MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism.

One of the main problems with the joint use of multiple drugs is that it may cause adverse drug interactions and side effects that damage the body. Therefore, it is important to predict potential drug interactions. However, most of the available prediction methods can only predict whether two drugs interact or not, whereas few methods can predict interaction events between two drugs. Accurately predicting interaction events of two drugs is more useful for researchers to study the mechanism of the interaction of two drugs. In the present study, we propose a novel method, MDF-SA-DDI, which predicts drug-drug interaction (DDI) events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. MDF-SA-DDI is mainly composed of two parts: multi-source drug fusion and multi-source feature fusion. First, we combine two drugs in four different ways and input the combined drug feature representation into four different drug fusion networks (Siamese network, convolutional neural network and two auto-encoders) to obtain the latent feature vectors of the drug pairs, in which the two auto-encoders have the same structure, and their main difference is the number of neurons in the input layer of the two auto-encoders. Then, we use transformer blocks that include self-attention mechanism to perform latent feature fusion. We conducted experiments on three different tasks with two datasets. On the small dataset, the area under the precision-recall-curve (AUPR) and F1 scores of our method on task 1 reached 0.9737 and 0.8878, respectively, which were better than the state-of-the-art method. On the large dataset, the AUPR and F1 scores of our method on task 1 reached 0.9773 and 0.9117, respectively. In task 2 and task 3 of two datasets, our method also achieved the same or better performance as the state-of-the-art method. More importantly, the case studies on five DDI events are conducted and achieved satisfactory performance. The source codes and data are available at https://github.com/ShenggengLin/MDF-SA-DDI.

Gut Microbiota Moderates Multimodal Brain Structure-Function Integration and Behavioral Cognition in Growth Hormone Deficient Children.

INTRODUCTION: Previous brain studies of growth hormone deficiency (GHD) often used single-modal neuroimaging, missing the complexity captured by multimodal data. Growth hormone affects gut microbiota and metabolism in GHD. However, from a gut-brain axis (GBA) perspective, the relationship between abnormal GHD brain development and microbiota alterations remains unclear. The ultimate goal is to uncover the manifestations underlying GBA abnormalities in GHD and idiopathic short stature (ISS). METHODS: Participants included 23 GHD and 25 ISS children. The fusion independent component analysis was applied to integrate multimodal brain data (high-resolution structural, diffusion tensor, and resting-state functional MRI) covering regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), and white matter fractional anisotropy (FA). Gut microbiome diversity and metabolites were analyzed using 16S sequencing and proton nuclear magnetic resonance (1H-NMR). Associations between multimodal neuroimaging and cognition were assessed using moderation analysis. RESULTS: Six independent components (IC) of ReHo, ALFF, and FA differed significantly between GHD and ISS patients, with three functional components linked to the processing speed index. GHD individuals showed higher levels of acetate, nicotinate, and lysine in microbiota metabolism. Higher alpha diversity in GHD strengthened connections between ReHo-IC1, ReHo-IC5, ALFF-IC1, and the processing speed index, while increasing agathobacter levels in ISS weakened the link between ALFF-IC1 and the speech comprehension index. CONCLUSIONS: Our findings uncover differing brain structure and functional fusion in GHD, alongside microbiota metabolism of short-chain fatty acids. Additionally, microbiome influences connections between neuroimaging and cognition, offering insight into diverse GBA patterns in GHD and ISS, enhancing our understanding of the disease's pathophysiology and interventions.

Development and Validation of a Combined MRI Radiomics, Imaging and Clinical Parameter-Based Machine Learning Model for Identifying Idiopathic Central Precocious Puberty in Girls.

