the Centers for Disease Control and Prevention (CDC) and WHO: fever, headache, injection site pain, tiredness, muscular pain, swollen lymph nodes, joint pain, paresthesia, dizziness, sleepiness, nausea, and abdominal pain. At the same stage, vaxEffect@UniMiB collects information about the vaccine administrated to the subject, such as its kind: Vaxzevria (AstraZeneca), Comirnaty (Pfizer/BioNTech), moderna-mRNA1273 (Moderna), or Janssen (Johnson & Johnson).To avoid cases of intentional or unintentional false reports by users, we checked the consistency of the submitted data at different time points, discarding nonconsistent data. Due the free access to the app and the data themselves, it was not possible to automatically detect fraudulent data reporting, but during a manual inspection, no fraudulent report was found.For each user, we labeled the registered data, defining whether they belonged to the first, second, or third vaccination dose. By keeping separate the 3 sets of records, we analyzed in each the number of ADRs registered by each user by separating into 2 groups subjects who reported more than 3 postvaccination symptoms and those who reported fewer than 3 symptoms. These 2 groups of subjects were compared in terms of sex and age by using the chi-square test .Through radar plots, we analyzed the kinds of ADRs reported by each user after every vaccine dose by keeping subjects separated in terms of gender and age.Using a Sankey diagram , we follThe total number of people vaccinated during the first wave of vaccine administration (March 2021) conducted at the Bicocca hub was 3712 (a third of the entire personnel of UniMiB and PoliMi). This partial coverage of the personnel can be ascribed to the fact that health care workers received the vaccine before the launching of the app, with the effect that not everyone may have necessarily participated in the survey presented in this work.During this wave, vaxEffect@UniMiB received a total of 2733 medical history questionnaires from individuals between the ages of 19 and 80 years. In particular, as shown in the Medical History column of vaxEffect@UniMiB allows characterizing the vaccinated population by the kind of administered vaccine. It is possible to observe that the administered vaccines were Vaxzevria , Comirnaty , moderna-mRNA1273 , and Janssen . The collected data made it possible to follow the proportion of each vaccine kind through the 3 doses .Stratification of subjects by gender revealed a slightly greater proportion of female  compared to male  subjects. When stratified by age, younger (<30 years) and middle-aged (30-55 years) subjects represented the most active users, together constituting 88.2% (n=2410) of the total participants. On the contrary, older users (>55 years) represented just 11.8% (n=323) of the total set of survey respondents. Age and sex distributions of users employed in UniMiB were in line with those in PoliMi.Once the medical history questionnaire was completed, 2360 (86.3%) and 1779 (65.1%) of all survey participants continued to use vaxEffect@UniMiB, reporting their vaccination data of, respectively, the first and the second dose campaign. Subsequently, 2226 (94.3%) and 1610 (90.5%) users who submitted their vaccination data after, respectively, the first and the second doses also reported their ADRs to the vaccination.In response to the first vaccine dose, we collected information about vaccination and ADRs for 2360 and 2226 subjects  reports.It should be noted that health care workers, receiving the vaccine before the launching of the app, may not have necessarily participated in the survey presented in this work. Moreover, due to deputed authority emergency plan, the hub invited all UniMiB employees and only part of PoliMi ones, the others being distributed to different hubs. This aspect further affected the number of reports received over the total number of subjects invited to the vaccination campaign.Thanks to vaxEffect@UniMiB, we collected vaccination and ADR data relative to the first vaccine dose for 2226 (60.0%) of the 3712 individuals vaccinated during the first dose campaign, to the second dose for 1610 (43.3%) of the 3718 individuals vaccinated during the second dose campaign, and, in a nonsponsored fashion, to the third dose for 169 (4.6%) individuals.Although a direct comparison of our reporting rate against traditional ones is not possible, a reporting rate of 120 questionnaires submitted to the National Pharmacovigilance Network for any 100,000 doses administered was reported, according to the Annual Report of Aifa  related to the surveillance of COVID-19 vaccine safety during December 27, 2020-December 26, 2021.Following a review of the currently available mobile apps for monitoring ADR reporting for medical devices, drugs, or vaccines, our app revealed to be one step ahead. Specifically, vaxEffect@UniMiB is the first successful attempt in tracking the same user over time and identifying putative temporal patterns in an anonymous fashion. With this ability, we were able to analyze in this study the trend that emerged through the 3 vaccine doses administered to the same user. The first vaccination resulted in the strongest response among individuals, given the high prevalence of subjects with 3 or more than 3 symptoms. As already evidenced by Haas et al , part ofA series of aspects contributed to a greater involvement from people in using this app to report their ADRs to vaccination.First, the user-friendly and interactive graphics of vaxEffect@UniMiB allowed ease of use of the app, resulting in a positive response rate after both the first and the second vaccination campaign. Although the response after the third campaign was lower compared to the previous 2 ones, it could be still considered a positive result. Indeed, during the third vaccination campaign, any reminder notification was sent to users, denoting a completely spontaneous participation of subjects in that phase. This result indicates the effectiveness of vaxEffect@UniMiB as an ADR-reporting system, although the active contribution of an institutional sponsorship is necessary.Another aspect that contributed to the increase in the usage of vaxEffect@UniMiB was its availability for both Android and iOS devices, which made it reachable to as many users as possible.Even more importantly, the protection of the submitted information assured users that high standards of confidentiality were maintained during data reporting. The architecture underlying vaxEffect@UniMiB ensured the anonymity of all the survey participants and also the privacy of their data, minimizing the illegitimate acquisition of information in the case of loss, theft, or fraudulent use of the device. These actions allowed guaranteeing proper use of the outcomes of the research, while preserving the possibility of tracking the same subject over time.Although these features allowed for a positive response from the public, we are also aware that following an even stronger sponsorship of the vaccination campaign, the 373 users who just compiled the medical questionnaire without then reporting any other data would continue using the app. Likewise, increased support in the dissemination of the app could encourage who partially submitted data for only 1 or 2 vaccine doses to complete the entire survey.We intend to also clarify that since the study presented in this work was a pilot study conducted on a selected voluntary population, we cannot exclude that our statistical measures could be affected by volunteer bias. This bias can occur at all stages of the study, and differences between volunteers and the target population are not restricted to sociodemographic factors but can include attitudes toward the study and institutions involved. However, the likelihood of volunteer bias increases as the refusal rate to volunteer increases. We observed a high rate of participation and a low rate of attraction until the third vaccination wave (when the Bicocca hub was not more in place and the in-person stimulus ended), probably due to the endorsement of participants’ anonymity and confidentiality.The impact of this bias is unknown, but the observed sample should be quite younger and have a higher level of education and income than the target population, showing a higher rate of participation. These characteristics have been associated with greater extension and severity, so we cannot exclude that we overestimated the prevalence of symptoms .This work being a pilot study, we chose to restrict the analyses to the respondents explicitly imputable to the hub itself. Consequently, data entry was strictly controlled since only ADRs to the administered vaccination were asked to be submitted. In this way, we were sure that all the reported information was not false-true ADRs due to other medical conditions of the user. However, as a suggestion for the future, if vaxEffect@UniMiB will be used in a different and more open context, it will be necessary to integrate a preprocessing phase to tame the possible distortions induced by the noncontrolled environment in which respondents report information.The results of this study showed that it is possible to leverage active citizenship engagement to increase the ADR-reporting rate using an agile and pervasive tool, such as a mobile app.The combination between the synergy of the physician who proposed the app and the flexibility of vaxEffect@UniMiB seems to be an excellent tool to ensure high response rates in situations in which a priori information is scarce and decisions need to be made in a short time.In addition to the promising results so far obtained, this work highlighted some practices that need to be carried out to foster approach efficacity, such as conspicuous support in the dissemination of the app to encourage more subjects to report their ADRs and steady user engagement to sustain data submission through the entire vaccination treatment when only partial information is reported.


