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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.

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.

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.

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.

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.

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.

In this paper we apply a predictive profiling method to genome copy number aberrations (CNA) in combination with gene expression and clinical data to identify molecular patterns of cancer pathophysiology. Predictive models and optimal feature lists for the platforms are developed by a complete validation SVM-based machine learning system. Ranked list of genome CNA sites (assessed by comparative genomic hybridization arrays – aCGH) and of differentially expressed genes (assessed by microarray profiling with Affy HG-U133A chips) are computed and combined on a breast cancer dataset for the discrimination of Luminal/ ER+ (Lum/ER+) and Basal-like/ER- classes. Different encodings are developed and applied to the CNA data, and predictive variable selection is discussed. We analyze the combination of profiling information between the platforms, also considering the pathophysiological data. A specific subset of patients is identified that has a different response to classification by chromosomal gains and losses and by differentially expressed genes, corroborating the idea that genomic CNA can represent an independent source for tumor classification.

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

Abstract Background Neuroblastoma (NB) represents the most frequent and aggressive form of extracranial solid tumor of infants. Although the overall survival of patients with NB has improved in the last years, more than 50% of high-risk patients still undergo a relapse. Thus, in the era of precision/personalized medicine, the need for high-risk NB patient-specific therapies is urgent. Methods Within the PeRsonalizEd Medicine (PREME) program, patient-derived NB tumors and bone marrow (BM)-infiltrating NB cells, derived from either iliac crests or tumor bone lesions, underwent to histological and to flow cytometry immunophenotyping, respectively. BM samples containing a NB cells infiltration from 1 to 50 percent, underwent to a subsequent NB cells enrichment using immune-magnetic manipulation. Then, NB samples were used for the identification of actionable targets and for the generation of 3D/tumor-spheres and Patient-Derived Xenografts (PDX) and Cell PDX (CPDX) preclinical models. Results Eighty-four percent of NB-patients showed potentially therapeutically targetable somatic alterations (including point mutations, copy number variations and mRNA over-expression). Sixty-six percent of samples showed alterations, graded as “very high priority”, that are validated to be directly targetable by an approved drug or an investigational agent. A molecular targeted therapy was applied for four patients, while a genetic counseling was suggested to two patients having one pathogenic germline variant in known cancer predisposition genes. Out of eleven samples implanted in mice, five gave rise to (C)PDX, all preserved in a local PDX Bio-bank. Interestingly, comparing all molecular alterations and histological and immunophenotypic features among the original patient’s tumors and PDX/CPDX up to second generation, a high grade of similarity was observed. Notably, also 3D models conserved immunophenotypic features and molecular alterations of the original tumors. Conclusions PREME confirms the possibility of identifying targetable genomic alterations in NB, indeed, a molecular targeted therapy was applied to four NB patients. PREME paves the way to the creation of clinically relevant repositories of faithful patient-derived (C)PDX and 3D models, on which testing precision, NB standard-of-care and experimental medicines.

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

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.

Introduction Depression is common in schizophrenia and is correlated with suicide risk and poor long-term outcomes. However, the presence of depressive symptoms is often underestimated in both research and treatment, particularly at the illness onset. Objectives The goals of this study were: (a) to longitudinally observe anxious-depressive symptom levels in patients with First Episode Schizophrenia (FES) during a 24 months of follow-up period, and (b) to examine their associations with other psychopathology and the intervention patients received in an “Early Intervention in Psychosis” (EIP) program during the follow-up period. Methods The Global Assessment of Functioning (GAF) and the Positive And Negative Syndrome Scale (PANSS) were completed by 159 FES patients both at baseline and across the follow-up. Data were analyzed by linear regression analysis and Spearman’s coefficients. Results Anxious-depressive symptoms had significant longitudinal associations with GAF deterioration and PANSS “Positive Symptoms”, “Negative Symptoms” and “Disorganization” subscores. During the follow-up period, FES participants significantly improved the level of anxious-depressive symptoms. This was significantly associated with the number of case management and individual psychotherapy meetings the patient engaged in, as well as with lower antipsychotic doses prescribed during the follow-up period. Conclusions In conclusion, anxious-depressive symptoms are prominent in FES and at the initial entry into EIP programs. Anxious-depressive symptom severity tends to diminish overtime, especially with the provision of specialized EIP treatments. However, since we did not have a control population studied in parallel, we cannot say whether these results are specific to the protocols of EIP programs or just to the intensity of engagement in care. Disclosure of Interest None Declared

