Targeting JMJD1C to selectively disrupt tumor Treg cell fitness enhances antitumor immunity.

Regulatory T (Treg) cells are critical for immune tolerance but also form a barrier to antitumor immunity. As therapeutic strategies involving Treg cell depletion are limited by concurrent autoimmune disorders, identification of intratumoral Treg cell-specific regulatory mechanisms is needed for selective targeting. Epigenetic modulators can be targeted with small compounds, but intratumoral Treg cell-specific epigenetic regulators have been unexplored. Here, we show that JMJD1C, a histone demethylase upregulated by cytokines in the tumor microenvironment, is essential for tumor Treg cell fitness but dispensable for systemic immune homeostasis. JMJD1C deletion enhanced AKT signals in a manner dependent on histone H3 lysine 9 dimethylation (H3K9me2) demethylase and STAT3 signals independently of H3K9me2 demethylase, leading to robust interferon-γ production and tumor Treg cell fragility. We have also developed an oral JMJD1C inhibitor that suppresses tumor growth by targeting intratumoral Treg cells. Overall, this study identifies JMJD1C as an epigenetic hub that can integrate signals to establish tumor Treg cell fitness, and we present a specific JMJD1C inhibitor that can target tumor Treg cells without affecting systemic immune homeostasis.

Constructing fine-grained spatiotemporal neonatal functional atlases with spectral functional network learning.

The early stages of human development are increasingly acknowledged as pivotal in laying the groundwork for subsequent behavioral and cognitive development. Spatiotemporal (4D) brain functional atlases are important in elucidating the development of human brain functions. However, the scarcity of such atlases for early life stages stems from two primary challenges: (1) the significant noise in functional magnetic resonance imaging (fMRI) that complicates the generation of high-quality atlases for each age group, and (2) the rapid and complex changes in the early human brain that hinder the maintenance of temporal consistency in 4D atlases. This study tackles these challenges by integrating low-rank tensor learning with spectral embedding, thereby proposing a novel, data-driven 4D functional atlas generation framework based on spectral functional network learning (SFNL). This method utilizes low-rank tensor learning to capture common functional connectivity (FC) patterns across different ages, thus optimizing FCs for each age group to improve the temporal consistency of functional networks. Incorporating spectral embedding aids in mitigating potential noise in FC networks derived from fMRI data by reconstructing networks in the spectral space. Utilizing SFNL-generated functional networks enables the creation of consistent and highly qualified spatiotemporal functional atlases. The framework was applied to the developing Human Connectome Project (dHCP) dataset, generating the first neonatal 4D functional atlases with fine-grained temporal and spatial resolutions. Experimental evaluations focusing on functional homogeneity, reliability, and temporal consistency demonstrated the superiority of our framework compared to existing methods for constructing 4D atlases. Additionally, network analysis experiments, including individual identification, functional systems development, and local efficiency assessments, further corroborate the efficacy and robustness of the generated atlases. The 4D atlases and related codes will be made publicly accessible (https://github.com/zhaoyunxi/neonate-atlases).

Personalized neoantigen vaccine enhances the therapeutic efficacy of bevacizumab and anti-PD-1 antibody in advanced non-small cell lung cancer.

Clinically, a considerable number of non-small cell lung cancer (NSCLC) patients are unable to receive or resist chemotherapy, and the efficacy of non-chemotherapy treatment strategies based on anti-angiogenic agents combined with immune checkpoint blockade is still unsatisfactory. Neoantigen vaccine, based on personalized tumor DNA mutations, could elicit tumor specific T cell infiltration into the tumor site, exerting potent anti-tumor efficacy. Here, we evaluated the feasibility and safety of a new antitumor strategy by adding neoantigen vaccine to the regimen of bevacizumab and anti-PD-1 antibody. Firstly, 7 novel immunogenic neoantigen peptides were identified and developed for neoantigen vaccine (LLCvac), which can elicit strong antitumor immune response in vivo. Then, in orthotopic lung cancer model, LLCvac further combining with bevacizumab and anti-PD-1 antibody exerted a stronger antitumor effect, exhibiting significant decrease of tumor volume without obvious toxicity. Furthermore, tumor immune microenvironment assessment also showed that the proportion of neoantigen-specific T cells in blood could be induced dramatically by the combined therapy. And a large amount of neoantigen-specific Ki67-positive CD8+ T cells were found in tumor tissues, which infiltrated tumor tissues effectively to kill tumor cells expressing identified neoantigens. Overall, these results suggested that this combined therapy could safely induce robust antitumor efficacy, serving as an effective chemotherapy-free strategy for NSCLC treatment.

