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Bridging gaps between educational stakeholders at the classroom, school, and system levels is essential to achieve sustainable change in primary and secondary education. However, transferring knowledge or building capacity within this network of loosely coupled stakeholders is demanding. The "brokerage" concept holds promise for studying these complex patterns of interaction, as it refers to how specific actors (brokers) link loosely coupled or disconnected individuals (brokering). However, different research traditions, in terms of theoretical frameworks and methodological approaches, and various stakeholders examined in their role as bridge builders make understanding the role of brokers, brokering, and brokerage in changing educational practice challenging. Therefore, the purpose of this study is to provide an overview of the current literature on these concepts in educational change research. In a systematic literature review based on 42 studies, we analyzed each study's theoretical assumptions, methodological approach, scope in terms of stakeholders involved, and empirical findings. First, the literature review revealed that research on educational change refers to four different theoretical frameworks when focusing on brokers, brokering, or brokerage. Second, our results indicate that predominantly qualitative approaches have been applied. Third, using content network graphs, we identified teachers and principals as among the most frequently analyzed brokers. Fourth, four relevant aspects of the empirical findings are presented: brokers' personal characteristics, conditions that enable brokering, successful brokering strategies, and outcomes of brokerage. Finally, we outline a future research agenda based on the empirical evidence base and shortcomings.

ABSTRACT Understanding mechanisms and predicting natural population responses to climate is a key goal of Ecology. However, studies explicitly linking climate to population dynamics remain limited. Antecedent effect models are a set of statistical tools that capitalize on the evidence provided by climate and population data to select time windows correlated with a response (e.g., survival, reproduction). Thus, these models can serve as both a predictive and exploratory tool. We compare the predictive performance of antecedent effect models against simpler models and showcase their exploratory analysis potential by selecting a case study with high predictive power. We fit three antecedent effect models: (1) weighted mean models (WMM), which weigh the importance of monthly anomalies based on a Gaussian curve, (2) stochastic antecedent models (SAM), which weigh the importance of monthly anomalies using a Dirichlet process, and (3) regularized regressions using the Finnish horseshoe model (FHM), which estimate a separate effect size for each monthly anomaly. We compare these approaches to a linear model using a yearly climatic predictor and a null model with no predictors. We use demographic data from 77 natural populations of 34 plant species ranging between seven and 36 years in length. We then fit models to the asymptotic population growth rate (λ) and its underlying vital rates: survival, development, and reproduction. We find that models including climate do not consistently outperform null models. We hypothesize that the effect of yearly climate is too complex, weak, and confounded by other factors to be easily predicted using monthly precipitation and temperature data. On the other hand, in our case study, antecedent effect models show biologically sensible correlations between two precipitation anomalies and multiple vital rates. We conclude that, in temporal datasets with limited sample sizes, antecedent effect models are better suited as exploratory tools for hypothesis generation.

Abstract Background Migraine lacks biomarkers that can trace the biological pathways of the disease and predict the effectiveness of treatments. Monoclonal antibodies targeting calcitonin gene-related peptide pathway – including erenumab – offer the opportunity of investigating potential migraine biomarkers due to their specific mechanism of action in preventing both episodic (EM) and chronic (CM) migraine. Our study aims at evaluating the expression levels of circulating microRNAs (miRNAs) according to migraine type, before and after treatment with erenumab and based on treatment response, in order to identify miRNAs with potential role as epigenetic biomarkers. Methods The study included women aged 25–50 years with EM or CM treated with erenumab according to clinical indications. MiRNAs expression levels were assessed before (baseline) and after a 16-week treatment with erenumab, 140 mg every four weeks (post-treatment). An extensive miRNAs profiling was performed by qRT-PCR in small, pooled groups of ≤ 8 women each, classified according to migraine frequency (EM and CM) and the degree of response to erenumab. The expression levels of selected miRNAs were also validated using single miRNA assays in each woman with EM and CM. Results During the study, 36 women with migraine (19 with EM and 17 with CM) out of 40 who were initially screened, performed the assessment of miRNA expression at baseline and post-treatment, Erenumab treatment significantly improved migraine burden in both EM and CM. MiRNA profiling revealed differential expression levels of a wide set of miRNAs (hsa-let-7d-3p, hsa-miR-106b-3p, hsa-miR-122-5p, hsa-miR-143-3p, hsa-miR-144-3p, hsa-miR-16-5p, hsa-miR-181a-5p, hsa-miR-221-3p, hsa-miR-25-3p, hsa-miR-29b-2-5p, hsa-miR-326, miR-363-3p, hsa-miR-424-5p, hsa-miR-485-3p, hsa-miR-532-5p, hsa-miR-543, hsa-miR-629-5p, hsa-miR-660-5p, hsa-miR-92a-3p) depending on treatment response. Among them, single miRNA assays confirmed the progressive decrease of hsa-miR-143-3p expression levels in relation to increasing response to erenumab in women with EM (7 with low, 6 with medium, and 6 with high response; p = 0.02). Additionally, single assays showed higher hsa-miR-34a-5p and hsa-miR-382-5p expression levels at baseline in women with CM compared with those with EM (p = 0.0002 and p = 0.0007, respectively), as well as their expression level decrease in women with CM from baseline to follow-up (p = 0.04 and p = 0.02, respectively). Conclusions Our study suggests that targeting the CGRP pathway in migraine changes the expression levels of certain miRNAs. These miRNA levels are linked to the levels of response to CGRP receptor blockage. Future research challenges include assigning specific functions to the modulated miRNAs to unravel pathways modulated by the disease and the treatment. Trial registration The study was registered in clinicaltrials.gov with code NCT04659226 and in the Novartis database with code CAMG334AIT05T.

