bluuebunny/crossref_metadata_2025_split huggingface dataset:
Chemical fingerprinting, antimicrobial, antioxidant, anti‐inflammatory, and anticancer potential of greenly synthesized silver nanoparticles from pistachio (Pistacia vera) nuts and senna (Cassia angustifolia Vahl.) leaves
Authors: Irshad Saba , Iftikhar Sabahat , Riaz Muhammad , Mahmood Azra , Mushtaq Afaq , Saleem Yasar , Shamim Rahat , Akter Quzi Sharmin 
Published: April 2024

AbstractThere is a growing interest in standardizing the biocompatible, cost‐effective, and eco‐friendly manufacturing techniques for metallic nanostructures due to their widespread applications in the industrial and medical sectors. In recent decades, green synthesis has been proven as the most suitable technique for synthesizing metal nanoparticles. The present research study investigates the use of Cassia angustifolia (senna) leaves and Pistacia vera (Pistachio) nuts to prepare crude aqueous extracts, ethanolic extracts, and biogenic silver nanoparticles (AgNPs). The prepared aqueous extracts were used as reducing, stabilizing, and capping agents for the production of silver nanoparticles. These AgNPs were characterized by scanning electron microscopy (SEM), Fourier‐transform infrared spectroscopy (FTIR), and ultraviolet–visible (UV–Vis) spectroscopy. The outcomes validated the formation of stable AgNPs with bioactive functional components. In vitro antibacterial, anticancer, anti‐inflammatory, and antioxidant potentials were assessed by Kirby–Bauer disk diffusion test, MIC test, MBC test, MTT assay, BSA denaturation inhibition assay, and DPPH antioxidant assay, respectively. Results confirmed that the tested plant extract possesses a variety of bioactive compounds with various biological activities and is therapeutically effective. These findings verified that C. angustifolia and P. vera are promising bioresources for the synthesis of therapeutic extracts and nanostructures with commendable therapeutic potency.
The reduction of the m6A methyltransferase METTL3 in granulosa cells is related to the follicular cysts in pigs
Authors: Cao Maosheng , Chen Xue , Wang Yueying , Chen Lu , Zhao Yun , Li Chunjin , Zhou Xu 
Published: April 2024

AbstractFollicular cysts are a common reproductive disorder in domestic animals that cause considerable economic losses to the farming industry. Effective prevention and treatment methods are lacking because neither the pathogenesis nor formation mechanisms of follicular cysts are well‐understood. In this study, we first investigated the granulosa cells (GCs) of cystic follicles isolated from pigs. We observed a significant reduction in the expression of methyltransferase‐like 3 (METTL3). Subsequent experiments revealed that METTL3 downregulation in GCs caused a decrease in m6A modification of pri‐miR‐21. This reduction further inhibited DGCR8 recognition and binding to pri‐miR‐21, dampening the synthesis of mature miR‐21‐5p. Additionally, the decrease in miR‐21‐5p promotes IL‐1β expression in GCs. Elevated IL‐1β activates the NFκB pathway, in turn upregulating apoptotic genes TNFa and BAX/BCL2. The subsequent apoptosis of GCs and inhibition of autophagy causes downregulation of CYP19A1 expression. These processes lower oestrogen secretion and contribute to follicular cyst formation. In conclusion, our findings provide a foundation for understanding and further exploring the mechanisms of follicular‐cyst development in farm animals. This work has important implications for treating ovarian disorders in livestock and could potentially be extended to humans.
Dual template (epitope) imprinted electrode for sensing bacterial protein with high selectivity
Authors: Srivastava Akriti , Harijan Manjeet , Prasad Rajniti , Singh Meenakshi 
Published: April 2024

