Transgenic Mice for the Translational Study of Neuropathic Pain and Dystonia

Murine models are fundamental in the study of clinical conditions and the development of new drugs and treatments. Transgenic technology has started to offer advantages in oncology, encompassing all research fields related to the study of painful syndromes. Knockout mice or mice overexpressing genes encoding for proteins linked to pain development and maintenance can be produced and pain models can be applied to transgenic mice to model the most disabling neurological conditions. Due to the association of movement disorders with sensitivity and pain processing, our group focused for the first time on the role of the torsinA gene GAG deletion—responsible for DYT1 dystonia—in baseline sensitivity and neuropathic responses. The aim of the present report are to review the complex network that exists between the chaperonine-like protein torsinA and the baseline sensitivity pattern—which are fundamental in neuropathic pain—and to point at its possible role in neurodegenerative diseases.     

What Are the Biomarkers for Immunotherapy in SCLC?

Small-cell lung cancer (SCLC) is an aggressive malignancy that exhibits a rapid doubling time, a high growth fraction, and the early development of widespread metastases. The addition of immune checkpoint inhibitors to first-line chemotherapy represents the first significant improvement of systemic therapy in several decades. However, in contrast to its effects on non-SCLC, the advantageous effects of immunotherapy addition are modest in SCLC. In particular, only a small number of SCLC patients benefit from immune checkpoint inhibitors. Additionally, biomarkers selection is lacking for SCLC, with clinical trials largely focusing on unselected populations. Here, we review the data concerning the major biomarkers for immunotherapy, namely, programmed death ligand 1 expression and tumour mutational burden. Furthermore, we explore other potential biomarkers, including the role of the immune microenvironment in SCLC, the role of genetic alterations, and the potential links between neurological paraneoplastic syndromes, serum anti-neuronal nuclear antibodies, and outcomes in SCLC patients treated with immunotherapy.     

Bioprospecting Antimicrobials from Lactiplantibacillus plantarum: Key Factors Underlying Its Probiotic Action

Lactiplantibacillus plantarum (L. plantarum) is a well-studied and versatile species of lactobacilli. It is found in several niches, including human mucosal surfaces, and it is largely employed in the food industry and boasts a millenary tradition of safe use, sharing a long-lasting relationship with humans. L. plantarum is generally recognised as safe and exhibits a strong probiotic character, so that several strains are commercialised as health-promoting supplements and functional food products. For these reasons, L. plantarum represents a valuable model to gain insight into the nature and mechanisms of antimicrobials as key factors underlying the probiotic action of health-promoting microbes. Probiotic antimicrobials can inhibit the growth of pathogens in the gut ensuring the intestinal homeostasis and contributing to the host health. Furthermore, they may be attractive alternatives to conventional antibiotics, holding potential in several biomedical applications. The aim of this review is to investigate the most relevant papers published in the last ten years, bioprospecting the antimicrobial activity of characterised probiotic L. plantarum strains. Specifically, it focuses on the different chemical nature, the action spectra and the mechanisms underlying the bioactivity of their antibacterial and antiviral agents. Emerging trends in postbiotics, some in vivo applications of L. plantarum antimicrobials, including strengths and limitations of their therapeutic potential, are addressed and discussed.     

Colocalization and Interaction Study of Neuronal JNK3, JIP1, and β-Arrestin2 Together with PSD95

c-Jun N-terminal kinases (JNKs) are stress-activated serine/threonine protein kinases belonging to the mitogen-activated protein kinase (MAPK) family. Among them, JNK3 is selectively expressed in the central nervous system, cardiac smooth muscle, and testis. In addition, it is the most responsive JNK isoform to stress stimuli in the brain, and it is involved in synaptic dysfunction, an essential step in neurodegenerative processes. JNK3 pathway is organized in a cascade of amplification in which signal transduction occurs by stepwise, highly controlled phosphorylation. Since different MAPKs share common upstream activators, pathway specificity is guaranteed by scaffold proteins such as JIP1 and β-arrestin2. To better elucidate the physiological mechanisms regulating JNK3 in neurons, and how these interactions may be involved in synaptic (dys)function, we used (i) super-resolution microscopy to demonstrate the colocalization among JNK3–PSD95–JIP1 and JNK3–PSD95–β-arrestin2 in cultured hippocampal neurons, and (ii) co-immunoprecipitation techniques to show that the two scaffold proteins and JNK3 can be found interacting together with PSD95. The protein-protein interactions that govern the formation of these two complexes, JNK3–PSD95–JIP1 and JNK3–PSD95–β-arrestin2, may be used as targets to interfere with their downstream synaptic events.     

