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Although cerebellar involvement across a wide range of cognitive and neuropsychiatric phenotypes is increasingly being recognized, previous large-scale studies in schizophrenia (SZ) have primarily focused on supratentorial structures. Hence, the across-sample reproducibility, regional distribution, associations with cerebrocortical morphology and effect sizes of cerebellar relative to cerebral morphological differences in SZ are unknown. We addressed these questions in 983 patients with SZ spectrum disorders and 1349 healthy controls (HCs) from 14 international samples, using state-of-the-art image analysis pipelines optimized for both the cerebellum and the cerebrum. Results showed that total cerebellar grey matter volume was robustly reduced in SZ relative to HCs (Cohens's d=-0.35), with the strongest effects in cerebellar regions showing functional connectivity with frontoparietal cortices (d=-0.40). Effect sizes for cerebellar volumes were similar to the most consistently reported cerebral structural changes in SZ (e.g., hippocampus volume and frontotemporal cortical thickness), and were highly consistent across samples. Within groups, we further observed positive correlations between cerebellar volume and cerebral cortical thickness in frontotemporal regions (i.e., overlapping with areas that also showed reductions in SZ). This cerebellocerebral structural covariance was strongest in SZ, suggesting common underlying disease processes jointly affecting the cerebellum and the cerebrum. Finally, cerebellar volume reduction in SZ was highly consistent across the included age span (16-66 years) and present already in the youngest patients, a finding that is more consistent with neurodevelopmental than neurodegenerative etiology. Taken together, these novel findings establish the cerebellum as a key node in the distributed brain networks underlying SZ.

Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.

Rationale Pharmacological stimulation of D2 receptors modulates prefrontal neural activity associated with working memory (WM) processing. The T allele of a functional single-nucleotide polymorphism (SNP) within DRD2 (rs1076560 G > T) predicts reduced relative expression of the D2S receptor isoform and less efficient neural cortical responses during WM tasks. Objective We used functional MRI to test the hypothesis that DRD2 rs1076560 genotype interacts with pharmacological stimulation of D2 receptors with bromocriptine on prefrontal responses during different loads of a spatial WM task ( N -Back). Methods Fifty-three healthy subjects (38 GG and 15 GT) underwent two 3-T functional MRI scans while performing the 1-, 2- and 3-Back versions of the N -Back WM task. Before the imaging sessions, either bromocriptine or placebo was administered to all subjects in a counterbalanced order. A factorial repeated-measures ANOVA within SPM8 ( p  < 0.05, family-wise error corrected) was used. Results On bromocriptine, GG subjects had reduced prefrontal activity at 3-Back together with a significant decrement in performance, compared with placebo. On the other hand, GT subjects had lower activity for the same level of performance at 1-Back but a trend for reduced behavioral performance in the face of unchanged activity at 2-Back. Conclusions These results indicate that bromocriptine stimulation modulates prefrontal activity in terms of disengagement or of efficiency depending on DRD2 genotype and working memory load.

Genome-Wide-Association studies have involved miR-137 in schizophrenia. However, the biology underlying this statistical evidence is unclear. Statistical polygenic risk for schizophrenia is associated with working memory, while other biological evidence involves miR-137 in emotion processing. We investigated the function of miR-137 target schizophrenia risk genes in humans. We identified a prefrontal co-expression pathway of schizophrenia-associated miR-137 targets and validated the association with miR-137 expression in neuroblastoma cells. Alleles predicting greater co-expression of this pathway were associated with greater prefrontal activation during emotion processing in two independent cohorts of healthy volunteers (N1=222; N2=136). Statistical polygenic risk for schizophrenia was instead associated with prefrontal activation during working memory. A co-expression pathway links miR-137 and its target genes to emotion processing and risk for schizophrenia. Low prefrontal miR-137 expression may be related with SCZ risk via increased expression of target risk genes, itself associated with increased prefrontal activation during emotion processing.

