bluuebunny/crossref_metadata_2025 huggingface dataset:
Mapping fine-scale seagrass disturbance using bi-temporal UAV-acquired images and multivariate alteration detection
Authors: Simpson Jamie , Davies Kevin P. , Barber Paul , Bruce Eleanor
Published: August 2024

Seagrasses provide critical ecosystem services but cumulative human pressure on coastal environments has seen a global decline in their health and extent. Key processes of anthropogenic disturbance can operate at local spatio-temporal scales that are not captured by conventional satellite imaging. Seagrass management strategies to prevent longer-term loss and ensure successful restoration require effective methods for monitoring these fine-scale changes. Current seagrass monitoring methods involve resource-intensive fieldwork or recurrent image classification. This study presents an alternative method using iteratively reweighted multivariate alteration detection (IR-MAD), an unsupervised change detection technique originally developed for satellite images. We investigate the application of IR-MAD to image data acquired using an unoccupied aerial vehicle (UAV). UAV images were captured at a 14-week interval over two seagrass beds in Brisbane Water, NSW, Australia using a 10-band Micasense RedEdge-MX Dual camera system. To guide sensor selection, a further three band subsets representing simpler sensor configurations (6, 5 and 3 bands) were also analysed using eight categories of seagrass change. The ability of the IR-MAD method, and for the four different sensor configurations, to distinguish the categories of change were compared using the Jeffreys-Matusita (JM) distance measure of spectral separability. IR-MAD based on the full 10-band sensor images produced the highest separability values indicating that human disturbances (propeller scars and other seagrass damage) were distinguishable from all other change categories. IR-MAD results for the 6-band and 5-band sensors also distinguished key seagrass change features. The IR-MAD results for the simplest 3-band sensor (an RGB camera) detected change features, but change categories were not strongly separable from each other. Analysis of IR-MAD weights indicated that additional visible bands, including a coastal blue band and a second red band, improve change detection. IR-MAD is an effective method for seagrass monitoring, and this study demonstrates the potential for multispectral sensors with additional visible bands to improve seagrass change detection.
Beyond hand-crafted features for pretherapeutic molecular status identification of pediatric low-grade gliomas
Authors: Kudus Kareem , Wagner Matthias W. , Namdar Khashayar , Bennett Julie , Nobre Liana , Tabori Uri , Hawkins Cynthia , Ertl-Wagner Birgit Betina , Khalvati Farzad
Published: August 2024

The use of targeted agents in the treatment of pediatric low-grade gliomas (pLGGs) relies on the determination of molecular status. It has been shown that genetic alterations in pLGG can be identified non-invasively using MRI-based radiomic features or convolutional neural networks (CNNs). We aimed to build and assess a combined radiomics and CNN non-invasive pLGG molecular status identification model. This retrospective study used the tumor regions, manually segmented from T2-FLAIR MR images, of 336 patients treated for pLGG between 1999 and 2018. We designed a CNN and Random Forest radiomics model, along with a model relying on a combination of CNN and radiomic features, to predict the genetic status of pLGG. Additionally, we investigated whether CNNs could predict radiomic feature values from MR images. The combined model (mean AUC: 0.824) outperformed the radiomics model (0.802) and CNN (0.764). The differences in model performance were statistically significant (p-values < 0.05). The CNN was able to learn predictive radiomic features such as surface-to-volume ratio (average correlation: 0.864), and difference matrix dependence non-uniformity normalized (0.924) well but was unable to learn others such as run-length matrix variance (− 0.017) and non-uniformity normalized (− 0.042). Our results show that a model relying on both CNN and radiomic-based features performs better than either approach separately in differentiating the genetic status of pLGGs, and that CNNs are unable to express all handcrafted features.
Organocatalytic synthetic route to esters and their application in hydrosilylation process
Authors: Mermela Aleksandra , Bołt Małgorzata , Mrzygłód Aleksandra , Żak Patrycja
Published: August 2024

A facile esterification of α,β-unsaturated aldehydes with alcohols has been developed for the synthesis of esters by using bulky N-heterocyclic (NHC) carbene as a metal-free and eco-friendly organocatalyst. This new protocol has been proved to be effective with a wide substrate scope, giving selective esters in yields greater than 84% under mild conditions. Moreover, proposed synthetic strategy enables modification of various types of silsesquioxanes (SQ) which cannot or are technically difficult to be carried out with known protocols. For the first time, a one-pot sequential esterification/hydrosilylation has been successfully carried out.
Polarization-diverse soliton transitions and deterministic switching dynamics in strongly-coupled and self-stabilized microresonator frequency combs
Authors: Wang Wenting , Aldhafeeri Alwaleed , Zhou Heng , Melton Tristan , Jiang Xinghe , Vinod Abhinav Kumar , Yu Mingbin , Lo Guo-Qiang , Kwong Dim-Lee , Wong Chee Wei
Published: August 2024