BACKGROUND: Idiopathic central precocious puberty (ICPP) impairs child development, without early intervention. The current reference standard, the gonadotropin-releasing hormone stimulation test, is invasive which may hinder diagnosis and intervention. PURPOSE: To develop a model for accurate diagnosis of ICPP, by integrating pituitary MRI, carpal bone age, gonadal ultrasound, and basic clinical data. STUDY TYPE: Retrospective. POPULATION: A total of 492 girls with PP (185 with ICPP and 307 peripheral precocious puberty [PPP]) were randomly divided by reference standard into training (75%) and internal validation (25%) data. Fifty-one subjects (16 with ICPP, 35 with PPP) provided by another hospital as external validation. FIELD STRENGTH/SEQUENCE: T1-weighted (spin echo [SE], fast SE, cube) and T2-weighted (fast SE-fat suppression) imaging at 3.0 T or 1.5 T. ASSESSMENT: Radiomics features were extracted from pituitary MRI after manual segmentation. Carpal bone age, ovarian, follicle and uterine volumes and endometrium presence were assessed from radiographs and gonadal ultrasound. Four machine learning methods were developed: a pituitary MRI radiomics model, an integrated image model (with pituitary MRI, gonadal ultrasound and bone age), a basic clinical model (with age and sex hormone data), and an integrated multimodal model combining all features. STATISTICAL TESTS: Intraclass correlation coefficients were used to assess consistency of segmentation. Receiver operating characteristic (ROC) curves and the Delong tests were used to assess and compare the diagnostic performance of models. P < 0.05 was considered statistically significant. RESULTS: The area under of the ROC curve (AUC) of the pituitary MRI radiomics model, integrated image model, basic clinical model, and integrated multimodal model in the training data was 0.668, 0.809, 0.792, and 0.860. The integrated multimodal model had higher diagnostic efficacy (AUC of 0.862 and 0.866 for internal and external validation). CONCLUSION: The integrated multimodal model may have potential as an alternative clinical approach to diagnose ICPP. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Bioinformatics Identification of Regulatory Genes and Mechanism Related to Hypoxia-Induced PD-L1 Inhibitor Resistance in Hepatocellular Carcinoma.

The combination of a PD-L1 inhibitor and an anti-angiogenic agent has become the new reference standard in the first-line treatment of non-excisable hepatocellular carcinoma (HCC) due to the survival advantage, but its objective response rate remains low at 36%. Evidence shows that PD-L1 inhibitor resistance is attributed to hypoxic tumor microenvironment. In this study, we performed bioinformatics analysis to identify genes and the underlying mechanisms that improve the efficacy of PD-L1 inhibition. Two public datasets of gene expression profiles, (1) HCC tumor versus adjacent normal tissue (N = 214) and (2) normoxia versus anoxia of HepG2 cells (N = 6), were collected from Gene Expression Omnibus (GEO) database. We identified HCC-signature and hypoxia-related genes, using differential expression analysis, and their 52 overlapping genes. Of these 52 genes, 14 PD-L1 regulator genes were further identified through the multiple regression analysis of TCGA-LIHC dataset (N = 371), and 10 hub genes were indicated in the protein-protein interaction (PPI) network. It was found that POLE2, GABARAPL1, PIK3R1, NDC80, and TPX2 play critical roles in the response and overall survival in cancer patients under PD-L1 inhibitor treatment. Our study provides new insights and potential biomarkers to enhance the immunotherapeutic role of PD-L1 inhibitors in HCC, which can help in exploring new therapeutic strategies.

Efficacy and safety of sodium oligomannate in the treatment of Alzheimer's disease.

To evaluate the efficacy and safety of sodium oligomannate in the treatment of Alzheimer's disease. Patients with mild-to-moderate AD were randomly divided into three groups, the scores of ADAS-Cog, ADL, CIBIC-plus, NPI and CSDD were evaluated at the 0th, 12th, 24th, 36th and 48th weeks of medication. Comparing the mean scores of each scale in each cycle of each group. Using SPSS21.0 software for measurement data using t test, Chi-square test was used for counting data. A total of 72 patients with AD were included. The difference of CIBIC-plus score at week 12(P=0.007) and 24(P=0.005), ADAS-Cog scores (P=0.01) at week 24 in GV-971 group was statistically significant compared with that in the control group. The CIBIC-plus score at week 24(P=0.01) and week 48 (P=0.04), CSDD scores at week 48(P=0.02) of GV-971 group was statistically significant compared with that of donepezil group. There were 2 cases of adverse reaction of increased stool frequency in GV-971 (5.67%), and 2 cases of adverse reaction of nausea in donepezil group (8.33%), the difference was statistically significant. GV-971 is as effective as donepezil in the treatment of Alzheimer's disease, and may even be better. It has good safety.

Flexible ultraviolet photodetector based on single ZnO microwire/polyaniline heterojunctions.

