In treatment-naive patients receiving ICI in combination with chemotherapy, the ORR, median PFS, and OS rate at 1 year were 52%, 6 months, and 88%, respectively. In second or subsequent lines, ICI monotherapy was associated with an ORR of 16%, a median PFS of 4 months, and a median OS of 10 months.
 
  ICIs are effective as monotherapy and in combination with platinum-doublet chemotherapy. Therefore, ICI-based treatments may be found as the current standard of care and benchmark for targeted therapies in HER2mu NSCLC.
 ICIs are effective as monotherapy and in combination with platinum-doublet chemotherapy. Therefore, ICI-based treatments may be found as the current standard of care and benchmark for targeted therapies in HER2mu NSCLC.We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19. The goals of TREC-COVID include the construction of a pandemic search test collection and the evaluation of IR methods for COVID-19. The challenge was conducted over five rounds from April to July 2020, with participation from 92 unique teams and 556 individual submissions. A total of 50 topics (sets of related queries) were used in the evaluation, starting at 30 topics for Round 1 and adding 5 new topics per round to target emerging topics at that state of the still-emerging pandemic. This paper provides a comprehensive overview of the structure and results of TREC-COVID. Specifically, the paper provides details on the background, task structure, topic structure, corpus, participation, pooling, assessment, judgments, results, top-performing systems, lessons learned, and benchmark datasets.
  The genetic architecture of Brugada syndrome (BrS) is emerging as an increasingly complex area of investigation.  https://www.selleckchem.com/products/nms-p937-nms1286937.html  The identification of genetically homogeneous populations can provide mechanistic insights and improve genotype-phenotype correlation.
 
  To characterize and define the clinical implications of a novel BrS founder mutation. Using a haplotype-based approach we investigated whether 2 SCN5A genetic variants could derive from founder events.
 
  Single nucleotide polymorphisms were genotyped in 201 subjects, haplotypes reconstructed, and mutational age estimated. Clinical phenotypes and historical records were collected.
 
  A SCN5A variant (c.3352C>T; p.Gln1118Ter) was identified in 3 probands with BrS originating from south Italy. The same mutation was identified in a proband from central Italy and in 1 U.S. resident subject with Italian ancestry. The 5 individuals carried a common core haplotype, whose frequency was extremely low in local noncarrier probands and in population controls (0%-6.06%). The clinical presentation included multigenerational dominant transmission of Brugada electrocardiographic pattern, high incidence of sudden cardiac death (SCD), and cardiac conduction defects (CCD). We reconstructed 7-generation pedigrees with common geographic origin. Variant's age estimates suggested that origin of the p.Gln1118Ter dates back 76 generations (95% confidence interval 28-200). A second SCN5A variant (c.5350G>A; p.Glu1784Lys) identified in the region did not show similar founder signal.
 
  p.Gln1118Ter is a novel BrS/CCD/SCD founder mutation. We illustrate how these findings provide insights on the inheritance patterns and phenotypes associated with SCN5A mutation.
 p.Gln1118Ter is a novel BrS/CCD/SCD founder mutation. We illustrate how these findings provide insights on the inheritance patterns and phenotypes associated with SCN5A mutation.
  To provide a review of the impact of high deductible health plans (HDHPs) on the utilizations of services required for optimal management of diabetes and subsequent health outcomes.
 
  Systematic literature review of studies published between January 1, 2000, and May 7, 2021, was conducted that examined the impact of HDHP on diabetes monitoring (eg, recommended laboratory and surveillance testing), routine care (eg, ambulatory appointments), medication management (eg, medication initiation, adherence), and acute health care utilization (eg, emergency department visits, hospitalizations, incident complications).
 
  Of the 303 reviewed articles, 8 were relevant. These studies demonstrated that HDHPs lower spending at the expense of reduced high-value diabetes monitoring, routine care, and medication adherence, potentially contributing to the observed increases in acute health care utilization. Additionally, patient out-of-pocket costs for recommended screenings doubled, and total health care expenditures increased by 49.4% for HDHP enrollees compared with enrollees in traditional health plans. Reductions in disease monitoring and routine care and increases in acute health care utilization were greatest in lower-income patients. None of the studies examined the impact of HDHPs on access to diabetes self-management education, technology use, or glycemic control.
 
  Although HDHPs reduce some health care utilization and costs, they appear to do so at the expense of limiting high-value care and medication adherence. Policymakers, providers, and payers should be more cognizant of the potential for negative consequences of HDHPs on patients' health.
 Although HDHPs reduce some health care utilization and costs, they appear to do so at the expense of limiting high-value care and medication adherence. Policymakers, providers, and payers should be more cognizant of the potential for negative consequences of HDHPs on patients' health.Over the past few decades, the number of health and 'omics-related data' generated and stored has grown exponentially. Patient information can be collected in real time and explored using various artificial intelligence (AI) tools in clinical trials; mobile devices can also be used to improve aspects of both the diagnosis and treatment of diseases. In addition, AI can be used in the development of new drugs or for drug repurposing, in faster diagnosis and more efficient treatment for various diseases, as well as to identify data-driven hypotheses for scientists. In this review, we discuss how AI is starting to revolutionize the life sciences sector.