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Nanoporous metals are a class of nanostructured materials finding extensive applications in multiple fields thanks to their unique properties attributed to their high surface area and interconnected nanoscale ligaments. They can be prepared following different strategies, but the deposition of an arbitrary pure porous metal is still challenging. Recently, a dry synthesis of nanoporous films based on the plasma treatment of metal thin layers deposited by physical vapour deposition has been demonstrated, as a general route to form pure nanoporous films from a large set of metals. An interesting aspect related to this approach is the possibility to apply the same methodology to deposit the porous films as a multilayer. In this way, it is possible to explore the properties of different porous metals in close contact. As demonstrated in this paper, interesting plasmonic properties emerge in a nanoporous Au-Ag bi-layer. The versatility of the method coupled with the possibility to include many different metals, provides an opportunity to tailor their optical resonances and to exploit the chemical and mechanical properties of components, which is of great interest to applications ranging from sensing, to photochemistry and photocatalysis., Competing Interests: Conflict of interest: Authors state no conflicts of interest., (© 2024 the author(s), published by De Gruyter, Berlin/Boston.)

MoS 2 is a two-dimensional layered transition metal dichalcogenide with unique electronic and optical properties. The fabrication of ultrathin MoS 2 is vitally important, since interlayer interactions in its ultrathin varieties will become thickness-dependent, providing thickness-governed tunability and diverse applications of those properties. Unlike with a number of studies that have reported detailed information on direct bandgap emission from MoS 2 monolayers, reliable experimental evidence for thickness-induced evolution or transformation of the indirect bandgap remains scarce. Here, the sulfurization of MoO 3 thin films with nominal thicknesses of 30 nm, 5 nm and 3 nm was performed. All sulfurized samples were examined at room temperature with spectroscopic ellipsometry and photoluminescence spectroscopy to obtain information about their dielectric function and edge emission spectra. This investigation unveiled an indirect-to-indirect crossover between the transitions, associated with two different Λ and K valleys of the MoS 2 conduction band, by thinning its thickness down to a few layers.

The role of software in healthcare is getting more and more pervasive. Nevertheless, manufacturers sometimes forget that these software are medical devices and must be certified according to the EU Medical Device Regulation 2017/745. In this work we propose a pipeline for developing a Medical Device Software (MDS) compliant with the regulations and certifiable. The pipeline includes the phase of requirements elicitation, risk assessment and analysis of effectiveness as key elements. The preparation of the technical file should be carried out in parallel with the MDS development. In the overall, it can be stated that the certification process starts with the conceptualization of the MDS and proceeds all along its design and implementation.

The tremendous prevalence and mortality of heart failure (HF), along with the social and economic impact of its consequences, make an appropriate disease management utmost important. In this context, telemedicine offers promising possibilities. Current clinical guidelines and technological solutions do not address the problem of monitoring at-risk patients and patients affected by mild HF for prevention purposes. The goal of this work is to design a service based on a telemedicine framework for the management of heart failure patients. The proposed service grounds the monitoring of the patient on a custom multi-sensor array that we designed and developed for the purpose. The description of the processes involved in the service was carried out by means of Process Modelling tools, and in particular through Swim Lane Activity Diagrams. The results look promising for the implementation of the service in a real-life scenario. The main strength of the service resides in a) the use of noninvasive monitoring technologies to include patients with a mild HF or at-risk patients; and b) the integration of hospital and territory services to grant continuity and coherence in the treatment.

Radiomics-based systems could improve the management of oncological patients by supporting cancer diagnosis, treatment planning, and response assessment. However, one of the main limitations of these systems is the generalizability and reproducibility of results when they are applied to images acquired in different hospitals by different scanners. Normalization has been introduced to mitigate this issue, and two main approaches have been proposed: one rescales the image intensities ( image normalization ), the other the feature distributions for each center ( feature normalization ). The aim of this study is to evaluate how different image and feature normalization methods impact the robustness of 93 radiomics features acquired using a multicenter and multi-scanner abdominal Magnetic Resonance Imaging (MRI) dataset. To this scope, 88 rectal MRIs were retrospectively collected from 3 different institutions (4 scanners), and for each patient, six 3D regions of interest on the obturator muscle were considered. The methods applied were min-max, 1st-99th percentiles and 3-Sigma normalization, z-score standardization, mean centering, histogram normalization, Nyul-Udupa and ComBat harmonization. The Mann-Whitney U-test was applied to assess features repeatability between scanners, by comparing the feature values obtained for each normalization method, including the case in which no normalization was applied. Most image normalization methods allowed to reduce the overall variability in terms of intensity distributions, while worsening or showing unpredictable results in terms of feature robustness, except for the z-score , which provided a slight improvement by increasing the number of statistically similar features from 9/93 to 10/93. Conversely, feature normalization methods positively reduced the overall variability across the scanners, in particular, 3 sigma , z_score and ComBat that increased the number of similar features (79/93). According to our results, it emerged that none of the image normalization methods was able to strongly increase the number of statistically similar features., (Creative Commons Attribution license.)

The home monitoring of patients affected by chronic heart failure (CHF) is of key importance in preventing acute episodes. Nevertheless, no wearable technological solution exists to date. A possibility could be offered by Cardiac Time Intervals extracted from simultaneous recordings of electrocardiographic (ECG) and phonocardiographic (PCG) signals. Nevertheless, the recording of a good-quality PCG signal requires accurate positioning of the stethoscope over the chest, which is unfeasible for a naïve user as the patient. In this work, we propose a solution based on multi-source PCG. We designed a flexible multi-sensor array to enable the recording of heart sounds by inexperienced users. The multi-sensor array is based on a flexible Printed Circuit Board mounting 48 microphones with a high spatial resolution, three electrodes to record an ECG and a Magneto-Inertial Measurement Unit. We validated the usability over a sample population of 42 inexperienced volunteers and found that all subjects could record signals of good to excellent quality. Moreover, we found that the multi-sensor array is suitable for use on a wide population of at-risk patients regardless of their body characteristics. Based on the promising findings of this study, we believe that the described device could enable the home monitoring of CHF patients soon.

The aim of this study is to present a personalized predictive model (PPM) with a machine learning (ML) system that is able to identify and classify patients with suspected prostate cancer (PCa) following mpMRI. We extracted all the patients who underwent fusion biopsy (FB) from March 2014 to December 2019, while patients from August 2020 to April 2021 were included as a validation set. The proposed system was based on the following four ML methods: a fuzzy inference system (FIS), the support vector machine (SVM), k-nearest neighbors (KNN), and self-organizing maps (SOMs). Then, a system based on fuzzy logic (FL) + SVM was compared with logistic regression (LR) and standard diagnostic tools. A total of 1448 patients were included in the training set, while 181 patients were included in the validation set. The area under the curve (AUC) of the proposed FIS + SVM model was comparable with the LR model but outperformed the other diagnostic tools. The FIS + SVM model demonstrated the best performance, in terms of negative predictive value (NPV), on the training set (78.5%); moreover, it outperformed the LR in terms of specificity (92.1% vs. 83%). Considering the validation set, our model outperformed the other methods in terms of NPV (60.7%), sensitivity (90.8%), and accuracy (69.1%). In conclusion, we successfully developed and validated a PPM tool using the FIS + SVM model to calculate the probability of PCa prior to a prostate FB, avoiding useless ones in 15% of the cases.

Finding the right time for weaning from ventilator is a difficult clinical decision. Several systems based on machine or deep learning are reported in literature. However, the results of these applications are not completely satisfactory and may be improved. An important aspect is represented by the features used as input of these systems. In this paper we present the results of the application of genetic algorithms to perform feature selection on a dataset containing 13688 patients under mechanical ventilation characterizing by 58 variables, extracted from the MIMIC III database. The results show that all features are important, but four of them are essential: 'Sedation_days', 'Mean_Airway_Pressure', 'PaO2', and 'Chloride'. This is only the initial step to obtain a tool to be added to the other clinical indices for minimize the risk of extubation failure.

Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside.

The digital healthcare workforce is usually composed of two major types of professionals: the healthcare workers, who are the users of eHealth, and the health informatics developers, who are usually computer scientists, biomedical engineers, or other technical experts. Health informatics educators have the responsibility to develop the appropriate skills for both, acting within their specific curricula. Here we present the experience of the Italian Society of Biomedical Informatics (SIBIM) and show that, whereas the technical curricula are widely covered with a large range of topics, the eHealth education in medical curricula is often limited to simple bioengineering and informatics skills, thus suggesting that eHealth associations and organizations at the national level should focus their efforts towards increasing the level of eHealth contents in medical schools.

Certification of Medical Device Software (MDS) according to the EU Medical Device Regulation 2017/745 requires demonstrating safety and effectiveness. Thus, the syllabus of a course on MDS development must provide tools for addressing these issues. To assure safety, risk analysis has to be performed using a four-step procedure. Effectiveness could be demonstrated by literature systematic review combined with meta-analysis, to compare the MDS performances with those of similar tools.

The aim of the study is to present and tune a fully automatic deep learning algorithm to segment colorectal cancers (CRC) on MR images, based on a U-Net structure. It is a multicenter study, including 3 different Italian institutions, that used 4 different MRI scanners. Two of them were used for training and tuning the systems, while the other two for the validation. The implemented algorithm consists of a pre-processing step to normalize and to highlight the tumoral area, followed by the CRC segmentation using different U-net structures. Automatic masks were compared with manual segmentations performed by three experienced radiologists, one at each center. The two best performing systems (called mdl2 and mdl3), obtained a median Dice Similarity Coefficient of 0.68(mdl2) - 0.69(mdl3), precision of 0.75(md/2) - 0.71(md/3), and recall of 0.69(mdl2) - 0.73(mdl3) on the validation set. Both systems reached high detection rates, 0.98 and 0.95, respectively, on the validation set. These encouraging results, if confirmed on larger dataset, might improve the management of patients with CRC, since it can be used as a fast and precise tool for further radiomics analyses. Clinical Relevance - To provide a reliable tool able to automatically segment CRC tumors that can be used as first step in future radiomics studies aimed at predicting response to chemotherapy and personalizing treatment.

. 'One for all, all for one': an example of community networks integration and coordination., Introduction: In order to build networks and activate more support services at home, it is essential to have information about the availlable resources., Aims: To describe the experience of creating an informative brochure on territorial services for people at home., Methods: The brochure was developed by collecting both from the web and from health care professionals data on services supplied by non-profit organizations., Results: The brochure is divided into sections, each corresponding to a type of service and presenting the questions that users may ask or the most frequent situations (e.g. "I can't grocery shopping any more"), the availlable services and contact information., Conclusions: A brochure providing information on the service providers available in the region is a useful tool for both patients and caregivers.

Background: In current precision prostate cancer (PCa) surgery era the identification of the best patients candidate for prostate biopsy still remains an open issue. The aim of this study was to evaluate if the prostate target biopsy (TB) outcomes could be predicted by using artificial intelligence approach based on a set of clinical pre-biopsy., Methods: Pre-biopsy characteristics in terms of PSA, PSA density, digital rectal examination (DRE), previous prostate biopsies, number of suspicious lesions at mp-MRI, lesion volume, lesion location, and Pi-Rads score were extracted from our prospectively maintained TB database from March 2014 to December 2019. Our approach is based on Fuzzy logic and associative rules mining, with the aim to predict TB outcomes., Results: A total of 1448 patients were included. Using the Frequent-Pattern growth algorithm we extracted 875 rules and used to build the fuzzy classifier. 963 subjects were classified whereas for the remaining 484 subjects were not classified since no rules matched with their input variables. Analyzing the classified subjects we obtained a specificity of 59.2% and sensitivity of 90.8% with a negative and the positive predictive values of 81.3% and 76.6%, respectively. In particular, focusing on ISUP ≥ 3 PCa, our model is able to correctly predict the biopsy outcomes in 98.1% of the cases., Conclusions: In this study we demonstrated that the possibility to look at several pre-biopsy variables simultaneously with artificial intelligence algorithms can improve the prediction of TB outcomes, outclassing the performance of PSA, its derivates and MRI alone., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

