Remimazolam has similar anesthetic effect and superior safety compared to propofol in elderly patients: A meta-analysis of randomized controlled trials.

BACKGROUND: The superiority between remimazolam and propofol for anesthesia is controversial in elderly patients (≥60 years). This meta-analysis aimed to systematically compare anesthetic effect and safety profile between remimazolam and propofol in elderly patients under any surgery. METHODS: Cochrane Library, Web of Science, and PubMed were searched until December 25, 2023 for relevant randomized controlled trials. RESULTS: Ten studies with 806 patients receiving remimazolam (experimental group) and 813 patients receiving propofol (control group) were included. Time to loss of consciousness [standard mean difference (SMD) (95% confidence interval (CI): 1.347 (-0.362, 3.055), p = 0.122] and recovery time [SMD (95% CI): -0.022 (-0.300, 0.257), p = 0.879] were similar between experimental and control groups. Mean arterial pressure at baseline minus 1 min after induction [SMD (95% CI): -1.800 (-3.250, -0.349), p = 0.015], heart rate at baseline minus 1 min after induction [SMD (95% CI): -1.041 (-1.537, -0.545), p < 0.001], incidences of hypoxemia [relative risk (RR) (95% CI): 0.247 (0.138, 0.444), p < 0.001], respiratory depression [RR (95% CI): 0.458 (0.300, 0.700), p < 0.001], bradycardia [RR (95% CI): 0.409 (0.176, 0.954), p = 0.043], hypotension [RR (95% CI): 0.415 (0.241, 0.714), p = 0.007], and injection pain [RR (95% CI): 0.172 (0.113, 0.263), p < 0.001] were lower in the experimental group compared to the control group. Postoperative nausea and vomiting was not different between groups [RR (95% CI): 1.194 (0.829, 1.718), p = 0.341]. Moreover, this meta-analysis displayed a low risk of bias, minimal publication bias, and good robustness. CONCLUSION: Remimazolam shows comparative anesthetic effect and better safety profile than propofol in elderly patients under any surgery.

LncRNA LINK-A Remodels Tissue Inflammatory Microenvironments to Promote Obesity.

High-fat diet (HFD)-induced obesity is a crucial risk factor for metabolic syndrome, mainly due to adipose tissue dysfunctions associated with it. However, the underlying mechanism remains unclear. This study has used genetic screening to identify an obesity-associated human lncRNA LINK-A as a critical molecule bridging the metabolic microenvironment and energy expenditure in vivo by establishing the HFD-induced obesity knock-in (KI) mouse model. Mechanistically, HFD LINK-A KI mice induce the infiltration of inflammatory factors, including IL-1ß and CXCL16, through the LINK-A/HB-EGF/HIF1α feedback loop axis in a self-amplified manner, thereby promoting the adipose tissue microenvironment remodeling and adaptive thermogenesis disorder, ultimately leading to obesity and insulin resistance. Notably, LINK-A expression is positively correlated with inflammatory factor expression in individuals who are overweight. Of note, targeting LINK-A via nucleic acid drug antisense oligonucleotides (ASO) attenuate HFD-induced obesity and metabolic syndrome, pointing out LINK-A as a valuable and effective therapeutic target for treating HFD-induced obesity. Briefly, the results reveale the roles of lncRNAs (such as LINK-A) in remodeling tissue inflammatory microenvironments to promote HFD-induced obesity.

A pixelated liquid perovskite array for high-sensitivity and high-resolution X-ray imaging scintillation screens.

