Uncovering the magnitude of African pangolin poaching with extensive nanopore DNA genotyping of seized scales.

Trade in pangolins is illegal, and yet tons of their scales and products are seized at various ports. These large seizures are challenging to process and comprehensively genotype for upstream provenance tracing and species identification for prosecution. We implemented a scalable DNA barcoding pipeline in which rapid DNA extraction and MinION sequencing were used to genotype a substantial proportion of pangolin scales subsampled from 2 record shipments seized in Singapore in 2019 (37.5 t). We used reference sequences to match the scales to phylogeographical regions of origin. In total, we identified 2346 cytochrome b (cytb) barcodes of white-bellied (Phataginus tricuspis) (from 1091 scales), black-bellied (Phataginus tetradactyla) (227 scales), and giant (Smutsia gigantea) (1028 scales) pangolins. Haplotype diversity was higher for P. tricuspis scales (121 haplotypes, 66 novel) than that for P. tetradactyla (22 haplotypes, 15 novel) and S. gigantea (25 haplotypes, 21 novel) scales. Of the novel haplotypes, 74.2% were likely from western and west-central Africa, suggesting potential resurgence of poaching and newly exploited populations in these regions. Our results illustrate the utility of extensively subsampling large seizures and outline an efficient molecular approach for rapid genetic screening that should be accessible to most forensic laboratories and enforcement agencies.

Revelación de la magnitud de la caza furtiva del pangolín africano mediante el genotipo extenso de nanoporos de ADN de escamas incautadas Resumen Aunque el mercado de pangolines es ilegal, se incautan toneladas de sus escamas y productos derivados en varios puertos comerciales. Es un reto procesar estas magnas incautaciones y obtener el genotipo completo para usarlo en la trazabilidad logística ascendente e identificación de la especie y así imponer sanciones. Implementamos una canalización escalable del código de barras de ADN en el cual usamos la extracción rápida de ADN y la secuenciación MinION para obtener el genotipo de una proporción sustancial de las escamas de pangolín submuestreadas en dos cargamentos incautados en 2019 en Singapur (37.5 t). Usamos secuencias referenciales para emparejar las escamas con las regiones filogeográficas de origen. Identificamos en total 2,346 códigos de citocromo b (cytb) del pangolín de vientre blanco (Phataginus tricuspis) (de 1,091 escamas), de vientre negro (P. tetradactyla) (227 escamas) y del pangolín gigante (Smutsia gigantea) (1,028 escamas). La diversidad de haplotipos fue mayor en las escamas de P. tricuspis (121 haplotipos, 66 nuevos) que en las de P. tetradactyla (22 haplotipos, 15 nuevos) y S. gigantea (25 haplotipos, 21 nuevos). De los haplotipos nuevos, el 74.2% probablemente provenía del occidente y centro­occidente de África, lo que sugiere un resurgimiento potencial de la caza furtiva y poblaciones recién explotadas en estas regiones. Nuestros resultados demuestran la utilidad de submuestrear extensivamente las grandes incautaciones y esboza una estrategia molecular eficiente para un análisis genético rápido que debería ser accesible para la mayoría de los laboratorios forenses y las autoridades de aplicación.

Differences in virus and immune dynamics for SARS-CoV-2 Delta and Omicron infections by age and vaccination histories.

Vaccination against COVID-19 was integral to controlling the pandemic that persisted with the continuous emergence of SARS-CoV-2 variants. Using a mathematical model describing SARS-CoV-2 within-host infection dynamics, we estimate differences in virus and immunity due to factors of infecting variant, age, and vaccination history (vaccination brand, number of doses and time since vaccination). We fit our model in a Bayesian framework to upper respiratory tract viral load measurements obtained from cases of Delta and Omicron infections in Singapore, of whom the majority only had one nasopharyngeal swab measurement. With this dataset, we are able to recreate similar trends in URT virus dynamics observed in past within-host modelling studies fitted to longitudinal patient data.We found that Omicron had higher R0,within values than Delta, indicating greater initial cell-to-cell spread of infection within the host. Moreover, heterogeneities in infection dynamics across patient subgroups could be recreated by fitting immunity-related parameters as vaccination history-specific, with or without age modification. Our model results are consistent with the notion of immunosenescence in SARS-CoV-2 infection in elderly individuals, and the issue of waning immunity with increased time since last vaccination. Lastly, vaccination was not found to subdue virus dynamics in Omicron infections as well as it had for Delta infections.This study provides insight into the influence of vaccine-elicited immunity on SARS-CoV-2 within-host dynamics, and the interplay between age and vaccination history. Furthermore, it demonstrates the need to disentangle host factors and changes in pathogen to discern factors influencing virus dynamics. Finally, this work demonstrates a way forward in the study of within-host virus dynamics, by use of viral load datasets including a large number of patients without repeated measurements.

