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Abstract Childhood adversity is associated with various clinical dimensions in psychosis; however, how genetic vulnerability shapes the adversity-associated psychopathological signature is yet to be studied. We studied data of 583 First Episode Psychosis (FEP) cases from the EU-GEI FEP case-control study, including Polygenic risk scores for major depressive disorder (MDD-PRS), bipolar disorder (BD-PRS) and schizophrenia (SZ-PRS); childhood adversity measured with the total score of the Childhood Trauma Questionnaire (CTQ); and positive, negative, depressive and manic psychopathological domains from a factor model of transdiagnostic dimensions. Genes and environment interactions were explored as a departure from a multiplicative effect of PRSs and total CTQ on each dimension. Analyses were adjusted for age, sex, 10 PCA, site of recruitment and for medication. A childhood adversity and PRS multiplicative interaction was observed between A) the CTQ and MDD-PRS on the predominance of positive (β = 0.42, 95% CI = [0.155, 0.682], p = 0.004); and depressive (β = 0.33, 95% CI = [0.071, 0.591], p = 0.013) dimensions; B) between the CTQ and BD-PRS on the positive dimension (β = 0.45, 95% CI = [0.106, 0.798], p = 0.010), and C) with the CTQ and SZ-PRS on the positive dimension (β = −0.34, 95% CI = [−0.660, −0.015], p = 0.040). Bonferroni corrected p-value of significance was set at 0.0125. In conclusion, despite being underpowered, this study suggests that genetic liability for MDD and BD may have a moderating effect on the sensibility of childhood adversity on depressive and positive psychotic dimensions. This supports the hypothesis of an affective pathway to psychosis in those exposed to childhood adversity.

Abstract Background Extracellular vesicles (EVs) are cell-secreted particles conceived as natural vehicles for intercellular communication. The capacity to entrap heterogeneous molecular cargoes and target specific cell populations through EV functionalization promises advancements in biomedical applications. However, the efficiency of the obtained EVs, the contribution of cell-exposed receptors to EV interactions, and the predictability of functional cargo release with potential sharing of high molecular weight recombinant mRNAs are crucial for advancing heterologous EVs in targeted therapy applications. Methods In this work, we selected the popular EV marker CD81 as a transmembrane guide for fusion proteins with a C-terminal GFP reporter encompassing or not Trastuzumab light chains targeting the HER2 receptor. We performed high-content imaging analyses to track EV-cell interactions, including isogenic breast cancer cells with manipulated HER2 expression. We validated the functional cargo delivery of recombinant EVs carrying doxorubicin upon EV-donor cell treatment. Then, we performed an in vivo study using JIMT-1 cells commonly used as HER2-refractory, trastuzumab-resistant model to detect a more than 2000 nt length recombinant mRNA in engrafted tumors. Results Fusion proteins participated in vesicular trafficking dynamics and accumulated on secreted EVs according to their expression levels in HEK293T cells. Despite the presence of GFP, secreted EV populations retained a HER2 receptor-binding capacity and were used to track EV-cell interactions. In time-frames where the global EV distribution did not change between HER2-positive (SK-BR-3) or -negative (MDA-MB-231) breast cancer cell lines, the HER2 exposure in isogenic cells remarkably affected the tropism of heterologous EVs, demonstrating the specificity of antiHER2 EVs representing about 20% of secreted bulk vesicles. The specific interaction strongly correlated with improved cell-killing activity of doxorubicin-EVs in MDA-MB-231 ectopically expressing HER2 and reduced toxicity in SK-BR-3 with a knocked-out HER2 receptor, overcoming the effects of the free drug. Interestingly, the fusion protein-corresponding transcripts present as full-length mRNAs in recombinant EVs could reach orthotopic breast tumors in JIMT-1-xenografted mice, improving our sensitivity in detecting penetrant cargoes in tissue biopsies. Conclusions This study highlights the quantitative aspects underlying the creation of a platform for secreted heterologous EVs and shows the limits of single receptor-ligand interactions behind EV-cell engagement mechanisms, which now become the pivotal step to predict functional tropism and design new generations of EV-based nanovehicles.