The primary sector is vital for growth and sustainable development in emerging countries. The combined effects of COVID-19 and geopolitical uncertainty on capital flows are likely to have profound impacts on many developing countries. In particular, decreased capital inflows into agriculture will negatively affect food security and growth. However, there remain limited literature on the role of capital inflows in this sector. In this paper, we examine the role that foreign capital inflows play in the development of the agricultural, forestry and fishing sectors in developing countries. Specifically, we use the panel vector autoregression approach that accounts for endogeneity. Using data from sixteen developing economies, we find that there exists bidirectional causality between foreign direct investments in agriculture, forestry and fishing and value added in these sectors. These bidirectional relationships reflect a cyclical effect between FDI and value added in the agriculture, forestry and fishing. The effect of FDI on value added in agriculture, forestry and fishing remains positive for up to five years in our model. This implies FDI has a medium- to long-term positive impact on value added in agriculture, forestry and fishing. The implication of this result is that countries with currently high FDI transaction costs or that have a generally less conducive investment environment can improve agriculture by eliminating these obstacles. This is because FDIs can lead to improved technologies and technical expertise, practices, management and other systems that benefit the host countries. Food security and environmental quality are crucial requirements for sustainable development. Investments that address both can enhance growth opportunities in poor and developing economies, where the contribution of agriculture to the economy tends to be relatively large and sustains a disproportionately large share of the population. Castañeda et al.  estimate2), methane (CH4) and nitrous oxide (N2O). Mitigating and adapting to climate change, improving food security, enhancing and maximizing employment, value added and growth will require substantial investments in the sector. The demand for food is estimated to increase by seventy percent by 2050 [The 2021 Food and Agricultural Organization (FAO) statistical yearbook estimates the global value added generated by agriculture, forestry and fishing to be US$3.5 trillion in 2018, growing by 73% in real terms between 2000 and 2019 . In Afri by 2050 . Accordi by 2050 , this gr by 2050 . One way by 2050 .Specifically, we investigate the role of foreign direct investments in agriculture, forestry and fishing in developing countries between 2001 and 2020. Our goal is to identify the degree to which foreign capital inflows contribute to value added in the sector. This issue is of great importance for several related reasons. First, the agricultural sector, especially in developing and poor countries, draw their funding largely from the public sector. Second, even prior to the COVID-19 pandemic, such funding was often insufficient  model that also allows us to address the potential issue of endogeneity. Specifically we consider FDI flows into this sector, partly motivated by the fact that the effect of FDI flows differs across sectors and industries see “” sectionThe literature on the effects of FDI on the agricultural sector or food security remains scant, despite the importance of this sector as well as evidence on the significant rise in global capital flows into and out of various sectors of the world’s economies. Our work draws from to three, albeit limited strands of literature on this topic, all built on two theoretical foundations: the modernization thesis and dependency theory; both tied to the discourse on globalization. The modernization thesis holds that foreign capital inflows produce growth that benefits both the host country and the source country. On the other hand, the dependency theory posits that foreign capital inflows increase income inequality and reduce food security  and (k × k) vectors of parameters to be estimated, where k is the number of parameter estimates. We use a 4-variable vector consisting of value added, FDI, official development, and gross fixed capital formation in agriculture, forestry and fishing. We use the share of value added in gross domestic product from agriculture, forestry and fishing as an exogenous variable in the model. The model allows us to examine the dynamic relationship between FDI in agriculture, forestry and fishing and the performance of this sector . It also estimates fixed effects by augmenting a standard vector autoregression with a cross-country dimension. This is done via the system generalized method of moments (GMM) estimation, which uses lagged values as instruments to address endogeneity and provide unbiased estimates. The lag length we use is based on the model selection criteria laid out by Abrigo and Love [Given the numerous strands of literature on the role of FDI in the economy, a wide variety of methods has been used in the literature. Time-series methods are commonly used to examine the relationship between FDI and agricultural growth. The choice of a particular method aims at addressing some of the intractable problems associated with the characteristics of the data. In particular, the problem of endogeneity remains underappreciated in the current literature. In this paper, we use the panel vector autoregression model, which treats our variables of interest in the model as endogenous. The model, first introduced by Holtz-Eakin et al. , is wideAll our data come from the Food and Agricultural Organization database.We consider the annual growth (in US$) rate of this variable, rather than its levels. Agriculture, forestry and fishing is represented by the value added (VA) in these three subsectors sector (measured in US$ 2015 prices in millions).We include official development assistance (DFA) or flows in agriculture, forestry and fishing. According to FAO , these aTable The results we report use a system GMM-style estimation of the panel-VAR. It fits homogeneous panel-VAR model by fitting a multivariate panel regression of each dependent variable on lags of itself and on lags of all other dependent variables using generalized method of moments . We follWe can examine the dynamic relationships between the variables of our model with the help of impulse response functions and variance decomposition measures. This requires that that the panel-VAR be invertible and has an infinite-order vector moving average representation . We can We conclude that our model is stable because all the moduli are smaller than one. The stability condition can be graphically illustrated, as given in Fig. There is no empirical test for the ordering in panel-VAR estimation. Instead, we simply use the ordering of variables specified in panel VAR estimation procedure. The impulse response (IRF) functions for our estimation are shown in Fig. Similarly, value added in agriculture, forestry and fishing serves as a pull factor to FDI, and this effect persists in the medium to long term. The relationship between value added and development assistance shows positive and self-reinforcing effects. Another variable with strong explanatory power in the model is the development assistance in agriculture, forestry and fishing. According to Fig. The COVID-19 pandemic and current geopolitical events have created high levels of uncertainty in food chains across the globe, and this issue will increasingly pull the agricultural sector into focus in policy circles for governments and international bodies. Among the factors that will likely be focused on is the role of investment and technology in the agricultural sector. FDI is a crucial determinant of agricultural production via technology transfer and skills that benefit host country’s farmers. Empirical analyses on this topic, especially in the context of the global pandemic and geopolitical conflict, remain limited. The goal of this paper is to contribute to the discourse. Specifically, we aim to examine the role of FDI and performance in agriculture, forestry and fishing. To achieve this, we apply a panel VAR model to data on sixteen developing countries over a twenty-year period. This model is particularly suitable because of its ability to deal with the problem of endogeneity. Our results show that FDI have medium- to long-term positive effects on value added in agriculture, forestry and fishing. This suggests that developing countries would benefit from eliminating policies that and other regulations that increase transaction costs for foreign investments. This includes improving institutional mechanisms that deter foreign investments—Amendolagine et al.  as well


Autoimmune liver diseases (AiLDs) are rare autoimmune conditions of the liver and the biliary tree with unknown etiology and limited treatment options. AiLDs are inherently characterized by a high degree of complexity, which poses great challenges in understanding their etiopathogenesis, developing novel biomarkers and risk-stratification tools, and, eventually, generating new drugs. Artificial intelligence (AI) is considered one of the best candidates to support researchers and clinicians in making sense of biological complexity. In this review, we offer a primer on AI and machine learning for clinicians, and discuss recent available literature on its applications in medicine and more specifically how it can help to tackle major unmet needs in AiLDs. Autoimmune liver diseases (AiLDs) are chronic diseases affecting the liver and the biliary tract, with a putative autoimmune pathogenesis, and include autoimmune hepatitis (AIH) , primaryNonetheless, high-throughput DNA and RNA sequencing technologies, digital pathology, and digital radiology are also progressively reaching this neglected field. A large amount of experimental and clinical data are increasingly available in the field , which rThis review aims to provide an introduction to AI for clinicians and to outline the current evidence about AI in medicine and the foreseeable applications in the field of AiLDs.labeled data is available, e.g., the number of days needed to recover; this value is the value of the target  of the problem at hand, and it is known for a set of the patients tracked in the historical record. When the value of the output is known and labeled, the problem falls into the area of supervised learning. When the output of interest is a quantity , the problem is referred to as a regression. On the contrary, if the output represents a quality , the problem would be defined as a classification.In 1959, Artur L. Samuel, a computer scientist, firstly introduced the expression “machine learning” in