Flexible ultraviolet (UV) photodetectors are considered as potential building blocks for future-oriented photoelectric applications such as flexible optical communication, image sensors, wearable devices and so on. In this work, high-performance UV photodetector was fabricated via a facile combination of single ZnO microwire (MW) and p-type polyaniline. Due to the formation of effective organic/inorganic p-n junction, the as-prepared flexible UV photodetector based on ZnO MW/polyaniline hybrid heterojunction exhibits high performance (responsivity ∼ 60 mA/W and detectivity ∼ 2.0 ×1011 Jones) at the reverse bias of -1 V under the UV illumination. The ZnO MW/polyaniline photodetector displays short response/recovery times (∼ 0.44 s/∼ 0.42 s), which is less than that of most reported UV photodetectors based on ZnO/polymer heterojunction. The fast response speed and recovery speed can be attributed to the high crystallinity of ZnO MW, built-in electric field in space-charge region and the passivation of oxygen traps on the surface. Further, the photodetector using ZnO MW/polyaniline junctions shows excellent flexibility and stability under bent conditions. This work opens a new way to design next-generation high-performance, low-cost and flexible optoelectronic devices for lab-on-a-chip applications.

Perfluoroalkyl-Promoted Synthesis of Perfluoroalkylated Pyrrolidine-Fused Coumarins with Methyl ß-Perfluoroalkylpropionates.

Employing the methyl ß-perfluoroalkylpropionate as the Michael acceptor, an efficient approach for the synthesis of perfluoroalkylated pyrrolidine-fused coumarins has been achieved. A tandem reaction involving [3 + 2] cycloaddition and intramolecular transesterification was proposed for the mechanism. The enhanced electrophilicity resulting from the strong electron-withdrawing ability of the perfluoroalkyl group was crucial for this tandem reaction.

An exploratory decision tree analysis to predict physical activity compliance rates in breast cancer survivors.

Background: The study of physical activity in cancer survivors has been limited to one cause, one effect relationships. In this exploratory study, we used recursive partitioning to examine multiple correlates that influence physical activity compliance rates in cancer survivors. Methods: African American breast cancer survivors (N = 267, Mean age = 54 years) participated in an online survey that examined correlates of physical activity. Recursive partitioning (RP) was used to examine complex and nonlinear associations between sociodemographic, medical, cancer-related, theoretical, and quality of life indicators. Results: Recursive partitioning revealed five distinct groups. Compliance with physical activity guidelines was highest (82% met guidelines) among survivors who reported higher mean action planning scores (P < 0.001) and lower mean barriers to physical activity (P = 0.035). Compliance with physical activity guidelines was lowest (9% met guidelines) among survivors who reported lower mean action and coping (P = 0.002) planning scores. Similarly, lower mean action planning scores and poor advanced lower functioning (P = 0.034), even in the context of higher coping planning scores, resulted in low physical activity compliance rates (13% met guidelines). Subsequent analyses revealed that body mass index (P = 0.019) and number of comorbidities (P = 0.003) were lowest in those with the highest compliance rates. Conclusion: Our findings support the notion that multiple factors determine physical activity compliance rates in African American breast cancer survivors. Interventions that encourage action and coping planning and reduce barriers in the context of addressing function limitations may increase physical activity compliance rates.

A novel center-based deep contrastive metric learning method for the detection of polymicrogyria in pediatric brain MRI.

Polymicrogyria (PMG) is a disorder of cortical organization mainly seen in children, which can be associated with seizures, developmental delay and motor weakness. PMG is typically diagnosed on magnetic resonance imaging (MRI) but some cases can be challenging to detect even for experienced radiologists. In this study, we create an open pediatric MRI dataset (PPMR) containing both PMG and control cases from the Children's Hospital of Eastern Ontario (CHEO), Ottawa, Canada. The differences between PMG and control MRIs are subtle and the true distribution of the features of the disease is unknown. This makes automatic detection of potential PMG cases in MRI difficult. To enable the automatic detection of potential PMG cases, we propose an anomaly detection method based on a novel center-based deep contrastive metric learning loss function (cDCM). Despite working with a small and imbalanced dataset our method achieves 88.07% recall at 71.86% precision. This will facilitate a computer-aided tool for radiologists to select potential PMG MRIs. To the best of our knowledge, our research is the first to apply machine learning techniques to identify PMG solely from MRI. Our code is available at: https://github.com/RichardChangCA/Deep-Contrastive-Metric-Learning-Method-to-Detect-Polymicrogyria-in-Pediatric-Brain-MRI. Our pediatric MRI dataset is available at: https://www.kaggle.com/datasets/lingfengzhang/pediatric-polymicrogyria-mri-dataset.