To cope with the high intra-subject variability of muscle activation intervals, a large amount of gait cycles is necessary to clearly document physiological or pathological muscle activity patterns during human locomotion. The Clustering for Identification of Muscle Activation Pattern (CIMAP) algorithm has been proposed to help clinicians obtaining a synthetic and clear description of normal and pathological muscle functions in human walking. Moreover, this algorithm allows the extraction of Principal Activations (PAs), defined as those muscle activations that are strictly necessary to perform a specific task and occur in every gait cycle. This contribution aims at assessing the impact of the number of gait cycles on the extraction of the PAs. Results demonstrated no statistically significant differences between PAs extracted considering different numbers of gait cycles, revealing, on average, similarity values higher than 0.88.Clinical Relevance-This contribution demonstrates the potential applicability of the CIMAP algorithm to the analysis of subjects affected by neurological disorders, for whom the assessment of motor functions may be of the uttermost importance and only a reduced number of gait cycles can be acquired.

Gait analysis applications in clinics are still uncommon, for three main reasons: (1) the considerable time needed to prepare the subject for the examination; (2) the lack of user-independent tools; (3) the large variability of muscle activation patterns observed in healthy and pathological subjects. Numerical indices quantifying the muscle coordination of a subject could enable clinicians to identify patterns that deviate from those of a reference population and to follow the progress of the subject after surgery or completing a rehabilitation program. In this work, we present two user-independent indices. First, a muscle-specific index (MFI) that quantifies the similarity of the activation pattern of a muscle of a specific subject with that of a reference population. Second, a global index (GFI) that provides a score of the overall activation of a muscle set. These two indices were tested on two groups of healthy and pathological children with encouraging results. Hence, the two indices will allow clinicians to assess the muscle activation, identifying muscles showing an abnormal activation pattern, and associate a functional score to every single muscle as well as to the entire muscle set. These opportunities could contribute to facilitating the diffusion of surface EMG analysis in clinics.

Purpose: To assess the use of telemedicine with phone-call visits as a practical tool to follow-up with patients affected by urological benign diseases, whose clinic visits had been cancelled during the acute phase of the COVID-19 pandemic., Methods: Patients were contacted via phone-call and a specific questionnaire was administered to evaluate the health status of these patients and to identify those who needed an "in-person" ambulatory visit due to the worsening of their condition. Secondarily, the patients' perception of a potential shift towards a "telemedicine" approach to the management of their condition and to indirectly evaluate their desire to return to "in-person" clinic visits., Results: 607 were contacted by phone-call. 87.5% (531/607) of the cases showed stability of the symptoms so no clinic in-person or emergency visits were needed. 81.5% (495/607) of patients were more concerned about the risk of contagion than their urological condition. The median score for phone visit comprehensibility and ease of communication of exams was 5/5; whilst patients' perception of phone visits' usefulness was scored 4/5. 53% (322/607) of the interviewees didn't own the basic supports required to be able to perform a real telemedicine consult according to the required standards., Conclusion: Telemedicine approach limits the number of unnecessary accesses to medical facilities and represents an important tool for the limitation of the risk of transmission of infectious diseases, such as COVID-19. However, infrastructures, health workers and patients should reach out to a computerization process to allow a wider diffusion of more advanced forms of telemedicine, such as televisit., (© 2021. Springer-Verlag GmbH Germany, part of Springer Nature.)

Quality of care and patient satisfaction are important aspects of high standard care. If clinical staff is subject to an elevated workload there is a possible decrease of both. This justifies the development of tools to quantify the workload and to find organizational changes that will normalize it. We have previously developed a simulation system to quantify the workload of the staff working in a regional reference center for the treatment of bleeding and hemorrhagic disorders. The goal of this new work is to simulate, through an agent-based model, the impact of adding a physician to the staff. Ten sets of initial parameters were defined to simulate ten typical weeks. Results show that the introduction of the new physician together with a second ambulatory room can reduce the workload of all the staff to the expected 8-hour. In this situation, in which the staff workload does not exceed the daily capacity, we may suppose that an increase in the quality of care and patient satisfaction will be possible.

The use of plant elicitors for controlling Pseudomonas syringae pv. actinidiae (Psa), the etiological agent of the kiwifruit bacterial canker (KBC), has been analysed in the past and, while salicylic acid (SA) seems to decrease disease susceptibility, methyl jasmonate (MJ) shows an opposite effect. However, the metabolic and genomic responses of Psa-infected plants following elicitation with these two compounds, as compared with non-elicited Psa-inoculated plants, are poorly understood, being the focus of this study. Micropropagated A. chinensis 'Hayward' plants were elicited with MJ or SA, and further inoculated with Psa. Fifteen days post-inoculation, Psa population in MJ-treated plants was increased by 7.4-fold, whereas SA elicitation led to decreased Psa colonization (0.5-fold), as compared with non-elicited inoculated plants. Additionally, elicitation with MJ or SA generally decreased polyphenols and lignin concentrations (by at least 20%) and increased total proteins (by at least 50%). MJ led to the upregulation of SOD, involved in plant antioxidant system, and reporter genes for the jasmonic acid (JA) (JIH and LOX1), abscisic acid (SnRK), SA (ICS1), and ethylene (ACAS1, ETR1 and SAM) pathways. Moreover, it increased ABA (40%) and decreased carotenoids (30%) concentrations. Contrastingly, comparing with non-elicited Psa-inoculated plants, SA application resulted in the downregulation of antioxidant system-related genes (SOD and APX) and of reporter genes for ethylene (ETR1) and JA (JIH and ETR1). This study contributes to the understanding of potential mechanisms involved in kiwifruit plant defences against Psa, highlighting the role of the JA, ABA and ethylene in plant susceptibility to the pathogen., (Copyright © 2021 Elsevier Masson SAS. All rights reserved.)