his seminal paper focusing on how a machine could learn the game of checkers . For inslabeled data is not available and the macro-category is called unsupervised learning. For example, unsupervised learning may refer to the task of splitting a set of patients into homogeneous subgroups with respect to a set of features (clustering) or rather to determine which diagnostic factors are correlated to each other (association mining). In these cases, there is no known, pre-defined and pre-labeled target of interest.In other cases, the set of All these tasks can potentially be addressed with a statistical approach. The main difference between statistics and ML is that ML does not require to make any assumptions concerning the statistical distribution of the considered features . Converstrain the model, while another part is not supplied to the training pipeline. In ML terminology, the former part is usually referred to as training set, while the latter is commonly denoted as test set. After having completed the modeling phase on the training set, the model itself is applied on the test set; in other words, the model will make predictions on a new and unknown subset of data, which simulates the foreseeable new data on which it will be applied in the future. Coming back to the first example about days to recover from a disease, the test set will involve patients not considered in the modeling phase on training. More complex and sophisticated scenarios also involve the introduction of a third dataset, referred to as the validation dataset; in this case, there is a dataset for training, a dataset for hyperparameter tuning (tuning of model parameters) on top of training, and another one for performance evaluation.Conversely, considering that ML approaches cannot be compared against any reference distribution for evaluation, it is harder to assess their performance. A common procedure to deal with this issue consists in comparing the model predictions against the data themselves. More specifically, a subset of data  is used to External validation should be performed for rigorous evaluation of the algorithm performance. In fact, deficiency and biases present in the training dataset may appear in external data that are either closer to the ideal target population or representative of minority populations. In this way, the risk of biased performance estimation is reduced and more consistent and robust conclusions can be drawn .root mean squared error  allows to understand how much the extracted model is effective on previously unseen data. When there is a strikingly good performance in the training set, but with a poor one in the test set, the model is overfitting data: in other words, it is too focused on replicating training data and is not able to generalize its predictive power.Evaluating, for instance, the unstructured data, such as documents or images  to discriminate the type of tissue and to detect new prognostic biomarkers. In this case, on top of data coming from a single Chinese cohort, the investigators have also leveraged the Cancer Genome Atlas (TCGA) to finely map the identified histological patterns and correlate them with tumor immune infiltrates and gene mutations. Of note, to take into account the different staining protocols between the training cohort and TCGA, they used a specific DL algorithm for standardization. This work represents a remarkable example of a pipeline that goes from slice preparation to the development of a risk score associated to histological patterns identified within the tumor, showing the ability of this score to stratify patients in higher- and lower-risk groups and to correlate score values with histological patterns and tumor-associated immune cells .More relevant to the field of AiLD are applications of WSI and DL to inflammatory diseases such as ulcerative colitis (UC). In a recent landmark paper showing data from an international multicenter consortium, investigators have created a new histological score for UC, which correlates with endoscopic findings and holds prognostic value. To generate the score, colonic biopsies were used and a group of expert pathologists annotated the slides by using a variety of histological scoring schemes already available. After a Delphi consensus among pathologists, the neutrophil infiltrate was deemed as the key element of disease activity and clinical outcome; importantly, the new score was created by using standard statistical methods. Subsequently, the DL algorithm was trained to learn how to identify neutrophil infiltrates within images and to differentiate between quiescent and active disease, achieving 86% accuracy. In our opinion, the interesting aspect of this study is the blended approach between classical and standardized statistical approaches  together with DL .WSI offers a more quantitative approach for assessment of liver fibrosis and steatosis, and novel data are available for HCC and transplant pathology . A dedicThe main limitations to the implementation of AI-based technologies to digital pathology are the lack of universal standards for data formatting, as compared to radiology, where the Digital Imaging and Communications in Medicine (DICOM) format is already the standard . The curIn addition, data quality is essential; in fact, histological slides comprise a highly heterogeneous information. Staining, thickness of the section, and presence of artifacts are known factors influencing the model performance in DL-based diagnostic models . The devAnother important limitation of current pipelines that is particularly relevant for the field of AiLD is their exclusive reliance on hematoxylin–eosin stainings. Despite the validated role of CK7 and orcein stainings in the differential diagnosis and staging of PBC  and PSC Overall, there are still some methodological limitations for the full implementation of digital pathology as a research tool and for its incorporation in the current clinical workflow; yet, there is active research aiming at addressing them.Liver biopsy has a pivotal role in the diagnostic and prognostic process of AiLDs. It is essential for the diagnosis of AIH and holds value in atypical cases of PBC and PSC; for all the three conditions, it provides a rich amount of information that can assist prognostication and guide treatment. While for PBC the pediatric onset is exceptional, AIH and PSC can arise at pediatric age, with features and disease course different to the adult onset.Computational pathology has several promising applications in the field of AiLDs. As the pathogenesis of these diseases is still obscure, the use of AI-assisted methods may aid in discovering pathogenetic clues for further investigation. Computational pathology may also assist in identifying core histological features of AiLDs that are still missing, and provide a more standardized approach for differential diagnosis . An even more promising application is for prognosis modeling and risk stratification.Nevertheless, there are some disease-specific traits that can influence AI implementation. In AIH, liver biopsy is the cornerstone of the diagnosis, and the available scoring systems unlikely lead to definite AIH diagnosis without histological evaluation. In addition, histology provides useful information on disease activity and stage . HistoloFor risk stratification in AIH, unsupervised ML approaches could be of interest to identify prognostic biomarkers of disease activity that can predict biochemical remission, when liver biopsy is performed at diagnosis, or relapse, if the histological assessment is performed before treatment withdrawal. Unsupervised learning could shed a light on morphological features peculiar of sub-phenotypes of AIH that are currently invisible to the human eye, highlighting populations of cells or morphological patterns that are not considered part of the histological spectrum of the disease –44. UnsuEven though only supervised learning approaches were employed, in another landmark paper from Stanford University, it was shown that the use of a computational pathology tool that automatically generates quantitative features can pinpoint structures that had not been called in action as prognostic biomarkers. This is a clear example of how the implementation of quantitative approaches, either supervised or unsupervised, can represent a way to detect unseen patterns that hold predictive and prognostic value .Multi-center, collaborative efforts can represent an asset by helping the collection of a huge amount of clinical and histological data to be analyzed through ML; large consortia such as European Reference Network for Rare Liver Diseases (ERN-RARE LIVER) or the International AIH Group can be leveraged to this end. Unsupervised techniques can be devised and validated in order to cluster individuals affected by AIH according to different prognostic trajectories.As regards autoimmune cholangiopathies, histological samples suitable for analyses are limited in number. The diagnostic accuracy of PBC-specific autoantibodies has determined a progressive reduction in the number of liver biopsies performed ; in PSC,As regards biomarker discovery and risk stratification, non-invasive tools are not accurate enough to depict the full cholestatic picture, and the variety of inflammatory and fibrotic patterns, together with the cellular milieu of biliary regeneration, are not captured by transient elastography or routine liver enzymes , 48. ThiAs regards disease classification and definition, unsupervised learning algorithms can be applied to detect sub-phenotypes that could prompt disease re-classification. The latter aspect is of particular interest for variant syndromes and for better characterization of the ductopenic variant of PBC.Overall, the introduction of ML in this field of medicine may at minimum generate new hypotheses and identify novel biomarkers; yet, robust studies validated in several cohorts will be required to change also everyday clinical practice.In the last decade, the remarkable progress in liver imaging techniques has helped to characterize several liver diseases from a qualitative and quantitative point of view. The foreseeable introduction of AI in medical practice together with the generation of a large amount of high-quality imaging data poses radiology as a key player in precision medicine , 49.To this end, two groups of AI-based techniques can be mentioned: radiomics, which relies on ML, and DL systems  . RadiomiWe can speculate that the ultimate goal of both techniques is the combination of radiological data with clinical and laboratory data and potentially other -omics, to develop more accurate predictive models that incorporate a wider spectrum of disease-related features , 53.data acquisition and normalization.The methodological