Highly Selective Ratiometric Sensors for Pb2+ Based on Luminescent Zn(II)-Coordination Polymers with Thiophenedicarboxylate. Crystal Structures and Spectroscopic Studies.

The development of luminescent coordination polymers for the selective sensing of Pb2+ in water constitutes an active area of research that impacts analytical, environmental, and inorganic chemistry. Herein, two novel water-stable 2D Zn-coordination polymers {[Zn2(H2O)2(tdc)2(bpy)]·(H2O)}n 1 and [Zn(tdc)(tmb)]n 2 (tdc = thiophenedicarboxylate; bpy = 4,4'-bipyridine and tmb = 4,4'-trimethylenebipyridine) were synthesized, structurally determined by single crystal X-ray diffraction, and studied in-depth as luminescent sensors for a series of cations (Ca2+, Mg2+, Mn2+, Fe2+, Co2+, Ni2+, Cu2+, Zn2+ Cd2+, Hg2+ and Pb2+) in 20% aqueous ethanol. These Zn-polymers possess photostability in 20% aqueous ethanol with a strong emission at 410 upon excitation at 330 nm and quantum yields of around Φ = 0.09. Under these conditions, Pb+2 can be efficiently sensed with polymer 2 through a fluorescent ratiometric response with selectivity over common interfering metal ions such as Cu2+, Cd2+ and Hg2+ in the micromolar concentration range (detection limit = 1.78 ± 10 µM). Such selectivity/affinity of Pb2+ over Hg2+ for luminescent chemosensors is still rare. On the basis of spectroscopic tools (1H NMR, far ATR-IR, PXRD), the X-ray crystal structure of 2, and Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopic analysis, the ratiometric fluorescent response is proposed via an efficient metal-ion exchange driven through interactions between thiophenedicarboxylate rings and Pb2+ ions. The use of flexible luminescent Zn-coordination polymers as sensors for selective and direct detection of Pb2+ in aqueous media has been unexplored until now.

Human gut microbiome, diet, and mental disorders.

Diet is one of the most important external factor shaping the composition and metabolic activities of the gut microbiome. The gut microbiome plays a crucial role in host health, including immune system development, nutrients metabolism, and the synthesis of bioactive molecules. In addition, the gut microbiome has been described as critical for the development of several mental disorders. Nutritional psychiatry is an emerging field of research that may provide a link between diet, microbial function, and brain health. In this study, we have reviewed the influence of different diet types, such as Western, Mediterranean, vegetarian, and ketogenic, on the gut microbiota composition and function, and their implication in various neuropsychiatric and psychological disorders.

Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects.

PURPOSE: This review provides an overview of the current state of artificial intelligence (AI) technology for automated detection of breast cancer in digital mammography (DM) and digital breast tomosynthesis (DBT). It aims to discuss the technology, available AI systems, and the challenges faced by AI in breast cancer screening. METHODS: The review examines the development of AI technology in breast cancer detection, focusing on deep learning (DL) techniques and their differences from traditional computer-aided detection (CAD) systems. It discusses data pre-processing, learning paradigms, and the need for independent validation approaches. RESULTS: DL-based AI systems have shown significant improvements in breast cancer detection. They have the potential to enhance screening outcomes, reduce false negatives and positives, and detect subtle abnormalities missed by human observers. However, challenges like the lack of standardised datasets, potential bias in training data, and regulatory approval hinder their widespread adoption. CONCLUSIONS: AI technology has the potential to improve breast cancer screening by increasing accuracy and reducing radiologist workload. DL-based AI systems show promise in enhancing detection performance and eliminating variability among observers. Standardised guidelines and trustworthy AI practices are necessary to ensure fairness, traceability, and robustness. Further research and validation are needed to establish clinical trust in AI. Collaboration between researchers, clinicians, and regulatory bodies is crucial to address challenges and promote AI implementation in breast cancer screening.

Lung Ultrasound Elastography by SWE2D and "Fibrosis-like" Computed Tomography Signs after COVID-19 Pneumonia: A Follow-Up Study.

