Vascular injury activates the ELK1/SND1/SRF pathway to promote vascular smooth muscle cell proliferative phenotype and neointimal hyperplasia.

BACKGROUND: Vascular smooth muscle cell (VSMC) proliferation is the leading cause of vascular stenosis or restenosis. Therefore, investigating the molecular mechanisms and pivotal regulators of the proliferative VSMC phenotype is imperative for precisely preventing neointimal hyperplasia in vascular disease. METHODS: Wire-induced vascular injury and aortic culture models were used to detect the expression of staphylococcal nuclease domain-containing protein 1 (SND1). SMC-specific Snd1 knockout mice were used to assess the potential roles of SND1 after vascular injury. Primary VSMCs were cultured to evaluate SND1 function on VSMC phenotype switching, as well as to investigate the mechanism by which SND1 regulates the VSMC proliferative phenotype. RESULTS: Phenotype-switched proliferative VSMCs exhibited higher SND1 protein expression compared to the differentiated VSMCs. This result was replicated in primary VSMCs treated with platelet-derived growth factor (PDGF). In the injury model, specific knockout of Snd1 in mouse VSMCs reduced neointimal hyperplasia. We then revealed that ETS transcription factor ELK1 (ELK1) exhibited upregulation and activation in proliferative VSMCs, and acted as a novel transcription factor to induce the gene transcriptional activation of Snd1. Subsequently, the upregulated SND1 is associated with serum response factor (SRF) by competing with myocardin (MYOCD). As a co-activator of SRF, SND1 recruited the lysine acetyltransferase 2B (KAT2B) to the promoter regions leading to the histone acetylation, consequently promoted SRF to recognize the specific CArG motif, and enhanced the proliferation- and migration-related gene transcriptional activation. CONCLUSIONS: The present study identifies ELK1/SND1/SRF as a novel pathway in promoting the proliferative VSMC phenotype and neointimal hyperplasia in vascular injury, predisposing the vessels to pathological remodeling. This provides a potential therapeutic target for vascular stenosis.

Theoretical and kinetic study of the H-atom abstraction reactions by H atom from alkyl cyclohexanes.

Reaction kinetics of hydrogen atom abstraction from six alkyl cyclohexanes, methyl cyclohexane (MCH), ethyl cyclohexane (ECH), n-propyl cyclohexane (nPCH), iso-propyl cyclohexane (iPCH), sec-butyl cyclohexane (sBCH) and iso-butyl cyclohexane (iBCH), by the H atom are systematically studied in this work. The M06-2X method combined with the 6-311++G(d,p) basis set is used to perform geometry optimization, frequency analysis and zero-point energy calculations for all species. The intrinsic reaction coordinate (IRC) calculations are performed to confirm the transition states connecting the reactants and products correctly. One-dimensional hindered rotors are used to treat the low frequency torsional models with potentials scanned at the M06-2X/6-31G level of theory. Electronic single-point energy calculations for all reactants, transition states, and products are performed at the QCISD(T)/CBS level of theory. High-pressure limiting rate constants of 39 reaction channels are obtained using conventional transition state theory with asymmetric Eckart tunneling corrections in the temperature range 298.15-2000 K. Reaction rate rules for H-atom abstraction by the H atom from alkyl cyclohexanes on primary, secondary and tertiary carbon sites on both the side chain and ring are provided. The obtained rate constants are given by the Arrhenius expression in the temperature range 500-2000 K, which can be used for the combustion kinetics model development for alkyl cyclohexanes.

"Multidisciplinary fast-track" care can significantly reduce risk of mortality among hip fracture patients at least 80 years old: a single-center retrospective study.

