bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research progress on application of dental stem cells in alveolar bone and periodontal regeneration
Authors: Liu Zhaoyong , Guo Chengbin , Guo Jing , Huang Shan , Du Peng 
Published: September 2024

AbstractPeriodontal disease is a prevalent inflammatory condition affecting the tissues and bone surrounding the teeth, posing significant challenges to oral health. Current treatments often fall short due to incomplete removal of bacteria and plaque, irreversible damage to gums and bone, and their cost and time‐consuming nature. Stem cell therapy offers a promising alternative that potentially addresses these limitations. This review delves into the biology, mechanisms, and applications of dental stem cells (DSCs) in alveolar bone and periodontal regeneration. We begin with an overview of various stem cell types and their characteristics, highlighting their role in tissue regeneration and the mechanisms involved. Subsequently, we examine preclinical and clinical studies that demonstrate the efficacy and safety of DSC therapy, showcasing their potential in restoring periodontal health. The discussion also extends to the critical challenges and future directions in the field, emphasizing the necessity for standardized protocols and rigorous research to fully integrate DSC‐based therapies into routine clinical practice. This review underscores the transformative potential of DSCs in regenerative dentistry, aiming to provide a comprehensive understanding that can propel further advancements and ultimately improve patient outcomes in periodontal therapy.
Clinical Features and Novel Pathogenic Variants of Chinese Patients With McLeod Syndrome and Chorea‐Acanthocytosis
Authors: Yu Hao , Li Ling , Li Xiaoyan , Liu Haipeng 
Published: September 2024

ABSTRACTBackgroundMcLeod syndrome (MLS) and chorea‐acanthocytosis (ChAc) are exceedingly rare diseases characterized by a variety of movement disorders including chorea, dystonia, and Parkinsonism. Genetic analysis plays a key role in early and accurate diagnosis, but relevant variants are still under investigation. This study aims to explore new pathogenic variants in Chinese patients with MLS and ChAc and to conduct a comprehensive analysis of the clinical heterogeneity among these patients.MethodsEighteen Chinese patients who presented with choreatic movements with negative HTT genetic testing were identified and underwent targeted next‐generation sequencing, verified by Sanger sequencing.ResultsTwo novel XK variants (c.970A>T, c.422_423del) were identified in three index MLS patients and six novel VPS13A variants (c.9219C>A, c.3467T>A, c.4208dup, c.9243_9246del, c.5364del, c.556‐290_697‐483del) in five index ChAc patients. One copy number variant of VPS13A (g.79827595_79828762del/c.556‐290_697‐483del) was firstly described in Chinese population.ConclusionAs the currently largest descriptive study of MLS and ChAc patients in China, this study expands on the clinical and genetic spectrum of XK and VPS13A, contributing to the clinical diagnosis of MLS and ChAc.
Theoretical Analysis and Field Investigation on Bearing Characteristics of the Long‐Core SDCM Pile Under Vertical Load in Multilayered Soil
Authors: Gong Zhiyu , Dai Guoliang , Liu Hongbo , Chen Xinsheng , Ouyang Haoran , Jiang Jianxiong 
Published: September 2024

ABSTRACTThe long‐core SDCM pile is a typical type of stiffened deep cement mixing (SDCM) pile, it could be widely exploited in coastal geotechnical engineering because of its high bearing capacity, low settlement, green, and economic advantages. The long‐core SDCM pile is constituted by a PHC pipe pile and cemented soil, the height of the PHC pipe pile is upward than the depth of the cemented soil reinforcement. This study implements a theoretical approach to load transfer analysis of the long‐core SDCM pile under vertical load in layer soil. Herein, the shear constitutive models of the DCM pile‐PHC pipe pile interface and the fictitious soil pile‐PHC pipe pile interface are double exponential models, the compression constitutive model of the soil under the pile and the shear constitutive models of the DCM pile–soil interface and the fictitious soil pile–soil interface are ideal elastic–plastic models. The results obtained from this calculation model can match well with the data from on‐site tests and other analytical solutions. The theoretical model is used to analyze the key parameters LD/LP, DD/DP, Ec, and Ep of the long‐core SDCM pile. The LD/LP and DD/DP are the critical parameters affecting the bearing characteristics, and the minor settlement is affected by the changes of Ec and Ep.
Determination of Compaction Parameters of Cement‐Lime Soils: Boosting‐Based Ensemble Models
Authors: Tilahun Yonas , Qinghua Xiao , Ashango Argaw Asha , Han Xiangyu 
Published: September 2024

