Quality-driven deep cross-supervised learning network for semi-supervised medical image segmentation.

Semi-supervised medical image segmentation presents a compelling approach to streamline large-scale image analysis, alleviating annotation burdens while maintaining comparable performance. Despite recent strides in cross-supervised training paradigms, challenges persist in addressing sub-network disagreement and training efficiency and reliability. In response, our paper introduces a novel cross-supervised learning framework, Quality-driven Deep Cross-supervised Learning Network (QDC-Net). QDC-Net incorporates both an evidential sub-network and an vanilla sub-network, leveraging their complementary strengths to effectively handle disagreement. To enable the reliability and efficiency of semi-supervised training, we introduce a real-time quality estimation of the model's segmentation performance and propose a directional cross-training approach through the design of directional weights. We further design a truncated form of sample-wise loss weighting to mitigate the impact of inaccurate predictions and collapsed samples in semi-supervised training. Extensive experiments on LA and Pancreas-CT datasets demonstrate that QDC-Net surpasses other state-of-the-art methods in semi-supervised medical image segmentation. Code release is available at https://github.com/Medsemiseg.

Secondary cavitation bubble dynamics during laser-induced bubble formation in a small container.

We investigated secondary cavitation bubble dynamics during laser-induced bubble formation in a small container with a partially confined free surface and elastic thin walls. We employed high-speed photography to record the dynamics of sub-mm-sized laser-induced bubbles and small secondary bubble clouds. Simultaneous light scattering and acoustic measurements were used to detect the oscillation times of laser-induced bubbles. We observed that the appearance of secondary bubbles coincides with a prolonged collapse phase and with re-oscillations of the laser-induced bubble. We observed an asymmetric distribution of secondary bubbles with a preference for the upstream side of the focus, an absence of secondary bubbles in the immediate vicinity of the laser focus, and a migration of laser-induced bubble toward secondary bubbles at large pulse energies. We found that secondary bubbles are created through heating of impurities to form initial nanobubble nuclei, which are further expanded by rarefaction waves. The rarefaction waves originate from the vibration of the elastic thin walls, which are excited either directly by laser-induced bubble or by bubble-excited liquid-mass oscillations. The oscillation period of thin walls and liquid-mass were Twall = 116 µs and Tlm ≈ 160 µs, respectively. While the amplitude of the wall vibrations increases monotonically with the size of laser-induced bubbles, the amplitude of liquid-mass oscillation undulates with increasing bubble size. This can be attributed to a phase shift between the laser-induced bubble oscillation and the liquid-mass oscillator. Mutual interactions between the laser-induced bubble and secondary bubbles reveal a fast-changing pressure gradient in the liquid. Our study provides a better understanding of laser-induced bubble dynamics in a partially confined environment, which is of practical importance for microfluidics and intraluminal laser surgery.

MLMFNet: A multi-level modality fusion network for multi-modal accelerated MRI reconstruction.

Magnetic resonance imaging produces detailed anatomical and physiological images of the human body that can be used in the clinical diagnosis and treatment of diseases. However, MRI suffers its comparatively longer acquisition time than other imaging methods and is thus vulnerable to motion artifacts, which ultimately lead to likely failed or even wrong diagnosis. In order to perform faster reconstruction, deep learning-based methods along with traditional strategies such as parallel imaging and compressed sensing come into play in recent years in this field. Meanwhile, in order to better analyze the diseases, it is also often necessary to acquire images in the same region of interest under different modalities, which yield images with different contrast levels. However, most of these aforementioned methods tend to use single-modal images for reconstruction, neglecting the correlation and redundancy information embedded in MR images acquired with different modalities. While there are works on multi-modal reconstruction, the information is yet to be efficiently explored. In this paper, we propose an end-to-end neural network called MLMFNet, which helps the reconstruction of the target modality by using information from the auxiliary modality across feature channels and layers. Specifically, this is highlighted by three components: (I) An encoder based on UNet with a single-stream strategy that fuses auxiliary and target modalities; (II) a decoder that tends to multi-level features from all layers of the encoder, and (III) a channel attention module. Quantitative and qualitative analyses are performed on a public brain dataset and knee brain dataset, which show that the proposed method achieves satisfying results in MRI reconstruction within the multi-modal context, and also demonstrate its effectiveness and potential to be used in clinical practice.

