-
Clinical & Translational Immunology 2023The leading cause of mortality in patients with rheumatoid arthritis is atherosclerotic cardiovascular disease (CVD). We have shown that murine arthritis impairs...
OBJECTIVES
The leading cause of mortality in patients with rheumatoid arthritis is atherosclerotic cardiovascular disease (CVD). We have shown that murine arthritis impairs atherosclerotic lesion regression, because of cellular cholesterol efflux defects in haematopoietic stem and progenitor cells (HSPCs), causing monocytosis and impaired atherosclerotic regression. Therefore, we hypothesised that improving cholesterol efflux using a Liver X Receptor (LXR) agonist would improve cholesterol efflux and improve atherosclerotic lesion regression in arthritis.
METHODS
mice were fed a western-type diet for 14 weeks to initiate atherogenesis, then switched to a chow diet to induce lesion regression and divided into three groups; (1) control, (2) K/BxN serum transfer inflammatory arthritis (K/BxN) or (3) K/BxN arthritis and LXR agonist T0901317 daily for 2 weeks.
RESULTS
LXR activation during murine inflammatory arthritis completely restored atherosclerotic lesion regression in arthritic mice, evidenced by reduced lesion size, macrophage abundance and lipid content. Mechanistically, serum from arthritic mice promoted foam cell formation, demonstrated by increased cellular lipid accumulation in macrophages and paralleled by a reduction in mRNA of the cholesterol efflux transporters , and . T0901317 reduced lipid loading and increased and expression in macrophages exposed to arthritic serum and increased ABCA1 levels in atherosclerotic lesions of arthritic mice. Moreover, arthritic clinical score was also attenuated with T0901317.
CONCLUSION
Taken together, we show that the LXR agonist T0901317 rescues impaired atherosclerotic lesion regression in murine arthritis because of enhanced cholesterol efflux transporter expression and reduced foam cell development in atherosclerotic lesions.
PubMed: 37091327
DOI: 10.1002/cti2.1446 -
The Journal of Investigative Dermatology Aug 2022Lipocalins are a family of secreted adipokines that regulate cell lipid metabolism and immune responses. Although we have previously revealed that LCN2 modulates...
Lipocalins are a family of secreted adipokines that regulate cell lipid metabolism and immune responses. Although we have previously revealed that LCN2 modulates neutrophil activation in psoriasis, the other roles of LCN2 in psoriatic local inflammation have remained elusive. In this study, we found that 24p3R, the well-known specific receptor of LCN2, was highly expressed in the lesional epidermis of patients with psoriasis. Silencing 24p3R (also known as slc22a17) alleviated hyperkeratosis, inflammatory cell infiltration, and overexpression of inflammatory mediators in an imiquimod-induced psoriasis-like mouse model. In vitro, LCN2 enhanced the expression of proinflammatory factors in primary keratinocytes, such as IL-1β, IL-23, CXCL1, and CXCL10, which was paralleled by enforced cholesterol biosynthetic signaling. Importantly, taking in vivo and in vitro approaches, we discovered the SREBP2, a vital transcriptional factor in cholesterol synthesis pathway, as the critical mediator of LCN2-induced keratinocyte activation, which bound to the promoter region of NLRC4. Suppressing SREBP2 in mice attenuated NLRC4 signaling and psoriasis-like dermatitis. Taken together, this study identifies the critical role of LCN2‒SREBP2‒NLRC4 axis in the pathogenesis of psoriasis and proposes 24p3R or SREBP2 as a potential therapeutic target for psoriasis.
Topics: Animals; Apoptosis Regulatory Proteins; Calcium-Binding Proteins; Dermatitis; Disease Models, Animal; Imiquimod; Inflammation; Keratinocytes; Lipocalin-2; Mice; Psoriasis; Skin; Sterol Regulatory Element Binding Protein 2
PubMed: 35120997
DOI: 10.1016/j.jid.2022.01.012 -
Computers in Biology and Medicine Jul 2022Accurate skin lesion segmentation plays a fundamental role in computer-aided melanoma analysis. Recently, some FCN-based methods have been proposed and achieved...
