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Objectives Digital breast tomosynthesis (DBT) increases sensitivity of mammography and is increasingly implemented in breast cancer screening. However, the large volume of images increases the risk of reading errors and reading time. This study aims to investigate whether the accuracy of breast radiologists reading wide-angle DBT increases with the aid of an artificial intelligence (AI) support system. Also, the impact on reading time was assessed and the stand-alone performance of the AI system in the detection of malignancies was compared to the average radiologist. Methods A multi-reader multi-case study was performed with 240 bilateral DBT exams (71 breasts with cancer lesions, 70 breasts with benign findings, 339 normal breasts). Exams were interpreted by 18 radiologists, with and without AI support, providing cancer suspicion scores per breast. Using AI support, radiologists were shown examination-based and region-based cancer likelihood scores. Area under the receiver operating characteristic curve (AUC) and reading time per exam were compared between reading conditions using mixed-models analysis of variance. Results On average, the AUC was higher using AI support (0.863 vs 0.833; p = 0.0025). Using AI support, reading time per DBT exam was reduced ( p < 0.001) from 41 (95% CI = 39–42 s) to 36 s (95% CI = 35– 37 s). The AUC of the stand-alone AI system was non-inferior to the AUC of the average radiologist (+0.007, p = 0.8115). Conclusions Radiologists improved their cancer detection and reduced reading time when evaluating DBT examinations using an AI reading support system. Key Points • Radiologists improved their cancer detection accuracy in digital breast tomosynthesis (DBT) when using an AI system for support, while simultaneously reducing reading time. • The stand-alone breast cancer detection performance of an AI system is non-inferior to the average performance of radiologists for reading digital breast tomosynthesis exams. • The use of an AI support system could make advanced and more reliable imaging techniques more accessible and could allow for more cost-effective breast screening programs with DBT.

Objectives To investigate whether artificial intelligence (AI) can reduce interval cancer in mammography screening. Materials and methods Preceding screening mammograms of 429 consecutive women diagnosed with interval cancer in Southern Sweden between 2013 and 2017 were analysed with a deep learning–based AI system. The system assigns a risk score from 1 to 10. Two experienced breast radiologists reviewed and classified the cases in consensus as true negative, minimal signs or false negative and assessed whether the AI system correctly localised the cancer. The potential reduction of interval cancer was calculated at different risk score thresholds corresponding to approximately 10%, 4% and 1% recall rates. Results A statistically significant correlation between interval cancer classification groups and AI risk score was observed ( p  < .0001). AI scored one in three (143/429) interval cancer with risk score 10, of which 67% (96/143) were either classified as minimal signs or false negative. Of these, 58% (83/143) were correctly located by AI, and could therefore potentially be detected at screening with the aid of AI, resulting in a 19.3% (95% CI 15.9–23.4) reduction of interval cancer. At 4% and 1% recall thresholds, the reduction of interval cancer was 11.2% (95% CI 8.5–14.5) and 4.7% (95% CI 3.0–7.1). The corresponding reduction of interval cancer with grave outcome (women who died or with stage IV disease) at risk score 10 was 23% (8/35; 95% CI 12–39). Conclusion The use of AI in screen reading has the potential to reduce the rate of interval cancer without supplementary screening modalities. Key Points • Retrospective study showed that AI detected 19% of interval cancer at the preceding screening exam that in addition showed at least minimal signs of malignancy. Importantly, these were correctly localised by AI, thus obviating supplementary screening modalities . • AI could potentially reduce a proportion of particularly aggressive interval cancers . • There was a correlation between AI risk score and interval cancer classified as true negative, minimal signs or false negative.

Background: The integration of artificial intelligence (AI) in digital mammography (DM) screening holds promise for early breast cancer detection, potentially enhancing accuracy and efficiency. However, AI performance is not identical to that of human observers. We aimed to identify common morphological image characteristics of true cancers that are missed by either AI or human screening when their interpretations are discrepant. Methods: Twenty-six breast cancer-positive cases, identified from a large retrospective multi-institutional digital mammography dataset based on discrepant AI and human interpretations, were included in a reader study. Ground truth was confirmed by histopathology or ≥1-year follow-up. Fourteen radiologists assessed lesion visibility, morphological features, and likelihood of malignancy. AI performance was evaluated using receiver operating characteristic (ROC) analysis and area under the curve (AUC). The reader study results were analyzed using interobserver agreement measures and descriptive statistics. Results: AI demonstrated high discriminative capability in the full dataset, with AUCs ranging from 0.903 (95% CI: 0.862–0.944) to 0.946 (95% CI: 0.896–0.996). Cancers missed by AI had a significantly smaller median size (9.0 mm, IQR 6.5–12.0) compared to those missed by human readers (21.0 mm, IQR 10.5–41.0) (p = 0.0014). Cancers in discrepant cases were often described as having ‘low visibility’, ‘indistinct margins’, or ‘irregular shape’. Calcifications were observed in 27% of human-missed cancers (42/154) versus 18% of AI-missed cancers (38/210). A very high likelihood of malignancy was assigned in 32.5% (50/154) of human-missed cancers compared to 19.5% (41/210) of AI-missed cancers. Overall inter-rater agreement was poor to fair (<0.40), indicating interpretation challenges of the selected images. Among the human-missed cancers, calcifications were more frequent (42/154; 27%) than among the AI-missed cancers (38/210; 18%) (p = 0.396). Furthermore, 50/154 (32.5%) human-missed cancers were deemed to have a very high likelihood of malignancy, compared to 41/210 (19.5%) AI-missed cancers (p = 0.8). Overall inter-rater agreement on the items assessed during the reader study was poor to fair (<0.40), suggesting that interpretation of the selected images was challenging. Conclusions: Lesions missed by AI were smaller and less often calcified than cancers missed by human readers. Cancers missed by AI tended to show lower levels of suspicion than those missed by human readers. While definitive conclusions are premature, the findings highlight the complementary roles of AI and human readers in mammographic interpretation.

ObjectiveTo uncover the microRNA (miRNA) interactome of the osteoarthritis (OA) pathophysiological process in the cartilage.MethodsWe performed RNA sequencing in 130 samples (n=35 and n=30 pairs for messenger RNA (mRNA) and miRNA, respectively) on macroscopically preserved and lesioned OA cartilage from the same patient and performed differential expression (DE) analysis of miRNA and mRNAs. To build an OA-specific miRNA interactome, a prioritisation scheme was applied based on inverse Pearson’s correlations and inverse DE of miRNAs and mRNAs. Subsequently, these were filtered by those present in predicted (TargetScan/microT-CDS) and/or experimentally validated (miRTarBase/TarBase) public databases. Pathway enrichment analysis was applied to elucidate OA-related pathways likely mediated by miRNA regulatory mechanisms.ResultsWe found 142 miRNAs and 2387 mRNAs to be differentially expressed between lesioned and preserved OA articular cartilage. After applying prioritisation towards likely miRNA-mRNA targets, a regulatory network of 62 miRNAs targeting 238 mRNAs was created. Subsequent pathway enrichment analysis of these mRNAs (or genes) elucidated that genes within the ‘nervous system development’ are likely mediated by miRNA regulatory mechanisms (familywise error=8.4×10−5). Herein NTF3 encodes neurotrophin-3, which controls survival and differentiation of neurons and which is closely related to the nerve growth factor.ConclusionsBy an integrated approach of miRNA and mRNA sequencing data of OA cartilage, an OA miRNA interactome and related pathways were elucidated. Our functional data demonstrated interacting levels at which miRNA affects expression of genes in the cartilage and exemplified the complexity of functionally validating a network of genes that may be targeted by multiple miRNAs.

Important advancements in the development of novel materials and designs have led to the creation of advanced mineral membranes with high packing densities and enhanced competitiveness in relation to polymeric and classic mineral membranes. Olive juice represents an underutilised source of phenolic and secoiridoid antioxidants, in which industrial valorisation is hindered by some technical limitations, particularly the effective removal of suspended solids during processing. The efficiency of two recrystallized silicon carbide-based microfiltration membranes with an equivalent industrial filtration packing density of 782 m2/m3 was evaluated. One of them had nominal pore sizes of 500 nm and was made of mixed oxides and the other had nominal pore sizes of 200 nm and was made of α-Al2O3. The 500 nm membrane demonstrated superior filtration flux and faster processing compared to the 200 nm membrane, though both achieved complete removal of suspended solids. A greater workload of the 500 nm membrane resulted in a progressive irreversible fouling, caused by the smallest-sized suspended particles and macromolecular colloids. Particle size had a greater impact on fouling than particle load. Both membrane treatments induced a spontaneous increase in the concentrations of up to 24 phenolic, secoiridoid and secoiridoidyl phenylethanoid conjugates. This effect can be considered as an additional benefit of the thus clarified olive juices. Further investigations are warranted to elucidate the underlying mechanisms driving these transformations.

ObjectivesTo 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.Methods430 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.ResultsThe 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.ConclusionMultimodal (ABUS + DM) AI systems for detecting breast cancer in women with dense breasts are a potential solution for breast screening in radiologist-scarce regions.Key pointsCombining AI systems from different modalities may increase the overall performance.Combined AI outperforms readers from single modalities for breast cancer detection.Combined AI can be an alternative to radiologists in breast imaging reading.

Cartilage has little intrinsic capacity for repair, so transplantation of exogenous cartilage cells is considered a realistic option for cartilage regeneration. We explored whether human-induced pluripotent stem cells (hiPSCs) could represent such unlimited cell sources for neo-cartilage comparable to human primary articular chondrocytes (hPACs) or human bone marrow-derived mesenchymal stromal cells (hBMSCs). For this, chondroprogenitor cells (hiCPCs) and hiPSC-derived mesenchymal stromal cells (hiMSCs) were generated from two independent hiPSC lines and characterized by morphology, flow cytometry, and differentiation potential. Chondrogenesis was compared to hBMSCs and hPACs by histology, immunohistochemistry, and RT-qPCR, while similarities were estimated based on Pearson correlations using a panel of 20 relevant genes. Our data show successful differentiations of hiPSC into hiMSCs and hiCPCs. Characteristic hBMSC markers were shared between hBMSCs and hiMSCs, with the exception of CD146 and CD45. However, neo-cartilage generated from hiMSCs showed low resemblances when compared to hBMSCs (53%) and hPACs (39%) characterized by lower collagen type 2 and higher collagen type 1 expression. Contrarily, hiCPC neo-cartilage generated neo-cartilage more similar to hPACs (65%), with stronger expression of matrix deposition markers. Our study shows that taking a stepwise approach to generate neo-cartilage from hiPSCs via chondroprogenitor cells results in strong similarities to neo-cartilage of hPACs within 3 weeks following chondrogenesis, making them a potential candidate for regenerative therapies. Contrarily, neo-cartilage deposited by hiMSCs seems more prone to hypertrophic characteristics compared to hPACs. We therefore compared chondrocytes derived from hiMSCs and hiCPCs with hPACs and hBMSCs to outline similarities and differences between their neo-cartilage and establish their potential suitability for regenerative medicine and disease modelling.

The objective of this study was to develop and characterize a novel hyaluronic acid (HA) 3D scaffold integrated with gelatin microparticles for sustained-delivery applications. To achieve this goal, the delivery microparticles were synthesized and thoroughly characterized, focusing on their crosslinking mechanisms (vanillin and genipin), degradation profiles, and release kinetics. Additionally, the cytotoxicity of the system was assessed, and its impact on the cell adhesion and distribution using mouse fibroblasts was examined. The combination of both biomaterials offers a novel platform for the gradual release of various factors encapsulated within the microparticles while simultaneously providing cell protection, support, and controlled factor dispersion due to the HA 3D scaffold matrix. Hence, this system offers a platform for addressing injure repair by continuously releasing specific encapsulated factors for optimal tissue regeneration. Additionally, by leveraging the properties of HA conjugates with small drug molecules, we can enhance the solubility, targeting capabilities, and cellular absorption, as well as prolong the system stability and half-life. As a result, this integrated approach presents a versatile strategy for therapeutic interventions aimed at promoting tissue repair and regeneration.

Purpose To investigate the effect of two extracts obtained from Agaricus bisporus on the mRNA expression of cholesterol-related genes. One of the extracts contained ergosterol and other fungal sterols (SFE) and the other contained β-glucans and fungal sterols (EβG). Methods Firstly, the dietary mixed micelles (DMMs) generated after in vitro digestion of standards and SFE were applied to Caco2 cells. Then, the lower compartment after a Caco2-transport experiment was applied to HepG2 cells. The mRNA expression was assessed in both cell lines by low-density arrays (LDA). Mice received the extracts, ergosterol or control drugs after 4 weeks of a high-cholesterol diet. The lipid profile of plasma, liver and feces was determined. LDA assays were performed in liver and intestines. Results The DMM fraction of SFE up-regulated the LDLR mRNA expression in Caco2 cells. The lower compartment after Caco2-transport experiments up-regulated LDLR and modulated several other lipid-related genes in HepG2 cells. In mice, SFE decreased TC/HDL ratio and reduced hepatic triglycerides paralleled with down-regulation of Dgat1 expression, while EβG did it without transcriptional changes. Addition of SFE or ergosterol induced in jejunum a similar transcriptional response to simvastatin and ezetimibe; they all down-regulated Srebf2 and Nr1h4 (FXR) genes. Conclusion Ergosterol-containing extracts from A. bisporus lowered hepatic triglyceride and modify the mRNA expression of cholesterol-related genes although the transcriptional regulation was unrelated to changes in plasma lipid profile. These extracts may be useful limiting hepatic steatosis and as bioactive ingredients to design novel functional foods preventing lifestyle-related diseases such as non-alcoholic fatty liver disease.

Background Lack of insight into factors that determine purity and quality of human iPSC (hiPSC)-derived neo-cartilage precludes applications of this powerful technology toward regenerative solutions in the clinical setting. Here, we set out to generate methylome-wide landscapes of hiPSC-derived neo-cartilages from different tissues-of-origin and integrated transcriptome-wide data to identify dissimilarities in set points of methylation with associated transcription and the respective pathways in which these genes act. Methods We applied in vitro chondrogenesis using hiPSCs generated from two different tissue sources: skin fibroblasts and articular cartilage. Upon differentiation toward chondrocytes, these are referred to as hFiCs and hCiC, respectively. Genome-wide DNA methylation and RNA sequencing datasets were generated of the hiPSC-derived neo-cartilages, and the epigenetically regulated transcriptome was compared to that of neo-cartilage deposited by human primary articular cartilage (hPAC). Results Methylome-wide landscapes of neo-cartilages of hiPSCs reprogrammed from two different somatic tissues were 85% similar to that of hPACs. By integration of transcriptome-wide data, differences in transcriptionally active CpGs between hCiC relative to hPAC were prioritized. Among the CpG-gene pairs lower expressed in hCiCs relative to hPACs, we identified genes such as MGP, GDF5, and CHAD enriched in closely related pathways and involved in cartilage development that likely mark phenotypic differences in chondrocyte states. Vice versa, among the CpG-gene pairs higher expressed, we identified genes such as KIF1A or NKX2-2 enriched in neurogenic pathways and likely reflecting off target differentiation. Conclusions We did not find significant variation between the neo-cartilages derived from hiPSCs of different tissue sources, suggesting that application of a robust differentiation protocol such as we applied here is more important as compared to the epigenetic memory of the cells of origin. Results of our study could be further exploited to improve quality, purity, and maturity of hiPSC-derived neo-cartilage matrix, ultimately to realize introduction of sustainable, hiPSC-derived neo-cartilage implantation into clinical practice.