The aim of this study was to assess the shear wave velocity by LUS elastography (SWE2D) for the evaluation of superficial lung stiffness after COVID-19 pneumonia, according to "fibrosis-like" signs found by Computed Tomography (CT), considering the respiratory function. Seventy-nine adults participated in the study 42 to 353 days from symptom onset. Paired evaluations (SWE2D and CT) were performed along with the assessment of arterial blood gases and spirometry, three times with 100 days in between. During the follow-up and within each evaluation, the SWE2D velocity changed over time (MANOVA, p < 0.05) according to the extent of "fibrosis-like" CT signs by lung lobe (ANOVA, p < 0.05). The variability of the SWE2D velocity was consistently related to the first-second forced expiratory volume and the forced vital capacity (MANCOVA, p < 0.05), which changed over time with no change in blood gases. Covariance was also observed with age and patients' body mass index, the time from symptom onset until hospital admission, and the history of diabetes in those who required intensive care during the acute phase (MANCOVA, p < 0.05). After COVID-19 pneumonia, SWE2D velocity can be related to the extent and regression of "fibrotic-like" involvement of the lung lobes, and it could be a complementary tool in the follow-up after COVID-19 pneumonia.

Grazing livestock move by Lévy walks: Implications for soil health and environment.

Grazing livestock plays an important role in the context of food security, agricultural sustainability and climate change. Understanding how livestock move and interact with their environment may offer new insights on how grazing practices impact soil and ecosystem functions at spatial and temporal scales where knowledge is currently limited. We characterized daily and seasonal grazing patterns using Global Positioning System (GPS) data from two grazing strategies: conventionally- and rotationally-grazed pastures. Livestock movement was consistent with the so-called Lévy walks, and could thus be simulated with Lévy-walk based probability density functions. Our newly introduced "Moovement model" links grazing patterns with soil structure and related functions by coupling animal movement and soil structure dynamics models, allowing to predict spatially-explicit changes in key soil properties. Predicted post-grazing management-specific bulk densities were consistent with field measurements and confirmed that rotational grazing produced similar disturbance as conventional grazing despite hosting higher stock densities. Harnessing information on livestock movement and its impacts in soil structure within a modelling framework can help testing and optimizing grazing strategies for ameliorating their impact on soil health and environment.

Functional annotation and comparative genomics analysis of Balamuthia mandrillaris reveals potential virulence-related genes.

Balamuthia mandrillaris is a pathogenic protozoan that causes a rare but almost always fatal infection of the central nervous system and, in some cases, cutaneous lesions. Currently, the genomic data for this free-living amoeba include the description of several complete mitochondrial genomes. In contrast, two complete genomes with draft quality are available in GenBank, but none of these have a functional annotation. In the present study, the complete genome of B. mandrillaris isolated from a freshwater artificial lagoon was sequenced and assembled, obtaining an assembled genome with better assembly quality parameter values than the currently available genomes. Afterward, the genome mentioned earlier, along with strains V039 and 2046, were subjected to functional annotation. Finally, comparative genomics analysis was performed, and it was found that homologous genes in the core genome potentially involved in the virulence of Acanthamoeba spp. and Trypanosoma cruzi. Moreover, eleven of fifteen genes were identified in the three strains described as potential target genes to develop new treatment approaches for B. mandrillaris infections. These results describe proteins in this protozoan's complete genome and help prioritize which target genes could be used to develop new treatments.

Intermediate Molecular Phenotypes to Identify Genetic Markers of Anthracycline-Induced Cardiotoxicity Risk.

Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex trait with a polygenic component that is mainly unidentified. We propose that levels of intermediate molecular phenotypes (IMPs) in the myocardium associated with histopathological damage could explain CDA susceptibility, so variants of genes encoding these IMPs could identify patients susceptible to this complication. Thus, a genetically heterogeneous cohort of mice (n = 165) generated by backcrossing were treated with doxorubicin and docetaxel. We quantified heart fibrosis using an Ariol slide scanner and intramyocardial levels of IMPs using multiplex bead arrays and QPCR. We identified quantitative trait loci linked to IMPs (ipQTLs) and cdaQTLs via linkage analysis. In three cancer patient cohorts, CDA was quantified using echocardiography or Cardiac Magnetic Resonance. CDA behaves as a complex trait in the mouse cohort. IMP levels in the myocardium were associated with CDA. ipQTLs integrated into genetic models with cdaQTLs account for more CDA phenotypic variation than that explained by cda-QTLs alone. Allelic forms of genes encoding IMPs associated with CDA in mice, including AKT1, MAPK14, MAPK8, STAT3, CAS3, and TP53, are genetic determinants of CDA in patients. Two genetic risk scores for pediatric patients (n = 71) and women with breast cancer (n = 420) were generated using machine-learning Least Absolute Shrinkage and Selection Operator (LASSO) regression. Thus, IMPs associated with heart damage identify genetic markers of CDA risk, thereby allowing more personalized patient management.

