bluuebunny/crossref_metadata_2025_split huggingface dataset:
The synthesis and characterization of an iron(VII) nitrido complex
Authors: Keilwerth Martin , Mao Weiqing , Malischewski Moritz , Jannuzzi Sergio A. V. , Breitwieser Kevin , Heinemann Frank W. , Scheurer Andreas , DeBeer Serena , Munz Dominik , Bill Eckhard , Meyer Karsten 
Published: January 2024

AbstractComplexes of iron in high oxidation states are captivating research subjects due to their pivotal role as active intermediates in numerous catalytic processes. Structural and spectroscopic studies of well-defined model complexes often provide evidence of these intermediates. In addition to the fundamental molecular and electronic structure insights gained by these complexes, their reactivity also affects our understanding of catalytic reaction mechanisms for small molecule and bond-activation chemistry. Here, we report the synthesis, structural and spectroscopic characterization of a stable, octahedral Fe(VI) nitrido complex and an authenticated, unique Fe(VII) species, prepared by one-electron oxidation. The super-oxidized Fe(VII) nitride rearranges to an Fe(V) imide through an intramolecular amination mechanism and ligand exchange, which is characterized spectroscopically and computationally. This enables combined reactivity and stability studies on a single molecular system of a rare high-valent complex redox pair. Quantum chemical calculations complement the spectroscopic parameters and provide evidence for a diamagnetic (S = 0) d2 Fe(VI) and a genuine S = 1/2, d1 Fe(VII) configuration of these super-oxidized nitrido complexes.
An efficient approach to study multi-polar fuzzy ideals of semirings
Authors: Bashir Shahida , Alharbi Talal , Mazhar Rabia , Khalid Issra , ul Hassan Afzal Muneeb , Riaz Chaudhry Nauman 
Published: January 2024

AbstractThe multi polar fuzzy (m-PF) set has an extensive range of implementations in real world problems related to the multi-polar information, multi-index and multi-attributes data. This paper introduces innovative extensions to algebraic structures. We present the definitions and some important results of m-polar fuzzy subsemirings (m-PFSSs), m-polar fuzzy ideals (m-PFIs), m-polar fuzzy generalized bi-ideals (m-PFGBIs), m-polar fuzzy bi-ideals (m-PFBIs) and m-polar fuzzy quasi-ideals (m-PFQIs) in semirings. The main contributions of the paper include the derivation and proof of key theorems that shed light on the algebraic interplay and computational aspects of m-polar fuzzy ideals (m-PFIs), m-polar fuzzy generalized bi-ideals (m-PFGBIs), m-polar fuzzy bi-ideals (m-PFBIs) and m-polar fuzzy quasi-ideals (m-PFQIs) in semirings along with examples. Moreover, this paper deals with several important properties of m-PFIs and characterizes regular and intra-regular semirings by the properties of these ideals.
Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus
Authors: Yan Yujie , Kehayias Christopher , He John , Aerts Hugo J. W. L. , Fitzgerald Kelly J. , Kann Benjamin H. , Kozono David E. , Guthier Christian V. , Mak Raymond H. 
Published: January 2024