Scintillators with high spatial resolution at a low radiation dose rate are desirable for X-ray medical imaging. A low radiation dose rate can be achieved using a sufficiently thick scintillator layer to absorb the incident X-ray energy completely, however, often at the expense of low spatial resolution due to the issue of optical crosstalk of scintillation light. Therefore, to achieve high sensitivity combined with high-resolution imaging, a thick scintillator with perfect light guiding properties is in high demand. Herein, a new strategy is developed to address this issue by embedding liquid scintillators into lead-containing fiber-optical plates (FOPs, n = 1.5) via the siphon effect. The liquid scintillator is composed of perovskite quantum dots (QDs)/2,5-diphenyloxazole (PPO) and the non-polar high-refractive index (n = 1.66) solvent α-bremnaphthalene. Benefiting from the pixelated and thickness-adjustable scintillators, the proposed CsPbBr3 QDs/PPO liquid scintillator-based X-ray detector achieves a detection limit of 79.1 µGy s-1 and a spatial resolution of 4.6 lp mm-1. In addition, it displays excellent tolerance against radiation (>34 h) and shows outstanding stability under ambient conditions (>160 h). This strategy could also be applied to other liquid scintillators (such as CsPbCl3 QDs and Mn:CsPbCl3 QDs). The combination of high sensitivity, high spatial resolution and stability, easy fabrication and maintenance, and a reusable substrate matrix makes these liquid scintillators a promising candidate for practical X-ray medical imaging applications.

Undoped Strained Ge Quantum Well with Ultrahigh Mobility of Two Million.

We develop a method to fabricate an undoped Ge quantum well (QW) under a 32 nm relaxed Si0.2Ge0.8 shallow barrier. The bottom barrier contains Si0.2Ge0.8 (650 °C) and Si0.1Ge0.9 (800 °C) such that variation of Ge content forms a sharp interface that can suppress the threading dislocation density (TDD) penetrating into the undoped Ge quantum well. The SiGe barrier introduces enough in-plane parallel strain (ε∥ strain -0.41%) in the Ge quantum well. The heterostructure field-effect transistors with a shallow buried channel obtain an ultrahigh two-dimensional hole gas (2DHG) mobility over 2 × 106 cm2/(V s) and a very low percolation density of (5.689 ± 0.062) × 1010 cm-2. The fractional indication is also observed at high density and high magnetic fields. This strained germanium as a noise mitigation material provides a platform for integration of quantum computation with a long coherence time and fast all-electrical manipulation.

LncRNA modulates Hippo-YAP signaling to reprogram iron metabolism.

Iron metabolism dysregulation is tightly associated with cancer development. But the underlying mechanisms remain poorly understood. Increasing evidence has shown that long noncoding RNAs (lncRNAs) participate in various metabolic processes via integrating signaling pathway. In this study, we revealed one iron-triggered lncRNA, one target of YAP, LncRIM (LncRNA Related to Iron Metabolism, also named ZBED5-AS1 and Loc729013), which effectively links the Hippo pathway to iron metabolism and is largely independent on IRP2. Mechanically, LncRIM directly binds NF2 to inhibit NF2-LATS1 interaction, which causes YAP activation and increases intracellular iron level via DMT1 and TFR1. Additionally, LncRIM-NF2 axis mediates cellular iron metabolism dependent on the Hippo pathway. Clinically, high expression of LncRIM correlates with poor patient survival, suggesting its potential use as a biomarker and therapeutic target. Taken together, our study demonstrated a novel mechanism in which LncRIM-NF2 axis facilitates iron-mediated feedback loop to hyperactivate YAP and promote breast cancer development.

lncRNA BREA2 promotes metastasis by disrupting the WWP2-mediated ubiquitination of Notch1.

Notch has been implicated in human cancers and is a putative therapeutic target. However, the regulation of Notch activation in the nucleus remains largely uncharacterized. Therefore, characterizing the detailed mechanisms governing Notch degradation will identify attractive strategies for treating Notch-activated cancers. Here, we report that the long noncoding RNA (lncRNA) BREA2 drives breast cancer metastasis by stabilizing the Notch1 intracellular domain (NICD1). Moreover, we reveal WW domain containing E3 ubiquitin protein ligase 2 (WWP2) as an E3 ligase for NICD1 at K1821 and a suppressor of breast cancer metastasis. Mechanistically, BREA2 impairs WWP2-NICD1 complex formation and in turn stabilizes NICD1, leading to Notch signaling activation and lung metastasis. BREA2 loss sensitizes breast cancer cells to inhibition of Notch signaling and suppresses the growth of breast cancer patient-derived xenograft tumors, highlighting its therapeutic potential in breast cancer. Taken together, these results reveal the lncRNA BREA2 as a putative regulator of Notch signaling and an oncogenic player driving breast cancer metastasis.