Clinical performance of metagenomic next-generation sequencing for diagnosis of invasive fungal disease after hematopoietic cell transplant.

Background: Timely diagnosis and appropriate antifungal therapy are critical for improving the prognosis of patients with invasive fungal disease (IFD) after hematopoietic stem cell transplantation (HSCT). We evaluated the performance of metagenomic next-generation sequencing (mNGS) and conventional microbiological testing (CMT), as well as the diagnosis, therapeutic management, and outcomes of IFD after HSCT. Methods: We retrospectively studied 189 patients who underwent HSCT and were considered at risk for IFD. In total, 46 patients with IFD were enrolled in this study. The IFD consensus was followed for classifying IFD incidents. Results: Forty-six patients were diagnosed with proven/probable (n = 12), possible (n = 27), and undefined (n = 7) IFD. Aspergillus was the most commonly detected fungal genus. Mucormycosis was found in 15 patients; two had Aspergillus, and one had Candida infections. Compared to CMT, mNGS significantly reduced the time required to identify pathogens (P = 0.0016). mNGS had a much higher sensitivity than CMT (84.78% vs. 36.96%; P < 0.0001). A total of 76.09% of patients received antifungal prophylaxis during fungal infections. All Pneumocystis infections occurred later than 100 days after transplantation. Among patients with Pneumocystis infection, 71.43% occurred following sulfonamide withdrawal, and subsequent treatment with sulfonamide alone or in combination with other drugs was effective. Based on the empirical antifungal treatment, the dosages, modes of administration, frequency of administration, or antifungal of 55.26% of the patients were changed according to the mNGS results. The 4-year overall survival rate of patients diagnosed with IFD after transplantation was 71.55% (95% CI, 55.18%-85.82%). Hypoproteinemia and corticosteroid use are independent risk factors for IFD. Conclusion: mNGS, which has a high sensitivity and a short detection time, aids in the diagnosis and prognosis of pathogenic fungi. As a powerful technology, mNGS can influence treatment decisions in patients with IFD following HSCT.

Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation.

Medical imaging serves as a crucial tool in current cancer diagnosis. However, the quality of medical images is often compromised to minimize the potential risks associated with patient image acquisition. Computer-aided diagnosis systems have made significant advancements in recent years. These systems utilize computer algorithms to identify abnormal features in medical images, assisting radiologists in improving diagnostic accuracy and achieving consistency in image and disease interpretation. Importantly, the quality of medical images, as the target data, determines the achievable level of performance by artificial intelligence algorithms. However, the pixel value range of medical images differs from that of the digital images typically processed via artificial intelligence algorithms, and blindly incorporating such data for training can result in suboptimal algorithm performance. In this study, we propose a medical image-enhancement scheme that integrates generic digital image processing and medical image processing modules. This scheme aims to enhance medical image data by endowing them with high-contrast and smooth characteristics. We conducted experimental testing to demonstrate the effectiveness of this scheme in improving the performance of a medical image segmentation algorithm.

A comprehensive study on the regulation of Compound Zaoren Granules on cAMP/CREB signaling pathway and metabolic disorder in CUMS-PCPA induced insomnia rats.