ABSTRACTThis study investigates the application of artificial intelligence (AI) models to predict soil compaction characteristics, specifically maximum dry density (MDD) and optimum moisture content (OMC), which are critical for stabilizing construction foundations. Traditional methods for determining MDD and OMC are labor‐intensive and often influenced by factors such as soil type, plasticity, and compaction energy (E). The research employed AI models, including random forest regression (RF‐R), gradient boosting regression (GB‐R), XGBoosting regressor (XGB‐R), and multilinear regression (ML‐R), trained on a comprehensive dataset of soil properties. For the first time, compaction energy has been used as an input variable to predict soil cement lime stabilized compaction parameters. Among the models, GB‐R demonstrated the highest prediction accuracy for MDD and OMC, outperforming RF‐R, XGB‐R, and ML‐R. The performance of built‐in models has been measured by three new index performance metrics: the a20‐index, the index of scatter (IS), and the index of agreement (IA), in addition to four common metrics. Taylor diagrams confirmed the robustness of these predictions during lab testing. A sensitivity analysis revealed that MDD and OMC were most influenced by plastic limit (PL), compaction energy (E), liquid limit (LL), and plasticity index (PI). Additionally, curve‐fitting techniques were applied to model the relationship between MDD, OMC, and these key factors. The results indicated that the GB‐R model, particularly when focused on essential features, provided superior accuracy compared to traditional regression methods, offering a reliable tool for soil stabilization assessments in construction.
Recent advances of silk‐fibroin‐based hydrogel in the field of antibacterial application
Authors: Zou Pan , Li Xiaoliang , Zhao Haiyun , Qu Shenhong , Jiang Zhichao , Lei Xi , Wu Cimin , Liu Ling 
Published: September 2024

AbstractSilk fibroin (SF) hydrogel has been popular in the wound dressing and tissue engineering fields for its good biocompatibility, biodegradation and water retention ability. The development of functional SF‐based hydrogels has gained attention in recent decades in medical fields. Multifunctional SF hydrogel is prepared by the addition and combination of therapeutic agents with cell growth promoting, antibacterial, antioxidant and anti‐inflammatory properties. In particular, the antibacterial activity of SF‐based hydrogel is essential in clinical practice. Despite this growing interest in functional silk protein hydrogels and their potential antimicrobial applications, a comprehensive review on this topic is currently lacking. Therefore, this review aims to provide an in‐depth understanding of the advanced status of the antibacterial application of SF‐based hydrogel, mainly focused on different components loaded. The review begins by briefly introducing the preparation methods of SF‐based hydrogel. Subsequently, the review summarizes diverse components loaded into the hydrogel to provide a comprehensive understanding in the antibacterial field. Furthermore, the article reviews the biomedical applications of antibacterial aspects including tissue engineering, sustained drug release, wound repair and adhesive use. Also the challenges associated with developing SF‐based hydrogels are analyzed alongside future prospects. We hope that this study will contribute to promoting the application and innovation of SF material in the clinical antibacterial field. © 2024 Society of Chemical Industry.
Use of cardiopulmonary exercise testing to identify mechanisms of exertional symptoms in children with long COVID
Authors: Lowe Adam , Sabati Arash , Bhatia Rajeev 
Published: September 2024

