A ROS storm generating nanocomposite for enhanced chemodynamic therapy through H2O2 self-supply, GSH depletion and calcium overload.

Catalytic generation of toxic hydroxyl radicals (˙OH) from hydrogen peroxide (H2O2) is an effective strategy for tumor treatment in chemodynamic therapy (CDT). However, the intrinsic features of the microenvironment in solid tumors, characterized by limited H2O2 and overexpressed glutathione (GSH), severely impede the accumulation of intracellular ˙OH, posing significant challenges. To circumvent these critical issues, in this work, a CaO2-based multifunctional nanocomposite with a surface coating of Cu2+ and L-buthionine sulfoximine (BSO) (named CaO2@Cu-BSO) is designed for enhanced CDT. Taking advantage of the weakly acidic environment of the tumor, the nanocomposite gradually disintegrates, and the exposed CaO2 nanoparticles subsequently decompose to produce H2O2, alleviating the insufficient supply of endogenous H2O2 in the tumor microenvironment (TME). Furthermore, Cu2+ detached from the surface of CaO2 is reduced by H2O2 and GSH to Cu+ and ROS. Then, Cu+ catalyzes H2O2 to generate highly cytotoxic ˙OH and Cu2+, forming a cyclic catalysis effect for effective CDT. Meanwhile, GSH is depleted by Cu2+ ions to eliminate possible ˙OH scavenging. In addition, the decomposition of CaO2 by TME releases a large amount of free Ca2+, resulting in the accumulation and overload of Ca2+ and mitochondrial damage in tumor cells, further improving CDT efficacy and accelerating tumor apoptosis. Besides, BSO, a molecular inhibitor, decreases GSH production by blocking γ-glutamyl cysteine synthetase. Together, this strategy allows for enhanced CDT efficiency via a ROS storm generation strategy in tumor therapy. The experimental results confirm and demonstrate the satisfactory tumor inhibition effect both in vitro and in vivo.

Weighted gene co-expression network analysis reveals key biomarkers and immune infiltration characteristics for bronchial epithelial cells from asthmatic patients.

BACKGROUND: Asthma ranks among the most prevalent non-communicable diseases worldwide. Previous studies have elucidated the significant role of the immune system in its pathophysiology. Nevertheless, the immune-related mechanisms underlying asthma are complex and still inadequately understood. Thus, our objective was to investigate novel key biomarkers and immune infiltration characteristics associated with asthma by employing integrated bioinformatics tools. METHODS: In this study, we conducted a weighted gene co-expression network analysis (WGCNA) to identify key modules and genes potentially implicated in asthma. Functional annotation of these key modules and genes was carried out through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Additionally, we constructed a protein-protein interaction (PPI) network using the STRING database to identify 10 hub genes. Furthermore, we evaluated the relative proportion of immune cells in bronchial epithelial cell samples from 20 healthy individuals and 88 asthmatic patients using CIBERSORT. Finally, we validated the hub genes and explored their correlation with immune infiltration. RESULTS: Furthermore, 20 gene expression modules and 10 hub genes were identified herein. Among them, complement component 3 (C3), prostaglandin I2 receptor (PTGIR), parathyroid hormone-like hormone (PTHLH), and C-X3-C motif chemokine ligand 1 (CX3CL1) were closely correlated with the infiltration of immune cells. They may be novel candidate biomarkers or therapeutic targets for asthma. Furthermore, B cells memory, and plasma cells might play an important role in immune cell infiltration after asthma. CONCLUSIONS: C3, PTGIR, CX3CL1, and PTHLH have important clinical diagnostic values and are correlated with infiltration of multiple immune cell types in asthma. These hub genes, B cells memory, and plasma cells may become important biological targets for therapeutic asthma drug screening and drug design.

Maternal exposure to beta-Cypermethrin disrupts placental development by dysfunction of trophoblast cells from oxidative stress.

