Aberrant stem cell and developmental programs in pediatric leukemia

Hematopoiesis is a finely orchestrated process, whereby hematopoietic stem cells give rise to all mature blood cells. Crucially, they maintain the ability to self-renew and/or differentiate to replenish downstream progeny. This process starts at an embryonic stage and continues throughout the human lifespan. Blood cancers such as leukemia occur when normal hematopoiesis is disrupted, leading to uncontrolled proliferation and a block in differentiation of progenitors of a particular lineage (myeloid or lymphoid). Although normal stem cell programs are crucial for tissue homeostasis, these can be co-opted in many cancers, including leukemia. Myeloid or lymphoid leukemias often display stem cell-like properties that not only allow proliferation and survival of leukemic blasts but also enable them to escape treatments currently employed to treat patients. In addition, some leukemias, especially in children, have a fetal stem cell profile, which may reflect the developmental origins of the disease. Aberrant fetal stem cell programs necessary for leukemia maintenance are particularly attractive therapeutic targets. Understanding how hijacked stem cell programs lead to aberrant gene expression in place and time, and drive the biology of leukemia, will help us develop the best treatment strategies for patients

Early prediction of bumblebee flight task using machine learning

This work demonstrates the development of a neural network algorithm able to determine the function of a bee's flight within six measurements (≈18 s with current radar technology) of its relative position on leaving a nest. Engineering advancements have created technology to track individual insects, unlocking research possibilities to investigate how bumblebees react to their environment in more detail. This includes how they discover and make use of resources. The development of an intelligent algorithm would allow for the automated monitoring of resource use and nest health. An imbalance of bee flight tasks may indicate a shortage of resources or over-reliance on a plant that may soon stop flowering. Recent developments using drones to track insects can benefit from an intelligent target acquisition system given limited drone battery life. Such knowledge will also benefit the tracking itself by allowing for customised flight parameters to match target flight patterns. Data captured by these tracking techniques are taxing to parse manually using human expertise. Artificial intelligence can produce meaningful knowledge faster with equal precision. In this work, a comparison between a neural network (NN), random forest (RF), and support vector machine (SVM) is provided to distinguish the best model for the task by comparing cross entropy loss and accuracy across the dataset, showing improved results as time goes on. In situations where the radar lost sight of the target, a purpose-built filter was created to mitigate signal losses. The generated model provides results with a peak accuracy of 92%. This model, combined with the filter, create an opportunity to monitor the number of bees leaving the nest for each flight task with smaller, cheaper, and stationary receiver solutions with shorter ranges by removing the need to track a bee for its entire flight to ascertain its errand

Maintaining a minimally invasive surgical service during a pandemic

PURPOSE: The safety of minimally invasive surgery (MIS) was questioned in the COVID-19 pandemic due to concern regarding disease spread. We continued MIS during the pandemic with appropriate protective measures. This study aims to assess the safety of MIS compared to Open Surgery (OS) in this setting. METHODS: Operations performed during 2020 lockdown were compared with operations from the same time-period in 2019 and 2021. Outcomes reviewed included all complications, respiratory complications, length of stay (LOS) and operating surgeon COVID-19 infections (OSI). RESULTS: In 2020, MIS comprised 52% of procedures. 29% of MIS 2020 had complications (2019: 24%, 2021: 15%; p = 0.08) vs 47% in OS 2020 (p = 0.04 vs MIS). 8.5% of MIS 2020 had respiratory complications (2019: 7.7%, 2021: 6.9%; p = 0.9) vs 10.5% in OS 2020 (p = 0.8 vs MIS). Median LOS[IQR] for MIS 2020 was 2.5[6] days vs 5[23] days in OS 2020 (p = 0.06). In 2020, 2 patients (1.2%) were COVID-19 positive (MIS: 1, OS: 1) and there were no OSI. CONCLUSION: Despite extensive use of MIS during the pandemic, there was no associated increase in respiratory or other complications, and no OSI. Our study suggests that, with appropriate protective measures, MIS can be performed safely despite high levels of COVID-19 in the population