Immunological Landscapes in Lung Transplantation: Insights from T Cell Profiling in BAL and PBMC.

Lung transplant recipients frequently encounter immune-related complications, including chronic lung allograft dysfunction (CLAD). Monitoring immune cells within the lung microenvironment is pivotal for optimizing post-transplant outcomes. This study examined the proportion of T cell subsets in paired bronchoalveolar lavage (BAL) and peripheral PBMC comparing healthy (n = 4) and lung transplantation patients (n = 6, no CLAD and n = 14 CLAD) using 14-color flow cytometry. CD4+ T cell proportions were reduced in CD3 cells in both PBMC and BAL, and positive correlations were discerned between T cell populations in peripheral PBMC and BAL, suggesting the prospect of employing less invasive PBMC sampling as a means of monitoring lung T cells. Furthermore, regulatory T cells (Tregs) were enriched in BAL when compared to peripheral PBMC for transplant recipients. A parallel positive correlation emerged between Treg proportions in BAL and peripheral PBMC, underscoring potential avenues for monitoring lung Tregs. Finally, the most promising biomarker was the Teff (CD8+Granzyme B+)-Treg ratio, which was higher in both the PBMC and BAL of transplant recipients compared to healthy individuals, and increased in the patients with CLAD compared to no CLAD and healthy patients. Conclusions: Distinct T cell profiles in BAL and peripheral PBMC underscore the significance of localized immune monitoring in lung transplantation. The Teff (CD8+granzyme B+)-Treg ratio, particularly within the context of CLAD, emerges as a promising blood and BAL biomarker reflective of inflammation and transplant-related complications. These findings emphasize the imperative need for personalized immune monitoring strategies that tailored to address the unique immunological milieu in post-transplant lungs.

Causal relationships between breast cancer risk factors based on mammographic features.

BACKGROUND: Mammogram risk scores based on texture and density defined by different brightness thresholds are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. We aimed to investigate causal relationships between these intercorrelated mammogram risk scores to determine their relevance to breast cancer aetiology. METHODS: We used digitised mammograms for 371 monozygotic twin pairs, aged 40-70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method. RESULTS: The mammogram risk scores were correlated within twin pairs and with each other (r = 0.22-0.81; all P < 0.005). We estimated that 28-92% of the associations between the risk scores could be attributed to causal relationships between the scores, with the rest attributed to familial confounders shared by the scores. There was consistent evidence for positive causal effects: of Cirrus, light areas, and bright areas on the brightest areas (accounting for 34%, 55%, and 85% of the associations, respectively); and of light areas and bright areas on Cirrus (accounting for 37% and 28%, respectively). CONCLUSIONS: In a mammogram, the lighter (less dense) areas have a causal effect on the brightest (highly dense) areas, including through a causal pathway via textural features. These causal relationships help us gain insight into the relative aetiological importance of different mammographic features in breast cancer. For example our findings are consistent with the brightest areas being more aetiologically important than lighter areas for screen-detected breast cancer; conversely, light areas being more aetiologically important for interval breast cancer. Additionally, specific textural features capture aetiologically independent breast cancer risk information from dense areas. These findings highlight the utility of ICE FALCON and family data in decomposing the associations between intercorrelated disease biomarkers into distinct biological pathways.

Gradual Self-Training via Confidence and Volume Based Domain Adaptation for Multi Dataset Deep Learning-Based Brain Metastases Detection Using Nonlocal Networks on MRI Images.

BACKGROUND: Research suggests that treatment of multiple brain metastases (BMs) with stereotactic radiosurgery shows improvement when metastases are detected early, providing a case for BM detection capabilities on small lesions. PURPOSE: To demonstrate automatic detection of BM on three MRI datasets using a deep learning-based approach. To improve the performance of the network is iteratively co-trained with datasets from different domains. A systematic approach is proposed to prevent catastrophic forgetting during co-training. STUDY TYPE: Retrospective. POPULATION: A total of 156 patients (105 ground truth and 51 pseudo labels) with 1502 BM (BrainMetShare); 121 patients with 722 BM (local); 400 patients with 447 primary gliomas (BrATS). Training/pseudo labels/validation data were distributed 84/51/21 (BrainMetShare). Training/validation data were split: 121/23 (local) and 375/25 (BrATS). FIELD STRENGTH/SEQUENCE: A 5 T and 3 T/T1 spin-echo postcontrast (T1-gradient echo) (BrainMetShare), 3 T/T1 magnetization prepared rapid acquisition gradient echo postcontrast (T1-MPRAGE) (local), 0.5 T, 1 T, and 1.16 T/T1-weighted-fluid-attenuated inversion recovery (T1-FLAIR) (BrATS). ASSESSMENT: The ground truth was manually segmented by two (BrainMetShare) and four (BrATS) radiologists and manually annotated by one (local) radiologist. Confidence and volume based domain adaptation (CAVEAT) method of co-training the three datasets on a 3D nonlocal convolutional neural network (CNN) architecture was implemented to detect BM. STATISTICAL TESTS: The performance was evaluated using sensitivity and false positive rates per patient (FP/patient) and free receiver operating characteristic (FROC) analysis at seven predefined (1/8, 1/4, 1/2, 1, 2, 4, and 8) FPs per scan. RESULTS: The sensitivity and FP/patient from a held-out set registered 0.811 at 2.952 FP/patient (BrainMetShare), 0.74 at 3.130 (local), and 0.723 at 2.240 (BrATS) using the CAVEAT approach with lesions as small as 1 mm being detected. DATA CONCLUSION: Improved sensitivities at lower FP can be achieved by co-training datasets via the CAVEAT paradigm to address the problem of data sparsity. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Single-Cell Profiling of Cells in the Lung of a Patient with Chronic Hypersensitivity Pneumonitis Reveals Inflammatory Niche with Abundant CD39+ T Cells with Functional ATPase Phenotype: A Case Study.

This study investigated immune cell characteristics in chronic hypersensitivity pneumonitis (HP), focusing on CD39-expressing cells' impact on inflammation and tissue remodelling. Lung tissue from an HP patient was analysed using single-cell transcriptomics, flow cytometry, and gene expression profiling. The tissue revealed diverse cell types like macrophages, T cells, fibroblasts, and regulatory T cells (Tregs). CD39-expressing Tregs exhibited heightened ATP hydrolysis capacity and regulatory gene expression. CD39hi cells displayed markers of both Tregs and proinflammatory Th17 cells, suggesting transitional properties. Communication networks involving molecules like SPP1, collagen, CSF1, and IL-1ß were identified, hinting at interactions between cell types in HP pathogenesis. This research provides insights into the immune response and cell interactions in chronic HP. CD39-expressing cells dual nature as Tregs and Th17 cells suggests a role in modulating lung inflammation, potentially affecting disease progression. These findings lay the groundwork for further research, underscoring CD39-expressing cells as potential therapeutic targets in HP.

A Methodological Approach to Identify Natural Compounds with Antifibrotic Activity and the Potential to Treat Pulmonary Fibrosis Using Single-Cell Sequencing and Primary Human Lung Macrophages.