Energy resolving dark-field imaging with dual phase grating interferometer.

X-ray dark-filed imaging is a powerful approach to quantify the dimension of micro-structures of the object. Often, a series of dark-filed signals have to be measured under various correlation lengths. For instance, this is often achieved by adjusting the sample positions by multiple times in Talbot-Lau interferometer. Moreover, such multiple measurements can also be collected via adjustments of the inter-space between the phase gratings in dual phase grating interferometer. In this study, the energy resolving capability of the dual phase grating interferometer is explored with the aim to accelerate the data acquisition speed of dark-filed imaging. To do so, both theoretical analyses and numerical simulations are investigated. Specifically, the responses of the dual phase grating interferometer at varied X-ray beam energies are studied. Compared with the mechanical position translation approach, the combination of such energy resolving capability helps to greatly shorten the total dark-field imaging time in dual phase grating interferometer.

Long non-coding RNA SNHG6 couples cholesterol sensing with mTORC1 activation in hepatocellular carcinoma.

Cholesterol contributes to the structural basis of biological membranes and functions as a signaling molecule, whose dysregulation has been associated with various human diseases. Here, we report that the long non-coding RNA (lncRNA) SNHG6 increases progression from non-alcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC) by modulating cholesterol-induced mTORC1 activation. Mechanistically, cholesterol binds ER-anchored FAF2 protein to promote the formation of a SNHG6-FAF2-mTOR complex. As a putative cholesterol effector, SNHG6 enhances cholesterol-dependent mTORC1 lysosomal recruitment and activation via enhancing FAF2-mTOR interaction at ER-lysosome contacts, thereby coordinating mTORC1 kinase cascade activation with cellular cholesterol biosynthesis in a self-amplified cycle to accelerate cholesterol-driven NAFLD-HCC development. Notably, loss of SNHG6 inhibits mTORC1 signaling and impairs growth of patient-derived xenograft liver cancer tumors, identifyifng SNHG6 as a potential target for liver cancer treatment. Together, our findings illustrate the crucial role of organelle-associated lncRNA in organelle communication, nutrient sensing, and kinase cascades.

LncRNAs in tumor metabolic reprogramming and immune microenvironment remodeling.

Evidence accumulated over the past decade has verified that long non-coding RNAs (lncRNAs) exert important functions in multiple cell programs. As a novel class of cellular regulatory molecules, lncRNAs interact with different molecules, such as DNA, RNA or proteins, depending on their subcellular distribution, to modulate gene transcription and kinase cascades. It has been widely clarified that lncRNAs play important roles in modulating metabolic reprogramming and reshaping the immune landscape and serve as hinges bridging tumor metabolism and anti-tumor immunity. Given these facts, lncRNAs, as putative regulators of tumor initiation and progression, have attracted extensive attention in recent years. In this review, we summarized the current research progress on the role of lncRNAs in tumor metabolic reprogramming and tumor-immune microenvironment remodeling, and conclude with our laboratory's contributions in advancing the clinical applications of lncRNAs.

Feasibility study of three-material decomposition in dual-energy cone-beam CT imaging with deep learning.

Objective.In this work, a dedicated end-to-end deep convolutional neural network, named as Triple-CBCT, is proposed to demonstrate the feasibility of reconstructing three different material distribution volumes from the dual-energy CBCT projection data.Approach.In Triple-CBCT, the features of the sinogram and the CT image are independently extracted and cascaded via a customized domain transform network module. This Triple-CBCT network was trained by numerically synthesized dual-energy CBCT data, and was tested with experimental dual-energy CBCT data of the Iodine-CaCl2solution and pig leg specimen scanned on an in-house benchtop system.Main results.Results show that the information stored in both the sinogram and CT image domains can be used together to improve the decomposition quality of multiple materials (water, iodine, CaCl2or bone) from the dual-energy projections. In addition, both the numerical and experimental results demonstrate that the Triple-CBCT is able to generate high-fidelity dual-energy CBCT basis images.Significance.An innovative end-to-end network that joints the sinogram and CT image domain information is developed to facilitate high quality automatic decomposition from the dual-energy CBCT scans.

Generalized deep iterative reconstruction for sparse-view CT imaging.

Sparse-view CT is a promising approach for reducing the x-ray radiation dose in clinical CT imaging. However, the CT images reconstructed from the conventional filtered backprojection algorithm suffer from severe streaking artifacts. Iterative reconstruction algorithms have been widely adopted to mitigate these streaking artifacts, but they may prolong the CT imaging time due to the intense data-specific computations. Recently, a model-driven deep learning CT image reconstruction method, which unrolls the iterative optimization procedures into a deep neural network, has shown exciting prospects for improving image quality and shortening the reconstruction time. In this work, we explore a generalized unrolling scheme for such an iterative model to further enhance its performance on sparse-view CT imaging. By using it, the iteration parameters, regularizer term, data-fidelity term and even the mathematical operations are all assumed to be learned and optimized via network training. Results from the numerical and experimental sparse-view CT imaging demonstrate that the newly proposed network with the maximum generalization provides the best reconstruction performance.

DIRECT-Net: A unified mutual-domain material decomposition network for quantitative dual-energy CT imaging.

PURPOSE: The purpose of this paper is to present an end-to-end deep convolutional neural network to improve the dual-energy CT (DECT) material decomposition performance. METHODS: In this study, we proposes a unified mutual-domain (sinogram domain and CT domain) material decomposition network (DIRECT-Net) for DECT imaging. By design, the DIRECT-Net has immediate access to mutual-domain data, and utilizes stacked convolution neural network layers for noise reduction and material decomposition. The training data are numerically generated following the fundamental DECT imaging physics. Numerical simulation of the XCAT digital phantom, experiments of a biological specimen, a calcium chloride phantom and an iodine solution phantom are carried out to evaluate the performance of DIRECT-Net. Comparisons are performed with different DECT decomposition algorithms. RESULTS: Results demonstrate that the proposed DIRECT-Net can generate water and bone basis images with less artifacts compared to the other decomposition methods. Additionally, the quantification errors of the calcium chloride (75-375 mg/cm3 ) and the iodine (2-20 mg/cm3 ) are less than 4%. CONCLUSIONS: An end-to-end material decomposition network is proposed for quantitative DECT imaging. The qualitative and quantitative results demonstrate that this new DIRECT-Net has promising benefits in improving the DECT image quality.

Performance evaluation of dual-energy CT and differential phase contrast CT in quantitative imaging applications.