Dissipative Kerr soliton microcombs in microresonators have enabled fundamental advances in chip-scale precision metrology, communication, spectroscopy, and parallel signal processing. Here we demonstrate polarization-diverse soliton transitions and deterministic switching dynamics of a self-stabilized microcomb in a strongly-coupled dispersion-managed microresonator driven with a single pump laser. The switching dynamics are induced by the differential thermorefractivity between coupled transverse-magnetic and transverse-electric supermodes during the forward-backward pump detunings. The achieved large soliton existence range and deterministic transitions benefit from the switching dynamics, leading to the cross-polarized soliton microcomb formation when driven in the transverse-magnetic supermode of the single resonator. Secondly, we demonstrate two distinct polarization-diverse soliton formation routes – arising from chaotic or periodically-modulated waveforms via pump power selection. Thirdly, to observe the cross-polarized supermode transition dynamics, we develop a parametric temporal magnifier with picosecond resolution, MHz frame rate and sub-ns temporal windows. We construct picosecond temporal transition portraits in 100-ns recording length of the strongly-coupled solitons, mapping the transitions from multiple soliton molecular states to singlet solitons. This study underpins polarization-diverse soliton microcombs for chip-scale ultrashort pulse generation, supporting applications in frequency and precision metrology, communications, spectroscopy and information processing.
An accurate and transferable machine learning interatomic potential for nickel
Authors: Gong Xiaoguo , Li Zhuoyuan , Pattamatta A. S. L. Subrahmanyam , Wen Tongqi , Srolovitz David J.
Published: August 2024

Nickel (Ni) is a magnetic transition metal with two allotropic phases, stable face-centered cubic (FCC) and metastable hexagonal close-packed (HCP), widely used in structural applications. Magnetism affects many mechanical and defect properties, but spin-polarized density functional theory (DFT) calculations are computationally inefficient for studying material behavior requiring large system sizes and/or long simulation times. Here we develop a “magnetism-hidden” machine-learning Deep Potential (DP) model for Ni without a descriptor for magnetic moments, using training datasets derived from spin-polarized DFT calculations. The DP-Ni model exhibits excellent transferability and representability for a wide-range of FCC and HCP properties, including (finite-temperature) lattice parameters, elastic constants, phonon spectra, and many defects. As an example of its applicability, we investigate the Ni FCC-HCP allotropic phase transition under (high-stress) uniaxial tensile loading. The high accurate DP model for magnetic Ni facilitates accurate large-scale atomistic simulations for complex phase transformation behavior and may serve as a foundation for developing interatomic potentials for Ni-based superalloys and other multi-principal component alloys.
SLL-1A-16 suppresses proliferation and induces autophagy in non-small-cell lung cancer cells via the AKT/mTOR signaling pathway
Authors: Luo Xiaoqin , Wang Jin , Wang Ruichang , Lian Jiabing , Guo Mengnan , Zhou Hongrui , Zhang Mengxue , Yang Zhe , Li Xiaolong , He Xianran , Bi Xiuli
Published: August 2024

Schematic of proposed mechanism for the treatment of the SLL-1A-16 inhibiting the proliferation in NSCLC.
High-entropy cobalt-free perovskite as a high-performing nanofiber cathode for solid oxide fuel cells
Authors: Fu Xinmin , Lü Shiquan , Meng Xiangwei , Sun Chuxiao , Wei Maobin , Jiang Haipeng , Gong Weijiang
Published: August 2024

Enhanced oxygen reduction reaction over a high-entropy cobalt-free perovskite with a nominal formula of La1/6Pr1/6Nd1/6Ba1/6Sr1/6Ca1/6FeO3−δ.
Integrating β-ketoenamine linkages into covalent organic frameworks toward efficient overall photocatalytic hydrogen peroxide production
Authors: Shu Chang , Xie Peixuan , Yang Xiaoju , Yang Xuan , Gao Hui , Tan Bien , Wang Xiaoyan
Published: August 2024

A β-ketoenamine-linked covalent organic framework photocatalyst was synthesized to achieve overall photocatalytic H2O2 production, which shows enhanced the exciton dissociation, oxidizing ability and the interaction with reactants.
Regulating the d-band center of Pt for highly effective H2 storage through toluene hydrogenation at low temperatures
Authors: Zhang Xingyu , Xu Ke , Liao Qiankang , Sun Yu , Zhang Sai
Published: August 2024

The d-band center of Pt nanoparticles has been demonstrated as an effective parameter for modulating their catalytic activity for toluene hydrogenation, thereby promoting the H2 strong in liquid organic molecules.
Explore human parsing modality for action recognition
Authors: Liu Jinfu , Ding Runwei , Wen Yuhang , Dai Nan , Meng Fanyang , Zhang Fang‐Lue , Zhao Shen , Liu Mengyuan
Published: August 2024

Multimodal‐based action recognition methods have achieved high success using pose and RGB modality. However, skeletons sequences lack appearance depiction and RGB images suffer irrelevant noise due to modality limitations. To address this, the authors introduce human parsing feature map as a novel modality, since it can selectively retain effective semantic features of the body parts while filtering out most irrelevant noise. The authors propose a new dual‐branch framework called ensemble human parsing and pose network (EPP‐Net), which is the first to leverage both skeletons and human parsing modalities for action recognition. The first human pose branch feeds robust skeletons in the graph convolutional network to model pose features, while the second human parsing branch also leverages depictive parsing feature maps to model parsing features via convolutional backbones. The two high‐level features will be effectively combined through a late fusion strategy for better action recognition. Extensive experiments on NTU RGB + D and NTU RGB + D 120 benchmarks consistently verify the effectiveness of our proposed EPP‐Net, which outperforms the existing action recognition methods. Our code is available at https://github.com/liujf69/EPP‐Net‐Action.
A robust low‐rank tensor completion model with sparse noise for higher‐order data recovery
Authors: Wang Min , Chen Zhuying , Zhang Shuyi
Published: August 2024

