Impact of a Categorical AI System for Digital Breast Tomosynthesis on Breast Cancer Interpretation by Both General Radiologists and Breast Imaging Specialists.

Purpose To evaluate performance improvements of general radiologists and breast imaging specialists when interpreting a set of diverse digital breast tomosynthesis (DBT) examinations with the aid of a custom-built categorical artificial intelligence (AI) system. Materials and Methods A fully balanced multireader, multicase reader study was conducted to compare the performance of 18 radiologists (nine general radiologists and nine breast imaging specialists) reading 240 retrospectively collected screening DBT mammograms (mean patient age, 59.8 years ± 11.3 [SD]; 100% women), acquired between August 2016 and March 2019, with and without the aid of a custom-built categorical AI system. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity across general radiologists and breast imaging specialists reading with versus without AI were assessed. Reader performance was also analyzed as a function of breast cancer characteristics and patient subgroups. Results Every radiologist demonstrated improved interpretation performance when reading with versus without AI, with an average AUC of 0.93 versus 0.87, demonstrating a difference in AUC of 0.06 (95% CI: 0.04, 0.08; P < .001). Improvement in AUC was observed for both general radiologists (difference of 0.08; P < .001) and breast imaging specialists (difference of 0.04; P < .001) and across all cancer characteristics (lesion type, lesion size, and pathology) and patient subgroups (race and ethnicity, age, and breast density) examined. Conclusion A categorical AI system helped improve overall radiologist interpretation performance of DBT screening mammograms for both general radiologists and breast imaging specialists and across various patient subgroups and breast cancer characteristics. Keywords: Computer-aided Diagnosis, Screening Mammography, Digital Breast Tomosynthesis, Breast Cancer, Screening, Convolutional Neural Network (CNN), Artificial Intelligence Supplemental material is available for this article. © RSNA, 2024.

Confined placental mosaicism: placental size and function evaluated on magnetic resonance imaging.

OBJECTIVES: Evidence regarding placental function in pregnancies complicated by confined placental mosaicism (CPM) is conflicting. We aimed to compare placental function between CPM and non-CPM pregnancies prenatally and at birth. A secondary objective was to evaluate the relationship between placental function and chromosomal subtype of CPM. METHODS: This was a retrospective study of pregnancies with CPM and control pregnancies delivered at a tertiary hospital in Denmark between 2014 and 2017. Placental volume and placental transverse relaxation time (T2*) were estimated on magnetic resonance imaging (MRI), fetal weight and uterine artery pulsatility index (UtA-PI) were estimated on ultrasound and fetoplacental ratio was assessed on MRI and at birth. These estimates of placental function were adjusted for gestational age and compared between groups using the Wilcoxon rank-sum test. Within the group of CPM pregnancies, measures of placental function were compared between those at high risk (chromosome numbers 2, 3, 7, 13 and 16) and those at low risk (chromosome numbers 5, 18 and 45X). RESULTS: A total of 90 pregnancies were included, of which 12 had CPM and 78 were controls. MRI and ultrasound examinations were performed at a median gestational age of 32.6 weeks (interquartile range, 24.7-35.3 weeks). On MRI assessment, CPM placentae were characterized by a lower placental T2* Z-score (P = 0.004), a lower fetoplacental ratio (P = 0.03) and a higher UtA-PI Z-score (P = 0.03), compared with non-CPM placentae. At birth, the fetoplacental ratio was significantly lower (P = 0.02) and placental weight Z-score was higher (P = 0.01) in CPM pregnancies compared with non-CPM pregnancies. High-risk CPM pregnancies showed a reduced placental T2* Z-score (P = 0.003), lower birth-weight Z-score (P = 0.041), earlier gestational age at delivery (P = 0.019) and higher UtA-PI Z-score (P = 0.028) compared with low-risk CPM pregnancies. Low-risk CPM pregnancies did not differ in any of these parameters from non-CPM pregnancies. CONCLUSIONS: CPM pregnancies are characterized by an enlarged and dysfunctional placenta. Placental function was highly related to the chromosomal type of CPM; placental dysfunction was seen predominantly in high-risk CPM pregnancies in which chromosomes 2, 3, 7, 13 or 16 were involved. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Placental volume, thickness and transverse relaxation time (T2*) estimated by magnetic resonance imaging in relation to small for gestational age at birth.