process has been classically divided in distinct phases . The presegmentation of region of interest or ROI). In most radiomics studies on liver diseases, the segmentation is performed by a radiologist as manual segmentation. Alternatively, the identification of ROI can be done by computer analysis through specific algorithms (automatic segmentation), with an optional input provided by the radiologist (semiautomatic segmentation). From the defined ROI, quantitative data are subsequently extracted (feature extraction). Informative data include both manual engineered features, like patterns of intensity, texture and shape, and abstract DL features. Of all the quantitative features extracted from the ROI, only the most informative will be retained, after feature selection through computational methods. Selected features are then fitted in a specific model of analysis. While a single modeling technique is often used, multiple-modeling methodology must be preferred to limit effects on prediction performance. Internal and possibly external validation of the model should be performed to avoid overfitting the model . The last and most important step of radiomics is to correlate the selected characteristics with the outcomes of interest to better characterize the disease . Recently, the radiomics quality score (RQS) has been proposed to evaluate if a radiomic study matches with defined quality criteria in all steps .Another important issue that limits the broader adoption of AI algorithms is the lack of interpretability, which is the gorithms   using iron-corrected T1 (cT1) relaxation maps provides an accurate, non-invasive quantitative biomarker of liver fibrosis and inflammation, recent works have shown that mpMRI, when applied to AIH patients, has a better performance in detecting residual disease activity than serological biomarkers , 69. MorIn conclusion, future implementations of radiomics and DL systems have the potential to improve our comprehension of the complexity of AiLDs. Looking forward, there are several potential future developments. Novel biomarkers can be investigated and validated; they may play a role as diagnostic, prognostic, and predictive tools to assist current clinical practice and clinical trials. Correlations of radiomic features with molecular parameters can enhance their contribution and potentially shed light on novel disease sub-phenotypes.From a genetic perspective, AiLD are complex traits ; in otheIn this paragraph, we focus our attention on the possible applications of ML on GWAS data, since they represent by far the largest data already available in this field.ML is considered a complementary tool in population genetics, where several methodological hurdles need to be overcome. Research in population genetics has mostly focused on the formalization and validation of statistical models that describe patterns of variations and their application to experimental molecular data . While cIn population genetics, the output could be represented by the status (case or control) or a continuous phenotype , and the features are the individual sample genotype data . Data fevia an unsupervised approach.The research question should be clear: does one want to predict outputs or to interpret data? The generative approach builds a model for two classes in a supervised manner, while the discriminative approach focuses only on separating them The main application of supervised ML in population genetics is to build a model to classify cases and controls based on SNPs. This approach has the research aim to leverage AI to create a feature ranking of the most significant genetic variants that are inherently specific for the disease of interest and to create a polygenic model that can complement PRS. Moreover, ML can incorporate other relevant information such as sex to create hybrid models that can risk stratify the genetic liability already at birth. A caveat that should be carefully considered is that only “pure” phenotypes should be included as cases, to avoid confounding.vs advanced disease, onset at younger age vs older age, positivity for specific autoantibodies). In this way, a predictive model is generated, taking advantage of the different contribution of variables within the training genotype data (Another possible application of supervised ML in AiLDs could be to identify novel predictive features (SNPs) associated with phenotype, possibly looking at biologically distinct sub-phenotypes of the disease  predict the outcome (the phenotype y). y can be a quantitative measure  or a binary outcome (case vs. control), so that linear or logistic models should be used, respectively.ML can also be helpful for studying epistasis, a so far neglected topic that may account for part of the missing heritability . Epistasvia an interaction with another locus, it turns out that a model that incorporates interaction may increase the power to detect the effect of the locus of interest to the phenotype. For example, if the locus of interest is A, it may be more interesting to compare a model where the effects of locus A and B, and their interactions are included in a model where all terms (either main or interaction) involving locus A are removed.If a locus of interest does have an influence on the phenotype and this happens Yet, in GWAS, several loci of interest should be investigated. The simple way is an exhaustive search of all possible pairs of loci studying interactions for each couple (two-locus interaction) or replicating the three degrees of freedom test iteratively. It is evident that a multiple testing issue arises and corrections should be employed, but this leads to the detection of only huge epistatic effects . Computaper se (see above). Another advantage is that there is a long line of research in computer science for problems like feature selection and data mining. For example, the Random forest method builds a tree-fashioned model measuring the effect of each SNP both individually and through interactions, generating a feature ranking, as compared to the list of p-values provided by PLINK, i.e., the gold standard software used in GWAS studies . Another strategy is to use ML, which does not demand a marginal effect in place. ML can overcome the obstacles encountered by traditional regression-based methods since it works without a prespecified model and explores different models to search for the most computationally efficient one, avoiding a comprehensive research . From a  studies , 90. Sin studies . An impo studies , 88.Overall, the investigation of gene–gene interactions has represented a methodological challenge for long. There is high expectation that AI-based pipelines can solve at least some issues, even though it is likely that large amounts of data will be required to fully recapitulate the network of gene–gene interactions.ML represents a potent tool for analyses of data derived from high-throughput sequencing. As for other scientific fields, the possible applications of ML can be (1) generation of models for classification; (2) clustering of individuals in groups; and (3) feature selection. In this section, we provide several examples of these different applications.The field of AiLD is still at the dawn of the big data era. Gene expression, proteomic, and metabolomic data come mainly from peripheral blood and single cohorts. More specifically, a recent study has shown that ML can discriminate between AIH and healthy controls based on gene expression profiles . A similhttps://opendata.lifebit.ai/) , epigenetic (DNA methylation and histone modifications), transcriptomic , proteomic, and metabolomic, with many others emerging . There ibit.ai/) . Despitebit.ai/) .Integrative multi-omics is a rapidly growing field within systems biology . Multi-ointerceptive medicine, which is the early interception of disease based on more accurate cellular and molecular diagnostics. In other words, the idea behind interceptive medicine is to combine several breakthrough technologies such as single-cell sequencing and DL to track the process of the disease in an unprecedented way, highlighting potential druggable pathways that are significant in the early phase of the disease before the fibrotic process occurs (for inflammatory disease) or the disease spreads throughout the body (for cancer). . Figure 3cancer). .The LifeTime initiative aims to develop cutting-edge pipelines that incorporate different single-cell technologies, to investigate cellular heterogeneity, and spatial molecular information, to better define the location of disease cells within the tissue. This information will be collected longitudinally by small/liquid biopsies and integrated with EHRs of patients. Organoid models derived from healthy and diseased individuals will represent the experimental arm of the pipeline. It is self-evident that the amount of information generated will need dedicated bioinformatics skills and infrastructures and, ultimately, ML. One of the envisioned final goals is the adoption of AI-based systems that will aid clinical decisions in everyday practice. This project identified five pillars: cancer, neurological disease, infections, cardiovascular diseases, and CIDs. As regards the latter ones, where AiLDs fall within, the urgent target is the investigation of cellular heterogeneity and how this influences disease course and differences in treatment response.a systems medicine approach to chronic inflammatory diseases) consortium , while its path towards the emulation of System 2 (that leads humans to deliberate and make conscious decisions) is still ongoing.Following Daniel Kahneman’s classification of human intelligence , Bengio interpretability, may be of particular interest in the field.This is why many researchers focus on the development and on the extension of rule-based modeling techniques, so that each prediction is motivated by the rule(s) determining it. This is the case, for instance, of decision trees  and the DL has outstanding potential, but it is challenging to interpret DL systems and translate them in clinical practice. The lack of explainability makes it difficult to identify and tackle biases present in training sets. Most importantly, the research aim should be clear. If the goal is automation and technological aid to humans, lack of interpretability is probably much less important. Yet, when models are devised to make predictions that can change medical decisions, it is ethically difficult to accept a model that cannot be tracked in its work , 108. FuThe encounter between ML and the biomedical field forces both specialists to learn something that is not typical of their domain . If clinOverall, we acknowledge that these statements could be outpaced by changes in the field, which is moving rapidly and may pose new