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Abstract Good data quality is vital for personalising plans in rehabilitation. Machine learning (ML) improves prognostics but integrating it with Multiple Imputation (MImp) for dealing missingness is an unexplored field. This work aims to provide post-stroke ambulation prognosis, integrating MImp with ML, and identify the prognostic influential factors. Stroke survivors in intensive rehabilitation were enrolled. Data on demographics, events, clinical, physiotherapy, and psycho-social assessment were collected. An independent ambulation at discharge, using the Functional Ambulation Category scale, was the outcome. After handling missingness using MImp, ML models were optimised, cross-validated, and tested. Interpretability techniques analysed predictor contributions. Pre-MImp, the dataset included 54.1% women, 79.2% ischaemic patients, median age 80.0 (interquartile range: 15.0). Post-MImp, 368 non-ambulatory patients on 10 imputed datasets were used for training, 80 for testing. The random forest (the validation best-performing algorithm) obtained 75.5% aggregated balanced accuracy on the test set. The main predictors included modified Barthel index, Fugl-Meyer assessment/motricity index, short physical performance battery, age, Charlson comorbidity index/cumulative illness rating scale, and trunk control test. This is among the first studies applying ML, together with MImp, to predict ambulation recovery in post-stroke rehabilitation. This pipeline reliably exploits the potential of incomplete datasets for healthcare prognosis, identifying relevant predictors.

Abstract Background Lung cancer (LC) is among the most common neoplasms, mostly caused by smoking. This study, carried out within the ACAB project, aims to provide local, updated and systematic estimates of years lived with disability (YLD) from LC due to smoking in the Tuscany region, Italy. Methods We estimated YLD for the year 2022 for the whole region and at subregional level by local health unit (LHU) using data from the Tuscany Cancer Registry and local surveys. YLD were calculated by applying the severity-specific LC prevalence, estimated with an incidence-based disease model, to the corresponding disability weight. The burden from smoking was computed by: modelling the prevalence of smokers with a Bayesian Dirichlet-Multinomial regression model; estimating the distribution of smokers by pack-years simulating individual smoking histories; collecting relative risks from the literature. Results In 2022 in Tuscany, LC caused 7.79 (95% uncertainty interval [UI] = 2.26, 17.27) and 25.50 (95% UI = 7.30, 52.68) YLDs per 100,000 females and males, respectively, with slight variations by LHU, and 53% and 66% of the YLDs were caused by smoking. Conclusion The updated estimates of the burden of LC attributable to smoking for the Tuscany region as a whole and for each LHU provide indications to inform strategic prevention plans and set public health priorities. The impact of smoking on YLDs from LC is not negligible and heterogeneous by LHU, thus requiring local interventions.

Abstract This study aims to assess the predictive capability of cylindrical Tumor Growth Rate (cTGR) in the prediction of early progression of well-differentiated gastro-entero-pancreatic tumours after Radio Ligand Therapy (RLT), compared to the conventional TGR. Fifty-eight patients were included and three CT scans per patient were collected at baseline, during RLT, and follow-up. RLT response, evaluated at follow-up according to RECIST 1.1, was calculated as a percentage variation of lesion diameters over time (continuous values) and as four different RECIST classes. TGR between baseline and interim CT was computed using both conventional (approximating lesion volume to a sphere) and cylindrical (called cTGR, approximating lesion volume to an elliptical cylinder) formulations. Receiver Operating Characteristic (ROC) curves were employed for Progressive Disease class prediction, revealing that cTGR outperformed conventional TGR (area under the ROC equal to 1.00 and 0.92, respectively). Multivariate analysis confirmed the superiority of cTGR in predicting continuous RLT response, with a higher coefficient for cTGR (1.56) compared to the conventional one (1.45). This study serves as a proof of concept, paving the way for future clinical trials to incorporate cTGR as a valuable tool for assessing RLT response.

Abstract We propose a compartmental model for investigating smoking dynamics in an Italian region (Tuscany). Calibrating the model on local data from 1993 to 2019, we estimate the probabilities of starting and quitting smoking and the probability of smoking relapse. Then, we forecast the evolution of smoking prevalence until 2043 and assess the impact on mortality in terms of attributable deaths. We introduce elements of novelty with respect to previous studies in this field, including a formal definition of the equations governing the model dynamics and a flexible modelling of smoking probabilities based on cubic regression splines. We estimate model parameters by defining a two-step procedure and quantify the sampling variability via a parametric bootstrap. We propose the implementation of cross-validation on a rolling basis and variance-based Global Sensitivity Analysis to check the robustness of the results and support our findings. Our results suggest a decrease in smoking prevalence among males and stability among females, over the next two decades. We estimate that, in 2023, 18% of deaths among males and 8% among females are due to smoking. We test the use of the model in assessing the impact on smoking prevalence and mortality of different tobacco control policies, including the tobacco-free generation ban recently introduced in New Zealand.

Background and aimsThis study aimed to explore the complex phenomenon of emotional dysregulation, particularly in adolescence, which is associated with many mental health disorders and problems. Increasing the knowledge of clinicians and researchers in this area can be helpful in guiding future treatment plans. The aim of the study was to investigate, from an exploratory perspective, which structural aspects of adolescent functioning (assessed using the Rorschach test and administered and scored according to the Comprehensive System, CS, by Exner) were associated with different dimensions of emotional dysregulation (evaluated using the Difficulties in Emotion Dysregulation Scale, DERS).MethodSecondary data were used for the study, which included 100 adolescents, with 50 in the clinical group (patients with complex trauma histories residing in therapeutic and socio-rehabilitative communities) and 50 in the nonclinical group (recruited from a scout group and middle and high schools). The two groups were compared on terms of the mean scores obtained in the DERS scales (one-tailed t-test) and the proportions of cases that obtained pathological values for selected Rorschach CS indicators (z-test). Partial correlations were calculated between the DERS scales and the Rorschach CS variables to explore which structural dimensions of functioning were associated with different characteristics of emotional dysregulation.ResultsThe results indicated that the two groups differed in their outcomes on all DERS scales, except for Awareness and Goals, and on four Rorschach CS variables (EgoIndex, a:p, Wsum6, and MOR). Some significant positive and negative correlations between the Rorschach CS variables and the DERS scales also emerged.ConclusionThese results suggest that the dimensions of functioning associated with emotional dysregulation are related to self-representation, relational immaturity, and thought processes character and characterize membership in a therapeutic community. The correlations described in the article warrants further consideration. Finally, the study’s limitations and future research prospects are presented.

IntroductionAlthough older adults are particularly vulnerable to the effects of climate change, they seem to be overall less concerned about it, and less inclined to support climate policies. The study aims to identify the communication strategies that have been evaluated in promoting awareness and/or climate friendly behaviors in older adults.MethodsWe searched multiple electronic databases for studies that evaluated the effects of any interventions aimed at communicating climate change to older persons (over 65 years) and assessed the results as awareness and /or behavioral changes. We selected quantitative, qualitative and mixed methods studies, and we also included systematic reviews for cross-referencing. Risk of bias of included studies was evaluated using different tools according to the study design.ResultsFrom a total of 5,486 articles, only 3 studies were included. One mixed-method study engaged older adults to assess the community vulnerability to climate change and to develop adaptation recommendations based on their perspectives; one qualitative study conducted focus groups to identify the more effective language, values and themes based on participants’ responses to narratives; one quantitative study utilized a 360-degree audio-visual platform allowing users to engage with immersive visualizations of sea-level rise scenarios.DiscussionDespite the paucity of literature, this review demonstrates the potential for different strategies to increase the awareness of older persons about climate change. The involvement of older adults in the communication process, the identification of their priorities, and the integration of technology in their daily lives are promising approaches but more research, including both quantitative and qualitative studies is recommended on this topic.Systematic review registerationFor further details about the protocol, this systematic review has been registered on PROSPERO on July 1, 2023 (https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023438256).

Human endogenous retroviruses (HERVs) are retroviral sequences integrated into 8% of the human genome resulting from ancient exogenous retroviral infections. Unlike endogenous retroviruses of other mammalian species, HERVs are mostly replication and retro-transposition defective, and their transcription is strictly regulated by epigenetic mechanisms in normal cells. A significant addition to the growing body of research reveals that HERVs’ aberrant activation is often associated with offsetting diseases like autoimmunity, neurodegenerative diseases, cancers, and chemoresistance. Adult T-cell leukemia/lymphoma (ATLL) is a very aggressive and chemoresistant leukemia caused by the human T-cell leukemia virus type 1 (HTLV-1). The prognosis of ATLL remains poor despite several new agents being approved in the last few years. In the present study, we compare the expression of HERV genes in CD8+-depleted PBMCs from HTLV-1 asymptomatic carriers and patients with acute ATLL. Herein, we show that HERVs are highly upregulated in acute ATLL. Our results further demonstrate that the oncoprotein Fra-2 binds the LTR region and activates the transcription of several HERV families, including HERV-H and HERV-K families. This raises the exciting possibility that upregulated HERV expression could be a key factor in ATLL development and the observed chemoresistance, potentially leading to new therapeutic strategies and significantly impacting the field of oncology and virology.

BackgroundIntensive treadmill training (TT) has been documented to improve gait parameters and functional independence in Parkinson’s Disease (PD), but the optimal intervention protocol and the criteria for tailoring the intervention to patients’ performances are lacking. TT may be integrated with augmented virtual reality (AVR), however, evidence of the effectiveness of this combined treatment is still limited. Moreover, prognostic biomarkers of rehabilitation, potentially useful to customize the treatment, are currently missing. The primary aim of this study is to compare the effects on gait performances of TT + AVR versus TT alone in II-III stage PD patients with gait disturbance. Secondary aims are to assess the effects on balance, gait parameters and other motor and non-motor symptoms, and patient’s satisfaction and adherence to the treatment. As an exploratory aim, the study attempts to identify biomarkers of neuroplasticity detecting changes in Neurofilament Light Chain concentration T0-T1 and to identify prognostic biomarkers associated to blood-derived Extracellular Vesicles.MethodsSingle-center, randomized controlled single-blind trial comparing TT + AVR vs. TT in II-III stage PD patients with gait disturbances. Assessment will be performed at baseline (T0), end of training (T1), 3 (T2) and 6 months (T3, phone interview) from T1. The primary outcome is difference in gait performance assessed with the Tinetti Performance-Oriented Mobility Assessment gait scale at T1. Secondary outcomes are differences in gait performance at T2, in balance and spatial–temporal gait parameters at T1 and T2, patients’ satisfaction and adherence. Changes in falls, functional mobility, functional autonomy, cognition, mood, and quality of life will be also assessed at different timepoints. The G*Power software was used to estimate a sample size of 20 subjects per group (power 0.95, α

IntroductionReliable and valid assessment tools are needed to evaluate and predict physical function in older hospitalized patients. The aim of this study is to develop the Italian version of the Elderly Mobility Scale (I-EMS) and to evaluate its validity and inter-rater reliability for use with geriatric inpatients.MethodsThe study consists of two phases: (i) translation, where EMS version 2 was translated into Italian by two teams, each comprising 2 bilingual physiotherapists; translations were back translated by 2 native English speakers, and a committee of 2 physiotherapists and 1 physician refined the initial I-EMS version, which was pilot-tested for clarity in a group of ten experienced geriatric physiotherapists; (ii) an observational study assessed I-EMS metrics (reliability, validity) in older Italian inpatients at IRCCS INRCA (Ancona, Italy) between September 2022 and April 2023.ResultsNo statistically significant differences were found between the scores of individual items and the total score assigned by different raters. The ICC for total I-EMS was 0.951, SEM was 1.10 and MDC95 was 3.06. The absolute agreement and weighted kappa for individual items ranged 80.32–100% and 8.2–1, respectively. The validity of I-EMS was supported by a significant (p

ObjectiveThe objective of this study is to verify the reliability and the concurrent and discriminant validity of the measurements of spasticity offered by the robotic device, quantifying the (1) test–retest reliability, (2) correlation with the clinical evaluation using the Modified Ashworth Scale (MAS), (3) inter-rater reliability between the two physiotherapists, and (4) ability to discriminate between healthy and stroke patients.MethodsA total of 20 stroke patients and 20 healthy volunteers participated in the study. Two physical therapists (PT1 and PT2) independently evaluated the hand spasticity of stroke subjects using the MAS. Spasticity was assessed, both in healthy and stroke patients, with the Amadeo device at three increasing velocities of passive movement for three consecutive repeated assessments, while raw data of force and position were collected through an external program.Data analysisThe intraclass correlation coefficient (ICC) and the weighted kappa were computed to estimate the reliability of the Amadeo device measurements, the inter-rater reliability of MAS, and the correlation between the MAS and Amadeo device measurements. The discriminant ability of the Amadeo device was assessed by comparing the stroke and healthy subjects' spasticity measurements with the percentage of agreements with 0 in MAS for healthy subjects.ResultsThe test–retest reliability of the Amadeo device was high with ICC at all three velocities (ICC = 0.908, 0.958, and 0.964, respectively) but lower if analyzed with weighted kappa correlation (0.584, 0.748, and 0.749, respectively) as mean values for each velocity. The correlation between Amadeo and the clinical scale for stroke patients with weighted kappa correlation was poor (0.280 ± 0.212 for PT1 and 0.290 ± 0.155 for PT2). The inter-rater reliability of the clinical MAS was high (ICC = 0.911).ConclusionBoth MAS and Amadeo spasticity scores showed good reliability. The Amadeo scores did not show a strong clinical correlation with the MAS in stroke patients. Hitherto, Amadeo evaluation shows trends that are consistent with the characteristics of spasticity, such as an increase in spasticity as the speed of muscle stretching increases. The ability of the device to discriminate between stroke patients and healthy controls is low. Future studies adopting an instrumental gold standard for spasticity may provide further insight into the validity of these measurements.

ABSTRACT: Objectives: The aim of this study was to evaluate the impact of resistance mutations on efficacy of dolutegravir-based two-drug regimens (2DR). Methods: Virologically suppressed patients with HIV-1 switching to dolutegravir + lamivudine or rilpivirine or to a dolutegravir-based three-drug regimen (3DR) with pre-baseline genotype were selected. Virological failure (VF) was defined as one HIV-RNA viral load (VL) >200 cps/mL or two consecutive VL >50 cps/mL; treatment failure (TF) was defined as VF or treatment discontinuation (TD). Resistance was defined as at least low-level resistance to at least one drug of the current regimen. Propensity score matching was used to conduct adjusted analyses within a competing risks framework. Results: A total of 971 dolutegravir-based regimens were selected: 339 (34.9%) 2DR and 632 (65.1%) 3DR. The adjusted cumulative 48-week incidence of VF was 4.2% (90% CI 3.1%–5.3%) with 2DR and 4.7% (90% CI 3.5%–5.8%) with 3DR. The cumulative 48-week incidence of TF was 15.8% (90% CI 13.9%–17.9%) with 2DR and 24.5% (90% CI 22.2%–27.0%) with 3DR. For VF, the estimated hazard ratio (HR) for 2DR vs. 3DR was 1.02 (90% CI: 0.78–1.34), with evidence of effect modification by low-level resistance (HR 3.96, 90% CI: 2.10–7.46). The estimated HR of TF for 2DR vs. 3DR was 0.54 (90% CI: 0.48–0.60). The 48-week cumulative incidence of TD was 11.7% (8.7%, 14.6%) in 2DR and 19.6% (16.9%, 22.4%) in 3DR. Conclusions: Dolutegravir-based 2DR showed high virological efficacy and durability; however, past resistance increased the risk of VF, but not of TD or TF.

Abstract Background Lack of effective peer-review process of predatory journals, resulting in more ambiguity in reporting, language and incomplete descriptions of processes might have an impact on the reliability of PEDro scale. The aim of this investigation was to compare the reliability of the PEDro scale when evaluating the methodological quality of RCTs published in predatory (PJs) and non-predatory (NPJs) journals, to more confidently select interventions appropriate for application to practice. Methods A selected sample of RCTs was independently rated by two raters randomly selected among 11 physical therapists. Reliability of each item of the PEDro scale and the total PEDro score were assessed by Cohen’s kappa statistic and percent of agreement and by Intraclass Correlation Coefficients (ICC) and the Standard Error of Measurement (SEM), respectively. The Chi-square test was used to compare the rate of agreement between PJs and NPJs. Results A total number of 298 RCTs were assessed (119 published in NPJs). Cronbach’s alphas were .704 and .845 for trials published in PJs and NPJs, respectively. Kappa values for individual scale items ranged from .14 to .73 for PJs and from .09 to .70 for NPJs. The ICC was .537 (95% CI .425—.634) and .729 (95% CI .632-.803), and SEM was 1.055 and 0.957 for PJs and NPJs, respectively. Inter-rater reliability in discriminating between studies of moderate to high and low quality was higher for NPJs (k = .57) than for PJs (k = .28). Conclusions Interrater reliability of PEDro score of RCTs published in PJs is lower than that of trials published in NPJs, likely also due to ambiguous language and incomplete reporting. This might make the detection of risk of bias more difficult when selecting interventions appropriate for application to practice or producing secondary literature.

BackgroundStroke represents the second preventable cause of death after cardiovascular disease and the third global cause of disability. In countries where national registries of the clinical quality of stroke care have been established, the publication and sharing of the collected data have led to an improvement in the quality of care and survival of patients. However, information on rehabilitation processes and outcomes is often lacking, and predictors of functional outcomes remain poorly explored. This paper describes a multicenter study protocol to implement a Stroke rehabilitation Registry, mainly based on a multidimensional assessment proposed by the Italian Society of Physical and Rehabilitation Medicine (PMIC2020), in a pilot Italian cohort of stroke survivors undergoing post-acute inpatient rehabilitation, to provide a systematic assessment of processes and outcomes and develop data-driven prediction models of functional outcomes.MethodsAll patients with a diagnosis of ischemic or haemorrhagic stroke confirmed by clinical assessment, admitted to intensive rehabilitation units within 30 days from the acute event, aged 18+, and providing informed consent will be enrolled. Measures will be taken at admission (T0), at discharge (T1), and at follow-up, 3 months (T2) and 6 months (T3) after the stroke. Assessment variables include anamnestic data, clinical and nursing complexity information and measures of body structures and function, activity and participation (PMIC2020), rehabilitation interventions, adverse events and discharge data. The modified Barthel Index will be our primary outcome. In addition to classical biostatistical analysis, learning algorithms will be cross-validated to achieve data-driven prognosis prediction models.ConclusionsThis study will test the feasibility of a stroke rehabilitation registry in the Italian health context and provide a systematic assessment of processes and outcomes for quality assessment and benchmarking. By the development of data-driven prediction models in stroke rehabilitation, this study will pave the way for the development of decision support tools for patient-oriented therapy planning and rehabilitation outcomes maximization.Clinical tial registrationThe registration on ClinicalTrials.gov is ongoing and under review. The identification number will be provided when the review process will be completed.

Abstract To develop predictive models of side effect occurrence in GEPNET treated with PRRT. Metastatic GEPNETs patients treated in our centre with PRRT (177Lu-Oxodotreotide) from 2019 to 2020 were considered. Haematological, liver and renal toxicities were collected and graded according to CTCAE v5. Patients were grouped according with ECOG-PS, number of metastatic sites, previous treatment lines and therapies received before PRRT. A FLIC model with backward selection was used to detect the most relevant predictors. A subsampling approach was implemented to assess variable selection stability and model performance. Sixty-seven patients (31 males, 36 females, mean age 63) treated with PRRT were considered and followed up for 30 weeks from the beginning of the therapy. They were treated with PRRT as third or further lines in 34.3% of cases. All the patients showed at least one G1–G2, meanwhile G3–G5 were rare events. No renal G3–G4 were reported. Line of PRRT administration, age, gender and ECOG-PS were the main predictors of haematological, liver and renal CTCAE. The model performance, expressed by AUC, was > 65% for anaemia, creatinine and eGFR. The application of FLIC model can be useful to improve GEPNET decision-making, allowing clinicians to identify the better therapeutic sequence to avoid PRRT-related adverse events, on the basis of patient characteristics and previous treatment lines.

During autumn 2020, Italy faced a second important SARS-CoV-2 epidemic wave. We explored the time pattern of the instantaneous reproductive number, R0(t), and estimated the prevalence of infections by region from August to December calibrating SIRD models on COVID-19-related deaths, fixing at values from literature Infection Fatality Rate (IFR) and average infection duration. A Global Sensitivity Analysis (GSA) was performed on the regional SIRD models. Then, we used Bayesian meta-analysis and meta-regression to combine and compare the regional results and investigate their heterogeneity. The meta-analytic R0(t) curves were similar in the Northern and Central regions, while a less peaked curve was estimated for the South. The maximum R0(t) ranged from 2.15 (South) to 2.61 (North) with an increase following school reopening and a decline at the end of October. The predictive performance of the regional models, assessed through cross validation, was good, with a Mean Absolute Percentage Error of 7.2% and 10.9% when considering prediction horizons of 7 and 14 days, respectively. Average temperature, urbanization, characteristics of family medicine and healthcare system, economic dynamism, and use of public transport could partly explain the regional heterogeneity. The GSA indicated the robustness of the regional R0(t) curves to different assumptions on IFR. The infectious period turned out to have a key role in determining the model results, but without compromising between-region comparisons.

Abstract Background Missing data are common in end-of-life care studies, but there is still relatively little exploration of which is the best method to deal with them, and, in particular, if the missing at random (MAR) assumption is valid or missing not at random (MNAR) mechanisms should be assumed. In this paper we investigated this issue through a sensitivity analysis within the ACTION study, a multicenter cluster randomized controlled trial testing advance care planning in patients with advanced lung or colorectal cancer. Methods Multiple imputation procedures under MAR and MNAR assumptions were implemented. Possible violation of the MAR assumption was addressed with reference to variables measuring quality of life and symptoms. The MNAR model assumed that patients with worse health were more likely to have missing questionnaires, making a distinction between single missing items, which were assumed to satisfy the MAR assumption, and missing values due to completely missing questionnaire for which a MNAR mechanism was hypothesized. We explored the sensitivity to possible departures from MAR on gender differences between key indicators and on simple correlations. Results Up to 39% of follow-up data were missing. Results under MAR reflected that missingness was related to poorer health status. Correlations between variables, although very small, changed according to the imputation method, as well as the differences in scores by gender, indicating a certain sensitivity of the results to the violation of the MAR assumption. Conclusions The findings confirmed the importance of undertaking this kind of analysis in end-of-life care studies.

Abstract Background The shape of the exposure-response curve describing the effects of air pollution on population health has crucial regulatory implications, and it is important in assessing causal impacts of hypothetical policies of air pollution reduction. Methods After having reformulated the problem of assessing the short-term impact of air pollution on health within the potential outcome approach to causal inference, we developed a method based on the generalized propensity score (GPS) to estimate the average dose-response function (aDRF) and quantify attributable deaths under different counterfactual scenarios of air pollution reduction. We applied the proposed approach to assess the impact of airborne particles with a diameter less than or equal to 10 μm (PM10) on deaths from natural, cardiovascular and respiratory causes in the city of Milan, Italy (2003-2006). Results As opposed to what is commonly assumed, the estimated aDRFs were not linear, being steeper for low-moderate values of exposure. In the case of natural mortality, the curve became flatter for higher levels; this behavior was less pronounced for cause-specific mortality. The effect was larger in days characterized by higher temperature. According to the curves, we estimated that a hypothetical intervention able to set the daily exposure levels exceeding 40 μg/m3 to exactly 40 would have avoided 1157 deaths (90%CI: 689, 1645) in the whole study period, 312 of which for respiratory causes and 771 for cardiovascular causes. These impacts were higher than those obtained previously from regression-based methods. Conclusion This novel method based on the GPS allowed estimating the average dose-response function and calculating attributable deaths, without requiring strong assumptions about the shape of the relationship. Its potential as a tool for investigating effect modification by temperature and its use in other environmental epidemiology contexts deserve further investigation.

This paper explores, by using suitable quantitative techniques, to what extent the intellectual proximity among scholarly journals is also proximity in terms of social communities gathered around the journals. Three fields are considered: statistics, economics and information and library sciences. Co-citation networks represent intellectual proximity among journals. The academic communities around the journals are represented by considering the networks of journals generated by authors writing in more than one journal (interlocking authorship: IA), and the networks generated by scholars sitting on the editorial board of more than one journal (interlocking editorship: IE). Dissimilarity matrices are considered to compare the whole structure of the networks. The CC, IE, and IA networks appear to be correlated for the three fields. The strongest correlation is between CC and IA for the three fields. Lower and similar correlations are obtained for CC and IE, and for IE and IA. The CC, IE, and IA networks are then partitioned in communities. Information and library sciences is the field in which communities are more easily detectable, whereas the most difficult field is economics. The degrees of association among the detected communities show that they are not independent. For all the fields, the strongest association is between CC and IA networks; the minimum level of association is between IE and CC. Overall, these results indicate that intellectual proximity is also proximity among authors and among editors of the journals. Thus, the three maps of editorial power, intellectual proximity, and authors communities tell similar stories.

Prostate cancer (PCa) is the most frequent male malignancy and the assessment of PCa aggressiveness, for which a biopsy is required, is fundamental for patient management. Currently, multiparametric (mp) MRI is strongly recommended before biopsy. Quantitative assessment of mpMRI might provide the radiologist with an objective and noninvasive tool for supporting the decision-making in clinical practice and decreasing intra- and inter-reader variability. In this view, high dimensional radiomics features and Machine Learning (ML) techniques, along with Deep Learning (DL) methods working on raw images directly, could assist the radiologist in the clinical workflow. The aim of this study was to develop and validate ML/DL frameworks on mpMRI data to characterize PCas according to their aggressiveness. We optimized several ML/DL frameworks on T2w, ADC and T2w+ADC data, using a patient-based nested validation scheme. The dataset was composed of 112 patients (132 peripheral lesions with Prostate Imaging Reporting and Data System (PI-RADS) score ≥ 3) acquired following both PI-RADS 2.0 and 2.1 guidelines. Firstly, ML/DL frameworks trained and validated on PI-RADS 2.0 data were tested on both PI-RADS 2.0 and 2.1 data. Then, we trained, validated and tested ML/DL frameworks on a multi PI-RADS dataset. We reported the performances in terms of Area Under the Receiver Operating curve (AUROC), specificity and sensitivity. The ML/DL frameworks trained on T2w data achieved the overall best performance. Notably, ML and DL frameworks trained and validated on PI-RADS 2.0 data obtained median AUROC values equal to 0.750 and 0.875, respectively, on unseen PI-RADS 2.0 test set. Similarly, ML/DL frameworks trained and validated on multi PI-RADS T2w data showed median AUROC values equal to 0.795 and 0.750, respectively, on unseen multi PI-RADS test set. Conversely, all the ML/DL frameworks trained and validated on PI-RADS 2.0 data, achieved AUROC values no better than the chance level when tested on PI-RADS 2.1 data. Both ML/DL techniques applied on mpMRI seem to be a valid aid in predicting PCa aggressiveness. In particular, ML/DL frameworks fed with T2w images data (objective, fast and non-invasive) show good performances and might support decision-making in patient diagnostic and therapeutic management, reducing intra- and inter-reader variability.

We introduce a methodology based on averaging similarity matrices with the aim of integrating the layers of a multiplex network into a single monoplex network. Multiplex networks are adopted for modelling a wide variety of real-world frameworks, such as multi-type relations in social, economic and biological structures. More specifically, multiplex networks are used when relations of different nature (layers) arise between a set of elements from a given population (nodes). A possible approach for analyzing multiplex similarity networks consists in aggregating the different layers in a single network (monoplex) which is a valid representation—in some sense—of all the layers. In order to obtain such an aggregated network, we propose a theoretical approach—along with its practical implementation—which stems on the concept of similarity matrix average. This methodology is finally applied to a multiplex similarity network of statistical journals, where the three considered layers express the similarity of the journals based on co-citations, common authors and common editors, respectively.

In this work, the ability of several bis-viologen axles to thread a series of heteroditopic tris(N-phenylureido)calix[6]arene wheels to give interwoven supramolecular complexes to the [3]pseudorotaxane type was studied. The unidirectionality of the threading process inside these nonsymmetric wheels allows the formation of highly preorganised [3]pseudorotaxane and [3]rotaxane species in which the macrocycles phenylureido moieties, functionalised with either ester, carboxylic, or hydroxymethyl groups, are facing each other. As verified by NMR and semiempirical computational studies, these latter compounds possess the correct spatial arrangement of their subcomponents, which could lead, in principle, upon proper bridging reaction, to the realisation of upper-to-upper molecular capsules that are based on calix[6]arene derivatives.

Carbon losses from forest degradation and disturbances are significant and growing sources of emissions in the Brazilian Amazon. Between 2003 and 2019, degradation and disturbance accounted for 44% of forest carbon losses in the region, compared with 56% from deforestation (forest clearing). We found that land tenure played a decisive role in explaining these carbon losses, with Undesignated Public Forests and Other Lands (e.g., private properties) accounting for the majority (82%) of losses during the study period. Illegal deforestation and land grabbing in Undesignated Public Forests widespread and increasingly are important drivers of forest carbon emissions from the region. In contrast, indigenous Territories and Protected Natural Areas had the lowest emissions, demonstrating their effectiveness in preventing deforestation and maintaining carbon stocks. These trends underscore the urgent need to develop reliable systems for monitoring and reporting on carbon losses from forest degradation and disturbance. Together with improved governance, such actions will be crucial for Brazil to reduce pressure on standing forests; strengthen Indigenous land rights; and design effective climate mitigation strategies needed to achieve its national and international climate commitments.