AbstractManual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-therapy planning is time-consuming and subject to variation between different observers. Artificial intelligence (AI) can assist with segmentation, but challenges exist in ensuring high-quality segmentation, especially for small, variable structures, such as the esophagus. We investigated the effect of variation in segmentation quality and style of physicians for training deep-learning models for esophagus segmentation and proposed a new metric, edge roughness, for evaluating/quantifying slice-to-slice inconsistency. This study includes a real-world cohort of 394 patients who each received radiation therapy (mainly for lung cancer). Segmentation of the esophagus was performed by 8 physicians as part of routine clinical care. We evaluated manual segmentation by comparing the length and edge roughness of segmentations among physicians to analyze inconsistencies. We trained eight multiple- and individual-physician segmentation models in total, based on U-Net architectures and residual backbones. We used the volumetric Dice coefficient to measure the performance for each model. We proposed a metric, edge roughness, to quantify the shift of segmentation among adjacent slices by calculating the curvature of edges of the 2D sagittal- and coronal-view projections. The auto-segmentation model trained on multiple physicians (MD1-7) achieved the highest mean Dice of 73.7 ± 14.8%. The individual-physician model (MD7) with the highest edge roughness (mean ± SD: 0.106 ± 0.016) demonstrated significantly lower volumetric Dice for test cases compared with other individual models (MD7: 58.5 ± 15.8%, MD6: 67.1 ± 16.8%, p < 0.001). A multiple-physician model trained after removing the MD7 data resulted in fewer outliers (e.g., Dice ≤ 40%: 4 cases for MD1-6, 7 cases for MD1-7, Ntotal = 394). While we initially detected this pattern in a single clinician, we validated the edge roughness metric across the entire dataset. The model trained with the lowest-quantile edge roughness (MDER-Q1, Ntrain = 62) achieved significantly higher Dice (Ntest = 270) than the model trained with the highest-quantile ones (MDER-Q4, Ntrain = 62) (MDER-Q1: 67.8 ± 14.8%, MDER-Q4: 62.8 ± 15.7%, p < 0.001). This study demonstrates that there is significant variation in style and quality in manual segmentations in clinical care, and that training AI auto-segmentation algorithms from real-world, clinical datasets may result in unexpectedly under-performing algorithms with the inclusion of outliers. Importantly, this study provides a novel evaluation metric, edge roughness, to quantify physician variation in segmentation which will allow developers to filter clinical training data to optimize model performance.
The effect of whole blood logistics on neutrophil non-specific activation and kinetics ex vivo
Authors: Li Chao , Farooqui Mehtab , Yada Ravi Chandra , Cai Joseph B. , Huttenlocher Anna , Beebe David J. 
Published: January 2024

AbstractWhile the exquisite sensitivity of neutrophils enables their rapid response to infection in vivo; this same sensitivity complicates the ex vivo study of neutrophils. Handling of neutrophils ex vivo is fraught with unwanted heterogeneity and alterations that can diminish the reproducibility of assays and limit what biological conclusions can be drawn. There is a need to better understand the influence of ex vivo procedures on neutrophil behavior to guide improved protocols for ex vivo neutrophil assessment to improve inter/intra-experimental variability. Here, we investigate how whole blood logistics (i.e., the procedure taken from whole blood collection to delivery of the samples to analytical labs and storage before neutrophil interrogation) affects neutrophil non-specific activation (i.e., baseline apoptosis and NETosis) and kinetics (i.e., activation over time). All the experiments (60+ whole blood neutrophil isolations across 36 blood donors) are performed by a single operator with optimized isolation and culture conditions, and automated image analysis, which together increase rigor and consistency. Our results reveal: (i) Short-term storage (< 8 h) of whole blood does not significantly affect neutrophil kinetics in subsequent two-dimensional (2D) cell culture; (ii) Neutrophils from long-term storage (> 24 h) in whole blood show significantly higher stability (i.e., less non-specific activation) compared to the control group with the isolated cells in 2D culture. (iii) Neutrophils have greater non-specific activation and accelerated kinetic profiles when stored in whole blood beyond 48 h.
Effect of interferon therapy on quality of life in patients with chronic hepatitis B
Authors: Zhang Mengdi , Wan Meijuan , Wang Wen , Lin Shumei , Zhang Xi 
Published: January 2024

AbstractInterferon therapy is the most effective treatment for achieving clinical cure in chronic hepatitis B (CHB) patients. However, the treatment outcomes of interferon therapy are uncertain, multiple side effects can occur during treatment, and the treatment is expensive. Although these characteristics may affect patients’ quality of life, research examining this topic is limited. We used a cross-sectional design to examine 100 CHB patients receiving interferon, 100 receiving nucleoside/nucleotide analogues, and 87 receiving non-antiviral treatment. Characteristic information, the Hepatitis B Quality of Life Instrument, Connor Davidson Resilience Scale, and Work Productivity and Activity Impairment Questionnaire were used to collect information. We found that quality of life in the interferon treatment group was higher than that in the non-antiviral treatment and nucleoside/nucleotide analogue treatment groups (p < 0.05). The factors influencing quality of life were resilience, presenteeism, hair loss, and antiviral treatment (p < 0.05). Although interferon therapy has some potential side effects, the results suggested that it did not negatively affect quality of life. Overall, interferon therapy did not have a major impact on CHB patients’ daily lives and work.
Artificial intelligence assisted patient blood and urine droplet pattern analysis for non-invasive and accurate diagnosis of bladder cancer
Authors: Demir Ramiz , Koc Soner , Ozturk Deniz Gulfem , Bilir Sukriye , Ozata Halil İbrahim , Williams Rhodri , Christy John , Akkoc Yunus , Tinay İlker , Gunduz-Demir Cigdem , Gozuacik Devrim 
Published: January 2024