Idiopathic pulmonary fibrosis (IPF) is the most common and lethal form of the interstitial pneumonias. The cause of the disease is unknown, and new therapies that stop or reverse disease progression are desperately needed. Recent advances in next-generation sequencing have led to an abundance of freely available, clinically relevant, organ-and-disease-specific, single-cell transcriptomic data, including studies from patients with IPF. We mined data from published IPF data sets and identified gene signatures delineating pro-fibrotic or antifibrotic macrophages and then used the Enrichr platform to identify compounds with the potential to drive the macrophages toward the antifibrotic transcriptotype. We then began testing these compounds in a novel in vitro phenotypic drug screening assay utilising human lung macrophages recovered from whole-lung lavage of patients with silicosis. As predicted by the Enrichr tool, glitazones potently modulated macrophage gene expression towards the antifibrotic phenotype. Next, we assayed a subset of the NatureBank pure compound library and identified the cyclobutane lignan, endiandrin A, which was isolated from the roots of the endemic Australian rainforest plant, Endiandra anthropophagorum, with a similar antifibrotic potential to the glitazones. These methods open new avenues of exploration to find treatments for lung fibrosis.

Alveolar crystal burden in stone workers with artificial stone silicosis.

BACKGROUND AND OBJECTIVE: An epidemic of silicosis has emerged due to a failure to control risks associated with exposure to high-silica content respirable dust generated while working with artificial stone products. Methods for quantification of alveolar crystal burden are needed to advance our understanding of the pathobiology of silica-related lung injury as well as assisting in the diagnosis, clinical management and prognostication of affected workers. The objective of this study was to develop and validate novel methods to quantify alveolar crystal burden in bronchoalveolar lavage (BAL) fluid from patients with artificial stone silicosis. METHODS: New methods to quantify and analyse alveolar crystal in BAL from patients with artificial stone silicosis were developed. Crystals were isolated and counted by microscopy and alveolar crystal burden was calculated using a standard curve generated by titration of respirable α-Quartz. The utility of the assay was then assessed in 23 patients with artificial stone silicosis. RESULTS: Alveolar crystal burden was greater in patients with silicosis (0.44 picograms [pg]/cell [0.08-3.49]) compared to patients with other respiratory diagnoses (0.057 pg/cell [0.01-0.34]; p < 0.001). Alveolar crystal burden was positively correlated with years of silica exposure (ρ = 0.49, p = 0.02) and with decline in diffusing capacity of the lungs for carbon monoxide (ρ = -0.50, p = 0.02). CONCLUSION: Alveolar crystal burden quantification differentiates patients with silicosis from patients with other respiratory disorders. Furthermore, crystal burden is correlated with the rate of decline in lung function in patients with artificial stone silicosis.

Nitrate Reductase Knockout Uncouples Nitrate Transport from Nitrate Assimilation and Drives Repartitioning of Carbon Flux in a Model Pennate Diatom.

The ecological prominence of diatoms in the ocean environment largely results from their superior competitive ability for dissolved nitrate (NO3-). To investigate the cellular and genetic basis of diatom NO3- assimilation, we generated a knockout in the nitrate reductase gene (NR-KO) of the model pennate diatom Phaeodactylum tricornutum In NR-KO cells, N-assimilation was abolished although NO3- transport remained intact. Unassimilated NO3- accumulated in NR-KO cells, resulting in swelling and associated changes in biochemical composition and physiology. Elevated expression of genes encoding putative vacuolar NO3- chloride channel transporters plus electron micrographs indicating enlarged vacuoles suggested vacuolar storage of NO3- Triacylglycerol concentrations in the NR-KO cells increased immediately following the addition of NO3-, and these increases coincided with elevated gene expression of key triacylglycerol biosynthesis components. Simultaneously, induction of transcripts encoding proteins involved in thylakoid membrane lipid recycling suggested more abrupt repartitioning of carbon resources in NR-KO cells compared with the wild type. Conversely, ribosomal structure and photosystem genes were immediately deactivated in NR-KO cells following NO3- addition, followed within hours by deactivation of genes encoding enzymes for chlorophyll biosynthesis and carbon fixation and metabolism. N-assimilation pathway genes respond uniquely, apparently induced simultaneously by both NO3- replete and deplete conditions.

Applying a new quantitative global breast MRI feature analysis scheme to assess tumor response to chemotherapy.