As a broad-spectrum and efficient insecticide, beta-Cypermethrin (ß-CYP) poses a health risk to pregnancy. It matters the mechanisms of maternal exposure to ß-CYP for impacting reproductive health. The placenta, a transient organ pivotal for maternal-fetal communication during pregnancy, plays a crucial role in embryonic development. The effect of ß-CYP exposure on the placenta and its underlying molecular mechanisms remain obscure. The objective of this study was to investigate the effect of ß-CYP exposure on placental development and the function of trophoblast, as well as the underlying mechanisms through CD-1 mouse model (1, 10, 20 mg/kg.bw) and in vitro HTR-8/SVneo cell model (12.5, 25, 50, 100 µM). We found slower weight gain and reduced uterine wet weight in pregnant mice with maternal exposure to ß-CYP during pregnancy, as well as adverse pregnancy outcomes such as uterine bleeding and embryo resorption. The abnormal placental development in response to ß-CYP was noticed, including imbalanced placental structure and disrupted labyrinthine vascular development. Trophoblasts, pivotal in placental development and vascular remodeling, displayed abnormal differentiation under ß-CYP exposure. This aberration was characterized by thickened trophoblast layers in the labyrinthine zone, accompanied by mitochondrial and endoplasmic reticulum swelling within trophoblasts. Further researches on human chorionic trophoblast cell lines revealed that ß-CYP exposure induced apoptosis in HTR-8/SVneo cells. This induction resulted in a notable decrease in migration and invasion abilities, coupled with oxidative stress and the inhibition of the Notch signaling pathway. N-acetylcysteine (an antioxidant) partially restored the impaired Notch signaling pathway in HTR-8/SVneo cells, and mitigated cellular functional damage attributed to ß-CYP exposure. Collectively, exposure to ß-CYP induced oxidative stress and then led to inhibition of the Notch signaling pathway and dysfunction of trophoblast cells, ultimately resulted in abnormal placenta and pregnancy. These findings indicate Reactive Oxygen Species as potential intervention targets to mitigate ß-CYP toxicity. The comprehensive elucidation contributes to our understanding of ß-CYP biosafety and offers an experimental basis for preventing and managing its reproductive toxicity.

Integrated analysis of scRNA-seq and bulk RNA-seq identifies FBXO2 as a candidate biomarker associated with chemoresistance in HGSOC.

Background: High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and aggressive histological subtype of epithelial ovarian cancer. Around 80% of individuals will experience a recurrence within five years because of resistance to chemotherapy, despite initially responding well to platinum-based treatment. Biomarkers associated with chemoresistance are desperately needed in clinical practice. Methods: We jointly analyzed the transcriptomic profiles of single-cell and bulk datasets of HGSOC to identify cell types associated with chemoresistance. Copy number variation (CNV) inference was performed to identify malignant cells. We subsequently analyzed the expression of candidate biomarkers and their relationship with patients' prognosis. The enrichment analysis and potential biological function of candidate biomarkers were explored. Then, we validated the candidate biomarker using in vitro experiments. Results: We identified 8871 malignant epithelial cells in a single-cell RNA sequencing dataset, of which 861 cells were associated with chemoresistance. Among these malignant epithelial cells, FBXO2 (F-box protein 2) is highly expressed in cells related to chemoresistance. Moreover, FBXO2 expression was found to be higher in epithelial cells from chemoresistance samples compared to those from chemosensitivity samples in a separate single-cell RNA sequencing dataset. Patients exhibiting elevated levels of FBXO2 experienced poorer outcomes in terms of both overall survival (OS) and progression-free survival (PFS). FBXO2 could impact chemoresistance by influencing the PI3K-Akt signaling pathway, focal adhesion, and ECM-receptor interactions and regulating tumorigenesis. The 50% maximum inhibitory concentration (IC50) of cisplatin decreased in A2780 and SKOV3 ovarian carcinoma cell lines with silenced FBXO2 during an in vitro experiment. Conclusions: We determined that FBXO2 is a potential biomarker linked to chemoresistance in HGSOC by combining single-cell RNA-seq and bulk RNA-seq dataset. Our results suggest that FBXO2 could serve as a valuable prognostic marker and potential target for drug development in HGSOC.

Unveiling the best predictive models for early­onset metastatic cancer: Insights and innovations (Review).

Cancer metastasis is the primary cause of cancer deaths. Metastasis involves the spread of cancer cells from the primary tumors to other body parts, commonly through lymphatic and vascular pathways. Key aspects include the high mutation rate and the capability of metastatic cells to form invasive tumors even without a large initial tumor mass. Particular emphasis is given to early metastasis, occurring in initial cancer stages and often leading to misdiagnosis, which adversely affects survival and prognosis. The present review highlighted the need for improved understanding and detection methods for early metastasis, which has not been effectively identified clinically. The present review demonstrated the clinicopathological and molecular characteristics of early­onset metastatic types of cancer, noting factors such as age, race, tumor size and location as well as the histological and pathological grade as significant predictors. In conclusion, the present review underscored the importance of early detection and management of metastatic types of cancer and called for improved predictive models, including advanced techniques such as nomograms and machine learning, so as to enhance patient outcomes, acknowledging the challenges and limitations of the current research as well as the necessity for further studies.

MuLAN: Multi-level attention-enhanced matching network for few-shot knowledge graph completion.

Recent years have witnessed increasing interest in the few-shot knowledge graph completion due to its potential to augment the coverage of few-shot relations in knowledge graphs. Existing methods often use the one-hop neighbors of the entity to enhance its embedding and match the query instance and support set at the instance level. However, such methods cannot handle inter-neighbor interaction, local entity matching and the varying significance of feature dimensions. To bridge this gap, we propose the Multi-Level Attention-enhanced matching Network (MuLAN) for few-shot knowledge graph completion. In MuLAN, a multi-head self-attention neighbor encoder is designed to capture the inter-neighbor interaction and learn the entity embeddings. Then, entity-level attention and instance-level attention are responsible for matching the query instance and support set from the local and global perspectives, respectively, while feature-level attention is utilized to calculate the weights of the feature dimensions. Furthermore, we design a consistency constraint to ensure the support instance embeddings are close to each other. Extensive experiments based on two well-known datasets (i.e., NELL-One and Wiki-One) demonstrate significant advantages of MuLAN over 11 state-of-the-art competitors. Compared to the best-performing baseline, MuLAN achieves 14.5% higher MRR and 13.3% higher Hits@K on average.

Single-cell RNA sequencing reveals distinct transcriptomic profiles and evolutionary patterns in lung cancer brain metastasis.

Background: Lung cancer metastasis to the brain presents significant clinical challenges. Therefore, elucidating its underlying mechanisms and characterizing its transcriptomic landscape is essential for developing therapeutic interventions. Methods: We analyzed two distinct single-cell RNA sequencing datasets of lung cancer metastasis to analyze the evolutionary trajectory of brain metastatic tumors. In addition, a systematic comparison of cell-cell interaction between tumor cells and lymphocytes was conducted within primary and brain metastatic tumors. Results: The brain metastatic tumors showed greater transcriptomic changes (reflected by a higher pseudotime) than tumors in the lymph nodes and primary tumors. Furthermore, our investigation has not only revealed specific shared ligand-receptor pairs in both mLN and mBrain, exemplified by the interaction between SPP1 and CD99 in T cells, but has also unveiled a diverse array of ligand-receptor pairs exclusive to the mBrain. Notably, this includes distinctive pairs such as APP and IL1 observed specifically in myeloid cells. Conclusion: The distinct microenvironment in the brain may influence the observed transcriptomic changes in tumors, emphasizing the significance of the specific environment in determining tumor behavior and therapeutic response.

Predictive role of NICU-related stress, postpartum depression trajectory and family coping on growth trajectory of moderate-to-late preterm infants: A longitudinal study.

AIMS: To describe the changes in moderate-to-late preterm infants' (MLPIs) growth during 12 months of corrected age (CA) and to examine the predictive role of NICU-related stress, postpartum depression trajectory and family coping ability on the physical developmental trajectory of MLPIs. DESIGN: A prospective longitudinal study. METHODS: There were 237 mother-infant dyads with at least two follow-up data records included. General characteristics and NICU-related stress were recorded from medical records at baseline. Infants' physical growth was measured at 40 weeks, 1, 3, 6, 9 and 12 months CA during outpatient follow-up. Maternal postpartum depressive symptoms and family coping ability were assessed by questionnaires at 1, 3, 6, 9 and 12 months CA and 1 month CA respectively. We investigated the modifiable factors inside and outside of NICU on the trajectories of physical growth in the first year in MLPIs, mainly by using latent growth curve models with time-varying covariates. RESULTS: The curved trajectories of weight, length and head circumference in the first year in MLPIs demonstrated gradually slowed growth rates and these infants were above the WHO growth standards for the same age and sex. The latent growth curve models indicated that more NICU-related stress was negatively associated with the weight and length at 40 weeks CA, and family coping ability (parent-child relationship) at 1 month CA was associated with the growth rate of weight. Besides, more NICU-related stress predicted faster length growth rate. The infants of mothers who were in the group of high-level postpartum depression trajectory had a slower growth rate of head circumference. CONCLUSIONS: Our study identified the modifiable factors along the care continuum influencing the trajectory of MLPIs' physical growth. Nurses should receive more training about infant stress measurement and family-centred care to work in partnership with parents so that MLPIs can reach their full developmental potential. Also, multidisciplinary interventions including stress reduction strategies, close psychological monitoring and education improving parent-infant relationships should be further developed to achieve optimizing growth in the first year of MLPIs. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: It is recommended that nurses pay attention to the long-term physical growth status of MLPIs, and closely support their families. Quantifying NICU-related stress and developing reduction strategies should be the priority for clinical staff during hospitalization. After discharge, persistent screening of depressive symptoms, psychological intervention and education about the parent-child relationship need to be included in the follow-up visits. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. The study only included patients who were research participants.

