bluuebunny/crossref_metadata_2025_split huggingface dataset:
Elotuzumab‐mediated ADCC with Th1‐like Vγ9Vδ2 T cells to disrupt myeloma–osteoclast interaction
Authors: Inoue Yusuke , Tenshin Hirofumi , Teramachi Jumpei , Sumitani Ryohei , Oda Asuka , Maeda Yusaku , Oura Masahiro , Sogabe Kimiko , Maruhashi Tomoko , Takahashi Mamiko , Fujii Shiro , Nakamura Shingen , Miki Hirokazu , Hara Tomoyo , Endo Itsuro , Kagawa Kumiko , Ozaki Shuji , Hiasa Masahiro , Harada Takeshi , Abe Masahiro 
Published: November 2024

AbstractMultiple myeloma (MM) cells and osteoclasts (OCs) activate with each other to cause drug resistance. Human Th1‐like Vγ9Vδ2 (γδ) T cells, important effectors against tumors, can be expanded and activated ex vivo by the aminobisphosphonate zoledronic acid in combination with IL‐2. We previously reported that the expanded γδ T cells effectively targeted and killed OCs as well as MM cells. Because the expanded γδ T cells expressed CD16 on their surface, we investigated the utilization of the expanded γδ T cells for antibody‐dependent cellular cytotoxicity (ADCC). Although the expanded γδ T cells alone induced cell death in MM cell lines, the addition of the anti‐SLAMF7 monoclonal antibody elotuzumab (ELO) further enhanced their cytotoxic activity only against SLAMF7‐expressing MM cell lines and primary MM cells. Intriguingly, ELO was also able to enhance γδ T cell‐induced cell death against OCs cultured alone, and against both MM cells and OCs in their coculture settings. SLAMF7 was found to be highly expressed in OCs differentiated in vitro from monocytes by receptor activator of nuclear factor‐κ B ligand and M‐CSF, although monocytes only marginally expressed SLAMF7. These results demonstrate that SLAMF7 is highly expressed in both MM cells and OCs, and that the ex vivo‐expanded γδ T cells can exert ELO‐mediated ADCC against SLAMF7‐expressing MM cells and OCs besides their direct cytotoxic activity. Further study is warranted for the innovative utilization of γδ T cells.
Leveraging genome‐wide association studies to better understand the etiology of cancers
Authors: Sonehara Kyuto , Okada Yukinori 
Published: November 2024

AbstractGenome‐wide association studies (GWAS) statistically assess the association between tens of millions of genetic variants in the whole genome and a phenotype of interest. Genome‐wide association studies enable the elucidation of polygenic inheritance of cancer, in which myriad low‐penetrance genetic variants collectively contribute to a substantial proportion of the heritable susceptibility. In addition to the robust genotype–phenotype associations provided by GWAS, combining GWAS data with functional genomic datasets or sophisticated statistical genetic methods unlocks deeper insights. Integrating genotype and molecular phenotyping data facilitates functional characterization of GWAS association signals through molecular quantitative trait loci mapping and transcriptome‐wide association studies. Furthermore, aggregating genome‐wide polygenic signals, including subthreshold associations, enables one to estimate genetic correlations across diverse phenotypes and helps in clinical risk predictions by evaluating polygenic risk scores. In this review, we begin by summarizing the rationale for GWAS of cancer, introduce recent methodological updates in the GWAS‐derived downstream analyses, and demonstrate their applications to GWAS of cancers.
Skeletal Phenotype in Mulibrey Nanism, A Monogenic Skeletal Dysplasia With Fibrous Dysplasia
Authors: Karlberg Susann , Toiviainen‐Salo Sanna , Lipsanen‐Nyman Marita , Mäkitie Outi 
Published: November 2024

ABSTRACTMulibrey nanism (MUL) is a monogenic growth disorder caused by mutations in TRIM37, with pre‐and postnatal growth failure, typical craniofacial features, perimyocardial heart disease, infertility and predisposition to tumors. Clinically, patients are gracile with relative macrocephaly, thin extremities, and narrow shoulders, but the full spectrum of skeletal features remains unknown. We conducted a cross‐sectional study in order to further clarify the skeletal phenotype. We assessed radiographs of the long bones and spine in 33 MUL patients, aged 4.5–48 years (14 females and 19 males, median age 16.7 years) for skeletal features. Hospital records were reviewed for clinical characteristics and fractures. Results confirmed significant skeletal abnormalities related to MUL. Skeletal changes were present in all patients; long bones were slender and bowed with broad metaphyses and narrow diaphysis, the cortices were thick, and medullary cavities were narrow. The vertebral bodies were tall. Fibrous dysplasia was found in 19/33 patients (58%); changes were monostotic in 58% and polyostotic in 42%. Altogether 17/33 patients (52%) had a history of fractures. This study confirms that in addition to short stature, patients with MUL have a specific skeletal dysplasia. Our findings suggest an important role for TRIM37 in cellular functions governing skeletal modelling and remodelling.
Integrating real‐world data and machine learning: A framework to assess covariate importance in real‐world use of alternative intravenous dosing regimens for atezolizumab
Authors: Vora Bianca , Jindal Ashutosh , Velasquez Erick , Lu James , Wu Benjamin 
Published: November 2024

AbstractThe increase in the availability of real‐world data (RWD), in combination with advances in machine learning (ML) methods, provides a unique opportunity for the integration of the two to explore complex clinical pharmacology questions. Here we present a recently developed RWD/ML framework that utilizes ML algorithms to understand the influence and importance of various covariates on the use of a given dose and schedule for drugs that have multiple approved dosing regimens. To demonstrate the application of this framework, we present atezolizumab as a use case on account of its three approved alternative intravenous (IV) dosing regimens. As expected, the real‐world use of atezolizumab has generally been increasing since 2016 for the 1200 mg every 3 weeks regimen and since 2019 for the 1680 mg every 4 weeks regimen. Out of the ML algorithms evaluated, XGBoost performed the best, as measured by the area under the precision–recall curve, with an emphasis on the under‐sampled class given the imbalance in the data. The importance of features was measured by Shapley Additive exPlanations (SHAP) values and showed metastatic breast cancer and use of protein‐bound paclitaxel as the most correlated with the use of 840 mg every 2 weeks. Although patient usage data for alternative IV dosing regimens are still maturing, these analyses provide initial insights on the use of atezolizumab and set up a framework for the re‐analysis of atezolizumab (at a future data cut) as well as application to other molecules with approved alternative dosing regimens.
Effect of selective dorsal rhizotomy on neuromuscular symptoms, muscle morphology, and motor function in children with spastic cerebral palsy
Authors: Verreydt Ineke , Hanssen Britta , Molenaers Guy , De Beukelaer Nathalie , Vandekerckhove Ines , Papageorgiou Eirini , Huenaerts Catherine , Ortibus Els , Van Campenhout Anja , Desloovere Kaat 
Published: November 2024

AbstractAimTo investigate the effect of selective dorsal rhizotomy (SDR) on an integrated outcome set 1‐year post‐SDR, in a cohort of children with spastic cerebral palsy (CP).MethodFifteen children with bilateral spastic CP (median age 8 years 8 months [interquartile range 3 years 3 months], 11 males, four females, eight in Gross Motor Function Classification System (GMFCS) level II, seven in GMFCS level III) were measured pre‐ and 1‐year post‐SDR. Clinical scales and goniometry assessed plantar flexor spasticity, range of motion, strength, and selectivity. Spasticity was also quantified via an instrumented assessment. Medial gastrocnemius macroscopic muscle morphology (absolute and normalized muscle belly, tendon and muscle‐tendon unit length, cross‐sectional area, muscle volume) was assessed using ultrasound. Gait profile score, ankle and knee gait variable scores, walking speed, cadence, and step length were extracted from gait analysis. Gross motor function was assessed using the Gross Motor Function Measure‐66 item set. Wilcoxon signed‐rank test was used to analyse pre‐ and post‐SDR changes. A reference database was used to qualitatively judge muscle growth post‐SDR with respect to muscle growth of children with spastic CP without SDR intervention.ResultsSignificant changes (p < 0.05) were seen for spasticity, selectivity, all absolute morphology parameters, normalized tendon and muscle‐tendon unit length, and all gait parameters, except walking speed and cadence. Muscle growth of children with and without SDR was comparable.InterpretationSDR is an effective spasticity reducing treatment and does not adversely affect natural muscle growth in spastic CP.
Integrative transcriptome analysis reveals Serpine2 promotes glomerular mesangial cell proliferation and extracellular matrix accumulation via activating ERK1/2 signalling pathway in diabetic nephropathy
Authors: Zheng Ting , Yang Ruhao , Li Xin , Dai Zhe , Xiang Hongyu 
Published: November 2024