The virtual staining method by quantitative phase imaging for label free lymphocytes based on self-supervised iteration cycle-consistent adversarial networks.

Quantitative phase imaging (QPI) provides 3D structural and morphological information for label free living cells. Unfortunately, this quantitative phase information cannot meet doctors' diagnostic requirements of the clinical "gold standard," which displays stained cells' pathological states based on 2D color features. To make QPI results satisfy the clinical "gold standard," the virtual staining method by QPI for label free lymphocytes based on self-supervised iteration Cycle-Consistent Adversarial Networks (CycleGANs) is proposed herein. The 3D phase information of QPI is, therefore, trained and transferred to a kind of 2D "virtual staining" image that is well in agreement with "gold standard" results. To solve the problem that unstained QPI and stained "gold standard" results cannot be obtained for the same label free living cell, the self-supervised iteration for the CycleGAN deep learning algorithm is designed to obtain a trained stained result as the ground truth for error evaluation. The structural similarity index of our virtual staining experimental results for 8756 lymphocytes is 0.86. Lymphocytes' area errors after converting to 2D virtual stained results from 3D phase information are less than 3.59%. The mean error of the nuclear to cytoplasmic ratio is 2.69%, and the color deviation from the "gold standard" is less than 6.67%.

Imaging the intracellular refractive index distribution (IRID) for dynamic label-free living colon cancer cells via circularly depolarization decay model (CDDM).

Label-free detection of intracellular substances for living cancer cells remains a significant hurdle in cancer pathogenesis research. Although the sensitivity of light polarization to intracellular substances has been validated, current studies are predominantly focused on tissue lesions, thus label-free detection of substances within individual living cancer cells is still a challenge. The main difficulty is to find specific detection methods along with corresponding characteristic parameters. With refractive index as an endogenous marker of substances, this study proposes a detection method of intracellular refractive index distribution (IRID) for label-free living colon cancer (LoVo) cells. Utilizing the circular depolarization decay model (CDDM) to calculate the degree of circular polarization (DOCP) modulated by the cell allows for the derivation of the IRID on the focal plane. Experiments on LoVo cells demonstrated the refractive index of single cell can be accurately and precisely measured, with precision of 10-3 refractive index units (RIU). Additionally, chromatin content during the interphases (G1, S, G2) of cell cycle was recorded at 56.5%, 64.4%, and 71.5%, respectively. A significantly finer IRID can be obtained compared to the phase measurement method. This method is promising in providing a dynamic label-free intracellular substances detection method in cancer pathogenesis studies.

Alternating projection combined with fast gradient projection (FGP-AP) method for intensity-only measurement optical diffraction tomography in LED array microscopy.

Optical diffraction tomography (ODT) is a powerful label-free measurement tool that can quantitatively image the three-dimensional (3D) refractive index (RI) distribution of samples. However, the inherent "missing cone problem," limited illumination angles, and dependence on intensity-only measurements in a simplified imaging setup can all lead to insufficient information mapping in the Fourier domain, affecting 3D reconstruction results. In this paper, we propose the alternating projection combined with the fast gradient projection (FGP-AP) method to compensate for the above problem, which effectively reconstructs the 3D RI distribution of samples using intensity-only images captured from LED array microscopy. The FGP-AP method employs the alternating projection (AP) algorithm for gradient descent and the fast gradient projection (FGP) algorithm for regularization constraints. This approach is equivalent to incorporating prior knowledge of sample non-negativity and smoothness into the 3D reconstruction process. Simulations demonstrate that the FGP-AP method improves reconstruction quality compared to the original AP method, particularly in the presence of noise. Experimental results, obtained from mouse kidney cells and label-free blood cells, further affirm the superior 3D imaging efficacy of the FGP-AP method.

Why is Superlubricity of Diamond-Like Carbon Rare at Nanoscale?