challenges, making these ones rapidly outdated.High expectations behind AI do exist ; yet, weWe mentioned the need for data standardization and explainability of models. It is also worth mentioning the issue of having a diverse dataset to have more generalized models; similarly to what is known in population genetics, where most of GWAS have been performed in populations of European origin, under-representing most of the other ancestries , there iReproducibility of ML studies is a hot topic. There is evidence that the majority of studies do not follow a rigorous and consistent methodology , 113. ReYet, the field should also start focusing more on clinical deployment rather than doing only retrospective analysis for validation purposes . DigitalWhile AiLDs will share general hurdles for the implementation of AI in medicine, there are also disease-specific obstacles. We believe that AiLDs share with other rare conditions the difficulty in having sufficient sample sizes for these data-hungry new methodologies; despite the birth of worldwide consortia, the field will never reach the numbers of cardiovascular or cancer specialties. The wide heterogeneity of phenotypes seen in the clinic represent another peculiar obstacle for supervised approaches, which require clear-cut phenotypes as outputs. On the other hand, unsupervised learning techniques, although much awaited in AiLD for their capacity to detect patterns within data and pinpoint sub-phenotypes that are only intuitively noticed by clinicians, need bigger samples than supervised ones.Overall, we are at a unique juncture in the history of medicine, with new technological avenues bringing together new challenges.The application of ML to big data in medicine, and more specifically in AiLDs, is challenging. Explainability of AI will be probably required by regulatory agencies to support systemic approaches and to accept their future translation into clinical protocols and pathways . AnotherNevertheless, despite all these potential obstacles to the application of AI in biomedicine, we should realize that this process could revolutionize the way we diagnose and treat our patients, which is the ultimate goal of translational research. Successful implementation will require investment in healthcare workforce education and technological infrastructures, together with involvement of patients and the creation of a culture of innovation and learning. To do so, we advocate that health services together with regulatory bodies should create a robust framework able to leverage the opportunities and address all the challenges that AI provides.The first draft of the manuscript was prepared by AG with the support of MS, LC, and DV. CM has created and processed all figures. All authors contributed to manuscript revision, and read and approved the submitted version.This research was partially supported by the Italian Ministry of University and Research (MIUR) - Department of Excellence project PREMIA (PREcision MedIcine Approach: bringing biomarker research to clinic).Alessio Gerussi, Miki Scaravaglio, Laura Cristoferi, Pietro Invernizzi, and Marco Carbone are members of the European Reference Network on Hepatological Diseases (ERN RARE-LIVER). This work has been supported by grants of the Italian Ministry of Health in the role of auto-reactive hepatic natural killer cells in the pathogenesis of primary biliary cholangitis (PE-2016-02363915) and in the biocompatible nano-assemblies to increase the safety and the efficacy of steroid treatment against liver inflammation (GR-2018-12367794). The authors thank AMAF Monza ONLUS and AIRCS for the unrestricted research funding.Author DV is employed by Rulex.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This study introduces a step-by-step, summarized overview of direct methanol fuel cell (DMFC) fundamentals, thermodynamic–electrochemical principles, and system evaluation factors. In addition, a parametric investigation of a JENNY 600S DMFC is conducted to simulate cell performance behavior under varying operating conditions. The system is mathematically modeled and solved in MATLAB and accounts for multi-irreversibilities such as the activation and ohmic and concentration overpotentials. The performance of the modeled system was validated against theoretical and experimental results from the literature. The results indicated that increasing the fuel cell’s operating temperature yields enhanced output cell voltages due to enhanced methanol oxidation reactions. Nevertheless, the maximum efficiency limits of the fuel cell tend to decrease with an increase in temperature. In addition, the model has also depicted that enhanced output cell voltages are associated with increased oxygen consumption, resulting in the lower exit flowrates of the reactants. The world’s population has been increasing rapidly, and according to the United Nations, it is estimated to reach approximately 10 billion in the next 30 years, as depicted in Consequently, this brought rise to electrochemical generators, such as fuel cells, for clean energy generation, as they are regarded as a better and more feasible alternative. Sir William Robert Grove, a Welsh inventor, physicist, and patent lawyer, hypothesized a reverse electrolysis process that could be used to generate electrical power. In 1893, his hypothesis was deemed successful upon building an electrochemical device known as a fuel cell. It is a green technology where hydrogen (fuel) is supplied from the anode side to a catalyst layer where it decomposes to form protons and electrons. The protons migrate to the cathode side through a proton exchange membrane, which serves as an electrolyte that acts as a barrier between two electrodes and an ion-conductive medium . The eleOne of the most crucial components of any fuel cell is the membrane electrode assembly (MEA), which includes a polymer membrane, electrodes (anode and cathode), a gas diffusion layer (GDL), and a catalyst layer (CL). Considerable research efforts are devoted to the development of MEAs because they are highly susceptible to degradation. In addition, electrolytes and bipolar plates are also important components of a fuel cell. Each of these components performs a different role, and together, they facilitate the generation of electrical power. Most fuel cells are maintenance-free due to the simplicity of the fuel cell structure and their long-lasting components, such as the electrodes, which are not consumed during the electrochemical reactions that occur. There are numerous types of fuel cells, as evident in 3OH) as fuel and a proton exchange membrane as the electrolyte. Currently, a significant portion of the global DMFC market is controlled by four major companies: SFC Energy AG, Oorja Corporation, Fujikura Ltd., and Siqens. As evident in A direct methanol fuel cell (DMFC) is a type of fuel cell that uses liquid methanol –(3). In general, the methanol fuel is oxidized in the presence of water at the anode catalyst layer, thus releasing electrons and protons during this process, as shown in Equation (1). The electrons then migrate through an external circuit to the cathode, whereas the protons are transported through the electrolyte membrane to the cathode. The protons and electrons then react with the oxygen supplied at the cathode catalyst layer to produce water, as shown in Equation (2). Anode:Cathode:Overall reaction:DMFC systems can be categorized as active, semi-passive, or passive depending upon the supply mode of the fuel and the oxidant. Active DMFCs, for instance, make use of pumps, sensors, heaters, fans, and various humidity components to facilitate the accurate control of the fuel and oxygen supply, thus producing the highest performance. Passive DMFCs, on the other hand, do not rely on auxiliary components for the supply of fuel/oxidant but instead depend on direct transportation from methanol tanks and ambient surroundings to the membrane electrode assembly with the aid of gravity and concentration gradients . This ca2). Even though it is regarded as a very stable gas and is essential for the survival of most living organisms and cycles within the ecosystem, it can accumulate in the atmosphere for long periods of time. Indeed, the increasing amount of fossil fuel being incinerated within factories, automobiles, etc., has accelerated the rate of CO2 emissions beyond acceptable natural levels, thus adversely affecting the global environment, as previously discussed. In addition to environmental concerns, the cell’s CO2 generation can have undesirable effects on the operation of a DMFC and hinder its performance. In other words, it introduces the risk of CO2 crossover through the electrolyte membrane due to pressure gradients between both electrodes. It has been proven that this crossover rate can increase with an increased current density due to higher CO2 production [2 release from the anode side more difficult, thus encouraging its crossover to the cathode and escaping into the environment. To overcome this hurdle, fuel cell systems can be equipped with CO2-capturing mechanisms. Such mechanisms allow for the separation of CO2 from gas streams, which also facilitates its purification for subsequent use [DMFCs, as previously mentioned, have several advantages over rechargeable batteries. Nevertheless, their commercialization has been hindered due to a few technical limitations. Methanol crossover is one major limitation that such fuel cells suffer from, which has ignited widespread attention in terms of the implemented research efforts to tackle this problem. This is a phenomenon that occurs due to the inherent water transport property of the Nafion membrane (manufactured by The Chemours Company in the USA) and the miscibility between methanol and water . This caoduction . This phuent use .2O4), zinc oxide (ZnO), and palladium–tin (IV) oxide composites (Pd/SnO2). Nevertheless, these catalysts are active for MORs only within alkaline conditions [The performance of any fuel cell highly relies on the type and performance of the various components that constitute the technology. For this reason, this section aims to provide a brief overview of the various types of components and materials of a typical DMFC. Electrodes, for instance, are essential components in any type of fuel cell. Electrodes are mainly made of porous carbon fibers. In general, carbon-based materials are commonly utilized as electrode materials owing to their long life cycles, high electrical conductivity, chemical stability, cost, and ability to operate under oxidizing media . In addinditions .Recently, nanostructured carbons, including carbon nanotubes, carbon nanofibers, and ordered mesoporous carbons (OMC), have been exploited as new carbon support structures for DMFC catalysts to enhance