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1.
Langmuir ; 2024 Jun 03.
Article in English | MEDLINE | ID: mdl-38830755

ABSTRACT

Silicon carbide, as a third-generation semiconductor material, plays a pivotal role in various advanced technological applications. Its exceptional stability under extreme conditions has garnered a significant amount of attention. These superior characteristics make silicon carbide an ideal candidate material for high-frequency, high-power electronic devices and applications in harsh environments. In particular, corrosion resistance in natural or artificially acidic and alkaline environments limits the practical application of many other materials. In fields such as chemical engineering, energy conversion, and environmental engineering, materials often face severe chemical erosion, necessitating materials with excellent chemical stability as foundational materials, carriers, or reaction media. Silicon carbide exhibits outstanding performance under these conditions, demonstrating significant resistance to corrosive substances such as hydrochloric acid, sulfuric acid, nitric acid, and alkaline substances such as potassium hydroxide and sodium hydroxide. Despite the well-known chemical stability of silicon carbide, the stability conditions of its different types (such as 3C-, 4H-, and 6H-SiC polycrystals) in acidic and alkaline environments, as well as the specific corrosion mechanisms and differences, warrant further investigation. This Review not only delves deeply into the detailed studies related to this topic but also highlights the current applications of different silicon carbide polycrystals in chemical reaction systems, energy conversion equipment, and recycling processes. Through a comprehensive analysis, this Review aims to bridge research gaps, offering a comparative analysis of the advantages and disadvantages between different polymorphs. It provides material scientists, engineers, and developers with a thorough understanding of silicon carbide's behavior in various chemical environments. This work will propel the research and development of silicon carbide materials under extreme conditions, especially in areas where chemical stability is crucial for device performance and durability. It lays a solid foundation for ultra-high-power, high-integration, high-reliability module architectures, supercomputing chips, and highly safe long-life batteries.

2.
IEEE Trans Med Imaging ; PP2024 May 27.
Article in English | MEDLINE | ID: mdl-38801690

ABSTRACT

It is an essential task to accurately diagnose cancer subtypes in computational pathology for personalized cancer treatment. Recent studies have indicated that the combination of multimodal data, such as whole slide images (WSIs) and multi-omics data, could achieve more accurate diagnosis. However, robust cancer diagnosis remains challenging due to the heterogeneity among multimodal data, as well as the performance degradation caused by insufficient multimodal patient data. In this work, we propose a novel multimodal co-attention fusion network (MCFN) with online data augmentation (ODA) for cancer subtype classification. Specifically, a multimodal mutual-guided co-attention (MMC) module is proposed to effectively perform dense multimodal interactions. It enables multimodal data to mutually guide and calibrate each other during the integration process to alleviate inter- and intra-modal heterogeneities. Subsequently, a self-normalizing network (SNN)-Mixer is developed to allow information communication among different omics data and alleviate the high-dimensional small-sample size problem in multi-omics data. Most importantly, to compensate for insufficient multimodal samples for model training, we propose an ODA module in MCFN. The ODA module leverages the multimodal knowledge to guide the data augmentations of WSIs and maximize the data diversity during model training. Extensive experiments are conducted on the public TCGA dataset. The experimental results demonstrate that the proposed MCFN outperforms all the compared algorithms, suggesting its effectiveness.

3.
Neural Netw ; 177: 106378, 2024 May 08.
Article in English | MEDLINE | ID: mdl-38761414

ABSTRACT

Transformer-based image denoising methods have shown remarkable potential but suffer from high computational cost and large memory footprint due to their linear operations for capturing long-range dependencies. In this work, we aim to develop a more resource-efficient Transformer-based image denoising method that maintains high performance. To this end, we propose an Efficient Wavelet Transformer (EWT), which incorporates a Frequency-domain Conversion Pipeline (FCP) to reduce image resolution without losing critical features, and a Multi-level Feature Aggregation Module (MFAM) with a Dual-stream Feature Extraction Block (DFEB) to harness hierarchical features effectively. EWT achieves a faster processing speed by over 80% and reduces GPU memory usage by more than 60% compared to the original Transformer, while still delivering denoising performance on par with state-of-the-art methods. Extensive experiments show that EWT significantly improves the efficiency of Transformer-based image denoising, providing a more balanced approach between performance and resource consumption.

