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.

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.

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.

Stand-Alone Use of Artificial Intelligence for Digital Mammography and Digital Breast Tomosynthesis Screening: A Retrospective Evaluation.

Background Use of artificial intelligence (AI) as a stand-alone reader for digital mammography (DM) or digital breast tomosynthesis (DBT) breast screening could ease radiologists' workload while maintaining quality. Purpose To retrospectively evaluate the stand-alone performance of an AI system as an independent reader of DM and DBT screening examinations. Materials and Methods Consecutive screening-paired and independently read DM and DBT images acquired between January 2015 and December 2016 were retrospectively collected from the Tomosynthesis Cordoba Screening Trial. An AI system computed a cancer risk score (range, 1-100) for DM and DBT examinations independently. AI stand-alone performance was measured using the area under the receiver operating characteristic curve (AUC) and sensitivity and recall rate at different operating points selected to have noninferior sensitivity compared with the human readings (noninferiority margin, 5%). The recall rate of AI and the human readings were compared using a McNemar test. Results A total of 15 999 DM and DBT examinations (113 breast cancers, including 98 screen-detected and 15 interval cancers) from 15 998 women (mean age, 58 years ± 6 [standard deviation]) were evaluated. AI achieved an AUC of 0.93 (95% CI: 0.89, 0.96) for DM and 0.94 (95% CI: 0.91, 0.97) for DBT. For DM, AI achieved noninferior sensitivity as a single (58.4%; 66 of 113; 95% CI: 49.2, 67.1) or double (67.3%; 76 of 113; 95% CI: 58.2, 75.2) reader, with a reduction in recall rate (P < .001) of up to 2% (95% CI: -2.4, -1.6). For DBT, AI achieved noninferior sensitivity as a single (77%; 87 of 113; 95% CI: 68.4, 83.8) or double (81.4%; 92 of 113; 95% CI: 73.3, 87.5) reader, but with a higher recall rate (P < .001) of up to 12.3% (95% CI: 11.7, 12.9). Conclusion Artificial intelligence could replace radiologists' readings in breast screening, achieving a noninferior sensitivity, with a lower recall rate for digital mammography but a higher recall rate for digital breast tomosynthesis. Published under a CC BY 4.0 license. See also the editorial by Fuchsjäger and Adelsmayr in this issue.

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.

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.

The role of TNFRSF11B in development of osteoarthritic cartilage.

OBJECTIVES: OA is a complex genetic disease with different risk factors contributing to its development. One of the genes, TNFRSF11B, previously identified with gain-of-function mutation in a family with early-onset OA with chondrocalcinosis, is among the highest upregulated genes in lesioned OA cartilage (RAAK-study). Here, we determined the role of TNFRSF11B overexpression in development of OA. METHODS: Human primary articular chondrocytes (9 donors RAAK study) were transduced using lentiviral particles with or without TNFRSF11B. Cells were cultured for 1 week in a 3 D in-vitro chondrogenic model. TNFRSF11B overexpression was confirmed by RT-qPCR, immunohistochemistry and ELISA. Effects of TNFRSF11B overexpression on cartilage matrix deposition, matrix mineralization, and genes highly correlated to TNFRSF11B in RNA-sequencing dataset (r >0.75) were determined by RT-qPCR. Additionally, glycosaminoglycans and collagen deposition were visualized with Alcian blue staining and immunohistochemistry (COL1 and COL2). RESULTS: Overexpression of TNFRSF11B resulted in strong upregulation of MMP13, COL2A1 and COL1A1. Likewise, mineralization and osteoblast characteristic markers RUNX2, ASPN and OGN showed a consistent increase. Among 30 genes highly correlated to TNFRSF11B, expression of only eight changed significantly, with BMP6 showing the highest increase (9-fold) while expression of RANK and RANKL remained unchanged indicating previously unknown downstream pathways of TNFRSF11B in cartilage. CONCLUSION: Results of our 3D in vitro chondrogenesis model indicate that upregulation of TNFRSF11B in lesioned OA cartilage may act as a direct driving factor for chondrocyte to osteoblast transition observed in OA pathophysiology. This transition does not appear to act via the OPG/RANK/RANKL triad common in bone remodeling.

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.

Using deep learning to assist readers during the arbitration process: a lesion-based retrospective evaluation of breast cancer screening performance.

