bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Role of Biotics as a Therapeutic Strategy for Oral Mucositis - A Systematic Review
Authors: Frey-Furtado Leonor , Magalhães Inês , Azevedo Maria João , Sampaio-Maia Benedita 
Published: June 2023

Abstract
Objectives
Oral mucositis (OM) is an acute and highly prevalent side effect of cancer treatments. Currently, there is no effective strategy for its prevention or treatment. This systematic review aimed to assess the effectiveness of biotics used as a therapeutic strategy for the management of OM.

Materials and Methods
The PRISMA checklist was followed and PubMed, Web of Science, and Scopus were screened for clinical and pre-clinical studies assessing the potential effects of biotics in OM. Inclusion criteria included in vivo studies related to oral mucositis evaluating the effect of biotics, and written in Portuguese, English, French, Spanish, or Dutch. The following exclusion criteria were used: systematic reviews and meta-analyses, reviews, case reports, opinion papers or comments, conference papers, letters without results, articles not related to oral therapy-induced mucositis or biotics, or in vitro articles that do not simulate oral mucositis.

Results
From a total of 1250 articles retrieved, 9 were included in this systematic review. Four clinical studies reported a reduction in oral mucositis occurrence with Lactobacillus species (Lactobacillus casei and Lactobacillus brevis CD2) and Bacillus clausii UBBC07. In pre-clinical studies, Lactococcus lactis genetically modified and Lactobacillus reuteri reduced the severity of OM and Streptococcus salivarius K12 also decreased the size of the ulcers.

Conclusion
The findings of this systematic review suggest that probiotic supplementation may potentially reduce the incidence of therapy-induced OM and decrease its severity in patients undergoing cancer treatment. However, the available evidence is marred by significant heterogeneity across studies.
Does Adsorption of Cd, Cu and Pb on Polymeric Silicic Acid Occur Under Acidic Conditions?
Authors: Stein Mathias , Buchweitz Maria , Mayer Pia , Rennert Thilo 
Published: June 2023

AbstractContamination with Cd, Cu and Pb is a major environmental issue. Cations of those metals may adsorb on negatively charged surfaces of polymeric silicic acid (pSi), altering their environmental fate. Aiming to elucidate the underlying mechanisms and the extent of adsorption, we conducted batch adsorption experiments at pH 4 to 6 and concentrations that excluded precipitation of solid phases. Zeta-potential measurements were conducted to monitor surface charge changes. In addition, isothermal titration calorimetry (ITC) was used to derive thermodynamic parameters of the interaction between the metals and pSi. Surprisingly, neither did batch-adsorption experiments reveal any metal adsorption on pSi after 24 h reaction time, nor did ITC experiments show any evidence for chemical adsorption of the metals, as no heat was released or absorbed during the experiments. However, zeta-potential measurements indicated weak electrostatic interactions between the negatively charged silanol groups and the metals. These electrostatic interactions may be the initial step of metal incorporation into the matrix of polymerizing silicic acid, which were spectroscopically proven in long-term experiments.
Association of periodontitis with handgrip strength and skeletal muscle mass in middle-aged US adults from NHANES 2013–2014
Authors: Bunte Kübra , Wiessner Christian , Bahat Gülistan , Erdogan Tugba , Cruz-Jentoft Alfonso J. , Zapf Antonia 
Published: June 2023

Abstract
Objectives
The relationship between periodontitis and sarcopenia parameters in middle-aged adults is largely unexplored. This study investigated the association between periodontitis and combined handgrip strength and skeletal muscle mass in middle-aged adults.

Materials and methods
A sub-cohort of 1912 individuals with complete periodontal and whole-body dual X-ray absorptiometry examinations from the 2013–2014 wave of the National Health and Nutrition Examination Survey (n = 10,175) were analyzed using fully adjusted multiple linear regression models for associations between periodontitis and skeletal muscle mass index (kg/m2) and combined handgrip strength (kg).