PURPOSE: The purpose of this study is to evaluate and compare the quantitative material decomposition performance of the dual-energy CT (DECT) and differential phase contrast CT (DPCT) via numerical observer studies. METHODS: The electron density ( ρ e $\rho _{{\rm e}}$ ) and the effective atomic number ( Z eff $Z_{{\rm eff}}$ ) are selected as the decomposition bases. The image domain based decomposition algorithms with certain noise suppression are used to extract the ρ e $\rho _{{\rm e}}$ and Z eff $\text{Z}_{{\rm eff}}$ information under three different spatial resolutions (0.3 mm, 0.1 mm, and 0.03 mm). The contrast-to-noise-ratio (CNR) and the numerical human observer model which is sensitive to the noise textures are investigated to compare the quantitative imaging performance of DECT and DPCT under varied radiation dose levels. RESULTS: The model observer results show that the DECT is superior to DPCT at 0.3 mm spatial resolution (300 mm object size); the DECT and DPCT show similar quantitative imaging performance at 0.1 mm spatial resolution (100 mm object size); and the DPCT outperforms the DECT by approximately 1.5 times for the 0.3 mm sized imaging target at 0.03 mm spatial resolution (30 mm object size). CONCLUSIONS: In conclusion, the DECT would be recommended to obtain ρ e $\rho _{{\rm e}}$ and Z eff $Z_{{\rm eff}}$ for the low spatial resolution quantitative imaging applications such as the diagnostic CT imaging. Whereas, the DPCT would be recommended for ultra high spatial resolution imaging tasks of small objects such as the micro-CT imaging. This study provides a reference to determine the most appropriate quantitative X-ray CT imaging method for a certain radiation dose level.

Sequential Growth of 2D/3D Double-Layer Perovskite Films with Superior X-Ray Detection Performance.

Perovskite materials in different dimensions show great potential in direct X-ray detection, but each with limitations stemming from its own intrinsic properties. Particularly, the sensitivity of two-dimensional (2D) perovskites is limited by poor carrier transport while ion migration in three-dimensional (3D) perovskites causes the baseline drifting problem. To circumvent these limitations, herein a double-layer perovskite film is developed with properly aligned energy level, where 2D (PEA)2 MA3 Pb4 I13 (PEA=2-phenylethylammonium, MA=methylammonium) is cascaded with vertically crystallized 3D MAPbI3 . In this new design paradigm, the 3D layer ensures fast carrier transport while the 2D layer mitigates ion migration, thus offering a high sensitivity and a greatly stabilized baseline. Besides, the 2D layer increases the film resistivity and enlarges the energy barrier for hole injection without compromising carrier extraction. Consequently, the double-layer perovskite detector delivers a high sensitivity (1.95 × 104 µC Gyair -1 cm-2 ) and a low detection limit (480 nGyair s-1 ). Also demonstrated is the X-ray imaging capacity using a circuit board as the object. This work opens up a new avenue for enhancing X-ray detection performance via cascade assembly of various perovskites with complementary properties.

Angular sensitivity of an x-ray differential phase contrast imaging system with real and virtual source images.

In this work, a novel, to the best of our knowledge, approach based on an x-ray thin lens imaging theory is proposed to predict the angular sensitivity responses of dual-phase-grating differential phase contrast (DPC) interferometers. Experimental validations have been performed to demonstrate the high accuracy of theoretical predictions using two different setups: one with real source images and the other with virtual source images. This new sensitivity calculation method is helpful to optimize the DPC imaging performance of a dual-phase-grating system.

DIR-DBTnet: Deep iterative reconstruction network for three-dimensional digital breast tomosynthesis imaging.

PURPOSE: The goal of this study is to develop a three-dimensional (3D) iterative reconstruction framework based on the deep learning (DL) technique to improve the digital breast tomosynthesis (DBT) imaging performance. METHODS: In this work, the DIR-DBTnet is developed for DBT image reconstruction by mapping the conventional iterative reconstruction (IR) algorithm to the deep neural network. By design, the DIR-DBTnet learns and optimizes the regularizer and the iteration parameters automatically during the network training with a large amount of simulated DBT data. Numerical, experimental, and clinical data are used to evaluate its performance. Quantitative metrics such as the artifact spread function (ASF), breast density, and the signal difference to noise ratio (SDNR) are measured to assess the image quality. RESULTS: Results show that the proposed DIR-DBTnet is able to reduce the in-plane shadow artifacts and the out-of-plane signal leaking artifacts compared to the filtered backprojection (FBP) and the total variation (TV)-based IR methods. Quantitatively, the full width half maximum (FWHM) of the measured ASF from the clinical data is 27.1% and 23.0% smaller than those obtained with the FBP and TV methods, while the SDNR is increased by 194.5% and 21.8%, respectively. In addition, the breast density obtained from the DIR-DBTnet network is more accurate and consistent with the ground truth. CONCLUSIONS: In conclusion, a deep iterative reconstruction network, DIR-DBTnet, has been proposed for 3D DBT image reconstruction. Both qualitative and quantitative analyses of the numerical, experimental, and clinical results demonstrate that the DIR-DBTnet has superior DBT imaging performance than the conventional algorithms.