In locally advanced (LA) laryngeal/hypopharyngeal squamous cell carcinoma (LHSCC), larynx preservation (LP) strategies aim at the cure of the disease while preserving a functional larynx, thus avoiding total laryngectomy and the associated impact on the quality of life. In the last decades, apart from transoral and open-neck organ preservation approaches, several non-surgical regimens have been investigated: radiotherapy alone, alternate, concurrent or sequential chemoradiation, and bioradiotherapy. Despite major progress, the identification of reliable and effective predictors for treatment response remains a clinical challenge. This review examines the current state of LP in LA-LHSCC and the need for predictive factors, highlighting the importance of the PRESERVE trial in addressing this gap. The PRESERVE trial represents a pivotal initiative aimed at finding the optimal therapy for laryngeal preservation specific to each patient through a retrospective analysis of data from previous LP trials and prospectively validating findings. The goal of the PRESERVE trial is to develop a comprehensive predictive classifier that integrates clinical, molecular, and multi-omics data, thereby enhancing the precision and efficacy of patient selection for LP protocols.

Abstract Purpose Patellofemoral joint instability (PFJI) can surgically be treated with a multitude of approaches, depending on the underlying pathology. In the presence of increased femoral anteversion, some authors have reported good results with a derotational distal femoral osteotomy (DeDFO). The purpose of the study was to investigate the indications, outcomes and complication rate of DeDFO for PFJI. Methods A systematic review was performed according to the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta‐analyses) by searching Medline, Embase, Web of Science and Cochrane Library databases through 1 December 2023. Included were levels 1–4 clinical studies of skeletally mature patients undergoing a DeDFO for PFJI irrespective of concomitant procedures. Study characteristics, indications, radiological and clinical outcomes, surgical technique and concomitant procedures, re‐dislocation and complication rate were all analysed, as was methodological quality. Results A total of 12 studies including 310 patients (325 knees) were included. Three studies were cohort studies, all others were case series. The mean patient age across the studies was 22 years, and the mean follow‐up was 29.4 months. Femoral anteversion cut‐off was between 20° and 30°. Every study included at least one concurrent soft tissue, bony or combined procedure. Across all studies, one case of re‐dislocation was reported (0.3%) and four implant or osteotomy‐related complications (1.2%) were reported. All studies reported a statistically significant increase in clinical scores. Conclusion This systematic review of DeDFO for patellofemoral instability in the presence of increased femoral anteversion demonstrates promising clinical results and an extremely low dislocation and complication rate. The heterogeneity of the cut‐off in anteversion and concomitant procedures, especially tibial tubercle osteotomy with seemingly identical results, indicates the need for high‐quality evidence for treating patellofemoral instability. Based upon this systematic review, we strongly recommend that DeDFO be added to the ‘menu à la carte’ of PFJI. Level of Evidence Level III Systematic Review.

In this two-part article we discuss the difference between a continuous and a discrete approach to trap modeling in the simulation of 3-D NAND Flash memories with polysilicon channel. In Part I we focus on threshold voltage $({\mathrm { V}}_{\mathrm { T}})$ fluctuations induced by traps and show that lower values for the average and rms ${\mathrm { V}}_{\mathrm { T}}$ arise when the discrete nature of traps is accounted for. We explain such differences in terms of a stronger percolation that leads to a lower number of filled traps in the discrete-trap case, and investigate such differences as a function of cell parameters and temperature. Finally, we compare the two approaches showing that a continuous trap model cannot reproduce the correct dependences resulting from a discrete treatment.

In Part II of this article we discuss the impact of a discrete treatment of traps on 3-D NAND Flash random telegraph noise (RTN). A higher RTN results when discrete traps are taken into account, that can only be explained by a stronger influence of the discrete charged traps on the current conduction, leading to more percolation. The effects are then investigated as a function of the cell parameters, showing that a continuous model for traps cannot reproduce the correct dependence.

Abstract Metamorphic fluids, faults, and shear zones are carriers of carbon from the deep Earth to shallower reservoirs. Some of these fluids are reduced and transport energy sources, like H2 and light hydrocarbons. Mechanisms and pathways capable of transporting these deep energy sources towards shallower reservoirs remain unidentified. Here we present geological evidence of failure of mechanically strong rocks due to the accumulation of CH4-H2-rich fluids at deep forearc depths, which ultimately reached supralithostatic pore fluid pressure. These fluids originated from adjacent reduction of carbonates by H2-rich fluids during serpentinization at eclogite-to-blueschist-facies conditions. Thermodynamic modeling predicts that the production and accumulation of CH4-H2-rich aqueous fluids can produce fluid overpressure more easily than carbon-poor and CO2-rich aqueous fluids. This study provides evidence for the migration of deep Earth energy sources along tectonic discontinuities, and suggests causal relationships with brittle failure of hard rock types that may trigger seismic activity at forearc depths.

Background/Objectives: This retrospective study aims to evaluate the safety and functional outcomes of simultaneous bilateral total knee arthroplasty (TKA) compared to two-stage bilateral TKA procedures. Osteoarthritis is the leading cause of knee joint replacement globally, so we sought to determine whether the one-stage approach could be deemed non-inferior to the two-stage method in terms of perioperative complications and postoperative recovery. Methods: We analyzed two cohorts: 43 patients underwent one-stage bilateral TKA, while 66 patients received two-stage TKA. The data collected included demographic details, comorbidities, postoperative complications, and functional outcomes assessed by the Oxford Knee Score (OKS), European Quality of Life, and Visual Analogue Scale scores at preoperative and five years postoperative follow-ups. Results: The results showed statistically significant improvements in quality-of-life measures for both groups, with the one-stage group exhibiting greater enhancement in OKS (p < 0.05). Complication rates were similar across both procedures, with no significant differences in infection rates or other adverse events. Conclusions: This study suggests that the one-stage approach to treat bilateral knee arthritis could reduce subjective stress and healthcare costs, presenting a safe alternative for appropriately selected patients. However, the findings highlight the necessity of considering patients’ overall health status when planning surgical interventions. Further research involving larger populations is recommended to validate our conclusions.

In this paper, we examined how kindergarteners' self-evaluation biases are related to behavioural self-regulation (SR) and learning goal orientation (GO). According to educational research and practice, fostering high and optimistic academic self-concepts promotes the setting of challenging goals and initiates effective behavioural SR processes. However, research on metacognition states that it is a match between academic self-concept and abilities that provides the optimal conditions for behavioural SR and a learning GO. There is theoretical and empirical evidence in favour of both positions, yet the correlates of self-evaluative tendencies may differ with children's different levels of achievement, which are rarely considered. This cross-sectional study used response surface analysis, an innovative research methodology capable of assessing the complex interaction of academic self-concept and academic abilities on the behavioural SR and GO of 147 kindergarten children (M = 6.47 years, SD = 0.39 years). Polynomial regression models were used to test the presence of a fit pattern in empirical data and offer a new perspective on the interaction of academic self-concept and academic abilities. Results showed that a fit is generally associated with better behavioural SR and a learning GO but that correlates of academic self-concept differ with different achievement levels and outcome measures. This study extends current knowledge, as it offers important insights on how to conceptualise and pursue questions regarding self-concepts and behavioural SR. At an applied level, the findings indicate that interventions with kindergarteners that target SR should take the interactions between self-evaluation biases and ability level into account.

