Pivotal role of micro-CT technology in setting up an optimized lung fibrosis mouse model for drug screening

Idiopathic pulmonary fibrosis (IPF) is a progressive disease with no curative pharmacological treatment. The most used animal model of IPF for anti-fibrotic drug screening is bleomycin (BLM)-induced lung fibrosis. However, several issues have been reported: the balance among disease resolution, an appropriate time window for therapeutic intervention and animal welfare remain critical aspects yet to be fully elucidated. In this study, C57Bl/6 male mice were treated with BLM via oropharyngeal aspiration (OA) following either double or triple administration. The fibrosis progression was longitudinally assessed by micro-CT every 7 days for 4 weeks after BLM administration. Quantitative micro-CT measurements highlighted that triple BLM administration was the ideal dose regimen to provoke sustained lung fibrosis up to 28 days. These results were corroborated with lung histology and Bronchoalveolar Lavage Fluid cells. We have developed a mouse model with prolonged lung fibrosis enabling three weeks of a curative therapeutic window for the screening of putative anti-fibrotic drugs. Moreover, we have demonstrated the pivotal role of longitudinal micro-CT imaging in reducing the number of animals required per experiment in which each animal can be its own control. This approach permits a valuable decrease in costs and time to develop disease animal models

Persistent Megalocystic Ovary Following in Vitro Fertilization in a Postpartum Patient with Polycystic Ovarian Syndrome

SummaryObjectiveOvarian hyperstimulation syndrome (OHSS) is more severe when pregnancy occurs, as the developing pregnancy produces human chorionic gonadotropin, which stimulates the ovary's persistent growth. If no pregnancy occurs, the syndrome will typically resolve within 1 week. In a maintained pregnancy, slow resolution of symptoms usually occurs over 1-2 months.Case ReportA 31-year-old woman, gravida 2, para 1, aborta 1, with polycystic ovary syndrome underwent in vitro fertilization (IVF) with clomiphene citrate and follicle-stimulating hormone/gonadotropin releasing hormone-antagonist stimulation. During transvaginal oocyte retrieval, enlarged bilateral ovaries were noted. She had an episode of OHSS after IVF/embryo transfer, for which paracentesis was performed three times. Pregnancy was achieved. Throughout antenatal examinations, bilateral ovaries were enlarged. She delivered a healthy baby by cesarean section at term. However, 1 month after delivery, the bilateral ovary had not shrunk, and levels of tumor markers CA125 and CA199 were 50.84 and 41.34 U/mL, respectively. At laparotomy for suspected malignancy, both adnexae formed “kissing ovaries”, which were multinodulated with yellow serous fluid. Specimens from wedge resection submitted for frozen section showed a benign ovarian cyst. The final pathology report showed bilateral follicle cysts.ConclusionWith the increasing use of gonadotropins in the management of infertility, ovarian enlargement secondary to hyperstimulation is common. Generally, symptoms appear between the 6th and 13th weeks of pregnancy and disappear thereafter. The hyperstimulated ovary often subsides after the first trimester. This case is unusual as the megalocystic ovary persisted after delivery. To the best of our knowledge, we report the first case of enlarged bilateral ovaries persisting 2 months after delivery

Three new pancreatic cancer susceptibility signals identified on chromosomes 1q32.1, 5p15.33 and 8q24.21.

Genome-wide association studies (GWAS) have identified common pancreatic cancer susceptibility variants at 13 chromosomal loci in individuals of European descent. To identify new susceptibility variants, we performed imputation based on 1000 Genomes (1000G) Project data and association analysis using 5,107 case and 8,845 control subjects from 27 cohort and case-control studies that participated in the PanScan I-III GWAS. This analysis, in combination with a two-staged replication in an additional 6,076 case and 7,555 control subjects from the PANcreatic Disease ReseArch (PANDoRA) and Pancreatic Cancer Case-Control (PanC4) Consortia uncovered 3 new pancreatic cancer risk signals marked by single nucleotide polymorphisms (SNPs) rs2816938 at chromosome 1q32.1 (per allele odds ratio (OR) = 1.20, P = 4.88x10 -15), rs10094872 at 8q24.21 (OR = 1.15, P = 3.22x10 -9) and rs35226131 at 5p15.33 (OR = 0.71, P = 1.70x10 -8). These SNPs represent independent risk variants at previously identified pancreatic cancer risk loci on chr1q32.1 ( NR5A2), chr8q24.21 ( MYC) and chr5p15.33 ( CLPTM1L- TERT) as per analyses conditioned on previously reported susceptibility variants. We assessed expression of candidate genes at the three risk loci in histologically normal ( n = 10) and tumor ( n = 8) derived pancreatic tissue samples and observed a marked reduction of NR5A2 expression (chr1q32.1) in the tumors (fold change -7.6, P = 5.7x10 -8). This finding was validated in a second set of paired ( n = 20) histologically normal and tumor derived pancreatic tissue samples (average fold change for three NR5A2 isoforms -31.3 to -95.7, P = 7.5x10 -4-2.0x10 -3). Our study has identified new susceptibility variants independently conferring pancreatic cancer risk that merit functional follow-up to identify target genes and explain the underlying biology

Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer.