Enhancing the X-Ray Differential Phase Contrast Image Quality With Deep Learning Technique.

OBJECTIVE: The purpose of this work is to investigate the feasibility of using deep convolutional neural network (CNN) to improve the image quality of a grating-based X-ray differential phase contrast imaging (XPCI) system. METHODS: In this work, a novel deep CNN based phase signal extraction and image noise suppression algorithm (named as XP-NET) is developed. The numerical phase phantom, the ex vivo biological specimen and the ACR breast phantom are evaluated via the numerical simulations and experimental studies, separately. Moreover, images are also evaluated under different low radiation levels to verify its dose reduction capability. RESULTS: Compared with the conventional analytical method, the novel XP-NET algorithm is able to reduce the bias of large DPC signals and hence increasing the DPC signal accuracy by more than 15%. Additionally, the XP-NET is able to reduce DPC image noise by about 50% for low dose DPC imaging tasks. CONCLUSION: This proposed novel end-to-end supervised XP-NET has a great potential to improve the DPC signal accuracy, reduce image noise, and preserve object details. SIGNIFICANCE: We demonstrate that the deep CNN technique provides a promising approach to improve the grating-based XPCI performance and its dose efficiency in future biomedical applications.

Model-driven phase retrieval network for single-shot x-ray Talbot-Lau interferometer imaging.

The single-shot x-ray Talbot-Lau interferometer-based differential phase contrast (DPC) imaging is able to accelerate time-consuming data acquisition; however, the extracted phase image suffers from severe image artifacts. Here, we propose to estimate the DPC image via a deep convolutional neural network (CNN) incorporated with the physical imaging model. Instead of training the CNN with thousands of labeled data beforehand, both phantom and biological specimen validation experiments show that high-quality DPC images can be automatically generated from only one single-shot projection image with a certain periodic moiré pattern. This work provides a new, to the best of our knowledge, paradigm for single-shot x-ray DPC imaging.

Adenovirus-mediated co-expression of ING4 and PTEN cooperatively enhances their antitumor activity in human hepatocellular carcinoma cells.

Both inhibitor of growth 4 (ING4) and phosphatase and tensin homolog deleted on chromosome 10 (PTEN) are well known as tumor suppressors that are closely related to tumor occurrence and progression. It was reported that ING4 and PTEN showed synergistic antitumor activities in nasopharyngeal carcinoma cells. The two tumor suppressors demonstrated synergistic effect on growth inhibition and apoptosis activation. In this study, we investigated their therapeutic potential in hepatocellular carcinoma (HCC) cells. Recombinant adenoviruses co-expressing ING4 and PTEN (Ad-ING4-PTEN) were constructed, and the antitumor effect on SMMC-7721 and HepG2 HCC cells was evaluated. Ad-ING4-PTEN cooperatively inhibited cell growth, stimulated apoptosis, and suppressed invasion in both HCC cells, and regulated cell cycle in SMMC-7721. Further studies showed that the combination of ING4 and PTEN by Ad-ING4-PTEN cooperatively enhanced the alteration of the expression of cell cycle-related proteins (p53, p21, and cyclin D1) and apoptotic factors (Bad, Bcl-2, Bcl-XL, and Bax), which are involved in the regulation of cell cycle and the activation of apoptotic pathways, leading to the synergistic antitumor effect. These results indicate that the combination of ING4 and PTEN may provide an effective therapeutic strategy for HCC.