Accurate skin lesion segmentation plays a fundamental role in computer-aided melanoma analysis. Recently, some FCN-based methods have been proposed and achieved promising results in lesion segmentation tasks. However, due to the variable shapes, different scales, noise interference, and ambiguous boundaries of skin lesions, the capabilities of lesion location and boundary delineation of these works are still insufficient. To overcome the above challenges, in this paper, we propose a novel Neighborhood Context Refinement Network (NCRNet) by using the coarse-to-fine strategy to achieve accurate skin lesion segmentation. The proposed NCRNet contains a shared encoder and two different but closely related decoders for locating the skin lesions and refining the lesion boundaries. Specifically, we first design the Parallel Attention Decoder (PAD), which can effectively extract and fuse the local detail information and global semantic information on multiple levels to locate skin lesions of different sizes and shapes. Then, based on the initial lesion location, we further design the Neighborhood Context Refinement Decoder (NCRD), aiming at leveraging the fine-grained multi-stage neighborhood context cues to refine the lesion boundaries continuously. Furthermore, the neighborhood-based deep supervision used in the NCRD can make the shared encoder pay more attention to the lesion boundary areas and promote convergence of the segmentation network. The public skin lesion segmentation dataset ISIC2017 is adopted to validate the effectiveness of the proposed NCRNet. Comprehensive experiments prove that the proposed NCRNet reaches the state-of-the-art performance than the other nine competitive methods and gets 78.62%, 86.55%, and 94.01% on Jaccard, Dice, and Accuracy, respectively.
Topics: Humans; Image Processing, Computer-Assisted; Melanoma; Neural Networks, Computer; Skin Diseases; Skin Neoplasms
PubMed: 35477048
DOI: 10.1016/j.compbiomed.2022.105545 -
European Radiology Nov 2023To compare examination time and image quality between artificial intelligence (AI)-assisted compressed sensing (ACS) technique and parallel imaging (PI) technique in MRI...
AI-assisted compressed sensing and parallel imaging sequences for MRI of patients with nasopharyngeal carcinoma: comparison of their capabilities in terms of examination time and image quality.
OBJECTIVE
To compare examination time and image quality between artificial intelligence (AI)-assisted compressed sensing (ACS) technique and parallel imaging (PI) technique in MRI of patients with nasopharyngeal carcinoma (NPC).
METHODS
Sixty-six patients with pathologically confirmed NPC underwent nasopharynx and neck examination using a 3.0-T MRI system. Transverse T2-weighted fast spin-echo (FSE) sequence, transverse T1-weighted FSE sequence, post-contrast transverse T1-weighted FSE sequence, and post-contrast coronal T1-weighted FSE were obtained by both ACS and PI techniques, respectively. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and duration of scanning of both sets of images analyzed by ACS and PI techniques were compared. The images from the ACS and PI techniques were scored for lesion detection, margin sharpness of lesions, artifacts, and overall image quality using the 5-point Likert scale.
RESULTS
The examination time with ACS technique was significantly shorter than that with PI technique (p < 0.0001). The comparison of SNR and CNR showed that ACS technique was significantly superior with PI technique (p < 0.005). Qualitative image analysis showed that the scores of lesion detection, margin sharpness of lesions, artifacts, and overall image quality were higher in the ACS sequences than those in the PI sequences (p < 0.0001). Inter-observer agreement was evaluated for all qualitative indicators for each method, in which the results showed satisfactory-to-excellent agreement (p < 0.0001).
CONCLUSION
Compared with the PI technique, the ACS technique for MR examination of NPC can not only shorten scanning time but also improve image quality.
CLINICAL RELEVANCE STATEMENT
The artificial intelligence (AI)-assisted compressed sensing (ACS) technique shortens examination time for patients with nasopharyngeal carcinoma, while improving the image quality and examination success rate, which will benefit more patients.
KEY POINTS
• Compared with the parallel imaging (PI) technique, the artificial intelligence (AI)-assisted compressed sensing (ACS) technique not only reduced examination time, but also improved image quality. • Artificial intelligence (AI)-assisted compressed sensing (ACS) pulls the state-of-the-art deep learning technique into the reconstruction procedure and helps find an optimal balance of imaging speed and image quality.
Topics: Humans; Artificial Intelligence; Nasopharyngeal Carcinoma; Magnetic Resonance Imaging; Signal-To-Noise Ratio; Nasopharyngeal Neoplasms; Artifacts
PubMed: 37219618
DOI: 10.1007/s00330-023-09742-6 -
Frontiers in Psychology 2017Traditionally, philosophers have appealed to the phenomenological similarity between visual experience and visual imagery to support the hypothesis that there is... (Review)
Review
Traditionally, philosophers have appealed to the phenomenological similarity between visual experience and visual imagery to support the hypothesis that there is significant overlap between the perceptual and imaginative domains. The current evidence, however, is inconclusive: while evidence from transcranial brain stimulation seems to support this conclusion, neurophysiological evidence from brain lesion studies (e.g., from patients with brain lesions resulting in a loss of mental imagery but not a corresponding loss of perception and vice versa) indicates that there are functional and anatomical dissociations between mental imagery and perception. Assuming that the mental imagery and perception do not overlap, at least, to the extent traditionally assumed, then the question arises as to what exactly mental imagery is and whether it parallels perception by proceeding via several functionally distinct mechanisms. In this review, we argue that even though there may not be a shared mechanism underlying vision for perception and conscious imagery, there is an overlap between the mechanisms underlying vision for action and unconscious visual imagery. On the basis of these findings, we propose a modification of Kosslyn's model of imagery that accommodates unconscious imagination and explore possible explanations of the quasi-pictorial phenomenology of conscious visual imagery in light of the fact that its underlying neural substrates and mechanisms typically are distinct from those of visual experience.