Results
The mean age of the study cohort was 43 (± 8.4) years and 49.4% of the participants were male. In total, 612 participants (32%) were determined to have periodontitis, of which 513 (26.8%) had non-severe (mild or moderate) periodontitis, and 99 (5.2%) had severe periodontitis. In unadjusted regression models, both non-severe and severe periodontitis were associated with SMMI (βnon-severe = 1.01, 95% CI 0.50; 1.52 and βsevere = 1.42, 95% CI 0.59; 2.25) but not with cHGS. After adjusting for age, sex, education, body mass index, bone mineral density, diabetic status, education, total energy intake, total protein intake, and serum vitamin D2 + D3, periodontitis was associated with cHGS (βnon-severe = -2.81, 95% CI − 4.7; − 1.15 and βsevere = − 2.73, 95% CI − 6.31; 0.83). The association between periodontitis and SMMI remained for non-severe periodontitis (βnon-severe = 0.07, 95% CI − 0.26; 0.40 and βsevere = 0.22, 95% CI − 0.34; 0.78).

Conclusion
The present study highlights the need of further prospective research to investigate the nature and direction of the relationship between periodontitis and sarcopenia indicators. Future studies can support the screening, prevention and clinical management of sarcopenia and periodontitis, and emphasize the interdisciplinary and complementary approach between the disciplines of geriatric medicine and periodontology.
Error Propagation from Sleep Stage Classification to Derived Sleep Parameters in Machine Learning on Data from Wearables
Authors: Hardarson Emil , Islind Anna Sigridur , Arnardottir Erna Sif , Óskarsdóttir María 
Published: June 2023

Abstract
Purpose of Review:
Automatic analysis of sleep is an important and active area of research. Machine learning models are commonly developed to classify time segments into sleep stages. The sleep stages can be used to calculate various sleep parameters, such as sleep efficiency and total sleep time. The machine learning models are typically trained to minimize the sleep stage classification error, but little is known about how error propagates from sleep stages to derived sleep parameters.

Recent findings:
We review recently published studies where machine learning was used to classify sleep stages using data from wearable devices. Using classification error statistics from these studies, we perform a Monte Carlo simulation to estimate sleep parameter error in a dataset of 197 hypnograms. This is, to our knowledge, the first attempt at evaluating how robust sleep parameter estimation is to misclassification of sleep stages.

Summary:
Our analysis suggests that a machine learning model capable of 90% accurate sleep stage classification (surpassing current state-of-art in wearable sleep tracking) may perform worse than a random guess in estimating some sleep parameters. Our analysis also indicates that sleep stage classification may not be a relevant target variable for machine learning on wearable sleep data and that regression models may be better suited to estimating sleep parameters. Finally, we propose a baseline model to use as a reference for sleep stage estimation accuracy. When applied to a test set, the baseline model predicts 2-, 3-, 4- and 5-class sleep stages with an accuracy of 74%, 54%, 46% and 35%, respectively
Machine learning and the James–Stein estimator
Authors: Efron Bradley 
Published: June 2023

AbstractIt is now 62 years since the publication of James and Stein’s seminal article on the estimation of a multivariate normal mean vector. The paper made a spectacular first impression on the statistical community through its demonstration of inadmissability of the maximum likelihood estimator. It continues to be influential, but not for the initial reasons. Empirical Bayes shrinkage estimation, now a major topic, found its early justification in the James–Stein formula. Less obvious downstream topics include Tweedie’s formula and Benjamini and Hochberg’s false discovery rate algorithm. This is a short and mainly non-technical review of the James–Stein rule and its effects on the machine learning era of statistical innovation.
Resilient Multi-range Radar Detection System for Autonomous Vehicles: A New Statistical Method
Authors: Enayati Javad , Asef Pedram , Wilson Peter 
Published: June 2023

AbstractCritical issues with current detection systems are their susceptibility to adverse weather conditions and constraint on the vertical field view of the radars limiting the ability of such systems to accurately detect the height of the targets. In this paper, a novel multi-range radar (MRR) arrangement (i.e. triple: long-range, medium-range, and short-range radars) based on the sensor fusion technique is investigated that can detect objects of different sizes in a level 2 advanced driver-assistance system. To improve the accuracy of the detection system, the resilience of the MRR approach is investigated using the Monte Carlo (MC) method for the first time. By adopting MC framework, this study shows that only a handful of fine-scaled computations are required to accurately predict statistics of the radar detection failure, compared to many expensive trials. The results presented huge computational gains for such a complex problem. The MRR approach improved the detection reliability with an increased mean detection distance (4.9% over medium range and 13% over long range radar) and reduced standard deviation over existing methods (30% over medium range and 15% over long-range radar). This will help establishing a new path toward faster and cheaper development of modern vehicle detection systems.
Ensemble-based 4DVarNet uncertainty quantification for the reconstruction of sea surface height dynamics
Authors: Beauchamp Maxime , Febvre Quentin , Fablet Ronan 
Published: June 2023