The tensor singular value decomposition‐based model has garnered increasing attention in addressing tensor recovery challenges. However, existing tensor recovery methods exhibit certain inherent limitations. Some ignore the simultaneous effects of noise and missing values, while most can't handle higher‐order tensors, which are not reflective of real‐world scenarios. The information redundancy within tensor data often leads to a prevailing low‐rank structure, making low‐rankness a vital prior in the tensor recovery process. To tackle this pressing issue, a robust low‐rank tensor recovery framework is proposed to rehabilitate higher‐order tensors corrupted by sparse noise and missing entries. In the model, the tensor nuclear norm derived for order‐d tensors (d 4) are employed as a representation of the low‐rank prior, while utilizing the ‐norm to model the sparse noise. To solve the proposed model, an efficient Alternating direction method of multipliers algorithm is developed. A series of experiments are performed on synthetic and real‐world datasets. The results show that the superior performance of the method compared with other algorithms dedicated to addressing order‐d tensor recovery challenges. Notably, in scenarios where the data is severely compromised (noise ratio 40%, sample ratio 70%), the algorithm consistently outperforms its competitors, achieving significantly improved results.
Coupled piecewise tri-stable stochastic resonance system driven by dual inputs and joint EMD/VMD analysis
Authors: Zhang Gang , Xiong Qing , Bi Lujie , Li Zhaorui
Published: August 2024

A novel piecewise tri-stable stochastic resonance (NPTSR) system is proposed to address the issue of output saturation caused by high-order terms limitation in this paper. Building upon this, the exploration is extended to a coupled piecewise tri-stable stochastic resonance system driven by dual inputs (DCPTSR). First, we uncover the influence of dual input interaction on output quality, finding that when the fRequencies of the two input signals are consistent, varying the amplitude of the driving signal can effectively enhance the output performance of the target signal. Secondly, by utilizing the adiabatic approximation theory, the steady-state probability density (SPD) and signal-to-noise ratio (SNR) of the DCPTSR system are derived, which allows us to analyze the effects of various parameters on both SPD and SNR. Next, three combined denoising systems, namely EMD-DCPTSR, VMD-DCPTSR, and SDCPTSR, are constructed by utilizing empirical mode decomposition (EMD), variational mode decomposition (VMD), and the stochastic resonance (SR). Through numerical simulations, we demonstrate that the combined denoising system outperforms the stand-alone SR system, and we analyze the stochastic resonance phenomenon of the DCPTSR system using the spectral amplification (SA) coefficient as an evaluation index. Finally, to assess practical applicability, these systems are deployed for bearing fault detection. The experimental results exhibit notable signal-to-noise gain improvements for the DCPTSR system compared to standalone SR systems by 0.7699 ∼ 9.4541 dB. The EMD-DCPTSR system shows signal-to-noise gain improvements of 0.3245 ∼ 1.1709 dB compared to the VMD-DCPTSR and SDCPTSR systems. Moreover, all three combined denoising systems outperform the standalone SR system in terms of signal processing capabilities. In conclusion, this paper extensively investigates the interaction between the two input signals in a dual-input system and studies the output performance of using EMD, VMD, and SR as preprocessing methods for the SR system. Through numerical simulations and practical engineering applications, we highlight the substantial advantages of combined denoising systems. These findings offer essential theoretical insights and promising prospects for engineering applications.
A dual-parameter Na-based PCF sensor with wide sensing bandwidth based SPR effect
Authors: Hu Linchuan , Li Jianshe , Yin Zhiyong , Gao Zhigang , Li Hongwei , Li Kaifeng , Zhang Zhibing , Li Shuguang
Published: August 2024

Wide sensing bandwidth is a prerequisite for dual-channel sensors to have a wide detection range and high sensitivity. To obtain a dual-channel optical fiber sensor with wide sensing bandwidth, a Na-based photonic crystal fiber sensor based on the surface plasmon resonance effect is proposed. The proposed sensor structure is numerically analyzed by finite element method. The sensor builds the fiber core in the center of the fiber, chooses alkali metal Na as the plasma material to stimulate the SPR effect, and polymethyl methacrylate and polydimethylsiloxane (PDMS) will be coated on the Na film as the protective layer. As a temperature-sensitive material, PDMS also takes into account the role of constructing temperature-sensing channels. The sensor bandwidth covers 600–1700 nm. The results show that when the RI range of the measured object is 1.33–1.42, the maximum sensitivity of the refractive index sensor is 11100 nm/RIU, and when the temperature range of the measured object is −40–20 °C, the maximum sensitivity of the temperature sensor is 6.4 nm/°C. Meanwhile, the loss peak of the sensor has a lower half-height width and greater detection accuracy. It has a good application prospect in the fields of biosensing and environmental monitoring.
Parametric controllable planar multi-scroll chaotic attractors in a 3-D memristive tabu learning single neuron model
Authors: Ding Ruoyu , Bao Han , Wang Kai , Xu Dan , Huang Liping
Published: August 2024

In this paper, a three-dimensional (3D) autonomous tabu learning single neuron model is proposed, which is achieved by using a sinusoidal activation function and introducing a memristor synapse. This model exhibits the remarkable capability to produce a series of planar multi-scroll chaotic attractors, and its unique feature lies in the ability to control the number of scrolls. The investigation of the planar multi-scroll chaotic attractors and its dynamical behaviors is conducted through the analysis of phase plane portraits, bifurcation diagrams, and spectral entropies. The numerical simulations unveil a compelling relationship between the number of chaotic scrolls and specific control parameters governing the model. To further validate the findings, a 3D autonomous tabu learning single neuron model is implemented on a digital hardware platform. In an effort to extend the practical significance of this research, the multi-scroll chaotic phenomenon generated by the proposed model is deployed for image encryption. The fusion of mathematical modeling, digital hardware implementation, and practical application underscores the universality and significance of the proposed single neuron model in the fields of chaotic systems and engineering applications.
In the twilight zone of protein sequence homology: do protein language models learn protein structure?
Authors: Kabir Anowarul , Moldwin Asher , Bromberg Yana , Shehu Amarda
Published: August 2024

Motivation

Protein language models based on the transformer architecture are increasingly improving performance on protein prediction tasks, including secondary structure, subcellular localization, and more. Despite being trained only on protein sequences, protein language models appear to implicitly learn protein structure. This paper investigates whether sequence representations learned by protein language models encode structural information and to what extent.

Results

We address this by evaluating protein language models on remote homology prediction, where identifying remote homologs from sequence information alone requires structural knowledge, especially in the “twilight zone” of very low sequence identity. Through rigorous testing at progressively lower sequence identities, we profile the performance of protein language models ranging from millions to billions of parameters in a zero-shot setting. Our findings indicate that while transformer-based protein language models outperform traditional sequence alignment methods, they still struggle in the twilight zone. This suggests that current protein language models have not sufficiently learned protein structure to address remote homology prediction when sequence signals are weak.

Availability and implementation

We believe this opens the way for further research both on remote homology prediction and on the broader goal of learning sequence- and structure-rich representations of protein molecules. All code, data, and models are made publicly available.
Improving protein function prediction by learning and integrating representations of protein sequences and function labels
Authors: Boadu Frimpong , Cheng Jianlin
Published: August 2024

Motivation

As fewer than 1% of proteins have protein function information determined experimentally, computationally predicting the function of proteins is critical for obtaining functional information for most proteins and has been a major challenge in protein bioinformatics. Despite the significant progress made in protein function prediction by the community in the last decade, the general accuracy of protein function prediction is still not high, particularly for rare function terms associated with few proteins in the protein function annotation database such as the UniProt.

Results

We introduce TransFew, a new transformer model, to learn the representations of both protein sequences and function labels [Gene Ontology (GO) terms] to predict the function of proteins. TransFew leverages a large pre-trained protein language model (ESM2-t48) to learn function-relevant representations of proteins from raw protein sequences and uses a biological natural language model (BioBert) and a graph convolutional neural network-based autoencoder to generate semantic representations of GO terms from their textual definition and hierarchical relationships, which are combined together to predict protein function via the cross-attention. Integrating the protein sequence and label representations not only enhances overall function prediction accuracy, but delivers a robust performance of predicting rare function terms with limited annotations by facilitating annotation transfer between GO terms.

Availability and implementation

https://github.com/BioinfoMachineLearning/TransFew.
splicekit: an integrative toolkit for splicing analysis from short-read RNA-seq
Authors: Rot Gregor , Wehling Arne , Schmucki Roland , Berntenis Nikolaos , Zhang Jitao David , Ebeling Martin
Published: August 2024

Motivation

Analysis of alternative splicing using short-read RNA-seq data is a complex process that involves several steps: alignment of reads to the reference genome, identification of alternatively spliced features, motif discovery, analysis of RNA-protein binding near donor and acceptor splice sites, and exploratory data visualization. To the best of our knowledge, there is currently no integrative open-source software dedicated to this task.

Results

Here, we introduce splicekit, a Python package that provides and integrates a set of existing and novel splicing analysis tools for conducting splicing analysis.

Availability and implementation

The software splicekit is open-source and available at Github (https://github.com/bedapub/splicekit) and via the Python Package Index.
Spaln3: improvement in speed and accuracy of genome mapping and spliced alignment of protein query sequences
Authors: Gotoh Osamu
Published: August 2024

Motivation

Spaln is the earliest practical tool for self-sufficient genome mapping and spliced alignment of protein query sequences onto a mammalian-sized eukaryotic genomic sequence. However, its computational speed has become inadequate for the analysis of rapidly growing genomic and transcript sequence data.

Results

The dynamic programming calculation of Spaln has been sped up in two ways: (i) the introduction of the multi-intermediate unidirectional Hirschberg method and (ii) SIMD-based vectorization. The new version, Spaln3, is ∼7 times faster than the latest Spaln version 2, and its gene prediction accuracy is consistently higher than that of Miniprot.