OBJECTIVES: To evaluate if artificial intelligence (AI) can discriminate recalled benign from recalled malignant mammographic screening abnormalities to improve screening performance. METHODS: A total of 2257 full-field digital mammography screening examinations, obtained 2011-2013, of women aged 50-69 years which were recalled for further assessment of 295 malignant out of 305 truly malignant lesions and 2289 benign lesions after independent double-reading with arbitration, were included in this retrospective study. A deep learning AI system was used to obtain a score (0-95) for each recalled lesion, representing the likelihood of breast cancer. The sensitivity on the lesion level and the proportion of women without false-positive ratings (non-FPR) resulting under AI were estimated as a function of the classification cutoff and compared to that of human readers. RESULTS: Using a cutoff of 1, AI decreased the proportion of women with false-positives from 89.9 to 62.0%, non-FPR 11.1% vs. 38.0% (difference 26.9%, 95% confidence interval 25.1-28.8%; p < .001), preventing 30.1% of reader-induced false-positive recalls, while reducing sensitivity from 96.7 to 91.1% (5.6%, 3.1-8.0%) as compared to human reading. The positive predictive value of recall (PPV-1) increased from 12.8 to 16.5% (3.7%, 3.5-4.0%). In women with mass-related lesions (n = 900), the non-FPR was 14.2% for humans vs. 36.7% for AI (22.4%, 19.8-25.3%) at a sensitivity of 98.5% vs. 97.1% (1.5%, 0-3.5%). CONCLUSION: The application of AI during consensus conference might especially help readers to reduce false-positive recalls of masses at the expense of a small sensitivity reduction. Prospective studies are needed to further evaluate the screening benefit of AI in practice. KEY POINTS: • Integrating the use of artificial intelligence in the arbitration process reduces benign recalls and increases the positive predictive value of recall at the expense of some sensitivity loss. • Application of the artificial intelligence system to aid the decision to recall a woman seems particularly beneficial for masses, where the system reaches comparable sensitivity to that of the readers, but with considerably reduced false-positives. • About one-fourth of all recalled malignant lesions are not automatically marked by the system such that their evaluation (AI score) must be retrieved manually by the reader. A thorough reading of screening mammograms by readers to identify suspicious lesions therefore remains mandatory.

AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation.

Background The workflow of breast cancer screening programs could be improved given the high workload and the high number of false-positive and false-negative assessments. Purpose To evaluate if using an artificial intelligence (AI) system could reduce workload without reducing cancer detection in breast cancer screening with digital mammography (DM) or digital breast tomosynthesis (DBT). Materials and Methods Consecutive screening-paired and independently read DM and DBT images acquired from January 2015 to December 2016 were retrospectively collected from the Córdoba Tomosynthesis Screening Trial. The original reading settings were single or double reading of DM or DBT images. An AI system computed a cancer risk score for DM and DBT examinations independently. Each original setting was compared with a simulated autonomous AI triaging strategy (the least suspicious examinations for AI are not human-read; the rest are read in the same setting as the original, and examinations not recalled by radiologists but graded as very suspicious by AI are recalled) in terms of workload, sensitivity, and recall rate. The McNemar test with Bonferroni correction was used for statistical analysis. Results A total of 15 987 DM and DBT examinations (which included 98 screening-detected and 15 interval cancers) from 15 986 women (mean age ± standard deviation, 58 years ± 6) were evaluated. In comparison with double reading of DBT images (568 hours needed, 92 of 113 cancers detected, 706 recalls in 15 987 examinations), AI with DBT would result in 72.5% less workload (P < .001, 156 hours needed), noninferior sensitivity (95 of 113 cancers detected, P = .38), and 16.7% lower recall rate (P < .001, 588 recalls in 15 987 examinations). Similar results were obtained for AI with DM. In comparison with the original double reading of DM images (222 hours needed, 76 of 113 cancers detected, 807 recalls in 15 987 examinations), AI with DBT would result in 29.7% less workload (P < .001), 25.0% higher sensitivity (P < .001), and 27.1% lower recall rate (P < .001). Conclusion Digital mammography and digital breast tomosynthesis screening strategies based on artificial intelligence systems could reduce workload up to 70%. Published under a CC BY 4.0 license.

Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis.