The aim of our study was to develop and validate a machine learning algorithm to predict response of individual HER2-amplified colorectal cancer liver metastases (lmCRC) undergoing dual HER2-targeted therapy. Twenty-four radiomics features were extracted after 3D manual segmentation of 141 lmCRC on pretreatment portal CT scans of a cohort including 38 HER2-amplified patients; feature selection was then performed using genetic algorithms. lmCRC were classified as nonresponders (R-), if their largest diameter increased more than 10% at a CT scan performed after 3 months of treatment, responders (R+) otherwise. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values in correctly classifying individual lesion and overall patient response were assessed on a training dataset and then validated on a second dataset using a Gaussian naïve Bayesian classifier. Per-lesion sensitivity, specificity, NPV and PPV were 89%, 85%, 93%, 78% and 90%, 42%, 73%, 71% respectively in the testing and validation datasets. Per-patient sensitivity and specificity were 92% and 86%. Heterogeneous response was observed in 9 of 38 patients (24%). Five of nine patients were carriers of nonresponder lesions correctly classified as such by our radiomics signature, including four of seven harboring only one nonresponder lesion. The developed method has been proven effective in predicting behavior of individual metastases to targeted treatment in a cohort of HER2 amplified patients. The model accurately detects responder lesions and identifies nonresponder lesions in patients with heterogeneous response, potentially paving the way to multimodal treatment in selected patients. Further validation will be needed to confirm our findings., (© 2020 Union for International Cancer Control.)

Introduction: Only a few studies have described the impacts, strengths and needs for further development of national licensing exams (NLE). To gain such insights regarding the Swiss NLE, which includes a multiple-choice and a standardised clinical skills exam, we explored the perceptions of involved experts and stakeholders., Methods: We explored participants' perceptions in four focus group discussions. The interviews were recorded, transcribed verbatim and qualitatively analysed using a thematic analysis approach., Results: The analysis resulted in five perceived impacts, two strengths and two needs for further developments of the NLE. Perceived impacts were (1) steering students' learning behaviour, (2) supporting teachers and assessors to align teaching to competencies, (3) elevating the importance of the Swiss Catalogue of Learning Objectives, (4) setting incentives for the further development of curricula, and (5) fostering the collaboration between the faculties of medicine. Perceived strengths were the blend of assessment formats, including their competency-based orientation, and the collaborative development approach. Perceived needs lay in the NLE's further development to sustain its fit for purpose and in incentives for people involved., Conclusion: According to our study, this NLE had, and has, notable impacts on medical education in Switzerland. Our insights can be useful for others planning a similar undertaking.

Surface electromyography (sEMG) is the main non-invasive tool used to record the electrical activity of muscles during dynamic tasks. In clinical gait analysis, a number of techniques have been developed to obtain and interpret the muscle activation patterns of patients showing altered locomotion. However, the body of knowledge described in these studies is very seldom translated into routine clinical practice. The aim of this work is to analyze critically the key factors limiting the extensive use of these powerful techniques among clinicians. A thorough understanding of these limiting factors will provide an important opportunity to overcome limitations through specific actions, and advance toward an evidence-based approach to rehabilitation based on objective findings and measurements., (Copyright © 2020 Agostini, Ghislieri, Rosati, Balestra and Knaflitz.)

Liver metastases (mts) from colorectal cancer (CRC) can have different responses to chemotherapy in the same patient. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts. 22 radiomic features (RF) were computed on pretreatment portal CT scans following a manual segmentation of mts. RFs were extracted from 7x7 Region of Interests (ROIs) that moved across the image by step of 2 pixels. Liver mts were classified as non-responder (R-) if their largest diameter increased more than 3 mm after 3 months of treatment and responder (R+), otherwise. Features selection (FS) was performed by a genetic algorithm and classification by a Support Vector Machine (SVM) classifier. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values were evaluated for all lesions in the training and validation sets, separately. On the training set, we obtained sensitivity of 86%, specificity of 67%, PPV of 89% and NPV of 61%, while, on the validation set, we reached a sensitivity of 73%, specificity of 47%, PPV of 64% and NPV of 57%. Specificity was biased by the low number of R- lesions on the validation set. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance- to personalize treatment of patients with metastastic colorectal cancer, based on the likelihood of response to chemotherapy of each liver metastasis.

Purpose: The first clinical genetic autoplanning algorithm (Genetic Planning Solution, GPS) was validated in ten radiotherapy centres for prostate cancer VMAT by comparison with manual planning (Manual)., Methods: Although there were large differences among centres in planning protocol, GPS was tuned with the data of a single centre and then applied everywhere without any centre-specific fine-tuning. For each centre, ten Manual plans were compared with autoGPS plans, considering dosimetric plan parameters and the Clinical Blind Score (CBS) resulting from blind clinician plan comparisons. AutoGPS plans were used as is, i.e. there was no patient-specific fine-tuning., Results: For nine centres, all ten plans were clinically acceptable. In the remaining centre, only one plan was acceptable. For the 91% acceptable plans, differences between Manual and AutoGPS in target coverage were negligible. OAR doses were significantly lower in AutoGPS plans (p < 0.05); rectum D 15% and D mean were reduced by 8.1% and 17.9%, bladder D 25% and D mean by 5.9% and 10.3%. According to clinicians, 69% of the acceptable AutoGPS plans were superior to the corresponding Manual plan. In case of preferred Manual plans (31%), perceived advantages compared to autoGPS were minor. QA measurements demonstrated that autoGPS plans were deliverable. A quick configuration adjustment in the centre with unacceptable plans rendered 100% of plans acceptable., Conclusion: A novel, clinically applied genetic autoplanning algorithm was validated in 10 centres for in total 100 prostate cancer patients. High quality plans could be generated at different centres without centre-specific algorithm tuning., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The aim of the study is to present a new Convolutional Neural Network (CNN) based system for the automatic segmentation of the colorectal cancer. The algorithm implemented consists of several steps: a pre-processing to normalize and highlights the tumoral area, the classification based on CNNs, and a post-processing aimed at reducing false positive elements. The classification is performed using three CNNs: each of them classifies the same regions of interest acquired from three different MR sequences. The final segmentation mask is obtained by a majority voting. Performances were evaluated using a semi-automatic segmentation revised by an experienced radiologist as reference standard. The system obtained Dice Similarity Coefficient (DSC) of 0.60, Precision (Pr) of 0.76 and Recall (Re) of 0.55 on the testing set. After applying the leave-one-out validation, we obtained a median DSC=0.58, Pr=0.74, Re=0.54. The promising results obtained by this system, if validated on a larger dataset, could strongly improve personalized medicine.

