No abstract

The novel COVID-19 outbreak has affected more than 200 countries and territories as of March 2020. Given that patients with cancer are generally more vulnerable to infections, systematic analysis of diverse cohorts of patients with cancer affected by COVID-19 is needed. We performed a multicenter study including 105 patients with cancer and 536 age-matched noncancer patients confirmed with COVID-19. Our results showed COVID-19 patients with cancer had higher risks in all severe outcomes. Patients with hematologic cancer, lung cancer, or with metastatic cancer (stage IV) had the highest frequency of severe events. Patients with nonmetastatic cancer experienced similar frequencies of severe conditions to those observed in patients without cancer. Patients who received surgery had higher risks of having severe events, whereas patients who underwent only radiotherapy did not demonstrate significant differences in severe events when compared with patients without cancer. These findings indicate that patients with cancer appear more vulnerable to SARS-COV-2 outbreak.SIgnIfICAnCe: Because this is the first large cohort study on this topic, our report will provide muchneeded information that will benefit patients with cancer globally. As such, we believe it is extremely important that our study be disseminated widely to alert clinicians and patients.

Background A vaccine to protect against COVID-19 is urgently needed. We aimed to assess the safety, tolerability, and immunogenicity of a recombinant adenovirus type-5 (Ad5) vectored COVID-19 vaccine expressing the spike glycoprotein of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strain. MethodsWe did a dose-escalation, single-centre, open-label, non-randomised, phase 1 trial of an Ad5 vectored COVID-19 vaccine in Wuhan, China. Healthy adults aged between 18 and 60 years were sequentially enrolled and allocated to one of three dose groups (5 × 10¹⁰, 1 × 10¹¹, and 1•5 × 10¹¹ viral particles) to receive an intramuscular injection of vaccine. The primary outcome was adverse events in the 7 days post-vaccination. Safety was assessed over 28 days post-vaccination. Specific antibodies were measured with ELISA, and the neutralising antibody responses induced by vaccination were detected with SARS-CoV-2 virus neutralisation and pseudovirus neutralisation tests. T-cell responses were assessed by enzyme-linked immunospot and flow-cytometry assays. This study is registered with ClinicalTrials.gov, NCT04313127. Findings Between March 16 and March 27, 2020, we screened 195 individuals for eligibility. Of them, 108 participants (51% male, 49% female; mean age 36•3 years) were recruited and received the low dose (n=36), middle dose (n=36), or high dose (n=36) of the vaccine. All enrolled participants were included in the analysis. At least one adverse reaction within the first 7 days after the vaccination was reported in 30 (83%) par ticipants in the low dose group, 30 (83%) participants in the middle dose group, and 27 (75%) participants in the high dose group. The most common injection site adverse reaction was pain, which was reported in 58 (54%) vaccine recipients, and the most commonly reported systematic adverse reactions were fever (50 [46%]), fatigue (47 [44%]), headache (42 [39%]), and muscle pain (18 [17%]. Most adverse reactions that were reported in all dose groups were mild or moderate in severity. No serious adverse event was noted within 28 days post-vaccination. ELISA antibodies and neutralising antibodies increased significantly at day 14, and peaked 28 days post-vaccination. Specific T-cell response peaked at day 14 post-vaccination. Interpretation The Ad5 vectored COVID-19 vaccine is tolerable and immunogenic at 28 days post-vaccination. Humoral responses against SARS-CoV-2 peaked at day 28 post-vaccination in healthy adults, and rapid specific T-cell responses were noted from day 14 post-vaccination. Our findings suggest that the Ad5 vectored COVID-19 vaccine warrants further investigation.

A receptor that mediates osteoprotegerin ligand (OPGL)-induced osteoclast differentiation and activation has been identified via genomic analysis of a primary osteoclast precursor cell cDNA library and is identical to the tumor necrosis factor receptor (TNFR) family member RANK. The RANK mRNA was highly expressed by isolated bone marrow-derived osteoclast progenitors and by mature osteoclasts in vivo. Recombinant OPGL binds specifically to RANK expressed by transfected cell lines and purified osteoclast progenitors. Transgenic mice expressing a soluble RANK-Fc fusion protein have severe osteopetrosis because of a reduction in osteoclasts, similar to OPG transgenic mice. Recombinant RANK-Fc binds with high affinity to OPGL in vitro and blocks osteoclast differentiation and activation in vitro and in vivo. Furthermore, polyclonal Ab against the RANK extracellular domain promotes osteoclastogenesis in bone marrow cultures suggesting that RANK activation mediates the effects of OPGL on the osteoclast pathway. These data indicate that OPGL-induced osteoclastogenesis is directly mediated through RANK on osteoclast precursor cells.

The EZH2 histone methyltransferase is highly expressed in germinal center (GC) B-cells and targeted by somatic mutations in B-cell lymphomas. Here we find that EZH2 deletion or pharmacologic inhibition suppresses GC formation and functions in mice. EZH2 represses proliferation checkpoint genes and helps establish bivalent chromatin domains at key regulatory loci to transiently suppress GC B-cell differentiation. Somatic mutations reinforce these physiological effects through enhanced silencing of EZH2 targets in B-cells, and in human B-cell lymphomas. Conditional expression of mutant EZH2 in mice induces GC hyperplasia and accelerated lymphomagenesis in cooperation with BCL2. GCB-type DLBCLs are mostly addicted to EZH2, regardless of mutation status, but not the more differentiated ABC-type DLBCLs, thus clarifying the therapeutic scope of EZH2 targeting.

No abstract

Profound neuronal dysfunction in the entorhinal cortex contributes to early loss of short-term memory in Alzheimer’s disease1–3. Here we show broad neuroprotective effects of entorhinal brain-derived neurotrophic factor (BDNF) administration in several animal models of Alzheimer’s disease, with extension of therapeutic benefits into the degenerating hippocampus. In amyloid-transgenic mice, BDNF gene delivery, when administered after disease onset, reverses synapse loss, partially normalizes aberrant gene expression, improves cell signaling and restores learning and memory. These outcomes occur independently of effects on amyloid plaque load. In aged rats, BDNF infusion reverses cognitive decline, improves age-related perturbations in gene expression and restores cell signaling. In adult rats and primates, BDNF prevents lesion-induced death of entorhinal cortical neurons. In aged primates, BDNF reverses neuronal atrophy and ameliorates age-related cognitive impairment. Collectively, these findings indicate that BDNF exerts substantial protective effects on crucial neuronal circuitry involved in Alzheimer’s disease, acting through amyloid-independent mechanisms. BDNF therapeutic delivery merits exploration as a potential therapy for Alzheimer’s disease.

We propose a technique for learning representations of parser states in transitionbased dependency parsers. Our primary innovation is a new control structure for sequence-to-sequence neural networksthe stack LSTM. Like the conventional stack data structures used in transitionbased parsing, elements can be pushed to or popped from the top of the stack in constant time, but, in addition, an LSTM maintains a continuous space embedding of the stack contents. This lets us formulate an efficient parsing model that captures three facets of a parser's state: (i) unbounded look-ahead into the buffer of incoming words, (ii) the complete history of actions taken by the parser, and (iii) the complete contents of the stack of partially built tree fragments, including their internal structures. Standard backpropagation techniques are used for training and yield state-of-the-art parsing performance.