Abstract Background When communicating risks to the general population, the format of the epidemiological results may affect individual reactions. In environmental epidemiology, no study has compared the use of different statistical formats in communicating results to the population. The aim of this paper is to investigate whether the degree of concern expressed by residents of a high environmental risk site, regarding epidemiological results on cancer mortality in the area where they live, is influenced by the statistical indicator used in communication. Methods A sample of residents in the high environmental risk area of Livorno (Italy) was randomized to respond to different questionnaires, in which the same epidemiological results were expressed by two alternative risk indexes: percent excess risk and time needed to harm, defined as the number of days that one has to wait for, on average, to observe 1 death in excess in respect to the baseline. Participants were asked to express their concern on a quantitative scale or to rank different diseases according to their impressions. The statistical analysis was performed using an Inverse Probability of Treatment Weighting approach based on propensity score, in order to account for sample stratification and adjust for unbalance between groups occurring despite randomization. Results The probability of high concern levels was larger under time needed to harm than under percent excess, with a difference between proportions of 6.7% (95% Confidence Interval, 0.6,12.8%). Mortality from sexual glands cancer was ranked as more worrisome and mortality from thyroid gland cancer as less worrisome under time needed to harm than under percent excess. No rank change was found for lung cancer. Larger differences between the two indicators arose in subjects with higher education or better numerical skills. Conclusions Communicating epidemiological results to the population is not a neutral task. The degree of concern and judgments when comparing results on different diseases may depend on the risk indicators used. Translating scientific results into lay language should not exempt from careful evaluation of the impact of this translation on lay people.

In this study, we investigated the cross-talk between mGlu1 and CB1 receptors in modulating GABA hippocampal output in whole-cell voltage clamp recordings in rat hippocampal acute slices, in organotypic hippocampal slices exposed to oxygen and glucose deprivation (OGD) and in gerbils subjected to global ischemia. CB1 receptor expression was studied using immunohistochemistry and the CA1 contents of anandamide (AEA) and 2-arachidonoylglycerol (2-AG) were measured by LC-MS/MS. Our results show that mGlu1 receptor antagonists enhance sIPSCs in CA1 pyramidal cells and the basal and ischemic hippocampal release of GABA in vivo in a manner that is mediated by CB1 receptor activation. In hippocampal slices exposed to OGD and in ischemic gerbils, mGlu1 receptor antagonists protected CA1 pyramidal cells against post-ischemic injury and this effect was reduced by CB1 receptor activation. OGD induced a transient increase in the hippocampal content of AEA and this effect is prevented by mGlu1 receptor antagonist. Finally, OGD induced a late disruption of CB1 receptors in the CA1 region and the effect was prevented when CA1 pyramidal cells were protected by mGlu1 antagonists. Altogether, these results suggest a cooperative interaction between mGlu1 receptors and the endocannabinoid system in the mechanisms that lead to post-ischemic neuronal death.

In order to standardize cellular hematology practices, the French-speaking Cellular Hematology Group (Groupe Francophone d’Hématologie Cellulaire, GFHC) focused on Perls’ stain. A national survey was carried out, leading to the proposal of recommendations on insoluble iron detection and quantification in bone marrow. The criteria presented here met with a “strong professional agreement” and follow the suggestions of the World Health Organization’s classification of hematological malignancies.

Background: Systemic sclerosis (SSc) alterations of the face and of the mouth cause aesthetic modifications and disability, impairing self-esteem and quality of life (QoL). The aim of this study was to verify the effects of two rehabilitation protocols on facial mimic and mouth opening. Methods: A total of 47 SSc patients (40 females and 7 males, mean age ± SD 59.08 ± 10.31 years), were consecutively selected: 22 were randomly assigned to protocol 1 [home exercises for temporomandibular joint (TMJ), mimic, masticatory and cervical spine muscles] and 25 to protocol 2 (home exercises and combined physiotherapeutic procedures performed by a physiotherapist). Each treatment had a duration of 12 weeks with a follow up of 8 weeks. TMJ dysfunction, orofacial involvement, disability, QoL, and safety were assessed at enrollment (T0), at the end of the treatment (T1), and at follow up (T2). Results: Both Protocol 1 and Protocol 2 induced significant improvements of some clinical and clinimetric parameters, but better results were obtained with Protocol 2. In the comparison between the effects of Protocol 1 and Protocol 2 at T1 and T2, a significant difference was observed only for Mouth Handicap in SSc [MHISS; Total ( p = 0.00178] and for MHISS Mouth opening ( p = 0.0098) at T1. No significant difference of indices of short-form 36 was observed. Conclusion: The present data suggest that TMJ involvement in SSc may be managed by rehabilitation treatments. The action of a physiotherapist prescribing and personalizing exercises may induce better therapeutic effects.

This article examines the impact of terrorism on voting behavior in the United States. We rely on an exhaustive list of terror attacks over the period 1970–2016 and exploit the inherent randomness of the success or failure of terror attacks to identify the political impacts of terrorism. We first confirm that the success of terror attacks is plausibly random by showing that it is orthogonal to potential confounders. We then show that on average successful attacks have no effect on presidential and non-presidential elections. As a benchmark, we also rely on a more naïve identification strategy using all the counties not targeted by terrorists as a comparison group. We show that using this naïve identification strategy leads to strikingly different results overestimating the effect of terror attacks on voting behavior. Overall, our results indicate that terrorism has less of an influence on voters than is usually thought.