Hydrogenated diamond-like carbon (HDLC) is a promising solid lubricant for its superlubricity which can benefit various industrial applications. While HDLC exhibits notable friction reduction in macroscale tests in inert or reducing environmental conditions, ultralow friction is rarely observed at the nanoscale. This study investigates this rather peculiar dependence of HDLC superlubricity on the contact scale. To attain superlubricity, HDLC requires i) removal of ≈2 nm-thick air-oxidized surface layer and ii) shear-induced transformation of amorphous carbon to highly graphitic and hydrogenated structure. The nanoscale wear depth exceeds the typical thickness of the air-oxidized layer, ruling out the possibility of incomplete removal of the air-oxidized layer. Raman analysis of transfer films indicates that shear-induced graphitization readily occurs at shear stresses lower than or comparable to those in the nanoscale test. Thus, the same is expected to occur at the nanoscale test. However, the graphitic transfer films are not detected in ex-situ analyses after nanoscale friction tests, indicating that the graphitic transfer films are pushed out of the nanoscale contact area due to the instability of transfer films within a small contact area. Combining all these observations, this study concludes the retention of highly graphitic transfer films is crucial to achieving HDLC superlubricity.

Inactivation of Malic Enzyme 1 in Endothelial Cells Alleviates Pulmonary Hypertension.

BACKGROUND: Pulmonary hypertension (PH) is a progressive cardiopulmonary disease with a high mortality rate. Although growing evidence has revealed the importance of dysregulated energetic metabolism in the pathogenesis of PH, the underlying cellular and molecular mechanisms are not fully understood. In this study, we focused on ME1 (malic enzyme 1), a key enzyme linking glycolysis to the tricarboxylic acid cycle. We aimed to determine the role and mechanistic action of ME1 in PH. METHODS: Global and endothelial-specific ME1 knockout mice were used to investigate the role of ME1 in hypoxia- and SU5416/hypoxia (SuHx)-induced PH. Small hairpin RNA and ME1 enzymatic inhibitor (ME1*) were used to study the mechanism of ME1 in pulmonary artery endothelial cells. Downstream key metabolic pathways and mediators of ME1 were identified by metabolomics analysis in vivo and ME1-mediated energetic alterations were examined by Seahorse metabolic analysis in vitro. The pharmacological effect of ME1* on PH treatment was evaluated in PH animal models induced by SuHx. RESULTS: We found that ME1 protein level and enzymatic activity were highly elevated in lung tissues of patients and mice with PH, primarily in vascular endothelial cells. Global knockout of ME1 protected mice from developing hypoxia- or SuHx-induced PH. Endothelial-specific ME1 deletion similarly attenuated pulmonary vascular remodeling and PH development in mice, suggesting a critical role of endothelial ME1 in PH. Mechanistic studies revealed that ME1 inhibition promoted downstream adenosine production and activated A2AR-mediated adenosine signaling, which leads to an increase in nitric oxide generation and a decrease in proinflammatory molecule expression in endothelial cells. ME1 inhibition activated adenosine production in an ATP-dependent manner through regulating malate-aspartate NADH (nicotinamide adenine dinucleotide plus hydrogen) shuttle and thereby balancing oxidative phosphorylation and glycolysis. Pharmacological inactivation of ME1 attenuated the progression of PH in both preventive and therapeutic settings by promoting adenosine production in vivo. CONCLUSIONS: Our findings indicate that ME1 upregulation in endothelial cells plays a causative role in PH development by negatively regulating adenosine production and subsequently dysregulating endothelial functions. Our findings also suggest that ME1 may represent as a novel pharmacological target for upregulating protective adenosine signaling in PH therapy.

An Implicit-Explicit Prototypical Alignment Framework for Semi-Supervised Medical Image Segmentation.