AbstractBackgroundLittle is known about the mechanisms of exercise intolerance and exertional symptoms in children with long COVID. Through utilization of cardiopulmonary exercise testing (CPET), this study is the first of its kind to evaluate exertional symptoms and attempt to identify potential mechanism of long COVID‐19 in children.ObjectiveTo determine if CPET will uncover potential reasons for persistent symptoms of long COVID when there is no indication of cardiopulmonary or upper airway disease.MethodsWe performed a retrospective chart review study involving children 6–17 years of age with symptoms of long COVID at Phoenix Children''s Hospital from January 1, 2021, to June 1, 2022. Symptoms included but were not limited to exercise intolerance, fatigue, shortness of breath, dyspnea on exertion, and chest pain. We recorded any measurable abnormalities present on CPET after comparing it to established normal reference ranges. Range, median, and SD of data points were calculated and p values were determined using the Mann–Whitney U and Fisher's exact test.ResultsTwenty‐three children with exertional symptoms consistent with long COVID were identified. The most frequent symptoms reported during exercise include dyspnea on exertion (35%), followed by chest pain (30%) and dizziness (13%). Nearly half of the children (47%) demonstrated decreased exercise capacity with 30% displaying limitations due to deconditioning, 22% limited by body habitus, and 13% due to bronchospasm. Other contributing factors include ventilation to perfusion mismatch and volitional hyperventilation.ConclusionDecreased aerobic activity due to multiple factors was found in 47% of children with a history of COVID‐19. This study illustrates the importance of ongoing research into this phenomenon to elucidate its mechanism and assist physicians in making decisions regarding referral to specialists for further testing.
Preoperative physiotherapy for improving the reserve capacity in a patient with esophageal cancer and frailty: A case report
Authors: Takahashi Yusuke , Okura Kazuki , Sakamoto Ririko , Hasegawa Kakeru , Nagaki Yushi , Wakita Akiyuki , Sato Yusuke 
Published: September 2024

AbstractIntroductionFrailty is significantly correlated with a higher incidence of medical complications during hospitalization after esophagectomy. As frailty is thought to be a reversible condition, improving the reserve capacity through preoperative physical therapy is expected to reduce the risk of postoperative pulmonary complications (PPCs). Herein, we report our experience with preoperative physical therapy in a patient with esophageal cancer who was considered to have inadequate fitness for surgery owing to poor physical performance.Case PresentationA 72‐year‐old man (height: 169.5 cm, weight: 54.7 kg, body mass index: 18.9 kg/m2) with esophagogastric junction tumors (cStage IIIA) was hospitalized and scheduled to undergo surgery based on preoperative screening. He was categorized as frail according to the revised Japanese version of the Cardiovascular Health Study criteria and the Mini Nutritional Assessment Short‐Form indicated severe malnutrition. We focused on physical therapy to improve exercise tolerance and prevent PPCs and devised a short‐term intensive physical therapy program comprising minimal exercises that the patient could perform efficiently. The program consisted of only inspiratory muscle training and aerobic exercises. His maximal inspiratory pressure (MIP) and 6‐min walking distance improved by 30 cm H2O and 145 m, respectively, on the day before surgery compared with those on day 8. The percentage predicted value of the MIP improved from 56.6% at the start of physical therapy to 102.9% on the day before surgery. On day 43, the patient underwent subtotal esophagectomy and was able to ambulate on postoperative day 5 without respiratory complications.ConclusionWe conducted a short‐term, intensive, and minimal preoperative physical therapy program for a patient with esophageal cancer who had physical frailty. Preoperative physical therapy to increase the reserve capacity may result in a favorable postoperative course even in patients with physical frailty.
Assimilating atmospheric motion vector winds using a feature track correction observation operator
Authors: Hoffman Ross N. , Liu Hui , Lukens Katherine E. , Garrett Kevin , Ide Kayo 
Published: September 2024

