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Malignant pleural mesothelioma is a rare cancer characterized by a very poor prognosis. Exposure to asbestos is the leading cause of malignant pleural mesothelioma. The preinvasive lesions, the mesothelial hyperplasia and its possible evolution are the focus of the majority of the studies aiming to identify the treatable phase of the disease. The role of BAP-1 and MTAP in the diagnosis of mesothelioma in situ and in the prognosis of malignant pleural mesothelioma is the main topic of recent studies. The management of preinvasive lesions in mesothelioma is still unclear and many aspects are the subject of debate. The diagnosis, the disease staging and the accurate, comprehensive assessment of patients are three key instants for an appropriate management of patients/the disease.

Rectal cancer is estimated to increase due to an expanding aging population, thus affecting elderly patients more frequently. The optimal surgical treatment for this type of patient remains controversial because they are often excluded from or underrepresented in trials. This meta-analysis aimed to evaluate the feasibility and the safety of robotic surgery in elderly patients (>70 years old) undergoing curative treatment for rectal cancer. Studies comparing elderly (E) and young (Y) patients submitted to robotic rectal resection were searched on PubMed, Embase, and the Cochrane Library. Data regarding surgical oncologic quality, post-operative, and survival outcomes were extracted. Overall, 322 patients underwent robotic resection (81 in the E group and 241 in the Y group) for rectal cancer. No differences between the two groups were found regarding distal margins and the number of nodes yielded (12.70 in the E group vs. 14.02 in the Y group, p = 0.16). No differences were found in conversion rate, postoperative morbidity, mortality, and length of stay. Survival outcomes were only reported in one study. The results of this study suggest that elderly patients can be submitted to robotic resection for rectal cancer with the same oncologic surgical quality offered to young patients, without increasing postoperative mortality and morbidity.

Background: Almost 30% of patients with rectal cancer (RC) who submit to comprehensive treatment experience relapse. Surveillance plays a leading role in early detection. The landmark approach provides a more flexible and dynamic framework for survival prediction. Objective: This large retrospective study aims to develop a machine learning algorithm to profile the patient prognosis, especially the risk and the onset of RC relapse after curative resection. Methods: A cohort of 2450 RC patients were analyzed using landmark analysis. Model A applied a classical cause-specific Cox approach with a landmarking approach, while Model B implemented a landmarking-based RSF (random survival forest) competing risk algorithm. The two models were compared in terms of predictive and interpretative ability. A bootstrapped validation strategy was employed to validate the model’s performance and prevent overfitting. The best-performing hyperparameters were selected systematically, ensuring the model’s robustness within the landmark approach. The study assessed these factors’ importance and interactions using RSF and compared the predictive accuracy to that of the classical Cox model. Results: Model B outperformed Model A (mean C-index 0.95 vs. 0.78), capturing complex interactions and providing dynamic, individualized relapse predictions. Clinical factors influencing survival outcomes were identified across time with the landmark approach allowing for more accurate and timely predictions. Conclusions: The landmark approach offers an improvement over traditional methods in survival analysis. By accommodating time-dependent variables and the evolving nature of patient data, this approach provides a precise tool for profiling RC survival, thereby supporting more informed and dynamic clinical decision-making.

Elasmobranchs are among the most threatened long-lived marine species worldwide, and incidental capture is a major source of mortality. The northern central Adriatic Sea, though one of the most overfished basins of the Mediterranean Sea, supports a very valuable marine biodiversity, including elasmobranchs. This study assesses the impact of the northern central Adriatic pelagic trawl fishery on common smooth-hound (Mustelus mustelus), spiny dogfish (Squalus acanthias), common eagle ray (Myliobatis aquila), and pelagic stingray (Pteroplatytrygon violacea) by examining incidental catches recorded between 2006 and 2015. The distribution of bycatch events was evaluated using geo-referenced data. Generalized Linear Models were computed to standardize the catch of the four species and to predict the relative abundance of bycatch events. Data analysis shows that most bycatch events involving all four species occurred in the northern Adriatic Sea. The models predicted significant, distinct temporal patterns of standardized catches in line with previous investigations. Water depth, season, and fishing region were the best predictors to explain bycatch events. The present data suggest that the northern Adriatic may be an important nursery area for several elasmobranchs. They also highlight the urgent need for a better understanding of the interactions between elasmobranchs and fisheries to develop and apply suitable, ad hoc management measures.

Fisheries bycatch is recognised as a global threat to vulnerable marine megafauna and historical data can contribute to quantify the magnitude of the impact. Here, we present a collection of three datasets generated between 2006 and 2019 by a monitoring programme on marine megafauna bycatch in one of the main Italian fisheries, the northern central Adriatic midwater pair trawl fishery. The three datasets consist of: (i) monitored fishing effort; (ii) bycatch and biological data of dolphins, sea turtles and elasmobranchs; (iii) and dolphin sightings. Some information included in these datasets has already proved to provide a unique opportunity to estimate total incidental capture of species of conservation concern and trends of their relative abundance over time in the northern - central Adriatic Sea. These datasets are expected to be considered by different end users to improve the conservation of species and fishery management approaches to assess the impact of a fishery on species of conservation concern.Measurement(s)marine megafauna bycatchTechnology Type(s)monitoring • digital curationFactor Type(s)temporal intervalSample Characteristic - OrganismTursiops truncatus • Caretta caretta • ElasmobranchsSample Characteristic - EnvironmentseaSample Characteristic - LocationAdriatic SeaMachine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.17152577

