Prognostic relevance of a T-type calcium channels gene signature in solid tumours: A correlation ready for clinical validation

BackgroundT-type calcium channels (TTCCs) mediate calcium influx across the cell membrane. TTCCs regulate numerous physiological processes including cardiac pacemaking and neuronal activity. In addition, they have been implicated in the proliferation, migration and differentiation of tumour tissues. Although the signalling events downstream of TTCC-mediated calcium influx are not fully elucidated, it is clear that variations in the expression of TTCCs promote tumour formation and hinder response to treatment.MethodsWe examined the expression of TTCC genes (all three subtypes; CACNA-1G, CACNA-1H and CACNA-1I) and their prognostic value in three major solid tumours (i.e. gastric, lung and ovarian cancers) via a publicly accessible database.ResultsIn gastric cancer, expression of all the CACNA genes was associated with overall survival (OS) among stage I-IV patients (all pConclusionsAlterations in CACNA gene expression are linked to tumour prognosis. Gastric cancer represents the most promising setting for further evaluation

Machine learning clinical decision support systems for surveillance: a case study on pertussis and RSV in children

We tested the performance of a machine learning (ML) algorithm based on signs and symptoms for the diagnosis of RSV infection or pertussis in the first year of age to support clinical decisions and provide timely data for public health surveillance. We used data from a retrospective case series of children in the first year of life investigated for acute respiratory infections in the emergency room from 2015 to 2020. We collected data from PCR laboratory tests for confirming pertussis or RSV infection, clinical symptoms, and routine blood testing results, which were used for the algorithm development. We used a LightGBM model to develop 2 sets of models for predicting pertussis and RSV infection: for each type of infection, we developed one model trained with the combination of clinical symptoms and results from routine blood test (white blood cell count, lymphocyte fraction and C-reactive protein), and one with symptoms only. All analyses were performed using Python 3.7.4 with Shapley values (Shap values) visualization package for predictor visualization. The performance of the models was assessed through confusion matrices. The models were developed on a dataset of 599 children. The recall for the pertussis model combining symptoms and routine laboratory tests was 0.72, and 0.74 with clinical symptoms only. For RSV infection, recall was 0.68 with clinical symptoms and laboratory tests and 0.71 with clinical symptoms only. The F1 score for the pertussis model was 0.72 in both models, and, for RSV infection, it was 0.69 and 0.75. ML models can support the diagnosis and surveillance of infectious diseases such as pertussis or RSV infection in children based on common symptoms and laboratory tests. ML-based clinical decision support systems may be developed in the future in large networks to create accurate tools for clinical support and public health surveillance

Phenotypic Changes Across a Geographic Gradient: The Case of Three Sympatric Dolphin Species

Phenotypic changes in the mammalian mandible can occur at different spatial and temporal scales. We investigated mandibular size and shape variation in three extant closely related dolphins (Cetacea, Odontoceti): Tursiops truncatus, Stenella coeruleoalba and Delphinus delphis in order to test the hypothesis that similar phenotypic changes occur across the same geographical gradient. Our data included 219 specimens representative of the following geographic locations: the Mediterranean Sea, the eastern north Atlantic and the North Sea. Each mandibula was photographed laterally and spatial positioning of eight homologous 2D landmarks was recorded. After applying generalised Procrustes analysis (GPA), intraspecific variation was first investigated between sexes and among populations to allow further pooling of samples. Size and shape differences among populations and species were investigated through multivariate ordination techniques (PCA), Procrustes ANOVA and allometric analyses. In all three species, Mediterranean populations clearly differed in mandible shape from the extra-Mediterranean ones. Among the three, the direction of geographic phenotypic changes was significantly similar in the striped and common dolphin, while the bottlenose dolphin was the most divergent species, differing both in size and allometric trajectory. Shape variation of the two former species highlighted a morphological convergence in the Atlantic, and a phenotypic divergence in the Mediterranean. Shape differences among the three dolphin species were interpreted in the light of different prey preferences, feeding strategies and habitat partitioning to avoid direct competition

Emotion based attentional priority for storage in visual short-term memory

A plethora of research demonstrates that the processing of emotional faces is prioritised over non-emotive stimuli when cognitive resources are limited (this is known as ‘emotional superiority’). However, there is debate as to whether competition for processing resources results in emotional superiority per se, or more specifically, threat superiority. Therefore, to investigate prioritisation of emotional stimuli for storage in visual short-term memory (VSTM), we devised an original VSTM report procedure using schematic (angry, happy, neutral) faces in which processing competition was manipulated. In Experiment 1, display exposure time was manipulated to create competition between stimuli. Participants (n = 20) had to recall a probed stimulus from a set size of four under high (150 ms array exposure duration) and low (400 ms array exposure duration) perceptual processing competition. For the high competition condition (i.e. 150 ms exposure), results revealed an emotional superiority effect per se. In Experiment 2 (n = 20), we increased competition by manipulating set size (three versus five stimuli), whilst maintaining a constrained array exposure duration of 150 ms. Here, for the five-stimulus set size (i.e. maximal competition) only threat superiority emerged. These findings demonstrate attentional prioritisation for storage in VSTM for emotional faces. We argue that task demands modulated the availability of processing resources and consequently the relative magnitude of the emotional/threat superiority effect, with only threatening stimuli prioritised for storage in VSTM under more demanding processing conditions. Our results are discussed in light of models and theories of visual selection, and not only combine the two strands of research (i.e. visual selection and emotion), but highlight a critical factor in the processing of emotional stimuli is availability of processing resources, which is further constrained by task demands

The politicisation of evaluation: constructing and contesting EU policy performance

Although systematic policy evaluation has been conducted for decades and has been growing strongly within the European Union (EU) institutions and in the member states, it remains largely underexplored in political science literatures. Extant work in political science and public policy typically focuses on elements such as agenda setting, policy shaping, decision making, or implementation rather than evaluation. Although individual pieces of research on evaluation in the EU have started to emerge, most often regarding policy “effectiveness” (one criterion among many in evaluation), a more structured approach is currently missing. This special issue aims to address this gap in political science by focusing on four key focal points: evaluation institutions (including rules and cultures), evaluation actors and interests (including competencies, power, roles and tasks), evaluation design (including research methods and theories, and their impact on policy design and legislation), and finally, evaluation purpose and use (including the relationships between discourse and scientific evidence, political attitudes and strategic use). The special issue considers how each of these elements contributes to an evolving governance system in the EU, where evaluation is playing an increasingly important role in decision making

Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. Results: Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas

New national and regional Annex I Habitat records: from #26 to #36

New Italian data on the distribution of the Annex I Habitats 1510*, 2130*, 2250*, 3180*, 3260, 5230*, 6410, 7140, 7220*, 9320 are reported in this contribution. Specifically, 14 new occurrences in Natura 2000 sites are presented and 20 new cells are added in the EEA 10 km × 10 km reference grid. The new data refer to the Italian administrative regions of Abruzzo, Apulia, Friuli Venezia Giulia, Liguria, Marche, Molise, Sardinia, Sicily, Tuscany and Umbria

Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts

Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify—CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. / Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. / Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from “slight” to “significant” in 80% of the cases. / Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology