Water-soluble Polypyrrole-Polybis(4-oxy benzene sulfonic acid)phosphazene Composites and Investigation of Their Performance as Cathode Binder in Li-ion Batteries.

Current electric storage systems eagerly focus on high-power and energy-dense Lithium-ion batteries to cope with increasing energy storage demands. Since cathode materials are one of the bottlenecks of these batteries, there is much interest in layered lithium-rich manganese oxide-based (LLMO) cathodes which can develop this technology. However, Initial Coulombic Efficiency (ICE) loss, poor rate performance and cycling instability issues are still persistent as problems to be solved for these materials. Recent research shows that water-soluble binders are effective in improving the performance of LLMO materials. Herein, we describe the synthesis, characterisation, and application of a series of water-soluble composites as a binder for LLMO cathodes. The PPy is introduced as part of the binder to improve the electronic conductivity and two different oxidants and various PPy to PSAP ratios were used to optimise the final properties. The electrochemical performance and morphology of the cathodes before and after cycling were investigated and compared with the conventional PVDF binder. The LLMO-2c electrode showed excellent charge-discharge performance, especially at 5 C and 10 C rates, and high cycling stability at 0.2 C whilst maintaining a final capacity of 184 mAh/g after 200 cycles, which is equal to 89.3 % capacity retention.

Identification of novel driver risk genes in CNV loci associated with neurodevelopmental disorders.

Copy-number variants (CNVs) are genome-wide structural variations involving the duplication or deletion of large nucleotide sequences. While these types of variations can be commonly found in humans, large and rare CNVs are known to contribute to the development of various neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD). Nevertheless, given that these NDD-risk CNVs cover broad regions of the genome, it is particularly challenging to pinpoint the critical gene(s) responsible for the manifestation of the phenotype. In this study, we performed a meta-analysis of CNV data from 11,614 affected individuals with NDDs and 4,031 control individuals from SFARI database to identify 41 NDD-risk CNV loci, including 24 novel regions. We also found evidence for dosage-sensitive genes within these regions being significantly enriched for known NDD-risk genes and pathways. In addition, a significant proportion of these genes was found to (1) converge in protein-protein interaction networks, (2) be among most expressed genes in the brain across all developmental stages, and (3) be hit by deletions that are significantly over-transmitted to individuals with ASD within multiplex ASD families from the iHART cohort. Finally, we conducted a burden analysis using 4,281 NDD cases from Decipher and iHART cohorts, and 2,504 neurotypical control individuals from 1000 Genomes and iHART, which resulted in the validation of the association of 162 dosage-sensitive genes driving risk for NDDs, including 22 novel NDD-risk genes. Importantly, most NDD-risk CNV loci entail multiple NDD-risk genes in agreement with a polygenic model associated with the majority of NDD cases.

Characterization of a Clinically and Biologically Defined Subgroup of Patients with Autism Spectrum Disorder and Identification of a Tailored Combination Treatment.

Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders (NDDs) with a high unmet medical need. The diagnosis of ASD is currently based on behavior criteria, which overlooks the diversity of genetic, neurophysiological, and clinical manifestations. Failure to acknowledge such heterogeneity has hindered the development of efficient drug treatments for ASD and other NDDs. DEPI® (Databased Endophenotyping Patient Identification) is a systems biology, multi-omics, and machine learning-driven platform enabling the identification of subgroups of patients with NDDs and the development of patient-tailored treatments. In this study, we provide evidence for the validation of a first clinically and biologically defined subgroup of patients with ASD identified by DEPI, ASD Phenotype 1 (ASD-Phen1). Among 313 screened patients with idiopathic ASD, the prevalence of ASD-Phen1 was observed to be ~24% in 84 patients who qualified to be enrolled in the study. Metabolic and transcriptomic alterations differentiating patients with ASD-Phen1 were consistent with an over-activation of NF-κB and NRF2 transcription factors, as predicted by DEPI. Finally, the suitability of STP1 combination treatment to revert such observed molecular alterations in patients with ASD-Phen1 was determined. Overall, our results support the development of precision medicine-based treatments for patients diagnosed with ASD.