INTRODUCTION: Placental magnetic resonance imaging (MRI) may be a valuable tool in the prediction of small for gestational age (SGA) at birth. MRI provides reliable estimates of placental volume and thickness. In addition, placental transverse relaxation time (T2*) may be directly related to placental function. This study aimed to explore and compare the predictive performance of three placental MRI parameters - volume, thickness and T2* - in relation to SGA at birth. METHODS: A mixed cohort of 85 pregnancies was retrieved from the placental MRI database at the study hospital. MRI was performed in a 1.5 T system at gestational weeks 15-41. In normal birthweight (BW) pregnancies [BW > -22 % of expected for gestational age (GA)], the correlation between each of the MRI parameters and GA was investigated by linear regression. The prediction of SGA was investigated by logistic regression analysis adjusted for GA at MRI. RESULTS: In normal BW pregnancies, a significant linear correlation was found between GA and each of the MRI parameters. Univariate analysis demonstrated that placental volume [odds ratio (OR) 0.97, p = 0.001] and placental T2* (OR 0.79, p = 0.003), but not placental thickness (OR 0.92, p = 0.862) were significant predictors of SGA. A multi-variate model including all three MRI parameters found that placental T2* was the only independent predictor of SGA (OR 0.81, p = 0.04). CONCLUSION: Among the MRI parameters investigated in this study, placental T2* was the only independent predictor of SGA in a multi-variate model. This finding underlines the strong position of T2*-weighted placental MRI in the prediction of SGA.

External Validation of an Ensemble Model for Automated Mammography Interpretation by Artificial Intelligence.

Importance: With a shortfall in fellowship-trained breast radiologists, mammography screening programs are looking toward artificial intelligence (AI) to increase efficiency and diagnostic accuracy. External validation studies provide an initial assessment of how promising AI algorithms perform in different practice settings. Objective: To externally validate an ensemble deep-learning model using data from a high-volume, distributed screening program of an academic health system with a diverse patient population. Design, Setting, and Participants: In this diagnostic study, an ensemble learning method, which reweights outputs of the 11 highest-performing individual AI models from the Digital Mammography Dialogue on Reverse Engineering Assessment and Methods (DREAM) Mammography Challenge, was used to predict the cancer status of an individual using a standard set of screening mammography images. This study was conducted using retrospective patient data collected between 2010 and 2020 from women aged 40 years and older who underwent a routine breast screening examination and participated in the Athena Breast Health Network at the University of California, Los Angeles (UCLA). Main Outcomes and Measures: Performance of the challenge ensemble method (CEM) and the CEM combined with radiologist assessment (CEM+R) were compared with diagnosed ductal carcinoma in situ and invasive cancers within a year of the screening examination using performance metrics, such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Results: Evaluated on 37 317 examinations from 26 817 women (mean [SD] age, 58.4 [11.5] years), individual model AUROC estimates ranged from 0.77 (95% CI, 0.75-0.79) to 0.83 (95% CI, 0.81-0.85). The CEM model achieved an AUROC of 0.85 (95% CI, 0.84-0.87) in the UCLA cohort, lower than the performance achieved in the Kaiser Permanente Washington (AUROC, 0.90) and Karolinska Institute (AUROC, 0.92) cohorts. The CEM+R model achieved a sensitivity (0.813 [95% CI, 0.781-0.843] vs 0.826 [95% CI, 0.795-0.856]; P = .20) and specificity (0.925 [95% CI, 0.916-0.934] vs 0.930 [95% CI, 0.929-0.932]; P = .18) similar to the radiologist performance. The CEM+R model had significantly lower sensitivity (0.596 [95% CI, 0.466-0.717] vs 0.850 [95% CI, 0.766-0.923]; P < .001) and specificity (0.803 [95% CI, 0.734-0.861] vs 0.945 [95% CI, 0.936-0.954]; P < .001) than the radiologist in women with a prior history of breast cancer and Hispanic women (0.894 [95% CI, 0.873-0.910] vs 0.926 [95% CI, 0.919-0.933]; P = .004). Conclusions and Relevance: This study found that the high performance of an ensemble deep-learning model for automated screening mammography interpretation did not generalize to a more diverse screening cohort, suggesting that the model experienced underspecification. This study suggests the need for model transparency and fine-tuning of AI models for specific target populations prior to their clinical adoption.

