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Abstract Introduction Despite an improved understanding of its pathogenesis, dry eye disease (DED) remains relatively underestimated and its treatment challenging. A better alignment between the clinical evaluation and the patient self-assessment also requires capturing the whole patient experience of DED. This project aimed to unveil this experience through narrative medicine (NM). Methods The project involved 38 expert centres in Italy and one in San Marino, targeting adult patients with DED, their informal caregivers and their treating ophthalmologists. Written narratives and sociodemographic and quality of life (QoL)-related data were anonymously collected through the project’s webpage. Narratives were analysed through MAXQDA (VERBI Software, Berlin, Germany), NM classifications and content analysis. Results A total of 171 patients with DED, 37 informal caregivers and 81 ophthalmologists participated in the research. DED was defined as a disabling condition by 19% of patients and 35% of caregivers; 70% of patients reported that a therapeutic alliance is an integral part of DED treatment and 32% hope for more effective therapies. Forty-four per cent of patients assessed their own QoL as good; however, DED emerged as importantly impacting work performance and social events. DED physical, emotional and economic burden and the cruciality of a trusting care relationship represent the main themes that emerged across all narratives, while empathy and effective treatment are among the factors favouring coping with DED. Conclusion This project marked a pioneering initiative investigating the lived experience of patients with DED through NM, simultaneously involving all viewpoints involved in the care pathway. NM enabled the unveiling of factors favouring the ability to cope with DED and its associated QoL implications and provided valuable insights to improve the therapeutic alliance.

Purpose. To assess and evaluate cytological changes of conjunctival epithelial cells collected by CIC and relative expression of CD15 before and after 2-month treatment with sodium hyaluronate 0.2% and 0.001% hydrocortisone among patients with dysregulatory inflammatory changes of the conjunctiva in type 2 diabetes mellitus. Methods. We prospectively enrolled 38 T2DM patients (76 eyes). All patients were further divided into two groups: Group I - study group (n=23; 46 eyes); Group II - controls (n=15; 30 eyes). Group I received one drop of sodium hyaluronate 0.2% and 0.001% hydrocortisone (HA/Hydrocortisone); group II – one drop of sodium hyaluronate two times daily for two months, respectively. Results. Relative expression of epithelial CD15 was significantly reduced after the treatment in Group I (p˂0,001) in contrast to Group II (p=0,8726). Absence of CD 15 expression was found in five cases (11%) before treatment and seven cases (15%) after HA/Hydrocortisone administration (p=0,7582). Macrophage infiltration was observed in 28% of participants upon baseline examination and it completely disappeared after treatment with HA/Hydrocortisone. These findings show that HA/hydrocortisone drops should be considered as an effective treatment for patients with dysregulated parainflammation of the conjunctiva. Conclusions. Treatment with hyaluronate/hydrocortisone eye drops showed a statistically significant improvement of conjunctival cytology and functional tests: lipid layer thickness (LLT), tear breakup time (TBUT), meibomian gland loss (MGL) along with reduction of relative expression of epithelial CD15 among T2DM patients.

Load limits, which appear to be routinely exceeded by trucks, occasionally result in road bridge failures. Therefore, predicting failures is crucial for safeguarding road safety. Past studies have largely focused on forecasting bridge failure event probability using the reliability analysis method, whilst occasionally accounting for vehicular overloading effects. Only recently, a study has investigated design traffic overloading event frequency using generalised linear regression models (GLRMs), including a power component and negative binomial regressions (NBRs). However, as far as the authors know, artificial neural network models (ANNMs) have never been applied to this field. This paper is an attempt to fill in these gaps. First a frequency-based metric of traffic overloading was adopted as a driver of failure probability. Second, two alternative ‘frequency’ models were specified, calibrated, and validated. The former was based on a GLRM, the latter on ANNMs. Then, these models were compared using regression plots (RPs), measures of errors (MoEs) and the ratio between the number of observed vs predicted design load overcoming events to evaluate their performance. The models analysed more than 2 million weigh-in-motion (WIM) data records from a pilot station on a bridge on a heavily used ring road in Brescia (Italy). Results showed that ANNMs outperformed GLRMs. ANNMs have a higher correlation coefficient (between predicted and target frequencies), lower MoEs, and a closer-to-unity ratio (between predicted and target frequencies). These findings may increase prediction accuracy of design traffic overloading events and give road authorities more effective traffic management to protect bridges from load hazards.

