Robust Artificial Intelligence Tool for Atrial Fibrillation Diagnosis: Novel Development Approach Incorporating Both Atrial Electrograms and Surface ECG and Evaluation by Head-to-Head Comparison With Hospital-Based Physician ECG Readers.

BACKGROUND: Atrial fibrillation (AF) increases risk of embolic stroke, and in postoperative patients, increases cost of care. Consequently, ECG screening for AF in high-risk patients is important but labor-intensive. Artificial intelligence (AI) may reduce AF detection workload, but AI development presents challenges. METHODS AND RESULTS: We used a novel approach to AI development for AF detection using both surface ECG recordings and atrial epicardial electrograms obtained in postoperative cardiac patients. Atrial electrograms were used only to facilitate establishing true AF for AI development; this permitted the establishment of an AI-based tool for subsequent AF detection using ECG records alone. A total of 5 million 30-second epochs from 329 patients were annotated as AF or non-AF by expert ECG readers for AI training and validation, while 5 million 30-second epochs from 330 different patients were used for AI testing. AI performance was assessed at the epoch level as well as AF burden at the patient level. AI achieved an area under the receiver operating characteristic curve of 0.932 on validation and 0.953 on testing. At the epoch level, testing results showed means of AF detection sensitivity, specificity, negative predictive value, positive predictive value, and F1 (harmonic mean of positive predictive value and sensitivity) as 0.970, 0.814, 0.976, 0.776, and 0.862, respectively, while the intraclass correlation coefficient for AF burden detection was 0.952. At the patient level, AF burden sensitivity and positive predictivity were 96.2% and 94.5%, respectively. CONCLUSIONS: Use of both atrial electrograms and surface ECG permitted development of a robust AI-based approach to postoperative AF recognition and AF burden assessment. This novel tool may enhance detection and management of AF, particularly in patients following operative cardiac surgery.

Targeted genetic screening in mice through haploid embryonic stem cells identifies critical genes in bone development.

Mutagenic screening is powerful for identifying key genes involved in developmental processes. However, such screens are successful only in lower organisms. Here, we develop a targeted genetic screening approach in mice through combining androgenetic haploid embryonic stem cells (AG-haESCs) and clustered regularly interspaced palindromic repeats/CRISPR-associated protein 9 (CRISPR-Cas9) technology. We produced a mutant semi-cloned (SC) mice pool by oocyte injection of AG-haESCs carrying constitutively expressed Cas9 and an single guide RNA (sgRNA) library targeting 72 preselected genes in one step and screened for bone-development-related genes through skeletal analysis at birth. This yielded 4 genes: Zic1 and Clec11a, which are required for bone development, and Rln1 and Irx5, which had not been previously considered. Whereas Rln1-/- mice exhibited small skeletal size only at birth, Irx5-/- mice showed skeletal abnormalities both in postnatal and adult phases due to decreased bone mass and increased bone marrow adipogenesis. Mechanistically, iroquois homeobox 5 (IRX5) promotes osteoblastogenesis and inhibits adipogenesis by suppressing peroxisome proliferator activated receptor γ (PPARγ) activation. Thus, AG-haESC-mediated functional mutagenic screening opens new avenues for genetic interrogation of developmental processes in mice.

Lkb1 deletion in periosteal mesenchymal progenitors induces osteogenic tumors through mTORC1 activation.

Bone osteogenic sarcoma has a poor prognosis as the exact cell of origin and the signaling pathways underling tumor formation remain undefined. Here, we report an osteogenic tumor mouse model based on the conditional knockout of liver kinase b1 (Lkb1; also known as Stk11) in Cathepsin K (Ctsk)-Cre expressing cells. Lineage tracing studies demonstrated that Ctsk-Cre could label a population of periosteal cells. The cells functioned as mesenchymal progenitors with regard to markers and functional properties. LKB1 deficiency increased proliferation and osteoblast differentiation of Ctsk+ periosteal cells, while downregulation of mTORC1 activity, using Raptor genetic mouse model or mTORC1 inhibitor treatment, ameliorated tumor progression of Ctsk-Cre Lkb1fllfl mice. Xenograft mouse models, using human osteosarcoma cell lines, also demonstrated that LKB1 deficiency promoted tumor formation, while mTOR inhibition suppressed xenograft tumor growth. In summary, we identified periosteum-derived Ctsk-Cre expressing cells as a cell of origin for osteogenic tumor and suggested the LKB1-mTORC1 pathway as a promising target for treatment of osteogenic tumor.