Detection of low numbers of bacterial cells in a pharmaceutical drug product using Raman spectroscopy and PLS-DA multivariate analysis.

Sterility testing is a laborious and slow process to detect contaminants present in drug products. Raman spectroscopy is a promising label-free tool to detect microorganisms and thus gaining relevance as a future alternative culture-free method for sterility testing in the pharmaceutical industry. However, reaching detection limits similar to standard procedures while keeping a high accuracy remains challenging, due to weak bacterial Raman signals. In this work, we show a new non-invasive approach focusing on detection of different bacteria in concentrations below 100 CFU per ml within drug product containers using Raman spectroscopy and multivariate data analysis. Even though Raman spectra from drug product with and without bacteria are similar, a partial least squared discriminant analysis (PLS-DA) model shows great performance to distinguish samples with bacterial contaminants in concentrations down to 10 CFU per ml. We used spiked samples with bacterial spores for model validation achieving a detection accuracy of 99%. Our results indicate the great potential of this rapid, and cost-effective approach to be used in quality control in the pharmaceutical industry.

The Effects of Hearing-Aid Amplification and Noise on Conversational Dynamics Between Normal-Hearing and Hearing-Impaired Talkers.

There is a long-standing tradition to assess hearing-aid benefits using lab-based speech intelligibility tests. Towards a more everyday-like scenario, the current study investigated the effects of hearing-aid amplification and noise on face-to-face communication between two conversational partners. Eleven pairs, consisting of a younger normal-hearing (NH) and an older hearing-impaired (HI) participant, solved spot-the-difference tasks while their conversations were recorded. In a two-block randomized design, the tasks were solved in quiet or noise, both with and without the HI participant receiving hearing-aid amplification with active occlusion cancellation. In the presence of 70 dB SPL babble noise, participants had fewer, slower, and less well-timed turn-starts, while speaking louder with longer inter-pausal units (IPUs, stretches of continuous speech surrounded by silence) and reducing their articulation rates. All these changes are indicative of increased communication effort. The timing of turn-starts by the HI participants exhibited more variability than that of their NH conversational partners. In the presence of background noise, the timing of turn-starts by the HI participants became even more variable, and their NH partners spoke louder. When the HI participants were provided with hearing-aid amplification, their timing of turn-starts became faster, they increased their articulation rate, and they produced shorter IPUs, all indicating reduced communication effort. In conclusion, measures of the conversational dynamics showed that background noise increased the communication effort, especially for the HI participants, and that providing hearing-aid amplification caused the HI participant to behave more like their NH conversational partner, especially in quiet situations.

Prediction of impending central-line-associated bloodstream infections in hospitalized cardiac patients: development and testing of a machine-learning model.

BACKGROUND: While modelling of central-line-associated blood stream infection (CLABSI) risk factors is common, models that predict an impending CLABSI in real time are lacking. AIM: To build a prediction model which identifies patients who will develop a CLABSI in the ensuing 24 h. METHODS: We collected variables potentially related to infection identification in all patients admitted to the cardiac intensive care unit or cardiac ward at Boston Children's Hospital in whom a central venous catheter (CVC) was in place between January 2010 and August 2020, excluding those with a diagnosis of bacterial endocarditis. We created models predicting whether a patient would develop CLABSI in the ensuing 24 h. We assessed model performance based on area under the curve (AUC), sensitivity and false-positive rate (FPR) of models run on an independent testing set (40%). FINDINGS: A total of 104,035 patient-days and 139,662 line-days corresponding to 7468 unique patients were included in the analysis. There were 399 positive blood cultures (0.38%), most commonly with Staphylococcus aureus (23% of infections). Major predictors included a prior history of infection, elevated maximum heart rate, elevated maximum temperature, elevated C-reactive protein, exposure to parenteral nutrition and use of alteplase for CVC clearance. The model identified 25% of positive cultures with an FPR of 0.11% (AUC = 0.82). CONCLUSIONS: A machine-learning model can be used to predict 25% of patients with impending CLABSI with only 1.1/1000 of these predictions being incorrect. Once prospectively validated, this tool may allow for early treatment or prevention.

Nitro-fatty acids decrease type I interferons and monocyte chemoattractant protein 1 in ex vivo models of inflammatory arthritis.

BACKGROUND: Inflammatory arthritis including rheumatoid arthritis (RA) and spondyloarthritis (SpA) is characterized by inflammation and destruction of the joints. Approximately one third of patients do not respond to first-line treatments. Nitro-fatty acids are bioactive lipids with anti-inflammatory properties and tissue-protective functions. The nitro-fatty acid 10-NO2-oleic acid (10-NO2-OA) is being tested in clinical trials for patients with fibrotic and inflammatory conditions. Here, we tested whether 10-NO2-OA could inhibit immune reactions involved in the inflammatory and joint destructive processes in inflammatory arthritis. METHODS: Synovial fluid and blood samples were obtained from 14 patients with active RA or SpA. The in vitro models consisted of synovial fluid mononuclear cells (SFMCs) cultured for 48 h, SFMCs cultured for 21 days, and fibroblast-like synovial cells (FLSs) co-cultured with peripheral blood mononuclear cells (PBMCs) for 48 h. Cells were treated with or without 10-NO2-OA or the tumor necrosis factor alpha (TNFα) inhibitor etanercept. Supernatants were analyzed for type I interferon, monocyte chemoattractant protein-1 (MCP-1), matrix metalloproteinase 3 (MMP3) and tartrate resistant acid phosphatase (TRAP). RESULTS: In SFMCs cultured for 48 h, 10-NO2-OA dose-dependently decreased the secretion of bioactive type I interferons and MCP-1 but not MMP3 (P = 0.032, P = 0.0001, and P = 0.58, respectively). Both MCP-1 and MMP3 were decreased by etanercept (P = 0.0031 and P = 0.026, respectively). In SFMCs cultured for 21 days, 10-NO2-OA significantly decreased the production of MCP-1 but not TRAP (P = 0.027 and P = 0.1523, respectively). Etanercept decreased the production of TRAP but not MCP-1 (P < 0.001 and P = 0.84, respectively). In co-cultures of FLSs and PBMCs, 10-NO2-OA decreased the production of MCP-1 (P < 0.0001). This decrease in MCP-1 production was not seen with etanercept treatment (P = 0.47). CONCLUSION: 10-NO2-OA decreased the release of MCP-1 in three models of inflammatory arthritis. Of particular interest, 10-NO2-OA inhibited type I interferon, and 10-NO2-OA was more effective in reducing MCP-1 production in cultures dominated by FLSs compared with etanercept. Our results encourage clinical investigations of 10-NO2-OA in patients with inflammatory arthritis.

Ignoring genotype by environment interaction in the genetic evaluation of dairy cattle reduces accuracy but may increase selection intensity.

Genotype by environment interaction (G×E) may exist for traits that are expressed in different environments. The G×E is often ignored in the genetic evaluation of selection candidates. We hypothesized that genetic gain in 2 environments is always higher when the true value of the genetic correlation (rg) between traits expressed in different environments is considered in the genetic evaluation. We tested this hypothesis by stochastic simulation of dairy cattle breeding programs in a mainstream and a niche environment. The rg was varied from 0 to 1 in steps of 0.1. We simulated the following 3 scenarios: 1Trait_1Index, 2Traits_1Index, and 2Traits_2Indices. The G×E was ignored in the genetic evaluation in the scenario with 1Trait and included in scenarios with 2Traits. Selection was based on the mainstream selection index in both environments in scenarios with 1Index. Selection in the mainstream environment was based on the mainstream selection index and selection in the niche environment was based on the niche selection index in the scenario with 2Indices. With moderate G×E (rg between 0.6 and 0.9), the highest genetic gain was achieved in the niche environment by selecting for the mainstream selection index and ignoring G×E. At lower rg, the highest genetic gain was achieved when considering G×E and selecting for the niche selection index. For the mainstream environment, it was never an advantage to ignore G×E. Therefore, although our hypothesis was confirmed in most cases, there were cases where ignoring G×E was the better option, and using the correct evaluation led to inferior genetic gain. The results of the current study can be used in animal breeding programs that encompass multiple environments.

Effects of Noise and Second Language on Conversational Dynamics in Task Dialogue.