ETHNOPHARMACOLOGICAL RELEVANCE: Compound Zaoren Granules (CZG), an optimized herbal formulation based on the traditional Chinese medicine prescription Suanzaoren decoction, are designed specifically for insomnia treatment. However, the mechanisms underlying its efficacy in treating insomnia are not yet fully understood. AIM OF THE STUDY: The research investigated the mechanisms of CZG's improvement in insomnia by regulating cAMP/CREB signaling pathway and metabolic profiles. METHODS: The main components of CZG were characterized by liquid chromatography-mass spectrometry (LC-MS). Subsequently, these validated components were applied to network pharmacological analysis to predict signaling pathways associated with insomnia. We evaluated the effect of CZG on BV-2 cells in vitro. We also evaluated the behavioral indexes of CUMS combined with PCPA induced insomnia in rats. HE staining and Nissl staining were used to observe the pathological damage of hippocampus. ELISA was used to detect the levels of various neurotransmitters, orexins, HPA axis, and inflammatory factors in insomnia rats. Then we detected the expression of cAMP/CREB signaling pathway through ELISA, WB, and IHC. Finally, the metabolomics was further analyzed by using UHPLC-QTOF-MS/MS to investigate the changes in the hippocampus of insomnia rats and the possible metabolic pathways were also speculated. RESULTS: The results of CZG in vitro experiments showed that CZG has protective and anti-inflammatory effects on LPS induced BV-2 cells. A total of 161 chemical components were identified in CZG. After conducting network pharmacology analysis through these confirmed components, we select the cAMP/CREB signaling pathway for further investigate. The behavioral research results on insomnia rats showed that CZG significantly prolonged sleep time, mitigated brain tissue pathological damage, and exhibited liver protective properties. CZG treats insomnia by regulating the content of various neurotransmitters, reducing levels of orexin, HPA axis, and inflammatory factors. It can also treat insomnia by upregulating the expression of the cAMP/CREB signaling pathway. Hippocampus metabolomics analysis identified 69 differential metabolites associated with insomnia. The metabolic pathways related to these differential metabolites have also been predicted. CONCLUSION: These results indicate that CZG can significantly prolong sleep time. CZG is used to treat insomnia by regulating various neurotransmitters, HPA axis, inflammatory factors, cAMP/CREB signaling pathways, and metabolic disorders.

Establishing a machine learning model based on dual-energy CT enterography to evaluate Crohn's disease activity.

OBJECTIVES: The simplified endoscopic score of Crohn's disease (SES-CD) is the gold standard for quantitatively evaluating Crohn's disease (CD) activity but is invasive. This study aimed to develop and validate a machine learning (ML) model based on dual-energy CT enterography (DECTE) to noninvasively evaluate CD activity. METHODS: We evaluated the activity in 202 bowel segments of 46 CD patients according to the SES-CD score and divided the segments randomly into training set and testing set at a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) was used for feature selection, and three models based on significant parameters were established based on logistic regression. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and clinical decision curves. RESULTS: There were 110 active and 92 inactive bowel segments. In univariate analysis, the slope of spectral curve in the venous phases (λHU-V) has the best diagnostic performance, with an area under the ROC curve (AUC) of 0.81 and an optimal threshold of 1.975. In the testing set, the AUC of the three models established by the 7 variables to differentiate CD activity was 0.81-0.87 (DeLong test p value was 0.071-0.766, p > 0.05), and the combined model had the highest AUC of 0.87 (95% confidence interval (CI): 0.779-0.959). CONCLUSIONS: The ML model based the DECTE can feasibly evaluate CD activity, and DECTE parameters provide a quantitative analysis basis for evaluating specific bowel activities in CD patients. CRITICAL RELEVANCE STATEMENT: The machine learning model based on dual-energy computed tomography enterography can be used for evaluating Crohn's disease activity noninvasively and quantitatively. KEY POINTS: Dual-energy CT parameters are related to Crohn's disease activity. Three machine learning models effectively evaluated Crohn's disease activity. Combined models based on conventional and dual-energy CT have the best performance.

Vicious Cycle-Breaking Lipid Nanoparticles Remodeling Multicellular Crosstalk to Reverse Liver Fibrosis.

During liver fibrogenesis, the reciprocal crosstalk among capillarized liver sinusoidal endothelial cells (LSECs), activated hepatic stellate cells (HSCs), and dysfunctional hepatocytes constructs a self-amplifying vicious cycle, greatly exacerbating the disease condition and weakening therapeutic effect. Limited by the malignant cellular interactions, the previous single-cell centric treatment approaches show unsatisfactory efficacy and fail to meet clinical demand. Herein, a vicious cycle-breaking strategy is proposed to target and repair pathological cells separately to terminate the malignant progression of liver fibrosis. Chondroitin sulfate-modified and vismodegib-loaded nanoparticles (CS-NPs/VDG) are designed to efficiently normalize the fenestrae phenotype of LSECs and restore HSCs to quiescent state by inhibiting Hedgehog signaling pathway. In addition, glycyrrhetinic acid-modified and silybin-loaded nanoparticles (GA-NPs/SIB) are prepared to restore hepatocytes function by relieving oxidative stress. The results show successful interruption of vicious cycle as well as distinct fibrosis resolution in two animal models through multiregulation of the pathological cells. This work not only highlights the significance of modulating cellular crosstalk but also provides a promising avenue for developing antifibrotic regimens.