TRIM65 knockout inhibits the development of HCC by polarization tumor-associated macrophages towards M1 phenotype via JAK1/STAT1 signaling pathway.

BACKGROUND & AIMS: Tumor-associated macrophages (TAMs) are main components of immune cells in tumor microenvironment (TME), and play a crucial role in tumor progression. Tripartite motif-containing protein 65 (TRIM65) has been associated with tumor progression. However, whether TRIM65 regulate the interaction of tumor cell and TAMs in HCC and the underlying mechanisms remain unknown. In this study, we investigated the role of TRIM65 in TME of HCC and explored its underlying mechanisms. METHODS: The relation of TRIM65 expression level with tumor grades, TNM stages, and worse prognosis of HCC patients was evaluated by bioinformatics analysis, as well as immune infiltration level of macrophages. TRIM65 shRNA was transfected into HepG2 cells, and TRIM65 overexpression plasmid was transfected into Huh7 cells, and the effect of TRIM65 on cell growth was examined by EdU assay. The mouse subcutaneous Hep1-6 tumor-bearing model with WT and TRIM65-/- mice was established to study the role of TRIM65 in HCC. Immunohistochemistry staining, Immunofluorescence staining, qRT-PCR and western blot were performed to evaluate the effect of TRIM65 on TAM infiltration, TAM polarization and JAK1/STAT1 signaling pathway. RESULTS: Bioinformatics analysis revealed that TRIM65 was upregulated in 16 types of cancer especially in HCC, and high level of TRIM65 was strongly correlated with higher tumor grades, TNM stages, and worse prognosis of patients with HCC as well as immune infiltration level of macrophages (M0, M1, and M2). Moreover, we observed that TRIM65 shRNA-mediated TRIM65 knockdown significantly inhibited the HepG2 cells growth while TRIM65 overexpression highly increased the Huh7 cells growth in vitro. TRIM65 knockout significantly inhibited the tumor growth as well as macrophages polarization towards M2 but promoted macrophages polarization towards M1 in vivo. Mechanistically, the results demonstrate that TRIM65 knockout promoted macrophage M1 polarization in conditioned medium-stimulated peritoneal macrophages and in tumor tissues by activating JAK1/STAT1 signaling pathway. CONCLUSIONS: Taken together, our study suggests that tumor cells utilize TRIM65-JAK1/STAT1 axis to inhibit macrophage M1 polarization and promote tumor growth, reveals the role of TRIM65 in TAM-targeting tumor immunotherapy.

The role of vasoactive intestinal peptide (VIP) in atropine-related inhibition of the progression of myopia.

OBJECTIVE: This study aimed to investigate the potential involvement of vasoactive intestinal polypeptide (VIP) in myopia development and its contribution to the mechanism of action of the anti-myopia drug, atropine. METHODS: Thirty-three-week-old guinea pigs were randomly divided into normal control (NC, n = 10), monocularly form-deprived (FDM, n = 10), and FDM treated with 1% atropine (FDM + AT, n = 10) groups. The diopter and axial length were measured at 0, 2, and 4 weeks. Guinea pig eyeballs were removed at week four, fixed, and stained for morphological changes. Immunohistochemistry (IHC) and in situ hybridization (ISH) were performed to evaluate VIP protein and mRNA levels. RESULTS: The FDM group showed an apparent myopic shift compared to the control group. The results of the H&E staining were as follows: the cells of the inner/outer nuclear layers and retinal ganglion cells were disorganized; the choroidal thickness (ChT), blood vessel lumen, and area were decreased; the sclera was thinner, with disordered fibers and increased interfibrillar space. IHC and ISH revealed that VIP's mRNA and protein expressions were significantly up-regulated in the retina of the FDM group. Atropine treatment attenuated FDM-induced myopic shift and fundus changes, considerably reducing VIP's mRNA and protein expressions. CONCLUSIONS: The findings of elevated VIP mRNA and protein levels observed in the FDM group indicate the potential involvement of VIP in the pathogenesis and progression of myopia. The ability of atropine to reduce this phenomenon suggests that this may be one of the molecular mechanisms for atropine to control myopia.