Multi-vendor robustness analysis of a commercial artificial intelligence system for breast cancer detection.

Purpose: Population-based screening programs for the early detection of breast cancer have significantly reduced mortality in women, but they are resource intensive in terms of time, cost, and workload and still have limitations mainly due to the use of 2D imaging techniques, which may cause overlapping of tissues, and interobserver variability. Artificial intelligence (AI) systems may be a valuable tool to assist radiologist when reading and classifying mammograms based on the malignancy of the detected lesions. However, there are several factors that can influence the outcome of a mammogram and thus also the detection capability of an AI system. The aim of our work is to analyze the robustness of the diagnostic ability of an AI system designed for breast cancer detection. Approach: Mammograms from a population-based screening program were scored with the AI system. The sensitivity and specificity by means of the area under the receiver operating characteristic (ROC) curve were obtained as a function of the mammography unit manufacturer, demographic characteristics, and several factors that may affect the image quality (age, breast thickness and density, compression applied, beam quality, and delivered dose). Results: The area under the curve (AUC) from the scoring ROC curve was 0.92 (95% confidence interval = 0.89 - 0.95). It showed no dependence with any of the parameters considered, as the differences in the AUC for different interval values were not statistically significant. Conclusion: The results suggest that the AI system analyzed in our work has a robust diagnostic capability, and that its accuracy is independent of the studied parameters.

Microbial Populations in Ruminal Liquid Samples from Young Beefmaster Bulls at Both Extremes of RFI Values.

The gut microbiota is involved in the productivity of beef cattle, but the impact of different analysis strategies on microbial composition is unclear. Ruminal samples were obtained from Beefmaster calves (n = 10) at both extremes of residual feed intake (RFI) values (5 with the lowest and 5 with the highest RFI) from two consecutive days. Samples were processed using two different DNA extraction methods. The V3 and V4 regions of the 16S rRNA gene were amplified using PCR and sequenced with a MiSeq instrument (Illumina). We analyzed 1.6 million 16S sequences from all 40 samples (10 calves, 2 time points, and 2 extraction methods). The abundance of most microbes was significantly different between DNA extraction methods but not between high-efficiency (LRFI) and low-efficiency (HRFI) animals. Exceptions include the genus Succiniclasticum (lower in LRFI, p = 0.0011), and others. Diversity measures and functional predictions were also mostly affected by DNA extraction methods, but some pathways showed significant differences between RFI levels (e.g., methylglyoxal degradation, higher in LRFI, p = 0.006). The results suggest that the abundance of some ruminal microbes is associated with feed efficiency and serves as a cautionary tale for the interpretation of results obtained with a single DNA extraction method.

Intermediate molecular phenotypes to identify genetic markers of anthracycline-induced cardiotoxicity risk.

Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex disease whose polygenic component is mainly unidentified. We propose that levels of intermediate molecular phenotypes in the myocardium associated with histopathological damage could explain CDA susceptibility; so that variants of genes encoding these intermediate molecular phenotypes could identify patients susceptible to this complication. A genetically heterogeneous cohort of mice generated by backcrossing (N = 165) was treated with doxorubicin and docetaxel. Cardiac histopathological damage was measured by fibrosis and cardiomyocyte size by an Ariol slide scanner. We determine intramyocardial levels of intermediate molecular phenotypes of CDA associated with histopathological damage and quantitative trait loci (ipQTLs) linked to them. These ipQTLs seem to contribute to the missing heritability of CDA because they improve the heritability explained by QTL directly linked to CDA (cda-QTLs) through genetic models. Genes encoding these molecular subphenotypes were evaluated as genetic markers of CDA in three cancer patient cohorts (N = 517) whose cardiac damage was quantified by echocardiography or Cardiac Magnetic Resonance. Many SNPs associated with CDA were found using genetic models. LASSO multivariate regression identified two risk score models, one for pediatric cancer patients and the other for women with breast cancer. Molecular intermediate phenotypes associated with heart damage can identify genetic markers of CDA risk, thereby allowing a more personalized patient management. A similar strategy could be applied to identify genetic markers of other complex trait diseases.