Abstract
Uncertainty quantification (UQ) plays a crucial role in data assimilation (DA) since it impacts both the quality of the reconstruction and near-future forecast. However, traditional UQ approaches are often limited in their ability to handle complex datasets and may have a large computational cost. In this paper, we present a new ensemble-based approach to extend the 4DVarNet framework, an end-to-end deep learning scheme backboned on variational DA used to estimate the mean of the state along a given DA window. We use conditional 4DVarNet simulations compliant with the available observations to estimate the 4DVarNet probability density function. Our approach enables to combine both the efficiency of 4DVarNet in terms of computational cost and validation performance with a fast and memory-saving Monte-Carlo based post-processing of the reconstruction, leading to the so-called En4DVarNet estimation of the state pdf. We demonstrate our approach in a case study involving the sea surface height: 4DVarNet is pretrained on an idealized Observation System Simulation Experiment (OSSE), then used on real-world dataset (OSE). The sampling of independent realizations of the state is made among the catalogue of model-based data used during training. To illustrate our approach, we use a nadir altimeter constellation in January 2017 and show how the uncertainties retrieved by combining 4DVarNet with the statistical properties of the training dataset lead to a relevant information providing in most cases a confidence interval compliant with the Cryosat-2 nadir alongtrack dataset kept for validation.
Amyloid β-based therapy for Alzheimer’s disease: challenges, successes and future
Authors: Zhang Yun , Chen Huaqiu , Li Ran , Sterling Keenan , Song Weihong 
Published: June 2023

AbstractAmyloid β protein (Aβ) is the main component of neuritic plaques in Alzheimer’s disease (AD), and its accumulation has been considered as the molecular driver of Alzheimer’s pathogenesis and progression. Aβ has been the prime target for the development of AD therapy. However, the repeated failures of Aβ-targeted clinical trials have cast considerable doubt on the amyloid cascade hypothesis and whether the development of Alzheimer’s drug has followed the correct course. However, the recent successes of Aβ targeted trials have assuaged those doubts. In this review, we discussed the evolution of the amyloid cascade hypothesis over the last 30 years and summarized its application in Alzheimer’s diagnosis and modification. In particular, we extensively discussed the pitfalls, promises and important unanswered questions regarding the current anti-Aβ therapy, as well as strategies for further study and development of more feasible Aβ-targeted approaches in the optimization of AD prevention and treatment.
Internalization of apoptotic cells during efferocytosis requires Mertk-mediated calcium influx
Authors: Yang Susumin , Min Chanhyuk , Moon Hyunji , Moon Byeongjin , Lee Juyeon , Jeon Jaeseon , Kwon Hagyeong , Jang Deokyun , Park Daeho 
Published: June 2023

AbstractPhagocytosis of apoptotic cells, called efferocytosis, requires calcium inside and outside of phagocytes. Due to its necessity, calcium flux is sophisticatedly modulated, and the level of intracellular calcium in phagocytes is ultimately elevated during efferocytosis. However, the role of elevated intracellular calcium in efferocytosis remains elusive. Here, we report that Mertk-mediated intracellular calcium elevation is necessary for internalization of apoptotic cells during efferocytosis. Drastic depletion of intracellular calcium abrogated the internalization step of efferocytosis by delaying phagocytic cup extension and closure. Especially, the defect of phagocytic cup closure for internalization of apoptotic cells was caused by impaired F-actin disassembly and the attenuated interaction of Calmodulin with myosin light chain kinase (MLCK), leading to diminished myosin light chain (MLC) phosphorylation. Genetic and pharmacological impairment of the Calmodulin-MLCK-MLC axis or Mertk-mediated calcium influx also resulted in inefficient efferocytosis due to a defect in internalization of the targets. Taken together, our observations imply that intracellular calcium elevation through Mertk-mediated calcium influx facilitates efferocytosis by inducing myosin II-mediated contraction and F-actin disassembly required for internalization of apoptotic cells.
Interferon-γ elicits the ocular surface pathology mimicking dry eye through direct modulation of resident corneal cells
Authors: Ko Jung Hwa , Kim Seonghwan , Ryu Jin Suk , Song Hyo Jeong , Oh Joo Youn 
Published: June 2023

