Purpose:The development of computer-aided diagnostic ͑CAD͒ methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography ͑CT͒ scans. The Lung Image Database Consortium ͑LIDC͒ and Image Database Resource Initiative ͑IDRI͒ completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute ͑NCI͒, further advanced by the Foundation for the National Institutes of Health ͑FNIH͒, and accompanied by the Food and Drug Administration ͑FDA͒ through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ͑"noduleՆ 3 mm," "noduleϽ 3 mm," and "non-noduleՆ 3 mm"͒. In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. Results:The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "noduleՆ 3 mm" by at least one radiologist, of which 928 ͑34.7%͒ received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. Conclusions:The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.

With the advent of complete genome sequences, large-scale functional analyses are generating new excitement in biology and medicine. To facilitate genomewide functional analyses, we developed a high-density cell array with quantitative and automated readout of cell fitness. Able to print at >×10 higher density on a standard microtiter plate area than currently possible, our cell array allows single-plate screening of the complete set of Saccharomyces cerevisiae gene-deletion library and significantly reduces the amount of small molecules and other materials needed for the study. We used this method to map the relation between genes and cell fitness in response to rapamycin, a medically important natural product that targets the eukaryotic kinase Tor. We discuss the implications for pharmacogenomics and the uncharted complexity in genotype-dependent drug response in molecularly targeted therapies. Our analysis leads to several basic findings, including a class of gene deletions that confer better fitness in the presence of rapamycin. This result provides insights into possible therapeutic uses of rapamycin/CCI-779 in the treatment of neurodegenerative diseases (including Alzheimer's, Parkinson's, and Huntington's diseases), and cautions the possible existence of similar rapamycin-enhanceable mutations in cancer. It is well established in yeast that although TOR2 has a unique rapamycin-insensitive function, TOR1 and TOR2 are interchangeable in the rapamycin-sensitive functions. We show that even the rapamycin-sensitive functions are distinct between TOR1 and TOR2 and map the functional difference to a ≈120-aa region at the N termini of the proteins. Finally, we discuss using cell-based genomic pattern recognition in designing electronic or optical biosensors.

Leukocyte telomere length (LTL), MUC5B rs35705950, and TOLLIP rs5743890 have been associated with idiopathic pulmonary fibrosis (IPF). In this observational cohort study, we assessed the associations between these genomic markers and outcomes of survival and rate of disease progression in patients with interstitial pneumonia with autoimmune features (IPAF, n=250) and connective tissue disease-associated interstitial lung disease (CTD-ILD, n=248). IPF (n=499) was used as a comparator. LTL of IPAF and CTD-ILD patients (mean age-adjusted log-transformed T/S of −0.05, [SD 0.29] and −0.04 [0.25], respectively) are longer than IPF (−0.17 [0.32]). For IPAF, LTL <10th percentile is associated with faster lung function decline compared to LTL ≥10th percentile (−6.43%/year versus −0.86%/year, p<0.0001) and worse transplant-free survival (HR 2.97 [95% CI 1.70–5.20], p=0.00014). The MUC5B rs35705950 minor allele frequency is greater for IPAF (23.2 [95% CI 18.8–28.2], p<0.0001) than controls and is associated with worse transplant-free IPAF survival (HR 1.92, [95% CI 1.18–3.13], p=0.0091). Rheumatoid arthritis-associated ILD (RA-ILD) has shorter LTL than non-RA CTD-ILD (−0.14 [SD 0.27] versus −0.01 [0.23], p=0.00055) and higher MUC5B minor allele frequency (34.6 [95% CI 24.4–46.3] versus 14.1 [9.8–20.0], p=0.00025). Neither LTL nor MUC5B are associated with transplant-free CTD-ILD survival. LTL and MUC5B minor allele frequency have different associations with lung function progression and survival for IPAF and CTD-ILD.

Childhood vaccine hesitancy has been studied extensively before the COVID-19 pandemic. The pandemic presented new barriers to pediatric vaccinations. Furthermore, the development of COVID-19 vaccines has complicated factors underlying vaccine hesitancy. We performed a cross-sectional mobile phone-based survey at Children's Hospital Los Angeles querying parents regarding perspectives on vaccines before and during the pandemic. Our primary aim was to understand the impact of the pandemic on routine childhood vaccine hesitancy. Secondarily, we examined intent to vaccinate, COVID-19 vaccine hesitancy, and key contributing demographic factors. Among 252 participants, we found overall increased childhood vaccine hesitancy (p = 0.006), increased risk perception (p = 0.006), and unchanged vaccine confidence during the COVID-19 pandemic. Increased hesitancy did not translate into decreased intent to vaccinate with routine childhood vaccines or influenza vaccines. During the pandemic, households with higher income (50-99 K, > 100 K) correlated with decreased routine childhood vaccine hesitancy, while Hispanic ethnicity and African American race had increased risk perception. For COVID-19 vaccine hesitancy, households with higher income (> 100 K) correlated with decreased hesitancy, while non-White ethnicity and race had increased risk perception. We found that routine childhood vaccine hesitancy increased during the COVID-19 pandemic, mainly due to increased risk perception. Key contributing demographic factors behind both childhood vaccine hesitancy and COVID-19 vaccine hesitancy included household income and race. Understanding factors behind routine childhood vaccine hesitancy is crucial to maintaining pediatric vaccination rates and promoting vaccine confidence during and after the COVID-19 pandemic.

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