Oocyte retrieval difficulties in women with ovarian endometriomas

Research question: What are the frequency, characteristics and consequences of technical diffiiculties encountered by physicians when carrying out oocyte retrieval in women with ovarian endometriomas? Design: We prospectively recruited women undergoing IVF and compared technical difficulties between women with (n = 56) and without (n = 227) endometriomas. Results: In exposed women, the cyst had to be transfixed in eight cases (14%, 95% CI 7 to 25%) and accidental contamination of the follicular fluid with the endometrioma content was recorded in nine women (16%, 95% CI 8 to 27%). Moreover, follicular aspiration was more frequently incomplete (OR 3.6, 95% CI 1.4 to 9.6). In contrast, the retrievals were not deemed to be more technically difficult by the physicians and the rate of oocytes retrieved per developed follicle did not differ. No pelvic infections or cyst ruptures were recorded (0%, 95% CI 0 to 5%). Conclusions: Oocyte retrieval in women with ovarian endometriomas is more problematic but the magnitude of these increased difficulties is modest

Registration of FC1740 and FC1741 multigerm, rhizomania-resistant sugar beet germplasm with resistance to multiple diseases

FC1740 (Reg No. GP-293, PI 681717) and FC1741 (Reg No. GP-294, PI 681718) sugar beet germplasm (Beta vulgaris L.) were developed by the USDA-ARS at Fort Collins, CO, Salinas, CA, and Kimberly, ID, in cooperation with the Beet Sugar Development Foundation, Denver, CO. These germplasm are diploid, multigerm sugar beet populations in normal cytoplasm, segregating for self-sterility (Sf:SsSs), genetic male sterility (A:aa), and hypocotyl color (R:rr). FC1740 and FC1741 have excellent resistance to rhizomania (Beet necrotic yellow vein virus). FC1740 was selected as homozygous resistant to markers linked to both Rz1 and Rz2 genes for rhizomania resistance. FC1741 was selected as homozygous to the marker linked to the Rz2 gene for resistance. Both germplasm also have resistance to beet curly top (Beet curly top virus) and Fusarium yellows (Fusarium oxysporum Schlechtend.:Fr. f. sp. betae (D. Stewart) W. C. Snyder & H. N. Hans. and other Fusarium spp.), as well as moderate resistance to Aphanomyces root rot (Aphanomyces cochlioides Drechs.). Neither line exhibited resistance to Cercospora leaf spot (Cercospora beticola Sacc.), Rhizoctonia crown and root rot (Rhizoctonia solani Kuhn.) or sugar beet root aphid (Pemphigus spp.). These germplasm provide sources from which to select disease-resistant, multigerm pollinator parents with either or both of the Rz1 and Rz2 sources of rhizomania resistance. Because they are from the same population, they also are useful as controls of known genetic background in comparing entries screened for rhizomania resistance conditioned by Rz1 or Rz2

Behavioral Indices of Neuropsychological Processing Implicated in Moral Domain Reasoning amongst Children and Adolescents

Moral domain theory posits that moral knowledge is organized in separate domains related to moral and socio-conventional rules, with the latter being reliant on a statement made by authority. Domains may be contingent on different neuropsychological processing that may vary with age. Behavioral indices were measured in three age groups, to detect differences in the neuropsychological processing allegedly involved in the evaluation of rule transgressions in different domains. Acceptance of the transgressions was also investigated. Twenty-four children, 32 early adolescents, and 31 adolescents judged acceptability of rule transgressions when an authority figure allowed the transgression. Across age, moral-rule transgressions were less accepted and took significantly longer to be evaluated. In evaluating moral rule scenarios, children had the longest reaction times. Older adolescents took the least amount of time evaluating socio-conventional rule scenarios. Results suggest differences in the neuropsychological processing underlying decision making for moral and socio-conventional domains and that rule comprehension and distinction amongst domains increase by age

Machine-learning Support to Individual Diagnosis of Mild Cognitive Impairment Using Multimodal MRI and Cognitive Assessments

