Post-mastectomy radiation therapy after breast reconstruction: from historic dogmas to practical expert agreements based on a large literature review of surgical and radiation therapy considerations.

Breast reconstruction (BR) after mastectomy is important to consider for a woman's body image enhancement and psychological well-being. Although post-mastectomy radiation (PMRT) significantly improves the outcome of patients with high-risk breast cancer (BC), PMRT after BR may affect cosmetic outcomes and may compromise the original goal of improving quality of life (QoL). With the lack of practical guidelines, it seems essential to work on a consensus and provide some "expert agreements" to offer patients the best option for PMRT after BR.

Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the () gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models.

Engineering Vacancies for the Creation of Antisite Defects in Chemical Vapor Deposition Grown Monolayer MoS and WS via Proton Irradiation.

It is critical to understand the laws of quantum mechanics in transformative technologies for computation and quantum information science applications to enable the ongoing second quantum revolution calls. Recently, spin qubits based on point defects have gained great attention, since these qubits can be initiated, selectively controlled, and read out with high precision at ambient temperature. The major challenge in these systems is controllably generating multiqubit systems while properly coupling the defects.

Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction set.

Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike (SARS-CoV-2)-ACE2 recognition mechanism. To this end, two deep mutational scanning studies traced the impact of all possible mutations across receptor binding domain (RBD) of Spike and catalytic domain of human ACE2. By concentrating on the interface mutations of these experimental data, we benchmarked six commonly used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, and UEP).

The impact of AI-based modeling on the accuracy of protein assembly prediction: Insights from CASP15.

In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines.