Accurate predictions on small data with a tabular foundation model.

Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science. The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories, gradient-boosted decision trees have dominated tabular data for the past 20 years.

Target-conditioned diffusion generates potent TNFR superfamily antagonists and agonists.

Despite progress in designing protein-binding proteins, the shape matching of designs to targets is lower than in many native protein complexes, and design efforts have failed for the tumor necrosis factor receptor 1 (TNFR1) and other protein targets with relatively flat and polar surfaces. We hypothesized that free diffusion from random noise could generate shape-matched binders for challenging targets and tested this approach on TNFR1. We obtain designs with low picomolar affinity whose specificity can be completely switched to other family members using partial diffusion.

Splenectomy Before Kidney Transplantation for Hypersplenism: A Case Report.

Hypersplenism, although a rare hematological complication seen in chronic kidney disease patients, poses a significant challenge for successful kidney transplantation due to potential complications such as cytopenias and inadequate immunosuppressive therapy. We present a 40-year old end-stage kidney disease patient on dialysis with hypersplenism who underwent a laparoscopic splenectomy prior to high immunological risk renal transplantation. Post-splenectomy, there was a remarkable improvement in cytopenias, and effective immunosuppressive therapy could be administered prior to renal transplantation.