Modeling drug-induced mitochondrial toxicity with human primary cardiomyocytes.

Mitochondrial toxicity induced by therapeutic drugs is a major contributor for cardiotoxicity, posing a serious threat to pharmaceutical industries and patients' lives. However, mitochondrial toxicity testing is not incorporated into routine cardiac safety screening procedures. To accurately model native human cardiomyocytes, we comprehensively evaluated mitochondrial responses of adult human primary cardiomyocytes (hPCMs) to a nucleoside analog, remdesivir (RDV). Comparison of their response to human pluripotent stem cell-derived cardiomyocytes revealed that the latter utilized a mitophagy-based mitochondrial recovery response that was absent in hPCMs. Accordingly, action potential duration was elongated in hPCMs, reflecting clinical incidences of RDV-induced QT prolongation. In a screen for mitochondrial protectants, we identified mitochondrial ROS as a primary mediator of RDV-induced cardiotoxicity. Our study demonstrates the utility of hPCMs in the detection of clinically relevant cardiac toxicities, and offers a framework for hPCM-based high-throughput screening of cardioprotective agents.

Research trends and hotspots in exercise rehabilitation for coronary heart disease: A bibliometric analysis.

Exercise rehabilitation can improve the prognosis of patients with coronary heart disease. However, a bibliometric analysis of the global exercise rehabilitation for coronary heart disease (CHD) research topic is lacking. This study investigated the development trends and research hotspots in the field of coronary heart disease and exercise rehabilitation. CiteSpace software was used to analyze the literature on exercise therapy for CHD in the Web of Science Core Collection database. We analyzed the data of countries/institutions, journals, authors, keywords, and cited references. A total of 3485 peer-reviewed papers were found, and the number of publications on the topic has steadily increased. The most productive country is the USA (1125), followed by China (477) and England (399). The top 3 active academic institutions are Research Libraries UK (RLUK) (236), Harvard University (152), and the University of California System (118). The most commonly cited journals are Circulation (2596), The most commonly cited references are "Exercise-based cardiac rehabilitation for coronary heart disease" (75), Lavie CJ had published the most papers (48). World Health Organization was the most influential author (334 citations). The research frontier trends in this field are body composition, participation, and function. Research on the effects of physical activity or exercise on patients with CHD is a focus of continuous exploration in this field. This study provides a new scientific perspective for exercise rehabilitation and CHD research and gives researchers valuable information for detecting the current research status, hotspots, and emerging trends for further research.

Multisensory stimulation bundles on sleep and neurobehavioral development in the first year after birth in very preterm infants: a randomized crossover controlled study protocol.

BACKGROUND: Disrupted sleep is believed to contribute to short- and long-term neurodevelopmental problems in very preterm infants (VPIs). This study presents a protocol for an evaluator-blinded, randomized crossover trial. It aims to assess the sleep efficiency of hospitalized VPIs by providing multisensory stimulation bundles. Furthermore, it aims to observe the intervention impacts on sleep during hospitalization of the VPIs and their sleep and neurodevelopmental outcomes during the first year of post-discharge follow-up. METHODS: The study will be conducted in the neonatology department of a tertiary pediatric teaching hospital. All the eligible VPIs will undergo two types of care in random order: "standard care" (2 weeks) and "standard care plus multisensory stimulation bundles," each lasting 2 weeks. A generated list of random numbers will be used for case sequence allocation. Sleep outcomes will be evaluated using the Actiwatch-2 Actigraph. Moreover, the amplitude-integrated electroencephalography and the Griffiths Mental Development Scales will be used to measure the neurodevelopmental outcomes during hospitalization and in the first year of follow-up of the VPIs. DISCUSSION: The intervention protocol of this study differs from that of other traditional interventions by producing precise and consistent supportive stimulations, similar to maternal tactile, auditory, posture, and visual effects for hospitalized VPIs. This protocol could be an effective measure to facilitate sleep and early neurodevelopment of VPIs. The expected outcomes will help confirm the implementation and generalization of the multisensory stimulation bundles' care protocol in neonatology departments. We expect that the study will positively impact hospitalized VPIs, especially in their sleep and early neurodevelopmental outcomes. It will also provide a new perspective regarding parent and infant interaction strategies, particularly for newborn intensive care units that limit visits because of the global spread of COVID-19. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR 2200059099. Registered on 25 April 2022, https://www.chictr.org.cn/showproj.html?proj=166980 ; the Hospital Research Ethics Committee (approval number: SCMCIRB-K2021086-1, Version 01), approved on 21 January 2022.

