Hydrogen Storage Materials for Mobile and Stationary Applications: Current State of the Art

One of the limitations to the widespread use of hydrogen as an energy carrier is its storage in a safe and compact form. Herein, recent developments in effective high-capacity hydrogen storage materials are reviewed, with a special emphasis on light compounds, including those based on organic porous structures, boron, nitrogen, and aluminum. These elements and their related compounds hold the promise of high, reversible, and practical hydrogen storage capacity for mobile applications, including vehicles and portable power equipment, but also for the large scale and distributed storage of energy for stationary applications. Current understanding of the fundamental principles that govern the interaction of hydrogen with these light compounds is summarized, as well as basic strategies to meet practical targets of hydrogen uptake and release. The limitation of these strategies and current understanding is also discussed and new directions proposed

Mature neutrophils suppress T cell immunity in ovarian cancer microenvironment

Epithelial ovarian cancer (EOC) often presents with metastases and ascites. Granulocytic myeloid-derived suppressor cells are an immature population that impairs antitumor immunity. Since suppressive granulocytes in the ascites of patients with newly diagnosed EOC were morphologically mature, we hypothesized that PMN were rendered suppressive in the tumor microenvironment (TME). Circulating PMN from patients were not suppressive but acquired a suppressor phenotype (defined as ≥1 log10 reduction of anti-CD3/CD28-stimulated T cell proliferation) after ascites supernatant exposure. Ascites supernatants (20 of 31 supernatants) recapitulated the suppressor phenotype in PMN from healthy donors. T cell proliferation was restored with ascites removal and restimulation. PMN suppressors also inhibited T cell activation and cytokine production. PMN suppressors completely suppressed proliferation in naive, central memory, and effector memory T cells and in engineered tumor antigen-specific cytotoxic T lymphocytes, while antigen-specific cell lysis was unaffected. Inhibition of complement C3 activation and PMN effector functions, including CR3 signaling, protein synthesis, and vesicular trafficking, abrogated the PMN suppressor phenotype. Moreover, malignant effusions from patients with various metastatic cancers also induced the C3-dependent PMN suppressor phenotype. These results point to PMN impairing T cell expansion and activation in the TME and the potential for complement inhibition to abrogate this barrier to antitumor immunity

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings