bluuebunny/crossref_metadata_2025_split huggingface dataset:
A systematic review of indicators and methods used to assess coastal to offshore marine ecosystems in the western Canadian Arctic
Authors: Bilous Miranda , Wight Kevin , Galappaththi Eranga K. , Dunmall Karen M. 
Published: March 2024

AbstractIntegration and coordination among monitoring programs are needed to better assess the impacts of climate change on Arctic marine ecosystems. The use of common indicators and methods could facilitate this comprehensive understanding. We completed a systematic review of published marine research in the western Canadian Arctic from 1962 to 2021 to identify the commonalities in methods and indicators present in assessments of coastal and offshore ecosystems. Most abundant in our sample were indicators addressing the environmental context, followed by indicators concerning trophic webs and biological organisms, and finally indicators associated with anthropogenic stressors and threats. Ship-based studies located far offshore were by far the most common and focused on indicators that characterized the physical environment and lower trophic levels. Commonalities in data collection methods suggest possibilities for standardization among programs for some parameters. Differences and data gaps highlighted areas for future coordination and the potential to integrate among indicators, especially as some indicators may span coastal to offshore ecosystems whereas individual monitoring programs may not. The results of this review could be used to identify and gather data into broad-spanning datasets. Overall, this systematic review highlights opportunities to link indicators and methods among coastal to offshore programs and will therefore facilitate connectivity and coordination of ecological research and monitoring in the western Canadian Arctic.
The diagnostic performance of ultrafast MRI to differentiate benign from malignant breast lesions: a systematic review and meta-analysis
Authors: Amitai Yoav , Freitas Vivianne A. R. , Golan Orit , Kessner Rivka , Shalmon Tamar , Neeman Rina , Mauda-Havakuk Michal , Mercer Diego , Sklair-Levy Miri , Menes Tehillah S. 
Published: March 2024

Abstract
Objectives
To assess the diagnostic performance of ultrafast magnetic resonance imaging (UF-DCE MRI) in differentiating benign from malignant breast lesions.

Materials and methods
A comprehensive search was conducted until September 1, 2023, in Medline, Embase, and Cochrane databases. Clinical studies evaluating the diagnostic performance of UF-DCE MRI in breast lesion stratification were screened and included in the meta-analysis. Pooled summary estimates for sensitivity, specificity, diagnostic odds ratio (DOR), and hierarchic summary operating characteristics (SROC) curves were pooled under the random-effects model. Publication bias and heterogeneity between studies were calculated.

Results
A final set of 16 studies analyzing 2090 lesions met the inclusion criteria and were incorporated into the meta-analysis. Using UF-DCE MRI kinetic parameters, the pooled sensitivity, specificity, DOR, and area under the curve (AUC) for differentiating benign from malignant breast lesions were 83% (95% CI 79–88%), 77% (95% CI 72–83%), 18.9 (95% CI 13.7–26.2), and 0.876 (95% CI 0.83–0.887), respectively. We found no significant difference in diagnostic accuracy between the two main UF-DCE MRI kinetic parameters, maximum slope (MS) and time to enhancement (TTE). DOR and SROC exhibited low heterogeneity across the included studies. No evidence of publication bias was identified (p = 0.585).

Conclusions
UF-DCE MRI as a stand-alone technique has high accuracy in discriminating benign from malignant breast lesions.

Clinical relevance statement
UF-DCE MRI has the potential to obtain kinetic information and stratify breast lesions accurately while decreasing scan times, which may offer significant benefit to patients.

Key Points
• Ultrafast breast MRI is a novel technique which captures kinetic information with very high temporal resolution.
• The kinetic parameters of ultrafast breast MRI demonstrate a high level of accuracy in distinguishing between benign and malignant breast lesions.
• There is no significant difference in accuracy between maximum slope and time to enhancement kinetic parameters.
Update DVO-Leitlinie 2023 „Prophylaxe, Diagnostik und Therapie der Osteoporose bei postmenopausalen Frauen und bei Männern ab dem 50. Lebensjahr“ – Was ist neu für die Rheumatologie?
Authors: Pfeil Alexander , Lange Uwe 
Published: March 2024

ZusammenfassungIm Oktober 2023 veröffentlichte der Dachverband der deutschsprachigen wissenschaftlichen osteologischen Gesellschaften e. V. (DVO) die überarbeitete Leitlinie zur „Prophylaxe, Diagnostik und Therapie der Osteoporose bei postmenopausalen Frauen und bei Männern ab dem 50. Lebensjahr“. Dieser Übersichtsartikel geht auf die Neuerungen der Leitlinie und deren Relevanz in der Betreuung von Betroffenen mit entzündlich-rheumatischen Erkrankungen ein.Eine zentrale Änderung der Leitlinie stellt die Umstellung des 10-Jahres-Frakturrisikos auf das 3‑Jahres-Frakturrisiko dar. Die Basisdiagnostik wird aktuell ohne definierte Frakturschwelle durchgeführt. Als weitere Schlüsselneuerung sind die Therapieschwellen für die spezifisch osteologische Therapie mit 3 % bis < 5 %, 5 % bis < 10 % und ab 10 % für Wirbelkörper- sowie Schenkelhalsfrakturen zu nennen. Bei einem 3‑Jahres-Frakturrisiko > 10 % ist primär eine osteoanabole Therapie durchzuführen und eine antiresorptive Therapie wird an die osteoanabole Therapie angeschlossen. Weiterführend sollten Patientinnen und Patienten mit einer Osteoporose sowie einer länger andauernden Glukokortikoidtherapie primär osteoanabol mittels Teriparatid behandelt werden. Zusammenfassend reflektieren die Änderungen der DVO-Leitlinie die aktuellen wissenschaftlichen Studienerkenntnisse in der Osteologie und führen zu einer detaillierten Differentialtherapie der Osteoporose.
Optical coherence tomography angiography suggests choriocapillaris perfusion deficit as etiology of acute macular neuroretinopathy
Authors: von der Burchard Claus , Gruben Arved , Roider Johann 
Published: March 2024

Abstract
Purpose
Acute macular neuroretinopathy (AMN) can cause sudden-onset and permanent scotoma in healthy young patients. Analysis of optical coherence tomography (OCT) and OCT angiography (OCTA) of AMN patients may provide insights into disease mechanism.

Methods
We conducted a retrospective study of consecutive SARS-Cov-2-related AMN patients that presented in our clinic between Jan 1st, 2022, and April 30th, 2023, within 30 days of symptom onset. Retinal vessel area density (VAD) of AMN lesions in OCTA was quantified and compared to an adjacent tissue control (ATC). This quantification was performed for the superficial vascular plexus (SVP), the intermediate capillary plexus (ICP), the deep capillary plexus (DCP), the choriocapillaris (CC), and choroid. Furthermore, en face OCT images were analyzed.

Results
Nine AMN patients were identified, 6 of these (4 female, 2 male, average age 25 years) fulfilled the inclusion criteria and were included into this study. Average time from symptom onset to OCTA was 14.3 days. No VAD differences between AMN and adjacent tissue were found in either retinal layer (SVP, ICP, DCP). In contrast, VAD in CC was reduced by 27% against the ATC (p = 0.007) and choroidal VAD was reduced by 41% (p = 0.017). Further analysis of en face OCT could show that the pathognomonic infrared hyporeflectivity in AMN is caused by photoreceptor alterations rather than changes in the inner retinal layers.

Conclusions
Our data suggests that a perfusion deficit in the choroidal layers is responsible for AMN rather than in the DCP, which is the predominant hypothesis in current literature.
The pattern-based interpretation of p53 immunohistochemical expression as a surrogate marker for TP53 mutations in colorectal cancer
Authors: Osakabe Mitsumasa , Yamada Noriyuki , Sugimoto Ryo , Uesugi Noriyuki , Nakao Eiichi , Honda Michitaka , Yanagawa Naoki , Sugai Tamotsu 
Published: March 2024

AbstractMutations in the TP53 gene, most commonly observed in colorectal cancer (CRC), play an essential role in colorectal carcinogenesis. Although p53 immunohistochemical (IHC) expression patterns have been argued to serve as an excellent surrogate marker for TP53 mutations, its performance has not been confirmed in CRC. We aimed to determine whether p53 IHC expression patterns accurately predict TP53 mutation status as examined by next-generation sequencing (NGS). We performed p53 IHC and sequencing of TP53 by NGS in 92 CRC cases with a microsatellite stable phenotype to investigate the correlation between TP53 mutation status and p53 IHC expression. The concordance between p53 IHC and TP53 mutation was 84/92 (91.3%) overall. However, 6 mutant cases were found in 39 cases with a wild-type IHC pattern. Additionally, there were two discordant cases in which an abnormal p53 IHC pattern (overexpression or cytoplasmic pattern) was found, while NGS detected wild-type p53. Therefore, the optimized p53 IHC performs well and serves as a surrogate test for TP53 mutation in CRC cases. Furthermore, it demonstrates excellent reproducibility between two independent experienced pathologists and may have novel clinical utility for molecular classification algorithms in CRC. We suggest that the four-tier classification of p53 IHC patterns is helpful to evaluate molecular colorectal carcinogenesis.
Longitudinal analysis of carotenoid content in preterm human milk
Authors: Uretzky Adi , Mandel Dror , Schwartz Anat , Kaganov Kira , Mezad-Koursh Daphna , Mangel Laurence , Lubetzky Ronit 
Published: March 2024

AbstractTo describe the variability in carotenoid content of human milk (HM) in mothers of very to extremely low birth weight preterm infants throughout lactation and to explore the relationship between lutein in HM and the occurrence of retinopathy of prematurity (ROP) in preterm infants. We recruited healthy mothers along with their preterm infants that were born at gestational age 24 + 2 to 29 + 6 weeks or with a birth weight under 1500 g and were exclusively breastfed HM. Each participant provided up to 7 HM samples (2–10 ml) on day 0–3 and once a week until 6 weeks. Additionally, when possible, a blood sample was collected from the infant at week 6. Concentrations of the major carotenoids (lutein, zeaxanthin, beta-carotene, and lycopene) in all HM and blood samples were assessed and compared. Thirty-nine mother-infant dyads were included and 184 HM samples and 21 plasma samples were provided. Mean lutein, zeaxanthin, beta-carotene, and lycopene concentration decreased as lactation progressed, being at their highest in colostrum samples (156.9 vs. 66.9 vs. 363.9 vs. 426.8 ng/ml, respectively). Lycopene (41%) and beta-carotene (36%) were the predominant carotenoids in colostrum and up to 2 weeks post-delivery. Inversely, the proportion of lutein and zeaxanthin increased with lactation duration to account for 45% of the carotenoids in mature HM. Lutein accounted for 58% of the carotenoids in infant plasma and only 28% in HM. Lutein content of transition and mature HM did not differ between mothers of ROP and non-ROP infants.Conclusion Carotenoid content of HM was dynamic and varied between mothers and as lactation progressed. Infant plasma displayed a distinct distribution of carotenoids from HM.
Integrative analysis with machine learning identifies diagnostic and prognostic signatures in neuroblastoma based on differentially DNA methylated enhancers between INSS stage 4 and 4S neuroblastoma
Authors: Li Shan , Mi Tao , Jin Liming , Liu Yimeng , Zhang Zhaoxia , Wang Jinkui , Wu Xin , Ren Chunnian , Wang Zhaoying , Kong Xiangpan , Liu Jiayan , Luo Junyi , He Dawei 
Published: March 2024

Abstract
Introduction
Accumulating evidence demonstrates that aberrant methylation of enhancers is crucial in gene expression profiles across several cancers. However, the latent effect of differently expressed enhancers between INSS stage 4S and 4 neuroblastoma (NB) remains elusive.

Methods
We utilized the transcriptome and methylation data of stage 4S and 4 NB patients to perform Enhancer Linking by Methylation/Expression Relationships (ELMER) analysis, discovering a differently expressed motif within 67 enhancers between stage 4S and 4 NB. Harnessing the 67 motif genes, we established the INSS stage related signature (ISRS) by amalgamating 12 and 10 distinct machine learning (ML) algorithms across 113 and 101 ML combinations to precisely diagnose stage 4 NB among all NB patients and to predict the prognosis of NB patients. Based on risk scores calculated by prognostic ISRS, patients were categorized into high and low-risk groups according to median risk score. We conducted comprehensive comparisons between two risk groups, in terms of clinical applications, immune microenvironment, somatic mutations, immunotherapy, chemotherapy and single-cell analysis. Ultimately, we empirically validated the differential expressions of two ISRS model genes, CAMTA2 and FOXD1, through immunochemistry staining.

Results
Through leave-one-out cross-validation, in both feature selection and model construction, we selected the random forest algorithm to diagnose stage 4 NB, and Enet algorithm to develop prognostic ISRS, due to their highest average C-index across five NB cohorts. After validations, the ISRS demonstrated a stable predictive capability, outperforming the previously published NB signatures and several clinic variables. We stratified NB patients into high and low-risk group based on median risk score, which showed the low-risk group with a superior survival outcome, an abundant immune infiltration, a decreased mutation landscape, and an enhanced sensitivity to immunotherapy. Single-cell analysis between two risk groups reveals biologically cellular variations underlying ISRS. Finally, we verified the significantly higher protein levels of CAMTA2 and FOXD1 in stage 4S NB, as well as their protective prognosis value in NB.

Conclusion
Based on multi-omics data and ML algorithms, we successfully developed the ISRS to enable accurate diagnosis and prognostic stratification in NB, which shed light on molecular mechanisms of spontaneous regression and clinical utilization of ISRS.
The long-term outcome of revision microdiscectomy for recurrent sciatica
Authors: Lequin M. B. , Verbaan D. , Schuurman P. R. , Tasche Saskia , Peul W. C. , Vandertop W. P. , Bouma G. J. 
Published: March 2024

Abstract
Purpose
To study the long-term outcome of revision microdiscectomy after classic microdiscectomy for lumbosacral radicular syndrome (LSRS).

Methods
Eighty-eight of 216 patients (41%) who underwent a revision microdiscectomy between 2007 and 2010 for MRI disc-related LSRS participated in this study. Questionnaires included visual analogue scores (VAS) for leg pain, RDQ, OLBD, RAND-36, and seven-point Likert scores for recovery, leg pain, and back pain. Any further lumbar re-revision operation(s) were recorded.

Results
Mean (SD) age was 59.8 (12.8), and median [IQR] time of follow-up was 10.0 years [9.0–11.0]. A favourable general perceived recovery was reported by 35 patients (40%). A favourable outcome with respect to perceived leg pain was present in 39 patients (45%), and 35 patients (41%) reported a favourable outcome concerning back pain. The median VAS for leg and back pain was worse in the unfavourable group (48.0/100 mm (IQR 16.0–71.0) vs. 3.0/100 mm (IQR 2.0–5.0) and 56.0/100 mm (IQR 27.0–74.0) vs. 4.0/100 mm (IQR 2.0–17.0), respectively; both p < 0.001). Re-revision operation occurred in 31 (35%) patients (24% same level same side); there was no significant difference in the rate of favourable outcome between patients with or without a re-revision operation.

Conclusion
The long-term results after revision microdiscectomy for LSRS show an unfavourable outcome in the majority of patients and a high risk of re-revision microdiscectomy, with similar results. Based on also the disappointing results of alternative treatments, revision microdiscectomy for recurrent LSRS seems to still be a valid treatment. The results of our study may be useful to counsel patients in making appropriate treatment choices.
An empirical investigation of challenges of specifying training data and runtime monitors for critical software with machine learning and their relation to architectural decisions
Authors: Heyn Hans-Martin , Knauss Eric , Malleswaran Iswarya , Dinakaran Shruthi 
Published: March 2024

AbstractThe development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the training data used during the development of ML models have major influences on the later behaviour of the system. Runtime monitors are used to provide guarantees for that behaviour. Runtime monitors for example check that the data at runtime is compatible with the data used to train the model. In a first step towards identifying challenges when specifying requirements for training data and runtime monitors, we conducted and thematically analysed ten interviews with practitioners who develop ML models for critical applications in the automotive industry. We identified 17 themes describing the challenges and classified them in six challenge groups. In a second step, we found interconnection between the challenge themes through an additional semantic analysis of the interviews. We explored how the identified challenge themes and their interconnections can be mapped to different architecture views. This step involved identifying relevant architecture views such as data, context, hardware, AI model, and functional safety views that can address the identified challenges. The article presents a list of the identified underlying challenges, identified relations between the challenges and a mapping to architecture views. The intention of this work is to highlight once more that requirement specifications and system architecture are interlinked, even for AI-specific specification challenges such as specifying requirements for training data and runtime monitoring.
Voluntary E-Learning Exercises Support Students in Mastering Statistics
Authors: Schwerter Jakob , Brahm Taiga 
Published: March 2024