The air in the Lombardy region, Italy, is one of the most polluted in Europe because of limited air circulation and high emission levels. There is a large scientific consensus that the agricultural sector has a significant impact on air quality. To support studies quantifying the role of the agricultural and livestock sectors on the Lombardy air quality, this paper presents a harmonised dataset containing daily values of air quality, weather, emissions, livestock, and land and soil use in the years 2016–2021, for the Lombardy region. The daily scale is obtained by averaging hourly data and interpolating other variables. In fact, the pollutant data come from the European Environmental Agency and the Lombardy Regional Environment Protection Agency, weather and emissions data from the European Copernicus programme, livestock data from the Italian zootechnical registry, and land and soil use data from the CORINE Land Cover project. The resulting dataset is designed to be used as is by those using air quality data for research.

Delay discounting (DD) is a quantifiable psychological phenomenon that regulates decision-making. Nevertheless, the neural substrates of DD and its relationship with other cognitive domains are not well understood. The orbitofrontal cortex (OFC) is a potential candidate for supporting the expression of DD, but due to its wide involvement in several psychological functions and neural networks, its central role remains elusive. In this study, healthy subjects underwent transcranial direct current stimulation (tDCS) while performing an intertemporal choice task for the quantification of DD and a working memory task. To selectively engage the OFC, two electrode configurations have been tested, namely, anodal Fp1–cathodal Fp2 and cathodal Fp1–anodal Fp2. Our results show that stimulation of the OFC reduces DD, independently from electrode configuration. In addition, no relationship was found between DD measures and either working memory performance or baseline impulsivity assessed through established tests. Our work will direct future investigations aimed at unveiling the specific neural mechanisms underlying the involvement of the OFC in DD, and at testing the efficacy of OFC tDCS in reducing DD in psychological conditions where this phenomenon has been strongly implicated, such as addiction and eating disorders.

Background: Metacognition refers to the ability to reflect on and regulate cognitive processes. Despite advances in neuroimaging and lesion studies, its neural correlates, as well as their interplay with other cognitive domains, remain poorly understood. The orbitofrontal cortex (OFC) is proposed as a potential substrate for metacognitive processing due to its contribution to evaluating and integrating reward-related information, decision-making, and self-monitoring. Methods: This study examined OFC involvement in metacognition using transcranial direct current stimulation (tDCS) while participants performed a two-alternative forced choice task with confidence ratings to assess their metacognitive sensitivity. Before stimulation, the subjects completed the Metacognitions Questionnaire-30 and a monetary intertemporal choice task for the quantification of delay discounting. Results: Linear mixed-effects models showed that anodal tDCS over the left OFC reduced participants’ metacognitive sensitivity compared to sham stimulation, leaving perceptual decision-making accuracy unaffected. Moreover, real stimulation increased self-reported confidence ratings compared to the sham. Significant correlations were found between metacognitive sensitivity and negative beliefs about thinking. Conclusions: These results highlight the potential involvement of the OFC in the processing of retrospective second-order judgments about decision-making performance. Additionally, they support the notion that OFC overstimulation contributes to metacognitive dysfunctions detected in clinical conditions, such as difficulties in assessing the reliability of one’s thoughts and decision outcomes.

The phenomenon of neural plasticity pertains to the intrinsic capacity of neurons to undergo structural and functional reconfiguration through learning and experiential interaction with the environment. These changes could manifest themselves not only as a consequence of various life experiences but also following therapeutic interventions, including the application of noninvasive brain stimulation (NIBS) and psychotherapy. As standalone therapies, both NIBS and psychotherapy have demonstrated their efficacy in the amelioration of psychiatric disorders’ symptoms, with a certain variability in terms of effect sizes and duration. Consequently, scholars suggested the convenience of integrating the two interventions into a multimodal treatment to boost and prolong the therapeutic outcomes. Such an approach is still in its infancy, and the physiological underpinnings substantiating the effectiveness and utility of combined interventions are still to be clarified. Therefore, this opinion paper aims to provide a theoretical framework consisting of compelling arguments as to why adding NIBS to psychotherapy can promote therapeutic change. Namely, we will discuss the physiological effects of the two interventions, thus providing a rationale to explain the potential advantages of a combined approach.

This study presents a comparative analysis of three predictive models with an increasing degree of flexibility: hidden dynamic geostatistical models (HDGM), generalised additive mixed models (GAMM), and the random forest spatiotemporal kriging models (RFSTK). These models are evaluated for their effectiveness in predicting PM2.5 concentrations in Lombardy (North Italy) from 2016 to 2020. Despite differing methodologies, all models demonstrate proficient capture of spatiotemporal patterns within air pollution data with similar out-of-sample performance. Furthermore, the study delves into station-specific analyses, revealing variable model performance contingent on localised conditions. Model interpretation, facilitated by parametric coefficient analysis and partial dependence plots, unveils consistent associations between predictor variables and PM2.5 concentrations. Despite nuanced variations in modelling spatiotemporal correlations, all models effectively accounted for the underlying dependence. In summary, this study underscores the efficacy of conventional techniques in modelling correlated spatiotemporal data, concurrently highlighting the complementary potential of Machine Learning and classical statistical approaches.