Presence and Size of Placental Lakes on 20-Week Fetal Anatomy Ultrasound and Obstetrical Outcomes.

Our objective was to determine if placental lake presence or size is associated with adverse pregnancy outcomes. This was a retrospective cohort of patients who had fetal anatomy ultrasounds at 18-22 weeks and delivered between 2018 and 2022. Placental lakes were classified as small (>2.0 to 3.9 cm) or large (≥4 cm). Multiple gestations, placenta previas, and placenta accretas were excluded. Outcomes included low birthweight, cesarean delivery, primary cesarean for non-reassuring fetal heart tracing, fetal growth restriction, preterm birth, and severe preeclampsia. A total of 1052 patients were included; 294 had placental lakes (204 small, 90 large). No differences in pregnancy outcomes were observed.

Protein Ensemble Generation Through Variational Autoencoder Latent Space Sampling.

Mapping the ensemble of protein conformations that contribute to function and can be targeted by small molecule drugs remains an outstanding challenge. Here, we explore the use of variational autoencoders for reducing the challenge of dimensionality in the protein structure ensemble generation problem. We convert high-dimensional protein structural data into a continuous, low-dimensional representation, carry out a search in this space guided by a structure quality metric, and then use RoseTTAFold guided by the sampled structural information to generate 3D structures. We use this approach to generate ensembles for the cancer relevant protein K-Ras, train the VAE on a subset of the available K-Ras crystal structures and MD simulation snapshots, and assess the extent of sampling close to crystal structures withheld from training. We find that our latent space sampling procedure rapidly generates ensembles with high structural quality and is able to sample within 1 Å of held-out crystal structures, with a consistency higher than that of MD simulation or AlphaFold2 prediction. The sampled structures sufficiently recapitulate the cryptic pockets in the held-out K-Ras structures to allow for small molecule docking.

A novel mechanoeffector role of fibroblast S100A4 in myofibroblast transdifferentiation and fibrosis.

Fibroblast to myofibroblast transdifferentiation mediates numerous fibrotic disorders, such as idiopathic pulmonary fibrosis (IPF). We have previously demonstrated that non-muscle myosin II (NMII) is activated in response to fibrotic lung extracellular matrix, thereby mediating myofibroblast transdifferentiation. NMII-A is known to interact with the calcium-binding protein S100A4, but the mechanism by which S100A4 regulates fibrotic disorders is unclear. In this study, we show that fibroblast S100A4 is a calcium-dependent, mechanoeffector protein that is uniquely sensitive to pathophysiologic-range lung stiffness (8-25 kPa) and thereby mediates myofibroblast transdifferentiation. Re-expression of endogenous fibroblast S100A4 rescues the myofibroblastic phenotype in S100A4 KO fibroblasts. Analysis of NMII-A/actin dynamics reveals that S100A4 mediates the unraveling and redistribution of peripheral actomyosin to a central location, resulting in a contractile myofibroblast. Furthermore, S100A4 loss protects against murine in vivo pulmonary fibrosis, and S100A4 expression is dysregulated in IPF. Our data reveal a novel mechanosensor/effector role for endogenous fibroblast S100A4 in inducing cytoskeletal redistribution in fibrotic disorders such as IPF.

Zero-shot mutation effect prediction on protein stability and function using RoseTTAFold.

Predicting the effects of mutations on protein function and stability is an outstanding challenge. Here, we assess the performance of a variant of RoseTTAFold jointly trained for sequence and structure recovery, RFjoint , for mutation effect prediction. Without any further training, we achieve comparable accuracy in predicting mutation effects for a diverse set of protein families using RFjoint to both another zero-shot model (MSA Transformer) and a model that requires specific training on a particular protein family for mutation effect prediction (DeepSequence). Thus, although the architecture of RFjoint was developed to address the protein design problem of scaffolding functional motifs, RFjoint acquired an understanding of the mutational landscapes of proteins during model training that is equivalent to that of recently developed large protein language models. The ability to simultaneously reason over protein structure and sequence could enable even more precise mutation effect predictions following supervised training on the task. These results suggest that RFjoint has a quite broad understanding of protein sequence-structure landscapes, and can be viewed as a joint model for protein sequence and structure which could be broadly useful for protein modeling.

Demographic Landscape of Cardiothoracic Surgeons and Residents at United States Training Programs.

BACKGROUND: Recruiting and promoting women and racial/ethnic minorities could help enhance diversity and inclusion in the academic cardiothoracic (CT) surgery workforce. However, the demographics of trainees and faculty at US training programs have not yet been studied. METHODS: Traditional, integrated (I-6), and fast-track (4+3) programs listed in the Accreditation Council for Graduate Medical Education (ACGME) public database were analyzed. Demographics of trainees and surgeons, including gender, race/ethnicity, subspecialty, and academic appointment (if applicable), were obtained from ACGME Data Resource Books, institutional websites, and public profiles. Chi-square and Cochran-Armitage trend tests were performed. RESULTS: In July 2020, 78 institutions had at least 1 CT surgery training program; 40 (51%) had only a traditional program, 20 (26%) traditional and I-6, 6 (8%) all 3 types of program, and 4 (5%) only I-6. The proportion of female trainees increased significantly from 2011 to 2019 (19% vs 24%, P < .001), with female I-6 trainees outnumbering female traditional trainees since 2018. Significant increases by race/ethnicity were observed overall and by program type, notably for Asian and Hispanic individuals in I-6 programs and Black individuals in traditional programs. Finally, of the 1175 CT surgeons identified, 633 (54%) were adult cardiac surgeons, 360 (37%) assistant professors, 116 (10%) women, and 33 (3%) Black. CONCLUSIONS: The demographic landscape of CT surgery trainees and faculty across multiple training pathways reflects increasing representation by gender and race/ethnicity. However, we must continue to work toward equitable representation in the workforce to benefit the diverse patients we treat.

Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14.

The trRosetta structure prediction method employs deep learning to generate predicted residue-residue distance and orientation distributions from which 3D models are built. We sought to improve the method by incorporating as inputs (in addition to sequence information) both language model embeddings and template information weighted by sequence similarity to the target. We also developed a refinement pipeline that recombines models generated by template-free and template utilizing versions of trRosetta guided by the DeepAccNet accuracy predictor. Both benchmark tests and CASP results show that the new pipeline is a considerable improvement over the original trRosetta, and it is faster and requires less computing resources, completing the entire modeling process in a median < 3 h in CASP14. Our human group improved results with this pipeline primarily by identifying additional homologous sequences for input into the network. We also used the DeepAccNet accuracy predictor to guide Rosetta high-resolution refinement for submissions in the regular and refinement categories; although performance was quite good on a CASP relative scale, the overall improvements were rather modest in part due to missing inter-domain or inter-chain contacts.

Biodegradable Nanocomposite Antimicrobials for the Eradication of Multidrug-Resistant Bacterial Biofilms without Accumulated Resistance.

Infections caused by multidrug-resistant (MDR) bacteria are a rapidly growing threat to human health, in many cases exacerbated by their presence in biofilms. We report here a biocompatible oil-in-water cross-linked polymeric nanocomposite that degrades in the presence of physiologically relevant biomolecules. These degradable nanocomposites demonstrated broad-spectrum penetration and elimination of MDR bacteria, eliminating biofilms with no toxicity to cocultured mammalian fibroblast cells. Notably, serial passaging revealed that bacteria were unable to develop resistance toward these nanocomposites, highlighting the therapeutic promise of this platform.