Promoting fairness in activity recognition algorithms for patient's monitoring and evaluation systems in healthcare.

Researchers face the challenge of defining subject selection criteria when training algorithms for human activity recognition tasks. The ongoing uncertainty revolves around which characteristics should be considered to ensure algorithmic robustness across diverse populations. This study aims to address this challenge by conducting an analysis of heterogeneity in the training data to assess the impact of physical characteristics and soft-biometric attributes on activity recognition performance. The performance of various state-of-the-art deep neural network architectures (tCNN, hybrid-LSTM, Transformer model) processing time-series data using the IntelliRehab (IRDS) dataset was evaluated. By intentionally introducing bias into the training data based on human characteristics, the objective is to identify the characteristics that influence algorithms in motion analysis. Experimental findings reveal that the CNN-LSTM model achieved the highest accuracy, reaching 88%. Moreover, models trained on heterogeneous distributions of disability attributes exhibited notably higher accuracy, reaching 51%, compared to those not considering such factors, which scored an average of 33%. These evaluations underscore the significant influence of subjects' characteristics on activity recognition performance, providing valuable insights into the algorithm's robustness across diverse populations. This study represents a significant step forward in promoting fairness and trustworthiness in artificial intelligence by quantifying representation bias in multi-channel time-series activity recognition data within the healthcare domain.

Ethical and regulatory challenges of AI technologies in healthcare: A narrative review.

Over the past decade, there has been a notable surge in AI-driven research, specifically geared toward enhancing crucial clinical processes and outcomes. The potential of AI-powered decision support systems to streamline clinical workflows, assist in diagnostics, and enable personalized treatment is increasingly evident. Nevertheless, the introduction of these cutting-edge solutions poses substantial challenges in clinical and care environments, necessitating a thorough exploration of ethical, legal, and regulatory considerations. A robust governance framework is imperative to foster the acceptance and successful implementation of AI in healthcare. This article delves deep into the critical ethical and regulatory concerns entangled with the deployment of AI systems in clinical practice. It not only provides a comprehensive overview of the role of AI technologies but also offers an insightful perspective on the ethical and regulatory challenges, making a pioneering contribution to the field. This research aims to address the current challenges in digital healthcare by presenting valuable recommendations for all stakeholders eager to advance the development and implementation of innovative AI systems.

Generating a novel synthetic dataset for rehabilitation exercises using pose-guided conditioned diffusion models: A quantitative and qualitative evaluation.

Machine learning has emerged as a promising approach to enhance rehabilitation therapy monitoring and evaluation, providing personalized insights. However, the scarcity of data remains a significant challenge in developing robust machine learning models for rehabilitation. This paper introduces a novel synthetic dataset for rehabilitation exercises, leveraging pose-guided person image generation using conditioned diffusion models. By processing a pre-labeled dataset of class movements for 6 rehabilitation exercises, the described method generates realistic human movement images of elderly subjects engaging in home-based exercises. A total of 22,352 images were generated to accurately capture the spatial consistency of human joint relationships for predefined exercise movements. This novel dataset significantly amplified variability in the physical and demographic attributes of the main subject and the background environment. Quantitative metrics used for image assessment revealed highly favorable results. The generated images successfully maintained intra-class and inter-class consistency in motion data, producing outstanding outcomes with distance correlation values exceeding the 0.90. This innovative approach empowers researchers to enhance the value of existing limited datasets by generating high-fidelity synthetic images that precisely augment the anthropometric and biomechanical attributes of individuals engaged in rehabilitation exercises.

Best practices for portfolio optimization by quantum computing, experimented on real quantum devices.