their catalytic activity. Among the various nanostructured carbon supports, OMCs are inducing widespread attention as support materials for DMFCs due to their high surface area, uniform mesopores, and high thermal and chemical stabilities . Apart fApart from a catalyst’s activity, its stability, durability, and cost are also imperative to account for. The synthesis process of the catalyst system should be simplified with inexpensive agents and suitable methods for mass production. Recent advances in catalysts show significantly improved MOR activity and durability, which has increased the potential commercialization of DMFC applications . DespiteIt should be stable under the cell’s operating conditions;It should possess high proton conductivity;It should have low permeability for methanol.Fuel cell efficiencies and power densities strongly depend on the conductivity of the employed electrolyte. DMFC applications mandate the utilization of acidic electrolytes to aid in the rejection of carbon dioxide. The electrolyte should acquire three main properties in a DMFC, which are as follows :It shoul2SO4, HClO4, and H3PO4;Inorganic acids, such as HSuperacids, such as trifluoromethanesulfonic acid (TFMSA);32−/HCO3−;Buffers, such as COAlkaline electrolytes, such as KOH.A proper choice of electrolyte can reduce methanol crossover, enhance electrochemical reactions, and lower the cell’s overall cost. It also plays a major role in determining the fuel permeation rate and choice of catalyst. Proton-conducting electrolytes have been preferred over alkaline electrolytes for several decades for practical reasons, e.g., to avoid carbonation and corrosion and acquire better electrolyte management . In addiHowever, due to extremely high methanol crossovers, this concept has been abandoned in favor of using Nafion.Significant progress in electrolyte development for DMFCs has been made; nevertheless, it is difficult at present to identify the best membrane. This is because the choice of a proper membrane stems from several considerations, including device application, range of operating conditions, and costs. One commercial membrane that offers a suitable compromise among the various requested properties is produced by Polyfuel . The PolIn a DMFC, bipolar plates separate the reactant gases from adjacent cells, electrically connect the cells, and act as a support structure. The bipolar plates have reactant flow channels on both sides, forming the anode and cathode compartments of unit cells on opposite sides of the bipolar plate. Most bipolar plates are made of stainless steel or graphite. Stainless steel plates are heavy components for portable power systems. Solid graphite plates are highly conductive, chemically inert, and resistant to corrosion; however, they are expensive and costly to manufacture . Flow chThe GDL enables the reactant gas to diffuse its way to the active layer . They are mainly fabricated from pure porous polytetrafluoroethylene (PTFE), where PTFE suspensions are mixed with sugar or ammonium carbonate. The mixture is then heated at elevated temperatures to induce bubbles that result in the formation of porous PTFE films . It is iIn order to investigate the impacts associated with all the aforementioned fuel cell components and be able to further develop them, characterizing the performance of a fuel cell that employs them is imperative, as it shall serve as an indicator of the feasibility of the different components. Power efficiency is a major performance metric that quantifies the performance of any fuel cell based on its electrical power output. Determining this performance metric within DMFC applications is challenging due to fuel crossover and the complex cell architecture of fuel circulation. Notably, a semiempirical model has been developed to evaluate the efficiency of a DMFC under various conditions . In thatThis case study is based on a DMFC manufactured by the SFC Energy Company. In general, the products of the company serve a wide range of applications within clean Energy and mobility, defense and security, and the oil and gas sectors. The investigated DMFC is commonly referred to as JENNY 600S. It utilizes liquid methanol as a fuel and weighs 1.7 kg. Consequently, the developed fuel cell system facilitates around 80% weight reduction as it limits the number of spare batteries that would have otherwise been required to power electrical-based applications. In addition, the system produces a nominal power of 25 W and has high energy with the capability of operating for up to 72 h with the use of only 3–4 350 mL cartridges. Using the technical data shown in The volumetric power density, on the other hand, can be obtained by dividing the fuel cell’s output power by the system’s total volume.Although the gravimetric and volumetric power densities are simple to calculate, they are also very descriptive. Thus, these two characteristics are normally used for power comparisons between systems.To study any type of fuel cell, Gibbs free energy calculations are essential. Gibbs free energy can be defined as the energy available to perform external work, neglecting any work performed by changes in pressure and/or volume. Since work performed by a change in volume/pressure between the input and output is not captured by the fuel cell, Gibbs free energy is ideal for fuel cell analysis. The change in the Gibbs free energy of the formation provides us with the required energy released from the system. This change is the difference between the Gibbs free energy of the products and reactants.Using the net DMFC reaction,The molar enthalpy The molar entropy T is the operating temperature of the DMFC.The molar Gibbs free energy of formation F is the Faraday constant, and z is the number of electrons per mole of fuel. In DMFCs, z is equal to six since one mol of methanol fuel provides six electrons.In fuel cells, if there are no losses in the fuel cell or if the process is reversible, then all the Gibbs free energy is converted into electrical energy. Therefore, the Gibbs free energy values are used to calculate the reversible theoretical voltage of the fuel cell, as shown below:Since fuels are usually burned to produce energy, the efficiency can be determined by comparing the electrical energy produced from the fuel cell with the heat that would be produced by normally burning the fuel. The enthalpy of combustion is taken to be the same as the change in the enthalpy of formation that was previously calculated. However, the energy produced by the fuel cell can be effectively taken from the change in the Gibbs free energy. The maximum efficiency limit is also known as the thermodynamic efficiency and can be calculated as shown below:I is the operatic electric current of the fuel cell, and n is the number of cells within the DMFC system. It is important to note that the JENNY 600 DMFC system utilizes 32 cells.One way of characterizing the performance of any fuel cell is to measure the inlet and exit flowrates of the oxidizing agent, fuel, and water and their rate of consumption within the system. The equations represented below aid with calculating the aforementioned parameters. To facilitate the formulations of the mass flowrates, it is imperative to calculate the output voltage  and (15), respectively.The rate of methanol consumption and water production can be calculated using Equations (17) and (18), respectively.Apart from the aforementioned flowrates, polarization curves are another effective way of characterizing the performance of a fuel cell by gaining insight into its voltage losses. These losses can be classified as activation losses, crossover losses, ohmic losses, and concentration losses. The activation losses are accounted for by utilizing Tafel’s equation .The equation below enables the calculation of the actual cell voltage, which accounts for all of the aforementioned voltage drops.This section commences by highlighting a few general performance characteristics of the investigated DMFC system and then proceeds to highlight the various irreversibilities that impede the performance of the fuel cell and how the performance of the fuel cell is impacted by varying its operating temperature.The variation of Gibbs free energy with temperature is plotted in The reversible cell voltage was plotted against temperature and Gibbs free energy change in formation, as shown in The maximum efficiency of the DMFC system was investigated at different operating temperatures, as shown in 2). Generally, concentration losses are usually more dominant under higher current densities [2), the output cell voltage is almost 0 V owing to the fact that the irreversibilities are highest at this operating current density.The variation in air inlet and air exit flowrates, as well as the consumption rates of oxygen, can be investigated by varying the cell’s operating voltage, as shown in ensities . The figTo ensure the results conform to the results in the literature, the actual output cell voltage, which takes into account the aforementioned irreversibilities, was validated against the output cell voltage curve from another study, which modeled a passive DMFC to evaluate its performance , as showThis study provides a concise overview of DMFCs and investigates the performance of a DMFC used for military applications known as JENNY 600S. The analysis included computing the basic performance characteristics of the fuel cell, such as the gravimetric and volumetric power densities of the cell; Gibbs free energy at various different operating temperatures; and its correlation with various other parameters. Moreover, the theoretical reversible voltage was calculated, its relationship with temperature was investigated, and an inverse relationship was evident. The results showed that as the temperature increases the theoretical voltage decreases owing to the decrease in Gibbs free energy with the increasing temperature. Furthermore, the efficiency limit of the fuel cell was examined with respect to temperature, and it was confirmed that the efficiency limits of the DMFC drop with increasing temperature. In addition, the inlet and exit flowrates of air and oxygen were obtained for the examined fuel cell. The results showed that the exit mass flowrate of air was always lower than that supplied due to the consumption of oxygen during electrochemical reactions. Finally, the polarization curve was plotted to examine the various overpotentials independently and their effect on the output cell voltage at different temperatures. It was evident that increasing the temperature of the fuel cell enhanced the output voltage owing to enhanced methanol oxidation reactions and lower activation overpotentials, which also resulted in the fuel cell operating at increased current densities.