AbstractEpitope imprinting has shown better prospects to synthesize synthetic receptors for proteins. Here, dual epitope imprinted polymer electrode (DEIP) matrix was fabricated on gold surface of electrochemical quartz crystal microbalance (EQCM) for recognition of target epitope sequence in blood samples of patients suffering from brain fever. Epitope sequences from outer membrane protein Por B of Neisseria meningitidis (MC58) bacteria predicted through immunoinformatic tools were chosen for imprinting. Self‐assembled monolayers (SAM) of cysteine appended epitope sequences on gold nanoparticles were subjected to polymerization prior to electrodeposition on gold coated EQCM electrode. The polymeric matrix was woven around the cysteine appended epitope SAMs through multiple monomers (3‐sulfo propyl methacrylate potassium salt (3‐SPMAP), benzyl methacrylate (BMA)) and crosslinker (N, N′‐methylene‐bis‐acrylamide). On extraction of the peptide sequences, imprinted cavities were able to selectively and specifically bind targeted epitope sequences in laboratory samples as well as ‘real’ samples of patients. Selectivity of sensor was examined through mismatched peptide sequences and certain plasma proteins also. The sensor was able to show specific binding towards the blood samples of infected patients, even in the presence of ‘matrix’ and other plasma proteins such as albumin and globulin. Even other peptide sequences, similar to epitope sequences only with one or two amino acid mismatches were also unable to show any binding. The analytical performance of DEIP‐EQCM sensor was tested through selectivity, specificity, matrix effect, detection limit (0.68–1.01 nM), quantification limit (2.05–3.05 nM) and reproducibility (RSD ~ 5%). Hence, a diagnostic tool for bacterium causing meningitis is successfully fabricated in a facile manner which will broaden the clinical access and make efficient population screening feasible.
Ganoderma lucidum: Insights on host range, diagnosis, and management strategies
Authors: Kumari Ashwani , Tapwal Ashwani , Thakur Neha 
Published: April 2024

AbstractForest ecosystems play an important role in upholding life on our planet. However, the onslaught of fungal pathogens like Ganoderma lucidum, poses a threat by decimating numerous tree species. G. lucidum identified as a root pathogen, causing root rot in numerous tree species of horticulture and forestry importance. The fungus initiates infection through basidiospores, which germinate and penetrate within roots and start to degrade lignocellulosic components of plant cells. Early‐stage detection of G. lucidum, is challenging, while in advance stages, the wood undergoes softening and a loss of tensile strength, rendering the disease incurable. Hence, effective management of G. lucidum necessitates a pivotal role of disease diagnostic techniques, which are currently underutilized or inadequately accessible. Subsequent implementation of suitable control measures becomes imperative to thwart disease occurrence and mitigate its impact in early stages, thus preserving the vitality of forest ecosystems. This study provides comprehensive overview of G. lucidum, covering taxonomy, pathogenicity, disease cycle, diagnosis and effective control measures, which will be helpful in formulating effective diagnostic techniques for early management of root rot disease.
Automatic Lenke classification of adolescent idiopathic scoliosis with deep learning
Authors: Zhang Baolin , Chen Kanghao , Yuan Haodong , Liao Zhiheng , Zhou Taifeng , Guo Weiming , Zhao Shen , Wang Ruixuan , Su Peiqiang 
Published: April 2024

AbstractPurposeThe Lenke classification system is widely utilized as the preoperative evaluation protocol for adolescent idiopathic scoliosis (AIS). However, manual measurement is susceptible to observer‐induced variability, which consequently impacts the evaluation of progression. The goal of this investigation was to develop an automated Lenke classification system utilizing innovative deep learning algorithms.MethodsUsing the database from the First Affiliated Hospital of Sun Yat‐sen University, the whole spinal x‐rays images were retrospectively collected. Specifically, images collection was divided into AIS and control group. The control group consisted of individuals who underwent routine health checks and did not have scoliosis. Afterwards, relative features of all images were annotated. Deep learning was implemented through the utilization of the key‐point based detection method to realize the vertebral detection, and Cobb angle measurement and scoliosis classification were performed based on relevant standards. Besides, the segmentation method was employed to achieve the recognition of lumbar vertebral pedicle to determine the type of lumbar spine modifier. Finally, the model performance was further quantitatively analyzed.ResultsIn the study, a total of 2082 spinal x‐ray images were collected from 407 AIS patients and 227 individuals in the control group. The model for vertebral detection achieved an F1‐score of 0.809 for curve type evaluation and an F1‐score of 0.901 for thoracic sagittal profile. The intraclass correlation efficient (ICC) of the Cobb angle measurement was 0.925. In the analysis of performance for vertebra pedicle segmentation model, the F1‐score of lumbar modification profile was 0.942, the intersection over union (IOU) of the target pixels was 0.827, and the Hausdorff distance (HD) was 6.565 ± 2.583 mm. Specifically, the F1‐score for ultimate Lenke type classifier was 0.885.ConclusionsThis study has constructed an automated Lenke classification system by employing the deep learning networks to achieve the recognition pattern and feature extraction. Our models require further validation in additional cases in the future.
Adult Normative Data for the OMNI‐Vocal Effort Scale (VES)
Authors: Morton‐Jones Mariah E. , Timmons Sund Lauren , Castro M. Eugenia , Hapner Edie R. 
Published: April 2024

