Divalent siRNAs are bioavailable in the lung and efficiently block SARS-CoV-2 infection.

The continuous evolution of SARS-CoV-2 variants complicates efforts to combat the ongoing pandemic, underscoring the need for a dynamic platform for the rapid development of pan-viral variant therapeutics. Oligonucleotide therapeutics are enhancing the treatment of numerous diseases with unprecedented potency, duration of effect, and safety. Through the systematic screening of hundreds of oligonucleotide sequences, we identified fully chemically stabilized siRNAs and ASOs that target regions of the SARS-CoV-2 genome conserved in all variants of concern, including delta and omicron. We successively evaluated candidates in cellular reporter assays, followed by viral inhibition in cell culture, with eventual testing of leads for in vivo antiviral activity in the lung. Previous attempts to deliver therapeutic oligonucleotides to the lung have met with only modest success. Here, we report the development of a platform for identifying and generating potent, chemically modified multimeric siRNAs bioavailable in the lung after local intranasal and intratracheal delivery. The optimized divalent siRNAs showed robust antiviral activity in human cells and mouse models of SARS-CoV-2 infection and represent a new paradigm for antiviral therapeutic development for current and future pandemics.

Molecular architecture and dynamics of SARS-CoV-2 envelope by integrative modeling.

Despite tremendous efforts, the exact structure of SARS-CoV-2 and related betacoronaviruses remains elusive. SARS-CoV-2 envelope is a key structural component of the virion that encapsulates viral RNA. It is composed of three structural proteins, spike, membrane (M), and envelope, which interact with each other and with the lipids acquired from the host membranes. Here, we developed and applied an integrative multi-scale computational approach to model the envelope structure of SARS-CoV-2 with near atomistic detail, focusing on studying the dynamic nature and molecular interactions of its most abundant, but largely understudied, M protein. The molecular dynamics simulations allowed us to test the envelope stability under different configurations and revealed that the M dimers agglomerated into large, filament-like, macromolecular assemblies with distinct molecular patterns. These results are in good agreement with current experimental data, demonstrating a generic and versatile approach to model the structure of a virus de novo.

Unravelling psychiatric heterogeneity and predicting suicide attempts in women with trauma-related dissociation using artificial intelligence Desentrañando la Heterogeneidad Psiquiátrica y Prediciendo los Intentos Suicidas en Mujeres con Disociación Relacionada con el Trauma Usando Inteligencia Artificial 使用人工智能揭示具有创伤相关解离女性的精神病学异质性和预测其自杀意图

Background: Suicide is a leading cause of death, and rates of attempted suicide have increased during the COVID-19 pandemic. The under-diagnosed psychiatric phenotype of dissociation is associated with elevated suicidal self-injury;however, it has largely been left out of attempts to predict and prevent suicide. Objective: We designed an artificial intelligence approach to identify dissociative patients and predict prior suicide attempts in an unbiased, data-driven manner. Method: Participants were 30 controls and 93 treatment-seeking female patients with posttraumatic stress disorder (PTSD) and various levels of dissociation, including some with the PTSD dissociative subtype and some with dissociative identity disorder (DID). Results: Unsupervised learning models identified patients along a spectrum of dissociation. Moreover, supervised learning models accurately predicted prior suicide attempts with an score up to 0.83. DID had the highest risk of prior suicide attempts, and distinct subtypes of dissociation predicted suicide attempts in PTSD and DID. Conclusions: These findings expand our understanding of the dissociative phenotype and underscore the urgent need to assess for dissociation to identify individuals at high-risk of suicidal self-injury. HIGHLIGHTS Dissociation, feelings of detachment and disruption in one's sense of self and surroundings, is associated with an elevated risk of suicidal self-injury;however, it has largely been left out of attempts to predict and prevent suicide. Using machine learning techniques, we found dissociative identity disorder had the highest risk of prior suicide attempts, and distinct subtypes of dissociation predicted suicide attempts in posttraumatic stress disorder and dissociative identity disorder. These findings underscore the urgent need to assess for dissociation to identify individuals at high-risk of suicidal self-injury.

PHILM2Web: A high-throughput database of macromolecular host-pathogen interactions on the Web.

During infection, the pathogen's entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host-pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein-protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen-Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research Dataset. This COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface; scientists can also download the entire database in bulk for offline processing. Database URL: http://philm2web.live.