Abstract Providing timely and satisficing End-of-Life care (EOLC) is a priority for healthcare systems since aging population and chronic diseases are boosting the global demand for care at end-of-life (EOL). In OECD countries the access to EOLC is insufficient. In Italy, the average rate of cancer patients assisted by the palliative care (PC) network at EOL was 28% in 2021, with high variability in the country. Among the Italian regions offering the best coverages, Tuscany has a rate of about 40%, but intraregional variation is marked as well. The study aims to explore the delivery of EOLC to adult cancer patients in public facilities in the Tuscany region through survey data collection among professionals. Two online surveys were delivered to Directors of community-based PC Functional-Units (FUs) and Directors of hospital-based medical-oncology units. All FU Directors responded to the survey (n = 14), and a response rate of 96% was achieved from hospital-unit Directors (n = 27). The results highlight the availability of numerous dedicated services, but reveal heterogeneity among and within organisations, including variations in the professionals involved, pathways, and tools adopted. Care continuity is supported by institutionalized collaboration between hospital and community settings, but hindered by fragmented care processes and heterogeneous transition pathways. Late referral to PC is perceived as a major constraint to EOLC. Developing structured pathways for patient transition to end-stage PC is crucial, and practices/processes should be uniformly implemented to ensure equity. Multi-professional care should be facilitated through tailored supporting tools. Both hospital-unit and FU Directors suggest developing shared pathways between organisations/professionals (82% and 80% respectively) and digital information sharing (61% and 80% respectively). Hospital and community-based professionals have similar perceptions about the concerns and challenges to EOLC provision in the region, but community-based professionals are more sensitive to the importance of improving communication on PC to the public and early discussing EOLC with caregivers. This finding suggests the need of enhancing hospital personnel’s awareness about these issues. Professional training and the capacity to assess patients’ needs and preferences should be improved. The identified needs can inform future research and interventions to improve the quality and outcomes of EOLC for cancer patients.

In the last few years, a plethora of biomarkers have been proposed for the differentiation of Parkinson’s disease (PD) from its mimics. Most of them consist of complex measures, often based on expensive technology, not easily employed outside research centers. MRI measures have been widely used to differentiate between PD and other parkinsonism. However, these measurements were often performed manually on small brain areas in small patient cohorts with intra- and inter-rater variability. The aim of the current review is to provide a comprehensive and updated overview of the literature on biomarkers commonly used to differentiate PD from its mimics (including parkinsonism and tremor syndromes), focusing on parameters derived by simple qualitative or quantitative measurements that can be used in routine practice. Several electrophysiological, sonographic and MRI biomarkers have shown promising results, including the blink-reflex recovery cycle, tremor analysis, sonographic or MRI assessment of substantia nigra, and several qualitative MRI signs or simple linear measures to be directly performed on MR images. The most significant issue is that most studies have been conducted on small patient cohorts from a single center, with limited reproducibility of the findings. Future studies should be carried out on larger international cohorts of patients to ensure generalizability. Moreover, research on simple biomarkers should seek measurements to differentiate patients with different diseases but similar clinical phenotypes, distinguish subtypes of the same disease, assess disease progression, and correlate biomarkers with pathological data. An even more important goal would be to predict the disease in the preclinical phase.

BackgroundThe concept of wellbeing is expansive and intricate, making it challenging to define precisely. Similarly, the instruments employed to assess wellbeing are complex and multifaceted. Therefore, it is more appropriate to refer to the notion of wellbeing as Health-Related Quality of Life (HRQoL), which is the central focus of many measures used to assess the feeling of wellbeing. This review aimed to identify the tools most commonly used to evaluate HRQoL in individuals with Parkinsonism—a group of movement disorders that negatively impact the quality of life due to the intricate interplay of symptoms, socio-demographic characteristics, and psychological factors. The main aim was to assess the psychometric properties of these measures in terms of validity and reliability.MethodsA literature review was conducted, focusing on research related to the assessment of HRQoL in connection to symptoms of Parkinsonism. This review included all studies that examined HRQoL using evaluation scales, exams, or self-reported questionnaires. The literature review was conducted using the databases Scopus and Web of Science and the search engine PubMed to identify studies published between 1996 and 2023. Only records that assessed HRQoL in individuals with Parkinson's disease and Parkinsonism were selected for evaluation.ResultsA total of 393 records were examined, and eight tools were identified as the most frequently used in the evaluation of HRQoL.DiscussionThe results show a significant gap in knowledge regarding the latent structure and measurement invariance of HRQoL measurements, which may have a significant influence on the interpretation of test outcomes. Moreover, there is a lack of clear divergent validity between HRQoL assessments and other tests used as predictors of HRQoL. This could represent a significant limitation, affecting the construct and criterion validity of HRQoL measures.

