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Purpose: This study aimed to identify a marker for freezability and in vitro fertility of sperm samples before freezing., Methods: Semen was collected from nine Nelore bulls; half of the ejaculate was used for seminal plasma cell-free DNA (cfDNA) quantification, and the other half was cryopreserved. Evaluation of sperm movement using computer-assisted semen analysis and plasma membrane integrity and stability, acrosomal integrity, apoptosis, and mitochondrial potential using flow cytometry were performed on fresh and frozen/thawed semen at 0, 3, 6, and 12 h after thawing. Frozen/thawed sperm was also used for in vitro embryo production. cfDNA was extracted from each bull, and the total DNA and number of cell-free mitochondrial DNA (cfmtDNA) copies were quantified. Semen from each animal was used for IVF, and cleavage, blastocyst formation, and cell counts were evaluated., Results: Two groups were formed and compared based on the concentrations of cfDNA and cfmDNA present: low-cfDNA and high-cfDNA and low-cfmtDNA and high-cfmtDNA. Up to 12 h post-thawing, there were no differences between the groups in the majority of the sperm parameters evaluated. Cleavage, day 6 and 7 blastocyst rates, and the number of cells were higher in the high cfDNA group than in the low cfDNA group. Similar results were observed for cfmtDNA, except for the number of cells, which was similar between the groups., Conclusion: The concentration of cfDNA and the relative number of copies of cfmtDNA in seminal plasma cannot predict the freezability of semen but can be used to predict in vitro embryo production., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: In PSMA-ligand PET/CT imaging, standardized evaluation frameworks and image-derived parameters are increasingly used to support prostate cancer staging. Clinical applicability remains challenging wherever manual measurements of numerous suspected lesions are required. Deep learning methods are promising for automated image analysis, typically requiring extensive expert-annotated image datasets to reach sufficient accuracy. We developed a deep learning method to support image-based staging, investigating the use of training information from two radiotracers., Methods: In 173 subjects imaged with 68 Ga-PSMA-11 PET/CT, divided into development (121) and test (52) sets, we trained and evaluated a convolutional neural network to both classify sites of elevated tracer uptake as nonsuspicious or suspicious for cancer and assign them an anatomical location. We evaluated training strategies to leverage information from a larger dataset of 18 F-FDG PET/CT images and expert annotations, including transfer learning and combined training encoding the tracer type as input to the network. We assessed the agreement between the N and M stage assigned based on the network annotations and expert annotations, according to the PROMISE miTNM framework., Results: In the development set, including 18 F-FDG training data improved classification performance in four-fold cross validation. In the test set, compared to expert assessment, training with 18 F-FDG data and the development set yielded 80.4% average precision [confidence interval (CI): 71.1-87.8] for identification of suspicious uptake sites, 77% (CI: 70.0-83.4) accuracy for anatomical location classification of suspicious findings, 81% agreement for identification of regional lymph node involvement, and 77% agreement for identification of metastatic stage., Conclusion: The evaluated algorithm showed good agreement with expert assessment for identification and anatomical location classification of suspicious uptake sites in whole-body 68 Ga-PSMA-11 PET/CT. With restricted PSMA-ligand data available, the use of training examples from a different radiotracer improved performance. The investigated methods are promising for enabling efficient assessment of cancer stage and tumor burden., (© 2021. The Author(s).)

Total metabolic tumor volume (TMTV), calculated from 18 F-FDG PET/CT baseline studies, is a prognostic factor in diffuse large B-cell lymphoma (DLBCL) whose measurement requires the segmentation of all malignant foci throughout the body. No consensus currently exists regarding the most accurate approach for such segmentation. Further, all methods still require extensive manual input from an experienced reader. We examined whether an artificial intelligence-based method could estimate TMTV with a comparable prognostic value to TMTV measured by experts. Methods: Baseline 18 F-FDG PET/CT scans of 301 DLBCL patients from the REMARC trial (NCT01122472) were retrospectively analyzed using a prototype software (PET Assisted Reporting System [PARS]). An automated whole-body high-uptake segmentation algorithm identified all 3-dimensional regions of interest (ROIs) with increased tracer uptake. The resulting ROIs were processed using a convolutional neural network trained on an independent cohort and classified as nonsuspicious or suspicious uptake. The PARS-based TMTV (TMTV PARS ) was estimated as the sum of the volumes of ROIs classified as suspicious uptake. The reference TMTV (TMTV REF ) was measured by 2 experienced readers using independent semiautomatic software. The TMTV PARS was compared with the TMTV REF in terms of prognostic value for progression-free survival (PFS) and overall survival (OS). Results: TMTV PARS was significantly correlated with the TMTV REF (ρ = 0.76; P < 0.001). Using PARS, an average of 24 regions per subject with increased tracer uptake was identified, and an average of 20 regions per subject was correctly identified as nonsuspicious or suspicious, yielding 85% classification accuracy, 80% sensitivity, and 88% specificity, compared with the TMTV REF region. Both TMTV results were predictive of PFS (hazard ratio, 2.3 and 2.6 for TMTV PARS and TMTV REF , respectively; P < 0.001) and OS (hazard ratio, 2.8 and 3.7 for TMTV PARS and TMTV REF , respectively; P < 0.001). Conclusion: TMTV PARS was consistent with that obtained by experts and displayed a significant prognostic value for PFS and OS in DLBCL patients. Classification of high-uptake regions using deep learning for rapidly discarding physiologic uptake may considerably simplify TMTV estimation, reduce observer variability, and facilitate the use of TMTV as a predictive factor in DLBCL patients., (© 2021 by the Society of Nuclear Medicine and Molecular Imaging.)

Purpose of Review: We provide a critical review of digital technologies in evidence-based treatments (EBTs) for mental health with a focus on the functions technologies are intended to serve. The review highlights issues related to clarity of purpose, usability, and assumptions related to EBT technology integration, branding, and packaging., Recent Findings: Developers continue to use technology in creative ways, often combining multiple functions to convey existing EBTs or to create new technology-enabled EBTs. Developers have a strong preference for creating and investigating whole-source, branded solutions related to specific EBTs, in comparison to developing or investigating technology tools related to specific components of behavior change, or developing specific clinical protocols that can be delivered via existing technologies. Default assumptions that new applications are required for each individual EBT, that EBTs are best served by the use of only one technology solution rather than multiple tools, and that an EBT-specific technology product should include or convey all portions of an EBT slow scientific progress and increase risk of usability issues that negatively impact uptake. We contend that a purposeful, functions-based approach should guide the selection, development, and application of technology in support of EBT delivery.

The Grande Rose of Reims Cathedral (France), a UNESCO Cultural Heritage Monument from the 13 th century, underwent several restoration works during the 20 th century. Its colours result from centuries of colour management from which little information remain. We used non-destructive and portable optical absorption spectroscopy to quantify glass colour and determine the colouring species on a large-scale study of this monumental window. We found six distinct colour groups, each containing both medieval and modern glasses, with colouring processes specific to each colour. This illustrates medieval glassmakers' mastering of glass colouring and modern glassmakers' management to reproduce medieval glasses colours. Full UV-visible-NIR energy range is necessary for determining the contribution of colouring elements as Fe 2+ and Cu 2+ . Systematic thickness measurements reveal an average glass thickness of 3 mm and demonstrate the major control of chromophore concentration on glass colour. Yellow, red and purple colours arise from a single chromophore each, suggesting the use of well-defined glassmaking techniques leading to robust colour reproducibility. By contrast, blue and green glasses show different chromophore combinations depending on production time, which suggests more diversity in glassmaking techniques.