In finance, portfolio optimization aims at finding optimal investments maximizing a trade-off between return and risks, given some constraints. Classical formulations of this quadratic optimization problem have exact or heuristic solutions, but the complexity scales up as the market dimension increases. Recently, researchers are evaluating the possibility of facing the complexity scaling issue by employing quantum computing. In this paper, the problem is solved using the Variational Quantum Eigensolver (VQE), which in principle is very efficient. The main outcome of this work consists of the definition of the best hyperparameters to set, in order to perform Portfolio Optimization by VQE on real quantum computers. In particular, a quite general formulation of the constrained quadratic problem is considered, which is translated into Quadratic Unconstrained Binary Optimization by the binary encoding of variables and by including constraints in the objective function. This is converted into a set of quantum operators (Ising Hamiltonian), whose minimum eigenvalue is found by VQE and corresponds to the optimal solution. In this work, different hyperparameters of the procedure are analyzed, including different ansatzes and optimization methods by means of experiments on both simulators and real quantum computers. Experiments show that there is a strong dependence of solutions quality on the sufficiently sized quantum computer and correct hyperparameters, and with the best choices, the quantum algorithm run on real quantum devices reaches solutions very close to the exact one, with a strong convergence rate towards the classical solution, even without error-mitigation techniques. Moreover, results obtained on different real quantum devices, for a small-sized example, show the relation between the quality of the solution and the dimension of the quantum processor. Evidences allow concluding which are the best ways to solve real Portfolio Optimization problems by VQE on quantum devices, and confirm the possibility to solve them with higher efficiency, with respect to existing methods, as soon as the size of quantum hardware will be sufficiently high.

A deep learning system to monitor and assess rehabilitation exercises in home-based remote and unsupervised conditions.

In the domain of physical rehabilitation, the progress in machine learning and the availability of cost-effective motion capture technologies have paved the way for innovative systems capable of capturing human movements, automatically analyzing recorded data, and evaluating movement quality. This study introduces a novel, economically viable system designed for monitoring and assessing rehabilitation exercises. The system enables real-time evaluation of exercises, providing precise insights into deviations from correct execution. The evaluation comprises two significant components: range of motion (ROM) classification and compensatory pattern recognition. To develop and validate the effectiveness of the system, a unique dataset of 6 resistance training exercises was acquired. The proposed system demonstrated impressive capabilities in motion monitoring and evaluation. Notably, we achieved promising results, with mean accuracies of 89% for evaluating ROM-class and 98% for classifying compensatory patterns. By complementing conventional rehabilitation assessments conducted by skilled clinicians, this cutting-edge system has the potential to significantly improve rehabilitation practices. Additionally, its integration in home-based rehabilitation programs can greatly enhance patient outcomes and increase access to high-quality care.

Lexicon-based sentiment analysis to detect opinions and attitude towards COVID-19 vaccines on Twitter in Italy.

The paper proposes a methodology based on Natural Language Processing (NLP) and Sentiment Analysis (SA) to get insights into sentiments and opinions toward COVID-19 vaccination in Italy. The studied dataset consists of vaccine-related tweets published in Italy from January 2021 to February 2022. In the considered period, 353,217 tweets have been analyzed, obtained after filtering 1,602,940 tweets with the word "vaccin". A main novelty of the approach is the categorization of opinion holders in four classes, Common users, Media, Medicine, Politics, obtained by applying NLP tools, enhanced with large-scale domain-specific lexicons, on the short bios published by users themselves. Feature-based sentiment analysis is enriched with an Italian sentiment lexicon containing polarized words, expressing semantic orientation, and intensive words which give cues to identify the tone of voice of each user category. The results of the analysis highlighted an overall negative sentiment along all the considered periods, especially for the Common users, and a different attitude of opinion holders towards specific important events, such as deaths after vaccination, occurring in some days of the examined 14 months.

The role of sequential normothermic regional perfusion and end-ischemic normothermic machine perfusion in liver transplantation from very extended uncontrolled donation after cardiocirculatory death.

The use of pre-procurement normothermic regional perfusion (NRP) allowed us to implement controlled DCD liver transplantation with results comparable to brain death donors, but the use of uncontrolled DCD is declining due to logistic challenges and the high incidence of post-transplant complications. In Italy, the mandatory stand-off period of 20 min for DCD donors has driven the combined use of NRP and ex-situ machine perfusion with the intent to counterbalance the negative impact of prolonged warm ischemia. Organ viability during NRP is based on duration of warm ischemia, regional perfusion flow, lactate, transaminases values and histology, and those used in Italy are the widest worldwide. However, this evaluation can be difficult, especially when the acute damage is particularly severe. The use of ex-situ NRP could provide a safe organ evaluation. In the period from 06/2020 to 06/2022, all DCD grafts exceeding NRP viability criteria at a single center were eventually evaluated using ex-situ normothermic machine perfusion. Machine perfusion viability criteria were based on lactate clearance, irrespectively to bile production, unless 1-h transaminases perfusate level were not exceeding 5000 IU/L. Three cases of uncontrolled DCD grafts in excess of NRP viability criteria underwent ex-situ graft evaluation. Two matched ex-situ normothermic machine perfusion viability criteria and were successfully transplanted. Both recipients are doing well after 26 and 5 months after surgery with no signs of ischemic cholangiopathy. This experience suggests that the sequential use of NRP and normothermic machine perfusion may further expand the boundaries of organ viability in uncontrolled DCD liver transplantation.

Maternal nutritional status and offspring childlessness: Evidence from the late-nineteenth to early-twentieth centuries in a group of Italian populations.

The role of maternal nutrition in affecting offspring fertility, through alteration of foetal programming, has been demonstrated in animal-based experiments. However, results from human populations appear inconsistent and sometimes contradictory, likely because they have been based on single famine events. In this paper, we adopt a different approach. We combine official annual time series of daily nutrient availability with a sample of women's reproductive histories from the 1961 Italian Census to investigate the role of maternal nutritional status in pregnancy on offspring childlessness. The analysis therefore covers cohorts of females born between 1861 and 1939. Our results show a negative association between calorie availability in pregnancy and the odds of offspring childlessness, whereas no association is found between protein availability and offspring childlessness. The consequences of poor calorie intake were aggravated during the summer, likely due to the participation of pregnant women in physically demanding work.

A conversational agent for querying Italian Patient Information Leaflets and improving health literacy.

In the last years, the rise of digital technologies has enormously augmented the possibility for people to access health information and consult online versions of Patient Information Leaflets (PILs), enabling them to improve their knowledge about medication and adherence to therapies. However, health information may often be difficult to consult and comprehend due to an excessively lengthy and undersized text, coupled with the presence of many incomprehensible medical terms. To face these issues, this paper proposes a conversational agent as a valuable solution to simplify health information retrieval and improve health literacy in Italian by codifying PILs and making them query-able in natural language. In particular, the system has been devised to: i) comprehend natural language questions on medicines of interest; ii) proactively ask the user or automatically infer from the dialog state all the missing information necessary to generate an answer; iii) extract the answer from a structured knowledge base built from PILs of registered drugs. An experimental study has been carried out to evaluate both the performance and usability of the proposed system. Results showed an adequate ability of the system to handle most of the dialogues started by participants correctly, good users satisfaction, and, thus, proved its feasibility and usefulness.

A Novel COVID-19 Data Set and an Effective Deep Learning Approach for the De-Identification of Italian Medical Records.

In the last years, the need to de-identify privacy-sensitive information within Electronic Health Records (EHRs) has become increasingly felt and extremely relevant to encourage the sharing and publication of their content in accordance with the restrictions imposed by both national and supranational privacy authorities. In the field of Natural Language Processing (NLP), several deep learning techniques for Named Entity Recognition (NER) have been applied to face this issue, significantly improving the effectiveness in identifying sensitive information in EHRs written in English. However, the lack of data sets in other languages has strongly limited their applicability and performance evaluation. To this aim, a new de-identification data set in Italian has been developed in this work, starting from the 115 COVID-19 EHRs provided by the Italian Society of Radiology (SIRM): 65 were used for training and development, the remaining 50 were used for testing. The data set was labelled following the guidelines of the i2b2 2014 de-identification track. As additional contribution, combined with the best performing Bi-LSTM + CRF sequence labeling architecture, a stacked word representation form, not yet experimented for the Italian clinical de-identification scenario, has been tested, based both on a contextualized linguistic model to manage word polysemy and its morpho-syntactic variations and on sub-word embeddings to better capture latent syntactic and semantic similarities. Finally, other cutting-edge approaches were compared with the proposed model, which achieved the best performance highlighting the goodness of the promoted approach.

SARS-CoV-2 Infections and COVID-19 Fatality: Estimation of Infection Fatality Ratio and Current Prevalence.