Introduction Patients suffering from mental disorders tend to be less adherent to the recommended therapies. Moreover, the COVID-19 pandemic had a global impact on physical and social well-being, which turned out stronger in the most fragile patients, like those with a mental condition. Objectives To assess whether the COVID-19 pandemic influenced the physical healthcare gap between patients with and without severe mental illness (SMI) treated for chronic conditions. Methods Data were retrieved from Healthcare Utilization Databases of Lombardy region (Italy). Prevalent users of antihypertensive drugs, statins or antidiabetic drugs, receiving healthcare in Lombardy during 2020, were identified. Among them, those with a previous diagnosis of schizophrenic or bipolar disorder were selected and matched with up to 3 patients without any sign of mental disorder by sex, age and number of contacts with the NHS during the previous year. 3 cohorts (not necessarily independent) were formed. High adherence to specific recommended drug therapies and discontinuation during 2020 were evaluated. Association between presence of SMI and high adherence was evaluated by using a log-binomial model (risk ratios, RR with 95% CI); a Cox model (hazard ratios, HR) was used for discontinuation. As comparison, same analyses were performed to the cohorts of prevalent users in 2019, to evaluate the impact of the COVID-19 pandemic. Results were stratified according to the type of mental disorder. Results 36'436, 14'136 and 12'597 prevalent users of antihypertensives, statins or antidiabetics respectively were identified, of which 25% with SMI (9'109, 3'536 and 3'152 respectively). During the pandemic period, for all the three cohorts, patients with mental illness had 10% lower probability of being adherent to the recommended drug therapies. The association between SMI and discontinuation was significant and varied among the three cohorts, with HR (95% CI): 1.27 (1.21; 1.33) for antihypertensives users, 1.16 (1.07; 1.26) for antidiabetics users and 1.08 (1.01; 1.16) for statins users. Compared with 2019 the gap remained similar, except for discontinuation of antidiabetics, where the gap diminished from 34% in 2019 to 16% in 2020. No differences between the two mental disorders were found. Conclusions Results show that suffering from a mental disorder in people with chronic physical conditions affects their adherence to recommended drug therapies. During the pandemic period, the restrictive measures adopted may have led to a better care by family members, counteracting any increase in the gap. The healthcare gap in patients suffering from mental illness remains an unsolved problem of primary importance for public health. Disclosure of Interest None Declared

Abstract Autophagy is important for the removal, degradation and recycling of damaged organelles, proteins, and lipids through the degradative action of lysosomes. In addition to its catabolic function, autophagy is important in cancer and viral-mediated tumorigenesis, including Human Papillomavirus (HPV) positive cancers. HPV infection is a major risk factor in a subset of head and neck cancer (HNC), for which no targeted therapies are currently available. Herein, we assessed autophagy function in HPV-positive HNC. We showed that HPV-positive HNC cells presented a transcriptional and functional impairment of the autophagic process compared to HPV-negative cells, which were reactivated by knocking down HPV E6/E7 oncoproteins, the drivers of cellular transformation. We found that the oncoprotein c-MYC was stabilized and triggered in HPV-positive cell lines. This resulted in the reduced binding of the MiT/TFE transcription factors to their autophagy targets due to c-MYC competition. Thus, the knock-down of c-MYC induced the upregulation of autophagic and lysosomal genes in HPV-positive HNC cells, as well as the increase of autophagic markers at the protein level. Moreover, HPV oncoprotein E7 upregulated the expression of the phosphatase inhibitor CIP2A, accounting for c-MYC upregulation and stability in HPV+ HNC cells. CIP2A mRNA expression negatively correlated with autophagy gene expression in tumor tissues from HNC patients, showing, for the first time, its implication in a transcriptional autophagic context. Both CIP2A and c-MYC knock-down, as well as pharmacological downregulation of c-MYC, resulted in increased resistance to cisplatin treatment. Our results not only show a novel way by which HPV oncoproteins manipulate the host machinery but also provide more insights into the role of autophagy in chemoresistance, with possible implications for targeted HPV-positive HNC therapy.

Abstract Background Machine learning (ML) represents a powerful tool to capture relationships between molecular alterations and cancer types and to extract biological information. Here, we developed a plain ML model aimed at distinguishing cancer types based on genetic lesions, providing an additional tool to improve cancer diagnosis, particularly for tumors of unknown origin. Methods TCGA data from 9,927 samples spanning 32 different cancer types were downloaded from cBioportal. A vector space model type data transformation technique was designed to build consistently homogeneous new datasets containing, as predictive features, calls for somatic point mutations and copy number variations at chromosome arm-level, thus allowing the use of the XGBoost classifier models. Considering the imbalance in the dataset, due to large difference in the number of cases for each tumor, two preprocessing strategies were considered: i) setting a percentage cut-off threshold to remove less represented cancer types, ii) dividing cancer types into different groups based on biological criteria and training a specific XGBoost model for each of them. The performance of all trained models was mainly assessed by the out-of-sample balanced accuracy (BACC) and the AUC scores. Results The XGBoost classifier achieved the best performance (BACC 77%; AUC 97%) on a dataset containing the 10 most represented tumor types. Moreover, dividing the 18 most represented cancers into three different groups (endocrine-related carcinomas, other carcinomas and other cancers),such analysis models achieved 78%, 71% and 86% BACC, respectively, with AUC scores greater than 96%. In addition, the model capable of linking each group to a specific cancer type reached 81% BACC and 94% AUC. Overall, the diagnostic potential of our model was comparable/higher with respect to others already described in literature and based on similar molecular data and ML approaches. Conclusions A boosted ML approach able to accurately discriminate different cancer types was developed. The methodology builds datasets simpler and more interpretable than the original data, while keeping enough information to accurately train standard ML models without resorting to sophisticated Deep Learning architectures. In combination with histopathological examinations, this approach could improve cancer diagnosis by using specific DNA alterations, processed by a replicable and easy-to-use automated technology. The study encourages new investigations which could further increase the classifier’s performance, for example by considering more features and dividing tumors into their main molecular subtypes.