Background: Understanding whether the cognitive profile of a patient indicates mild cognitive impairment (MCI) or performance levels within normality is often a clinical challenge. The use of resting-state functional magnetic resonance imaging (RS-fMRI) and machine learning may represent valid aids in clinical settings for the identification of MCI patients. Methods: Machine-learning models were computed to test the classificatory accuracy of cognitive, volumetric [structural magnetic resonance imaging (sMRI)] and blood oxygen level dependent-connectivity (extracted from RS-fMRI) features, in single-modality and mixed classifiers. Results: The best and most significant classifier was the RS-fMRI+Cognitive mixed classifier (94% accuracy), whereas the worst performing was the sMRI classifier (∼80%). The mixed global (sMRI+RS-fMRI+Cognitive) had a slightly lower accuracy (∼90%), although not statistically different from the mixed RS-fMRI+Cognitive classifier. The most important cognitive features were indices of declarative memory and semantic processing. The crucial volumetric feature was the hippocampus. The RS-fMRI features selected by the algorithms were heavily based on the connectivity of mediotemporal, left temporal, and other neocortical regions. Conclusion: Feature selection was profoundly driven by statistical independence. Some features showed no between-group differences, or showed a trend in either direction. This indicates that clinically relevant brain alterations typical of MCI might be subtle and not inferable from group analysis

Transition of pediatric patients with bronchiectasis to adult medical care in the Northern Territory: A retrospective chart audit

BackgroundBronchiectasis is increasingly being recognized to exist in all settings with a high burden of disease seen in First Nations populations. With increasing numbers of pediatric patients with chronic illnesses surviving into adulthood, there is more awareness on examining the transition from pediatric to adult medical care services. We undertook a retrospective medical chart audit to describe what processes, timeframes, and supports were in place for the transition of young people (≥14 years) with bronchiectasis from pediatric to adult services in the Northern Territory (NT), Australia.MethodsParticipants were identified from a larger prospective study of children investigated for bronchiectasis at the Royal Darwin Hospital, NT, from 2007 to 2022. Young people were included if they were aged ≥14 years on October 1, 2022, with a radiological diagnosis of bronchiectasis on high-resolution computed tomography scan. Electronic and paper-based hospital medical records and electronic records from NT government health clinics and, where possible, general practitioner and other medical service attendance were reviewed. We recorded any written evidence of transition planning and hospital engagement from age ≥14 to 20 years.ResultsOne hundred and two participants were included, 53% were males, and most were First Nations people (95%) and lived in a remote location (90.2%). Nine (8.8%) participants had some form of documented evidence of transition planning or discharge from pediatric services. Twenty-six participants had turned 18 years, yet there was no evidence in the medical records of any young person attending an adult respiratory clinic at the Royal Darwin Hospital or being seen by the adult outreach respiratory clinic.ConclusionThis study demonstrates an important gap in the documentation of delivery of care, and the need to develop an evidence-based transition framework for the transition of young people with bronchiectasis from pediatric to adult medical care services in the NT

Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation:A Multi-center, Multi-vendor, and Multi-disease Study

Background: Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (1H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters.Purpose: Develop a generalizable CNN for lung segmentation in 1H-MRI, robust to pathology, acquisition protocol, vendor, and center.Study type: Retrospective.Population: A total of 809 1H-MRI scans from 258 participants with various pulmonary pathologies (median age (range): 57 (6–85); 42% females) and 31 healthy participants (median age (range): 34 (23–76); 34% females) that were split into training (593 scans (74%); 157 participants (55%)), testing (50 scans (6%); 50 participants (17%)) and external validation (164 scans (20%); 82 participants (28%)) sets.Field Strength/Sequence: 1.5-T and 3-T/3D spoiled-gradient recalled and ultrashort echo-time 1H-MRI.Assessment: 2D and 3D CNNs, trained on single-center, multi-sequence data, and the conventional spatial fuzzy c-means (SFCM) method were compared to manually delineated expert segmentations. Each method was validated on external data originating from several centers. Dice similarity coefficient (DSC), average boundary Hausdorff distance (Average HD), and relative error (XOR) metrics to assess segmentation performance.Statistical Tests: Kruskal–Wallis tests assessed significances of differences between acquisitions in the testing set. Friedman tests with post hoc multiple comparisons assessed differences between the 2D CNN, 3D CNN, and SFCM. Bland–Altman analyses assessed agreement with manually derived lung volumes. A P value of &lt;0.05 was considered statistically significant.Results: The 3D CNN significantly outperformed its 2D analog and SFCM, yielding a median (range) DSC of 0.961 (0.880–0.987), Average HD of 1.63 mm (0.65–5.45) and XOR of 0.079 (0.025–0.240) on the testing set and a DSC of 0.973 (0.866–0.987), Average HD of 1.11 mm (0.47–8.13) and XOR of 0.054 (0.026–0.255) on external validation data.Data Conclusion: The 3D CNN generated accurate 1H-MRI lung segmentations on a heterogenous dataset, demonstrating robustness to disease pathology, sequence, vendor, and center.Evidence Level: 4.Technical Efficacy: Stage 1.</p

