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

Qalb ta’ Tifel

Ġabra ta’ poeżiji u proża li tinkludi: Lil Malta ta’ Dun Karm – Fuq il-Fruntiera ta’ Dun Karm – Jum li jibqa’ jissemma ta’ Ġużè Chetcuti – Xbihet Malta ta’ Arthur V. Vassallo – Għanja ta’ Mħabba ta’ Ġużè Chetcuti – Żewġ Poeżijiet tal-kittieb Malti Antonio Calleja ta’ A. C. – Twettiqa ta’ Calleja – Lil Ħabib Għeluq Sninu ta’ Calleja – Taħt is-Salib ta’ R. M. B. – Lil Dun Karm ta’ Fran. Camilleri – Frak mill-weraq ta’ Byron ta’ A. C. – Irrid Immur Ta’ Xbiex ta’ Fran. Camilleri – Il-Cottonera fil-Ħamrun ta’ N. Biancardi – Qalb ta’ Tifel ta’ R. M. B.N/

Mainstreaming nature-based solutions for climate resilient infrastructure in peri-urban sub-Saharan Africa

Despite a growing recognition of the importance of designing, rehabilitating, and maintaining green infrastructure to provide essential ecosystem services and adapt to climate change, many decision makers in sub-Saharan Africa continue to favour engineered solutions and short term economic growth at the expense of natural landscapes and longer term sustainability agendas. Existing green infrastructure is typically maintained in more affluent suburbs, inadvertently perpetuating historic inequalities. This is in part because there remains a lack of fine-grained, comparative evidence on the barriers and enablers to mainstreaming green infrastructure in peri-urban areas. Here, we developed an analytical framework based on a review of 155 studies, screened to include 29 studies in 24 countries. Results suggest eight overarching categories of interconnected barriers to green infrastructure in peri-urban areas. Using a combinatorial mixed method approach, we then surveyed households in nine settlements in drought-prone Windhoek (n=330) and seven settlements in flood-prone Dar es Salaam (n=502) and conducted key informant interviews (n=118). Peri-urban residents in Windhoek and Dar es Salaam indicated 18 forms of green infrastructure and 47 derived ecosystem services. The most frequently reported barriers were financial (40.8%), legal and institutional barriers (35.8%) followed by land use change and spatial trade-offs (33%) and finally ecosystem disservices (30.6%). The most significant barriers in Dar es Salaam were legal and institutional (22.7%) and in Windhoek were land use change and spatial trade-offs (24.4%). At the household level, the principal barrier was financial; at community and municipal levels the main barriers were related to design, performance, and maintenance; while at the national level, the main barriers were legal and institutional. Embracing institutional cultures of adaptive policymaking, equitable partnerships, co-designing futures, integrated landscape management and experimental innovation have potential to scale long term maintenance for urban green infrastructure and foster agency, creativity and more transformative relationships and outcomes

Lill-kelb

Ġabra ta’ poeżiji u proża li tinkludi: Kemm hu kbir il-amar t’Alla! ta’ R. M. B. – Liż-żahrija ta’ V. M. B. – Il-Ġilju u l-warda ta’ P. P. M. B. – Sika trid tiżżewweġ.... ta’ R. M. B. – Qassis ġdid ta’ Dun Karm – Mhux dejjem tiġi żewġ ta’ T. Z. – Il-bandiera tagħna ta’ Mons. Gauci – Il-ħalliel tal-mejtin ta’ N. Biancardi – Lill-kelb ta’ Dun Karm.N/

A dual-channel deep learning approach for lung cavity estimation from hyperpolarized gas and proton MRI