The quality of patients care delivery is thought to be strongly affected by the physicians' workload. In this study we present an Agent-Based model of the processes during a typical working day. We simulated the current scenario and a possible scenario concerning the introduction of a second ambulatory as a potential improvement in the center organization. Our results validated the reliability of the model and showed that the introduction of a second ambulatory averagely reduces the daily physician' workload.

Rapid estimates of the central venous pressure (CVP) can be helpful to administer early fluid therapy or to manage cardiac preload in intensive care units, operating rooms or emergency rooms in order to start and monitor an adequate medical therapy. Invasive CVP measurements have inherent and non-negligible complication rates as well as great expenditures. Several noninvasive methods of CVP measurements, like ultrasound-guided techniques, are available, but require trained skills and special equipment which might not be at hand in all situations. Our purpose was to evaluate the feasibility and accuracy of CVP estimates assessed upon the height of hand veins collapse (HVC) using invasively measured CVP as the gold standard. The HVC was determined by slowly lifting the patient's hand while watching the dorsal hand veins to collapse. The vertical distance from the dorsal hand to a transducer air zero port was noted and converted to mmHg. The observer was blinded to the simultaneously measured CVP values, which were categorized as low (<7 mmHg), normal (7-12 mmHg) and high (>12 mmHg). Measurements were performed in 82 patients who had a median [IQR] age of 67 [60;74]. Median CVP was 12 [8;15] mmHg and the median absolute difference between the measured HVC and CVP was 4 [2;7] mmHg. The Spearman correlation coefficient between CVP and HVC was 0.55, 95%-CI [0.35;0.69]. Overall CVP categorization was correct in 45% of the cases. HVC had a sensitivity of 92% for a low CVP with a negative predictive value of 98%. A high HVC had a sensitivity of 29% but a high specificity of 94% for a high CVP. The overall performance of observing the hand vein collapse to estimate CVP was only moderate in the intensive care setting. However, the median difference to the CVP was low and HVC identifies a low CVP with a high sensitivity and excellent negative predictive value., (Copyright © 2019 Elsevier Inc. All rights reserved.)

The global regulatory community continues to explore new approaches to supervising innovation. Among these, so-called 'regulatory sandboxes' are proving to be particularly appealing to the financial sector as it seeks to promote technological and data-driven innovation. These 'playgrounds' for innovation are also spreading to other regulated sectors such as telecommunications, data protection, energy, and environmental protection, allowing participants to test new business models and technologies under the supervision of regulators while the applicable rules are temporarily relaxed. Regulators can provide targeted guidance to sandbox participants, including help with understanding how an innovation fits within the existing regulatory framework. As of July 2019, at least 50 sandboxes were either in operation or under consideration globally. Significantly, many of these are based in countries that host large numbers of displaced persons. This commentary explores the potential role for sandboxes in promoting responsible humanitarian innovation. Through a review of the literature, we discuss the stated benefits of the sandbox approach while also acknowledging notable risks and challenges to their sustained deployment. We then reflect on how the humanitarian sector might engage regulators and other stakeholders through sandboxes to develop and catalyze innovations that better serve those affected by humanitarian crises, including the displaced., (© 2019 The Authors. Global Policy published by Durham University and John Wiley & Sons Ltd.)

Purpose: Data collection in clinical trials is becoming complex, with a huge number of variables that need to be recorded, verified, and analyzed to effectively measure clinical outcomes. In this study, we used data warehouse (DW) concepts to achieve this goal. A DW was developed to accommodate data from a large clinical trial, including all the characteristics collected. We present the results related to baseline variables with the following objectives: developing a data quality (DQ) control strategy and improving outcome analysis according to the clinical trial primary end points., Methods: Data were retrieved from the electronic case reporting forms (eCRFs) of the phase III, multicenter MCL0208 trial (ClinicalTrials.gov identifier: NCT02354313) of the Fondazione Italiana Linfomi for younger patients with untreated mantle cell lymphoma (MCL). The DW was created with a relational database management system. Recommended DQ dimensions were observed to monitor the activity of each site to handle DQ management during patient follow-up. The DQ management was applied to clinically relevant parameters that predicted progression-free survival to assess its impact., Results: The DW encompassed 16 tables, which included 226 variables for 300 patients and 199,500 items of data. The tool allowed cross-comparison analysis and detected some incongruities in eCRFs, prompting queries to clinical centers. This had an impact on clinical end points, as the DQ control strategy was able to improve the prognostic stratification according to single parameters, such as tumor infiltration by flow cytometry, and even using established prognosticators, such as the MCL International Prognostic Index., Conclusion: The DW is a powerful tool to organize results from large phase III clinical trials and to effectively improve DQ through the application of effective engineered tools.

We study here experimentally, numerically and using a lubrication approach, the shape, velocity and lubrication film thickness distribution of a droplet rising in a vertical Hele-Shaw cell. The droplet is surrounded by a stationary immiscible fluid and moves purely due to buoyancy. A low density difference between the two media helps to operate in a regime with capillary number Ca lying between 0.03 and 0.35, where Ca =μ o U d / γ is built with the surrounding oil viscosity γ 0 , the droplet velocity U d and surface tension γ . The experimental data show that in this regime the droplet velocity is not influenced by the thickness of the thin lubricating film and the dynamic meniscus. For iso-viscous cases, experimental and three-dimensional numerical results of the film thickness distribution agree well with each other. The mean film thickness is well captured by the Aussillous & Quéré ( Phys. Fluids , vol. 12 (10), 2000, pp. 2367-2371) model with fitting parameters. The droplet also exhibits the 'catamaran' shape that has been identified experimentally for a pressure-driven counterpart (Huerre et al., Phys. Rev. Lett ., vol. 115 (6), 2015, 064501). This pattern has been rationalized using a two-dimensional lubrication equation. In particular, we show that this peculiar film thickness distribution is intrinsically related to the anisotropy of the fluxes induced by the droplet's motion.