Abstract Background Italy can be viewed as a laboratory to assess the quality of mental healthcare delivered in a community-oriented system, especially for severe mental disorders, such as personality disorders. Although initiatives based on clinical indicators for assessing the quality of mental healthcare have been developed by transnational-organisations, there is still no widespread practice of measuring the quality of care pathways delivered to patients with severe mental disorders in a community-oriented system, especially using administrative healthcare databases. The aim of the study is to evaluate the quality of care delivered to patients with personality disorders taken-in-care by mental health services of four Italian regions (Lombardy, Emilia-Romagna, Lazio, Sicily). Methods A set of thirty-three clinical indicators, concerning accessibility, appropriateness, continuity, and safety of care, was implemented using regional healthcare utilization databases, containing data on mental health treatments and diagnosis, hospital admissions, outpatient interventions and exams and drug prescriptions. RESULTS 31,688 prevalent patients with personality disorders treated in 2015 were identified, of whom 2,331 newly taken-in-care. One-in-10 patients received a standardized assessment, the treatment discontinuity affected half of the cases. 12.7% of prevalent patients received at least one hospitalization, 10.6% in the newly taken-in-care cohort. 6-out-of-10 patients had contact with community-services within 14 days from hospital discharge. Access to psychotherapy and psychoeducational treatments was low and delivered with a low intensity. The median of psychosocial interventions per person-year was 19.1 and 9.4, respectively, in prevalent and newly taken-in-care cases. Nearly 50% of patients received pharmacological treatments. Conclusions Healthcare utilization databases were used to systematically evaluate and assess service delivery across regional mental health systems; suggesting that in Italy the public mental health services provide to individuals with personality disorders suboptimal treatment paths.

Abstract Background Mental health (MH) care often exhibits uneven quality and poor coordination of physical and MH needs, especially for patients with severe mental disorders. This study tests a Population Health Management (PHM) approach to identify patients with severe mental disorders using administrative health databases in Italy and evaluate, manage and monitor care pathways and costs. A second objective explores the feasibility of changing the payment system from fee-for-service to a value-based system (e.g., increased care integration, bundled payments) to introduce performance measures and guide improvement in outcomes. Methods Since diagnosis alone may poorly predict condition severity and needs, we conducted a retrospective observational study on a 9,019-patient cohort assessed in 2018 (30.5% of 29,570 patients with SMDs from three Italian regions) using the Mental Health Clustering Tool (MHCT), developed in the United Kingdom, to stratify patients according to severity and needs, providing a basis for payment for episode of care. Patients were linked (blinded) with retrospective (2014–2017) physical and MH databases to map resource use, care pathways, and assess costs globally and by cluster. Two regions (3,525 patients) provided data for generalized linear model regression to explore determinants of cost variation among clusters and regions. Results Substantial heterogeneity was observed in care organization, resource use and costs across and within 3 Italian regions and 20 clusters. Annual mean costs per patient across regions was €3,925, ranging from €3,101 to €6,501 in the three regions. Some 70% of total costs were for MH services and medications, 37% incurred in dedicated mental health facilities, 33% for MH services and medications noted in physical healthcare databases, and 30% for other conditions. Regression analysis showed comorbidities, resident psychiatric services, and consumption noted in physical health databases have considerable impact on total costs. Conclusions The current MH care system in Italy lacks evidence of coordination of physical and mental health and matching services to patient needs, with high variation between regions. Using available assessment tools and administrative data, implementation of an episodic approach to funding MH could account for differences in disease phase and physical health for patients with SMDs and introduce performance measurement to improve outcomes and provide oversight.