AbstractBladder cancer is one of the most common cancer types in the urinary system. Yet, current bladder cancer diagnosis and follow-up techniques are time-consuming, expensive, and invasive. In the clinical practice, the gold standard for diagnosis remains invasive biopsy followed by histopathological analysis. In recent years, costly diagnostic tests involving the use of bladder cancer biomarkers have been developed, however these tests have high false-positive and false-negative rates limiting their reliability. Hence, there is an urgent need for the development of cost-effective, and non-invasive novel diagnosis methods. To address this gap, here we propose a quick, cheap, and reliable diagnostic method. Our approach relies on an artificial intelligence (AI) model to analyze droplet patterns of blood and urine samples obtained from patients and comparing them to cancer-free control subjects. The AI-assisted model in this study uses a deep neural network, a ResNet network, pre-trained on ImageNet datasets. Recognition and classification of complex patterns formed by dried urine or blood droplets under different conditions resulted in cancer diagnosis with a high specificity and sensitivity. Our approach can be systematically applied across droplets, enabling comparisons to reveal shared spatial behaviors and underlying morphological patterns. Our results support the fact that AI-based models have a great potential for non-invasive and accurate diagnosis of malignancies, including bladder cancer.
Lack of imbalance between the master regulators TTF1/NKX2-1 and ΔNp63/p40 implies adverse prognosis in non-small cell lung cancer
Authors: Vescio Martina , Bulloni Matteo , Pelosi Giuseppe , Pattini Linda 
Published: January 2024

AbstractThe transcription factors TTF1/NKX2-1 and ΔNp63/p40 are the counterposed molecular markers associated with the main Non-Small Cell Lung Cancer subtypes: TTF1 for adenocarcinoma, p40 for squamous cell carcinoma. Although they generally display a mutually exclusive expression, some exceptions exist simultaneously lacking or (very rarely) expressing both markers, either pattern being associated to poor prognosis. Hence, we quantitatively analyzed the relationship between their coordinated activity and prognosis. By analyzing the respective downstream transcriptional programs of the two genes, we defined a simple quantitative index summarizing the amount of mutual exclusivity between their activities, called Mean Absolute Activity (MAA). Systematic analysis of the MAA index in a dataset of 1018 NSCLC samples replicated on a validation dataset of 275 showed that the loss of imbalance between TTF-1 and p40 corresponds to a steady, progressive reduction in both overall and recurrence-free survival. Coherently, samples correspondent to more balanced activities were enriched for pathways related to increased malignancy and invasiveness. Importantly, multivariate analysis showed that the prognostic significance of the proposed index MAA is independent of other clinical variables including stage, sex, age and smoke exposure. These results hold irrespectively of tumor morphology across NSCLC subtypes, providing a unifying description of different expression patterns.
Theoretical optimisation of a novel gas sensor using periodically closed resonators
Authors: Zaky Zaky A. , Al-Dossari M. , Sharma Arvind , Hendy Ahmed S. , Aly Arafa H. 
Published: January 2024