4.
Article in English | MEDLINE | ID: mdl-38630567

ABSTRACT

The B-mode ultrasound based computer-aided diagnosis (CAD) has demonstrated its effectiveness for diagnosis of Developmental Dysplasia of the Hip (DDH) in infants, which can conduct the Graf's method by detecting landmarks in hip ultrasound images. However, it is still necessary to explore more valuable information around these landmarks to enhance feature representation for improving detection performance in the detection model. To this end, a novel Involution Transformer based U-Net (IT-UNet) network is proposed for hip landmark detection. The IT-UNet integrates the efficient involution operation into Transformer to develop an Involution Transformer module (ITM), which consists of an involution attention block and a squeeze-and-excitation involution block. The ITM can capture both the spatial-related information and long-range dependencies from hip ultrasound images to effectively improve feature representation. Moreover, an Involution Downsampling block (IDB) is developed to alleviate the issue of feature loss in the encoder modules, which combines involution and convolution for the purpose of downsampling. The experimental results on two DDH ultrasound datasets indicate that the proposed IT-UNet achieves the best landmark detection performance, indicating its potential applications.

5.
IEEE Trans Med Imaging ; PP2024 Apr 16.
Article in English | MEDLINE | ID: mdl-38625767

ABSTRACT

Identifying the progression stages of Alzheimer's disease (AD) can be considered as an imbalanced multi-class classification problem in machine learning. It is challenging due to the class imbalance issue and the heterogeneity of the disease. Recently, graph convolutional networks (GCNs) have been successfully applied in AD classification. However, these works did not handle the class imbalance issue in classification. Besides, they ignore the heterogeneity of the disease. To this end, we propose a novel cost-sensitive weighted contrastive learning method based on graph convolutional networks (CSWCL-GCNs) for imbalanced AD staging using resting-state functional magnetic resonance imaging (rs-fMRI). The proposed method is developed on a multi-view graph constructed using the functional connectivity (FC) and high-order functional connectivity (HOFC) features of the subjects. A novel cost-sensitive weighted contrastive learning procedure is proposed to capture discriminative information from the minority classes, encouraging the samples in the minority class to provide adequate supervision. Considering the heterogeneity of the disease, the weights of the negative pairs are introduced into contrastive learning and they are computed based on the distance to class prototypes, which are automatically learned from the training data. Meanwhile, the cost-sensitive mechanism is further introduced into contrastive learning to handle the class imbalance issue. The proposed CSWCL-GCN is evaluated on 720 subjects (including 184 NCs, 40 SMC patients, 208 EMCI patients, 172 LMCI patients and 116 AD patients) from the ADNI (Alzheimer's Disease Neuroimaging Initiative). Experimental results show that the proposed CSWCL-GCN outperforms state-of-the-art methods on the ADNI database.

6.
Protein Expr Purif ; 219: 106480, 2024 Jul.
Article in English | MEDLINE | ID: mdl-38588871

ABSTRACT

Mpox is a zoonotic disease that was once endemic in Africa countries caused by mpox virus. However, cases recently have been confirmed in many non-endemic countries outside of Africa. The rapidly increasing number of confirmed mpox cases poses a threat to the international community. In-depth studies of key viral factors are urgently needed, which will inform the design of multiple antiviral agents. Mpox virus A41L gene encodes a secreted protein, A41, that is nonessential for viral replication, but could affect the host response to infection via interacting with chemokines. Here, mpox virus A41 protein was expressed in Sf9 cells, and purified by affinity chromatography followed by gel filtration. Surface plasmon resonance spectroscopy showed that purified A41 binds a certain human chemokine CXCL8 with the equilibrium dissociation constant (KD) being 1.22 × 10-6 M. The crystal structure of mpox virus A41 protein was solved at 1.92 Å. Structural analysis and comparison revealed that mpox virus A41 protein adopts a characteristic ß-sheet topology, showing minor differences with that of vaccinia virus. These preliminary structural and functional studies of A41 protein from mpox virus will help us better understand its role in chemokine subversion, and contributing to the knowledge to viral chemokine binding proteins.