Multi-modal artificial intelligence for the combination of automated 3D breast ultrasound and mammograms in a population of women with predominantly dense breasts.

OBJECTIVES: To assess the stand-alone and combined performance of artificial intelligence (AI) detection systems for digital mammography (DM) and automated 3D breast ultrasound (ABUS) in detecting breast cancer in women with dense breasts. METHODS: 430 paired cases of DM and ABUS examinations from a Asian population with dense breasts were retrospectively collected. All cases were analyzed by two AI systems, one for DM exams and one for ABUS exams. A selected subset (n = 152) was read by four radiologists. The performance of AI systems was based on analysis of the area under the receiver operating characteristic curve (AUC). The maximum Youden's index and its associated sensitivity and specificity were also reported for each AI systems. Detection performance of human readers in the subcohort of the reader study was measured in terms of sensitivity and specificity. RESULTS: The performance of the AI systems in a multi-modal setting was significantly better when the weights of AI-DM and AI-ABUS were 0.25 and 0.75, respectively, than each system individually in a single-modal setting (AUC-AI-Multimodal = 0.865; AUC-AI-DM = 0.832, p = 0.026; AUC-AI-ABUS = 0.841, p = 0.041). The maximum Youden's index for AI-Multimodal was 0.707 (sensitivity = 79.4%, specificity = 91.2%). In the subcohort that underwent human reading, the panel of four readers achieved a sensitivity of 93.2% and specificity of 32.7%. AI-multimodal achieves superior or equal sensitivity as single human readers at the same specificity operating points on the ROC curve. CONCLUSION: Multimodal (ABUS + DM) AI systems for detecting breast cancer in women with dense breasts are a potential solution for breast screening in radiologist-scarce regions.

Relationship of persistent lymphocytosis, antibody titers, and proviral load with expression of interleukin-12, interferon-γ, interleukin-2, interleukin-4, interleukin-10, and transforming growth factor-ß in cows infected with bovine leukemia virus from a high-prevalence dairy complex.

Bovine leukemia virus (BLV) subclinical infection promotes persistent lymphocytosis (PL), which is related to susceptibility and progression to lymphoma. Moreover, lymphocyte counts directly correlate with BLV antibody titers and proviral load, and cell immune responses are considered atypical due to immune suppression. In order to determine the relationship of PL, antibody titers, and proviral load with interleukin (IL)-12, interferon (IFN)-γ, IL-2, IL-4, IL-10, and transforming growth factor (TGF)-ß expression in a 3-month interval, 58 cows were selected (30 BLV+ and 28 BLV-) from a high-prevalence dairy herd to complete 3 monthly blood samplings for the assessment of PL, BLV antibody titers, BLV proviral load, and IL-12, IFN-γ, IL-2, IL-4, IL-10, and TGF-ß expression. At sampling conclusion, the BLV-infected cows were grouped according to PL, BLV proviral load, and BLV antibody titers as follows: BLV+PL+ (n = 16) and BLV+PL- (n = 14); high proviral load (HPL) (n = 18) and low proviral load (LPL) (n = 13); high antibody titers (HAT) (n = 17) and low antibody titers (LAT) (n = 14). The BLV+PL+ cows showed significantly higher proviral load and antibody titers than the BLV+PL- group; however, the former suggested spread presumably unrelated to lymphoma outcome, because HPL was observed in PL- cows in the last sampling. Consistent with the data, a higher antibody response strongly indicated BLV susceptibility since it was linked to PL+ occurrence and a cytokine profile compatible with immune suppression. Furthermore, a reversion to lower antibody titers was observed in cows with HPL far ahead of time, most likely due to long-term immune suppression. In addition, high expression of IL-10 and TGF-ß was associated with reduced IL-12, IFN-γ, IL-2, and IL-4 expression alongside PL, HAT, and HPL in BLV-infected cows, suggesting an IL-10- and TGF-ß-induced immune suppression. The IL-10 expression was increasing throughout, implying disease progression, as described. In conclusion, the proliferative expansion of lymphocytes known as PL might enhance a regulatory-rich cell population (Bregs and/or Tregs) that secretes IL-10 and TGF-ß, leading to immune suppression. Further studies must be conducted regarding the types of regulatory cells involved in BLV-induced immune suppression.