Brain Tissue Segmentation (BTS) in young children and neonates is not a trivial task due to peculiar characteristics of the developing brain. The aim of this study is to present the preliminary results of new atlas-free BTS (afBTS) algorithm of MR images for pediatric applications, based on clustering. The algorithm works on axial T1, T2 and FLAIR sequences. First, the Cerebrospinal Fluid (CSF) is identified using the Region Growing algorithm. The remaining voxels are processed with the k-means algorithm in order to separate White Matter (WM) and Grey Matter (GM). The afBTS algorithm was applied to a population of 13 neonates; the segmentations were evaluated by two expert pediatric neuroradiologists and compared with an atlas-based algorithm. The results were promising: afBTS allowed reconstruction of WM and CSF with an image quality comparable to the reference of standard while lower segmentation quality was obtained for the GM segmentation.

Heavy workloads in healthcare have been often associated to adverse clinical outcomes. To reduce workloads, an optimal scheduling of clinical staff resources is not enough, but particular attention must be payed to work organization and task characteristics. Moreover, interruptions during the clinical practice contribute to increase perceived workloads. In this study we analyzed and characterized the physicians' workload in an Italian center for the treatment of thrombotic and bleeding disorders. First, all clinical and administrative processes performed in the center were analyzed by means of two process modelling tools. Then, the quantification of the physicians' workload and the characterization of interruptions during practice were conducted. From our results it emerged that the task that mainly impacts on the workload is ambulatory care (42% of total workload) while interruptions produce a delay of almost 15 minutes per day and mainly occur during visits. Including all activities, the total daily workload per physician was 8 hours on average. In this time breaks were not taken into account. Concluding, from our analysis it is evident that the physicians' workload in the analyzed center is heavy and interruptions represent a source of delay in the workflow, that impact the physicians' workload.

Human Activity Recognition (HAR) refers to an emerging area of interest for medical, military, and security applications. However, the identification of the features to be used for activity classification and recognition is still an open point. The aim of this study was to compare two different feature sets for HAR. Particularly, we compared a set including time, frequency, and time-frequency domain features widely used in literature ( FeatSet&#95;A ) with a set of time-domain features derived by considering the physical meaning of the acquired signals ( FeatSet&#95;B ). The comparison of the two sets were based on the performances obtained using four machine learning classifiers. Sixty-one healthy subjects were asked to perform seven different daily activities wearing a MIMU-based device. Each signal was segmented using a 5-s window and for each window, 222 and 221 variables were extracted for the FeatSet&#95;A and FeatSet&#95;B respectively. Each set was reduced using a Genetic Algorithm (GA) simultaneously performing feature selection and classifier optimization. Our results showed that Support Vector Machine achieved the highest performances using both sets (97.1% and 96.7% for FeatSet&#95;A and FeatSet&#95;B respectively). However, FeatSet&#95;B allows to better understand alterations of the biomechanical behavior in more complex situations, such as when applied to pathological subjects.

Inertial microfluidics is an active field of research that deals with crossflow positioning of the suspended entities in microflows. Until now, the majority of the studies have focused on the behavior of rigid particles in order to provide guidelines for microfluidic applications such as sorting and filtering. Deformable entities such as bubbles and droplets are considered in fewer studies despite their importance in multiphase microflows. In this paper, we show that the trajectory of bubbles flowing in rectangular and square microchannels can be controlled by tuning the balance of forces acting on them. A T-junction geometry is employed to introduce bubbles into a microchannel and analyze their lateral equilibrium position in a range of Reynolds (1 < Re < 40) and capillary numbers (0.1 < Ca < 1). We find that the Reynolds number (Re), the capillary number (Ca), the diameter of the bubble (D[combining macron]), and the aspect ratio of the channel are the influential parameters in this phenomenon. For instance, at high Re, the flow pushes the bubble towards the wall while large Ca or D[combining macron] moves the bubble towards the center. Moreover, in the shallow channels, having aspect ratios higher than one, the bubble moves towards the narrower sidewalls. One important outcome of this study is that the equilibrium position of bubbles in rectangular channels is different from that of solid particles. The experimental observations are in good agreement with the performed numerical simulations and provide insights into the dynamics of bubbles in laminar flows which can be utilized in the design of flow based multiphase flow reactors.

Urine osmolality (UOsm) is considered the most accurate measure of urine concentration and is used to assess body fluid homeostasis and renal function. We performed analytical validation of freezing point depression measurement of canine UOsm, to establish reference intervals (RIs) and to determine the effect of age, sex, and reproductive status on UOsm in dogs. Clinically healthy dogs ( n = 1,991) were retrospectively selected and stratified in groups by age (young [0-12 mo], adults [13-84 mo], and seniors [>84 mo]), sex (females and males), and reproductive status (intact and neutered). RIs were calculated for each age group. Intra- and inter-assay coefficients of variation were <1% in all cases. Good linearity ( r 2 = 1, p < 0.001) and recovery (89-98%) were observed. The limit of detection and limit of quantification were zero. Urine specific gravity and UOsm had a highly significant positive correlation ( r = 0.96, p < 0.001) but had inconsistent agreement. The 95% RI for canine UOsm was 369-2,416 mOsm/kg in young and adult dogs, and 366-2,178 mOsm/kg in seniors. Senior dogs had a significantly lower UOsm than young and adult dogs ( p < 0.000). Neutered females had a significantly lower UOsm than intact female dogs ( p < 0.002). These results indicate that the method evaluated is adequate for UOsm measurement and that RIs based on age and reproductive status should be used in dogs.