Subject(s)
Viral Proteins , Humans , Viral Proteins/genetics , Viral Proteins/chemistry , Viral Proteins/metabolism , Viral Proteins/biosynthesis , Viral Proteins/isolation & purification , Crystallography, X-Ray , Animals , Interleukin-8/genetics , Interleukin-8/chemistry , Interleukin-8/metabolism , Gene Expression , Sf9 Cells , Recombinant Proteins/chemistry , Recombinant Proteins/genetics , Recombinant Proteins/isolation & purification , Recombinant Proteins/metabolism , Recombinant Proteins/biosynthesis , Yatapoxvirus/genetics , Yatapoxvirus/chemistry , Yatapoxvirus/metabolism
7.
IEEE Trans Med Imaging ; PP2024 Feb 19.
Article in English | MEDLINE | ID: mdl-38373131

ABSTRACT

Deep learning (DL) has proven highly effective for ultrasound-based computer-aided diagnosis (CAD) of breast cancers. In an automatic CAD system, lesion detection is critical for the following diagnosis. However, existing DL-based methods generally require voluminous manually-annotated region of interest (ROI) labels and class labels to train both the lesion detection and diagnosis models. In clinical practice, the ROI labels, i.e. ground truths, may not always be optimal for the classification task due to individual experience of sonologists, resulting in the issue of coarse annotation to limit the diagnosis performance of a CAD model. To address this issue, a novel Two-Stage Detection and Diagnosis Network (TSDDNet) is proposed based on weakly supervised learning to improve diagnostic accuracy of the ultrasound-based CAD for breast cancers. In particular, all the initial ROI-level labels are considered as coarse annotations before model training. In the first training stage, a candidate selection mechanism is then designed to refine manual ROIs in the fully annotated images and generate accurate pseudo-ROIs for the partially annotated images under the guidance of class labels. The training set is updated with more accurate ROI labels for the second training stage. A fusion network is developed to integrate detection network and classification network into a unified end-to-end framework as the final CAD model in the second training stage. A self-distillation strategy is designed on this model for joint optimization to further improves its diagnosis performance. The proposed TSDDNet is evaluated on three B-mode ultrasound datasets, and the experimental results indicate that it achieves the best performance on both lesion detection and diagnosis tasks, suggesting promising application potential.

8.
Comput Biol Med ; 168: 107821, 2024 01.
Article in English | MEDLINE | ID: mdl-38064844

ABSTRACT

With the widespread application of digital orthodontics in the diagnosis and treatment of oral diseases, more and more researchers focus on the accurate segmentation of teeth from intraoral scan data. The accuracy of the segmentation results will directly affect the follow-up diagnosis of dentists. Although the current research on tooth segmentation has achieved promising results, the 3D intraoral scan datasets they use are almost all indirect scans of plaster models, and only contain limited samples of abnormal teeth, so it is difficult to apply them to clinical scenarios under orthodontic treatment. The current issue is the lack of a unified and standardized dataset for analyzing and validating the effectiveness of tooth segmentation. In this work, we focus on deformed teeth segmentation and provide a fine-grained tooth segmentation dataset (3D-IOSSeg). The dataset consists of 3D intraoral scan data from more than 200 patients, with each sample labeled with a fine-grained mesh unit. Meanwhile, 3D-IOSSeg meticulously classified every tooth in the upper and lower jaws. In addition, we propose a fast graph convolutional network for 3D tooth segmentation named Fast-TGCN. In the model, the relationship between adjacent mesh cells is directly established by the naive adjacency matrix to better extract the local geometric features of the tooth. Extensive experiments show that Fast-TGCN can quickly and accurately segment teeth from the mouth with complex structures and outperforms other methods in various evaluation metrics. Moreover, we present the results of multiple classical tooth segmentation methods on this dataset, providing a comprehensive analysis of the field. All code and data will be available at https://github.com/MIVRC/Fast-TGCN.