Availability and implementation

https://github.com/ogotoh/spaln.
The structure of the environment influences the patterns and genetics of local adaptation
Authors: Booker Tom R
Published: August 2024

Environmental heterogeneity can lead to spatially varying selection, which can, in turn, lead to local adaptation. Population genetic models have shown that the pattern of environmental variation in space can strongly influence the evolution of local adaptation. In particular, when environmental variation is highly autocorrelated in space local adaptation will more readily evolve. However, there have been few attempts to test this prediction empirically or characterize the consequences it would have for the genetic architecture underlying local adaptation. In this study, I analyze a large-scale provenance trial conducted on lodgepole pine and find suggestive evidence that spatial autocorrelation in environmental variation is related to the strength of local adaptation that has evolved in that species. Motivated by those results, I use simulations to model local adaptation to different spatial patterns of environmental variation. The simulations confirm that local adaptation is expected to increase with the degree of spatial autocorrelation in the selective environment, but also show that highly heterogeneous environments are more likely to exhibit high variation in local adaptation, a result not previously described. I find that the spatial pattern of environmental variation influences the genetic architectures underlying local adaptation. In highly autocorrelated environments, the genetic architecture of local adaptation tends to be composed of high-frequency alleles with small phenotypic effects. In weakly autocorrelated environments, locally adaptive alleles may have larger phenotypic effects but are present at lower frequencies across species’ ranges and experience more evolutionary turnover. Overall, this work emphasizes the profound importance that the spatial pattern of selection can have on the evolution of local adaptation and how spatial autocorrelation should be considered when formulating hypotheses in ecological and genetic studies.
Anisogamy and the Darwin–Bateman paradigm
Authors: Janicke Tim
Published: August 2024

The Darwin–Bateman paradigm advanced as the central concept to explain the evolutionary origin of sex differences. However, debates regarding its theoretical underpinnings persist, particularly with respect to the role of anisogamy in sexual selection. The theoretical work presented by Lehtonen and Parker suggests that the initial split in gamete production rate drives sex differences in sexual selection but that any further variation in the degree of anisogamy is not expected to alter the strength of sexual selection in males and females. Here, I discuss the historical background of a recently emerged controversy and present empirical data that corroborate the theoretical predictions. Lehtonen and Parker’s contribution refines our understanding of the Darwin–Bateman paradigm by providing a broad theory for large-scale patterns of sex differences that can be observed in nature. Further understanding of how demographic and environmental factors influence sexual selection is essential to predict the vast diversity of sex differences across the tree of life, beyond the primordial impact of anisogamy.
Carbon metabolism of a novel isolate from Lacticaseibacillus rhamnosus Probio-M9 derived through space mutant
Authors: Sun Yue , Su Xin , Zhao Lixia , Sun Tiansong , Liu Wenjun
Published: August 2024

Aims

Carbon source is a necessary nutrient for bacterial strain growth. In industrial production, the cost of using different carbon sources varies greatly. Moreover, the complex environment in space may cause metabolic a series of changes in the strain, and this method has been successfully applied in some basic research. To date, space mutagenesis is still limited number of studies, particularly in carbon metabolism of probiotics.

Methods and results

HG-R7970-41 was isolated from bacterium suspension (Probio-M9) after space flight, which can produce capsular polysaccharide after space mutagenesis. Phenotype Microarray (PM) was used to evaluated the metabolism of HG-R7970-41 in 190 single carbon sources. RNA sequencing and total protein identification of two strains revealed their different carbon metabolism mechanisms. PM results demonstrated the metabolism of 10 carbon sources were different between Probio-M9 and HG-R7970-41. Transcriptomic and proteomic analyses revealed that this change in carbon metabolism of HG-R7970-41 mainly related to changes in phosphorylation and the glycolysis pathway. Based on the metabolic mechanism of different carbon sources and related gene cluster analysis, we found that the final metabolic activities of HG-R7970-41 and Probio-M9 were mainly regulated by PTS-specific membrane embedded permease, carbohydrate kinase and two rate-limiting enzymes (phosphofructokinase and pyruvate kinase) in the glycolysis pathway. The expanded culture test also confirmed that HG-R7970-41 had different metabolic characteristics from original strain.

Conclusions

These results suggested that space environment could change carbon metabolism of Probio-M9. The new isolate (HG-R7970-41) showed a different carbon metabolism pattern from the original strain mainly by the regulation of two rate-limiting enzymes.
Evaluation of YouTube as a Health Education Resource for Skin Graft Procedures
Authors: Torres Grant B , Brondeel Kimberley C , Sledge Trey R , Nus Bradley M , Kamma Sai A , Wu Kylie , Branski Ludwik K
Published: August 2024

Recent studies indicate that YouTube has become a primary source of healthcare information for patients. Videos about Skin Graft Procedures (SGP) on YouTube have accumulated millions of views, yet there lacks a publication investigating the educational quality of this content. With current literature revealing misleading healthcare information found on YouTube, this study aims to evaluate the educational quality of videos related to SGP. YouTube was searched for various terms such as “Skin Graft Procedures” and “Skin Graft Surgery.” One hundred and five videos were assessed, with 21 excluded. Four independent reviewers rated the material with the Global Quality Scale (GQS) (5 = highest quality, 1 = lowest quality) to judge educational value. Viewership, source, modality, and date of upload were also collected from each video and compiled for further analysis. The average GQS was 2.60 amongst all videos, with videos led by physicians recording significantly higher scores than those not led by physicians (P < .01). In comparing educational modalities, physician-led presentations provided the highest educational value, whereas live surgeries and consumer-friendly content contained low educational quality (P < .01). Assessing videos split into cohorts based on viewership noted a significantly higher GQS in videos with lower view counts (P < .05). Skin graft videos on YouTube largely provide low-quality information. Videos performed by physicians, particularly physician-led presentations, significantly improved the educational quality of skin graft content. Physicians must involve themselves in enhancing the quality of online content to better guide patients in navigating treatment options and making healthcare decisions.
Reviving the Lieb–Schultz–Mattis theorem in open quantum systems
Authors: Zhou Yi-Neng , Li Xingyu , Zhai Hui , Li Chengshu , Gu Yingfei
Published: August 2024

In closed systems, the celebrated Lieb–Schultz–Mattis (LSM) theorem states that a one-dimensional locally interacting half-integer spin chain with translation and spin rotation symmetries cannot have a non-degenerate gapped ground state. However, the applicability of this theorem is diminished when the system interacts with a bath and loses its energy conservation. In this letter, we propose that the LSM theorem can be revived in the entanglement Hamiltonian when the coupling to the bath renders the system short-range correlated. Specifically, we argue that the entanglement spectrum cannot have a non-degenerate minimum, isolated by a gap from other states. We further support the results with numerical examples where a spin-$1/2$ system is coupled to another spin-$3/2$ chain serving as the bath. Compared with the original LSM theorem that primarily addresses UV–IR correspondence, our findings reveal that the UV data and topological constraints also play a pivotal role in shaping the entanglement in open quantum many-body systems.
Biomimetic ROS-responsive hyaluronic acid nanoparticles loaded with methotrexate for targeted anti-atherosclerosis
Authors: Li Bingyi , He Mei , Xu Zichen , Zhang Qianting , Zhang Liyuan , Zhao Shuang , Cao Yu , Mou Nianlian , Wang Yi , Wang Guixue
Published: August 2024

Atherosclerosis (AS), an inflammatory disease characterized by lipid accumulation, has a high global incidence and mortality rate. Recently, nanotherapeutic approaches that target pathological sites and improve drug bioavailability and biocompatibility hold great promise for AS treatment. In this study, a biomimetic ROS-responsive hyaluronic acid–based nanomaterial was prepared for targeted anti-AS. Specifically, a safe ROS-responsive carrier based on hyaluronic acid (HSP) was prepared to load methotrexate (MTX), a drug known for its ability to enhance lipid excretion, resulting in the formation of MTX-loaded nanoparticles (MTXNPs). Furthermore, the macrophage membrane was coated on the surface of MTXNPs to obtain MM/MTXNPs. Both MTXNPs and MM/MTXNPs exhibited ROS responsiveness and demonstrated excellent biocompatibility. In vitro experiments revealed that MM/MTXNPs could evade macrophage phagocytosis and exhibited high uptake rates by inflamed endothelial cells. MM/MTXNPs also reduced lipid accumulation in foam cells. In vivo experiments showed that MM/MTXNPs exhibited superior accumulation at AS plaque sites, facilitated by the surface membrane layer containing integrin α4β1 and CD47, resulting in an enhanced therapeutic effect in inhibiting plaque development compared to free MTX and MTXNPs. Therefore, HSP represents a promising nanocarrier to load hydrophobic MTX, enabling effective and biocompatible enhancement of AS treatment.
The Impact of Artificial Intelligence-Assisted Learning on Nursing Students' Ethical Decision-making and Clinical Reasoning in Pediatric Care
Authors: Shin Hyewon , De Gagne Jennie C. , Kim Sang Suk , Hong Minjoo
Published: August 2024

The integration of artificial intelligence such as ChatGPT into educational frameworks marks a pivotal transformation in teaching. This quasi-experimental study, conducted in September 2023, aimed to evaluate the effects of artificial intelligence–assisted learning on nursing students' ethical decision-making and clinical reasoning. A total of 99 nursing students enrolled in a pediatric nursing course were randomly divided into two groups: an experimental group that utilized ChatGPT and a control group that used traditional textbooks. The Mann-Whitney U test was employed to assess differences between the groups in two primary outcomes: (a) ethical standards, focusing on the understanding and applying ethical principles, and (b) nursing processes, emphasizing critical thinking skills and integrating evidence-based knowledge. The control group outperformed the experimental group in ethical standards and demonstrated better clinical reasoning in nursing processes. Reflective essays revealed that the experimental group reported lower reliability but higher time efficiency. Despite artificial intelligence's ability to offer diverse perspectives, the findings highlight that educators must supplement artificial intelligence technology with strategies that enhance critical thinking, careful data selection, and source verification. This study suggests a hybrid educational approach combining artificial intelligence with traditional learning methods to bolster nursing students' decision-making processes and clinical reasoning skills.
Factors influencing the prognosis of patients with cardiogenic shock treated with extracorporeal membrane oxygenation: A protocol for systematic review and meta-analysis
Authors: Hang Ming-Ming , Shen Mei-Rong , Chen Tie-Long , Wang Yu
Published: August 2024

Background:

Cardiogenic shock is a clinical syndrome caused by primary heart disease that results in decreased cardiac output and insufficient systemic perfusion. A study was conducted to determine what factors affect survival in patients with cardiogenic shock treated with extracorporeal membrane oxygenation (ECMO).

Methods:

A systematic search was conducted across various databases, including CKNI, VIP, Wan Fang, CBM, Embase, PubMed, Cochrane Library, and Web of Science databases, to gather factors linked to the prognosis of patients with cardiogenic shock who underwent ECMO treatment. The search period for each database was set to conclude on April 30, 2024.

Results:

The findings suggest that, in comparison to the death group, the lactic acid levels of the survival group after treatment were significantly lower (95% confidence interval [CI]: −0.79, −0.58). In addition, the creatinine levels of the survival group after treatment were also significantly lower than those of the death group (95% CI: −0.39, −0.14). Furthermore, the troponin levels in the survival group after treatment were lower than those in the death group (95% CI: −0.32, 0.04), and the total bilirubin levels in the survival group after treatment were also lower than those in the death group (95% CI: −0.62, −0.23).

Conclusions:

According to the study, total bilirubin, creatinine, and lactic acid levels were lower in the survival group than in the death group when ECMO was used to treat cardiogenic patients, suggesting a better prognosis for patients with cardiogenic shock. Therefore, total bilirubin, creatinine, and lactic acid could be influential factors in the prognosis of survival in patients with cardiogenic shock.
Sex differences in clinical profile and risk factors of cerebral venous sinus thrombosis at a high-altitude area: A 10-year retrospective cross-sectional study
Authors: Zarimeidani Fatemeh , Rahmati Rahem , Mokary Yousef , Azizollahi Setayesh , Ebrahimi Rasool , Raeisi Shahraki Hadi , Bayati Asghar , Jivad Nahid
Published: August 2024

Cerebral venous sinus thrombosis (CVST) is an uncommon disorder with an increased mortality risk. Data on the sex-specific clinical profile and related factors can be practical in clinical diagnosis and management. Thus, we aimed to assess CVST characteristics in Shahrekord, the most elevated city among the centers of the provinces of Iran, referred to as the Roof of Iran. In this retrospective cross-sectional study, we reviewed the hospital records of 127 CVST patients between April 2013 and June 2023 to analyze the demographic characteristics, clinical profile, disease outcomes, and risk factors. STROBE guidelines were used to report this study. Females comprised 65.3% of the study. Females’ age was almost equal to male patients (37.51 ± 13.95 years vs 38.09 ± 16.18 years, P = .832), and more than half of the patients (59.1%) were married. Clinical characteristics showed no significant difference between the sexes. In contrast to males, females had multifarious risk factors. However, the main risk factors for both sexes were CVST and hypertension. Most patients had 1 sinus affected (55.9%), and the location of thrombosis was mainly in the right transverse sinus in males (38.63%) and superior sagittal sinus in females (33.73%). However, those with more than 2 affected sinuses were mostly women. Regarding radiological findings, sinusitis was significantly seen in men (18.18% vs 3.61%). Among laboratory data, females significantly had higher erythrocyte sedimentation rate levels than males (21.66 ± 24.39 vs 9.60 ± 8.55). This study indicates that among CVST patients, men have significantly higher occurrences of sinusitis, and higher levels of erythrocyte sedimentation rate were observed in women. However, no other significant differences were found. More extensive studies are essential to fully comprehend the sex-specific aspects of CVST.
The clinical efficacy of combined ESA and Roxadustat treatment for renal anemia in hemodialysis patients with secondary hyperparathyroidism: A case series
Authors: Zhong Jing-jing , Wang Ming-li , Zheng Gao-feng , Li Ming-peng , Chen De-zheng
Published: August 2024

Rationale:

Pharmacological mechanism of Roxadustat in the treatment of renal anemia.

Patient concerns:

To investigate the efficacy and safety of combined Roxadustat and erythropoiesis stimulator (ESA) treatment of renal anemia in hemodialysis patients with secondary hyperparathyroidism.

Diagnoses:

A retrospective analysis was conducted on hemodialysis patients with renal anemia and secondary hyperparathyroidism treated with ESAs alone, who were admitted to our hospital from March 2022 to December 2022.

Interventions:

The patients were treated with Roxadustat combined with ESAs for 3 months, during which oral iron supplementation was given, and the changes in Hb levels and laboratory-related indicators before and after the combined treatment were analyzed.

Outcomes:

The results showed that a total of 13 patients received combination therapy, with a significant increase in Hb compared to ESAs alone (t = −3.955, P = .002). The Hb qualification rate was 38.46%, and the ∆Hb response rate was 76.92%. The parathyroid hormone significantly decreased with a statistically significant difference (Z = −2.062b, P = .039). Hemoglobin (RBC), total iron binding capacity, and serum ferritin (male) were significantly increased compared to ESAs alone. Total cholesterol and low-density lipoprotein were significantly lower than ESAs alone. The differences in the changes in the above indicators were statistically significant (P < .05). There was no statistically significant difference in changes in other laboratory-related indicators (P > .05). No adverse reactions were observed during the combined treatment of 13 patients.

Lessons subsections:

The combination of Roxadustat and ESAs can effectively improve renal anemia in hemodialysis patients with secondary hyperparathyroidism, as well as improve indicators of hyperparathyroidism and blood lipid levels with high levels of safety. This combined treatment thus provides a new and safe treatment method for these patients.
Clinical outcomes of hydrofiber foam dressing in the treatment of abrasion wounds: A retrospective study
Authors: Yen Patrick Szu-Ying , Chen Rong-Fu , Liu Yao-Hua , Lin Yun-Nan
Published: August 2024

Mopeds are a popular mode of transportation in Taiwan, yet collision accidents often lead to abrasion requiring medical attention. While hydrofiber foam dressing (HFD) have proven to help control exudates, reduce the frequency of dressing changes, and preserve more viable tissue, there is currently no established clinical guideline for the treatment of abrasion wounds with dressings. Therefore, the aim of this study was to investigate the clinical outcomes of abrasions treated with hydrofiber foam dressings. A total of 127 patients treated with HFDs between June 2018 and January 2021 were included in this study. Abrasions were categorized into 3 grades: grade I (n = 0) involving the epidermal layer, grade II (n = 89) involving the dermal layer, and grade III (n = 38) involving subcutaneous layer, bone or tendon. Statistical analysis showed that there was a significant difference in healing time (P < 0.001) and the number of outpatient department (OPD) visits (P = 0.013) between grade II and grade III. However, there was no significant difference in the number of dressing pieces used. This study offers a clinical reference for the use of hydrofiber foam dressings in treating different grades of abrasion wounds caused by moped accidents.
Uterine tumors mimicking ovarian sex cord tumors with rhabdoid differentiation: a clinicopathologic study of 4 cases: A case series analysis
Authors: Li Hongling , Xie Le , Zhang Jinhui , Xu Yuanyuan , Wu Xingyan , Chen Zengwei , Mao Rongjun
Published: August 2024

Rationale:

Uterine tumors resembling ovarian sex cord tumors (UTROSCT) with rhabdoid features are uncommon mesenchymal neoplasms exhibiting diverse histological patterns, including significant rhabdoid morphology. A thorough comprehension of their clinicopathologic features is crucial for precise diagnosis and effective management.

Patient concerns:

This study presents 4 cases of UTROSCT with rhabdoid features, diagnosed in patients aged 31 to 58. Varied recurrence patterns were observed, including similar recurrent lesions to the primary tumors with subsequent mortality, initial invasion and lymph node metastasis, and presence of only primary tumor.

Diagnoses:

Histopathological examination revealed diverse morphological patterns, prominently featuring rhabdoid differentiation. Immunohistochemical analysis showed expression of hormone receptors, sex cord, smooth muscle, and epithelial markers, notably WT1, CD56, and CD99. Molecular analysis identified ESR1-NCOA2 fusions and ESR1 and NCOA2/3 rearrangements, indicating a potential association between these genetic alterations and extensive rhabdoid differentiation.

Interventions:

Various treatments were administered post-recurrence, including chemotherapy and targeted therapies. However, poor clinical outcomes were observed in all cases.

Outcomes:

Despite aggressive treatments, including chemotherapy and targeted therapies, poor clinical outcomes were observed, highlighting the aggressive nature of UTROSCT with significant rhabdoid differentiation.

Lessons:

This case series emphasizes the importance of detailed pathological reporting, comprehensive molecular testing, and thorough tumor staging in UTROSCT cases with rhabdoid features. Enhanced understanding of the clinicopathologic characteristics of UTROSCT with rhabdoid differentiation is crucial for accurate diagnosis, prognostication, and management strategies.
Pilon fracture with central articular surface collapse treated by posterior tibial fenestration indirect reduction and compression technique: Two cases report
Authors: Sang Chaohui , Zeng Langqing , Zhong Huafeng , Wang Qiang , Zeng Lulu
Published: August 2024

Rationale:

Central collapsed fracture blocks traditionally require either an anteromedial or anterolateral approach for reduction. However, existing techniques face challenges such as soft tissue damage and compromised tibial strength, especially in pilon fractures with central articular surface collapse and an intact anterior cortex, as classified under 43B2.3 in the 2018 Orthopaedic Trauma Association/Association for the Study of Internal Fixation Fracture and Dislocation Classification Compendium.

Patient concerns:

We address the management of pilon fractures with central articular surface collapse, focusing on 2 cases where conventional reduction techniques posed a risk to soft tissues and tibial integrity.

Diagnoses:

The patients presented with pilon fractures characterized by a central articular surface collapse and an intact anterior cortex, aligning with the 43B2.3 classification.

Interventions:

A novel approach was employed, utilizing posterior tibial fenestration and indirect reduction with compression techniques. This method leveraged the talus as a template for precise articular surface realignment.

Outcomes:

Both cases demonstrated excellent reduction of the distal tibial articular surface and achieved favorable functional recovery of the ankle, evidenced by high American Orthopedic Foot and Ankle Society Ankle Hindfoot Scale scores during the 3-year follow-up.

Lessons:

The posterior tibial fenestration technique offers significant advantages for distal tibial pilon fracture reduction. It allows for precise articular realignment, facilitates bone grafting, and minimizes soft tissue and cortical bone disruption. This method is particularly effective for pilon fractures with an intact anterolateral cortex and central articular collapse, providing a valuable surgical alternative.