Long-term survival of renal allografts depends on the chronic immune response and is probably influenced by the initial injury caused by ischemia and reperfusion. Hypoxia-inducible transcription ...factors (HIFs) are essential for adaptation to low oxygen. Normoxic inactivation of HIFs is regulated by oxygen-dependent hydroxylation of specific prolyl-residues by prolyl-hydroxylases (PHDs). Pharmacological inhibition of PHDs results in HIF accumulation with subsequent activation of tissue-protective genes. We examined the effect of donor treatment with a specific PHD inhibitor (FG-4497) on graft function in the Fisher-Lewis rat model of allogenic kidney transplantation (KTx). Orthotopic transplantation of the left donor kidney was performed after 24 h of cold storage. The right kidney was removed at the time of KTx (acute model) or at day 10 (chronic model). Donor animals received a single dose of FG-4497 (40 mg/kg i.v.) or vehicle 6 h before donor nephrectomy. Recipients were followed up for 10 days (acute model) or 24 weeks (chronic model). Donor preconditioning with FG-4497 resulted in HIF accumulation and induction of HIF target genes, which persisted beyond cold storage. It reduced acute renal injury (serum creatinine at day 10: 0.66 ± 0.20 vs. 1.49 ± 1.36 mg/dL; P < 0.05) and early mortality in the acute model and improved long-term survival of recipient animals in the chronic model (mortality at 24 weeks: 3 of 16 vs. 7 of 13 vehicle-treated animals; P < 0.05). In conclusion, pretreatment of organ donors with FG-4497 improves short- and long-term outcomes after allogenic KTx. Inhibition of PHDs appears to be an attractive strategy for organ preservation that deserves clinical evaluation.
MicroRNAs (miRNAs) recently emerged as means of communication between insulin-sensitive tissues to mediate diabetes development and progression, and as such they present a valuable proxy for ...epigenetic alterations associated with type 2 diabetes. In order to identify miRNA markers for the precursor of diabetes called prediabetes, we applied a translational approach encompassing analysis of human plasma samples, mouse tissues and an in vitro validation system. MiR-652-3p, miR-877-5p, miR-93-5p, miR-130a-3p, miR-152-3p and let-7i-5p were increased in plasma of women with impaired fasting glucose levels (IFG) compared to those with normal fasting glucose and normal glucose tolerance (NGT). Among these, let-7i-5p and miR-93-5p correlated with fasting blood glucose levels. Human data were then compared to miRNome data obtained from islets of Langerhans and adipose tissue of 10-week-old female New Zealand Obese mice, which differ in their degree of hyperglycemia and liver fat content. Similar to human plasma, let-7i-5p was increased in adipose tissue and islets of Langerhans of diabetes-prone mice. As predicted by the in silico analysis, overexpression of let-7i-5p in the rat β-cell line INS-1 832/12 resulted in downregulation of insulin signaling pathway components (Insr, Rictor, Prkcb, Clock, Sos1 and Kcnma1). Taken together, our integrated approach highlighted let-7i-5p as a potential regulator of whole-body insulin sensitivity and a novel marker of prediabetes in women.
Synaptogenesis, the generation and maturation of functional synapses between nerve cells, is an essential step in the development of neuronal networks in the brain. It is thought to be triggered by ...members of the neuroligin family of postsynaptic cell adhesion proteins, which may form transsynaptic contacts with presynaptic α- and β-neurexins and have been implicated in the etiology of autism. We show that deletion mutant mice lacking neuroligin expression die shortly after birth due to respiratory failure. This respiratory failure is a consequence of reduced GABAergic/glycinergic and glutamatergic synaptic transmission and network activity in brainstem centers that control respiration. However, the density of synaptic contacts is not altered in neuroligin-deficient brains and cultured neurons. Our data show that neuroligins are required for proper synapse maturation and brain function, but not for the initial formation of synaptic contacts.
Punzi-loss Abudinén, F; Bertemes, M; Bilokin, S ...
The European physical journal. C, Particles and fields,
02/2022, Letnik:
82, Številka:
2
Journal Article
Recenzirano
Odprti dostop
We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics ...experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example.
Punzi-loss Abudinén, F.; Bertemes, M.; Bilokin, S. ...
The European physical journal. C, Particles and fields,
2022/2, Letnik:
82, Številka:
2
Journal Article
Recenzirano
Odprti dostop
We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics ...experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example.
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