University of California, San Francisco

About: University of California, San Francisco is a education organization based out in San Francisco, California, United States. It is known for research contribution in the topics: Population & Health care. The organization has 83381 authors who have published 186236 publications receiving 12068420 citations. The organization is also known as: UCSF & UC San Francisco.

Risk alleles for multiple sclerosis identified by a genomewide study.

Abstract: �Background Multiple sclerosis has a clinically significant heritable component. We conducted a genomewide association study to identify alleles associated with the risk of multiple sclerosis. Methods We used DNA microarray technology to identify common DNA sequence variants in 931 family trios (consisting of an affected child and both parents) and tested them for association. For replication, we genotyped another 609 family trios, 2322 case subjects, and 789 control subjects and used genotyping data from two external control data sets. A joint analysis of data from 12,360 subjects was performed to estimate the overall significance and effect size of associations between alleles and the risk of multiple sclerosis. Results A transmission disequilibrium test of 334,923 single-nucleotide polymorphisms (SNPs) in 931 family trios revealed 49 SNPs having an association with multiple sclerosis (P<1×10 −4 ); of these SNPs, 38 were selected for the second-stage analysis. A comparison between the 931 case subjects from the family trios and 2431 control subjects identified an additional nonoverlapping 32 SNPs (P<0.001). An additional 40 SNPs with less stringent P values (<0.01) were also selected, for a total of 110 SNPs for the second-stage analysis. Of these SNPs, two within the interleukin-2 receptor α gene (IL2RA) were strongly associated with multiple sclerosis (P = 2.96×10 −8 ), as were a nonsynonymous SNP in the interleukin-7 receptor α gene (IL7RA) (P = 2.94×10 −7 ) and multiple SNPs in the HLA-DRA locus (P = 8.94×10 −81 ).

Cardiologist-Level Arrhythmia Detection and Classification in Ambulatory Electrocardiograms Using a Deep Neural Network

Abstract: Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow1. Widely available digital ECG data and the algorithmic paradigm of deep learning2 present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide variety of diagnostic classes has not been previously reported. Here, we develop a deep neural network (DNN) to classify 12 rhythm classes using 91,232 single-lead ECGs from 53,549 patients who used a single-lead ambulatory ECG monitoring device. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiologists, the DNN achieved an average area under the receiver operating characteristic curve (ROC) of 0.97. The average F1 score, which is the harmonic mean of the positive predictive value and sensitivity, for the DNN (0.837) exceeded that of average cardiologists (0.780). With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes. These findings demonstrate that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. If confirmed in clinical settings, this approach could reduce the rate of misdiagnosed computerized ECG interpretations and improve the efficiency of expert human ECG interpretation by accurately triaging or prioritizing the most urgent conditions. Analysis of electrocardiograms using an end-to-end deep learning approach can detect and classify cardiac arrhythmia with high accuracy, similar to that of cardiologists.

Dysregulated pH: a perfect storm for cancer progression.

Abstract: Although cancer is a diverse set of diseases, cancer cells share a number of adaptive hallmarks. Dysregulated pH is emerging as a hallmark of cancer because cancers show a 'reversed' pH gradient with a constitutively increased intracellular pH that is higher than the extracellular pH. This gradient enables cancer progression by promoting proliferation, the evasion of apoptosis, metabolic adaptation, migration and invasion. Several new advances, including an increased understanding of pH sensors, have provided insight into the molecular basis for pH-dependent cell behaviours that are relevant to cancer cell biology. We highlight the central role of pH sensors in cancer cell adaptations and suggest how dysregulated pH could be exploited to develop cancer-specific therapeutics.

Genome Sequence of an Obligate Intracellular Pathogen of Humans: Chlamydia trachomatis

Abstract: Analysis of the 1,042,519-base pair Chlamydia trachomatis genome revealed unexpected features related to the complex biology of chlamydiae. Although chlamydiae lack many biosynthetic capabilities, they retain functions for performing key steps and interconversions of metabolites obtained from their mammalian host cells. Numerous potential virulence-associated proteins also were characterized. Several eukaryotic chromatin-associated domain proteins were identified, suggesting a eukaryotic-like mechanism for chlamydial nucleoid condensation and decondensation. The phylogenetic mosaic of chlamydial genes, including a large number of genes with phylogenetic origins from eukaryotes, implies a complex evolution for adaptation to obligate intracellular parasitism.