Semi-supervised learning methods have been explored to mitigate the scarcity of pixel-level annotation in medical image segmentation tasks. Consistency learning, serving as a mainstream method in semi-supervised training, suffers from low efficiency and poor stability due to inaccurate supervision and insufficient feature representation. Prototypical learning is one potential and plausible way to handle this problem due to the nature of feature aggregation in prototype calculation. However, the previous works have not fully studied how to enhance the supervision quality and feature representation using prototypical learning under the semi-supervised condition. To address this issue, we propose an implicit-explicit alignment (IEPAlign) framework to foster semi-supervised consistency training. In specific, we develop an implicit prototype alignment method based on dynamic multiple prototypes on-the-fly. And then, we design a multiple prediction voting strategy for reliable unlabeled mask generation and prototype calculation to improve the supervision quality. Afterward, to boost the intra-class consistency and inter-class separability of pixel-wise features in semi-supervised segmentation, we construct a region-aware hierarchical prototype alignment, which transmits information from labeled to unlabeled and from certain regions to uncertain regions. We evaluate IEPAlign on three medical image segmentation tasks. The extensive experimental results demonstrate that the proposed method outperforms other popular semi-supervised segmentation methods and achieves comparable performance with fully-supervised training methods.

Laser induced spherical bubble dynamics in partially confined geometry with acoustic feedback from container walls.

We investigated laser-induced cavitation dynamics in a small container with elastic thin walls and free or partially confined surface both experimentally and by numerical investigations. The cuvette was only 8-25 times larger than the bubble in its center. The liquid surface was either free, or two thirds were confined by a piston-shaped pressure transducer. Different degrees of confinement were realized by filling the liquid up to the transducer surface or to the top of the cuvette. For reference, some experiments were performed in free liquid. We recorded the bubble dynamics simultaneously by high-speed photography, acoustic measurements, and detection of probe beam scattering. Simultaneous single-shot recording of radius-time curves and oscillation times enabled to perform detailed investigations of the bubble dynamics as a function of bubble size, acoustic feedback from the elastic walls, and degree of surface confinement. The bubble dynamics was numerically simulated using a Rayleigh-Plesset model extended by terms describing the acoustically mediated feedback from the bubble's environment. Bubble oscillations were approximately spherical as long as no secondary cavitation by tensile stress occurred. Bubble expansion was always similar to the dynamics in free liquid, and the environment influenced mainly the collapse phase and subsequent oscillations. For large bubbles, strong confinement led to a slight reduction of maximum bubble size and to a pronounced reduction of the oscillation time, and both effects increased with bubble size. The joint action of breakdown-induced shock wave and bubble expansion excites cuvette wall vibrations, which produce alternating pressure waves that are focused onto the bubble. This results in a prolongation of the collapse phase and an enlargement of the second oscillation, or in time-delayed re-oscillations. The details of the bubble dynamics depend in a complex manner on the degree of surface confinement and on bubble size. Numerical simulations of the first bubble oscillation agreed well with experimental data. They suggest that the alternating rarefaction/compression waves from breakdown-induced wall vibrations cause a prolongation of the first oscillation. By contrast, liquid mass movement in the cuvette corners result in wall vibrations causing late re-oscillations. The strong and rich interaction between the bubble and its surroundings may be relevant for a variety of applications such as intraluminal laser surgery and laser-induced cavitation in microfluidics.

LED array microscopy system correction method with comprehensive error parameters optimized by phase smoothing criterion.

LED array microscopy is a novel computational imaging technique that can achieve two-dimensional (2D) phase imaging and three-dimensional (3D) refractive index imaging with both high resolution and a large field of view. Although its experimental setup is simple, the errors caused by LED array position and light source central wavelength obviously decrease the quality of reconstructed results. To solve this problem, comprehensive error parameters optimized by the phase smoothing criterion are put forward in this paper. The central wavelength error and 3D misalignment model with six freedom degree errors of LED array are considered as the comprehensive error parameters when the spatial positional and optical features of arbitrarily placed LED array are unknown. Phase smoothing criterion is also introduced to the cost function for optimizing comprehensive error parameters to improve the convergence results. Compared with current system correction methods, the simulation and experimental results show that the proposed method in this paper has the best reconstruction accuracy, which can be well applied to an LED array microscope system with unknown positional and optical features of the LED array.