Background: Mildly elevated lactate levels (i.e., 1-2 mmol/L) are increasingly recognized as a prognostic finding in critically ill patients. One of several possible underlying mechanisms, microcirculatory dysfunction, can be assessed at the bedside using sublingual direct in vivo microscopy. We aimed to evaluate the association between relative hyperlactatemia, microcirculatory flow, and outcome., Methods: This study was a predefined subanalysis of a multicenter international point prevalence study on microcirculatory flow abnormalities, the Microcirculatory Shock Occurrence in Acutely ill Patients (microSOAP). Microcirculatory flow abnormalities were assessed with sidestream dark-field imaging. Abnormal microcirculatory flow was defined as a microvascular flow index (MFI) < 2.6. MFI is a semiquantitative score ranging from 0 (no flow) to 3 (continuous flow). Associations between microcirculatory flow abnormalities, single-spot lactate measurements, and outcome were analyzed., Results: In 338 of 501 patients, lactate levels were available. For this substudy, all 257 patients with lactate levels ≤ 2 mmol/L (median [IQR] 1.04 [0.80-1.40] mmol/L) were included. Crude ICU mortality increased with each lactate quartile. In a multivariable analysis, a lactate level > 1.5 mmol/L was independently associated with a MFI < 2.6 (OR 2.5, 95% CI 1.1-5.7, P = 0.027)., Conclusions: In a heterogeneous ICU population, a single-spot mildly elevated lactate level (even within the reference range) was independently associated with increased mortality and microvascular flow abnormalities. In vivo microscopy of the microcirculation may be helpful in discriminating between flow- and non-flow-related causes of mildly elevated lactate levels., Trial Registration: ClinicalTrials.gov, NCT01179243 . Registered on August 3, 2010.

Medical datasets are usually affected by several problems, such as missing values, inconsistencies, redundancies, that can influence the data mining process and the extraction of useful knowledge. For these reasons, a preprocessing phase should be performed for improving the overall quality of data and, consequently, of the information that may be discovered from them. In this study we applied five steps of data preprocessing to improve the quality of a large dataset derived from a multicenter clinical trial. Our dataset included 298 patients enrolled in a prospective, multicenter, clinical trial, characterized by 22 input variables and one class variable (MIPI value). In particular, data coming from different medical centers were firstly integrated to obtain a homogeneous dataset. The latter was normalized to scale all variables into smaller and similar intervals. Then, all missing values were estimated by means of an imputation step. The complete dataset was finally discretized and reduced to remove redundant variables and decrease the amount of data to be managed. The improvement of data quality after each step was evaluated by means of the patients' classification accuracy using the KNN classifier. Our results showed that the proposed pipeline produced an increment of more than 20% of the classification performances. Moreover, the highest growth of accuracy was obtained after missing value imputation, whereas the discretization and feature selection steps allowed for a significant reduction of variables to be managed, without any deterioration of the information contained in data.

During cyclic movements, the number of muscle activations and their timing are different from cycle to cycle. In a previous study, the CIMAP algorithm was proposed for grouping cycles showing similar EMG activation intervals, using dendrogram clustering. Even if the algorithm demonstrated good performances on a healthy population, the intra-cluster variability decreased when applied to datasets from pathological subjects. In this work we propose an optimized version of the CIMAP, comparing the performances of 8 different combinations of parameters used for the dendrogram construction. The cut-off point is also modified. The new and the original version of the algorithm are compared, in terms of intra-cluster variability, considering a population of 60 subjects, both healthy and pathological. The results show that the new CIMAP allows for obtaining clusters with lower variability with respect to the original version of the algorithm (p <; 0.001).

In the last 10 years the European population aged 65 years and over grew of 2.3%, with Italy having the highest share of elderly persons in the total population. OPLON (OPportunities for active and healthy LONgevity) is a project supported by the Italian Ministry of Education, Universities, and Research aiming to identify and prevent frailty and to improve the life quality of elderly subjects. The main goal of OPLON is to develop a "Care&Cure" model for the management of subjects with different morbidities and co-morbidities, adaptable to the subject's risk level and to the regional contexts. In this study we analyzed four Italian telemedicine experiences addressed to chronic, geriatric or partially self-sufficient subjects. Each of them was exhaustively described by means of three process modelling tools (synopsis, workflow and swimlane activity diagrams). Starting from this analysis, we defined a general model of tele-monitoring and tele-assistance of frail and pre-frail people with different needs and pathologies. The proposed model was characterized by three macro processes (enrollment, assessment and assistance) and four groups of actors (patient, general practitioner/specialist physician, multidisciplinary team, and healthcare professionals). Combining this model with a detailed analysis of regulations and legislations in force both at local and national level, it will be possible to design the complete and efficient "Care&Cure" model.

Joint position error (JPE) is frequently used to assess proprioception in rehabilitation and sport science. During position-reposition tests the subject is asked to replicate a specific target angle (e.g. 30° of knee flexion) for a specific number of times. The aim of this study is to find an effective method to estimate JPE from the joint kinematic signal. Forty healthy subjects were tested to assess knee joint position sense. Three different methods of JPE estimation are described and compared using a hierarchical clustering approach. Overall, the 3 methods showed a high degree of similarity, ranging from 88% to 100%. We concluded that it is preferable to use the more user-independent method, in which the operator does not have to manually place "critical" markers.

The extraction of muscle synergies in human locomotion may be biased by the kind of pre-processing applied to electromyographic (EMG) data. The aim of this contribution is to analyze the differences in the muscle synergies extracted using a standard pre-processing procedure and a new procedure. The new procedure is based on the selection of the muscle's principal activations (necessary actuations of the muscle to accomplish its specific biomechanical task during gait), discarding secondary activations (with an auxiliary function in motor control). EMG signals were recorded from 12 muscles of a healthy volunteer who was asked to walk, at self-selected pace, for 5 minutes. A dataset of 193 gait cycles was collected and divided into 19 epochs of 10 concatenated gait cycles. The application of the new pre-processing procedure provided 5 instead of 6 muscle synergies accurately reconstructing the original EMG data matrix, and clearer and more stable neural activation commands. The new preprocessing procedure may be easily extended to the extraction of muscle synergies in other cyclic movements, such as running, stair climbing, cyclo-ergometer exercising, and swimming.

Complications associated with Toxocara canis infection are rare. We present a case of a patient with Staphylococcus aureus endocarditis as a complication of an endomyocardial fibrosis caused by T canis . The epidemiological, pathological, and clinical features of this rare complication are described here.