Subject(s)
Imaging, Three-Dimensional , Tooth , Humans , Imaging, Three-Dimensional/methods , Tooth/diagnostic imaging , Models, Dental
9.
IEEE Trans Med Imaging ; 43(3): 902-915, 2024 Mar.
Article in English | MEDLINE | ID: mdl-37815963

ABSTRACT

Computer-aided diagnosis (CAD) can help pathologists improve diagnostic accuracy together with consistency and repeatability for cancers. However, the CAD models trained with the histopathological images only from a single center (hospital) generally suffer from the generalization problem due to the straining inconsistencies among different centers. In this work, we propose a pseudo-data based self-supervised federated learning (FL) framework, named SSL-FT-BT, to improve both the diagnostic accuracy and generalization of CAD models. Specifically, the pseudo histopathological images are generated from each center, which contain both inherent and specific properties corresponding to the real images in this center, but do not include the privacy information. These pseudo images are then shared in the central server for self-supervised learning (SSL) to pre-train the backbone of global mode. A multi-task SSL is then designed to effectively learn both the center-specific information and common inherent representation according to the data characteristics. Moreover, a novel Barlow Twins based FL (FL-BT) algorithm is proposed to improve the local training for the CAD models in each center by conducting model contrastive learning, which benefits the optimization of the global model in the FL procedure. The experimental results on four public histopathological image datasets indicate the effectiveness of the proposed SSL-FL-BT on both diagnostic accuracy and generalization.


Subject(s)
Algorithms , Diagnosis, Computer-Assisted
10.
Organics ; 4(4): 459-489, 2023 Dec.
Article in English | MEDLINE | ID: mdl-38084108

ABSTRACT

Alkoxy radicals have been identified as versatile intermediates in synthetic chemistry in the last few decades. Over the last decade, various catalytic processes for the in situ generation of alkoxy radicals have been explored, leading to the development of new synthetic methodologies based on alkoxy radicals. In this review, we provided a comprehensive review of recent developments in the utilization of alkoxy radicals in diverse organic transformations, natural product synthesis, and the late-stage modification of bioactive molecules through the implementation of the photoredox methodology.

11.
Org Lett ; 25(49): 8792-8796, 2023 12 15.
Article in English | MEDLINE | ID: mdl-38059767

ABSTRACT

A heterobifunctional cross-linker with one sulfhydryl-reactive dinitroimidazole end and another amine-reactive N-hydroxysuccinimide (NHS) ester end was designed and synthesized. The two motifs of this cross-linker, dinitroimidazole and NHS ester, proved to react with thiol and amine, respectively, in an orthogonal way. The cross-linker was further applied to construct stapled peptides of different sizes and mono- and dual functionalization (including biotinylation, PEGylation, and fluorescence labeling) of protein.


Subject(s)
Cysteine , Lysine , Nitroimidazoles , Peptides , Amines , Cross-Linking Reagents , Imidazoles/chemistry , Peptides/chemistry , Proteins , Sulfhydryl Compounds , Nitroimidazoles/chemistry
12.
Environ Sci Pollut Res Int ; 30(58): 122774-122790, 2023 Dec.
Article in English | MEDLINE | ID: mdl-37978124

ABSTRACT

Facing the problem of a lack of endogenous incentive mechanisms for the development of green finance, we regard blockchain technology as an institutional technology and elevate it to the height of governance mechanisms. Using a dynamic stochastic general equilibrium (DSGE) model framework, we compare and analyze its effects with traditional supportive policies such as fiscal subsidies. The modeling simulation results show that the blockchain green finance platform model is conducive to better promoting the development of green finance. Subsequently, we construct a financial technology development index centered on blockchain technology and empirically test the impact of blockchain financial technology on the level of green finance development from both the supply and demand sides. The results show that the development of blockchain financial technology has significantly increased the scale of green credit issuance and effectively eased the financing constraints of green enterprises, reducing financing costs. We conduct an economic analysis of the impact of blockchain financial technology on the development of green finance, providing a feasible path for the integration and development of green finance and financial technology.


Subject(s)
Blockchain , Computer Simulation , Head , Health Facilities , Technology , China , Economic Development
13.
Front Oncol ; 13: 1248830, 2023.
Article in English | MEDLINE | ID: mdl-37869091

ABSTRACT

Papillary thyroid carcinoma (PTC) is the most common pathological type of thyroid malignancy and also has an excellent prognosis. Primary thyroid lymphoma (PTL) is rare and has a poor prognosis. The co-occurrence of both malignancies is extremely rare, and the preoperative diagnosis is rather difficult. We report the case of a patient with both PTC and PTL in the setting of Hashimoto's thyroiditis (HT). A 59-year-old female patient was referred to our department for progressive enlargement of the thyroid gland over a few months. The imaging results demonstrated an enlarged thyroid and a mass in the thyroid. Total thyroidectomy and bilateral central neck node dissection were conducted. The final diagnosis of the coexistence of thyroid diffuse large B cell lymphoma and PTC was confirmed by histopathology and immunohistochemistry. The patient received radiation therapy and six cycles of chemotherapy combined with targeted therapy, including rituximab, cyclophosphamide, doxorubicin, vindesine, and prednisone (R-CHOP). After 6 months of follow-up, neither tumor has recurred. It is important for physicians to keep PTL in mind for differential diagnosis in HT patients with sudden thyroid enlargement.

14.
Int Wound J ; 2023 Sep 22.
Article in English | MEDLINE | ID: mdl-37736955

ABSTRACT

Diabetic foot ulcer, is a chronic complication afflicting individuals with diabetes, continue to increase worldwide, immensely burdening society. Programmed cell death, which includes apoptosis, autophagy, ferroptosis, necroptosis and pyroptosis, has been increasingly implicated in the pathogenesis of diabetic foot ulcer. This review is based on an exhaustive examination of the literature on 'programmed cell death' and 'diabetic foot ulcers' via PubMed. The findings revealed that natural bioactive compounds, noncoding RNAs and certain proteins play crucial roles in the healing of diabetic foot ulcers through various forms of programmed cell death, including apoptosis, autophagy, ferroptosis and pyroptosis.

15.
IEEE J Biomed Health Inform ; 27(12): 5926-5936, 2023 Dec.
Article in English | MEDLINE | ID: mdl-37725722

ABSTRACT

The multi-scale information among the whole slide images (WSIs) is essential for cancer diagnosis. Although the existing multi-scale vision Transformer has shown its effectiveness for learning multi-scale image representation, it still cannot work well on the gigapixel WSIs due to their extremely large image sizes. To this end, we propose a novel Multi-scale Efficient Graph-Transformer (MEGT) framework for WSI classification. The key idea of MEGT is to adopt two independent efficient Graph-based Transformer (EGT) branches to process the low-resolution and high-resolution patch embeddings (i.e., tokens in a Transformer) of WSIs, respectively, and then fuse these tokens via a multi-scale feature fusion module (MFFM). Specifically, we design an EGT to efficiently learn the local-global information of patch tokens, which integrates the graph representation into Transformer to capture spatial-related information of WSIs. Meanwhile, we propose a novel MFFM to alleviate the semantic gap among different resolution patches during feature fusion, which creates a non-patch token for each branch as an agent to exchange information with another branch by cross-attention mechanism. In addition, to expedite network training, a new token pruning module is developed in EGT to reduce the redundant tokens. Extensive experiments on both TCGA-RCC and CAMELYON16 datasets demonstrate the effectiveness of the proposed MEGT.


Subject(s)
Electric Power Supplies , Semantics , Humans
16.
bioRxiv ; 2023 Sep 12.
Article in English | MEDLINE | ID: mdl-37745577

ABSTRACT

Huntington disease (HD) is an incurable neurodegenerative disease characterized by neuronal loss and astrogliosis. One hallmark of HD is the selective neuronal vulnerability of striatal medium spiny neurons. To date, the underlying mechanisms of this selective vulnerability have not been fully defined. Here, we employed a multi-omic approach including single nucleus RNAseq (snRNAseq), bulk RNAseq, lipidomics, HTT gene CAG repeat length measurements, and multiplexed immunofluorescence on post-mortem brain tissue from multiple brain regions of HD and control donors. We defined a signature of genes that is driven by CAG repeat length and found it enriched in astrocytic and microglial genes. Moreover, weighted gene correlation network analysis showed loss of connectivity of astrocytic and microglial modules in HD and identified modules that correlated with CAG-repeat length which further implicated inflammatory pathways and metabolism. We performed lipidomic analysis of HD and control brains and identified several lipid species that correlate with HD grade, including ceramides and very long chain fatty acids. Integration of lipidomics and bulk transcriptomics identified a consensus gene signature that correlates with HD grade and HD lipidomic abnormalities and implicated the unfolded protein response pathway. Because astrocytes are critical for brain lipid metabolism and play important roles in regulating inflammation, we analyzed our snRNAseq dataset with an emphasis on astrocyte pathology. We found two main astrocyte types that spanned multiple brain regions; these types correspond to protoplasmic astrocytes, and fibrous-like - CD44-positive, astrocytes. HD pathology was differentially associated with these cell types in a region-specific manner. One protoplasmic astrocyte cluster showed high expression of metallothionein genes, the depletion of this cluster positively correlated with the depletion of vulnerable medium spiny neurons in the caudate nucleus. We confirmed that metallothioneins were increased in cingulate HD astrocytes but were unchanged or even decreased in caudate astrocytes. We combined existing genome-wide association studies (GWAS) with a GWA study conducted on HD patients from the original Venezuelan cohort and identified a single-nucleotide polymorphism in the metallothionein gene locus associated with delayed age of onset. Functional studies found that metallothionein overexpressing astrocytes are better able to buffer glutamate and were neuroprotective of patient-derived directly reprogrammed HD MSNs as well as against rotenone-induced neuronal death in vitro. Finally, we found that metallothionein-overexpressing astrocytes increased the phagocytic activity of microglia in vitro and increased the expression of genes involved in fatty acid binding. Together, we identified an astrocytic phenotype that is regionally-enriched in less vulnerable brain regions that can be leveraged to protect neurons in HD.

17.
Acta Radiol ; 64(11): 2841-2848, 2023 Nov.
Article in English | MEDLINE | ID: mdl-37644799

ABSTRACT

BACKGROUND: Osteoporosis can cause bone fractures and disability, but early diagnosis faces challenges. Our proposed diagnostic indicators offer a new approach for early detection, which benefits early identification. PURPOSE: To determine the most appropriate threshold for predicting osteoporosis in patients with each section of vertebral body. MATERIAL AND METHODS: A retrospective analysis of 210 patients, including 646 vertebrae, who had both abdominal computed tomography (CT) and dual-energy X-ray absorptiometry (DXA) within six months. The correlation between DXA T-score and CT Hounsfield units (HU) values was tested by Pearson. The area under the curve (AUC) was calculated using the threshold obtained from the regression equation. RESULTS: The thresholds matching the T-score of -2.5 were 85, 95, 85, and 90 HU for the upper axial plane of the vertebral body (Lau), the middle axial plane of the vertebral body (Lam), the lower axial plane of the vertebral body (Lad), and the mid-sagittal plane of the vertebral body (Lsm), respectively. Defining osteoporosis using CT as Lau ≤ 85, Lam ≤ 95, Lad ≤ 85, or Lsm ≤ 90 HU had a specificity of 88.1% (116/134) and sensitivity of 90.8% (69/76) for distinguishing DXA osteoporosis of the lumbar spine in 210 patients. T-score ≤-2.5 defined as Lau ≤85 or Lam ≤95 or Lad ≤85 or Lsm ≤90 HU had a specificity of 85.9% (275/320) and sensitivity of 82.8% (270/326) for DXA T-score ≤-2.5 in 646 lumbar vertebrae. CONCLUSION: CT HU values obtained based on different sections of the vertebral body in abdominal CT can be used as a supplementary measure to assess osteoporosis.


Subject(s)
Bone Density , Osteoporosis , Humans , Retrospective Studies , Osteoporosis/diagnostic imaging , Tomography, X-Ray Computed/methods , Absorptiometry, Photon/methods , Lumbar Vertebrae/diagnostic imaging
18.
Blood Purif ; 52(7-8): 609-620, 2023.
Article in English | MEDLINE | ID: mdl-37591223

ABSTRACT

BACKGROUND: Hyperphosphatemia is associated with cardiovascular morbidity and mortality in adults with chronic kidney disease (CKD). Drug therapy has an irreplaceable role in the management of hyperphosphatemia. OBJECTIVES: We aimed to compare and rank phosphorus-lowering drugs, including phosphate binder and nonphosphate binder, in hyperphosphatemia adults with CKD. METHODS: We did a systematic review and frequentist random-effect network meta-analysis. We searched in PubMed, Cochrane Library, Web of Science, and Embase from inception to February 1, 2023, for randomized controlled trials of 12 phosphorus-lowering drugs in adults with hyperphosphatemia and CKD. Primary outcomes were efficacy (changes in serum phosphorus) and acceptability (treatment withdrawals due to any cause). We ranked each drug according to the value of surface under the cumulative ranking curve. We applied the Confidence in Network Meta-Analysis frameworks to rate the certainty of evidence. This study was registered with PROSPERO, number CRD42022322270. RESULTS: We identified 2,174 citations, and of these, we included 94 trials comprising 14,459 participants and comparing 13 drugs or placebo. In terms of efficacy, except for niacinamide, all drugs lowered the level of serum phosphorus compared with placebo, with mean difference ranging between -1.61 (95% credible interval [CrI], -2.60 to -0.62) mg/dL for magnesium carbonate and -0.85 (-1.66 to -0.05) mg/dL for bixalomer. Only ferric citrate with odds ratios 0.56 (95% CrI: 0.36-0.89) was significantly associated with fewer dropouts for acceptability. Of the 94 trials, 43 (46%), 7 (7%), and 44 (47%) trials were rated as high, moderate, and low risk of bias, respectively, the certainty of the evidence was moderate to very low. CONCLUSIONS: Magnesium carbonate has the best phosphorus-lowering effect in hyperphosphatemia adults with CKD; considering efficacy and acceptability, ferric citrate shows evidence to be the most appropriate drug with or without dialysis.


Subject(s)
Hyperphosphatemia , Renal Insufficiency, Chronic , Humans , Adult , Hyperphosphatemia/drug therapy , Hyperphosphatemia/etiology , Network Meta-Analysis , Renal Dialysis , Renal Insufficiency, Chronic/complications , Renal Insufficiency, Chronic/drug therapy
19.
Clin. transl. oncol. (Print) ; 25(8): 2408-2418, aug. 2023. graf
Article in English | IBECS | ID: ibc-222418