PurposeTo establish normative values for the OMNI‐Vocal Effort Scale (VES) in healthy adults without voice complaints. Secondary objective is to determine if there are differences in perceived vocal effort across age groups and between sexes.Study DesignProspective data collection across groups.MethodA nine‐item survey was administered by speech‐language pathologists with specialization in voice to consenting adults 18 years or older. Participants underwent an auditory perceptual evaluation of voice and answered questions regarding age, history of voice problems, history of voice surgery, smoking history and hearing loss. Participants were instructed to rate their perceived vocal effort in conversational speech using the OMNI‐VES. Multivariant analysis was conducted.ResultsTwo hundred and fifty‐one participants were recruited. The majority of adults without voice complaints reported that producing conversational voice was within the “extremely easy” to “easy” range, 0–3 (92.4%).ConclusionsThis study provides preliminary data for perceived vocal effort. The OMNI‐VES may be a useful tool in understanding changes in perceived vocal effort as a result of treatment for voice disorders. Further normative data are needed between sexes, across the gender spectrum, and older adult populations. Future directions include examining the magnitude of difference between numeric values on the scale and use of the scale with other dysphonic populations.Level of Evidence3 Laryngoscope, 134:3726–3731, 2024
Diagnostic Yield of Patients Undergoing Evaluation of Possible Superior Canal Dehiscence
Authors: Bartholomew Ryan A. , Williams Marta M. , Wang Brigette L. , Garcia Alejandro , Lee Daniel J. , Corrales C. Eduardo , Bhattacharyya Neil 
Published: April 2024

ObjectiveTo characterize the diagnostic yield of patients undergoing evaluation for superior canal dehiscence syndrome (SCDS), and identify alternative conditions diagnosed in patients suspected of, but not ultimately diagnosed with, SCDS.MethodsDiagnostically undifferentiated adult patients suspected of having SCDS were identified between 2016 and 2021 at a tertiary academic medical system. Patients were categorized by diagnostic testing, radiographic superior semicircular canal (SSC) abnormality, symptoms, evaluating clinician specialty, operative intervention, and diagnosis. Differences among groups were assessed for statistical significance.ResultsOf 1242 candidate patients, 477 met inclusion criteria—evaluation by a clinician with SCDS on their differential diagnosis prior to diagnostic imaging. The mean (SD) age was 53.0 (15.0) years and 70.6% were female. A total of 364 patients underwent subsequent diagnostic imaging, and among these, 164 (45.1%) had a radiographic SSC abnormality with 99 (27.2%) receiving a diagnosis of SCDS (two cases of “near dehiscence syndrome”). One third (33.3%) of patients with SCDS underwent operative repair. Most clinicians with the initial suspicion for SCDS were otolaryngologists (90.6%), who had greater diagnostic yield than clinicians from other specialties (22.2% vs. 6.7%, p = 0.012). Patients not diagnosed with SCDS alternatively received 21 unique diagnoses and 52.1% (138/265) were not definitively diagnosed with any condition.ConclusionsThis study characterizes the diagnostic incidence, or yield, of newly identified radiographic SSC abnormalities (45.1%) and SCDS (27.2%) among people suspected of having SCDS. Considerable overlap in presentation between SCDS and other conditions exists, and there is need for improvement in efficiently diagnosing patients with SCDS and audio‐vestibular complaints in general.Level of EvidenceIII Laryngoscope, 134:4095–4100, 2024
Response of soil microorganisms to soil fertility in the process of vegetation rehabilitation of degraded Pinus massoniana forest
Authors: Wang Xiaopeng , Wang He , Zhou Man , Zhuo Zuopin , Lin Gengen , Zhang Yue , Jiang Fangshi , Huang Yanhe , Lin Jinshi 
Published: April 2024