PURPOSE: To develop a new quantitative global kinetic breast magnetic resonance imaging (MRI) features analysis scheme and assess its feasibility to assess tumor response to neoadjuvant chemotherapy. MATERIALS AND METHODS: A dataset involving breast MR images acquired from 151 cancer patients before neoadjuvant chemotherapy was used. Among them, 63 patients had complete response (CR) and 88 had partial response (PR) to chemotherapy based on the RECIST criterion. A computer-aided detection (CAD) scheme was applied to segment breast region depicted on the breast MR images and computed a total of 10 kinetic image features to represent parenchyma enhancement either from the entire two breasts or the bilateral asymmetry between the two breasts. To classify between CR and PR cases, we tested an attribution selected classifier that integrates with an artificial neural network and a Wrapper Subset Evaluator. The classifier was trained and tested using a leave-one-case-out (LOCO)-based cross-validation method. The area under a receiver operating characteristic curve (AUC) was computed to assess classifier performance. RESULTS: From the pool of initial 10 features, four features were selected by more than 90% times in the LOCO cross-validation iterations. Among them, three represent the bilateral asymmetry of kinetic features between two breasts. Using the classifier yielded AUC = 0.83 ± 0.04, which is significantly higher than using each individual feature to classify between CR and PR cases (P < 0.05). CONCLUSION: This study demonstrated that quantitative analysis of global kinetic features computed from breast MRI-acquired prechemotherapy has potential to generate a useful clinical marker that is associated with tumor response to neoadjuvant chemotherapy. J. Magn. Reson. Imaging 2016;44:1099-1106.

Early prediction of clinical benefit of treating ovarian cancer using quantitative CT image feature analysis.

BACKGROUND: In current clinical trials of treating ovarian cancer patients, how to accurately predict patients' response to the chemotherapy at an early stage remains an important and unsolved challenge. PURPOSE: To investigate feasibility of applying a new quantitative image analysis method for predicting early response of ovarian cancer patients to chemotherapy in clinical trials. MATERIAL AND METHODS: A dataset of 30 patients was retrospectively selected in this study, among which 12 were responders with 6-month progression-free survival (PFS) and 18 were non-responders. A computer-aided detection scheme was developed to segment tumors depicted on two sets of CT images acquired pre-treatment and 4-6 weeks post treatment. The scheme computed changes of three image features related to the tumor volume, density, and density variance. We analyzed performance of using each image feature and applying a decision tree to predict patients' 6-month PFS. The prediction accuracy of using quantitative image features was also compared with the clinical record based on the Response Evaluation Criteria in Solid Tumors (RECIST) guideline. RESULTS: The areas under receiver operating characteristic curve (AUC) were 0.773 ± 0.086, 0.680 ± 0.109, and 0.668 ± 0.101, when using each of three features, respectively. AUC value increased to 0.831 ± 0.078 when combining these features together. The decision-tree classifier achieved a higher predicting accuracy (76.7%) than using RECIST guideline (60.0%). CONCLUSION: This study demonstrated the potential of using a quantitative image feature analysis method to improve accuracy of predicting early response of ovarian cancer patients to the chemotherapy in clinical trials.

MicroRNA-34c is associated with emphysema severity and modulates SERPINE1 expression.

BACKGROUND: MicroRNAs (MiRNA) are small non-coding RNAs that regulate gene expression. The aim of this study was to identify miRNAs differentially expressed between mild and moderately emphysematous lung, as well as their functional target mRNAs. Resected lung from patients with COPD undergoing lung cancer surgery was profiled using miRNA (Agilent Human miRNA profiler G4470 V1.01) and mRNA (OperonV2.0) microarrays. Cells of lung origin (BEAS-2B and HFL1) were profiled using mRNA microarrays (Illumina HumanHT-12 V3) after in vitro manipulation. RESULTS: COPD patients had mean (SD) age 68 (6) years, FEV1 72 (17)% predicted and gas transfer (KCO) 70 (10)% predicted. Five miRNAs (miR-34c, miR-34b, miR-149, miR-133a and miR-133b) were significantly down-regulated in lung from patients with moderate compared to mild emphysema as defined by gas transfer (p < 0.01). In vitro upregulation of miR-34c in respiratory cells led to down-regulation of predicted target mRNAs, including SERPINE1, MAP4K4, ZNF3, ALDOA and HNF4A. The fold change in ex-vivo expression of all five predicted target genes inversely correlated with that of miR-34c in emphysematous lung, but this relationship was strongest for SERPINE1 (p = 0.05). CONCLUSION: Differences in miRNA expression are associated with emphysema severity in COPD patients. MiR-34c modulates expression of its putative target gene, SERPINE1, in vitro in respiratory cell lines and ex vivo in emphysematous lung tissue.

