Mum's Facebook kidney plea prompts'mind-blowing' gift from total stranger A woman who shared a desperate plea for a kidney on Facebook has undergone a lifesaving transplant after a stranger offered to be her donor. Karen Davey, 41, had exhausted every avenue before turning to social media, where she said she did not want to leave her family without a mother. Gary Summerfield saw the mum-of-four's post and, after learning he was a match, donated one of his kidneys via an operation in July. I thought this lady needs one of my kidneys more than I need two of them, said Gary. Karen's family said they would never be able to repay Gary for his generosity, adding that it had given her another chance to be a mum.

Gene-edited pig kidneys could offer a lifeline to patients stuck waiting for a human donor. A woman has lived with a pig kidney for more than nine months without needing dialysis, a record-setting achievement. The woman underwent the operation at Massachusetts General Hospital on November 22 last year. As of Thursday, the kidney has been functioning for 285 days, according to Cambridge, Massachusetts-based eGenesis, the biotech company that used Crispr gene editing to make the organ fit for human transplantation. Previously, the record for a person living with a transplanted pig kidney was 271 days, set by Tim Andrews. Most people in need of a kidney transplant wait three to five years on average to receive one, though it can take longer depending on blood type and other factors.

Juan Carlos Izpisua Belmonte Believes Aging May Come Down to a Cell's Identity Crisis Follow this author to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. For decades, Juan Carlos Izpisua Belmonte focused on unlocking the secrets of human life at its earliest stages. A pioneer in developmental biology and a long-time professor at the Salk Institute, he used Shinya Yamanaka's game-changing discovery of four genes that can essentially rewind the clock--turning adult cells to a younger state--to better understand how a fertilized egg turns into a human. Izpisua Belmonte's groundbreaking studies include creating the first embryo containing both human and monkey cells in 2021 to better study the steps that occur well before birth.

Data and finance professionals are competing in Excel obstacle courses--amassing huge followings and keeping the Microsoft program relevant. For many people, Microsoft Excel is to be avoided at all costs. Maybe you lied about being "proficient" with the spreadsheet program to pad out your résumé and have lived in fear of being found out ever since. Or it could be that you do have a grasp on a few basic formulas but find the interface too labyrinthine to truly master. Still, like it or not, Excel remains an integral part of businesses, with Microsoft claiming "hundreds of millions" of users worldwide.

Plus: Space data centers don't exist yet, but people already oppose them. Supercooled kidneys have been transplanted into pigs in a "landmark achievement" When it comes to organ donation, time is everything. As soon as an organ has been removed from a donor's body, it starts to deteriorate. Surgeons have only a matter of hours to get it into a recipient. In most cases, organs will be kept on ice during that time, at around 4 C (39 F). They cannot be frozen--in previous attempts, ice has formed, causing all kinds of damage.

Large language models (LLMs) have achieved significant performance progress in various natural language processing applications. However, LLMs still struggle to meet the strict requirements for accuracy and reliability in the medical field and face many challenges in clinical applications. Existing clinical diagnostic evaluation benchmarks for evaluating medical agents powered by LLMs have severe limitations. Firstly, most existing medical evaluation benchmarks face the risk of data leakage or contamination.

Yet, training still treats these logits in isolation--either supervising only the final, highest-resolution logits or applying deep supervision with identical loss weights at every scale--without exploring mixed-scale combinations. Consequently, the decoder output misses the complementary cues that arise only when coarse and fine predictions are fused. To address this issue, we introduce LoMix (Logits Mixing), a Neural Architecture Search (NAS)-inspired, differentiable plug-and-play module that generates new mixed-scale outputs and learns how exactly each of them should guide the training process. More precisely, LoMix mixes the multi-scale decoder logits with four lightweight fusion operators: addition, multiplication, concatenation, and attentionbased weighted fusion, yielding a rich set of synthetic "mutant" maps. Every original or mutant map is given a softplus loss weight that is co-optimized with network parameters, mimicking a one-step architecture search that automatically discovers the most useful scales, mixtures, and operators. Plugging LoMix into recent U-shaped architectures (i.e., PVT-V2-B2 backbone with EMCAD decoder) on Synapse 8-organ dataset improves DICE by +4.2% over single-output supervision, +2.2% over deep supervision, and +1.5% over equally weighted additive fusion, all with zero inference overhead. When training data are scarce (e.g., one or two labeled scans, 5% of the trainset), the advantage grows to +9.23%, underscoring LoMix's data efficiency. Across four benchmarks and diverse U-shaped networks, LoMiX improves DICE by up to +13.5% over single-output supervision, confirming that learnable weighted mixed-scale fusion generalizes broadly while remaining data efficient, fully interpretable, and overhead-free at inference. Our implementation is available at https://github.com/SLDGroup/LoMix.

A.1 Proof of Monotonicity and Submodularity In Equation (3a), we stated the objective of the knapsack cover to be Remark 1. f+M is monotonically increasing. A.2 Knapsack Cover To find a solution to problem 3, we use the greedy algorithm proposed by Badanidiyuru and Vondrák [2], which deals with submodular maximization subject to a system of lknapsack constraints and with pmatroid constraints. We present an adapted version of the algorithm in Algorithm 2 where l = 1. Theparameter allows us to 16 trade-off solution time and solution quality. In this work, we set = 0.2.