Introduction The prognostic prediction of outcomes in individuals at clinical high-risk for psychosis (CHR-P) is still a significant clinical challenge. Among multiple baseline variables of risk calculator models, the role of ongoing pharmacological medications has been partially neglected, despite meta-analytical evidence of higher risk of psychosis transition associated with baseline prescription exposure to antipsychotics (AP) in CHR-P individuals. In particular, baseline AP exposure in CHR-P individuals may be considered as a functional equivalent of the psychometric transition to psychosis, as already postulated in the original ‘Ultra High-Risk’ model. Objectives The main aim of the current study was to test the hypothesis that ongoing AP need at baseline indexes a subgroup of CHR-P individuals with more severe psychopathology and worse prognostic trajectories along a 1-year follow-up period. Methods This research was settled within the ‘Parma At-Risk Mental States’ program. Baseline and 1-year follow-up assessment included the Positive And Negative Syndrome Scale (PANSS) and the Global Assessment of Functioning (GAF). CHR-P individuals who were taking AP medications at entry were included in the CHR-P-AP+ subgroup. The remaining participants were grouped as CHR-P-AP-. The acquisition of drug and outcome information was collected both at baseline and across the follow-up period. Finally, logistic regression analyses with dichotomized 1-year outcome parameters (previously showing statistically significant differences in inter-group comparisons) as dependent measures and sociodemographic and clinical characteristics as independent variables were also performed. Results Hundred and seventy-eight CHR-P individuals (aged 12–25 years) were enrolled (91 CHR-P-AP+, 87 CHR-P-AP-). Compared to CHR-P AP-, CHR-P AP+ individuals had older age, greater baseline PANSS ‘Positive Symptoms’ and ‘Negative Symptoms’ factor subscores and a lower GAF score. At the end of our follow-up, CHR-P-AP+ subjects showed higher rates of psychosis transition, new hospitalizations and urgent/non-planned visits compared to CHRP- AP- individuals. Conclusions The current study suggests that AP need is a significant prognostic variable in cohorts of CHR-P individuals and should be included in the current risk calculators. In particular, the results of this study conducted in a realworld clinical setting indicate that the rate of CHR-P individuals who were already exposed to AP at the time of CHR-P status ascription was higher than those reported in recent meta-analyses on this topic. Moreover, our findings confirm that baseline AP prescription appears to increase psychotic transition risk. Disclosure of Interest None Declared

ObjectiveTo investigate the performance of structural MRI cortical and subcortical morphometric data combined with blink-reflex recovery cycle (BRrc) values using machine learning (ML) models in distinguishing between essential tremor (ET) with resting tremor (rET) and classic ET.MethodsWe enrolled 47 ET, 43 rET patients and 45 healthy controls (HC). All participants underwent brain 3 T-MRI and BRrc examination at different interstimulus intervals (ISIs, 100–300 msec). MRI data (cortical thickness, volumes, surface area, roughness, mean curvature and subcortical volumes) were extracted using Freesurfer on T1-weighted images. We employed two decision tree-based ML classification algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) combining MRI data and BRrc values to differentiate between rET and ET patients.ResultsML models based exclusively on MRI features reached acceptable performance (AUC: 0.85–0.86) in differentiating rET from ET patients and from HC. Similar performances were obtained by ML models based on BRrc data (AUC: 0.81–0.82 in rET vs. ET and AUC: 0.88–0.89 in rET vs. HC). ML models combining imaging data (cortical thickness, surface, roughness, and mean curvature) together with BRrc values showed the highest classification performance in distinguishing between rET and ET patients, reaching AUC of 0.94 ± 0.05. The improvement in classification performances when BRrc data were added to imaging features was confirmed by both ML algorithms.ConclusionThis study highlights the usefulness of adding a simple electrophysiological assessment such as BRrc to MRI cortical morphometric features for accurately distinguishing rET from ET patients, paving the way for a better classification of these ET syndromes.