In managing road infrastructures, a key benchmark is the 85th percentile of vehicle speeds (V85). While V85 can be derived from spot speed samples, these are often lacking on each urban road. Thus, prediction models become valuable tools for examining the relationship between V85 and road characteristics. Although various models exist for rural roads, the impact of roadside characteristics and markings on V85 in urban road networks has been partially investigated, and the effect of traffic calming measures remains fragmented.This study aims to address these gaps by applying a methodology that sheds light on the effects of some variables that influence V85 on urban roads. Specifically, the methodology selects and segments roads along the urban road network of the municipality of Brescia (Italy) and collects data on both road characteristics and 48,000+ spot speed information. Following data cleansing, it processes these data according to three different multiple regression models to analyse the influence of various predictors on V85. Once the best model is estimated, its performance is evaluated, and the final list of significant predictors is obtained.The results revealed that V85 increases with longer homogeneous segments, greater distance to successive intersections, bituminous conglomerate roads with more lanes, and the presence of trees, visible road markings, and posted speed limits. Conversely, V85 decreases in the presence of on-street parking and other obstacles (e.g., walls and road posts), when the density of road intersections and pedestrian crossings increases, when the left crossbar width increases and when the land use crossed is commercial or office, residential or industrial. Nevertheless, no significant effect was found for several traffic calming measures included in the model.These findings can assist road authorities in verifying road operating conditions and planning infrastructure interventions to reduce speeds, thereby creating a safer urban environment for all users.

Fare evasion is a critical threat for Transit Agencies (TAs) and/or Public Transport Companies (PTCs) everywhere, especially in Proof-of-Payment Transit Systems (POP-TSs). The research on fare evasion risk is limited and based on econometric models restricting time characterization to a single period. This paper aims to enhance the use of fare evasion risk over several periods for possible real-time deterrence against fare evasion. The paper moves from an existing framework, identifying the factors of fare evasion and risk exposure in terms of frequency (or probability) and severity (or vulnerability), and adopts Artificial Neural Networks (ANNs) to shed light on the intricate nexus between these components, estimating the fare evasion risk for every (segment of a) route. Next, the risk index is evaluated for each time period of interest. The predictions are ranked and represented by time-dependent dashboards to recognize routes with high-risk evasion that require deterrence strategies. Some real-time strategies are simulated from fare inspection logs, passenger surveys, and probability distributions on data collected in three years. In conclusion, this research provides actionable insights for TAs/PTCs in dealing with fare compliance and can be integrated into any bus transit management system.

IntroductionThe human brain is an intricate structure composed of interconnected modular networks, whose organization is known to balance the principles of segregation and integration, enabling rapid information exchange and the generation of coherent brain states. Segregation involves the specialization of brain regions for specific tasks, while integration facilitates communication among these regions, allowing for efficient information flow. Several factors influence this balance, including maturation, aging, and the insurgence of neurological disorders like epilepsy, stroke, or cancer. To gain insights into information processing and connectivity recovery, we devised a controllable in vitro model to mimic and investigate the effects of different segregation and integration ratios over time.MethodsWe designed a cross-shaped polymeric mask to initially establish four independent sub-populations of cortical neurons and analyzed how the timing of its removal affected network development. We evaluated the morphological and functional features of the networks from 11 to 18 days in vitro (DIVs) with immunofluorescence techniques and micro-electrode arrays (MEAs).ResultsThe removal of the mask at different developmental stages of the network lead to strong variations in the degree of intercommunication among the four assemblies (altering the segregation/integration balance), impacting firing and bursting parameters. Early removal (after 5 DIVs) resulted in networks with a level of integration similar to homogeneous controls (without physical constraints). In contrast, late removal (after 15 DIVs) hindered the formation of strong inter-compartment connectivity, leading to more clustered and segregated assemblies.DiscussionA critical balance between segregation and integration was observed when the mask was removed at DIV 10, allowing for the formation of a strong connectivity among the still-separated compartments, thus demonstrating the existence of a time window in network development in which it is possible to achieve a balance between segregation and integration.