Background The high volume of data in digital breast tomosynthesis (DBT) and the lack of agreement on how to best implement it in screening programs makes its use challenging. Purpose To compare radiologist performance when reading single-view wide-angle DBT images with and without an artificial intelligence (AI) system for decision and navigation support. Materials and Methods A retrospective observer study was performed with bilateral mediolateral oblique examinations and corresponding synthetic two-dimensional images acquired between June 2016 and February 2018 with a wide-angle DBT system. Fourteen breast screening radiologists interpreted 190 DBT examinations (90 normal, 26 with benign findings, and 74 with malignant findings), with the reference standard being verified by using histopathologic analysis or at least 1 year of follow-up. Reading was performed in two sessions, separated by at least 4 weeks, with a random mix of examinations being read with and without AI decision and navigation support. Forced Breast Imaging Reporting and Data System (categories 1-5) and level of suspicion (1-100) scores were given per breast by each reader. The area under the receiver operating characteristic curve (AUC) and the sensitivity and specificity were compared between conditions by using the public-domain iMRMC software. The average reading times were compared by using the Wilcoxon signed rank test. Results The 190 women had a median age of 54 years (range, 48-63 years). The examination-based reader-averaged AUC was higher when interpreting results with AI support than when reading unaided (0.88 [95% CI: 0.84, 0.92] vs 0.85 [95% CI: 0.80, 0.89], respectively; P = .01). The average sensitivity increased with AI support (64 of 74, 86% [95% CI: 80%, 92%] vs 60 of 74, 81% [95% CI: 74%, 88%]; P = .006), whereas no differences in the specificity (85 of 116, 73.3% [95% CI: 65%, 81%] vs 83 of 116, 71.6% [95% CI: 65%, 78%]; P = .48) or reading time (48 seconds vs 45 seconds; P = .35) were detected. Conclusion Using a single-view digital breast tomosynthesis (DBT) and artificial intelligence setup could allow for a more effective screening program with higher performance, especially in terms of an increase in cancers detected, than using single-view DBT alone. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Chan and Helvie in this issue.

Cartilage from human-induced pluripotent stem cells: comparison with neo-cartilage from chondrocytes and bone marrow mesenchymal stromal cells.

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.

Impact of exercise training after bariatric surgery on cardiometabolic risk factors: a systematic review and meta-analysis of controlled trials.

The purpose of this systematic review was to provide updated evidence synthesis of the effectiveness of exercise training in patients with obesity undergoing bariatric surgery to improve cardio-metabolic risk. We systematically searched the MEDLINE, EMBASE, Scopus, Cochrane, and Web of Science databases. The studies selected were those in which an exercise-based intervention was performed after bariatric surgery, a control group was present, and at least one of the following outcomes was investigated: VO2max or VO2peak, resting heart rate (RHR), blood pressure, lipid profile, glucose, and insulin. The study quality was assessed using the PEDro scale and the data were meta-analyzed with a random effects model, comparing control groups to intervention groups using standardized measurements. Twenty articles were included in the systematic review and fourteen (70%) in the meta-analysis. Significant differences were observed between the control and intervention groups (always in favor of exercise) for absolute VO2max / VO2peak (ES = 0.317; 95% CI = 0.065, 0.569; p = 0.014), VO2max / peak relative to body weight (ES = 0.673; 95% CI = 0.287, 1.060; p = 0.001), HDL cholesterol (ES = 0.22; 95% CI = 0.009, 0.430; p = 0.041) and RHR (ES = -0.438; 95% CI = -0.753, -0.022; p = 0.007). No effects were observed for either systolic or diastolic blood pressure. Exercise training for patients undergoing bariatric surgery appears to be effective in improving absolute and relative VO2max / VO2peak, HDL cholesterol and reducing the RHR. More intervention studies using (better) exercise interventions are needed before discarding their effects on other cardiometabolic risk factors. This systematic review and meta-analysis has been registered in Prospero (CRD42020153398).

Can artificial intelligence reduce the interval cancer rate in mammography screening?

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.

Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study.

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.

Correlation between retinal nerve fibre layer thickness and white matter lesions in Alzheimer's disease.

OBJECTIVES: Early diagnosis in Alzheimer's disease (AD) is crucial in order to implement new therapeutic strategies. The retina is embryologically related to the brain. Thus, the possible usefulness of optical coherence tomography (OCT) in the early detection of AD is currently being studied. Our aim was to study the relationship between retinal nerve fiber layer (RNFL) thickness and AD. METHODS: We undertook an observational, analytical, cross-sectional study with consecutive sampling of 32 patients with AD or mild cognitive impairment and a group of healthy controls (C). The total number of eyes studied was 64. An ophthalmological and a comprehensive neuropsychological evaluation were performed in all participants. Quantification of white matter lesions and study of atrophy of the hippocampus by cerebral magnetic resonance were also performed. RESULTS: We observed a significant linear trend towards a thinning of RNFL as the degree of cognitive deterioration increased, in the superior and temporal quadrants of the retina. A significant correlation was also noted between the mean thickness of the RNFL of the left temporal quadrant and occipital white matter lesions (r = -0.579, p = 0.038). CONCLUSIONS: OCT could be a safe, rapid noninvasive tool providing useful biomarkers in the early detection of cognitive deterioration and AD.

3D-printed anatomical models of the cystic duct and its variants, a low-cost solution for an in-house built simulator for laparoscopic surgery training.

