The announcement coincides with the USCAP 112th Annual Meeting held March 11-16 at the New Orleans Ernest N. Morial Convention Center in New Orleans, Louisiana. "We are delighted to further extend our partnership in this emerging era of personalized medicine." The increasing prevalence of chronic conditions is predicted to intensify the urgency of pathologists seeking to adopt innovative digital pathology solutions to improve existing patient diagnostic imaging methods and reduce the high cost associated with traditional diagnostics. Technological advances in the past decade and the numerous benefits of digital pathology are driving the practice to become widely adopted. Agilent and Visiopharm have collaborated since 2020, developing an integrated solution comprising Visiopharm's portfolio of leading artificial intelligence (AI)-driven precision pathology software and Agilent's automated pathology staining solutions.

With 5G still in phased stages of development and deployment, it may seem premature to plan for the next generation of wireless communication technology. But with ambitious goals that build upon the current generation, it's not too early to begin addressing the technological, regulatory, geographical, and educational challenges that will be required to make ubiquitous 6G a reality This next generation of wireless technology is expected to bring even faster speeds, lower latency, and more bandwidth to instantly deliver massive amounts of data to and from more devices across decentralised, intelligent networks. Historically, technology research begins 10-15 years prior to the development of new industrial standards. Like 5G, some of 6G will be an evolution but some will be revolutionary, and taken as a whole, we can expect a step-function increase in technical capability. It envisages a society that by year 2030 is data-driven, enabled by near-instant, unlimited wireless connectivity.

Agilent Technologies Inc. announced it has acquired advanced artificial intelligence (AI) technology developed by Virtual Control, an AI and machine learning software developer that creates innovative analysis solutions in lab testing. Agilent will integrate the software, known as ACIES, into its industry-leading gas chromatography and mass spectrometry (GS/MS) platforms to improve the productivity, efficiency and accuracy of high-throughput labs the company serves around the world. "We're extremely pleased to be adding these additional capabilities to our product lineup." With the acquisition, Agilent obtained the software and other assets associated with ACIES. As part of the transaction, core members of the ACIES team also became Agilent employees.

Multiplex immunofluorescence and immunohistochemistry benefit patients by allowing cancer pathologists to identify several proteins expressed on the surface of cells, enabling cell classification, better understanding of the tumour micro-environment, more accurate diagnoses, prognoses, and tailored immunotherapy based on the immune status of individual patients. However, they are expensive and time consuming processes which require complex staining and imaging techniques by expert technicians. Hoechst staining is much cheaper and easier to perform, but is not typically used in this case as it binds to DNA rather than to the proteins targeted by immunofluorescent techniques, and it was not previously thought possible to differentiate cells expressing these proteins based only on DNA morphology. In this work we show otherwise, training a deep convolutional neural network to identify cells expressing three proteins (T lymphocyte markers CD3 and CD8, and the B lymphocyte marker CD20) with greater than 90% precision and recall, from Hoechst 33342 stained tissue only. Our model learns previously unknown morphological features associated with expression of these proteins which can be used to accurately differentiate lymphocyte subtypes for use in key prognostic metrics such as assessment of immune cell infiltration,and thereby predict and improve patient outcomes without the need for costly multiplex immunofluorescence.