Potential Schizophrenia Disease-Related Genes Prediction Using Metagraph Representations Based on a Protein-Protein Interaction Keyword Network: Framework Development and Validation.

BACKGROUND: Schizophrenia is a serious mental disease. With increased research funding for this disease, schizophrenia has become one of the key areas of focus in the medical field. Searching for associations between diseases and genes is an effective approach to study complex diseases, which may enhance research on schizophrenia pathology and lead to the identification of new treatment targets. OBJECTIVE: The aim of this study was to identify potential schizophrenia risk genes by employing machine learning methods to extract topological characteristics of proteins and their functional roles in a protein-protein interaction (PPI)-keywords (PPIK) network and understand the complex disease-causing property. Consequently, a PPIK-based metagraph representation approach is proposed. METHODS: To enrich the PPI network, we integrated keywords describing protein properties and constructed a PPIK network. We extracted features that describe the topology of this network through metagraphs. We further transformed these metagraphs into vectors and represented proteins with a series of vectors. We then trained and optimized our model using random forest (RF), extreme gradient boosting, light gradient boosting machine, and logistic regression models. RESULTS: Comprehensive experiments demonstrated the good performance of our proposed method with an area under the receiver operating characteristic curve (AUC) value between 0.72 and 0.76. Our model also outperformed baseline methods for overall disease protein prediction, including the random walk with restart, average commute time, and Katz models. Compared with the PPI network constructed from the baseline models, complementation of keywords in the PPIK network improved the performance (AUC) by 0.08 on average, and the metagraph-based method improved the AUC by 0.30 on average compared with that of the baseline methods. According to the comprehensive performance of the four models, RF was selected as the best model for disease protein prediction, with precision, recall, F1-score, and AUC values of 0.76, 0.73, 0.72, and 0.76, respectively. We transformed these proteins to their encoding gene IDs and identified the top 20 genes as the most probable schizophrenia-risk genes, including the EYA3, CNTN4, HSPA8, LRRK2, and AFP genes. We further validated these outcomes against metagraph features and evidence from the literature, performed a features analysis, and exploited evidence from the literature to interpret the correlation between the predicted genes and diseases. CONCLUSIONS: The metagraph representation based on the PPIK network framework was found to be effective for potential schizophrenia risk genes identification. The results are quite reliable as evidence can be found in the literature to support our prediction. Our approach can provide more biological insights into the pathogenesis of schizophrenia.

Splitting phenomenon of ferromagnetic resonance spectra in NiFe films deposited on periodically rippled sapphire substrates.

The splitting phenomenon of ferromagnetic resonance (FMR) spectra of Ni80Fe20 (NiFe) films deposited on periodically rippled sapphire substrates is studied experimentally and with the help of micromagnetic simulation. The analyses show that the splitting of FMR spectra is related to the periodic ripple topography of films. When the applied magnetic field is perpendicular to the ripple direction, the effective field of periodically rippled films becomes inhomogeneous. The splitting of ferromagnetic resonance spectra originates from localized FMR peaks corresponding to different regions with different effective field intensities in the rippled structure. Furthermore, the relative intensity and position between the split mode and the main FMR mode can be changed by designing ripple topography. This work would help understand the splitting phenomenon of FMR spectra for these NiFe films deposited on the periodically rippled sapphire substrates.

A Multilabel Text Classifier of Cancer Literature at the Publication Level: Methods Study of Medical Text Classification.

BACKGROUND: Given the threat posed by cancer to human health, there is a rapid growth in the volume of data in the cancer field and interdisciplinary and collaborative research is becoming increasingly important for fine-grained classification. The low-resolution classifier of reported studies at the journal level fails to satisfy advanced searching demands, and a single label does not adequately characterize the literature originated from interdisciplinary research results. There is thus a need to establish a multilabel classifier with higher resolution to support literature retrieval for cancer research and reduce the burden of screening papers for clinical relevance. OBJECTIVE: The primary objective of this research was to address the low-resolution issue of cancer literature classification due to the ambiguity of the existing journal-level classifier in order to support gaining high-relevance evidence for clinical consideration and all-sided results for literature retrieval. METHODS: We trained a multilabel classifier with scalability for classifying the literature on cancer research directly at the publication level to assign proper content-derived labels based on the "Bidirectional Encoder Representation from Transformers (BERT) + X" model and obtain the best option for X. First, a corpus of 70,599 cancer publications retrieved from the Dimensions database was divided into a training and a testing set in a ratio of 7:3. Second, using the classification terminology of International Cancer Research Partnership cancer types, we compared the performance of classifiers developed using BERT and 5 classical deep learning models, such as the text recurrent neural network (TextRNN) and FastText, followed by metrics analysis. RESULTS: After comparing various combined deep learning models, we obtained a classifier based on the optimal combination "BERT + TextRNN," with a precision of 93.09%, a recall of 87.75%, and an F1-score of 90.34%. Moreover, we quantified the distinctive characteristics in the text structure and multilabel distribution in order to generalize the model to other fields with similar characteristics. CONCLUSIONS: The "BERT + TextRNN" model was trained for high-resolution classification of cancer literature at the publication level to support accurate retrieval and academic statistics. The model automatically assigns 1 or more labels to each cancer paper, as required. Quantitative comparison verified that the "BERT + TextRNN" model is the best fit for multilabel classification of cancer literature compared to other models. More data from diverse fields will be collected to testify the scalability and extensibility of the proposed model in the future.

Identification of Colletotrichum aenigma as the new causal agent of leaf blight disease on Aucuba japonica Thunb., and screenings of effective fungicides for its sustainable management.

Aucuba japonica Thunb is an evergreen woody ornamental plant with significant economic and ecological values. It also produces aucubin, showing a variety of biological activities. It is widely planted in the southwest region of China, including karst landscape areas in Guizhou Province. In January 2022, a serious leaf blight disease was observed on the leaves of A. japonica in the outdoor gardens of Guizhou University, Guiyang, Guizhou, China. The causal agent was identified as Colletotrichum aenigma through amplification and sequencing of the internal transcribed spacer (ITS) region, translation of the chitin synthase (CHS) and actin (ACT) genes, and morphological characterizations. Koch's postulates were confirmed by its pathogenicity on healthy leaves, including re-isolation and identification. To our knowledge, this is the first report of C. aenigma causing leaf blight on A. japonica worldwide. To identify pathogen characteristics that could be utilized for future disease management, the effects of temperature and light on mycelial growth, conidia production, and conidial germination, and the effects of humidity on conidial germination were studied. Optimal temperatures for mycelial growth of C. aenigma BY827 were 25-30°C, while 15°C and 35°C were favorable for conidia production. Concurrently, alternating 10-h light and 14-h dark, proved to be beneficial for mycelial growth and conidial germination. Additionally, conidial germination was enhanced at 90% humidity. In vitro screenings of ten chemical pesticides to assess their efficacy in suppressing C. aenigma representative strain BY827. Among them, difenoconazole showed the best inhibition rate, with an EC50 (concentration for 50% of maximal effect) value of 0.0148 µg/ml. Subsequently, field experiment results showed that difenoconazole had the highest control efficiency on A. japonica leaf blight (the decreasing rate of disease incidence and decreasing rate of disease index were 44.60 and 47.75%, respectively). Interestingly, we discovered that C. aenigma BY827 may develop resistance to mancozeb, which is not reported yet among Colletotrichum spp. strains. In conclusion, our study provided new insights into the causal agent of A. japonica leaf blight, and the effective fungicides evaluated provided an important basis and potential resource for the sustainable control of A. japonica leaf blight caused by C. aenigma in the field.

A Deep Learning Model for the Normalization of Institution Names by Multisource Literature Feature Fusion: Algorithm Development Study.