Summary Mitochondria have been the focus of extensive research for decades since their dysfunction is linked to more than 150 distinct human disorders. Despite considerable efforts, researchers have only been able to skim the surface of the mitochondrial social complexity and the impact of inter-organelle and inter-organ communication alterations on human health. While some progress has been made in deciphering connections among mitochondria and other cytoplasmic organelles through direct (i.e., contact sites) or indirect (i.e., inter-organelle trafficking) crosstalk, most of these efforts have been restricted to a limited number of proteins involved in specific physiological pathways or disease states. This research bottleneck is further narrowed by our incomplete understanding of the cellular alteration timeline in a specific pathology, which prevents the distinction between a primary organelle dysfunction and the defects occurring due to the disruption of the organelle’s interconnectivity. In this perspective, we will (i) summarize the current knowledge on the mitochondrial crosstalk within cell(s) or tissue(s) in health and disease, with a particular focus on neurodegenerative disorders, (ii) discuss how different large-scale and targeted approaches could be used to characterize the different levels of mitochondrial social complexity, and (iii) consider how investigating the different expression patterns of mitochondrial proteins in different cell types/tissues could represent an important step forward in depicting the distinctive architecture of inter-organelle communication.

Abstract Background Indocyanine Green (ICG) fluorescence-guided surgery is widely used for intraoperative visualization of lymphatic structures. To date, there are no reports indicating this dye being used in lymph node biopsies for suspected or relapsed lymphoma. Methods Between October 2021 and June 2022, 12 patients underwent a fluorescence-guided laparoscopic lymph node biopsy (FGLLB) using ICG. The following was retrospectively evaluated: the dosage of ICG, the injection site, the number of patients where fluorescence was obtained after ICG administration, and additionally, the parameters indicating the outcome of the surgical procedure. Results The median duration of the surgery was 90 min. A laparotomy conversion was required in one case due to bleeding. Fluorescence was obtained in 10/12 (83.3%) patients by means of subcutaneous/perilesional injection in six of the patients, and intravenously in the other four. Hospitalization had a mean duration of three days. There were no major postoperative complications. FGLLB was used in seven patients to follow lymphoproliferative disease progression, and in five patients to establish a diagnosis. In all cases, FGLLB provided the information necessary for the correct diagnosis. Conclusions Fluorescence with ICG offers a simple and safe method for detecting pathological lymph nodes. FGLLB in suspected intra-abdominal lymphoma can largely benefit from this new opportunity which has not yet been tested to date. Further studies with large case series are needed to confirm its efficacy.

Background: Robot-assisted surgery ensures minimal invasiveness; since the expiry of the Da Vinci patent, new robotic systems have entered the market. Recently, the Hugo RAS received CE approval for several surgical procedures. However, more is needed to know about skill acquisition at the new simulator. Objective: This study aims to analyse the factors impacting basic surgical skills at the Hugo RAS simulator. Design, setting, and participants: We present a cross-sectional study involving 71 participants of different backgrounds invited to a hands-on session with the Hugo RAS simulator voluntarily. All of them had no prior expertise with the system. Participants were recruited among medical/nurse students, residents, and laparoscopic and robotic surgeons. Intervention: All participants underwent a hands-on “pick and place” exercise at the Hugo RAS simulator; the metrics of a second-round pick and place exercise were recorded. Outcome measurements and statistical analysis: Metrics were analysed with regard to the following variables: demographics, videogame use, and prior surgical experience (no surgical expertise, experience with laparoscopy, and experience with robotic console). Results and limitations: All participants completed the test. Of them, 77.5% were naïve to surgery, 8.5% had prior laparoscopic expertise, and 14.1% had prior robotic console experience. The time to complete the pick and place exercise was significantly lower (p

In proof-of-payment transit systems worldwide, fare inspection is the most widely adopted strategy against fare evasion from transit authorities and public transport companies. Although these actors attempt to make the inspectors’ work as easy, effective, and equitable as possible, several issues need to be analysed according to a unifying approach, i.e., “How, where and when to inspect”, “Who and why evades the fare”, “How many and how to distribute inspectors” as well as improve the inspectors’ effectiveness. Since no study exists in the literature investigating all these issues together, this paper aims to fill this gap with a review of several key papers that covered the full spectrum of relevant literature which, whole or partially, focused on fare inspection. Results show fare inspection is a beneficial strategy against fare evasion, but there are still many challenges and research limits that should be overcome in the years ahead. A possible research agenda is provided. It calls for specific options (i.e., data collection and fare evasion risk in hotspot definition, digital support and bottom-up approaches, size of inspection staff and scheduling of inspectors under realistic conditions and follower responses, effectiveness of actions focused on the visibility of fare inspection, managing interactions between fare inspectors and passengers) and integrated approaches (i.e., linking the planning, organisation, and activities of fare inspection with who and why evade). Nevertheless, even if this review may not be conclusive, these results support a unifying literature development on fare inspection.