This study provides a framework for measuring conversational dynamics between conversational partners (interlocutors). Conversations from 20 pairs of young, normal-hearing, native-Danish talkers were recorded when speaking in both quiet and noise (70 dBA sound pressure level [SPL]) and in Danish and English. Previous studies investigating the intervals from when one talker stops talking to when the next one starts, termed floor-transfer offsets (FTOs), suggest that typical turn-taking requires interlocutors to predict when the current talker will finish their turn. We hypothesized that adding noise and/or speaking in a second language (L2) would increase the communication difficulty and result in longer and more variable FTOs. The median and interquartile range of FTOs increased slightly in noise, and in L2, there was a small increase in interquartile range but a small decrease in the median of FTO durations. It took the participants longer to complete the task in both L2 and noise, indicating increased communication difficulty. The average duration of interpausal units, that is, units of connected speech surrounded by silences of 180 ms or more, increased by 18% in noise and 8% in L2. These findings suggest that talkers held their turn for longer, allowing more time for speech understanding and planning. In L2, participants spoke slower, and in both L2 and noise, they took fewer turns. These changes in behavior may have offset some of the increased difficulty when communicating in noise or L2. We speculate that talkers prioritize the maintenance of turn-taking timing over other speech measures.

Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach.

Breast cancer remains a global challenge, causing over 600,000 deaths in 2018 (ref. 1). To achieve earlier cancer detection, health organizations worldwide recommend screening mammography, which is estimated to decrease breast cancer mortality by 20-40% (refs. 2,3). Despite the clear value of screening mammography, significant false positive and false negative rates along with non-uniformities in expert reader availability leave opportunities for improving quality and access4,5. To address these limitations, there has been much recent interest in applying deep learning to mammography6-18, and these efforts have highlighted two key difficulties: obtaining large amounts of annotated training data and ensuring generalization across populations, acquisition equipment and modalities. Here we present an annotation-efficient deep learning approach that (1) achieves state-of-the-art performance in mammogram classification, (2) successfully extends to digital breast tomosynthesis (DBT; '3D mammography'), (3) detects cancers in clinically negative prior mammograms of patients with cancer, (4) generalizes well to a population with low screening rates and (5) outperforms five out of five full-time breast-imaging specialists with an average increase in sensitivity of 14%. By creating new 'maximum suspicion projection' (MSP) images from DBT data, our progressively trained, multiple-instance learning approach effectively trains on DBT exams using only breast-level labels while maintaining localization-based interpretability. Altogether, our results demonstrate promise towards software that can improve the accuracy of and access to screening mammography worldwide.

Phase II trial of carboplatin and bevacizumab in patients with breast cancer brain metastases.

BACKGROUND: We aimed to examine the safety and efficacy of bevacizumab and carboplatin in patients with breast cancer brain metastases. METHODS: We enrolled patients with breast cancer and > 1 measurable new or progressive brain metastasis. Patients received bevacizumab 15 mg/kg intravenously (IV) on cycle 1 day 1 and carboplatin IV AUC = 5 on cycle 1 day 8. Patients with HER2-positive disease also received trastuzumab. In subsequent cycles, all drugs were administered on day 1 of each cycle. Contrast-enhanced brain MRI was performed at baseline, 24-96 h after the first bevacizumab dose (day + 1), and every 2 cycles. The primary endpoint was objective response rate in the central nervous system (CNS ORR) by composite criteria. Associations between germline VEGF single nucleotide polymorphisms (rs699947, rs2019063, rs1570360, rs833061) and progression-free survival (PFS) and overall survival (OS) were explored, as were associations between early (day + 1) MRI changes and outcomes. RESULTS: Thirty-eight patients were enrolled (29 HER2-positive, 9 HER2-negative); all were evaluable for response. The CNS ORR was 63% (95% CI, 46-78). Median PFS was 5.62 months and median OS was 14.10 months. As compared with an Eastern Cooperative Oncology Group performance status (ECOG PS) of 0, patients with ECOG PS 1-2 had significantly worse PFS and OS (all P < 0.01). No significant associations between VEGF genotypes or early MRI changes and clinical outcomes were observed. CONCLUSIONS: The combination of bevacizumab and carboplatin results in a high rate of durable objective response in patients with brain metastases from breast cancer. This regimen warrants further investigation. TRIAL REGISTRATION: NCT01004172 . Registered 28 October 2009.

Depression among men in ART treatment: a register-based national cohort study.