A 43-year-old female patient had a necrotizing soft tissue infection in the temporal region. Because of the necrotic temporoparietal fascia, auricular reconstruction was attempted using the temporalis muscle flap; the flap was successfully placed. The use of the temporalis muscle flap was considered a treatment option for salvaging the auricle. Necrotizing soft tissue infection (NSTI) is a rapidly progressive cutaneous soft tissue infection with a high mortality rate. It most commonly occurs in the perineal area and lower extremities ,2 but raA 43-year-old woman presented to our department with chief complaints of pain and skin ulceration around the right auricle. At 13 days before presentation, the patient was diagnosed with herpes zoster at the first branch of the right trigeminal nerve.Streptococcus aureus. Antibiotic treatment was started with meropenem and daptomycin. Meropenem was administered for 6 days and daptomycin for 4 days. This regimen was subsequently de-escalated to cefazolin. Antibiotics were administered for 22 days, at which point acute signs of infection had subsided.During the first hospital visit, skin and subcutaneous tissue necrosis and pus drainage were mainly observed on the posterior surface of the right auricle. The right auricle and the surrounding temporal area were erythematous and swollen . Vital sAn emergency debridement was performed. The necrotic skin was excised, and additional skin incisions were made so that the necrosis of the subcutaneous tissues could be confirmed. The necrotic subcutaneous tissue and temporal fascia were excised one layer at a time. Visually, the temporalis muscle did not appear to have obvious necrosis. Furthermore, the upper portion of the auricle was detached by debridement, and a color change occurred due to insufficient blood flow. To salvage the auricle, the temporalis muscle flap was elevated above the periosteum and inverted to cover the auricle . A seconAt 66 days after the first debridement, the patient underwent reconstructive surgery by creating an auriculotemporal sulcus and skin grafting. The upper auriculotemporal sulcus region was incised, the VY advancement flap was created in the auricular region, and the exposed auricular cartilage was covered by the flap. The ulcer surface of the temporal region was covered with a split-thickness skin graft from the parietal region . The patNSTI has been classified in various ways based on the pathology and site of lesions, and the superficial fascia is often the main site of infection and necrosis, known as ‘necrotizing fasciitis’ . HoweverOnly a few reports of NSTI in the auricle and temporal region have been reported. In the head and face, reconstruction is difficult due to the limited skin and soft tissues. In the present case, the infection extended to the superficial and deep temporal fascia, which were necrotic. The fascia has lesser blood flow than the muscle and is more likely the primary site of infection; however, because the fascia is a tough tissue that simultaneously protects the muscle, the underlying muscle may be uninfected. Hence, although the infection was severe that the upper part of the auricle was detached, the temporalis muscle was protected by the superficial and deep temporal fascia, and the infection did not extend to the temporalis muscle.Generally, the auricular region reconstruction often involves the use of local skin flaps made from the adjacent skin, temporoparietal fascia, or mastoid facia . TherefoThe scalp reconstruction methods for exposed cranial bone include local skin flaps, tissue expander techniques, and, in extensive cases, free flap using the latissimus dorsi muscle, or the greater omentum may also be an option . Two-staWe have described a case of NSTI around the right auricle in which half of the right auricle was detached from the temporal region by debridement; however, the auricle was salvaged by creating the temporalis muscle flap. The auricular reconstruction was significant not only in terms of appearance but also in function, such as the ability to wear a mask. In cases of severe infection, the temporalis muscle flap may be an option for salvaging the auricle as much as possible.Consent for publication was obtained from the patient.


Implementing competency-based medical education (CBME) in post-graduate medical education (PGME) is a complex process that requires multiple systemic changes in a complex system that is simultaneously engaged in multiple initiatives. These initiatives often compete for attention during the implementation of CBME and produce unintended and unanticipated consequences. Understanding the impact of this context is necessary for evaluating the effectiveness of CBME. The purpose of the study was to identify factors, such as contexts and processes, that contribute to the implementation of CBME.n = 16) and faculty , and semi-structured interviews with program directors (n = 18), and program administrators (n = 12) from 2018 to 2021. Data were analyzed using a template analysis based on a coding framework that was developed from a sample of transcripts, the context-mechanism-outcomes framework for realist evaluations, and the core components of CBME.We conducted a realist evaluation using data collected from 15 programs through focus groups with residents  concurrent to, but separate from, the implementation of CBME. This context influenced participants’ participation, communication, and adaptation during CBME implementation, which led to change fatigue and unmet expectations for the collection and use of assessment data.Rival initiatives, such as the concurrent implementation of a new CMS, can have an impact on how programs implement CBME and greatly affect the outcomes of CBME. Mitigating the effects of rivals for attention with flexibility, clear communication, and training can facilitate effective implementation of CBME.The online version contains supplementary material available at 10.1186/s12909-022-03661-8. In 2017, specialty training programs in Canada began a staged implementation of a hybrid version of competency-based medical education (CBME) called Competence by Design (CBD) –7. TheseImplementation evaluations provide valuable insight into how contexts and processes contribute to outcomes , but manWe conducted a realist evaluation of the implementation of CBD from 2018 to 2021. The study was approved by the UM Health Research Ethics Board (REB Study H2017:313). All methods were carried out in accordance with relevant guidelines and regulations.Realist evaluations start with the assumption that it is not an intervention that works, but the participants’ engagement with an intervention that produces particular outcomes. The focus is on what works for whom, under which circumstances, and why , 15, 16.All UM specialty training programs who implemented CBME from 2017–2020 were invited to participate in the realist evaluation. Fifteen of the eligible 32 programs agreed to participate. Participants were recruited from these programs and included residents, faculty members, program directors (PD), and program administrators (PA).n = 16) and faculty members , and semi-structured interviews with PDs (n = 18 with 11 different PDs), and PAs (n = 12 with 6 different PAs). The research team developed and revised the interview and focus group guides to reflect the goals of each realist evaluation phase including the specific contexts, mechanisms, and outcomes we explored during each phase  theory gleaning 2018–2019); 2) theory refining (2020); and 3) theory consolidation (2021) in order to test and refine our program theory [–2019; 2)We conducted a template analysis of the interviews and focus groups . During The research team has a variety of experience and expertise that affected how we collected, analyzed, and interpreted our data. CR, BC, and JH are PhD-trained researchers who brought experience with conducting qualitative research and program evaluation in medical education to data collection and analysis. Additionally, CR and JH are involved in the CBME steering committee in which CR leads a CBME evaluation working group and JH offers educational support to programs. MKC and GS are educational leaders and clinicians in programs that had begun preparation but not yet launched CBME during the data collection period. They brought their experience with implementing educational innovations, facilitating faculty development, and teaching in specialty training programs to the interpretation of data.Through our iterative analysis of interviews and focus groups, we identified how the implementation of CBD was affected by multiple concurrent initiatives, which rivaled for the attention of residents, faculty members, and staff. These concurrent initiatives included new PGME accreditation standards, organizational changes in the healthcare system, and from 2020 onward, changes associated with the COVID-19 pandemic; however, the biggest rival initiative was the concurrent implementation of two new curriculum management systems (CMS) early on in the staged implementation of CBD. Participants consistently identified the