Unrelated Donor Peripheral Blood Stem Cell Transplantation for Patients with ß-Thalassemia Major Based on a Novel Conditioning Regimen.

Allogeneic hematopoietic stem cell transplantation (HSCT) is the only available curative treatment for patients with ß-thalassemia major (ß-TM). However, the problem of finding a suitable sibling donor with well-matched human leukocyte antigens is still a major obstacle to curing these patients. With the progress in high-resolution HLA typing technology and supportive care, outcomes after allogeneic HSCT from an HLA well-matched unrelated donor (UD) now approach those of well-matched sibling donors. However, UD HSCT is hampered by an increased risk of graft-versus-host disease and transplant-related mortality. Here we report the outcome of transplantation in patients with ß-TM using a novel WZ-14-TM transplant protocol, based on cyclophosphamide, intravenous busulfan, fludarabine, and antithymocyte globulin, in our center. Forty-eight patients between 2 and 11 years of age with ß-TM received HLA well-matched UD peripheral blood stem cell transplantation following the WZ-14-TM protocol. All of the transplanted patients achieved donor engraftment. The incidences of grade II to IV acute and chronic graft-versus-host disease were 8.3% and 8.3%, respectively. The overall survival and thalassemia-free survival rates were both 100%. This encouraging result suggests that the WZ-14-TM protocol is a feasible and safe conditioning regime for patients with ß-TM undergoing UD HSCT.

A Sample Size Extractor for RCT Reports.

Sample size is an important indicator of the power of randomized controlled trials (RCTs). In this paper, we designed a total sample size extractor using a combination of syntactic and machine learning methods, and evaluated it on 300 Covid-19 abstracts (Covid-Set) and 100 generic RCT abstracts (General-Set). To improve the performance, we applied transfer learning from a large public corpus of annotated abstracts. We achieved an average F1 score of 0.73 on the Covid-Set testing set, and 0.60 on the General-Set using exact matches. The F1 scores for loose matches on both datasets were over 0.74. Compared with the state-of-the-art tool, our extractor reports total sample sizes directly and improved F1 scores by at least 4% without transfer learning. We demonstrated that transfer learning improved the sample size extraction accuracy and minimized human labor on annotations.

The COVID-19 Trial Finder.

Clinical trials are the gold standard for generating reliable medical evidence. The biggest bottleneck in clinical trials is recruitment. To facilitate recruitment, tools for patient search of relevant clinical trials have been developed, but users often suffer from information overload. With nearly 700 coronavirus disease 2019 (COVID-19) trials conducted in the United States as of August 2020, it is imperative to enable rapid recruitment to these studies. The COVID-19 Trial Finder was designed to facilitate patient-centered search of COVID-19 trials, first by location and radius distance from trial sites, and then by brief, dynamically generated medical questions to allow users to prescreen their eligibility for nearby COVID-19 trials with minimum human computer interaction. A simulation study using 20 publicly available patient case reports demonstrates its precision and effectiveness.

A general approach to detecting migration events in digital trace data.

Empirical research on migration has historically been fraught with measurement challenges. Recently, the increasing ubiquity of digital trace data-from mobile phones, social media, and related sources of 'big data'-has created new opportunities for the quantitative analysis of migration. However, most existing work relies on relatively ad hoc methods for inferring migration. Here, we develop and validate a novel and general approach to detecting migration events in trace data. We benchmark this method using two different trace datasets: four years of mobile phone metadata from a single country's monopoly operator, and three years of geo-tagged Twitter data. The novel measures more accurately reflect human understanding and evaluation of migration events, and further provide more granular insight into migration spells and types than what are captured in standard survey instruments.