Implementable deep learning for multi-sequence proton MRI lung segmentation: a multi-center, multi-vendor, and multi-disease study

Background Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (1H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters. Purpose Develop a generalizable CNN for lung segmentation in 1H-MRI, robust to pathology, acquisition protocol, vendor, and center. Study type Retrospective. Population A total of 809 1H-MRI scans from 258 participants with various pulmonary pathologies (median age (range): 57 (6–85); 42% females) and 31 healthy participants (median age (range): 34 (23–76); 34% females) that were split into training (593 scans (74%); 157 participants (55%)), testing (50 scans (6%); 50 participants (17%)) and external validation (164 scans (20%); 82 participants (28%)) sets. Field Strength/Sequence 1.5-T and 3-T/3D spoiled-gradient recalled and ultrashort echo-time 1H-MRI. Assessment 2D and 3D CNNs, trained on single-center, multi-sequence data, and the conventional spatial fuzzy c-means (SFCM) method were compared to manually delineated expert segmentations. Each method was validated on external data originating from several centers. Dice similarity coefficient (DSC), average boundary Hausdorff distance (Average HD), and relative error (XOR) metrics to assess segmentation performance. Statistical Tests Kruskal–Wallis tests assessed significances of differences between acquisitions in the testing set. Friedman tests with post hoc multiple comparisons assessed differences between the 2D CNN, 3D CNN, and SFCM. Bland–Altman analyses assessed agreement with manually derived lung volumes. A P value of <0.05 was considered statistically significant. Results The 3D CNN significantly outperformed its 2D analog and SFCM, yielding a median (range) DSC of 0.961 (0.880–0.987), Average HD of 1.63 mm (0.65–5.45) and XOR of 0.079 (0.025–0.240) on the testing set and a DSC of 0.973 (0.866–0.987), Average HD of 1.11 mm (0.47–8.13) and XOR of 0.054 (0.026–0.255) on external validation data. Data Conclusion The 3D CNN generated accurate 1H-MRI lung segmentations on a heterogenous dataset, demonstrating robustness to disease pathology, sequence, vendor, and center. Evidence Level 4. Technical Efficacy Stage 1

Longitudinal lung function assessment of patients hospitalised with COVID-19 using 1H and 129Xe lung MRI

BACKGROUND: Microvascular abnormalities and impaired gas transfer have been observed in patients with COVID-19. The progression of pulmonary changes in these patients remains unclear. RESEARCH QUESTION: Do patients hospitalised due to COVID-19 without evidence of architectural distortion on structural imaging show longitudinal improvements in lung function measured using 1H and 129Xe magnetic resonance imaging between 6-52 weeks after hospitalisation? STUDY DESIGN AND METHODS: Patients who were hospitalised due to COVID-19 pneumonia underwent a pulmonary 1H and 129Xe MRI protocol at 6, 12, 25 and 51 weeks after hospital admission in a prospective cohort study between 11/2020 and 02/2022. Imaging protocol: 1H ultra-short echo time, contrast enhanced lung perfusion, 129Xe ventilation, 129Xe diffusion weighted and 129Xe spectroscopic imaging of gas exchange. RESULTS: 9 patients were recruited (57±14 [median±interquartile range] years, 6/9 male). Patients underwent MRI at 6 (N=9), 12 (N=9), 25 (N=6) and 51 (N=8) weeks after hospital admission. Patients with signs of interstitial lung damage were excluded. At 6 weeks, patients demonstrated impaired 129Xe gas transfer (red blood cell to membrane fraction) but lung microstructure was not increased (apparent diffusion coefficient and mean acinar airway dimensions). Minor ventilation abnormalities present in four patients were largely resolved in the 6-25 week period. At 12 weeks, all patients with lung perfusion data (N=6) showed an increase in both pulmonary blood volume and flow when compared to 6 weeks, though this was not statistically significant. At 12 weeks, significant improvements in 129Xe gas transfer were observed compared to 6-week examinations, however 129Xe gas transfer remained abnormally low at weeks 12, 25 and 51. INTERPRETATION: 129Xe gas transfer was impaired up to one year after hospitalisation in patients who were hospitalised due to COVID-19 pneumonia, without evidence of architectural distortion on structural imaging, whereas lung ventilation wa normal at 52 weeks