AbstractBackgroundDiabetic nephropathy (DN) is one of the main causes of end‐stage renal disease (ESRD), but its mechanism has not been clearly studied. We utilized integrative transcriptome analysis to explore the pathogenesis of DN.MethodsWe conducted an analysis by combining bulk dataset and single‐cell transcriptome dataset. Through this approach, we identified that Serpine2 may regulate the ‘collagen‐containing extracellular matrix’ pathway involved in DN. Subsequently, we established DN animal and cell models using db/db mice and mesangial cells (MCs) to validate the role of Serpine2 in DN. In the animal model, we detected the expression level of Serpine2 in DN using western blotting (WB) and immunofluorescence (IF) assays. To further clarify the molecular mechanism of Serpine2 in DN, we knocked down Serpine2 and observed its effects on MCs proliferation and extracellular matrix (ECM) accumulation.ResultsOur single‐cell analysis of DN models highlighted a pivotal role for MCs in the disease's initiation. Next, through Cytoscape analysis of differentially expressed genes (DEGs) in MCs, we identified the following 10 hub genes: Acta2, Angpt2, Ccn1, Col4a1, Col4a2, Col8a1, Kdr, Thbs1, Tpm4 and Serpine2. Subsequently, we identified that Serpine2 and Kdr were also significantly DEGs in the bulk analysis of glomeruli. Additionally, our integrated gene set enrichment analysis of bulk dataset and single‐cell RNA dataset revealed that the ‘collagen‐containing extracellular matrix’ was a key pathway in DN progression. Serpine2 was one of the crucial genes involved in regulating this pathway. Therefore, we speculated that the regulation of the ‘collagen‐containing extracellular matrix’ pathway by Serpine2 was an important mechanism. Importantly, WB and IF staining confirmed that Serpine2 expression was upregulated in the MCs of diabetic mice. Knockdown of Serpine2 in cultured MCs alleviated high‐glucose‐induced excessive MCs proliferation and ECM accumulation. Finally, we found that ERK agonist Ro 67‐7476 eliminated the effect of Serpine2 siRNA.ConclusionsIn summary, Serpine2 regulates MCs proliferation and ECM synthesis through activation of the ERK1/2 pathway, which is an important pathogenesis mechanism of DN. These findings offer fresh perspectives on the mechanisms of glomerulosclerosis in DN pathogenesis and may provide new targets for treating DN.
Cerebral cortical encephalitis in adults with myelin oligodendrocyte glycoprotein antibody‐associated disease: A national case series
Authors: Pace Samuel , Messina Silvia , Chen Bo , Tanasescu Radu , Papathanasiou Athanasios , Constantinescu Cris S. , Leite Maria I. , Willis Mark D. , Johnston Janet A. , Palace Jacqueline , Dobson Ruth 
Published: November 2024