Gait alterations are observed even years after hip replacement surgery. Such long-lasting alterations may arise from a global change of the motor control strategies. The aim of this work is to investigate the changes in gait motor control strategies of patients after hip replacement surgery by analyzing the frequency of occurrence (OF) of myoelectric activation patterns. We studied five lower limb muscles during gait, in hip prosthesis patients and controls. We found that patients adopt a motor control strategy that tends to prefer "simplified" myoelectric patterns, showing a smaller number of activations within the gait cycle. This altered motor control was present both on the prosthesis and the sound side, and did not improve during the 12-month follow-up. The reduced number of activations is even more evident in lateral hamstrings and gluteus medius, which are the muscles more affected by hip replacement surgery. Furthermore, this study demonstrated that the OF is a sensitive parameter able to discover subtle changes in motor control strategies. Its use can be extended to other studies involving motor coordination, motor learning and motor control adaptations.

This study reports the effects on antimicrobial, antioxidant, migration and disintegrability activities of ternary nanocomposite films based on poly(lactic acid) incorporating two biobased nanofillers, (cellulose nanocrystals (CNC) and lignin nanoparticles (LNP)), in two different amounts (1 and 3% wt.). Results from antimicrobial tests revealed a capacity to inhibit the Gram negative bacterial growth of Xanthomonas axonopodis pv. vesicatoria and Xanthomonas arboricola pv. pruni along the time, offering innovative opportunities against dangerous bacterial plant pathogens. LNP proved to be highly efficient in antioxidation activity, based on the disappearance of the absorption band at 517nm of the free radical, 2,2-diphenyl-1-picrylhydrazyl (DPPH) upon reduction by an antiradical compound; moreover the combination of LNP and CNC generates a synergistic positive effect in the antioxidation response of PLA ternary films. Furthermore, all the studied formulations showed a disintegrability value up to 90% after 15days of incubation in composting conditions. Migration results showed that the films can be considered suitable for application in food packaging field., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Objective: Since 2011, the new national final examination in human medicine has been implemented in Switzerland, with a structured clinical-practical part in the OSCE format. From the perspective of the national Working Group, the current article describes the essential steps in the development, implementation and evaluation of the Federal Licensing Examination Clinical Skills (FLE CS) as well as the applied quality assurance measures. Finally, central insights gained from the last years are presented., Methods: Based on the principles of action research, the FLE CS is in a constant state of further development. On the foundation of systematically documented experiences from previous years, in the Working Group, unresolved questions are discussed and resulting solution approaches are substantiated (planning), implemented in the examination (implementation) and subsequently evaluated (reflection). The presented results are the product of this iterative procedure., Results: The FLE CS is created by experts from all faculties and subject areas in a multistage process. The examination is administered in German and French on a decentralised basis and consists of twelve interdisciplinary stations per candidate. As important quality assurance measures, the national Review Board (content validation) and the meetings of the standardised patient trainers (standardisation) have proven worthwhile. The statistical analyses show good measurement reliability and support the construct validity of the examination. Among the central insights of the past years, it has been established that the consistent implementation of the principles of action research contributes to the successful further development of the examination., Conclusion: The centrally coordinated, collaborative-iterative process, incorporating experts from all faculties, makes a fundamental contribution to the quality of the FLE CS. The processes and insights presented here can be useful for others planning a similar undertaking.

In the last decade, the use of information technology (IT) in healthcare has taken a growing role. In fact, the adoption of an increasing number of computer tools has led to several benefits related to the process of patient care and allowed easier access to social and health care resources. At the same time this trend gave rise to new challenges related to the implementation of these new technologies. Software used in healthcare can be classified as medical devices depending on the way they are used and on their functional characteristics. If they are classified as medical devices they must satisfy specific regulations. The aim of this work is to present a software development framework that can allow the production of safe and high quality medical device software and to highlight the correspondence between each software development phase and the appropriate standard and/or regulation.

Throughout the medical equipment life cycle, preventive maintenance is considered one of the most important stages that should be managed properly. However, the need for better management and control by giving a reasonable prioritization for preventive maintenance becomes essential. The purpose of this study is to develop a comprehensive framework for preventive maintenance priority of medical equipment using Quality Function Deployment (QFD) and Fuzzy Logic (FL). The quality function deployment is proposed in order to identify the most important criteria that could impact preventive maintenance priority decision; meanwhile the role of the fuzzy logic is to generate a priority index of the list of equipment considering those criteria. The model validation was carried out on 140 pieces of medical equipment belonging to two hospitals. In application, we propose to classify the priority index into five classes. The results indicate that the strong correlation existence between risk-based criteria and preventive maintenance priority decision.

Clinical Pathways (CPs) are evidence-based recommendation for treating a diagnosis and an effective instrument to decrease undesired practice variability and improve clinician performance. Deviations from CPs might just as well reduce quality of care. Moreover they can be associated to possible adverse events. In this perspective, we developed and tested a system for comparing a patient trajectory (PT) with the corresponding CP in order to recognize significant variations. To measure adherence, a Clinical Pathway Deviation Index (CPDI) was constructed as the weighted-sum of five indicators. To build the indicators three different tools for CPs modeling have been tested. Only two of them proved suitable for our system. A preliminary analysis has been carried out using data of 24 real PTs. The aim of this paper is to present the system and to characterize CPDI performances.

Preventive maintenance is a core function of clinical engineering, and it is essential to guarantee the correct functioning of the equipment. The management and control of maintenance activities are equally important to perform maintenance. As the variety of medical equipment increases, accordingly the size of maintenance activities increases, the need for better management and control become essential. This paper aims to develop a new model for preventive maintenance priority of medical equipment using quality function deployment as a new concept in maintenance of medical equipment. We developed a three-domain framework model consisting of requirement, function, and concept. The requirement domain is the house of quality matrix. The second domain is the design matrix. Finally, the concept domain generates a prioritization index for preventive maintenance considering the weights of critical criteria. According to the final scores of those criteria, the prioritization action of medical equipment is carried out. Our model proposes five levels of priority for preventive maintenance. The model was tested on 200 pieces of medical equipment belonging to 17 different departments of two hospitals in Piedmont province, Italy. The dataset includes 70 different types of equipment. The results show a high correlation between risk-based criteria and the prioritization list.