BACKGROUND: "Multidisciplinary fast-track" (MFT) care can accelerate recovery and improve prognosis after surgery, but whether it is effective in older people after hip fracture surgery is unclear. METHODS: We retrospectively compared one-year all-cause mortality between hip fracture patients at least 80 years old at our institution who underwent hip fracture surgery between January 2014 and December 2018 and who then received MFT or conventional care. Multivariable regression was used to assess the association between MFT care and mortality after adjustment for confounders. RESULTS: The final analysis included 247 patients who received MFT care and 438 who received conventional orthopedic care. The MFT group showed significantly lower one-year mortality (8.9% vs. 14.4%, P = 0.037). Log-rank testing of Kaplan-Meier survival curves confirmed the survival advantage. However, the two groups did not differ significantly in rates of mortality during hospitalization or at 30 or 90 days after surgery. Regression analysis confirmed that MFT care was associated with lower risk of one-year mortality (hazard ratio [HR] 0.47, 95% confidence interval [CI] 0.281-0.788, P = 0.04), and the survival benefit was confirmed in subgroups of patients with anemia (HR 0.453, 95% CI 0.268-0.767, P = 0.003) and patients with American Society of Anesthesiologists grade III (HR 0.202, 95% CI 0.08-0.51, P = 0.001). CONCLUSIONS: MFT care can reduce one-year mortality among hip fracture patients at least 80 years old. This finding should be verified and extended in multi-center randomized controlled trials.

Federated learning for medical image analysis: A survey.

Machine learning in medical imaging often faces a fundamental dilemma, namely, the small sample size problem. Many recent studies suggest using multi-domain data pooled from different acquisition sites/centers to improve statistical power. However, medical images from different sites cannot be easily shared to build large datasets for model training due to privacy protection reasons. As a promising solution, federated learning, which enables collaborative training of machine learning models based on data from different sites without cross-site data sharing, has attracted considerable attention recently. In this paper, we conduct a comprehensive survey of the recent development of federated learning methods in medical image analysis. We have systematically gathered research papers on federated learning and its applications in medical image analysis published between 2017 and 2023. Our search and compilation were conducted using databases from IEEE Xplore, ACM Digital Library, Science Direct, Springer Link, Web of Science, Google Scholar, and PubMed. In this survey, we first introduce the background of federated learning for dealing with privacy protection and collaborative learning issues. We then present a comprehensive review of recent advances in federated learning methods for medical image analysis. Specifically, existing methods are categorized based on three critical aspects of a federated learning system, including client end, server end, and communication techniques. In each category, we summarize the existing federated learning methods according to specific research problems in medical image analysis and also provide insights into the motivations of different approaches. In addition, we provide a review of existing benchmark medical imaging datasets and software platforms for current federated learning research. We also conduct an experimental study to empirically evaluate typical federated learning methods for medical image analysis. This survey can help to better understand the current research status, challenges, and potential research opportunities in this promising research field.

DomainATM: Domain adaptation toolbox for medical data analysis.

Domain adaptation (DA) is an important technique for modern machine learning-based medical data analysis, which aims at reducing distribution differences between different medical datasets. A proper domain adaptation method can significantly enhance the statistical power by pooling data acquired from multiple sites/centers. To this end, we have developed the Domain Adaptation Toolbox for Medical data analysis (DomainATM) - an open-source software package designed for fast facilitation and easy customization of domain adaptation methods for medical data analysis. The DomainATM is implemented in MATLAB with a user-friendly graphical interface, and it consists of a collection of popular data adaptation algorithms that have been extensively applied to medical image analysis and computer vision. With DomainATM, researchers are able to facilitate fast feature-level and image-level adaptation, visualization and performance evaluation of different adaptation methods for medical data analysis. More importantly, the DomainATM enables the users to develop and test their own adaptation methods through scripting, greatly enhancing its utility and extensibility. An overview characteristic and usage of DomainATM is presented and illustrated with three example experiments, demonstrating its effectiveness, simplicity, and flexibility. The software, source code, and manual are available online.

Addressing multi-site functional MRI heterogeneity through dual-expert collaborative learning for brain disease identification.

Several studies employ multi-site rs-fMRI data for major depressive disorder (MDD) identification, with a specific site as the to-be-analyzed target domain and other site(s) as the source domain. But they usually suffer from significant inter-site heterogeneity caused by the use of different scanners and/or scanning protocols and fail to build generalizable models that can well adapt to multiple target domains. In this article, we propose a dual-expert fMRI harmonization (DFH) framework for automated MDD diagnosis. Our DFH is designed to simultaneously exploit data from a single labeled source domain/site and two unlabeled target domains for mitigating data distribution differences across domains. Specifically, the DFH consists of a domain-generic student model and two domain-specific teacher/expert models that are jointly trained to perform knowledge distillation through a deep collaborative learning module. A student model with strong generalizability is finally derived, which can be well adapted to unseen target domains and analysis of other brain diseases. To the best of our knowledge, this is among the first attempts to investigate multi-target fMRI harmonization for MDD diagnosis. Comprehensive experiments on 836 subjects with rs-fMRI data from 3 different sites show the superiority of our method. The discriminative brain functional connectivities identified by our method could be regarded as potential biomarkers for fMRI-related MDD diagnosis.

Unsupervised contrastive graph learning for resting-state functional MRI analysis and brain disorder detection.

Resting-state functional magnetic resonance imaging (rs-fMRI) helps characterize regional interactions that occur in the human brain at a resting state. Existing research often attempts to explore fMRI biomarkers that best predict brain disease progression using machine/deep learning techniques. Previous fMRI studies have shown that learning-based methods usually require a large amount of labeled training data, limiting their utility in clinical practice where annotating data is often time-consuming and labor-intensive. To this end, we propose an unsupervised contrastive graph learning (UCGL) framework for fMRI-based brain disease analysis, in which a pretext model is designed to generate informative fMRI representations using unlabeled training data, followed by model fine-tuning to perform downstream disease identification tasks. Specifically, in the pretext model, we first design a bi-level fMRI augmentation strategy to increase the sample size by augmenting blood-oxygen-level-dependent (BOLD) signals, and then employ two parallel graph convolutional networks for fMRI feature extraction in an unsupervised contrastive learning manner. This pretext model can be optimized on large-scale fMRI datasets, without requiring labeled training data. This model is further fine-tuned on to-be-analyzed fMRI data for downstream disease detection in a task-oriented learning manner. We evaluate the proposed method on three rs-fMRI datasets for cross-site and cross-dataset learning tasks. Experimental results suggest that the UCGL outperforms several state-of-the-art approaches in automated diagnosis of three brain diseases (i.e., major depressive disorder, autism spectrum disorder, and Alzheimer's disease) with rs-fMRI data.

From electronic structure to model application for alkyl cyclohexane combustion chemistry: H-atom abstraction reactions by HȮ2 radical.

Chemical kinetic studies of hydrogen atom abstraction reactions by hydroperoxyl (HȮ2) radical from six alkyl cyclohexanes of methyl cyclohexane (MCH), ethyl cyclohexane (ECH), n-propyl cyclohexane (nPCH), iso-propyl cyclohexane (iPCH), sec-butyl cyclohexane (sBCH), and iso-butyl cyclohexane (iBCH) are carried out systematically through high-level ab initio calculations. Geometry optimizations and frequency calculations for all species involved in the reactions are performed at the M06-2X/6-311++G(d,p) level of theory. Electronic single-point energy calculations are calculated at the UCCSD(T)-F12a/cc-pVDZ-F12 level of theory, with zero-point energy corrections. High-pressure limit rate constants for the reactions of alkyl cyclohexanes + HȮ2, in the temperature range of 500-2000 K, are calculated using conventional transition state theory taking asymmetric Eckart tunneling corrections and the one-dimensional hindered rotor approximation into consideration. Elementary reaction rate constants and branching ratios for each alkyl cyclohexane species were investigated, and rate constant rules of primary, secondary, and tertiary sites on the side-chain and the ring are provided here. Additionally, temperature-dependent thermochemical properties for reactants and products were also obtained in this work. The updated kinetics and thermochemistry data are used in the alkyl cyclohexane mechanisms to investigate their effects on ignition delay time predictions of shock tube and rapid compression machine data, and species concentrations from a jet-stirred reactor. It is found that these investigated reactions promote ignition delay times in the temperature range of 800-1200 K and also improve the prediction of cyclic olefin species formation which stems from the decomposition of fuel radicals.

Association between polymorphisms of the GSDMB gene and allergic rhinitis risk in the Chinese population: a case-control study.