Monitoring optoporated process on mammalian cells by real-time measurement of membrane resealing time.

Significance: Resealing time based loading efficiency of optoporation is the key parameter for drug or gene delivery. This work describes a comparatively simple optical approach to directly measure the cell membrane resealing time of the gold nanoparticle mediated photoporation. Aim: To establish a membrane potential detection optical system, which can provide a direct measurement of resealing time of the optoporated cells. Approach: Voltage sensitive dye has been used to label the gold nanoparticle covered cell before laser activation and the resealing time was estimated from the voltage change due to the fluorescence light intensity change before and after laser activation. The approach has been validated by the simulated data based on diffusion model and Monte Carlo simulation and the experimental data obtained from a flow cytometry analysis. Results: The measured resealing time after perforation varied from 28.6 to 163.8 s on Hela cells when the irradiation fluence was increased, with a correlation coefficient (R2) of 0.9938. This result is in agreement with the resealing time (1-2 min) of photothermal porated Hela cells measured by electrical impedance method. The intracellular delivery efficiency of extracellular macromolecular under the same irradiation fluence depends mainly on diffusion velocity rather than pore size. Conclusion: The method described here can be used to directly measure resealing time of optoporated cells for accurately estimating the loading efficiency on discovering the mechanism of optoporation.

αKG-driven RNA polymerase II transcription of cyclin D1 licenses malic enzyme 2 to promote cell-cycle progression.

Increased metabolic activity usually provides energy and nutrients for biomass synthesis and is indispensable for the progression of the cell cycle. Here, we find a role for α-ketoglutarate (αKG) generation in regulating cell-cycle gene transcription. A reduction in cellular αKG levels triggered by malic enzyme 2 (ME2) or isocitrate dehydrogenase 1 (IDH1) depletion leads to a pronounced arrest in G1 phase, while αKG supplementation promotes cell-cycle progression. Mechanistically, αKG directly binds to RNA polymerase II (RNAPII) and increases the level of RNAPII binding to the cyclin D1 gene promoter via promoting pre-initiation complex (PIC) assembly, consequently enhancing cyclin D1 transcription. Notably, αKG addition is sufficient to restore cyclin D1 expression in ME2- or IDH1-depleted cells, facilitating cell-cycle progression and proliferation in these cells. Therefore, our findings indicate a function of αKG in gene transcriptional regulation and cell-cycle control.

Cellular redox homeostasis maintained by malic enzyme 2 is essential for MYC-driven T cell lymphomagenesis.

T cell lymphomas (TCLs) are a group of rare and heterogeneous tumors. Although proto-oncogene MYC has an important role in driving T cell lymphomagenesis, whether MYC carries out this function remains poorly understood. Here, we show that malic enzyme 2 (ME2), one of the NADPH-producing enzymes associated with glutamine metabolism, is essential for MYC-driven T cell lymphomagenesis. We establish a CD4-Cre; Myc flox/+transgenic mouse mode, and approximately 90% of these mice develop TCL. Interestingly, knockout of Me2 in Myc transgenic mice almost completely suppresses T cell lymphomagenesis. Mechanistically, by transcriptionally up-regulating ME2, MYC maintains redox homeostasis, thereby increasing its tumorigenicity. Reciprocally, ME2 promotes MYC translation by stimulating mTORC1 activity through adjusting glutamine metabolism. Treatment with rapamycin, an inhibitor of mTORC1, blocks the development of TCL both in vitro and in vivo. Therefore, our findings identify an important role for ME2 in MYC-driven T cell lymphomagenesis and reveal that MYC-ME2 circuit may be an effective target for TCL therapy.

Position-Aware Relation Learning for RGB-Thermal Salient Object Detection.

