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Abstract The brain responds to experience through modulation of synaptic transmission, that is synaptic plasticity. An increase in the strength of synaptic transmission is manifested as long-term potentiation (LTP), while a decrease in the strength of synaptic transmission is expressed as long-term depression (LTD). Most of the studies of synaptic plasticity have been carried out by induction via electrophysiological stimulation. It is largely unknown in which behavioural tasks such synaptic plasticity occurs. Moreover, some stimuli can induce both LTP and LTD, thus making it difficult to separately study the different forms of synaptic plasticity. Two studies have shown that an aversive memory task – inhibitory avoidance learning and contextual fear conditioning – physiologically and selectively induce LTP and an LTP-like molecular change, respectively, in the hippocampus in vivo. Here, we show that a non-aversive behavioural task – exploration of new space – physiologically and selectively elicits a biochemical change in the hippocampus that is a hallmark of LTP. Specifically, we found that exploration of new space induces an increase in the phosphorylation of GluA1(Ser831), without affecting the phosphorylation of GluA1(Ser845), which are biomarkers of early-LTP and not NMDAR-mediated LTD. We also show that exploration of new space engenders the phosphorylation of the translational regulator S6K and the expression of Arc, which are features of electrophysiologically-induced late-LTP in the hippocampus. Therefore, our results show that exploration of new space is a novel non-aversive behavioural paradigm that elicits molecular changes in vivo that are analogous to those occurring during early- and late-LTP, but not during NMDAR-mediated LTD.

This study investigates the performance of flax fiber and epoxy resin composites, focusing on fatigue testing, which is scarcely reported in the literature. Fatigue behavior is evaluated under full reverse bending loading conditions: R = −1. Apart of the definition of the classic S-N curve, the analysis deeply investigated the evolution of the hysteresis leaf generated during each cycle over the entire fatigue life. This approach allows evaluating interesting parameters such as the evolution of the loss factor and the apparent modulus over life, being these informations fundamental for practical application of natural-fiber composites in engineering applications.

Although rare, C3 glomerulopathy (C3G) is increasingly recognized thanks to the currently available diagnostic skills. C3G is not a single disease but a group of disorders with distinct pathogenesis and progression. Thus, an essential step for its management remains an in-depth characterization of the specific form and the identification of underlying conditions, which may also impact treatment choices as well. Among these entities, an emerging condition is the association of C3G with monoclonal gammopathy, which confers poor outcomes. Overall, diagnosis of C3G remains challenging, and determining the appropriate treatment remains unclear. Conventional immunosuppressive therapy has proven ineffective in such cases, while clone-directed therapies have shown promising results in small interventional studies and case series. Here, we report a case of a patient affected by C3G with monoclonal gammopathy of renal significance who experienced rapid deterioration of kidney function requiring replacement therapy. After the failure of first-line treatment, a switch to the anti-CD38 therapy with daratumumab resulted in the progressive improvement of the patient’s kidney function, leading to the discontinuation of hemodialysis after approximately 10 months. Serial renal biopsies were also performed to study the disease’s evolution in response to the treatment. Based on the description of this single case, we have comprehensively reviewed available studies on daratumumab use in patients with C3G associated with monoclonal gammopathy to provide insights for the design of prospective studies which aim to enhance the management of such poor prognosis disease.

Abstract After spinal cord injury (SCI), patients face many physical and psychological issues including intestinal dysfunction and comorbidities, strongly affecting quality of life. The gut microbiota has recently been suggested to influence the course of the disease in these patients. However, to date only two studies have profiled the gut microbiota in SCI patients, months after a traumatic injury. Here we characterized the gut microbiota in a large Italian SCI population, within a short time from a not only traumatic injury. Feces were collected within the first week at the rehabilitation center (no later than 60 days after SCI), and profiled by 16S rRNA gene-based next-generation sequencing. Microbial profiles were compared to those publicly available of healthy age- and gender-matched Italians, and correlated to patient metadata, including type of SCI, spinal unit location, nutrition and concomitant antibiotic therapies. The gut microbiota of SCI patients shows distinct dysbiotic signatures, i.e. increase in potentially pathogenic, pro-inflammatory and mucus-degrading bacteria, and depletion of short-chain fatty acid producers. While robust to most host variables, such dysbiosis varies by lesion level and completeness, with the most neurologically impaired patients showing an even more unbalanced microbial profile. The SCI-related gut microbiome dysbiosis is very likely secondary to injury and closely related to the degree of completeness and severity of the lesion, regardless of etiology and time interval. This microbial layout could variously contribute to increased gut permeability and inflammation, potentially predisposing patients to the onset of severe comorbidities.

Cancer cells fuel growth and energy demands by increasing their NAD+ biosynthesis dependency, which therefore represents an exploitable vulnerability for anti-cancer strategies. CD38 is a NAD+-degrading enzyme that has become crucial for anti-MM therapies since anti-CD38 monoclonal antibodies represent the backbone for treatment of newly diagnosed and relapsed multiple myeloma patients. Nevertheless, further steps are needed to enable a full exploitation of these strategies, including deeper insights of the mechanisms by which CD38 promotes tumorigenesis and its metabolic additions that could be selectively targeted by therapeutic strategies. Here, we present evidence that CD38 upregulation produces a pervasive intracellular-NAD+ depletion, which impairs mitochondrial fitness and enhances oxidative stress; as result, genetic or pharmacologic approaches that aim to modify CD38 surface-level prime MM cells to NAD+-lowering agents. The molecular mechanism underlying this event is an alteration in mitochondrial dynamics, which decreases mitochondria efficiency and triggers energetic remodeling. Overall, we found that CD38 handling represents an innovative strategy to improve the outcomes of NAD+-lowering agents and provides the rationale for testing these very promising agents in clinical studies involving MM patients.

A central question of machine learning is how deep nets manage to learn tasks in high dimensions. An appealing hypothesis is that they achieve this feat by building a representation of the data where information irrelevant to the task is lost. For image datasets, this view is supported by the observation that after (and not before) training, the neural representation becomes less and less sensitive to diffeomorphisms acting on images as the signal propagates through the network. This loss of sensitivity correlates with performance and surprisingly correlates with a gain of sensitivity to white noise acquired during training. Which are the mechanisms learned by convolutional neural networks (CNNs) responsible for the these phenomena? In particular, why is the sensitivity to noise heightened with training? Our approach consists of two steps. (1) Analyzing the layer-wise representations of trained CNNs, we disentangle the role of spatial pooling in contrast to channel pooling in decreasing their sensitivity to image diffeomorphisms while increasing their sensitivity to noise. (2) We introduce model scale-detection tasks, which qualitatively reproduce the phenomena reported in our empirical analysis. In these models we can assess quantitatively how spatial pooling affects these sensitivities. We find that the increased sensitivity to noise observed in deep ReLU networks is a mechanistic consequence of the perturbing noise piling up during spatial pooling, after being rectified by ReLU units. Using odd activation functions like tanh drastically reduces the CNNs’ sensitivity to noise.