BACKGROUND: The normalization of institution names is of great importance for literature retrieval, statistics of academic achievements, and evaluation of the competitiveness of research institutions. Differences in authors' writing habits and spelling mistakes lead to various names of institutions, which affects the analysis of publication data. With the development of deep learning models and the increasing maturity of natural language processing methods, training a deep learning-based institution name normalization model can increase the accuracy of institution name normalization at the semantic level. OBJECTIVE: This study aimed to train a deep learning-based model for institution name normalization based on the feature fusion of affiliation data from multisource literature, which would realize the normalization of institution name variants with the help of authority files and achieve a high specification accuracy after several rounds of training and optimization. METHODS: In this study, an institution name normalization-oriented model was trained based on bidirectional encoder representations from transformers (BERT) and other deep learning models, including the institution classification model, institutional hierarchical relation extraction model, and institution matching and merging model. The model was then trained to automatically learn institutional features by pretraining and fine-tuning, and institution names were extracted from the affiliation data of 3 databases to complete the normalization process: Dimensions, Web of Science, and Scopus. RESULTS: It was found that the trained model could achieve at least 3 functions. First, the model could identify the institution name that is consistent with the authority files and associate the name with the files through the unique institution ID. Second, it could identify the nonstandard institution name variants, such as singular forms, plural changes, and abbreviations, and update the authority files. Third, it could identify the unregistered institutions and add them to the authority files, so that when the institution appeared again, the model could identify and regard it as a registered institution. Moreover, the test results showed that the accuracy of the normalization model reached 93.79%, indicating the promising performance of the model for the normalization of institution names. CONCLUSIONS: The deep learning-based institution name normalization model trained in this study exhibited high accuracy. Therefore, it could be widely applied in the evaluation of the competitiveness of research institutions, analysis of research fields of institutions, and construction of interinstitutional cooperation networks, among others, showing high application value.

Comparison of setup accuracy of optical surface image versus orthogonal x-ray images for VMAT of the left breast using deep-inspiration breath-hold.

To compare the setup accuracy of optical surface image (OSI) versus orthogonal x-ray images (2DkV) using cone beam computed tomography (CBCT) as ground truth for radiotherapy of left breast cancer in deep-inspiration breath-hold (DIBH). Ten left breast DIBH patients treated with volumetric modulated arc therapy (VMAT) were studied retrospectively. OSI, 2DkV, and CBCT were acquired weekly at treatment setup. OSI, 2DkV, and CBCT were registered to planning CT or planning DRR based on a breast surface region of interest (ROI), bony anatomy (chestwall and sternum), and both bony anatomy and breast surface, respectively. These registrations provided couch shifts for each imaging system. The setup errors, or the difference in couch shifts between OSI and CBCT were compared to those between 2DkV and CBCT. A second OSI was acquired during last beam delivery to evaluate intrafraction motion. The median absolute setup errors were (0.21, 0.27, 0.23 cm, 0.6°, 1.3°, 1.0°) for OSI, and (0.26, 0.24, 0.18 cm, 0.9°, 1.0°, 0.6°) for 2DkV in vertical, longitudinal and lateral translations, and in rotation, roll and pitch, respectively. None of the setup errors was significantly different between OSI and 2DkV. For both systems, the systematic and random setup errors were ≤0.6 cm and ≤1.5° in all directions. Nevertheless, larger setup errors were observed in some sessions in both systems. There was no correlation between OSI and CBCT whereas there was modest correlation between 2DkV and CBCT. The intrafraction motion in DIBH detected by OSI was small with median absolute translations <0.2 cm, and rotations ≤0.4°. Though OSI showed comparable and small setup errors as 2DkV, it showed no correlation with CBCT. We concluded that to achieve accurate setup for both bony anatomy and breast surface, daily 2DkV can't be omitted following OSI for left breast patients treated with DIBH VMAT.

The Malus domestica metal tolerance protein MdMTP11.1 was involved in the detoxification of excess manganese in Arabidopsis thaliana.

Ion homeostasis is maintained in plant cells by specialized transporters. However, functional studies on Mn transporters in apple trees have not been reported. MdMTP11.1, which encodes a putative Mn-MTP transporter in Malus domestica, was expressed highly in leaves and induced by Mn stress. Subcellular localization analysis of the MdMTP11.1-GFP fusion protein indicated that MdMTP11.1 was targeted to the Golgi. Meanwhile, overexpression of MdMTP11.1 in Arabidopsis thaliana conferred increased resistance to plants under toxic Mn levels, as evidenced by increased biomass of whole plant and length of primary root. Analysis of Mn bioaccumulation indicated that overexpression of MdMTP11.1 effectively reduced the content of Mn in every subcellular component and chemical forms when the plants were subjected with Mn stress. The majority of Mn of action were bound to cell wall and combined with un-dissolved phosphate. Besides, contents of malondialdehyde (MDA), proline and hydrogen peroxide (H2O2) were significantly lower, while content of chlorophyll and activities of CAT, SOD, POD and APX were significantly higher in MdMTP11.1-over-expressing plants compared with that in wild type plants under Mn stress. Taken together, these results suggest that MdMTP11.1 is a Mn specific transporter localized to the Golgi can maintain the phenotype, reduce the Mn accumulation and alleviate damage of oxidative stress, conferring the positive role of Mn tolerance.