Salient object detection (SOD) is an important task in computer vision that aims to identify visually conspicuous regions in images. RGB-Thermal SOD combines two spectra to achieve better segmentation results. However, most existing methods for RGB-T SOD use boundary maps to learn sharp boundaries, which lead to sub-optimal performance as they ignore the interactions between isolated boundary pixels and other confident pixels. To address this issue, we propose a novel position-aware relation learning network (PRLNet) for RGB-T SOD. PRLNet explores the distance and direction relationships between pixels by designing an auxiliary task and optimizing the feature structure to strengthen intra-class compactness and inter-class separation. Our method consists of two main components: A signed distance map auxiliary module (SDMAM), and a feature refinement approach with direction field (FRDF). SDMAM improves the encoder feature representation by considering the distance relationship between foreground-background pixels and boundaries, which increases the inter-class separation between foreground and background features. FRDF rectifies the features of boundary neighborhoods by exploiting the features inside salient objects. It utilizes the direction relationship of object pixels to enhance the intra-class compactness of salient features. In addition, we constitute a transformer-based decoder to decode multispectral feature representation. Experimental results on three public RGB-T SOD datasets demonstrate that our proposed method not only outperforms the state-of-the-art methods, but also can be integrated with different backbone networks in a plug-and-play manner. Ablation study and visualizations further prove the validity and interpretability of our method.

Improved Simulated-Daylight Photodynamic Therapy and Possible Mechanism of Ag-Modified TiO2 on Melanoma.

Simulated-daylight photodynamic therapy (SD-PDT) may be an efficacious strategy for treating melanoma because it can overcome the severe stinging pain, erythema, and edema experienced during conventional PDT. However, the poor daylight response of existing common photosensitizers leads to unsatisfactory anti-tumor therapeutic effects and limits the development of daylight PDT. Hence, in this study, we utilized Ag nanoparticles to adjust the daylight response of TiO2, acquire efficient photochemical activity, and then enhance the anti-tumor therapeutic effect of SD-PDT on melanoma. The synthesized Ag-doped TiO2 showed an optimal enhanced effect compared to Ag-core TiO2. Doping Ag into TiO2 produced a new shallow acceptor impurity level in the energy band structure, which expanded optical absorption in the range of 400-800 nm, and finally improved the photodamage effect of TiO2 under SD irradiation. Plasmonic near-field distributions were enhanced due to the high refractive index of TiO2 at the Ag-TiO2 interface, and then the amount of light captured by TiO2 was increased to induce the enhanced SD-PDT effect of Ag-core TiO2. Hence, Ag could effectively improve the photochemical activity and SD-PDT effect of TiO2 through the change in the energy band structure. Generally, Ag-doped TiO2 is a promising photosensitizer agent for treating melanoma via SD-PDT.

ME2 Promotes Hepatocellular Carcinoma Cell Migration through Pyruvate.

Cancer metastasis is still a major challenge in clinical cancer treatment. The migration and invasion of cancer cells into surrounding tissues and blood vessels is the primary step in cancer metastasis. However, the underlying mechanism of regulating cell migration and invasion are not fully understood. Here, we show the role of malic enzyme 2 (ME2) in promoting human liver cancer cell lines SK-Hep1 and Huh7 cells migration and invasion. Depletion of ME2 reduces cell migration and invasion, whereas overexpression of ME2 increases cell migration and invasion. Mechanistically, ME2 promotes the production of pyruvate, which directly binds to ß-catenin and increases ß-catenin protein levels. Notably, pyruvate treatment restores cell migration and invasion of ME2-depleted cells. Our findings provide a mechanistic understanding of the link between ME2 and cell migration and invasion.

Mitotic recombinatory evolution in acute leukemia.

A cohort of leukemia cases is presented with ancillary testing that includes microarray studies, karyotyping, FISH, and RNA sequencing to illustrate clonal evolution. Common evolution etiology with each case is apparent homologous mitotic recombination (HMR). The cohort includes: four cases of Pre B-cell acute lymphoblastic leukemia (B-ALL) with a single translocation derivative (19)t(1;19)(q23.3;p13.3), an acute myelogenous leukemia (AML) case with a paracentric inversion of 11q13.3q23 in both homologues confirmed as a rare KMT2A-MAML2 gene fusion, and a transplant patient in AML relapse with a t(6;11)(6q27;q23) and evolution to an additional derivative 6 chromosome. The PBX1-TCF3 fusion in the t(1;19) B-ALL subgroup has long been associated with clones that show either the balanced translocation (∼25%) or the unbalanced single derivative 19 (∼75%).  Evidence from the CMAs and FISH is consistent with HMR initiating at either the PBX1 translocation breakpoint or at a more proximal long arm site that mediates the evolution to the unbalanced form. This is contrary to the previous assumptions of either nondisjunction duplication of the normal homologue with loss of the translocation derivative 1 or an original trisomy 1 that loses the translocation derivative 1. Relapse from an unrelated transplant donor created unique allele dosage ratios in the microarray of the AML patient with the t(6;11) KMT2A-AFDN fusion.  An HMR-based evolution initiation site proximal to the 6q27 AFDN fusion gene is evident in the microarray of chromosome 6, the known oncogenic fusion derivative. The HMR selection driver in both AML cases is very likely associated with the DNA doubling of the oncogenic fusions in 6q and 11q, respectively. Since the oncogenic derivatives in the 1;19 cases are clearly the retained derivative 19, selection for the HMR clonal evolution in 1q is apparently based on the known proliferative advantage of extra copies of 1q in B-ALL and other malignancies. Although selection-based HMR can effectively initiate at any site proximal to a driver gene fusion, it appears that the translocation breaksite is common for many translocations. In addition, evidence from HMR evolution related distal 11q mutations, numerous unbalanced CCND1/IGH translocations, and the double MAML2/KMT2A presented in this study suggest that a recombinatorial "hot spot" exists near the CCND1 gene in many rearrangements or mutations within 11q.

p53 protects against alcoholic fatty liver disease via ALDH2 inhibition.

The tumor suppressor p53 is critical for tumor suppression, but the regulatory role of p53 in alcohol-induced fatty liver remains unclear. Here, we show a role for p53 in regulating ethanol metabolism via acetaldehyde dehydrogenase 2 (ALDH2), a key enzyme responsible for the oxidization of alcohol. By repressing ethanol oxidization, p53 suppresses intracellular levels of acetyl-CoA and histone acetylation, leading to the inhibition of the stearoyl-CoA desaturase-1 (SCD1) gene expression. Mechanistically, p53 directly binds to ALDH2 and prevents the formation of its active tetramer and indirectly limits the production of pyruvate that promotes the activity of ALDH2. Notably, p53-deficient mice exhibit increased lipid accumulation, which can be reversed by ALDH2 depletion. Moreover, liver-specific knockdown of SCD1 alleviates ethanol-induced hepatic steatosis caused by p53 loss. By contrast, overexpression of SCD1 in liver promotes ethanol-induced fatty liver development in wild-type mice, while it has a mild effect on p53-/- or ALDH2-/- mice. Overall, our findings reveal a previously unrecognized function of p53 in alcohol-induced fatty liver and uncover pyruvate as a natural regulator of ALDH2.

FFVN: An explicit feature fusion-based variational network for accelerated multi-coil MRI reconstruction.

Magnetic Resonance Imaging (MRI) is a leading diagnostic imaging modality that supports high contrast of soft tissues with no invasiveness or radiation. Nonetheless, it suffers from long scan time owing to the inherent physics in its data acquisition process, hampering its development and applications. Traditional strategies such as Compressed Sensing (CS) and Parallel Imaging (PI) allow for MRI acceleration via sub-sampling strategy, and multiple coils, respectively. When Deep Learning (DL) joins in, both strategies get re-vitalized to achieve even faster reconstruction in various reconstruction methods, among which the variational network is a previously proposed method that combines the mathematical structure of variational models with DL for fast MRI reconstruction. However, in our study we observe that the information of MR features is either not efficiently or explicitly exploited in former works based on the variational network. Instead, we introduce a variational network with explicit feature fusion that combines the CS, PI, with DL for accelerated multi-coil MRI reconstruction. By explicitly leveraging the extra information via feature fusion following feature extraction, our proposed method achieves comparably satisfying performance to the state-of-the-art methods without too much computation overhead on a public multi-coil brain dataset under 5-fold and 10-fold acceleration.