AbstractAtmospheric motion vector (AMV) winds have positive impacts in operational numerical weather prediction (NWP) systems. These impacts might be improved with better treatment of the following error characteristics of AMVs. First, AMVs may have wind errors due to height assignment errors. Second, AMVs may have additional wind‐speed biases in addition to those due to height assignment errors. Third, AMVs are representative of motion in a possibly thick atmospheric layer, not a single atmospheric level. Previous work proposed a variational feature track correction (FTC) method in which an observation operator is implemented that averages the NWP background winds optimally in the vertical. Here, a prototype feature track correction observation operator (FTC‐OO) is implemented in the NOAA/NCEP data assimilation (DA) system. The parameters describing the vertical averaging are determined offline based on previous DA cycles. The FTC‐OO reduces the observation minus background standard deviation by about 4%. Global observing‐system experiments (OSEs) are performed comparing the FTC‐OO with the operational observation operator. The forecast verification sample is 41 10‐day forecasts. The OSEs show that the FTC‐OO improves forecast skill, primarily for tropical geopotential height. Additional OSEs are performed that include Aeolus wind observations. The hypothesis that the Aeolus winds would enhance the impact of the FTC‐OO was not borne out in these experiments—the Aeolus observations alone have a significant positive impact, but the impact of the FTC method in the presence of the Aeolus
observations is neither enhanced nor degraded compared with the impact of the FTC method alone.
Spatial Distribution Control of Room‐Temperature Single Photon Emitters in the Telecom Range from GaN Thin Films Grown on Patterned Sapphire Substrates
Authors: Kim Hyemin , Song Yong‐Ho , Ko Young‐Ho , Cho Yong‐Hoon 
Published: September 2024

AbstractSpatial distribution‐controlled single‐photon emitters operating is demonstrated in telecom wavelength range at room‐temperature with GaN thin film grown on a patterned sapphire substrate (PSSs) with varying pattern sizes and dimensions. The analysis focuses on the various optical properties of defects within the GaN thin film, particularly their interactions with PSS. The confocal fluorescence mapping at room temperature revealed localized single‐photon emitters inside the GaN layers located between the patterns. In addition, compared to conventional sapphire substrates, PSS can scatter photons outside the total reflection cone, thereby enhancing the light extraction efficiency. From the samples, the single photon emission is observed in the telecom wavelength ranging from 1.1 to 1.35 µm at room temperature, which is critical for advancing quantum communication technologies, and elucidate how the physical characteristics of PSS influence the performance and efficiency of GaN‐based single‐photon emitters.
Hip Dips Technique: Filling Lateral Depressions with Hyaluronic Acid of Large Particles
Authors: Lourenco Luciana , Medeiros Helena , Ferreira Luísa , Favoretto Dias de Oliveira Natasha , Lopes Roberta , Sigrist Rosa 
Published: September 2024

Abstract

Introduction
Hip dips, often referred to as the pronounced trochanteric depression, can be caused by athletic muscular definition or the ageing process. This depression might impact the desirable contour of the buttocks in some female patients.

Patients and Methods
A technique is described for female patients exhibiting moderate to severe degrees of lateral trochanteric depression. This technique utilizes a specific marking, a well-thought-out plan and a designated product. Eleven patients were selected to use this technique to improve their trochanteric depression and enhance their buttocks contour without undergoing surgery. The product used was Sofiderm Subskin, which was applied at the intermediate subcutaneous layer.

Results
The authors report favourable aesthetic results with the proposed technique, and the patients expressed high satisfaction levels.

Discussion
Though safe for injection, the lateral gluteal depression can still be challenging to expand. The product chosen has both a high G prime and a large molecular size, contributing to its resistance to deformation.

Conclusion
The hip dips technique using hyaluronic acid for the augmentation of lateral depression has shown to be minimally invasive; it provides quick results without significant risks or downtime.
A Secure Authenticated Healthcare Data Analysis Mechanism in IoMT‐Enabled Healthcare
Authors: Wazid Mohammad , Nautiyal Shivansh , Das Ashok Kumar , Shetty Sachin , Islam SK Hafizul 
Published: September 2024

ABSTRACTWe frequently wait long when we go to the hospital for a doctor's appointment. However, many people cannot afford to stay in such lengthy lines in today's busy world. Daily, we observe that manually performing such tasks takes considerable time, and automating such processes can save time. Securing the information the user provides, medical staff, and devices is crucial. Before accessing such information, the user must be authenticated to ensure that unauthorized persons cannot retrieve patient records. Internet‐of‐Things (IoT) has revolutionized the healthcare industry with the emergence of IoT‐based healthcare systems, called Internet‐of‐Medical‐Things (IoMT). It offers efficient and personalized patient care. However, the vast amounts of sensitive patient data transmitted and stored within these systems raise serious concerns about data security and privacy. This article proposes a secure healthcare data analysis mechanism with user sign‐in authentication capability for the IoMT‐enabled healthcare system (SA‐IoMTH). It addresses the unique security challenges faced by these systems. The formal security verification using the scyther tool and a security analysis of SA‐IoMTH are provided, proving its security against various possible attacks. The proposed SA‐IoMTH outperforms the other existing approaches. Finally, a smartphone application is also implemented for SA‐IoMTH to check its usefulness in real‐world scenarios.
Privacy Re‐Identification Attacks on Tabular GANs
Authors: Alshantti Abdallah , Rasheed Adil , Westad Frank 
Published: September 2024

ABSTRACTGenerative models are effective in producing realistic tabular synthetic data that resembles the properties and distribution of real datasets. While synthetic data has numerous applications across various domains, generative models are susceptible to overfitting, which can lead to the leakage of sensitive information from training data. Privacy attacks exacerbate this issue by attempting to identify original data records from synthetic data, especially when the attacker possesses some knowledge about the generative model. In this work, we investigate the privacy risks associated with using generative adversarial networks (GANs) to create tabular synthetic datasets. More specifically, we develop privacy reconstruction attacks designed to identify training samples by minimizing their proximity to synthetic records. Our experimental analysis considers various scenarios of reconstruction attacks, in which attackers have different levels of access to the generative models. Additionally, we propose multi‐objective optimization using evolutionary algorithms to perturb synthetic samples closer to original training data points. The experimental results show that reconstruction attacks can effectively identify training samples, with privacy threats significantly increasing when attackers have access to the generative model. Furthermore, our findings indicate that using evolutionary algorithms in reconstruction attacks further heightens the risk of identifying confidential samples. Comparing our attacks against state‐of‐the‐art privacy attacks on tabular GANs further reveals that our reconstructions attacks are considerably more effective in recovering real data records.
Understanding machine learning predictions of wastewater treatment plant sludge with explainable artificial intelligence
Authors: Nasir Fuad Bin , Li Jin 
Published: September 2024

AbstractThis study investigates the use of machine learning (ML) models for wastewater treatment plant (WWTP) sludge predictions and explainable artificial intelligence (XAI) techniques for understanding the impact of variables behind the prediction. Three ML models, random forest (RF), gradient boosting machine (GBM), and gradient boosting tree (GBT), were evaluated for their performance using statistical indicators. Input variable combinations were selected through different feature selection (FS) methods. XAI techniques were employed to enhance the interpretability and transparency of ML models. The results suggest that prediction accuracy depends on the choice of model and the number of variables. XAI techniques were found to be effective in interpreting the decisions made by each ML model. This study provides an example of using ML models in sludge production prediction and interpreting models applying XAI to understand the factors influencing it. Understandable interpretation of ML model prediction can facilitate targeted interventions for process optimization and improve the efficiency and sustainability of wastewater treatment processes.Practitioner Points
Explainable artificial intelligence can play a crucial role in promoting trust between machine learning models and their real‐world applications.
Widely practiced machine learning models were used to predict sludge production of a United States wastewater treatment plant.
Feature selection methods can reduce the required number of input variables without compromising model accuracy.
Explainable artificial intelligence techniques can explain driving variables behind machine learning prediction.