new CMS and described mechanisms and outcomes that directly impacted the implementation of CBME.rivalry for attention that affected the implementation of CBME in multiple ways (Table The UM’s first cohort to launch CBME also implemented a new CMS, ePortfolio , at the ys Table . One wasparticipation in new CBME practices. For example, some programs reported that some faculty members did not know their university e-mail addresses so were unable to register and login to the new CMS and, as a result, were not completing assessments. The impact was greatest during the first two years of implementation when there were two rapid changes in CMS, the current CMS requires a university e-mail address to register, and other technical limitations or poor communication of changes to the ongoing development of the CMS. Since the current CMS is a faculty-wide initiative, common challenges associated with learning a new software program decreased in the following years as all residents, faculty members, and administrators gained more experience with the CMS even prior to launching CBME. However, the software has continued to change and develop and access issues remain, continuing to impact faculty members’ and residents’ use of the CMS to engage in CBME.We identified three key mechanisms, or actions and reactions, in response to the concurrent implementation of new CMS: participation, communication, and adaptation Table . During communication about the implementation of CBME was dominated by topics related to the new CMS. In particular, faculty, staff, and resident development initiatives focused on how to gain access to and use the CMS, turning people’s attention away from other important CBME-related topics. PDs and PAs reported providing large group sessions on the CMS in preparation before the launch of CBME and then following up individually with faculty members over the first several months post-launch. Following the first year of implementation of the current CMS, the focus on CMS training in preparation for CBME lessened for programs whose faculty members gained experience using the system with non-CBME residents and with residents from other CBME programs. However, the focus on CMS training remained for programs with small numbers of residents and who do not often host off-service residents.In addition, adaptation became an important and common approach to navigating the limitations of a developing CMS. In response to faculty members’ challenges with accessing the CMS and to reduce other barriers to its use, some residents initially began printing off assessments for faculty to complete and for PAs to upload manually. This approach was further adapted when residents began bringing mobile devices to encourage faculty members to complete assessments immediately following an observation. Additionally, the CMS initially lacked easy methods for tracking completion of assessments and for developing useful reports on resident progress for competence committees. Many individuals and programs developed additional methods for tracking and reporting progress outside of the CMS to fill in this gap.Finally, change fatigue due to the concurrent implementation of two new CMS and CBME. Change fatigue was further exacerbated by the increased workloads brought on by learning a new CMS, training others to use the CMS, and developing workarounds to address the limitations of the CMS. This focus on the new CMS, particularly for the first two cohorts to implement CBME, often led to the conflation of the CMS with CBME.The concurrent implementation of a new CMS and resulting mechanisms contributed to the outcomes of the implementation of CBME Table . Severalunmet expectations for the collection and use of CBME assessment data. Programs reported that they were not collecting as much assessment data as expected, both in terms of quantity and quality. This deficiency was largely due to faculty members’ delayed interactions with the CMS either because of challenges with accessing the CMS or limitations for tracking completed assessments. As a result of delayed or incomplete assessments, residents’ engagement in CBME practices, particularly their ability to learn from assessment, impeded their learning experience. However, even though the expectations for the quantity and quality of resident data had not been met, programs reported that the data they do collect has helped the competence committees to earlier identification of problems with resident performance sooner than in the previous time-based model.Second, as a result of the ways that people reacted to the concurrent implementation of a new CMS, programs reported Some outcomes of the implementation of CBME, namely change fatigue and unmet expectations, were heavily influenced by the context of multiple concurrent initiatives that rivaled for participants attention, primarily the concurrent implementation of two new CMS, that generated the mechanisms of participation, communication and adaptation. Further, we found that the mechanisms and outcomes related to rivalries for attention are interconnected and do not lend themselves to discrete CMO configurations as depicted in Fig. The findings demonstrate how a simultaneous activities and initiatives can rival for the attention of residents, faculty members, and staff when implementing CBME, and in particular, how a new CMS can rival for people’s attention when implemented concurrently with CBME. Our initial program theory focused on the contexts and mechanisms within the specialty training programs, but our analysis suggests that some of the outcomes of the implementation of CBME are influenced by the contexts of the academic health sciences system. This context includes the decisions that affect CBME implementation but are not exclusive to CBME, such as the concurrent implementation of a new CMS. This concurrent implementation affected how people participated in new CBME practices, changed communication strategies as programs prepared for and launched CBME, and prompted adaptations to mitigate limitations of the CMS and facilitate CBME practices. Residents, faculty members, and staff experienced change fatigue as they navigated multiple new ways of working. Additionally, expectations for CBME to improve the information about resident progress and the learning experience were challenged by ongoing changes and limitations of the new CMS.The influential role of CMS on CBME has also been identified in previous research. CMS are a key factor in the success of CBME, particularly for completing, tracking and reporting assessment data , 22–24. There are a number of ways to mitigate the challenges imposed by implementing a new CMS for CBME. First, developing a timeline for implementing the new CMS that allows ample time for training and piloting the new CMS prior to the launch of CBME can reduce potential change fatigue, identify its limitations in low stakes circumstances, and clarify the boundaries between the CMS and CBME. Second, developing a clear communication strategy for changes to the CMS and comprehensive CMS training and technical support that is separate, yet complementary to CBME-specific faculty, resident, and staff development initiatives can help alleviate the burden on PDs and PAs to support teachers and residents as they navigate the new practices associated with CBME, 23, 24.More broadly, while CBME is intended to provide greater standardization in medical training and social accountability, embracing flexibility and contextualization during implementation can help programs navigate the challenges associated with concurrent initiatives that are expected, such as changes to accreditation standards, and unexpected, such as the COVID-19 pandemic . AdditioThere are several strengths and limitations of our study. The strengths of the study are that the realist evaluation provided insight into the contexts and processes that result in outcomes that can be sometimes difficult to identify using other evaluation approaches. This methodology allowed us to identify novel issues that related to context that can have a profound impact on the implementation of educational innovations.There are several limitations to the study. We discuss the impact of the concurrent implementation of a new CMS and CBME at a single institution, and therefore expect there to be variation in how different concurrent initiatives impact the implementation of CBME and other major curricular changes at different locations. Our decision to rely on qualitative data also limited our ability to test the degree to which rival initiatives impacted CBME outcomes and the strength of these associations.Our realist evaluation demonstrated that the UM’s implementation of CBME for specialty training programs was greatly affected by the implementation of a new CMS that was concurrent with, yet separate from CBME. Such decisions made at the institutional level in the academic health sciences context can have a significant impact on programs and individuals’ actions and reactions when implementing CBME and can ultimately affect the outcomes of CBME.Additional file 1.