BACKGROUND: Allergic rhinitis (AR) is a great risk factor for developing asthma, and its pathogenesis is affected by various factors, such as gene and environment. GSDMB is related to allergic diseases. Our purpose is to explore the correlation of single nucleotide polymorphisms (SNPs) in GSDMB and AR risk in the Chinese population. METHODS: We performed a case-control study including 1005 cases and 1004 controls. Rs2305479, rs4795400, and rs12450091 in GSDMB were geneotyped using Agena MassARRAY. The relationships between GSDMB SNPs and AR risk were assessed by logistic regression analysis in PLINK1.9. RESULTS: Our study showed that rs4795400 was a protective factor for AR in overall (TT vs. CC: OR = 0.66, p = 0.009; TT vs. CC/TC: OR = 0.67, p = 0.008; additive: OR = 0.87, p = 0.042 males, people with BMI ≤ 24, and living in wind-blown sand area. Rs2305479 was associated with a reduced AR risk in males (TT vs. CC: OR = 0.47, p = 0.014; TT vs. CC/TC: OR = 0.43, p = 0.004). However, rs12450091 was a risk factor for AR in people living in the loess hilly region (CC: OR = 4.75, p = 0.047). The levels of EO and EO_per in the case group were significantly higher than those in the control group (p < 0.05). CONCLUSION: This study indicated that GSDMB polymorphisms (rs4795400, rs2305479, and rs12450091) were associated with AR susceptibility. Further studies are required to confirm our findings and to clarify the functional relationship.

Recent Progress in Mass Spectrometry-Based Metabolomics in Major Depressive Disorder Research.

Major depressive disorder (MDD) is a serious mental illness with a heavy social burden, but its underlying molecular mechanisms remain unclear. Mass spectrometry (MS)-based metabolomics is providing new insights into the heterogeneous pathophysiology, diagnosis, treatment, and prognosis of MDD by revealing multi-parametric biomarker signatures at the metabolite level. In this comprehensive review, recent developments of MS-based metabolomics in MDD research are summarized from the perspective of analytical platforms (liquid chromatography-MS, gas chromatography-MS, supercritical fluid chromatography-MS, etc.), strategies (untargeted, targeted, and pseudotargeted metabolomics), key metabolite changes (monoamine neurotransmitters, amino acids, lipids, etc.), and antidepressant treatments (both western and traditional Chinese medicines). Depression sub-phenotypes, comorbid depression, and multi-omics approaches are also highlighted to stimulate further advances in MS-based metabolomics in the field of MDD research.

Microglia in depression: an overview of microglia in the pathogenesis and treatment of depression.

Major depressive disorder is a highly debilitating psychiatric disorder involving the dysfunction of different cell types in the brain. Microglia are the predominant resident immune cells in the brain and exhibit a critical role in depression. Recent studies have suggested that depression can be regarded as a microglial disease. Microglia regulate inflammation, synaptic plasticity, and the formation of neural networks, all of which affect depression. In this review, we highlighted the role of microglia in the pathology of depression. First, we described microglial activation in animal models and clinically depressed patients. Second, we emphasized the possible mechanisms by which microglia recognize depression-associated stress and regulate conditions. Third, we described how antidepressants (clinical medicines and natural products) affect microglial activation. Thus, this review aimed to objectively analyze the role of microglia in depression and focus on potential antidepressants. These data suggested that regulation of microglial actions might be a novel therapeutic strategy to counteract the adverse effects of devastating mental disorders.

Apolipoprotein M and Sphingosine-1-Phosphate Receptor 1 Promote the Transendothelial Transport of High-Density Lipoprotein.