COVID-19 is one of the most important problems for public health, according to the number of deaths associated to this pathology reported so far. However, from the epidemiological point of view, the dimension of the problem is still unknown, since the number of actual cases of SARS-CoV-2 infected people is underestimated, due to limited testing. This paper aims at estimating the actual Infection Fatality Ratio (number of deaths with respect to the number of infected people) and the actual current prevalence (number of infected people with respect to the entire population), both in a specific population and all over the world. With this aim, this paper proposes a method to estimate Infection Fatality Ratio of a still ongoing infection, based on a daily estimation, and on the relationship between this estimation and the number of tests performed per death. The method has been applied using data about COVID-19 from Italy. Results show a fatality ratio of about 0.9%, which is lower than previous findings. The number of actual infected people in Italy is also estimated, and results show that (i) infection started at the end of January 2020; (ii) a maximum number of about 100,000 new cases in one day was reached at the beginning of March 2020; (iii) the estimated cumulative number of infections at the beginning of October 2020 is about 4.2 million cases in Italy (more than 120 million worldwide, if a generalization is conjectured as reasonable). Therefore, the prevalence at the beginning of October 2020 is estimated at about 6.9% in Italy (1.6% worldwide, if a generalization is conjectured).

An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian.

Over the last decade industrial and academic communities have increased their focus on sentiment analysis techniques, especially applied to tweets. State-of-the-art results have been recently achieved using language models trained from scratch on corpora made up exclusively of tweets, in order to better handle the Twitter jargon. This work aims to introduce a different approach for Twitter sentiment analysis based on two steps. Firstly, the tweet jargon, including emojis and emoticons, is transformed into plain text, exploiting procedures that are language-independent or easily applicable to different languages. Secondly, the resulting tweets are classified using the language model BERT, but pre-trained on plain text, instead of tweets, for two reasons: (1) pre-trained models on plain text are easily available in many languages, avoiding resource- and time-consuming model training directly on tweets from scratch; (2) available plain text corpora are larger than tweet-only ones, therefore allowing better performance. A case study describing the application of the approach to Italian is presented, with a comparison with other Italian existing solutions. The results obtained show the effectiveness of the approach and indicate that, thanks to its general basis from a methodological perspective, it can also be promising for other languages.

The "Polonium In Vivo" Study: Polonium-210 in Bronchial Lavages of Patients with Suspected Lung Cancer.

Few studies have reported on polonium-210, a decay breakdown product of radon-222 and lead-210, in human lungs and there has been no study in patients with suspected lung cancer. The main aim of this "Polonium in vivo" study was to evaluate polonium-210 radioactivity in bronchopulmonary systems of smoker, ex-smoker and never smoker patients with suspected lung cancer. Alpha-spectrometric analyses were performed on bronchial lavage (BL) fluids from two Italian hospitals in 2013-2016. Socio-demographic, smoking, occupational and spirometric characteristics, lung cancer confirmation and histologic type and radon-222 concentration in patients' homes were collected. Seventy BL samples from never (n = 13), former (n = 35) and current smokers (n = 22) were analyzed; polonium-210 was detected in all samples from current and former smokers and in 54% of samples from never smokers (p < 0.001; median values: 1.20, 1.43 and 0.40 mBq, respectively). Polonium-210 levels were significantly higher in COPD versus no COPD patients (median value: 3.60 vs. 0.97 mBq; p = 0.007); former and current smokers, without and with COPD, had significantly increased polonium-210 levels (p = 0.012); 96% of confirmed versus 69% of non-confirmed lung cancer patients recorded detectable polonium-210 levels (p = 0.018). A polonium-210 detectable activity was measured in BL samples from all current and former smokers. Polonium-210 in the lungs could be the result of lead-210 entrapment, which, with its half-life of 22 years, could provide a continuous emission of alpha radioactivity, even many years after quitting, thus proposing a possible explanation for the onset of lung cancer, particularly in former smokers.

Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set.

The COrona VIrus Disease 19 (COVID-19) pandemic required the work of all global experts to tackle it. Despite the abundance of new studies, privacy laws prevent their dissemination for medical investigations: through clinical de-identification, the Protected Health Information (PHI) contained therein can be anonymized so that medical records can be shared and published. The automation of clinical de-identification through deep learning techniques has proven to be less effective for languages other than English due to the scarcity of data sets. Hence a new Italian de-identification data set has been created from the COVID-19 clinical records made available by the Italian Society of Radiology (SIRM). Therefore, two multi-lingual deep learning systems have been developed for this low-resource language scenario: the objective is to investigate their ability to transfer knowledge between different languages while maintaining the necessary features to correctly perform the Named Entity Recognition task for de-identification. The systems were trained using four different strategies, using both the English Informatics for Integrating Biology & the Bedside (i2b2) 2014 and the new Italian SIRM COVID-19 data sets, then evaluated on the latter. These approaches have demonstrated the effectiveness of cross-lingual transfer learning to de-identify medical records written in a low resource language such as Italian, using one with high resources such as English.

Liver transplantation in Jehovah's witnesses: 13 consecutive cases at a single institution.

BACKGROUND: Jehovah's Witnesses represent a tremendous clinical challenge when indicated to liver transplantation because they refuse blood transfusion on religious grounds and the procedure is historically associated with potential massive peri-operative blood loss. We herein describe a peri-operative management pathway with strategies toward a transfusion-free environment with the aim not only of offering liver transplant to selected Jehovah's Witnesses patients but also, ultimately, of translating this practice to all general surgical procedures. METHODS: This is a retrospective review of prospective medical records of JW patients who underwent LT at our Institution. The peri-operative multimodal strategy to liver transplantation in Jehovah's Witnesses includes a pre-operative red cell mass optimization package and the intra-operative use of normovolemic haemodilution, veno-venous bypass and low central venous pressure. RESULTS: In a 9-year period, 13 Jehovah's Witness patients received liver transplantation at our centre representing the largest liver transplant program from deceased donors in Jehovah's Witnesses patients reported so far. No patient received blood bank products but 3 had fibrinogen concentrate and one tranexamic acid to correct ongoing hyper-fibrinolysis. There were 4 cases of acute kidney injury (one required extracorporeal renal replacement treatment) and one patient needed vasoactive medications to support blood pressure for the first 2 postoperative days. Two patients underwent re-laparotomy. Finally, of the 13 recipients, 12 were alive at the 1 year follow-up interview and 1 died due to septic complications. CONCLUSIONS: Our experience confirms that liver transplantation in selected Jehovah's Witnesses patients can be feasible and safe provided that it is carried out at a very experienced centre and according to a multidisciplinary approach.

Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radiomics and fuzzy classification.

MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the prediction is obtained timely, before or during the early phase of treatment. Artificial intelligence can address the problem, by learning from examples and building classification models. In particular, fuzzy logic has shown its suitability for medical applications, in order to manage uncertain data, and to build transparent rule-based classifiers. In previous works, clinical, dosimetric and image-based features were considered separately, to find different possible predictors of parotid shrinkage. On the other hand, a few works reported possible image-based predictors of xerostomia, while the combination of different types of features has been little addressed. OBJECTIVE: This paper proposes the application of a novel machine learning approach, based on both statistics and fuzzy logic, aimed at the classification of patients at risk of i) parotid gland shrinkage and ii) 12-months xerostomia. Both problems are addressed with the aim of individuating predictors and models to classify respective outcomes. METHODS: Knowledge is extracted from a real dataset of radiotherapy patients, by means of a recently developed method named Likelihood-Fuzzy Analysis, based on the representation of statistical information by fuzzy rule-based models. This method enables to manage heterogeneous variables and missing data, and to obtain interpretable fuzzy models presenting good generalization power (thus high performance), and to measure classification confidence. Numerous features are extracted to characterize patients, coming from different sources, i.e. clinical features, dosimetric parameters, and radiomics-based measures obtained by texture analysis of Computed Tomography images. A learning approach based on the composition of simple models in a more complicated one allows to consider the features separately, in order to identify predictors and models to use when only some data source is available, and obtaining more accurate results when more information can be combined. RESULTS: Regarding parotid shrinkage, a number of good predictors is detected, some already known and confirmed here, and some others found here, in particular among radiomics-based features. A number of models are also designed, some using single features and others involving models composition to improve classification accuracy. In particular, the best model to be used at the initial treatment stage, and another one applicable at the half treatment stage are identified. Regarding 12-months toxicity, some possible predictors are detected, in particular among radiomics-based features. Moreover, the relation between final parotid shrinkage rate and 12-months xerostomia is evaluated. The method is compared to the naïve Bayes classifier, which reveals similar results in terms of classification accuracy and best predictors. The interpretable fuzzy rule-based models are explicitly presented, and the dependence between predictors and outcome is explained, thus furnishing in some cases helpful insights about the considered problems. CONCLUSION: Thanks to the performance and interpretability of the fuzzy classification method employed, predictors of both parotid shrinkage and xerostomia are detected, and their influence on each outcome is revealed. Moreover, models for predicting parotid shrinkage at initial and half radiotherapy stages are found.

Octogenarian donors in liver transplantation grant an equivalent perioperative course to ideal young donors.

BACKGROUND: Use of grafts from very old donors for liver transplantation is controversial. AIM: To compare the perioperative course of patients receiving liver grafts from young ideal vs octogenarian donors. METHODS: Analysis of the perioperative course of patients receiving liver grafts from young, ideal (18-39 years) vs octogenarian (≥80years) deceased donors between 2001 and 2014. RESULTS: 346 patients were studied: 179 (51.7%) received grafts aged 18-39 years whereas 167 (48.3%) received a graft from a donor aged ≥80years. Intra-operative cardiovascular (p=0.2), coagulopathy (p=0.5) and respiratory (p=1.0) complications and incidence of reperfusion syndrome (p=0.3) were similar. Patients receiving a young graft required more fresh frozen plasma units (p≤0.03) but did not differ for the need of packed red cells (p=0.2) and platelet (p=0.3) transfusions. Median ICU stay was identical (p=0.4). Patients receiving octogenarian vs young grafts did not differ in terms of death or re-transplant (p=1.0) during the ICU stay. Similar cardiovascular, respiratory, renal, infectious and neurological postoperative complication rates were observed in the two groups. CONCLUSIONS: Octogenarian donors in liver transplantation grant an equivalent perioperative course to ideal young donors.

Radon levels in underground workplaces: a map of the Italian regions.

The indoor radon exposition is a widely recognised health hazard, so specific laws and regulations have been produced in many countries and so-called radon-risk maps have consequently been produced. In Italy the regulation applies to general workplaces and a national survey was carried out in the 1990s to evaluate the exposure to radon in dwellings. Failing a national coordinated mapping programme, some Italian regions performed a survey to identify radon-prone areas, nevertheless with different methodologies. In this work a national map of the average annual radon concentration levels in underground workplaces, obtained from the results of 8695 annual indoor radon measurements carried out by U-Series laboratory between 2003 and 2010, was presented. Due to underground locations, the mean radon concentration is higher than that from previous map elaborated for dwellings and a significant radon concentration was also found in Regions traditionally considered as low-risk areas.

Intravenous fenoldopam for early acute kidney injury after liver transplantation.

PURPOSE: Acute kidney injury remains a serious complication after orthotopic liver transplantation. To date, several 'renal-protective' agents have been explored in this setting but with conflicting and disappointing results. Therefore, our aim is to evaluate the effects of fenoldopam in liver transplant patients with an established renal injury. METHODS: In this prospective study, intravenous fenoldopam 0.1 µg/kg/min was administered to consecutive liver transplant patients with postoperative (within 7 days from surgery) stage 2 acute kidney injury (AKI) according to the Acute Kidney Injury Network classification. Actual glomerular filtration rate (GFR; calculated by the iohexol plasma clearance), serum creatinine (SCr) and cystatin C (SCyC) were used to assess the effect of the medication on the patients. RESULTS: During the study, 295 patients underwent liver transplant. Fifty-one patients (17.6%) met the inclusion criteria and the data from 48 patients were analysed. SCr and SCyC levels decreased (p < 0.001 after 48 h; p < 0.0001 after 72 h) and GFR increased (p < 0.001 after 24 h; p < 0.0001 after 72 h). When compared to a cohort of comparable patients with AKI from our historical series, the patients in the present study showed better SCr and SCyC levels. It was not necessary to discontinue the infusion of fenoldopam in any patient because of the occurrence of adverse events potentially attributable to it. CONCLUSION: We showed that fenoldopam was capable of improving some renal function parameters in postoperative liver transplantation patients with on-going AKI. This preliminary study now sets the stage for a multicenter, randomized, placebo-controlled trial in order to provide definite evidence.