The aim of this study is to perform a bibliometric and demographic analysis of the 10 most productive researchers over the past 7 years in relation to the selection of key topics in andrology. We conducted a bibliometric analysis in December 2023, using Scopus Researcher Discovery. Seven major andrological topics were identified: erectile dysfunction (T1), premature ejaculation (T2), male infertility (T3), Peyronie’s disease (T4), male hypogonadism (T5), priapism (T6), and low sexual desire (T7). A total of 10 researchers per topic were identified (51 unique authors). Most were urologists (65%) or endocrinologists (23%). Most authors belonged to centers in the USA (35.7%) and Italy (32.9%). The majority of authors were male (77.1%) and over 40 years old (83.3% of authors of known age). The mean (standard deviation (SD)) H-index of the 10 top researchers was 42.6 (24.6), ranging from 6 to 119. Male infertility was the topic with the most articles (1110) written by the top 10 researchers and the most cited (20,585). Erectile dysfunction was the second topic for articles (678) and third for citations after male hypogonadism (6046 vs. 11,384). The mean (SD) impact factor of the journals in which the andrological articles were published varied from 3.5 (0.53) for premature ejaculation to 5.33 (1.85) for hypogonadism.

Previous literature showed a complex interpretation of recall tasks due to the complex relationship between Executive Functions (EF) and Long Term Memory (M). The Test of Memory Strategies (TMS) could be useful for assessing this issue, because it evaluates EF and M simultaneously. This study aims to explore the validity of the TMS structure, comparing the models proposed by Vaccaro et al. (2022) and evaluating the measurement invariance according to three countries (Italy, Spain, and Portugal) through Confirmatory Factor Analysis (CFA). Four hundred thirty-one healthy subjects (Age mean = 54.84, sd = 20.43; Education mean = 8.85, sd =4.05; M = 177, F = 259) were recruited in three countries (Italy, Spain, and Portugal).Measurement invariance across three country groups was evaluated through Structural Equation modeling. Also, convergent and divergent validity were examined through the correlation between TMS and classical neuropsychological tests. CFA outcomes suggested that the best model was the three-dimensional model, in which list 1 and list2 reflect EF, list 3 reflects a mixed factor of EF and M (EFM) and list4 and list5 reflect M. This result is in line with the theory that TMS decreases EF components progressively. TMS was metric invariant to the country, but scalar invariance was not tenable. Finally, the factor scores of TMS showed convergent validity with the classical neuropsychological tests.The overall results support cross-validation of TMS in the three countries considered.

ABSTRACT Background: To evaluate the 10-year functional outcomes (primary) and frequency and predictors of BPH surgical retreatment (secondary) after ThuLEP. Materials and Methods: A single-center retrospective analysis of consecutive patients undergoing ThuLEP between 2010 and 2013 was performed. Inclusion criteria were: age ≥ 40 years, prostate volume (PV) ≥ 80 mL, International Prostate Symptom Score (IPSS)-Total score ≥ 8 points. IPSS-Total score was the primary outcome, and BPH surgical retreatment rate was the secondary outcome. Paired t-test, McNemar test, and Wilcoxon signed-rank test were used to compare variables. Logistic regression analysis was performed to evaluate predictors of surgical retreatment. Results: A total of 410 patients with a mean ±SD age of 63.9 ± 9.7 years and a PV of 115.6 ± 28.6 mL were included. Mean ±SD follow-up was 108.2 ± 29.6 months. IPSS-Total score was significantly improved at 1 year compared to baseline (23.3 ± 4.7 vs. 10.3 ± 3.8; p

This study explores the synergistic integration of remote sensing (RS) and Historical Landscape Characterisation (HLC) methodology as an innovative, multi-scalar and holistic approach to enhance archaeological planning. The goal is to maximize the effectiveness of the investigations, optimizing data collection and improving the contextual understanding of the sites. In fact, these methodologies can significantly contribute to the documentation, conservation, planning and valorisation of archaeological areas. By integrating RS data with features detected by HLC, a complete picture is obtained that facilitates a deeper understanding of the landscape and historical dynamics. This article will explain the combined approach of RS and HLC, presenting some methodologies key to improving the precision and effectiveness of archaeological planning. This integration facilitates the sustainable preservation of archaeological resources and contributes to the conscious management of cultural heritage in the context of contemporary development. The paper demonstrates, through a case study, how the application of the two methodologies (RS and HLC) in an integrated form can provide an exhaustive interpretation of the territory in which the archaeological area is located, which can represent an exhaustive knowledge base on which to set up effective processes for the strategic territorial planning of archaeological areas.

Abstract Bi-allelic pathogenic variants in PRKN are the most common cause of autosomal recessive Parkinson’s disease (PD). 647 patients with PRKN-PD were included in this international study. The pathogenic variants present were characterised and investigated for their effect on phenotype. Clinical features and progression of PRKN-PD was also assessed. Among 133 variants in index cases (n = 582), there were 58 (43.6%) structural variants, 34 (25.6%) missense, 20 (15%) frameshift, 10 splice site (7.5%%), 9 (6.8%) nonsense and 2 (1.5%) indels. The most frequent variant overall was an exon 3 deletion (n = 145, 12.3%), followed by the p.R275W substitution (n = 117, 10%). Exon3, RING0 protein domain and the ubiquitin-like protein domain were mutational hotspots with 31%, 35.4% and 31.7% of index cases presenting mutations in these regions respectively. The presence of a frameshift or structural variant was associated with a 3.4 ± 1.6 years or a 4.7 ± 1.6 years earlier age at onset of PRKN-PD respectively (p

Background: Medical students are at high risk for mental disorders, and the COVID-19 pandemic might have exacerbated psychological distress. However, no data are available for the southern part of Italy. The SMS-ME (Sicilian Medical Students’ MEntal health) project aimed to estimate the prevalence of and predictors of depression, anxiety, stress, and suicidal ideation in a sample of Sicilian medical students. Methods: A web-based cross-sectional survey was carried out from September 2022 to December 2022. The study protocol investigated sociodemographic factors and clinical data including Depression, Anxiety, Stress Scale-21 (DASS-21), and a specific question addressing suicidal ideation frequency over the last six months. Multivariate regression models were assessed to examine the association between symptoms and relevant predictors and then regressed their residuals with suicidal thought frequency. Result: We collected 1,866 records (age=22.5, SD=3.4; 65.2 % females). One out of four students presented highly severe depression (25 %) and referred to the presence of some suicidal ideation in the six months preceding the interview (26.1 %). DASS-21 scores, especially depression (F(5, 1,828)=58.8, p = 6.59–57), increasingly predicted the frequency of suicidal thoughts when above the sample's mean. Limitations: The cross-sectional study design does not allow inferences on temporal relationships and the self-report strategy could be intrinsically biased by the person's feelings at the time of the interview. Conclusions: High prevalence of anxiety and depressive symptoms and suicidal thoughts were observed among Sicilian medical students. The DASS-21 was a good predictor for suicidal ideation that Universities could use as a simple tool to assess the need for psychological healthcare in this population.

Aims Previous population-based studies have identified suicidal ideation (SI) as a potential risk marker for psychosis. We aimed to investigate the prevalence of previous SI in a large sample of patients with first episode of psychosis accepted to early intervention services (EIS) in South London and Maudsley (SLaM) NHS Foundation Trust using clinical records. We further aimed to investigate differences in patients with and without recorded SI according to age at diagnosis, gender, ethnicity and neighbourhood deprivation. Methods We designed a retrospective cohort using the Clinical Record Interactive System. Included were patients who were accepted by SLaM EIS from 2015–2018 and received a psychotic disorder diagnosis (n = 1658). We used a natural language processing algorithm that searches deidentified textual clinical records, returning a binary variable indicating presence or absence of SI recorded at any time prior to acceptance to EIS. The algorithm has high precision (97%) and inter-rater reliability (Cohen's k 92%). The t-test was used to compare mean age at first diagnosis in patients with and without recorded SI, while chi-squared tests evaluated differences according to gender, ethnicity and tertiles of index of multiple deprivation (based on 2015 postcode). The significance threshold was p = 0.05. Results The cohort included 1658 patients, of whom 656 (39.6%) were female. The natural language processing algorithm identified 600 patients (36.2%) who had SI recorded in their clinical records at any time prior to acceptance by EIS. On average, patients with recorded SI were younger at first diagnosis of psychotic disorder (mean 27.7 years, standard deviation 10.5) compared with patients without recorded SI (mean 30.1 years, standard deviation 11.2; p < 0.001). There was little evidence for differences on gender (p = 0.950), ethnicity (p = 0.059) or deprivation index (p = 0.597). Conclusion Approximately 1 in 3 patients attending SLaM EIS had evidence of SI recorded prior to acceptance by EIS. Consistent with previous studies, the current findings emphasise the high prevalence of SI in this clinical population. Compared with those without SI, patients with recorded SI were on average 2–3 years younger at diagnosis. This may reflect general population age differences in prevalence of suicidal ideation; increased severity of illness with earlier age of onset; or patterns of contact with services which facilitated earlier diagnosis. There was little evidence that patients with and without recorded SI differed significantly on gender, ethnicity or neighbourhood deprivation. Prospective studies would be helpful to assess whether SI is a risk marker for first episode of psychosis.