AbstractThis study investigates using the phononic crystal with periodically closed resonators as a greenhouse gas sensor. The transfer matrix and green methods are used to investigate the dispersion relation theoretically and numerically. A linear acoustic design is proposed, and the waveguides are filled with gas samples. At the center of the structure, a defect resonator is used to excite an acoustic resonant peak inside the phononic bandgap. The localized acoustic peak is shifted to higher frequencies by increasing the acoustic speed and decreasing the density of gas samples. The sensitivity, transmittance of the resonant peak, bandwidth, and figure of merit are calculated at different geometrical conditions to select the optimum dimensions. The proposed closed resonator gas sensor records a sensitivity of 4.1 Hz m−1 s, a figure of merit of 332 m−1 s, a quality factor of 113,962, and a detection limit of 0.0003 m s−1. As a result of its high performance and simplicity, the proposed design can significantly contribute to gas sensors and bio-sensing applications.
Lifestyle and behavioural changes in older adults during the Covid-19 pandemic are associated with subjective cognitive complaints
Authors: Zsuffa Janos Andras , Katz Sandor , Koszovacz Vanda , Berente Dalida Borbala , Kamondi Anita , Csukly Gabor , Mangialasche Francesca , Rocha Ana Sabsil Lopez , Kivipelto Miia , Horvath Andras Attila 
Published: January 2024

AbstractSubjective cognitive complaints (SCC) is a self-reported experience of persistently impaired cognitive functions which could be the earliest red flag of neurocognitive disorders. The COVID-19 pandemic and related restriction measures changed the lifestyle and behaviour of older adults. The aim of this study was to assess the relation of these changes and SCC status in Hungary. This cross-sectional study analysed the data of 359 elderly Hungarians who filled out the WW-FINGERS-SARS-CoV2 survey. A quarter of the respondents (n:88) reported SCC in connection with the pandemic. We compared sociodemographic features, health status, lifestyle, and social life parameters between subjects with reported SCC and without. To eliminate the potential interrelation across group differences, stepwise logistic regression was applied. Participants with SCC showed the following characteristics, compared to individuals without: (1) they were older; (2) they were more likely to be women; (3) they had a higher number of chronic disorders; (4) showed more prominent impairment in physical mobility; (5) had worse sleep quality; (6) spent less time with family; and (7) used internet more frequently during the pandemic (allp’s < 0.001). Logistic regression highlighted that only two parameters were related to SCC status independently, the physical mobility (ability to walk 500 m without difficulties; OR = 1.186;p < 0.001; 95%CI = 1.101, 1.270) and changes in time spent with grandchildren (OR = 1.04;p = 0.015; 95%CI = 1.008, 1.073). Our study draws attention to the importance of physical mobility and quality time with family as key factors in the cognitive well-being of elderly people.
Identification of VWA5A as a novel biomarker for inhibiting metastasis in breast cancer by machine-learning based protein prioritization
Authors: Koh Jiwon , Jeong Dabin , Park Soo Young , Han Dohyun , Kim Da Sol , Kim Ha Yeon , Kim Hyeyoon , Yang Sohyeon , Kim Sun , Ryu Han Suk 
Published: January 2024

AbstractDistant metastasis is the leading cause of death in breast cancer (BC). The timing of distant metastasis differs according to subtypes of BCs and there is a need for identification of biomarkers for the prediction of early and late metastasis. To identify biomarker candidates whose abundance level can discriminate metastasis types, we performed a high-throughput proteomics assay using tissue samples from BCs with no metastasis, late metastasis, and early metastasis, processed data with machine learning-based feature selection, and found that low VWA5A could be responsible for shorter duration of metastasis-free interval. Low expression of VWA5A gene in METABRIC cohort was associated with poor survival in BCs, especially in hormone receptor (HR)-positive BCs. In-vitro experiments confirmed tumor suppressive effect of VWA5A on BCs in HR+ and triple-negative BC cell lines. We found that expression of VWA5A can be assessed by immunohistochemistry (IHC) on archival tissue samples. Decreasing nuclear expression of VWA5A was significantly associated with advanced T stage and lymphatic invasion in consecutive BCs of all subtypes. We discovered lower expression of VWA5A as the potential biomarker for metastasis-prone BCs, and our results support the clinical utility of VWA5A IHC, as an adjunctive tools for prognostication of BCs.
Stochastic resonance in 2D materials based memristors
Authors: Roldán J. B. , Cantudo A. , Torres J. J. , Maldonado D. , Shen Yaqing , Zheng Wenwen , Yuan Yue , Lanza M. 
Published: January 2024