PubMed: 28588527
DOI: 10.3389/fpsyg.2017.00799 -
Diagnostics (Basel, Switzerland) Jan 2022Patients with new-onset malignant spinal lesions often have an urgent need for local spine intervention and systemic therapy. For optimal management, it is crucial to...
Patients with new-onset malignant spinal lesions often have an urgent need for local spine intervention and systemic therapy. For optimal management, it is crucial to diagnose the underlying disease as quickly and reliably as possible. The aim of our current study was to determine the feasibility, sensitivity, specificity, and diagnostic certainty of complementary cytological evaluation of spinal lesions suspected of malignancy. In 44 patients, we performed histopathological biopsies and in parallel cytologic preparations from the malignant site. Cytological smears were prepared and stained for May-Grunwald and Giemsa. Bone biopsies were histopathologically analyzed according to the existing standard-of-care practices. In 42 of 44 cases (95%), a cytological sample was successfully obtained. In 40 cases (95.2%, Cohen's kappa: 0.77), the cytological diagnosis agreed with the histological diagnosis regarding the identification of a malignant lesion. This resulted in a sensitivity of 97% and a specificity of 80% as well as a diagnostic safety of 95%. Cytological analysis in the context of spinal surgery proved sufficient to establish a diagnosis of malignancy or its exclusion, expanding the existing diagnostic spectrum. Furthermore, implementation of this process as a routine clinical diagnostic might shorten the time to diagnosis and improve the treatment of this vulnerable patient group.
PubMed: 35204401
DOI: 10.3390/diagnostics12020310 -
IEEE Transactions on Medical Imaging Jun 2023We present a novel deep network (namely BUSSeg) equipped with both within- and cross-image long-range dependency modeling for automated lesions segmentation from breast...
We present a novel deep network (namely BUSSeg) equipped with both within- and cross-image long-range dependency modeling for automated lesions segmentation from breast ultrasound images, which is a quite daunting task due to (1) the large variation of breast lesions, (2) the ambiguous lesion boundaries, and (3) the existence of speckle noise and artifacts in ultrasound images. Our work is motivated by the fact that most existing methods only focus on modeling the within-image dependencies while neglecting the cross-image dependencies, which are essential for this task under limited training data and noise. We first propose a novel cross-image dependency module (CDM) with a cross-image contextual modeling scheme and a cross-image dependency loss (CDL) to capture more consistent feature expression and alleviate noise interference. Compared with existing cross-image methods, the proposed CDM has two merits. First, we utilize more complete spatial features instead of commonly used discrete pixel vectors to capture the semantic dependencies between images, mitigating the negative effects of speckle noise and making the acquired features more representative. Second, the proposed CDM includes both intra- and inter-class contextual modeling rather than just extracting homogeneous contextual dependencies. Furthermore, we develop a parallel bi-encoder architecture (PBA) to tame a Transformer and a convolutional neural network to enhance BUSSeg's capability in capturing within-image long-range dependencies and hence offer richer features for CDM. We conducted extensive experiments on two representative public breast ultrasound datasets, and the results demonstrate that the proposed BUSSeg consistently outperforms state-of-the-art approaches in most metrics.
Topics: Female; Humans; Artifacts; Image Processing, Computer-Assisted; Neural Networks, Computer; Semantics; Ultrasonography, Mammary
PubMed: 37018315
DOI: 10.1109/TMI.2022.3233648 -
European Radiology Jul 2023To investigate the feasibility of deep learning-based MRI (DL-MRI) in its application in shoulder imaging and compare its performance with conventional MR imaging...
OBJECTIVE
To investigate the feasibility of deep learning-based MRI (DL-MRI) in its application in shoulder imaging and compare its performance with conventional MR imaging (non-DL-MRI).
METHODS
This retrospective study was approved by the local ethics committee. Seventy consecutive patients who had been examined with both DL-MRI and non-DL-MRI were enrolled for the image quality and lesion diagnosis comparison. Another 400 patients had been examined only with DL-MRI. Their images' quality was assessed by 20 radiologists using a satisfaction survey. The Kendall W test was performed to assess interobserver agreement. The Wilcoxon test was performed to compare the image quality. For lesion diagnosis, the interobserver and interstudy agreement were evaluated by kappa analysis.
RESULTS
The scan time of DL-MRI (6 min 1 s) was nearly 50% decreased compared with that of non-DL-MRI (11 min 25 s). The image quality was higher in both PDWI (4.85 ± 0.31 for DL, and 4.73 ± 0.29 for non-DL) and T2WI (4.95 ± 0.2 for DL, and 4.74 ± 0.41 for non-DL) of DL-MRI. Good interobserver agreement was found for the image quality of all the MR sequences on both DL-MRI (Kendall W: 0.588~0.902) and non-DL-MRI (Kendall W: 0751~0.865). Both the SNRs and |CNR| were significantly higher in PDWI and T2WI of DL-MRI. High interobserver and interstudy agreements for the lesions in non-DL-MRI and DL-MRI (kappa value = 0.913 to 1.000) were observed. The results of the image quality satisfaction survey in 400 patients receiving DL-MRI in the shoulder obtained 5 scores among all the radiologists.
CONCLUSION
Shoulder DL-MRI can greatly reduce the scan time, while improve imaging quality of PDWI and T2WI compared to non-DL-MRI.
KEY POINTS
• Shoulder 2D DL-MRI can greatly reduce the whole scan time and improve imaging quality of both PDWI and T2WI compared to conventional parallel MRI. • Shoulder 2D DL-MRI could be a clinical routine with greatly improved work efficiency in the future.
Topics: Humans; Shoulder; Retrospective Studies; Deep Learning; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Algorithms
PubMed: 36826500
DOI: 10.1007/s00330-023-09470-x -
Frontiers in Cell and Developmental... 2021DNA interstrand crosslinks (ICLs) are covalently bound DNA lesions, which are commonly induced by chemotherapeutic drugs, such as cisplatin and mitomycin C or endogenous... (Review)
Review
DNA interstrand crosslinks (ICLs) are covalently bound DNA lesions, which are commonly induced by chemotherapeutic drugs, such as cisplatin and mitomycin C or endogenous byproducts of metabolic processes. This type of DNA lesion can block ongoing RNA transcription and DNA replication and thus cause genome instability and cancer. Several cellular defense mechanism, such as the Fanconi anemia pathway have developed to ensure accurate repair and DNA replication when ICLs are present. Various structure-specific nucleases and translesion synthesis (TLS) polymerases have come into focus in relation to ICL bypass. Current models propose that a structure-specific nuclease incision is needed to unhook the ICL from the replication fork, followed by the activity of a low-fidelity TLS polymerase enabling replication through the unhooked ICL adduct. This review focuses on how, in parallel with the Fanconi anemia pathway, PCNA interactions and ICL-induced PCNA ubiquitylation regulate the recruitment, substrate specificity, activity, and coordinated action of certain nucleases and TLS polymerases in the execution of stalled replication fork rescue via ICL bypass.
PubMed: 34262911
DOI: 10.3389/fcell.2021.699966 -
Ocular Oncology and Pathology Sep 2021To report retinal function findings on the choroidal nevus.
PURPOSE
To report retinal function findings on the choroidal nevus.
METHODS
Prospective descriptive case series of 7 patients ( = 7 eyes) presenting a melanocytic choroidal lesion consistent with choroidal nevus and no other ocular disease. Baseline evaluation included measurement of best-corrected visual acuity (BCVA), color and near-infrared fundus pictures, and spectral-domain OCT (Heidelberg Engineering). Retinal function was tested with microperimetry (MAIA; CenterVUE, Padova) using a standard grid (µP1) and a linear grid (µP2) that distribute test points on retinal areas that overlaid the choroidal lesion as well as lesion-free areas equidistantly to the fovea in 3 parallel lines. mfERG was performed following the International Society for Clinical Electrophysiology of Vision (ISCEV) recommendation using a 61-hexyagon protocol.
RESULTS
BCVA was 20/25 (0.1 logMAR) or better in all 7 eyes. Microperimetry showed central stable fixation on all eyes, with mean ± SE sensitivity threshold significantly decreased on retinal areas overlaying the lesions (µP1): 21.8 ± 0.6 dB versus 25.2 ± 0.9 dB on nonaffected retinal areas ( < 0.001). Sensitivity was also decreased on µP2: 23.7 ± 0.2 dB for areas overlying the nevi and 25.7 ± 0.3 dB for the nonaffected retina ( < 0.001). mfERG responses showed no focal amplitude or implicit-time changes on the retina in the topographical region corresponding to the nevus for all patients.
CONCLUSION
Our results indicate that choroidal nevi may cause significant retinal sensitivity impairment, as shown by microperimetry, but preserved mfERG response indicates that the retinal function may be only partially impaired.
PubMed: 34604202
DOI: 10.1159/000515561