AbstractThe rehabilitation of diverse and three‐dimensional forest vegetation patterns is crucial for preventing forest degradation and improving soil fertility. However, the relationship between soil microbial community and soil fertility was not clear. To accurately assess the capability of vegetation restoration measures on the real impact on degraded soil ecosystems. We selected three vegetation rehabilitation models of degraded Pinus massoniana forests in typical soil erosion areas in China as the research objects, with untreated bare land as the control. All three vegetation construction patterns increased the abundance and diversity of soil bacteria and fungi, thereby enhancing the stability of the soil ecosystem. Additionally, the vegetation rehabilitation models also altered the community structure of soil bacteria and fungi in the degraded P. massoniana forests. The pH and soil fertility index (IFI) were the main factors leading to variations in the community structure of the soil bacteria and fungi. Among them, the grass‐planting model showed a significantly greater improvement in the soil fertility of degraded P. massoniana forests than the shrub‐planting and arbor‐planting models. Furthermore, Ascomycota, Basidiomycota, and Glomeromycota exhibited the most significant response to IFI, indicating their potential as indicator microorganisms for soil fertility changes. The improvement in soil fertility in degraded P. massoniana forests was influenced primarily by the increase in urease activity (S‐UE) according to the vegetation rehabilitation models (84.20%, p = 0.000). In conclusion, the grass‐planting system effectively improved the soil ecosystem quality of degraded P. massoniana forests in southern erosion‐prone areas of China and was suitable for further application.
Surface Topology of Redox‐ and Thermoresponsive Nanogel Droplets
Authors: Hofmann Doreen , Sychev Dmitrii , Zagradska‐Paromova Zlata , Bittrich Eva , Auernhammer Günter K. , Gaitzsch Jens 
Published: April 2024

AbstractHydrogels are usually depicted as a homogenous polymer block with a distinct surface. While defects in the polymer structure are looked into frequently, structural irregularities on the hydrogel surface are often neglected. In this work, thin hydrogel layers of ≈100 nm thickness (nanogels) are synthesized and characterized for their structural irregularities, as they represent the surface of macrogels. The nanogels contain a main‐chain responsiveness (thermo responsive) and a responsiveness in the cross‐linking points (redox responsive). By combining data from ellipsometry using box‐model and two‐segment‐model analysis, as well as atomic force microscopy, a more defined model of the nanogel surface can be developed. Starting with a more densely cross‐linked network at the silica wafer surface, the density of cross‐linking gradually decreases toward the hydrogel–solvent interface. Thermo‐responsive behavior of the main chain affects the entire network equally as all chain segments change solubility. Cross‐linker‐based redox‐responsiveness, on the other hand, is only governed by the inner, more cross‐linked layers of the network. Such dual responsive nanogels hence allow for developing a more detailed model of a hydrogel surface from free radical polymerization. It provides a better understanding of structural defects in hydrogels and how they are affected by responsive functionalities.
Automatic assessment of DWI‐ASPECTS for acute ischemic stroke based on deep learning
Authors: Fang Ting , Jiang Zhuoyun , Zhou Yuxi , Jia Shouqiang , Zhao Jiaqi , Nie Shengdong 
Published: April 2024