Role of Bevacizumab on Vascular Endothelial Growth Factor in Apolipoprotein E Deficient Mice after Traumatic Brain Injury

Traumatic brain injury (TBI) disrupts the blood–brain barrier (BBB). Vascular endothelial growth factor (VEGF) is believed to play a key role in TBI and to be overexpressed in the absence of apolipoprotein E (ApoE). Bevacizumab, a VEGF inhibitor, demonstrated neuroprotective activity in several models of TBI. However, the effects of bevacizumab on Apo-E deficient mice are not well studied. The present study aimed to evaluate VEGF expression and the effects of bevacizumab on BBB and neuroinflammation in ApoE^−/− mice undergoing TBI. Furthermore, for the first time, this study evaluates the effects of bevacizumab on the long-term consequences of TBI, such as atherosclerosis. The results showed that motor deficits induced by controlled cortical impact (CCI) were accompanied by increased brain edema and VEGF expression. Treatment with bevacizumab significantly improved motor deficits and significantly decreased VEGF levels, as well as brain edema compared to the control group. Furthermore, the results showed that bevacizumab preserves the integrity of the BBB and reduces the neuroinflammation induced by TBI. Regarding the effects of bevacizumab on atherosclerosis, it was observed for the first time that its ability to modulate VEGF in the acute phase of head injury prevents the acceleration of atherosclerosis. Therefore, the present study demonstrates not only the neuroprotective activity of bevacizumab but also its action on the vascular consequences related to TBI.     

A General Approach to L(h, k)-Label Interconnection Networks

Given two non-negative integers h and k,an L(h,k)-labeling of a graph G=(V,E) is a function from the set V to a set of colors,such that adjacent nodes take colors at distance at least h,and nodes at distance 2 take colors at distance at least k.The aim of the L(h,k)-labeling problem is to minimize the greatest used color.Since the decisional version of this problem is NP-complete,it is important to investigate particular classes of graphs for which the problem can be efficiently solved.It is well known that the most common interconnection topologies,such as Butterfly-like,Bene(?),CCC,Trivalent Cayley networks,are all characterized by a similar structure:they have nodes organized as a matrix and connections are divided into layers.So we naturally introduce a new class of graphs,called (1×n)-multistage graphs,containing the most common interconnection topologies,on which we study the L(h,k)-labeling.A general algorithm for L(h,k)-labeling these graphs is presented,and from this method an efficient L(2,1)-labeling for Butterfly and CCC networks is derived.Finally we describe a possible generalization of our approach.     

Biomarkers Related to Synaptic Dysfunction to Discriminate Alzheimer’s Disease from Other Neurological Disorders

Recently, the synaptic proteins neurogranin (Ng) and α-synuclein (α-Syn) have attracted scientific interest as potential biomarkers for synaptic dysfunction in neurodegenerative diseases. In this study, we measured the CSF Ng and α-Syn concentrations in patients affected by AD (n = 69), non-AD neurodegenerative disorders (n-AD = 50) and non-degenerative disorders (n-ND, n = 98). The concentrations of CSF Ng and α-Syn were significantly higher in AD than in n-AD and n-ND. Moreover, the Aβ42/Ng and Aβ42/α-Syn ratios showed statistically significant differences between groups and discriminated AD patients from n-AD patients, better than Ng or α-Syn alone. Regression analyses showed an association of higher Ng concentrations with MMSE < 24, pathological Aβ 42/40 ratios, pTau, tTau and the ApoEε4 genotype. Aβ 42/Ng was associated with MMSE < 24, an AD-related FDG-PET pattern, the ApoEε4 genotype, pathological Aβ 42 levels and Aβ 42/40 ratios, pTau, and tTau. Moreover, APO-Eε4 carriers showed higher Ng concentrations than non-carriers. Our results support the idea that the Aβ 42/Ng ratio is a reliable index of synaptic dysfunction/degeneration able to discriminate AD from other neurological conditions.     

Static analysis of Linear Logic programming

Linear Logic is gaining momentum in computer science because it offers a unified framework and a common vocabulary for studying and analyzing different aspects of programming and computation. We focus here on models where computation is identified with proof search in the sequent system of Linear Logic. A proof normalization procedure, called “focusing”, has been proposed to make the problem of proof search tractable. Correspondingly, there is a normalization procedure mapping formulae of Linear Logic into a syntactic fragment of that logic, calledLinLog, where the focusing normalization for proofs can be most conveniently expressed. In this paper, we propose to push this compilation/normalization process further, by applying abstract interpretation and partial evaluation techniques to (focused) proofs inLinLog. These techniques provide information concerning the evolution of the computational resources (formulae) during the execution (proof construction). The practical outcome that we expect from this theoretical effort is the definition of a general tool for statically analyzing and reasoning about the runtime behavior of programs in frameworks where computations can be accounted for in terms of proof search in Linear Logic.