Objective: ApoM enriches S1P (sphingosine-1-phosphate) within HDL (high-density lipoproteins) and facilitates the activation of the S1P1 (S1P receptor type 1) by S1P, thereby preserving endothelial barrier function. Many protective functions exerted by HDL in extravascular tissues raise the question of how S1P regulates transendothelial HDL transport. Approach and Results: HDL were isolated from plasma of wild-type mice, Apom knockout mice, human apoM transgenic mice or humans and radioiodinated to trace its binding, association, and transport by bovine or human aortic endothelial cells. We also compared the transport of fluorescently-labeled HDL or Evans Blue, which labels albumin, from the tail vein into the peritoneal cavity of apoE-haploinsufficient mice with (apoE-haploinsufficient mice with endothelium-specific knockin of S1P1) or without (control mice, ie, apoE-haploinsufficient mice without endothelium-specific knockin of S1P1) endothelium-specific knockin of S1P1. The binding, association, and transport of HDL from Apom knockout mice and human apoM-depleted HDL by bovine aortic endothelial cells was significantly lower than that of HDL from wild-type mice and human apoM-containing HDL, respectively. The binding, uptake, and transport of 125I-HDL by human aortic endothelial cells was increased by an S1P1 agonist but decreased by an S1P1 inhibitor. Silencing of SR-BI (scavenger receptor BI) abrogated the stimulation of 125I-HDL transport by the S1P1 agonist. Compared with control mice, that is, apoE-haploinsufficient mice without endothelium-specific knockin of S1P1, apoE-haploinsufficient mice with endothelium-specific knockin of S1P1 showed decreased transport of Evans Blue but increased transport of HDL from blood into the peritoneal cavity and SR-BI expression in the aortal endothelium. Conclusions: ApoM and S1P1 promote transendothelial HDL transport. Their opposite effect on transendothelial transport of albumin and HDL indicates that HDL passes endothelial barriers by specific mechanisms rather than passive filtration.

Membrane-cloaked polydopamine modified mesoporous silica nanoparticles for cancer therapy.

To improve the shortcomings of narrow therapeutic range and low bioavailability of traditional preparations, a composite drug carrier that combines the advantages of biological carriers and synthetic carriers was prepared in this project. The biomimetic nano-delivery system outer membrane vesicles-polydopamine-mesoporous silica nanoparticle (OMVs-PDA-MSN-DOX) for oral administration is composed of OMVs ofEscherichia colias shell and doxorubicin-loaded MSN modified by PDA as core. Several characterization techniques thoroughly examined the nano-drug delivery system to confirm its surface morphology and chemical property. OMVs-PDA-MSN-DOX with a particle size of 150 nm showed significant cell selectivity and safety. We demonstrated that OMVs are capable of protecting pH-sensitive nanostructure from the oral route of administration in the short term. Importantly, OMVs-PDA-MSN-DOX could facilitate intestinal adhesion and improve DOX bioavailability. Overall, the OMVs-cloaked nanocarrier provides an efficient delivery platform for the oral targeting treatment of cancer with pH-sensitive nano-formulations.

Maltose-functional metal-organic framework assisted laser desorption/ionization mass spectrometry for small biomolecule determination.

A series of functional metal-organic frameworks (MOFs) were facilely prepared through an one-pot procedure or post-synthetic modification strategy and used as matrices in laser desorption ionization mass spectrometry (LDI-MS). Compared with traditional organic matrices and other MOFs, maltose-functional MOF MIL-101-maltose demonstrated ultrahigh ionization efficiency, free matrix background, uniform crystallization, and good dispersibility. A simple, general, and efficient LDI-MS platform was developed for rapid detection of various small biomolecules using MIL-101-maltose as matrix, providing several advantages including low sample consumption of 500 nL, short analysis time of few seconds, strong salt tolerance (500 mM NaCl), and satisfactory reproducibility. The MIL-101-maltose matrix was used for serum glucose determination and successfully distinguished the diabetic patients from the healthy controls. This work provides a generic LDI-MS platform for fast determination of small biomolecules with high potential in clinical diagnosis and disease monitoring.

Antisense oligonucleotide-mediated inhibition of angiopoietin-like protein 3 increases reverse cholesterol transport in mice.