OBJECTIVES: To explore a method to create affordable anatomical models of the biliary tree that are adequate for training laparoscopic cholecystectomy with an in-house built simulator. METHODS: We used a fused deposition modeling 3D printer to create molds of Acrylonitrile Butadiene Styrene (ABS) from Digital Imaging and Communication on Medicine (DICOM) images, and the molds were filled with silicone rubber. Thirteen surgeons with 4-5-year experience in the procedure evaluated the molds using a low-cost in-house built simulator utilizing a 5-point Likert-type scale. RESULTS: Molds produced through this method had a consistent anatomical appearance and overall realism that evaluators agreed or definitely agreed (4.5/5). Evaluators agreed on recommending the mold for resident surgical training. CONCLUSIONS: 3D-printed molds created through this method can be applied to create affordable high-quality educational anatomical models of the biliary tree for training laparoscopic cholecystectomy.

Loss assessment in random crystal polarity gallium phosphide microdisks grown on silicon.

III-V semiconductors grown on silicon recently appeared as a promising platform to decrease the cost of photonic components and circuits. For nonlinear optics, specific features of the III-V crystal arising from the growth on the nonpolar Si substrate and called antiphase domains (APDs) offer a unique way to engineer the second-order properties of the semiconductor compound. Here we demonstrate the fabrication of microdisk resonators at the interface between a gallium-phosphide layer and its silicon substrate. The analysis of the whispering gallery mode quality factors in the devices allows the quantitative assessment of losses induced by a controlled distribution of APDs in the GaP layer and demonstrates the relevance of such a platform for the development of polarity-engineered III-V nonlinear photonic devices on silicon.

A retrospective study of DNA prevalence of porcine parvoviruses in Mexico and its relationship with porcine circovirus associated disease.

Worldwide, many emerging porcine parvoviruses (PPVs) have been linked to porcine circovirus-2 (PCV2) associated disease (PCVAD), which includes post-weaning multi-systemic wasting syndrome (PMWS), PCV2-related reproductive failure (PCV2-RF), as well as other syndromes. To determine the DNA prevalence of PPVs and their relationship with PMWS and PCV2-RF in Mexico, 170 formalin-fixed paraffin-embedded tissues were selected from archival collections to detect PPVs using a nested polymerase chain reaction. The tissues were composed of 50 PMWS cases, 20 age-matched tissues from healthy pigs, 56 PCV2-related reproductive failure (PCV2+ -RF) cases, and 44 PCV2- -RF cases. Overall, PPV2 and PPV6 were the most prevalent species (90.0% and 74.7%, respectively). In 8-11 week old pigs, the highest prevalence was for PPV6 and PPV3. Concerning reproductive failure, the PCV2-affected farms had a significantly higher prevalence for PPV6 (61.6%) and PPV5 (36.4%) than the PCV2-unaffected farms (35.0% and 5.0%, respectively). The concurrent infection rate was high, being significant for PPV2/PPV4 and PPV1/PPV5 within the PMWS cases and for PPV6/PPV5 among the PCV2+ -RF tissues. PPV5 showed a significant relationship with PMWS, whereas PPV5 and PPV6 were significant for PCVAD. The prevalence and coinfection rate of PPVs in Mexico were markedly higher than that described in other countries, denoting that PPV5 and PPV6 might have a potential role in PCVAD in Mexico. It is concluded that it is likely that the density population of pigs in Mexico is contributing to high PPV inter-species and PCV2 coinfections which might lead to a different pathogenic outcome.

Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System.

Purpose To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods An enriched retrospective, fully crossed, multireader, multicase, HIPAA-compliant study was performed. Screening digital mammographic examinations from 240 women (median age, 62 years; range, 39-89 years) performed between 2013 and 2017 were included. The 240 examinations (100 showing cancers, 40 leading to false-positive recalls, 100 normal) were interpreted by 14 Mammography Quality Standards Act-qualified radiologists, once with and once without AI support. The readers provided a Breast Imaging Reporting and Data System score and probability of malignancy. AI support provided radiologists with interactive decision support (clicking on a breast region yields a local cancer likelihood score), traditional lesion markers for computer-detected abnormalities, and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), specificity and sensitivity, and reading time were compared between conditions by using mixed-models analysis dof variance and generalized linear models for multiple repeated measurements. Results On average, the AUC was higher with AI support than with unaided reading (0.89 vs 0.87, respectively; P = .002). Sensitivity increased with AI support (86% [86 of 100] vs 83% [83 of 100]; P = .046), whereas specificity trended toward improvement (79% [111 of 140]) vs 77% [108 of 140]; P = .06). Reading time per case was similar (unaided, 146 seconds; supported by AI, 149 seconds; P = .15). The AUC with the AI system alone was similar to the average AUC of the radiologists (0.89 vs 0.87). Conclusion Radiologists improved their cancer detection at mammography when using an artificial intelligence system for support, without requiring additional reading time. Published under a CC BY 4.0 license. See also the editorial by Bahl in this issue.