IntroductionDistinguishing tremor-dominant Parkinson's disease (tPD) from essential tremor with rest tremor (rET) can be challenging and often requires dopamine imaging. This study aimed to differentiate between these two diseases through a machine learning (ML) approach based on rest tremor (RT) electrophysiological features and structural MRI data.MethodsWe enrolled 72 patients including 40 tPD patients and 32 rET patients, and 45 control subjects (HC). RT electrophysiological features (frequency, amplitude, and phase) were calculated using surface electromyography (sEMG). Several MRI morphometric variables (cortical thickness, surface area, cortical/subcortical volumes, roughness, and mean curvature) were extracted using Freesurfer. ML models based on a tree-based classification algorithm termed XGBoost using MRI and/or electrophysiological data were tested in distinguishing tPD from rET patients.ResultsBoth structural MRI and sEMG data showed acceptable performance in distinguishing the two patient groups. Models based on electrophysiological data performed slightly better than those based on MRI data only (mean AUC: 0.92 and 0.87, respectively; p = 0.0071). The top-performing model used a combination of sEMG features (amplitude and phase) and MRI data (cortical volumes, surface area, and mean curvature), reaching AUC: 0.97 ± 0.03 and outperforming models using separately either MRI (p = 0.0001) or EMG data (p = 0.0231). In the best model, the most important feature was the RT phase.ConclusionMachine learning models combining electrophysiological and MRI data showed great potential in distinguishing between tPD and rET patients and may serve as biomarkers to support clinicians in the differential diagnosis of rest tremor syndromes in the absence of expensive and invasive diagnostic procedures such as dopamine imaging.

Abstract Random Survival Forests (RSF) has recently showed better performance than statistical survival methods as Cox proportional hazard (CPH) in predicting conversion risk from mild cognitive impairment (MCI) to Alzheimer’s disease (AD). However, RSF application in real-world clinical setting is still limited due to its black-box nature. For this reason, we aimed at providing a comprehensive study of RSF explainability with SHapley Additive exPlanations (SHAP) on biomarkers of stable and progressive patients (sMCI and pMCI) from Alzheimer’s Disease Neuroimaging Initiative. We evaluated three global explanations—RSF feature importance, permutation importance and SHAP importance—and we quantitatively compared them with Rank-Biased Overlap (RBO). Moreover, we assessed whether multicollinearity among variables may perturb SHAP outcome. Lastly, we stratified pMCI test patients in high, medium and low risk grade, to investigate individual SHAP explanation of one pMCI patient per risk group. We confirmed that RSF had higher accuracy (0.890) than CPH (0.819), and its stability and robustness was demonstrated by high overlap (RBO > 90%) between feature rankings within first eight features. SHAP local explanations with and without correlated variables had no substantial difference, showing that multicollinearity did not alter the model. FDG, ABETA42 and HCI were the first important features in global explanations, with the highest contribution also in local explanation. FAQ, mPACCdigit, mPACCtrailsB and RAVLT immediate had the highest influence among all clinical and neuropsychological assessments in increasing progression risk, as particularly evident in pMCI patients’ individual explanation. In conclusion, our findings suggest that RSF represents a useful tool to support clinicians in estimating conversion-to-AD risk and that SHAP explainer boosts its clinical utility with intelligible and interpretable individual outcomes that highlights key features associated with AD prognosis. Graphical Abstract