Additive manufacturing (AM) is one of the most sustainable manufacturing processes since it could build parts directly from a computer-aided design (CAD) model simplifying the production of complex geometries, and they are generally more environmentally friendly using only the exact amount of material. Despite this qualitative consideration, the quantitative convenience in terms of energy consumption has not yet been extensively investigated. In the present paper, a model is proposed to improve understanding of AM energy use by applying a novel classification system for machine components, generating, as a result, the characteristic parameters specific for each material and useful for estimating energy consumption providing a simple tool for the companies that would evaluate the technology convenience considering also the energetical component. The main outcome is represented by the characteristics parameters for the main materials used in the material extrusion process and an approach for evaluating the energy consumption a priori with a prevision error of less than 10%.

Extracellular vesicles (EVs) are ubiquitous masters of intercellular communication, being detectable in tissues, circulation, and body fluids. Their complex cargo reflects the (patho)physiologic status of the cells from which they originate. Due to these properties, the potential of EVs, and in particular exosomes, to serve as biomarkers or therapeutics has grown exponentially over the past decade. On one side, numerous studies have demonstrated that EV-associated nucleic acids and proteins are implicated in cancer progression, as well as neurodegenerative, infectious, and autoimmune disorders. On the other, the therapeutic use of EVs secreted by various cell types, and in particular stem/progenitor cells, present significant advantages in comparison to the corresponding parental cells, such as the less complex production and storage conditions. In this review, we examine some of the major pre-clinical studies dealing with EVs and exosomes, that led to the development of numerous completed clinical trials.

PurposeThe aim of our study is to analyse mid- to long-term severe adult spinal deformity (ASD) surgery outcomes by comparing three-column osteotomies (3CO) and multiple anterior interbody fusion cages (AC).Materials and methodsThe PRISMA flowchart was used to systematically review the literature. Only articles with a minimum 24-month follow-up were examined, and 11 articles were included. The following radiological parameters were observed: pelvic incidence (PI), pelvic tilt (PT), lumbar lordosis (LL), sagittal vertical axis (SVA), Cobb angle and T1-sacrum plumbline. Clinical outcome was assessed using the visual analogue scale (VAS) and Oswestry disability index (ODI) scores. The main complications were analysed, and the two groups were compared.ResultsExcept for age, the two populations were homogeneous. Both techniques had the same number of posterior instrumented levels (7.4 ± 1.7). The AC group had a mean 3 ± 1.4 interbody fusions per patient. In the PSO group, all patients had 1 3CO and 89.8% of the osteotomies were performed at L2 or L3 vertebrae. No difference was observed between the two groups in terms of clinical outcomes. Both techniques were effective in sagittal parameters restoration with a final PI–LL mismatch = 4.4°. The PSO group had a statistically higher rate of intraoperative blood loss (p = 0.036), major complications, pseudoarthrosis and dural tears (p < 0.001).ConclusionBoth PSO and multiple AC are effective in treating ASD. Multiple AC seems more suitable when treating older patients because of a lower intraoperative blood loss, lower rate of major complications and fewer number of revision surgeries.

Extracellular vesicles (EVs) are particles naturally released from cells, delimited by a lipid bilayer, carrying functionally active biological molecules. In addition to their physiological role in cellular communication, the interest of the scientific community has recently turned to the use of EVs as vehicles for delivering therapeutic molecules. Several attempts are being made to ameliorate drug encapsulation and targeting, but these efforts are thwarted if the starting material does not meet stringent quality criteria. Here, we take a step back to the sources and isolation procedures that could guarantee significant improvements in the purification of EVs to be used as drug carriers, highlighting the advantages and shortcomings of each approach.

Xylella fastidiosa subsp. pauca (Xfp) is one of the most dangerous plant pathogens in the world. Identified in 2013 in olive trees in south–eastern Italy, it is spreading to the Mediterranean countries. The bacterium is transmitted by insects that feed on sap, and causes rapid wilting in olive trees. The paper explores the use of Unmanned Aerial Vehicle (UAV) in combination with a multispectral radiometer for early detection of infection. The study was carried out in three olive groves in the Apulia region (Italy) and involved four drone flights from 2017 to 2019. To classify Xfp severity level in olive trees at an early stage, a combined method of geostatistics and discriminant analysis was implemented. The results of cross-validation for the non-parametric classification method were of overall accuracy = 0.69, mean error rate = 0.31, and for the early detection class of accuracy 0.77 and misclassification probability 0.23. The results are promising and encourage the application of UAV technology for the early detection of Xfp infection.