Measures implemented in many countries to contain the COVID-19 pandemic resulted in a change in lifestyle with unpredictable consequences on physical and mental health. We aimed at identifying the variables associated with psychological distress during the lockdown between April and May 2020 in the Italian academic population. We conducted a multicenter cross-sectional online survey (IO CONTO 2020) within five Italian universities. Among about 240,000 individuals invited to participate through institutional communications, 18 120 filled the questionnaire. Psychological distress was measured by the self-administered Hospital Anxiety and Depression Scale (HADS). The covariates collected included demographic and lifestyle characteristics, trust in government, doctors and scientists. Associations of covariates with influenza-like symptoms or positive COVID-19 test and with psychological distress were assessed by multiple regression models at the local level; a meta-analysis of the results was then performed. Severe levels of anxiety or depression were reported by 20% of the sample and were associated with being a student or having a lower income, irrespective of their health condition and worries about contracting the virus. The probability of being severely anxious or depressed also depended on physical activity: compared to those never exercising, the highest OR being for those who stopped during lockdown (1.53; 95% CI, 1.28 to 1.84) and the lowest for those who continued (0.78; 95% CI, 0.64 to 0.95). Up to 21% of severe cases of anxiety or depression might have been avoided if during lockdown participants had continued to exercise as before. Socioeconomic insecurity contributes to increase mental problems related to the COVID-19 pandemic and to the measures to contain it. Maintaining or introducing an adequate level of physical activity is likely to mitigate such detrimental effects. Promoting safe practice of physical activity should remain a public health priority to reduce health risks during the pandemic.

Facing the SARS-CoV-2 epidemic requires intensive testing on the population to early identify and isolate infected subjects. During the first emergency phase of the epidemic, RT-qPCR on nasopharyngeal (NP) swabs, which is the most reliable technique to detect ongoing infections, exhibited limitations due to availability of reagents and budget constraints. This stressed the need to develop screening procedures that require fewer resources and are suitable to be extended to larger portions of the population. RT-qPCR on pooled samples from individual NP swabs seems to be a promising technique to improve surveillance. We performed preliminary experimental analyses aimed to investigate the performance of pool testing on samples with low viral load and we evaluated through Monte Carlo (MC) simulations alternative screening protocols based on sample pooling, tailored to contexts characterized by different infection prevalence. We focused on the role of pool size and the opportunity to develop strategies that take advantage of natural clustering structures in the population, e.g. families, school classes, hospital rooms. Despite the use of a limited number of specimens, our results suggest that, while high viral load samples seem to be detectable even in a pool with 29 negative samples, positive specimens with low viral load may be masked by the negative samples, unless smaller pools are used. The results of MC simulations confirm that pool testing is useful in contexts where the infection prevalence is low. The gain of pool testing in saving resources can be very high, and can be optimized by selecting appropriate group sizes. Exploiting natural groups makes the definition of larger pools convenient and potentially overcomes the issue of low viral load samples by increasing the probability of identifying more than one positive in the same pool.

With the aim of studying the spread of the SARS-CoV-2 infection in the Tuscany region of Italy during the first epidemic wave (February-June 2020), we define a compartmental model that accounts for both detected and undetected infections and assumes that only notified cases can die. We estimate the infection fatality rate, the case fatality rate, and the basic reproduction number, modeled as a time-varying function, by calibrating on the cumulative daily number of observed deaths and notified infected, after fixing to plausible values the other model parameters to assure identifiability. The confidence intervals are estimated by a parametric bootstrap procedure and a Global Sensitivity Analysis is performed to assess the sensitivity of the estimates to changes in the values of the fixed parameters. According to our results, the basic reproduction number drops from an initial value of 6.055 to 0 at the end of the national lockdown, then it grows again, but remaining under 1. At the beginning of the epidemic, the case and the infection fatality rates are estimated to be 13.1% and 2.3%, respectively. Among the parameters considered as fixed, the average time from infection to recovery for the not notified infected appears to be the most impacting one on the model estimates. The probability for an infected to be notified has a relevant impact on the infection fatality rate and on the shape of the epidemic curve. This stresses the need of collecting information on these parameters to better understand the phenomenon and get reliable predictions.

Screening plans for prevention and containment of SARS-CoV-2 infection should take into account the epidemic context, the fact that undetected infected individuals may transmit the disease and that the infection spreads through outbreaks, creating clusters in the population. In this paper, we compare through simulations the performance of six screening plans based on poorly sensitive individual tests, in detecting infection outbreaks at the level of single classes in a typical European school context. The performance evaluation is done by simulating different epidemic dynamics within the class during the four weeks following the day of the initial infection. The plans have different costs in terms of number of individual tests required for the screening and are based on recurrent evaluations on all students or subgroups of students in rotation. Especially in scenarios where the rate of contagion is high, at an equal cost, testing half of the class in rotation every week appears to be better in terms of sensitivity than testing all students every two weeks. Similarly, testing one-fourth of the students every week is comparable with testing all students every two weeks, despite the first one is a much cheaper strategy. In conclusion, we show that in the presence of natural clusters in the population, testing subgroups of individuals belonging to the same cluster in rotation may have a better performance than testing all the individuals less frequently. The proposed simulations approach can be extended to evaluate more complex screening plans than those presented in the paper.