AbstractStochastic resonance is an essential phenomenon in neurobiology, it is connected to the constructive role of noise in the signals that take place in neuronal tissues, facilitating information communication, memory, etc. Memristive devices are known to be the cornerstone of hardware neuromorphic applications since they correctly mimic biological synapses in many different facets, such as short/long-term plasticity, spike-timing-dependent plasticity, pair-pulse facilitation, etc. Different types of neural networks can be built with circuit architectures based on memristive devices (mostly spiking neural networks and artificial neural networks). In this context, stochastic resonance is a critical issue to analyze in the memristive devices that will allow the fabrication of neuromorphic circuits. We do so here with h-BN based memristive devices from different perspectives. It is found that the devices we have fabricated and measured clearly show stochastic resonance behaviour. Consequently, neuromorphic applications can be developed to account for this effect, that describes a key issue in neurobiology with strong computational implications.
New orphan disease therapies from the proteome of industrial plasma processing waste- a treatment for aceruloplasminemia
Authors: Zanardi Alan , Nardini Ilaria , Raia Sara , Conti Antonio , Ferrini Barbara , D’Adamo Patrizia , Gilberti Enrica , DePalma Giuseppe , Belloli Sara , Monterisi Cristina , Coliva Angela , Rainone Paolo , Moresco Rosa Maria , Mori Filippo , Zurlo Giada , Scali Carla , Natali Letizia , Pancanti Annalisa , Giovacchini Pierangelo , Magherini Giulio , Tovani Greta , Salvini Laura , Cicaloni Vittoria , Tinti Cristina , Tinti Laura , Lana Daniele , Magni Giada , Giovannini Maria Grazia , Gringeri Alessandro , Caricasole Andrea , Alessio Massimo 
Published: January 2024

AbstractPlasma-derived therapeutic proteins are produced through an industrial fractionation process where proteins are purified from individual intermediates, some of which remain unused and are discarded. Relatively few plasma-derived proteins are exploited clinically, with most of available plasma being directed towards the manufacture of immunoglobulin and albumin. Although the plasma proteome provides opportunities to develop novel protein replacement therapies, particularly for rare diseases, the high cost of plasma together with small patient populations impact negatively on the development of plasma-derived orphan drugs. Enabling therapeutics development from unused plasma fractionation intermediates would therefore constitute a substantial innovation. To this objective, we characterized the proteome of unused plasma fractionation intermediates and prioritized proteins for their potential as new candidate therapies for human disease. We selected ceruloplasmin, a plasma ferroxidase, as a potential therapy for aceruloplasminemia, an adult-onset ultra-rare neurological disease caused by iron accumulation as a result of ceruloplasmin mutations. Intraperitoneally administered ceruloplasmin, purified from an unused plasma fractionation intermediate, was able to prevent neurological, hepatic and hematological phenotypes in ceruloplasmin-deficient mice. These data demonstrate the feasibility of transforming industrial waste plasma fraction into a raw material for manufacturing of new candidate proteins for replacement therapies, optimizing plasma use and reducing waste generation.
The Russia-Ukraine war decreases food affordability but could reduce global greenhouse gas emissions
Authors: van Meijl Hans , Bartelings Heleen , van Berkum Siemen , Cui Hao David , Kristkova Zuzana Smeets , van Zeist Willem Jan 
Published: January 2024

AbstractThe Russian invasion of Ukraine has the potential to exacerbate food insecurity around the world, as both countries are major exporters of grains and other agricultural products. In this model-based scenario study, medium-term effects of the war are quantified on agricultural production, trade flows, market prices, food security, land use, and greenhouse gas emissions. The scenarios assess the possible consequences of macro-economic and agricultural production impacts in Ukraine, trade sanctions against Russia, and conflict-related energy price developments for global trade, food security, and greenhouse gas emissions. From a food security perspective, we conclude that there is enough food on the global level, but higher food and energy prices cause problems for low-income populations, spending a large part of their income on staple foods. Agricultural production and area expansion in parts of the world other than Ukraine and Russia could pose a risk to biodiversity and lead to higher greenhouse gas emissions related to land. However, total greenhouse gas emissions might decrease as lower emissions from less use of fossil energy due to higher energy and fertilizer prices in the whole economy dominate additional emissions resulting from land use change.