Reestablishment of recipient-associated microbiota in the lung allograft is linked to reduced risk of bronchiolitis obliterans syndrome.

RATIONALE: Bronchiolitis obliterans syndrome (BOS) is the primary limiting factor for long-term survival after lung transplantation, and has previously been associated with microbial infections. OBJECTIVES: To cross-sectionally and longitudinally characterize microbial communities in allografts from transplant recipients with and without BOS using a culture-independent method based on high-throughput sequencing. METHODS: Allografts were sampled by bronchoalveolar lavage, and microbial communities were profiled using 16S rRNA gene amplicon pyrosequencing. Community profiles were compared using the weighted Unifrac metric and the relationship between microbial populations, BOS, and other covariates was explored using PERMANOVA and logistic regression. MEASUREMENTS AND MAIN RESULTS: Microbial communities in transplant patients fell into two main groups: those dominated by Pseudomonas or those dominated by Streptococcus and Veillonella, which seem to be mutually exclusive lung microbiomes. Aspergillus culture was also negatively correlated with the Pseudomonas-dominated group. The reestablishment of dominant populations present in patients pretransplant, notably Pseudomonas in individuals with cystic fibrosis, was negatively correlated with BOS. CONCLUSIONS: Recolonization of the allograft by Pseudomonas in individuals with cystic fibrosis is not associated with BOS. In general, reestablishment of pretransplant lung populations in the allograft seems to have a protective effect against BOS, whereas de novo acquisition of microbial populations often belonging to the same genera may increase the risk of BOS.

Association between computed tissue density asymmetry in bilateral mammograms and near-term breast cancer risk.

This study investigated association between bilateral mammographic density asymmetry and near-term breast cancer risk. A data base of digital mammograms acquired from 690 women was retrospectively collected. All images were originally interpreted as negative by radiologists. During the next subsequent screening examinations (between 12 and 36 months later), 230 women were diagnosed positive for cancer, 230 were recalled for additional diagnostic workups and proved to be benign, and 230 remained negative (not recalled). We applied a computerized scheme to compute the differences of five image features between the left and right mammograms, and trained an artificial neural network (ANN) to compute a bilateral mammographic density asymmetry score. Odds ratios (ORs) were used to assess associations between the ANN-generated scores and risk of women having detectable cancers during the next screening examinations. A logistic regression method was applied to test for trend as a function of the increase in ANN-generated scores. The results were also compared with ORs computed using other existing cancer risk factors. The ORs showed an increasing risk trend with the increase in ANN-generated scores (from 1.00 to 9.07 between positive and negative case groups). The regression analysis also showed a significant increase trend in slope (p < 0.05). No significant increase trends of the ORs were found when using woman's age, subjectively rated breast density, or family history of breast cancer. This study demonstrated that the computed bilateral mammographic density asymmetry had potential to be used as a new risk factor to improve discriminatory power in predicting near-term risk of women developing breast cancer.

Genetic Aspects of Mammographic Density Measures Associated with Breast Cancer Risk.

Cumulus, Altocumulus, and Cirrocumulus are measures of mammographic density defined at increasing pixel brightness thresholds, which, when converted to mammogram risk scores (MRSs), predict breast cancer risk. Twin and family studies suggest substantial variance in the MRSs could be explained by genetic factors. For 2559 women aged 30 to 80 years (mean 54 years), we measured the MRSs from digitized film mammograms and estimated the associations of the MRSs with a 313-SNP breast cancer polygenic risk score (PRS) and 202 individual SNPs associated with breast cancer risk. The PRS was weakly positively correlated (correlation coefficients ranged 0.05−0.08; all p < 0.04) with all the MRSs except the Cumulus-white MRS based on the "white but not bright area" (correlation coefficient = 0.04; p = 0.06). After adjusting for its association with the Altocumulus MRS, the PRS was not associated with the Cumulus MRS. There were MRS associations (Bonferroni-adjusted p < 0.04) with one SNP in the ATXN1 gene and nominally with some ESR1 SNPs. Less than 1% of the variance of the MRSs is explained by the genetic markers currently known to be associated with breast cancer risk. Discovering the genetic determinants of the bright, not white, regions of the mammogram could reveal substantial new genetic causes of breast cancer.

Familial Aspects of Mammographic Density Measures Associated with Breast Cancer Risk.

Cumulus, Cumulus-percent, Altocumulus, Cirrocumulus, and Cumulus-white are mammogram risk scores (MRSs) for breast cancer based on mammographic density defined in effect by different levels of pixel brightness and adjusted for age and body mass index. We measured these MRS from digitized film mammograms for 593 monozygotic (MZ) and 326 dizygotic (DZ) female twin pairs and 1592 of their sisters. We estimated the correlations in relatives (r) and the proportion of variance due to genetic factors (heritability) using the software FISHER and predicted the familial risk ratio (FRR) associated with each MRS. The ρ estimates ranged from: 0.41 to 0.60 (standard error [SE] 0.02) for MZ pairs, 0.16 to 0.26 (SE 0.05) for DZ pairs, and 0.19 to 0.29 (SE 0.02) for sister pairs (including pairs of a twin and her non-twin sister), respectively. Heritability estimates were 39% to 69% under the classic twin model and 36% to 56% when allowing for shared non-genetic factors specific to MZ pairs. The FRRs were 1.08 to 1.17. These MRSs are substantially familial, due mostly to genetic factors that explain one-quarter to one-half as much of the familial aggregation of breast cancer that is explained by the current best polygenic risk score.

A novel computer-aided lung nodule detection system for CT images.

PURPOSE: The paper presents a complete computer-aided detection (CAD) system for the detection of lung nodules in computed tomography images. A new mixed feature selection and classification methodology is applied for the first time on a difficult medical image analysis problem. METHODS: The CAD system was trained and tested on images from the publicly available Lung Image Database Consortium (LIDC) on the National Cancer Institute website. The detection stage of the system consists of a nodule segmentation method based on nodule and vessel enhancement filters and a computed divergence feature to locate the centers of the nodule clusters. In the subsequent classification stage, invariant features, defined on a gauge coordinates system, are used to differentiate between real nodules and some forms of blood vessels that are easily generating false positive detections. The performance of the novel feature-selective classifier based on genetic algorithms and artificial neural networks (ANNs) is compared with that of two other established classifiers, namely, support vector machines (SVMs) and fixed-topology neural networks. A set of 235 randomly selected cases from the LIDC database was used to train the CAD system. The system has been tested on 125 independent cases from the LIDC database. RESULTS: The overall performance of the fixed-topology ANN classifier slightly exceeds that of the other classifiers, provided the number of internal ANN nodes is chosen well. Making educated guesses about the number of internal ANN nodes is not needed in the new feature-selective classifier, and therefore this classifier remains interesting due to its flexibility and adaptability to the complexity of the classification problem to be solved. Our fixed-topology ANN classifier with 11 hidden nodes reaches a detection sensitivity of 87.5% with an average of four false positives per scan, for nodules with diameter greater than or equal to 3 mm. Analysis of the false positive items reveals that a considerable proportion (18%) of them are smaller nodules, less than 3 mm in diameter. CONCLUSIONS: A complete CAD system incorporating novel features is presented, and its performance with three separate classifiers is compared and analyzed. The overall performance of our CAD system equipped with any of the three classifiers is well with respect to other methods described in literature.

ADAM28: a potential oncogene involved in asbestos-related lung adenocarcinomas.