AbstractUniversity students often learn statistics in large classes, and in such learning environments, students face an exceptionally high risk of failure. One reason for this is students’ frequent statistics anxiety. This study shows how students can be supported using e-learning exercises with automated knowledge of correct response feedback, supplementing a face-to-face lecture. To this end, we surveyed 67 undergraduate social science students at a German university and observed their weekly e-learning exercises. We aggregated students’ exercise behavior throughout the semester to explain their exam performance. To control for participation bias, we included essential predictors of educational success, such as prior achievement, motivation, personality traits, time preferences, and goals. We applied a double selection procedure based on the machine learning method Elastic Net to include an optimal but sparse set of control variables. The e-learning exercises indirectly promoted the self-regulated learning techniques of retrieval practice and spacing and provided corrective feedback. Working on the e-learning exercises increased students’ performance on the final exam, even after controlling for the rich set of control variables. Two-thirds of students used our designed e-learning exercises; however, only a fraction of students spaced out the exercises, although students who completed the exercises during the semester and were not cramming at the end benefited additionally. Finally, we discuss how the results of our study inform the literature on retrieval practice, spacing, feedback, and e-learning in higher education.
Mapping ethical, legal, and social implications (ELSI) of preimplantation genetic testing (PGT)
Authors: Alon Ido , Bussod Ilona , Ravitsky Vardit 
Published: March 2024

Abstract
Purpose
Preimplantation Genetic Testing (PGT) has attracted considerable ethical, legal, and social scrutiny, but academic debate often fails to reflect clinical realities.

Methods
Addressing this disconnect, a review of 506 articles from 1999 to 2019 across humanities and social sciences was conducted to synthesize the Ethical, Legal, and Social Implications (ELSI) of PGT. This review mined PubMed, WoS, and Scopus databases, using both MeSH terms and keywords to map out the research terrain.

Results
The findings reveal a tenfold increase in global research output on PGT’s ELSI from 1999 to 2019, signifying rising interest and concern. Despite heightened theoretical discourse on selecting “optimal” offspring, such practices were scarcely reported in clinical environments. Conversely, critical issues like PGT funding and familial impacts remain underexplored. Notably, 86% of the ELSI literature originates from just 12 countries, pointing to a research concentration.

Conclusion
This review underscores an urgent need for ELSI research to align more closely with clinical practice, promoting collaborations among ethicists, clinicians, policymakers, and economists. Such efforts are essential for grounding debates in practical relevance, ultimately steering PGT towards ethical integrity, societal acceptance, and equitable access, aiming to harmonize PGT research with real-world clinical concerns, enhancing the relevance and impact of future ethical discussions.
Causality-Driven Intra-class Non-equilibrium Label-Specific Features Learning
Authors: Ge Wenxin , Wang Yibin , Xu Yuting , Cheng Yusheng 
Published: March 2024

AbstractIn multi-label learning, label-specific feature learning can effectively avoid some ineffectual features that interfere with the classification performance of the model. However, most of the existing label-specific feature learning algorithms improve the performance of the model for classification by constraining the solution space through label correlation. The non-equilibrium of the label distribution not only leads to some spurious correlations mixed in with the calculated label correlations but also diminishes the performance of the classification model. Causal learning can improve the classification performance and robustness of the model by capturing real causal relationships from limited data. Based on this, this paper proposes a causality-driven intra-class non-equilibrium label-specific features learning, named CNSF. Firstly, the causal relationship between the labels is learned by the Peter-Clark algorithm. Secondly, the label density of all instances is calculated by the intra-class non-equilibrium method, which is used to relieve the non-equilibrium distribution of original labels. Then, the correlation of the density matrix is calculated using cosine similarity and combined with causality to construct the causal density correlation matrix, to solve the problem of spurious correlation mixed in the label correlation obtained by traditional methods. Finally, the causal density correlation matrix is used to induce label-specific feature learning. Compared with eight state-of-the-art multi-label algorithms on thirteen datasets, the experimental results prove the reasonability and effectiveness of the algorithms in this paper.
Die Verschiebung des Sagbaren in organisationalen Schweigepraktiken
Authors: Lorenz-Sinai Friederike 
Published: March 2024

ZusammenfassungIm Beitrag wird der Frage nachgegangen, wie sich das Sagbare in organisationalen Schweigepraktiken im Kontext von Machtmissbrauch und Gewalt gegen Kinder und Jugendliche durch Mitarbeiter:innen verschiebt. Anhand einer Fallstudie im Bereich der stationären Eingliederungshilfe wird diskutiert, wie Übergriffe im professionellen Sprechen und Schreiben umgedeutet und ausgelassen werden können. Deutlich wird, wie in solchen Praktiken ein Schweigen über Gewalt in organisationalen Routinen und Praktiken über Jahre unaufwändig vollzogen werden kann. Der Artikel endet mit einem Resümee der Befunde und Überlegungen zu den Herausforderungen von Supervision in Bezug auf verschwiegene Gewaltkonstellationen in Organisationen.