In 2020, 146,063 deaths due to pancreatic cancer are estimated to occur in Europe and the United States combined. To identify common susceptibility alleles, we performed the largest pancreatic cancer GWAS to date, including 9040 patients and 12,496 controls of European ancestry from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4). Here, we find significant evidence of a novel association at rs78417682 (7p12/TNS3, P = 4.35 × 10-8). Replication of 10 promising signals in up to 2737 patients and 4752 controls from the PANcreatic Disease ReseArch (PANDoRA) consortium yields new genome-wide significant loci: rs13303010 at 1p36.33 (NOC2L, P = 8.36 × 10-14), rs2941471 at 8q21.11 (HNF4G, P = 6.60 × 10-10), rs4795218 at 17q12 (HNF1B, P = 1.32 × 10-8), and rs1517037 at 18q21.32 (GRP, P = 3.28 × 10-8). rs78417682 is not statistically significantly associated with pancreatic cancer in PANDoRA. Expression quantitative trait locus analysis in three independent pancreatic data sets provides molecular support of NOC2L as a pancreatic cancer susceptibility gene

Genetic determinants of telomere length and risk of pancreatic cancer: A PANDoRA study

Telomere deregulation is a hallmark of cancer. Telomere length measured in lymphocytes (LTL) has been shown to be a risk marker for several cancers. For pancreatic ductal adenocarcinoma (PDAC) consensus is lacking whether risk is associated with long or short telomeres. Mendelian randomization approaches have shown that a score built from SNPs associated with LTL could be used as a robust risk marker. We explored this approach in a large scale study within the PANcreatic Disease ReseArch (PANDoRA) consortium. We analyzed 10 SNPs (ZNF676-rs409627, TERT-rs2736100, CTC1-rs3027234, DHX35-rs6028466, PXK-rs6772228, NAF1-rs7675998, ZNF208-rs8105767, OBFC1-rs9420907, ACYP2-rs11125529 and TERC-rs10936599) alone and combined in a LTL genetic score (“teloscore”, which explains 2.2% of the telomere variability) in relation to PDAC risk in 2,374 cases and 4,326 controls. We identified several associations with PDAC risk, among which the strongest were with the TERT-rs2736100 SNP (OR = 1.54; 95%CI 1.35–1.76; p = 1.54 × 10−10) and a novel one with the NAF1-rs7675998 SNP (OR = 0.80; 95%CI 0.73–0.88; p = 1.87 × 10−6, ptrend = 3.27 × 10−7). The association of short LTL, measured by the teloscore, with PDAC risk reached genome-wide significance (p = 2.98 × 10−9 for highest vs. lowest quintile; p = 1.82 × 10−10 as a continuous variable). In conclusion, we present a novel genome-wide candidate SNP for PDAC risk (TERT-rs2736100), a completely new signal (NAF1-rs7675998) approaching genome-wide significance and we report a strong association between the teloscore and risk of pancreatic cancer, suggesting that telomeres are a potential risk factor for pancreatic cancer

Polygenic and multifactorial scores for pancreatic ductal adenocarcinoma risk prediction

Most cases of pancreatic ductal adenocarcinoma (PDAC) are asymptomatic in early stages, and the disease is typically diagnosed in advanced phases, resulting in very high mortality. Tools to identify individuals at high risk of developing PDAC would be useful to improve chances of early detection

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

A fully automated deep learning pipeline for micro-CT-imaging-based densitometry of lung fibrosis murine models

Idiopathic pulmonary fibrosis, the archetype of pulmonary fibrosis (PF), is a chronic lung disease of a poor prognosis, characterized by progressively worsening of lung function. Although histology is still the gold standard for PF assessment in preclinical practice, histological data typically involve less than 1% of total lung volume and are not amenable to longitudinal studies. A miniaturized version of computed tomography (µCT) has been introduced to radiologically examine lung in preclinical murine models of PF. The linear relationship between X-ray attenuation and tissue density allows lung densitometry on total lung volume. However, the huge density changes caused by PF usually require manual segmentation by trained operators, limiting µCT deployment in preclinical routine. Deep learning approaches have achieved state-of-the-art performance in medical image segmentation. In this work, we propose a fully automated deep learning approach to segment right and left lung on µCT imaging and subsequently derive lung densitometry. Our pipeline first employs a convolutional network (CNN) for pre-processing at low-resolution and then a 2.5D CNN for higher-resolution segmentation, combining computational advantage of 2D and ability to address 3D spatial coherence without compromising accuracy. Finally, lungs are divided into compartments based on air content assessed by density. We validated this pipeline on 72 mice with different grades of PF, achieving a Dice score of 0.967 on test set. Our tests demonstrate that this automated tool allows for rapid and comprehensive analysis of µCT scans of PF murine models, thus laying the ground for its wider exploitation in preclinical settings