ABSTRACT

Background Osteosarcoma is a malignant tumor that can present with pain in the bones, joints, and local masses. The incidence is highest in adolescents, and the most common sites are the distal femur, proximal tibia and proximal humerus metaphyseal. Doxorubicin is the first-line chemotherapeutic agent for the treatment of osteosarcoma, but it has many side effects. Cannabidiol is a non-psychoactive plant cannabinoid cannabinol (CBD) that has been shown to be effective against osteosarcoma; however, the molecular targets and mechanisms of CBD action in osteosarcoma remain unclear. Methods Cell proliferation, migration, invasion and colony formation were analyzed using two drugs alone or in combination to evaluate their inhibitory effects on the malignant characteristics of OS cells. Apoptosis and the cell cycle were detected by flow cytometry. The synergistic inhibitory effect of doxorubicin/cannabidiol on tumors was also detected in nude mouse xenotransplantation models. Results Through analysis of two osteosarcoma cell lines, MG63 and U2R, it was found that the cannabidiol/doxorubicin combination treatment synergistically inhibited growth, migration and invasion and induced apoptosis, blocking G2 stagnation in OS cells. Further mechanistic exploration suggests that the PI3K-AKT-mTOR pathway and MAPK pathway play an important role in the synergistic inhibitory effect of the two drugs in osteosarcoma. Finally, in vivo experimental results showed that the cannabidiol/doxorubicin combination treatment significantly reduced the number of tumor xenografts compared to cannabidiol alone or doxorubicin alone. Conclusions Our findings in this study suggest that cannabidiol and doxorubicin have a synergistic anticancer effect on OS cells, and their combined application may be a promising treatment strategy for OS (AU)


Subject(s)
Animals , Mice , Antineoplastic Agents/therapeutic use , Bone Neoplasms/drug therapy , Bone Neoplasms/pathology , Cannabidiol/therapeutic use , Doxorubicin/therapeutic use , Drug Synergism , Cell Line, Tumor , Cell Proliferation , Phosphatidylinositol 3-Kinase , Apoptosis
20.
Front Neurosci ; 17: 1198219, 2023.
Article in English | MEDLINE | ID: mdl-37483351

ABSTRACT

The pathological involvement of the central nervous system in SARS-CoV2 (COVID-19) patients is established. The burden of pathology is most pronounced in the brain stem including the medulla oblongata. Hypoxic/ischemic damage is the most frequent neuropathologic abnormality. Other neuropathologic features include neuronophagia, microglial nodules, and hallmarks of neurodegenerative diseases: astrogliosis and microglial reactivity. It is still unknown if these pathologies are secondary to hypoxia versus a combination of inflammatory response combined with hypoxia. It is also unknown how astrocytes react to neuroinflammation in COVID-19, especially considering evidence supporting the neurotoxicity of certain astrocytic phenotypes. This study aims to define the link between astrocytic and microglial pathology in COVID-19 victims in the inferior olivary nucleus, which is one of the most severely affected brain regions in COVID-19, and establish whether COVID-19 pathology is driven by hypoxic damage. Here, we conducted neuropathologic assessments and multiplex-immunofluorescence studies on the medulla oblongata of 18 COVID-19, 10 pre-pandemic patients who died of acute respiratory distress syndrome (ARDS), and 7-8 control patients with no ARDS or COVID-19. The comparison of ARDS and COVID-19 allows us to identify whether the pathology in COVID-19 can be explained by hypoxia alone, which is common to both conditions. Our results showed increased olivary astrogliosis in ARDS and COVID-19. However, microglial density and microglial reactivity were increased only in COVID-19, in a region-specific manner. Also, olivary hilar astrocytes increased YKL-40 (CHI3L1) in COVID-19, but to a lesser extent than ARDS astrocytes. COVID-19 astrocytes also showed lower levels of Aquaporin-4 (AQP4), and Metallothionein-3 in subsets of COVID-19 brain regions. Cluster analysis on immunohistochemical attributes of astrocytes and microglia identified ARDS and COVID-19 clusters with correlations to clinical history and disease course. Our results indicate that olivary glial pathology and neuroinflammation in the COVID-19 cannot be explained solely by hypoxia and suggest that failure of astrocytes to upregulate the anti-inflammatory YKL-40 may contribute to the neuroinflammation. Notwithstanding the limitations of retrospective studies in establishing causality, our experimental design cannot adequately control for factors external to our design. Perturbative studies are needed to confirm the role of the above-described astrocytic phenotypes in neuroinflammation.

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