Procept BioRobotics, a Silicon Valley-based surgical robotics company, recently raised nearly $120 million in private equity to commercialize a treatment for a prevalent prostate condition known as benign prostatic hyperplasia (BPH). BPH, also known as enlarged prostate, affects around half of men age 60 or older and 90 percent of men age 85 or older. Founded in 1999, Procept has pioneered the first commercially available autonomous tissue removal robot to treat BPH. The company's AQUABEAM system uses autonomous robotics and advanced imaging to deliver a heat-free waterjet that removes enlarged prostate tissue. The company has presented clinical research suggesting its method carries less risk of side effects than the current surgical gold-standard, known as TURP.
"An important strength of the study is that the machine learning technology was applied to trichrome-stained histologic images of routine kidney biopsy samples without any special processing or manipulation other than digital scanning," the team noted, "which allowed us to directly compare the results of the machine learning analysis with those derived from the clinical pathological report on the same specimens."
The authors conducted a prospective trial to assess the feasibility of real time central molecular assessment of kidney transplant biopsy samples from 10 North American or European centers. Biopsy samples taken 1 day to 34 years posttransplantation were stabilized in RNAlater, sent via courier overnight at ambient temperature to the central laboratory, and processed (29 h workflow) using microarrays to assess T cell– and antibody-mediated rejection (TCMR and ABMR, respectively). Of 538 biopsy samples submitted, 519 (96%) were sufficient for microarray analysis (average length, 3 mm). Automated reports were generated without knowledge of histology and HLA antibody, with diagnoses assigned based on Molecular Microscope Diagnostic System (MMDx) classifier algorithms and signed out by one observer. Agreement between MMDx and histology (balanced accuracy) was 77% for TCMR, 77% for ABMR, and 76% for no rejection.
Balancing fairness and efficiency in resource allocation is a classical economic and computational problem. The price of fairness measures the worst-case loss of economic efficiency when using an inefficient but fair allocation rule; for indivisible goods in many settings, this price is unacceptably high. One such setting is kidney exchange, where needy patients swap willing but incompatible kidney donors. In this work, we close an open problem regarding the theoretical price of fairness in modern kidney exchanges. We then propose a general hybrid fairness rule that balances a strict lexicographic preference ordering over classes of agents, and a utilitarian objective that maximizes economic efficiency. We develop a utility function for this rule that favors disadvantaged groups lexicographically; but if cost to overall efficiency becomes too high, it switches to a utilitarian objective. This rule has only one parameter which is proportional to a bound on the price of fairness, and can be adjusted by policymakers. We apply this rule to real data from a large kidney exchange and show that our hybrid rule produces more reliable outcomes than other fairness rules.
'The idea of understanding a disease from an evolutionary viewpoint to inform drug design still resonates today in how Exscientia is approaching the design of anticancer agents. 'I spent a season at the GlaxoWellcome labs in Stevenage making the compounds I'd designed, and vividly remember the excitement of discovering the first molecule we'd made was active.' These included topics such as the druggable genome, ligand efficiency and network pharmacology – all of which are familiar topics to drug discovery chemists today. An early success involved feeding historical data for the project that discovered erectile dysfunction drug tadalafil (Cialis) into the evolutionary drug design model.