Background Hyperpolarized gas MRI can quantify regional lung ventilation via biomarkers, including the ventilation defect percentage (VDP). VDP is computed from segmentations derived from spatially co-registered functional hyperpolarized gas and structural proton (1H)-MRI. Although acquired at similar lung inflation levels, they are frequently misaligned, requiring a lung cavity estimation (LCE). Recently, single-channel, mono-modal deep learning (DL)-based methods have shown promise for pulmonary image segmentation problems. Multichannel, multimodal approaches may outperform single-channel alternatives. Purpose We hypothesized that a DL-based dual-channel approach, leveraging both 1H-MRI and Xenon-129-MRI (129Xe-MRI), can generate LCEs more accurately than single-channel alternatives. Study Type Retrospective. Population A total of 480 corresponding 1H-MRI and 129Xe-MRI scans from 26 healthy participants (median age [range]: 11 [8–71]; 50% females) and 289 patients with pulmonary pathologies (median age [range]: 47 [6–83]; 51% females) were split into training (422 scans [88%]; 257 participants [82%]) and testing (58 scans [12%]; 58 participants [18%]) sets. Field Strength/Sequence 1.5-T, three-dimensional (3D) spoiled gradient-recalled 1H-MRI and 3D steady-state free-precession 129Xe-MRI. Assessment We developed a multimodal DL approach, integrating 129Xe-MRI and 1H-MRI, in a dual-channel convolutional neural network. We compared this approach to single-channel alternatives using manually edited LCEs as a benchmark. We further assessed a fully automatic DL-based framework to calculate VDPs and compared it to manually generated VDPs. Statistical Tests Friedman tests with post hoc Bonferroni correction for multiple comparisons compared single-channel and dual-channel DL approaches using Dice similarity coefficient (DSC), average boundary Hausdorff distance (average HD), and relative error (XOR) metrics. Bland–Altman analysis and paired t-tests compared manual and DL-generated VDPs. A P value < 0.05 was considered statistically significant. Results The dual-channel approach significantly outperformed single-channel approaches, achieving a median (range) DSC, average HD, and XOR of 0.967 (0.867–0.978), 1.68 mm (37.0–0.778), and 0.066 (0.246–0.045), respectively. DL-generated VDPs were statistically indistinguishable from manually generated VDPs (P = 0.710). Data Conclusion Our dual-channel approach generated LCEs, which could be integrated with ventilated lung segmentations to produce biomarkers such as the VDP without manual intervention. Evidence Level 4. Technical Efficacy Stage 1

Cases of albinism and leucism in amphibians in Italy : new reports

Findings of abnormally pigmented amphibian individuals provide interesting insights on intraspecific phenotypic variability as well as on variation among populations inhabiting different habitats. Amphibian coloration is determined by chromatophores (specific epidermal cells), and a variety of abnormalities related to them have been reported. In this study we reported cases of albinism and leucism in six species of Italian amphibians, including some endemic species. For some taxa, like Hydromantes sarrabusensis, H. flavus, H. supramontis and Bufo viridis, we describe the first observations of albinism and leucism

PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation

Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization and quantification of regional lung ventilation; however, these techniques require specialized equipment and exogenous contrast, limiting clinical adoption. Physiologically-informed techniques to map proton (1H)-MRI ventilation have been proposed. These approaches have demonstrated moderate correlation with hyperpolarized gas MRI. Recently, deep learning (DL) has been used for image synthesis applications, including functional lung image synthesis. Here, we propose a 3D multi-channel convolutional neural network that employs physiologically-informed ventilation mapping and multi-inflation structural 1H-MRI to synthesize 3D ventilation surrogates (PhysVENeT). The dataset comprised paired inspiratory and expiratory 1H-MRI scans and corresponding hyperpolarized gas MRI scans from 170 participants with various pulmonary pathologies. We performed fivefold cross-validation on 150 of these participants and used 20 participants with a previously unseen pathology (post COVID-19) for external validation. Synthetic ventilation surrogates were evaluated using voxel-wise correlation and structural similarity metrics; the proposed PhysVENeT framework significantly outperformed conventional 1H-MRI ventilation mapping and other DL approaches which did not utilize structural imaging and ventilation mapping. PhysVENeT can accurately reflect ventilation defects and exhibits minimal overfitting on external validation data compared to DL approaches that do not integrate physiologically-informed mapping

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