AbstractDespite accumulating evidence indicating a key role of interferon-γ (IFN-γ)-producing immune cells in ocular infection and immunity, little is known about the direct effects of IFN-γ on resident corneal cells or on the ocular surface. Here, we report that IFN-γ impacts corneal stromal fibroblasts and epithelial cells to promote inflammation, opacification, and barrier disruption on the ocular surface, leading to dry eye. Our results demonstrated that IFN-γ dose-dependently induced cytotoxicity, pro-inflammatory cytokine/chemokine production, and expression of major histocompatibility complex class II and CD40 in cultures of corneal stromal fibroblasts and epithelial cells while increasing myofibroblast differentiation of corneal stromal fibroblasts. In mice, subconjunctival IFN-γ administration caused corneal epithelial defects and stromal opacity in dose- and time-dependent manners while promoting neutrophil infiltration and inflammatory cytokine expression in the cornea. Moreover, IFN-γ reduced aqueous tear secretion and the number of conjunctival goblet cells responsible for mucinous tear production. Together, our findings suggest that IFN-γ induces the ocular surface changes characteristic of dry eye disease at least in part through its direct effects on resident corneal cells.
Programmable spatial deformation by controllable off-center freestanding 4D printing of continuous fiber reinforced liquid crystal elastomer composites
Authors: Wang Qingrui , Tian Xiaoyong , Zhang Daokang , Zhou Yanli , Yan Wanquan , Li Dichen 
Published: June 2023

AbstractOwing to their high deformation ability, 4D printed structures have various applications in origami structures, soft robotics and deployable mechanisms. As a material with programmable molecular chain orientation, liquid crystal elastomer is expected to produce the freestanding, bearable and deformable three-dimensional structure. However, majority of the existing 4D printing methods for liquid crystal elastomers can only fabricate planar structures, which limits their deformation designability and bearing capacity. Here we propose a direct ink writing based 4D printing method for freestanding continuous fiber reinforced composites. Continuous fibers can support freestanding structures during the printing process and improve the mechanical property and deformation ability of 4D printed structures. In this paper, the integration of 4D printed structures with fully impregnated composite interfaces, programmable deformation ability and high bearing capacity are realized by adjusting the off-center distribution of the fibers, and the printed liquid crystal composite can carry a load of up to 2805 times its own weight and achieve a bending deformation curvature of 0.33 mm−1 at 150 °C. This research is expected to open new avenues for creating soft robotics, mechanical metamaterials and artificial muscles.
Fast-exchanging spirocyclic rhodamine probes for aptamer-based super-resolution RNA imaging
Authors: Englert Daniel , Burger Eva-Maria , Grün Franziska , Verma Mrigank S. , Lackner Jens , Lampe Marko , Bühler Bastian , Schokolowski Janin , Nienhaus G. Ulrich , Jäschke Andres , Sunbul Murat 
Published: June 2023

AbstractLive-cell RNA imaging with high spatial and temporal resolution remains a major challenge. Here we report the development of RhoBAST:SpyRho, a fluorescent light-up aptamer (FLAP) system ideally suited for visualizing RNAs in live or fixed cells with various advanced fluorescence microscopy modalities. Overcoming problems associated with low cell permeability, brightness, fluorogenicity, and signal-to-background ratio of previous fluorophores, we design a novel probe, SpyRho (Spirocyclic Rhodamine), which tightly binds to the RhoBAST aptamer. High brightness and fluorogenicity is achieved by shifting the equilibrium between spirolactam and quinoid. With its high affinity and fast ligand exchange, RhoBAST:SpyRho is a superb system for both super-resolution SMLM and STED imaging. Its excellent performance in SMLM and the first reported super-resolved STED images of specifically labeled RNA in live mammalian cells represent significant advances over other FLAPs. The versatility of RhoBAST:SpyRho is further demonstrated by imaging endogenous chromosomal loci and proteins.
Single-cell profiling of lncRNA expression during Ebola virus infection in rhesus macaques
Authors: Santus Luisa , Sopena-Rios Maria , García-Pérez Raquel , Lin Aaron E. , Adams Gordon C. , Barnes Kayla G. , Siddle Katherine J. , Wohl Shirlee , Reverter Ferran , Rinn John L. , Bennett Richard S. , Hensley Lisa E. , Sabeti Pardis C. , Melé Marta 
Published: June 2023