Increasing Donor-Acceptor Interactions and Particle Dispersibility of Covalent Triazine Frameworks for Higher Crystallinity and Enhanced Photocatalytic Activity.

Covalent triazine frameworks (CTFs) have recently emerged as an efficient class of photocatalysts due to their structural diversity and excellent stability. Nevertheless, the synthetic reactions of CTFs have usually suffered from poor reversibility, resulting in a low crystallinity of the materials. Here, we report the introduction of methoxy groups on the monomer 2,5-diphenylthiazolo[5,4-d]thiazole to reinforce interlayer π-π interactions of the resulting donor-acceptor type CTFs, which improved crystallinity, further increasing the visible light absorption range and allowing for efficient separation and transport of carriers. The morphology is strongly correlated to the wettability, which has a significant impact on the mass transfer capacity and photocatalytic activity in the photocatalytic reaction. To further improve crystallinity and photocatalytic activity, CTF-NWU-T3 photocatalysts in a bowl shape were prepared using a SiO2 template. The energy band structure, photocatalytic hydrogen evolution, and pollutant degradation efficiency of involved materials were investigated. The donor-acceptor type CTF-NWU-T3 with a bowl-shaped morphology, synthesized using the template method and the introduction of methoxy groups, exhibited an excellent photocatalytic hydrogen production rate of 32064 µmol·h-1·g-1. This study highlights the significance of improving donor-acceptor interactions and increasing the dispersibility of catalyst particles in dispersion to enhance the photocatalytic activity of heterogeneous photocatalysts.

An Autologous Macrophage-Based Phenotypic Transformation-Collagen Degradation System Treating Advanced Liver Fibrosis.

In advanced liver fibrosis (LF), macrophages maintain the inflammatory environment in the liver and accelerate LF deterioration by secreting proinflammatory cytokines. However, there is still no effective strategy to regulate macrophages because of the difficulty and complexity of macrophage inflammatory phenotypic modulation and the insufficient therapeutic efficacy caused by the extracellular matrix (ECM) barrier. Here, AC73 and siUSP1 dual drug-loaded lipid nanoparticle is designed to carry milk fat globule epidermal growth factor 8 (MFG-E8) (named MUA/Y) to effectively inhibit macrophage proinflammatory signals and degrade the ECM barrier. MFG-E8 is released in response to the high reactive oxygen species (ROS) environment in LF, transforming macrophages from a proinflammatory (M1) to an anti-inflammatory (M2) phenotype and inducing macrophages to phagocytose collagen. Collagen ablation increases AC73 and siUSP1 accumulation in hepatic stellate cells (HSCs) and inhibits HSCs overactivation. Interestingly, complete resolution of liver inflammation, significant collagen degradation, and HSCs deactivation are observed in methionine choline deficiency (MCD) and CCl4 models after tail vein injection of MUA/Y. Overall, this work reveals a macrophage-focused regulatory treatment strategy to eliminate LF progression at the source, providing a new perspective for the clinical treatment of advanced LF.

Global estimates of gap-free and fine-scale CO2 concentrations during 2014-2020 from satellite and reanalysis data.

Carbon dioxide (CO2) is a crucial greenhouse gas with substantial effects on climate change. Satellite-based remote sensing is a commonly used approach to detect CO2 with high precision but often suffers from extensive spatial gaps. Thus, the limited availability of data makes global carbon stocktaking challenging. In this paper, a global gap-free column-averaged dry-air mole fraction of CO2 (XCO2) dataset with a high spatial resolution of 0.1° from 2014 to 2020 is generated by the deep learning-based multisource data fusion, including satellite and reanalyzed XCO2 products, satellite vegetation index data, and meteorological data. Results indicate a high accuracy for 10-fold cross-validation (R2 = 0.959 and RMSE = 1.068 ppm) and ground-based validation (R2 = 0.964 and RMSE = 1.010 ppm). Our dataset has the advantages of high accuracy and fine spatial resolution compared with the XCO2 reanalysis data as well as that generated from other studies. Based on the dataset, our analysis reveals interesting findings regarding the spatiotemporal pattern of CO2 over the globe and the national-level growth rates of CO2. This gap-free and fine-scale dataset has the potential to provide support for understanding the global carbon cycle and making carbon reduction policy, and it can be freely accessed at https://doi.org/10.5281/zenodo.7721945.

