Ask about this productRelated genes to: SARS2 antibody
- Gene:
- SARS2 NIH gene
- Name:
- seryl-tRNA synthetase 2, mitochondrial
- Previous symbol:
- SARSM
- Synonyms:
- FLJ20450, mtSerRS, SerRSmt, SARS, SERS, SYS
- Chromosome:
- 19q13.2
- Locus Type:
- gene with protein product
- Date approved:
- 2002-10-09
- Date modifiied:
- 2016-11-09
Related products to: SARS2 antibody
Related articles to: SARS2 antibody
- Data scarcity limits the characterization of protein fitness landscapes and the development of accurate variant effect prediction models. To address this challenge, we introduce fitness translocation, a data augmentation strategy that generates synthetic variants for a target protein by leveraging variant fitness data previously measured in homologous proteins. Using embeddings from protein language models, the method computes the difference between each homolog variant and its wild type and applies these offsets to the target wild-type embedding to create synthetic variants in embedding space. - Source: PubMed
Publication date: 2026/06/20
Mialland AdrienFukunaga ShuzoKatsuki RikuDong YunfeiYamaguchi HidekiSaito Yutaka - The human nasopharynx is colonized by a diverse community of commensal microbiota linked to many respiratory diseases, yet their associations with the host remain unclear. - Source: PubMed
Publication date: 2026/06/22
Ye XiangyuYue MolinLee SojinLi AndrewWang-Erickson Anna FForno ErickEddens TaylorShaikh NaderChen Wei - - Source: PubMed
- Early identification of patients at risk of severe pneumonia during Omicron SARS-CoV-2 infection is critical for optimizing care and allocating resources. While clinical markers provide insights, imaging-derived radiomics features may enhance prognostic accuracy. We developed a multimodal predictive model combining Delta Radiomics features from serial chest CT scans with clinical data, including blood biochemical markers and lymphocyte subsets. The primary prediction target was severe/critical Omicron pneumonia during hospitalization. Mild and moderate cases were grouped as non-severe disease, whereas severe and critical cases were defined as the severe class for binary classification. The model was trained on 91 patients from the first center, internally validated on 23 patients, and externally tested on 32 patients from a second center. Machine learning algorithms including Logistic Regression, Random Forest, and MLP were applied, and a nomogram was constructed for individualized risk prediction. The combined model showed high discrimination in the training cohort and maintained favorable performance in the internal validation and independent external test cohorts, achieving AUCs of 0.885 and 0.875, respectively. The Delta Radiomics signature, particularly with MLP, showed comparatively stable predictive performance. These findings suggest the added value of temporal CT-derived radiomics when integrated with clinical biomarkers, although further validation in larger prospective cohorts is required. Integrating temporal imaging features with clinical data offers a non-invasive method for early prediction of severe/critical Omicron pneumonia, supporting individualized triage and more efficient allocation of medical resources. - Source: PubMed
Publication date: 2026/06/21
Ye XiaoxianDai XuhaoGong ShengpingZhou YingyingZhu TingSong BinbinYang JimingGe XiaoqinRen JiangpingShi CongCao Yijian - The global burden of respiratory viral disease is shaped by two enduring threats: influenza, responsible for 290,000-650,000 annual deaths, and coronaviruses, exemplified by the catastrophic SARS-CoV-2 pandemic that caused over 7 million confirmed fatalities and profound socioeconomic disruption. Current strain-specific vaccines remain inherently reactive, incapable of anticipating antigenic drift, reassortment, or zoonotic emergence. A paradigm shift toward universal vaccines-designed to target evolutionarily conserved viral epitopes and confer durable, broad-spectrum protection across strains, subtypes, and viral genera-represents the most strategically consequential frontier in contemporary vaccinology and pandemic preparedness. - Source: PubMed
Publication date: 2026/06/21
Ikrar TarunaMuchsin WachyudiSophian Alfi