Supported by an abundance of experimental and genetic evidence, angiopoietin-like protein 3 (ANGPTL3) has emerged as a promising therapeutic target for cardiovascular disease. ANGPTL3 is primarily produced by the liver and is a potent modulator of plasma lipids and lipoproteins. Experimental models and subjects with loss-of-function Angptl3 mutations typically present with lower levels of HDL-C than noncarriers. The effect of ANGPTL3 on HDL-C is typically attributed to its function as an inhibitor of the enzyme endothelial lipase. The ability to facilitate reverse cholesterol transport (RCT), the transport of cholesterol from peripheral tissues back to the liver, is a proposed antiatherogenic property of HDL. However, the effect of ANGPTL3 inhibition on RCT remains unclear. Here, we performed a series of dose-response and RCT studies using an Angptl3 antisense oligonucleotide (ASO) in mouse models with varying plasma lipid profiles ranging from moderately to severely hyperlipidemic. Angptl3 ASO-mediated reduction in HDL-C was limited to the model with moderate lipidemia, where the majority of plasma cholesterol was associated with HDL. Surprisingly, regardless of the effect on HDL-C, treatment with the Angptl3 ASO enhanced RCT in all models tested. The observations from the RCT assays were confirmed in HDL clearance studies, where mice treated with the Angptl3 ASO displayed increased plasma clearance and hepatic uptake of labeled HDL. The results from our studies suggest that inhibition of ANGPTL3 not only reduces levels of proatherogenic lipids but also improves HDL-mediated RCT.

Translation of Tudor-SN, a novel terminal oligo-pyrimidine (TOP) mRNA, is regulated by the mTORC1 pathway in cardiomyocytes.

The mechanisms that regulate cell-cycle arrest of cardiomyocytes during heart development are largely unknown. We have previously identified Tudor staphylococcal nuclease (Tudor-SN) as a cell-cycle regulator and have shown that its expression level was closely related to cell-proliferation capacity. Herein, we found that Tudor-SN was highly expressed in neonatal mouse myocardia, but it was lowly expressed in that of adults. Using Data Base of Transcription Start Sites (DBTSS), we revealed that Tudor-SN was a terminal oligo-pyrimidine (TOP) mRNA. We further confirmed that the translational efficiency of Tudor-SN mRNA was controlled by the mammalian target of rapamycin complex 1 (mTORC1) pathway, as revealed via inhibition of activated mTORC1 in primary neonatal mouse cardiomyocytes and activation of silenced mTORC1 in adult mouse myocardia; additionally, this result was recapitulated in H9c2 cells. We also demonstrated that the downregulation of Tudor-SN in adult myocardia was due to inactivation of the mTORC1 pathway to ensure that heart growth was in proportion to that of the rest of the body. Moreover, we revealed that Tudor-SN participated in the mTORC1-mediated regulation of cardiomyocytic proliferation, which further elucidated the correlation between Tudor-SN and the mTORC1 pathway. Taken together, our findings suggest that the translational efficiency of Tudor-SN is regulated by the mTORC1 pathway in myocardia and that Tudor-SN is involved in mTORC1-mediated regulation of cardiomyocytic proliferation and cardiac development.

Separation and determination of 3-hydroxyaspartate by online concentration capillary electrophoresis/laser-induced fluorescence with microwave-assisted derivatization.

A chiral analytical method was proposed based on capillary electrophoresis with laser-induced fluorescence detection coupled with microwave-assisted derivatization for the simultaneous baseline separation and sensitive detection of four stereoisomers of 3-hydroxyaspartate. The derivatization reaction of 3-hydroxyaspartate with 4-chloro-7-nitrobenzo-2-oxa-1,3-diazole was greatly accelerated by microwave irradiation. Under the optimized conditions, the derivatization yield was increased by 20% and the derivatization time was shortened by 20 min when compared with those from conventional water bath heating. In addition, the sensitivity was improved by online sample concentration methods. The detection limit of l-threo-3-hydroxyaspartate obtained by large-volume sample stacking with polarity switching was 5.3 nmol/L, which was around 1000-fold lower than that of the capillary electrophoresis/laser-induced fluorescence without stacking. The excellent analytical performance in terms of linearity and precision was also achieved. Furthermore, the developed method was successfully applied to the determination of 3-hydroxyaspartate in the spiked urine, and satisfactory recoveries were obtained ranging from 90.5 to 107.0%.

Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images.