STUDY QUESTION: Are male factor infertility or remaining childless risk factors for unipolar depression among men in assisted reproductive technology (ART) treatment? SUMMARY ANSWER: Male factor infertility was not associated with a significantly increased risk of unipolar depression and men remaining childless did not have a significantly increased risk of developing unipolar depression compared to men in ART treatment who became fathers. WHAT IS KNOWN ALREADY: Men in medically assisted reproduction due to male factor infertility are more distressed and have more negative emotions such as feelings of loss, stigma and low self-esteem compared to men in fertility treatment due to other infertility diagnosis. Stress is in general a risk factor for depression. However, previous studies show conflicting results whether male factor infertility is a risk factor for depression. STUDY DESIGN SIZE DURATION: This national, register-based cohort study consisted of 37 913 cohabitant male partners of women in ART treatment recorded in the Danish IVF register (1994-2009). Via a national register, the men's personal identification number data were linked to the Danish Psychiatric Central Research Register (PCRR) (1969-2009) which records psychiatric diagnoses including unipolar depression, based on the ICD-8 and ICD-10 classification system. PARTICIPANTS/MATERIALS SETTING METHODS: The full cohort of male partners (n = 37 913) was included in the initial analysis on prevalence of unipolar depression before or after ART treatment initiation. The association between male factor infertility and unipolar depression diagnosis after initiating ART treatment was analysed with Cox regression analysis in a sub-study population of men with the exclusion of men having a depression prior to ART treatment or not having full data on educational level and infertility diagnosis (n = 34 817). MAIN RESULTS AND THE ROLE OF CHANCE: Overall, 1.2% (n = 446) of the men were diagnosed with unipolar depression either before initiating ART treatment (n = 146) or during follow-up (n = 300). In all, 76.0% of men with depression prior to or after ART treatment achieved fatherhood compared to 82.3% of men without depression (P < 0.001). In the sub-study population (n = 34 817, which included 266 men with a unipolar depression diagnosis), male factor infertility was not associated a significantly increased risk of depression (adjusted hazard ratio (aHR) = 1.04, 95% CI: 0.79-1.36, P = 0.804), and ART-treated men who remained childless did not have a significantly increased risk of developing depression compared to ART treated men who became fathers (aHR = 1.13, 95% CI: 0.87-1.48, P = 0.355). LIMITATIONS REASONS FOR CAUTIONS: Only severe cases of depression are recorded and included in this national register-based study given that only men with clinically diagnosed unipolar depression recorded in a psychiatric hospital (in-patient and out-patient) are included in the Danish PCRR. It is difficult to completely rule out an association between the exposures and depression as this outcome is so rare, and therefore the results are still statistically uncertain despite a large cohort. Furthermore, only men in ART treatment were included in this study, and caution should be taken in generalising findings to the total population of men in all areas of medically assisted reproduction or infertile men who have not sought treatment. WIDER IMPLICATIONS OT THE FINDINGS: This large national cohort study suggests that despite evidence showing that male factor infertility is a potential severe stressor for men, which can increase psychological distress and negative emotions, infertile men in ART treatment and men remaining childless after ART are not at a significantly increased risk of developing clinically diagnosed unipolar depression. STUDY FUNDING/COMPETING INTERESTS: C.S.S. was funded by unrestricted research grants received by Lone Schmidt from The Danish Health Insurance Foundation (J.nr. 2008B105) and Merck Sharp & Dohme (MSD). The sponsors had no influence on how data were retrieved and analysed or on the conclusions of the study. C.S.S. and L.S. have declared conflicts of interests; the remaining co-authors have no conflicts of interests to declare. TRIAL REGISTRATION NUMBER: Not applicable.

Novel approach to incorporate information about recessive lethal genes increases the accuracy of genomic prediction for mortality traits.

The genetic underpinnings of calf mortality can be partly polygenic and partly due to deleterious effects of recessive lethal alleles. Prediction of the genetic merits of selection candidates should thus take into account both genetic components contributing to calf mortality. However, simultaneously modeling polygenic risk and recessive lethal allele effects in genomic prediction is challenging due to effects that behave differently. In this study, we present a novel approach where mortality risk probabilities from polygenic and lethal allele components are predicted separately to compute the total risk probability of an individual for its future offspring as a basis for selection. We present methods for transforming genomic estimated breeding values of polygenic effect into risk probabilities using normal density and cumulative distribution functions and show computations of risk probability from recessive lethal alleles given sire genotypes and population recessive allele frequencies. Simulated data were used to test the novel approach as implemented in probit, logit, and linear models. In the simulation study, the accuracy of predicted risk probabilities was computed as the correlation between predicted mortality probabilities and observed calf mortality for validation sires. The results indicate that our novel approach can greatly increase the accuracy of selection for mortality traits compared with the accuracy of predictions obtained without distinguishing polygenic and lethal gene effects.

Effect of genomic selection and genotyping strategy on estimation of variance components in animal models using different relationship matrices.

BACKGROUND: The traditional way to estimate variance components (VC) is based on the animal model using a pedigree-based relationship matrix (A) (A-AM). After genomic selection was introduced into breeding programs, it was anticipated that VC estimates from A-AM would be biased because the effect of selection based on genomic information is not captured. The single-step method (H-AM), which uses an H matrix as (co)variance matrix, can be used as an alternative to estimate VC. Here, we compared VC estimates from A-AM and H-AM and investigated the effect of genomic selection, genotyping strategy and genotyping proportion on the estimation of VC from the two methods, by analyzing a dataset from a commercial broiler line and a simulated dataset that mimicked the broiler population. RESULTS: VC estimates from H-AM were severely overestimated with a high proportion of selective genotyping, and overestimation increased as proportion of genotyping increased in the analysis of both commercial and simulated data. This bias in H-AM estimates arises when selective genotyping is used to construct the H-matrix, regardless of whether selective genotyping is applied or not in the selection process. For simulated populations under genomic selection, estimates of genetic variance from A-AM were also significantly overestimated when the effect of genomic selection was strong. Our results suggest that VC estimates from H-AM under random genotyping have the expected values. Predicted breeding values from H-AM were inflated when VC estimates were biased, and inflation differed between genotyped and ungenotyped animals, which can lead to suboptimal selection decisions. CONCLUSIONS: We conclude that VC estimates from H-AM are biased with selective genotyping, but are close to expected values with random genotyping.VC estimates from A-AM in populations under genomic selection are also biased but to a much lesser degree. Therefore, we recommend the use of H-AM with random genotyping to estimate VC for populations under genomic selection. Our results indicate that it is still possible to use selective genotyping in selection, but then VC estimation should avoid the use of genotypes from one side only of the distribution of phenotypes. Hence, a dual genotyping strategy may be needed to address both selection and VC estimation.

Distinct expression patterns of TLR transcripts in human oocytes and granulosa cells from primordial and primary follicles.

Ovulation has long been regarded as a process resembling an inflammatory response. Previously, luteinizing hormone (LH) was shown to induce Toll-like receptor 2 (TLR2) and TLR4 in granulosa cells from preovulatory hormone-dependent follicles. However, whether this could already initiate before the hormone-dependent phase is currently unknown. The aim of this study was to investigate TLR genes in human oocytes and granulosa cells from primordial and primary ovarian follicles during the hormone-independent phase. A class-comparison study of existing oocyte and granulosa cell RNA sequencing transcriptomes from primordial (n = 539 follicles) and primary (n = 261) follicles collected from three patients was examined. This revealed a distinct expression pattern of TLR3, TLR4 and TLR5 transcripts. Interestingly, the TLR3 protein was differentially detected in both the oocyte and the granulosa cells in primordial and primary follicles, suggesting that TLR3 is maternally contributed both as mRNA and protein. Intracellularly, the compartmentalized TLR3 dot-like staining in the intersection between the oocyte and the surrounding primordial granulosa cells. The TLR4 protein was detected in both primordial and primary follicles, with a notable staining in the granulosa cells. We functionally challenged ovaries in vitro, by polyinosinic:polycytidylic acid (poly I:C) and LPS, known to activate TLR3 and TLR4, respectively, and found a tendency for increased IL-6 production, which was particular evident in the LPS-treated group. Based on the expression of TLRs, it is notably that human primordial and primary follicles express genes that would allow them to respond to innate immune proteins and cytokines during follicle activation.

Possibilities for a specific breeding program for organic dairy production.

Organic dairy production differs from conventional dairy production in many aspects. However, breeding programs for the 2 production systems are the same in most countries. Breeding goals (BG) might be different for the 2 production systems and genotype × environment interaction may exist between organic and conventional dairy production, both of which have an effect on genetic gain in different breeding strategies. Other aspects also need to be considered, such as the application of multiple ovulation and embryo transfer (MOET), which is not allowed in organic dairy production. The general aim of this research was to assess different environment-specific breeding strategies for organic dairy production. The specific aim was to study differences in BG weights and include the effect of genotype × environment interaction, MOET, and the selection of breeding bulls from the conventional environment. Different scenarios were simulated. In the current scenario, the present-day situation for dairy production in Denmark was emulated as much as possible. The BG was based on a conventional dairy production system, MOET was applied in both environments, and conventional bulls could be selected as breeding bulls in the organic environment. Four alternative scenarios were simulated, all with a specific organic BG in the organic breeding program but differences in the usage of MOET and the selection of conventional bulls as breeding bulls. Implementation of a specific BG in organic dairy production slightly increased genetic gain in the aggregate genotype compared with the breeding program that is currently implemented in organic dairy production. Not using embryo transfer or only selecting breeding bulls from the organic environment decreased genetic gain in the aggregate genotype by as much as 24%. However, the use of embryo transfer is debatable because this is not allowed according to current regulations for organic dairy production. Assessing genetic gain on trait levels showed that a significant increase for functional traits was possible compared with the current breeding program in the organic environment without a decrease in genetic gain in the aggregate genotype. This difference on trait level was even more present when selection of conventional bulls as breeding bulls in the organic environment was not possible. This finding is very relevant when breeding for the desired cow in organic dairy production.

Quantification of Tumor Oxygenation Based on FMISO PET: Influence of Location and Oxygen Level of the Well-Oxygenated Reference Region.

Tumor hypoxia may play a fundamental role in determining the radiotherapy outcome for several cancer types. Functional imaging with hypoxia specific radiotracers offers a way to visualize and quantify regions of increased radioresistance, which may benefit from dose escalation strategies. Conversion of the uptake in positron emission tomography (PET) images into oxygenation maps offers a way to quantitatively characterize the microenvironment. However, normalization of the uptake with respect to a well-oxygenated reference volume (WOV), which should be properly selected, is necessary when using conversion functions. This study aims at assessing the sensitivity of quantifying tumor oxygenation based on 18F-fluoromisonidazole (FMISO) PET with respect to the choice of the location and the oxygenation level of the WOV in head and neck cancer patients. WOVs varying not only in shape and location but also with respect to the assigned pO2 level were considered. pO2 values other than the standard 60 mmHg were selected according to the specific tissue type included in the volume. For comparison, the volume which would be considered as hypoxic based on a tissue-to-muscle ratio equal to 1.4 was also delineated, as conventionally done in clinical practice. Hypoxia mapping strategies are found highly sensitive to selection of the location of well-oxygenated region, but also on its assigned oxygenation level, which is crucial for hypoxia-guided adaptive dose escalation strategies.

Twenty years with diabetes and amputations: a retrospective population-based cohort study.

AIM: To investigate the trends in non-traumatic lower limb amputation in people with and without diabetes. METHODS: From the Danish National Patient Register, all people with either type 1 or type 2 diabetes (n = 462 743) as well as a group of people without diabetes from the general population (n = 1 388 886) were identified and separated into three groups based on diabetes type. Among these, 17 265 amputations were identified between 1997 and 2017 and stratified into trans-femoral amputations, trans-tibial amputations and amputations below the ankle using surgical codes. Annual changes were described using least-squares linear regression. RESULTS: The yearly mean decrease in incidence rate of amputation per 1000 person-years was -0.032 [95% CI: -0.062, -0.001], -0.022 [-0.032, -0.012] and -0.006 [-0.009, -0.003] for trans-femoral amputation, -0.072 [-0.093, -0.052], -0.090 [-0.102, -0.078] and -0.015 [-0.016, -0.013] for trans-tibial amputation, and -0.055 [-0.080, -0.020], -0.075 [-0.090, -0.060] and -0.011 [-0.014, -0.007] for amputation below the ankle in people with type 1 diabetes, people with type 2 diabetes and people without diabetes, respectively. CONCLUSIONS: Over recent decades, the incidence of amputation has decreased significantly in people with diabetes and in the general population without diabetes.