Being a driver of failure consequences, forecasting the severity of events where design traffic load limits on bridges have been exceeded (DLEEs) is fundamental for road safety. Previous research has focused on estimating failure consequences by direct and indirect cost metrics. Only recently has research assessed severity unconventionally, in which the type of DLEEs was predicted by applying econometric models through Binomial Logistic Regression (BLR). Since machine learning models using Artificial Neural Networks (ANN) have not yet been explored, this study will enhance the literature as follows. First, two different ‘severity’ models were set up as a function of bridge-side, temporal-context, and traffic load hazard variables. Whilst the former relied on a BLR, the latter used an ANN. Second, the performance of these models was assessed using confusion matrixes, some performance indicators, and a cross-entropy parameter. Raw Weigh-In-Motion data on 7.4 M+ individual vehicle transits on a bridge along a primary roadway in Brescia (Italy) were processed. Although a similarly strong performance was achieved for BLR and ANN, the results indicated that ANN was able to predict severity records with a higher level of confidence than BLR on the case study dataset, with the cross-entropy of the ANN less than one third of that of the BLR. These analyses can support road authority traffic management to safeguard bridges from traffic load hazards. Finally, this study recommends future developments, such as considering the structural effects of traffic loads in the modelling, prioritizing traffic management actions among bridges at network level, and exploring the impact of ANN models in risk assessment.

Vehicle operating speed is a crucial factor for road safety, as it strictly affects occurrence and severity of crashes. Usually, 85th percentile of the operating speed distributions (i.e., V85) in free-flow traffic condition is widely accepted as a base value of consistency evaluation for homogenous portion of existing roads. Although the computation of V85 is simple, many road authorities cannot collect speed data for each road. Therefore, providing prediction models could be a useful tool to investigate the relationship between V85 and road characteristics. The literature proposed several models to account it. However, to the best of our knowledge, the effects of some road geometric characteristics, road markings and signs, traffic data, type of terrain and the simultaneous consideration of different road categories on the V85 prediction were not completely analyzed. This paper fills this gap by isolating key variables that mostly affect V85. In doing so, 60 000+ car spot speed data were collected along the county road network of the province of Brescia (Italy), and then processed by multiple regression models. The main findings show that V85 increases owing to the presence of a wider or paved shoulder, visible road median markings, a higher number of lanes and a higher percentage of cars with respect to the total traffic flow. Conversely, V85 decreases as the road axis curvature, the number of accesses and rate of forbidden overtaking increase. In addition, the presence of visible road external markings and the surrounding mountainous terrain contribute to decreasing V85. The overall findings may support road authorities to verify roads’ operating conditions and, possibly, adjust the speed limits, especially for existing roads.

Abstract Cholangiocarcinoma (CCA) is a rare cancer characterized by a global increasing incidence. Extracellular vesicles (EV) contribute to many of the hallmarks of cancer through transfer of their cargo molecules. The sphingolipid (SPL) profile of intrahepatic CCA (iCCA)-derived EVs was characterized by liquid chromatography-tandem mass spectrometry analysis. The effect of iCCA-derived EVs as mediators of inflammation was assessed on monocytes by flow cytometry. iCCA-derived EVs showed downregulation of all SPL species. Of note, poorly-differentiated iCCA-derived EVs showed a higher ceramide and dihydroceramide content compared with moderately-differentiated iCCA-derived EVs. Of note, higher dihydroceramide content was associated with vascular invasion. Cancer-derived EVs induced the release of pro-inflammatory cytokines in monocytes. Inhibition of synthesis of ceramide with Myriocin, a specific inhibitor of the serine palmitoyl transferase, reduced the pro-inflammatory activity of iCCA-derived EVs, demonstrating a role for ceramide as mediator of inflammation in iCCA. In conclusion, iCCA-derived EVs may promote iCCA progression by exporting the excess of pro-apoptotic and pro-inflammatory ceramides.