L'infection subclinique par le virus de la leucémie bovine (BLV) favorise une lymphocytose persistante (PL), qui est liée à la susceptibilité et à la progression vers le lymphome. De plus, le nombre de lymphocytes est directement corrélé aux titres d'anticorps BLV et à la charge provirale, et les réponses immunitaires cellulaires sont considérées comme atypiques en raison de la suppression immunitaire. Afin de déterminer la relation entre PL, les titres d'anticorps et la charge provirale avec l'interleukine (IL)-12, l'interféron (IFN)-γ, l'IL-2, l'IL-4, l'IL-10 et l'expression du facteur de croissance transformant (TGF)-ß dans un intervalle de 3 mois, 58 vaches ont été sélectionnées (30 BLV+ et 28 BLV−) à partir d'un troupeau laitier à forte prévalence pour compléter trois prélèvements sanguins mensuels pour l'évaluation de PL, des titres d'anticorps BLV, de la charge provirale BLV et l'expression d'IL-12, IFN-γ, d'IL-2, d'IL-4, d'IL-10 et TGF-ß. À la fin de l'échantillonnage, les vaches infectées par le BLV ont été regroupées en fonction du PL, de la charge provirale du BLV et des titres d'anticorps du BLV comme suit : BLV+PL+ (n = 16) et BLV+PL− (n = 14); charge provirale élevée (HPL) (n = 18) et charge provirale faible (LPL) (n = 13); titres d'anticorps élevés (HAT) (n = 17) et titres d'anticorps faibles (LAT) (n = 14). Les vaches BLV+PL+ ont montré une charge provirale et des titres d'anticorps significativement plus élevés que le groupe BLV+PL−; cependant, le premier suggère une propagation vraisemblablement sans rapport avec l'issue du lymphome, car HPL a été observé chez les vaches PL− lors du dernier échantillonnage. Conformément aux données, une réponse anticorps plus élevée indiquait fortement une sensibilité au BLV puisqu'elle était liée à l'apparition de PL+ et à un profil de cytokines compatible avec la suppression immunitaire. De plus, un retour à des titres d'anticorps plus faibles a été observé chez les vaches atteintes de HPL bien avant le temps, probablement en raison d'une immunosuppression à long terme. De plus, une expression élevée d'IL-10 et de TGF-ß était associée à une expression réduite d'IL-12, d'IFN-γ, d'IL-2 et d'IL-4 aux côtés de PL, HAT et HPL chez les vaches infectées par le BLV, suggérant une immunosuppression induite par IL-10 et le TGF-ß. L'expression d'IL-10 augmentait tout au long, impliquant une progression de la maladie, comme décrit. En conclusion, l'expansion proliférative des lymphocytes connus sous le nom de PL pourrait renforcer une population de cellules riches en régulation (Bregs et/ou Tregs) qui sécrète d'IL-10 et du TGF-ß, conduisant à une suppression immunitaire. D'autres études doivent être menées sur les types de cellules régulatrices impliquées dans la suppression immunitaire induite par le BLV.(Traduit par Docteur Serge Messier).

Stabilization of three-dimensional charge order through interplanar orbital hybridization in PrxY1-xBa2Cu3O6+δ.

The shape of 3d-orbitals often governs the electronic and magnetic properties of correlated transition metal oxides. In the superconducting cuprates, the planar confinement of the [Formula: see text] orbital dictates the two-dimensional nature of the unconventional superconductivity and a competing charge order. Achieving orbital-specific control of the electronic structure to allow coupling pathways across adjacent planes would enable direct assessment of the role of dimensionality in the intertwined orders. Using Cu L3 and Pr M5 resonant x-ray scattering and first-principles calculations, we report a highly correlated three-dimensional charge order in Pr-substituted YBa2Cu3O7, where the Pr f-electrons create a direct orbital bridge between CuO2 planes. With this we demonstrate that interplanar orbital engineering can be used to surgically control electronic phases in correlated oxides and other layered materials.

Distribution and Current State of Molecular Genetic Characterization in Pathogenic Free-Living Amoebae.

Free-living amoebae (FLA) are protozoa widely distributed in the environment, found in a great diversity of terrestrial biomes. Some genera of FLA are linked to human infections. The genus Acanthamoeba is currently classified into 23 genotypes (T1-T23), and of these some (T1, T2, T4, T5, T10, T12, and T18) are known to be capable of causing granulomatous amoebic encephalitis (GAE) mainly in immunocompromised patients while other genotypes (T2, T3, T4, T5, T6, T10, T11, T12, and T15) cause Acanthamoeba keratitis mainly in otherwise healthy patients. Meanwhile, Naegleria fowleri is the causative agent of an acute infection called primary amoebic meningoencephalitis (PAM), while Balamuthia mandrillaris, like some Acanthamoeba genotypes, causes GAE, differing from the latter in the description of numerous cases in patients immunocompetent. Finally, other FLA related to the pathologies mentioned above have been reported; Sappinia sp. is responsible for one case of amoebic encephalitis; Vermamoeba vermiformis has been found in cases of ocular damage, and its extraordinary capacity as endocytobiont for microorganisms of public health importance such as Legionella pneumophila, Bacillus anthracis, and Pseudomonas aeruginosa, among others. This review addressed issues related to epidemiology, updating their geographic distribution and cases reported in recent years for pathogenic FLA.

Misleading eosinophil counts in migration-associated malaria: do not miss hidden helminthic co-infections.

BACKGROUND: Lower eosinophil counts observed during acute malaria episodes could hide helminth-related eosinophilia. METHOD: Retrospective observational study with sub-Saharan migrants with imported malaria from May-2007 to May-2020. Absolute eosinophil count was determined upon diagnosis at hospital admission and at least once after clearance of parasitemia. Helminthic co-infections were investigated by searching for stool and urine parasites, serology for Strongyloides spp. and Schistosoma spp., and Knott and/or saponin tests for blood microfilariae. RESULTS: A total of 259 patients were included. Most of them were male (n = 237; 91.5%) and VFR travelers (n = 241; 93.1%). 131 patients (50.6%) were diagnosed with probable schistosomiasis, 15 (5.8%) with confirmed schistosomiasis, 16 (6.2%) with strongyloidiasis, 4 (1.6%) with soil-transmitted helminthiasis, and 4 (1.6%) with filariasis (Mansonella perstans). Prevalence of eosinophilia increased from 2.7% on admission to 32.5% during outpatient follow-up. Eosinophilia did not appear until several weeks after hospital discharge in up to 24% of the confirmed helminthic co-infections and in 61.1% of patients with probable schistosomiasis. Eosinophilia was associated with confirmed schistosomiasis and mansonellosis while 56.2% and 75% of cases with strongyloidiasis and soil-transmitted worms did not present eosinophilia at any time, respectively. CONCLUSIONS: Regardless of the absence of eosinophilia, patients hospitalized because of acute imported malaria might benefit from the screening of the main parasitic diseases, allowing for earlier diagnosis and treatment.

Cyclobutane Pyrimidine Dimer Hyperhotspots as Sensitive Indicators of Keratinocyte UV Exposure†.

The dominant DNA damage generated by UV exposure is the cyclobutane pyrimidine dimer (CPD), which alters skin cell physiology and induces cell death and mutation. Genome-wide nucleotide-resolution analysis of CPDs in melanocytes and fibroblasts has identified "CPD hyperhotspots", pyrimidine-pyrimidine sites hundreds of fold more susceptible to the generation of CPDs than the genomic average. Identifying hyperhotspots in keratinocytes could enable measuring individual past UV exposure in small skin samples and predicting future skin cancer risk. We therefore exposed neonatal human epidermal keratinocytes to narrowband UVB and quantified CPDs using the adductSeq high-throughput DNA sequencing method. Keratinocytes contained thousands of CPD hyperhotspots, with a UVB-sensitivity up to 550 fold greater than the genomic average. As with melanocytes, the most sensitive sites were located in promoter regions at ETS-family transcription factor binding sequence motifs, near RNA processing genes. Moreover, they lay at sequence motifs bound to ETS1 in CpG islands. These genes were specifically upregulated in skin and the CPD hyperhotspots were mutated in a fraction of keratinocyte cancers. Crucially for their biological importance and practical application, CPD hyperhotspot locations and UV-sensitivity ranking demonstrated high reproducibility across experiments and across skin donors. CPD hyperhotspots are therefore sensitive indicators of UV exposure.

Patient-derived heterogeneous breast phantoms for advanced dosimetry in mammography and tomosynthesis.

BACKGROUND: Understanding the magnitude and variability of the radiation dose absorbed by the breast fibroglandular tissue during mammography and digital breast tomosynthesis (DBT) is of paramount importance to assess risks versus benefits. Although homogeneous breast models have been proposed and used for decades for this purpose, they do not accurately reflect the actual heterogeneous distribution of the fibroglandular tissue in the breast, leading to biases in the estimation of dose from these modalities. PURPOSE: To develop and validate a method to generate patient-derived, heterogeneous digital breast phantoms for breast dosimetry in mammography and DBT. METHODS: The proposed phantoms were developed starting from patient-based models of compressed breasts, generated for multiple thicknesses and representing the two standard views acquired in mammography and DBT, that is, cranio-caudal (CC) and medio-lateral-oblique (MLO). Internally, the breast phantoms were defined as consisting of an adipose/fibroglandular tissue mixture, with a nonspatially uniform relative concentration. The parenchyma distributions were obtained from a previously described model based on patient breast computed tomography data that underwent simulated compression. Following these distributions, phantoms with any glandular fraction (1%-100%) and breast thickness (12-125 mm) can be generated, for both views. The phantoms were validated, in terms of their accuracy for average normalized glandular dose (Dg N) estimation across samples of patient breasts, using 88 patient-specific phantoms involving actual patient distribution of the fibroglandular tissue in the breast, and compared to that obtained using a homogeneous model similar to those currently used for breast dosimetry. RESULTS: The average Dg N estimated for the proposed phantoms was concordant with that absorbed by the patient-specific phantoms to within 5% (CC) and 4% (MLO). These Dg N estimates were over 30% lower than those estimated with the homogeneous models, which overestimated the average Dg N by 43% (CC), and 32% (MLO) compared to the patient-specific phantoms. CONCLUSIONS: The developed phantoms can be used for dosimetry simulations to improve the accuracy of dose estimates in mammography and DBT.

An Artificial Intelligence-based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload.

Background Developments in artificial intelligence (AI) systems to assist radiologists in reading mammograms could improve breast cancer screening efficiency. Purpose To investigate whether an AI system could detect normal, moderate-risk, and suspicious mammograms in a screening sample to safely reduce radiologist workload and evaluate across Breast Imaging Reporting and Data System (BI-RADS) densities. Materials and Methods This retrospective simulation study analyzed mammographic examination data consecutively collected from January 2014 to December 2015 in the Danish Capital Region breast cancer screening program. All mammograms were scored from 0 to 10, representing the risk of malignancy, using an AI tool. During simulation, normal mammograms (score < 5) would be excluded from radiologist reading and suspicious mammograms (score > recall threshold [RT]) would be recalled. Two radiologists read the remaining mammograms. The RT was fitted using another independent cohort (same institution) by matching to the radiologist sensitivity. This protocol was further applied to each BI-RADS density. Screening outcomes were measured using the sensitivity, specificity, workload, and false-positive rate. The AI-based screening was tested for noninferiority sensitivity compared with radiologist screening using the Farrington-Manning test. Specificities were compared using the McNemar test. Results The study sample comprised 114 421 screenings for breast cancer in 114 421 women, resulting in 791 screen-detected, 327 interval, and 1473 long-term cancers and 2107 false-positive screenings. The mean age of the women was 59 years ± 6 (SD). The AI-based screening sensitivity was 69.7% (779 of 1118; 95% CI: 66.9, 72.4) and was noninferior (P = .02) to the radiologist screening sensitivity of 70.8% (791 of 1118; 95% CI: 68.0, 73.5). The AI-based screening specificity was 98.6% (111 725 of 113 303; 95% CI: 98.5, 98.7), which was higher (P < .001) than the radiologist specificity of 98.1% (111 196 of 113 303; 95% CI: 98.1, 98.2). The radiologist workload was reduced by 62.6% (71 585 of 114 421), and 25.1% (529 of 2107) of false-positive screenings were avoided. Screening results were consistent across BI-RADS densities, although not significantly so for sensitivity. Conclusion Artificial intelligence (AI)-based screening could detect normal, moderate-risk, and suspicious mammograms in a breast cancer screening program, which may reduce the radiologist workload. AI-based screening performed consistently across breast densities. © RSNA, 2022 Online supplemental material is available for this article.

Mutation in the CCAL1 locus accounts for bidirectional process of human subchondral bone turnover and cartilage mineralization.

OBJECTIVES: To study the mechanism by which the readthrough mutation in TNFRSF11B, encoding osteoprotegerin (OPG) with additional 19 amino acids at its C-terminus (OPG-XL), causes the characteristic bidirectional phenotype of subchondral bone turnover accompanied by cartilage mineralization in chondrocalcinosis patients. METHODS: OPG-XL was studied by human induced pluripotent stem cells expressing OPG-XL and two isogenic CRISPR/Cas9-corrected controls in cartilage and bone organoids. Osteoclastogenesis was studied with monocytes from OPG-XL carriers and matched healthy controls followed by gene expression characterization. Dual energy X-ray absorptiometry scans and MRI analyses were used to characterize the phenotype of carriers and non-carriers of the mutation. RESULTS: Human OPG-XL carriers relative to sex- and age-matched controls showed, after an initial delay, large active osteoclasts with high number of nuclei. By employing hiPSCs expressing OPG-XL and isogenic CRISPR/Cas9-corrected controls to established cartilage and bone organoids, we demonstrated that expression of OPG-XL resulted in excessive fibrosis in cartilage and high mineralization in bone accompanied by marked downregulation of MGP, encoding matrix Gla protein, and upregulation of DIO2, encoding type 2 deiodinase, gene expression, respectively. CONCLUSIONS: The readthrough mutation at CCAL1 locus in TNFRSF11B identifies an unknown role for OPG-XL in subchondral bone turnover and cartilage mineralization in humans via DIO2 and MGP functions. Previously, OPG-XL was shown to affect binding between RANKL and heparan sulphate (HS) resulting in loss of immobilized OPG-XL. Therefore, effects may be triggered by deficiency in the immobilization of OPG-XL Since the characteristic bidirectional pathophysiology of articular cartilage calcification accompanied by low subchondral bone mineralization is also a hallmark of OA pathophysiology, our results are likely extrapolated to common arthropathies.

Interval Cancer Detection Using a Neural Network and Breast Density in Women with Negative Screening Mammograms.

Background Inclusion of mammographic breast density (BD) in breast cancer risk models improves accuracy, but accuracy remains modest. Interval cancer (IC) risk prediction may be improved by combining assessments of BD and an artificial intelligence (AI) cancer detection system. Purpose To evaluate the performance of a neural network (NN)-based model that combines the assessments of BD and an AI system in the prediction of risk of developing IC among women with negative screening mammography results. Materials and Methods This retrospective nested case-control study performed with screening examinations included women who developed IC and women with normal follow-up findings (from January 2011 to January 2015). An AI cancer detection system analyzed all studies yielding a score of 1-10, representing increasing likelihood of malignancy. BD was automatically computed using publicly available software. An NN model was trained by combining the AI score and BD using 10-fold cross-validation. Bootstrap analysis was used to calculate the area under the receiver operating characteristic curve (AUC), sensitivity at 90% specificity, and 95% CIs of the AI, BD, and NN models. Results A total of 2222 women with IC and 4661 women in the control group were included (mean age, 61 years; age range, 49-76 years). AUC of the NN model was 0.79 (95% CI: 0.77,0.81), which was higher than AUC of the AI cancer detection system or BD alone (AUC, 0.73 [95% CI: 0.71, 0.76] and 0.69 [95% CI: 0.67, 0.71], respectively; P < .001 for both). At 90% specificity, the NN model had a sensitivity of 50.9% (339 of 666 women; 95% CI: 45.2, 56.3) for prediction of IC, which was higher than that of the AI system (37.5%; 250 of 666 women; 95% CI: 33.0, 43.7; P < .001) or BD percentage alone (22.4%; 149 of 666 women; 95% CI: 17.9, 28.5; P < .001). Conclusion The combined assessment of an artificial intelligence detection system and breast density measurements enabled identification of a larger proportion of women who would develop interval cancer compared with either method alone. Published under a CC BY 4.0 license.