AbstractLong non-coding RNAs (lncRNAs) are involved in numerous biological processes and are pivotal mediators of the immune response, yet little is known about their properties at the single-cell level. Here, we generate a multi-tissue bulk RNAseq dataset from Ebola virus (EBOV) infected and not-infected rhesus macaques and identified 3979 novel lncRNAs. To profile lncRNA expression dynamics in immune circulating single-cells during EBOV infection, we design a metric, Upsilon, to estimate cell-type specificity. Our analysis reveals that lncRNAs are expressed in fewer cells than protein-coding genes, but they are not expressed at lower levels nor are they more cell-type specific when expressed in the same number of cells. In addition, we observe that lncRNAs exhibit similar changes in expression patterns to those of protein-coding genes during EBOV infection, and are often co-expressed with known immune regulators. A few lncRNAs change expression specifically upon EBOV entry in the cell. This study sheds light on the differential features of lncRNAs and protein-coding genes and paves the way for future single-cell lncRNA studies.
Spatial distribution, sources and health risk assessment of heavy metals in topsoil around oil and natural gas drilling sites, Andhra Pradesh, India
Authors: Aradhi Keshav Krishna , Dasari Babu Mallesh , Banothu Dasaram , Manavalan Satyanarayanan 
Published: June 2023

AbstractSoils are usually the interface between human activity and environmental components that must be conserved and protected. As a result of rising industrialization and urbanization, activities such as exploration and extraction operations lead to the release of heavy metals into the environment. This study presents distribution of six heavy metals (As, Cr, Cu, Ni, Pb and Zn) in 139 top soil samples collected in and around oil and natural gas drilling sites at a sampling density of 1 site/12 km2. The results indicated the concentration ranged from 0.1 to 16 mg/kg for As, 3–707 mg/kg for Cr, 7–2324 mg/kg for Cu, 14–234 mg/kg for Ni, 9–1664 mg/kg for Pb, and 60–962 mg/kg for Zn. The contamination of soil was estimated on the basis of Index of geo accumulation (Igeo), enrichment factor (Ef), and contamination factor (Cf). Further, spatial distribution pattern maps indicated that the pollution levels for Cu, Cr, Zn, and Ni were higher around drilling sites of the study area relative to other regions. Using exposure factors for the local population and references from the USEPA’s integrated database, potential ecological risk indices (PERI) and health risk assessments were made. The hazard index (HI) values of Pb (in adults) and Cr, Pb (in children) exceeded the recommended limit of HI = 1, indicating the non-carcinogenic risks. Total carcinogenic risk (TCR) calculations revealed Cr (in adults) and As, Cr (in children) levels in soils exceeded the threshold value of 1.0E − 04, indicating significant carcinogenic risk due to high metal concentrations in the study area. These results may assist in determining the soil’s present state and its effect due to extraction strategies used during drilling process and initiate few remedial techniques, particularly for proper management strategies in farming activities to decrease point and non-point source of contamination.
Study on distribution of sidewall earth pressure on open caissons considering soil arching effect
Authors: Yang Bai , Shi Qingye , Zhou Hexiang , Qin Chao , Xiao Weiwei 
Published: June 2023

AbstractBased on the soil arching effect theory, the magnitude and distribution of sidewall earth pressure on open caissons when the embedded depth is large was analyzed by using theory of non-limit state earth pressure theory and horizontal differential element method. The theoretical formula was deduced. The theoretical calculation results are compared with the field test results and centrifugal model test results respectively. The results show that when the embedded depth of the open caisson is large, the distribution of earth pressure on the side wall of the open caisson first increases with the increase of embedded depth, reaches a peak value, and then sharply decreases. The peak point is located at 2/3 ~ 4/5 of the embedded depth. In engineering practice, when the embedded depth of the open caisson is 40 m, the relative error between the field test value and the theoretical calculation value is − 55.8% ~ 1.2%, with an average error of 13.8%. When the equivalent embedded depth of the open caisson in the centrifugal model test is 36 m, the relative error between the centrifugal model test value and the theoretical calculation value is − 20.1% ~ 68.0%, with an average error of 10.6%, The results are consistent well. The results of this article provides reference for the design and construction of open caisson.