Understanding chest CT imaging of the coronavirus disease 2019 (COVID-19) will help detect infections early and assess the disease progression. Especially, automated severity assessment of COVID-19 in CT images plays an essential role in identifying cases that are in great need of intensive clinical care. However, it is often challenging to accurately assess the severity of this disease in CT images, due to variable infection regions in the lungs, similar imaging biomarkers, and large inter-case variations. To this end, we propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification. Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice). A multi-task multi-instance deep network (called M 2 UNet) is then developed to assess the severity of COVID-19 patients and also segment the lung lobe simultaneously. Our M 2 UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment (with a unique hierarchical multi-instance learning strategy). Here, the context information provided by segmentation can be implicitly employed to improve the performance of severity assessment. Extensive experiments were performed on a real COVID-19 CT image dataset consisting of 666 chest CT images, with results suggesting the effectiveness of our proposed method compared to several state-of-the-art methods.

Multi-Dimensional Organic Mass Cytometry: Simultaneous Analysis of Proteins and Metabolites on Single Cells.

Mass cytometry is attracting significant attention for enabling spatiotemporal high-throughput single-cell analysis. As the first demonstration of the simultaneous detection of single-cell proteins and untargeted metabolites, a multi-dimensional organic mass-cytometry system was established by a simple microfluidic chip connected to a nanoelectrospray mass spectrometer, providing useful heterogeneous information about the cells. A series of mass probes with online-dissociated mass tags were developed, ensuring the semi-quantification of cell-surface proteins and the compatibility of endogenous metabolite detection at the single-cell level. Six cell surface antigens and ≈100 metabolites from three ovarian-cancer cell types and two breast-cancer cell types were successfully monitored and contributed to highly sensitive and specific cell typing. Doxorubicin-resistant cancer-cell analysis confirmed the applications in distinguishing rare cell phenotypes. The proposed system is simple, extensible, and promising for cell typing, drug-resistance analysis of tumor cells, and clinical diagnosis and therapy at the single-cell level.

Targeted Deletion of Hepatocyte Abca1 Increases Plasma HDL (High-Density Lipoprotein) Reverse Cholesterol Transport via the LDL (Low-Density Lipoprotein) Receptor.

OBJECTIVE: The role of hepatocyte Abca1 (ATP binding cassette transporter A1) in trafficking hepatic free cholesterol (FC) into plasma versus bile for reverse cholesterol transport (RCT) is poorly understood. We hypothesized that hepatocyte Abca1 recycles plasma HDL-C (high-density lipoprotein cholesterol) taken up by the liver back into plasma, maintaining the plasma HDL-C pool, and decreasing HDL-mediated RCT into feces. Approach and Results: Chow-fed hepatocyte-specific Abca1 knockout (HSKO) and control mice were injected with human HDL radiolabeled with 125I-tyramine cellobiose (125I-TC; protein) and 3H-cholesteryl oleate (3H-CO). 125I-TC and 3H-CO plasma decay, plasma HDL 3H-CO selective clearance (ie, 3H-125I fractional catabolic rate), liver radiolabel uptake, and fecal 3H-sterol were significantly greater in HSKO versus control mice, supporting increased plasma HDL RCT. Twenty-four hours after 3H-CO-HDL injection, HSKO mice had reduced total hepatic 3H-FC (ie, 3H-CO hydrolyzed to 3H-FC in liver) resecretion into plasma, demonstrating Abca1 recycled HDL-derived hepatic 3H-FC back into plasma. Despite similar liver LDLr (low-density lipoprotein receptor) expression between genotypes, HSKO mice treated with LDLr-targeting versus control antisense oligonucleotide had slower plasma 3H-CO-HDL decay, reduced selective 3H-CO clearance, and decreased fecal 3H-sterol excretion that was indistinguishable from control mice. Increased RCT in HSKO mice was selective for 3H-CO-HDL, since macrophage RCT was similar between genotypes. CONCLUSIONS: Hepatocyte Abca1 deletion unmasks a novel and selective FC trafficking pathway that requires LDLr expression, accelerating plasma HDL-selective CE uptake by the liver and promoting HDL RCT into feces, consequently reducing HDL-derived hepatic FC recycling into plasma.