AbstractBackgroundAlberta Stroke Program Early Computed Tomography Score (ASPECTS) is a standardized semi‐quantitative method for early ischemic changes in acute ischemic stroke.PurposeHowever, ASPECTS is still affected by expert experience and inconsistent results between readers in clinical. This study aims to propose an automatic ASPECTS scoring model based on diffusion‐weighted imaging (DWI) mode to help clinicians make accurate treatment plans.MethodsEighty‐two patients with stroke were included in the study. First, we designed a new deep learning network for segmenting ASPECTS scoring brain regions. The network is improved based on U‐net, which integrates multiple modules. Second, we proposed using hybrid classifiers to classify brain regions. For brain regions with larger areas, we used brain grayscale comparison algorithm to train machine learning classifiers, while using hybrid feature training for brain regions with smaller areas.ResultsThe average DICE coefficient of the segmented hindbrain area can reach 0.864. With the proposed hybrid classifier, our method performs significantly on both region‐level ASPECTS and dichotomous ASPECTS. The sensitivity and accuracy on the test set are 95.51% and 93.43%, respectively. For dichotomous ASPECTS, the intraclass correlation coefficient (ICC) between our automated ASPECTS score and the expert reading was 0.87.ConclusionsThis study proposed an automated model for ASPECTS scoring of patients with acute ischemic stroke based on DWI images. Experimental results show that the method of segmentation first and then classification is feasible. Our method has the potential to assist physicians in the Alberta Stroke Program with early CT scoring and clinical stroke diagnosis.
Adaptive optimized backstepping tracking control for full‐state constrained nonlinear strict‐feedback systems without using barrier Lyapunov function method
Authors: Zhu Boyan , Xu Ning , Zong Guangdeng , Zhao Xudong 
Published: April 2024

AbstractIn this article, the problem of adaptive optimal tracking control is studied for nonlinear strict‐feedback systems. While not directly measurable, the states of these systems are subject to both time‐varying and asymmetric constraints. Bypassing the conventional barrier Lyapunov function method, the constrained system is transformed into its unconstrained counterpart, thereby obviating the need for feasibility conditions. A specially designed reinforcement learning (RL) algorithm, featuring an observer‐critic‐actor architecture, is deployed in an adaptive optimal control scheme to ensure the stabilization of the converted unconstrained system. Within this architecture, the observer estimates the unmeasurable system states, the critic evaluates the control performance, and the actor executes the control actions. Furthermore, enhancements to the RL algorithm lead to relaxed conditions of persistent excitation, and the design methodology for the observer overcomes the restrictions imposed by the Hurwitz equation. The Lyapunov stability theorem is applied for two primary purposes: to ascertain the boundedness of all signals within the closed‐loop system, and to ensure the accuracy of the output signal in tracking the desired reference trajectory. Finally, numerical and practical simulations are provided to corroborate the effectiveness of the proposed control strategy.
A Deep Learning Framework for Analysis of the Eustachian Tube and the Internal Carotid Artery
Authors: Amanian Ameen , Jain Aseem , Xiao Yuliang , Kim Chanha , Ding Andy S. , Sahu Manish , Taylor Russell , Unberath Mathias , Ward Bryan K. , Galaiya Deepa , Ishii Masaru , Creighton Francis X. 
Published: April 2024

AbstractObjectiveObtaining automated, objective 3‐dimensional (3D) models of the Eustachian tube (ET) and the internal carotid artery (ICA) from computed tomography (CT) scans could provide useful navigational and diagnostic information for ET pathologies and interventions. We aim to develop a deep learning (DL) pipeline to automatically segment the ET and ICA and use these segmentations to compute distances between these structures.Study DesignRetrospective cohort.SettingTertiary referral center.MethodsFrom a database of 30 CT scans, 60 ET and ICA pairs were manually segmented and used to train an nnU‐Net model, a DL segmentation framework. These segmentations were also used to develop a quantitative tool to capture the magnitude and location of the minimum distance point (MDP) between ET and ICA. Performance metrics for the nnU‐Net automated segmentations were calculated via the average Hausdorff distance (AHD) and dice similarity coefficient (DSC).ResultsThe AHD for the ET and ICA were 0.922 and 0.246 mm, respectively. Similarly, the DSC values for the ET and ICA were 0.578 and 0.884. The mean MDP from ET to ICA in the cartilaginous region was 2.6 mm (0.7‐5.3 mm) and was located on average 1.9 mm caudal from the bony cartilaginous junction.ConclusionThis study describes the first end‐to‐end DL pipeline for automated ET and ICA segmentation and analyzes distances between these structures. In addition to helping to ensure the safe selection of patients for ET dilation, this method can facilitate large‐scale studies exploring the relationship between ET pathologies and the 3D shape of the ET.