Asbestos-related lung cancer accounts for 4-12% of all lung cancers worldwide. Since putative mechanisms of carcinogenesis differ between asbestos and tobacco induced lung cancers, tumors induced by the two agents may be genetically distinct. To identify gene expression biomarkers associated with asbestos-related lung tumorigenicity we performed gene expression array analysis on tumors of 36 patients with primary lung adenocarcinoma, comparing 12 patients with lung asbestos body counts above levels associated with urban dwelling (ARLC-AC: asbestos-related lung cancer-adenocarcinoma) with 24 patients with no asbestos bodies (NARLC-AC: non-asbestos related lung cancer-adenocarcinoma). Genes differentially expressed between ARLC-AC and NARLC-AC were identified on fold change and P value, and then prioritized using gene ontology. Candidates included ZNRF3, ADAM28, PPP1CA, IRF6, RAB3D, and PRDX1. Expression of these six genes was technically and biologically replicated by qRT-PCR in the training set and biologically validated in three independent test sets. ADAM28, encoding a disintegrin and metalloproteinase domain protein that interacts with integrins, was consistently upregulated in ARLC across all four datasets. Further studies are being designed to investigate the possible role of this gene in asbestos lung tumorigenicity, its potential utility as a marker of asbestos related lung cancer for purposes of causal attribution, and its potential as a treatment target for lung cancers arising in asbestos exposed persons.

3D axial-attention for lung nodule classification.

PURPOSE: In recent years, Non-Local-based methods have been successfully applied to lung nodule classification. However, these methods offer 2D attention or limited 3D attention to low-resolution feature maps. Moreover, they still depend on a convenient local filter such as convolution as full 3D attention is expensive to compute and requires a big dataset, which might not be available. METHODS: We propose to use 3D Axial-Attention, which requires a fraction of the computing power of a regular Non-Local network (i.e., self-attention). Unlike a regular Non-Local network, the 3D Axial-Attention network applies the attention operation to each axis separately. Additionally, we solve the invariant position problem of the Non-Local network by proposing to add 3D positional encoding to shared embeddings. RESULTS: We validated the proposed method on 442 benign nodules and 406 malignant nodules, extracted from the public LIDC-IDRI dataset by following a rigorous experimental setup using only nodules annotated by at least three radiologists. Our results show that the 3D Axial-Attention model achieves state-of-the-art performance on all evaluation metrics, including AUC and Accuracy. CONCLUSIONS: The proposed model provides full 3D attention, whereby every element (i.e., pixel) in the 3D volume space attends to every other element in the nodule effectively. Thus, the 3D Axial-Attention network can be used in all layers without the need for local filters. The experimental results show the importance of full 3D attention for classifying lung nodules.

CASPIANET++: A multidimensional Channel-Spatial Asymmetric attention network with Noisy Student Curriculum Learning paradigm for brain tumor segmentation.

Convolutional neural networks (CNNs) have been used quite successfully for semantic segmentation of brain tumors. However, current CNNs and attention mechanisms are stochastic in nature and neglect the morphological indicators used by radiologists to manually annotate regions of interest. In this paper, we introduce a channel and spatial wise asymmetric attention (CASPIAN) by leveraging the inherent structure of tumors to detect regions of saliency. To demonstrate the efficacy of our proposed layer, we integrate this into a well-established convolutional neural network (CNN) architecture to achieve higher Dice scores, with less GPU resources. Also, we investigate the inclusion of auxiliary multiscale and multiplanar attention branches to increase the spatial context crucial in semantic segmentation tasks. The resulting architecture is the new CASPIANET++, which achieves Dice Scores of 91.19%, 87.6% and 81.03% for whole tumor, tumor core and enhancing tumor respectively. Furthermore, driven by the scarcity of brain tumor data, we investigate the Noisy Student method for segmentation tasks. Our new Noisy Student Curriculum Learning paradigm, which infuses noise incrementally to increase the complexity of the training images exposed to the network, further boosts the enhancing tumor region to 81.53%. Additional validation performed on the BraTS2020 data shows that the Noisy Student Curriculum Learning method works well without any additional training or finetuning.