AbstractBackground and purposeMyelin oligodendrocyte glycoprotein (MOG) antibody‐associated disease (MOGAD) is a relatively recently described disease, most commonly presenting with optic neuritis and longitudinally extensive transverse myelitis. Cerebral cortical encephalitis is a rare manifestation of MOGAD.MethodsWe identified patients presenting with cerebral cortical encephalitis with positive MOG antibodies in serum across a large specialized service. Demographic and clinical information were collected. We describe clinical and laboratory characteristics, treatment response, and subsequent relapse risk in adults presenting with this phenotype.ResultsWe identified eight patients meeting clinical criteria for cerebral cortical encephalitis with MOG antibodies. All had seizures; four had focal onset seizures with or without secondary generalization. Two patients exhibited encephalopathy, and six demonstrated focal neurological deficits at presentation. All had fluid‐attenuated inversion recovery hyperintensities. Five of eight displayed cerebral swelling, and two of eight displayed leptomeningeal enhancement. Where cerebrospinal fluid (CSF) results were available, five of seven had CSF pleocytosis, protein was raised in two of seven, and one patient had oligoclonal bands unique to CSF. Median time to seizure control was 1.25 months, and all clinical features and magnetic resonance imaging abnormalities resolved. Four of eight patients (50%) had a clinical relapse, with a median time to relapse of 6.4 months.ConclusionsCerebral cortical encephalitis appears to share similar CSF findings, steroid responsiveness, and risk of relapse with other clinical manifestations of MOGAD. This informs treatment decisions and patient counselling.
Intelligent Computing for Crop Monitoring in CIoT: Leveraging AI and Big Data Technologies
Authors: Ahmed Imran , Ahmad Misbah , Ghazouani Haythem , Barhoumi Walid , Jeon Gwanggil 
Published: November 2024

ABSTRACTConsumer Internet of Things (CIoT) has revolutionised agriculture by integrating intelligent computing, artificial intelligence and big data technologies in crop monitoring. This paper explores the application of intelligent computing and deep learning methodologies in crop monitoring within the CIoT framework. In CIoT‐based crop monitoring, a vision sensor collects real‐time data from crop leaf images. The image dataset is processed using state‐of‐the‐art deep learning models and intelligent computing algorithms. This integration enables the early detection of crop diseases by leveraging computer vision and deep learning. Intelligent computing systems provide accurate disease classification, real‐time alerts, and actionable recommendations for optimised crop management practises. This advanced system empowers farmers to make data‐driven decisions, such as irrigation optimization, targeted pesticide application and nutrient supplementation, to maximise crop productivity and minimise losses. A benchmark dataset of leaf images is used, and a deep learning based model is presented for classifying healthy and diseased leaves. Experimental results demonstrate an accuracy rate of 0.98, with detailed validation, including dataset size and model parameters. Key benefits of intelligent computing in CIoT‐based crop monitoring include enhanced resource efficiency, reduced environmental impact, and improved sustainability. The paper also addresses the challenges of implementing AI and big data technologies, such as data privacy, security, interoperability and resource management in agricultural settings.
Analysis of Different Modality of Data to Diagnose Parkinson's Disease Using Machine Learning and Deep Learning Approaches: A Review
Authors: Arnab Sheikh Bahauddin , Palash Md Istakiak Adnan , Islam Rakibul , Ovi Hemal Hossain , Yousuf Mohammad Abu , Uddin Md Zia 
Published: November 2024

ABSTRACTThe dynamic nature of Parkinson's disease (PD) is that it gradually impacts regions of the brain that are responsible for the production of the dopamine hormone. Despite continuous efforts, no effective treatment or preventative approach exists for PD. Nonetheless, the disease can be detected. Our goal is to create a Machine Learning and Deep Learning‐based system that can detect Parkinson's disease from a variety of data sources with high accuracy, sensitivity, specificity and interpretability. However, there have been significant advancements in the field of research, especially the use of artificial intelligence in the Parkinson's disease diagnostic process. We reviewed articles that were released between 2018 and 2024, concentrating on the most current studies that had been published. We chose 70 research articles for our review paper based on a set of criteria from a variety of online databases, including IEEExpress, medical databases like PubMed, Google Scholar, ResearchGate and ScienceDirect, and various publishers, including Elsevier, Taylor & Francis, Springer, MDPI, Plos One and so forth. According to our review, the majority of works make use of voice data. Our review study found that the highest accuracy level of most papers was above 90%, and the most commonly used algorithms were CNN and SVM. The main goal of this review study is to look into and put together information about the different ways that artificial intelligence, especially Machine Learning, can be used to find Parkinson's disease. Using diverse data gathered from multiple public and private datasets, we can infer that the application of artificial intelligence, particularly Machine Learning algorithms, for identifying Parkinson's disease plays a crucial role in the medical field.
Paradox of Shorter Residual Fatigue Life due to Omission of Low‐Amplitude Cycles and Its Significance for Testing
Authors: Pokorný Pavel , Vojtek Tomáš , Kubíček Radek , Jambor Michal , Náhlík Luboš , Hutař Pavel 
Published: November 2024