Research trends in transient receptor potential vanilloid in cardiovascular disease: Bibliometric analysis and visualization.

Background: Transient receptor potential vanilloid (TRPV) is one of the transient receptor potential protein groups; cardiovascular system disease is a crucial cause of mortality among people globally. Objective: This article is intended to accomplish a bibliometric analysis of the trends and public interest since TRPV was reported for the first time. Methods: The article summarized the Web of Science (WOS) Core Collection on the relationship between TRPV and cardiovascular system disease each year from 2000 to 2021. Data extraction and visualization were completed by R package bibliometrix. Keyword citation burst and co-citation networks were generated and produced by CiteSpace. The map evaluating the distribution of country and region was painted in GunnMap 2 (lert.co.nz). The ranking was performed using the Standard Competition Ranking method. Co-authorship and co-occurrence were analyzed with VOSviewer. Results: After removing duplicated data, books, conference proceedings, and articles of uncertain age, 493 were included, and 17 were excluded. The pattern of publication years showed that the number of publications increased rapidly from 2008 to 2021 with no peak in the number of publications until 2021. The geographical distribution pattern revealed a considerable gap in the number of publications between the United States, China, and other countries, with East Asian institutions leading the world in this area. The pattern of co-authorship showed that 77 institutions were divided into 19 clusters, each covering one country or region.These results suggest that intercontinental cooperation among institutions should be strengthened. The core authors section displayed the change in the most published authors. Keyword analysis listed six burst keywords. Co-citation analysis of references from 2011 to 2021 showed the number and centrality of citations to leading articles. Conclusion: Our findings reveal trends and public interest in transient receptor potential vanilloid for cardiovascular disease. These findings suggest that the field has experienced significant growth since 2008, with the United States and China in dominant positions. Our findings also suggest that intercontinental cooperation should be strengthened, and that future research hotspots may focus on pharmacological mechanisms and in-depth exploration of drug clinical trials and new clinical disease application areas such as hypertension, diabetes, and cardiac arrhythmias, which could serve as a foundation for further research.

Post-transplantation cyclophosphamide as GVHD prophylaxis in allogenic hematopoietic stem cell transplantation: Recent advances and modification.

Allogenic hematopoietic stem cell transplantation (allo-HSCT) is the most important therapeutic option for hematological disorders, although graft-versus-host disease (GVHD) remains the main cause of mortality. Post-transplantation cyclophosphamide (PTCY) induces immune tolerance and is associated with a low incidence of GVHD and non-relapse mortality. Therefore, PTCY has emerged as a safe and effective GVHD prophylaxis in haploidentical transplantation and has been expanded to matched related or unrelated donor and mismatched unrelated donor HSCT. On the basis of current understanding of the mechanisms of PTCY and antithymocyte globulin (ATG) in the prevention of GVHD, growing evidence suggests that the combination of ATG and PTCY could improve allo-HSCT clinical outcomes. Further research will focus on optimizing PTCY regimens by modifying the timing of administration or adding other immunosuppressive agents.

Progress on the pathological tissue microenvironment barrier-modulated nanomedicine.

Imbalance in the tissue microenvironment is the main obstacle to drug delivery and distribution in the human body. Before penetrating the pathological tissue microenvironment to the target site, therapeutic agents are usually accompanied by three consumption steps: the first step is tissue physical barriers for prevention of their penetration, the second step is inactivation of them by biological molecules, and the third step is a cytoprotective mechanism for preventing them from functioning on specific subcellular organelles. However, recent studies in drug-hindering mainly focus on normal physiological rather than pathological microenvironment, and the repair of damaged physiological barriers is also rarely discussed. Actually, both the modulation of pathological barriers and the repair of damaged physiological barriers are essential in the disease treatment and the homeostasis maintenance. In this review, we present an overview describing the latest advances in the generality of these pathological barriers and barrier-modulated nanomedicine. Overall, this review holds considerable significance for guiding the design of nanomedicine to increase drug efficacy in the future.