ABSTRACTThe work investigates nonvalidity of the common presumption that the nondamaging cycles do not influence residual fatigue life. Paradoxically, application of the full loading spectrum (more cycles) resulted in approximately 2.3 times longer life of the fatigue crack growth specimens than application of the spectrum with 33% of the smallest amplitudes omitted. Unlike in humid air (controlled relative humidity of 50% at 23°C), the effect disappeared in a dry‐air chamber (relative humidity <10% at 23°C), where both fatigue lives were short. The mechanism responsible for these effects was identified as the oxide‐induced crack closure, an extrinsic mechanism unrelated to material damage. Oxide debris developed on fracture surfaces was observed by light and scanning electron microscopy, whereas crack closure was measured during the experiments. The presented counterintuitive behavior in humid air may result in wrong assessment or prediction of components residual fatigue lives, which can be nonconservative in some scenarios.
The effect of employee mobility on firm innovation
Authors: Ciccone Stephen J. , Li Huimin , Liu Yixin 
Published: November 2024

AbstractWe study the effect of employee mobility on firm innovation. Using an occupation‐based measure of employee mobility, we find that firms with more mobile workforces are associated with greater patent quantity and quality and higher innovation efficiency. This effect is more pronounced for firms with higher labor intensity, greater business diversification, and lower unionization rates. Both the private market value of innovation and the effectiveness of innovation to generate revenues increase with higher employee mobility. Consistent results are found using a quasi‐experimental shock, which helps address endogeneity concerns. Our findings suggest that employee mobility has a profound impact on innovation.
Foreign Aid, Infrastructure, and the Inclusive Growth Agenda in Sub‐Saharan Africa
Authors: Ofori Isaac K. , Ojong Nathanael , Gbolonyo Emmanuel Y. 
Published: November 2024

ABSTRACTThis study examines the contingency effect of infrastructure (disaggregated into physical and digital) in the relationship between foreign aid and inclusive growth in Sub‐Saharan Africa (SSA). We employ macro data for 41 SSA countries and the dynamic system GMM estimator for the empirical analysis. We find that (i) foreign aid promotes inclusive growth in SSA, (ii) whereas transport infrastructure enhances inclusive growth, energy infrastructure, sanitation infrastructure, and digital infrastructure are statistically insignificant, and (iii) only transport infrastructure amplifies the inclusive growth‐enhancing effect of foreign aid. Across the digital and physical infrastructure domains, we find that the contingency effect of the latter is rather remarkable. Our threshold analysis also indicates that for digital infrastructure and transport infrastructure to condition complementary policies to foster inclusive growth in SSA, minimum thresholds of 22% and 57.8% are required. We conclude that comprehensive transport infrastructural development is key if foreign aid is to enhance inclusive growth in SSA.
Enhancing hydration resistance and mechanical properties of dolomite refractory through ZnO nanoparticle incorporation
Authors: Chamhaydari Mohammad Ali Rajabi , Ahmadimoghadam Hajar , Nilforoushan Mohammad Reza 
Published: November 2024

AbstractThe study aims to enhance the hydration resistance of dolomite refractory by investigating the influence of ZnO nanoparticles on their physical and mechanical properties. Four samples with varying ZnO concentrations (0, 0.5, 1, and 2 wt%) were prepared. The main identified phases were magnesia (MgO) and lime (CaO). The microstructure of the dolomite refractory shown irregularly shaped grains dispersed within a matrix phase, with an increase in ZnO nanoparticles, leading to larger and more abundant grains. The incorporation of ZnO nanoparticles resulted in improved density, hydration resistance, and cold compressive strength, attributed to a reduction in porosity. The sample containing 2 wt% ZnO nanoparticles displayed the highest cold compressive strength and thermal expansion coefficient compared to the pure sample. The addition of ZnO enhanced the dolomite's compressive strength by approximately 46%. These findings suggest that ZnO nanoparticles can enhance the properties of dolomite refractory, particularly in terms of hydration resistance.