Multi-Interface Electromagnetic Wave Absorbing Material Based on Liquid Marble Microstructures Anchored to SEBS.

The direct application of liquid marbles in electromagnetic wave (EMW) absorption is challenging due to their poor stability, susceptibility to gravitational collapse, and shaping difficulties. To address this issue, a novel strategy is proposed to incorporate liquid marble microstructures (NaCl/nano-SiO2) encapsulated in organic phases (Octadecane) into the rubber-matrix (SEBS) using the ultrasound-assisted emulsion blending method. The resulting NaCl/SiO2/Octadecane microstructures anchored to SEBS offer a substantial solid-liquid interface consisting of NaCl solution and SiO2. When subjected to an alternating electromagnetic (EM) field, the water molecules and polysorbate within SiO2 exhibit heightened responsiveness to the EM field, and the movement of Na+ and Cl- within these microstructures leads to their accumulation at the solid-liquid interface, creating an asymmetric ion distribution. This phenomenon facilitates enhanced interfacial polarization, thereby contributing to the material's EMW absorption properties. Notably, the latex with 16 wt% SEBS (E-3), exhibiting a surface morphology similar to human cell tissues, achieves complete absorption of X-band (fE = 4.20 GHz, RLmin = -33.87 dB). Moreover, the latex demonstrates light density (0.78 g cm-3) and environmental stability. This study not only highlights the predominant loss mechanism in rubber-based wave-absorbing materials but also provides valuable insights into the design of multifunctional wave-absorbing materials.

Macrophages-derived exo-miR-4449 induced by Cryptococcus affects HUVEC permeability and promotes pyroptosis in BEAS-2B via the HIC1 pathway.

Macrophages have recently been discovered to assume a significant role in the progression of cryptococcosis. However, the potential involvement of macrophage-derived exosomes in the pathogenesis of cryptococcosis remains uncertain. In this study, we investigated the changes of microRNAs in macrophage exosomes (exo-miRNAs) in cryptococcal infections and the role of markedly altered exo-miRNAs in the modulation of Human Umbilical Vein Endothelial Cells (HUVEC) permeability and ROS accumulation and pyroptosis in Human Bronchial Epithelioid Cells (BEAS-2B). Techniques such as microarray analysis and real-time quantitative PCR were used to detect different exo-miRNAs and to screen for the most highly expressed exo-miRNAs. Then its mimics were transfected into HUVEC to study its effect on the monolayer permeability of HUVEC. Finally, the relationship between this exo-miRNAs and the ROS accumulation and pyroptosis was verified by bioinformatics analysis. The results showed that five exo-miRNAs were overexpressed and two exo-miRNAs were reduced, among which, exo-miR-4449 was expressed at the highest level. Exo-miR-4449 could be internalized by HUVEC and enhanced its monolayer permeability. Moreover, exo-miR-4449 was found to promote ROS accumulation and pyroptosis in BEAS-2B through HIC1 pathway. Thus, exo-miR-4449 plays an important role in the pathogenesis of cryptococcosis and holds promise as a significant biomarker for treatment.

Metasurface-Assisted Quantum Nonlocal Weak-Measurement Microscopy.

In standard quantum weak measurements, preselection and postselection of quantum states are implemented in the same photon. Here we go beyond this restrictive setting and demonstrate that the preselection and postselection can be performed in two different photons, if the two photons are polarization entangled. The Pancharatnam-Berry phase metasurface is incorporated in the weak measurement system to perform weak coupling between probe wave function and spin observable. By introducing nonlocal weak measurement into the microscopy imaging system, it allows us to remotely switch different microscopy imaging modes of pure-phase objects, including bright-field, differential, and phase reconstruction. Furthermore, we demonstrate that the nonlocal weak-measurement scheme can prevent almost all environmental noise photons from detection and thus achieves a higher image contrast than the standard scheme at a low photon level. Our results provide the possibility to develop a quantum nonlocal weak-measurement microscope for label-free imaging of transparent biological samples.

Integrating Prior Chemical Knowledge into the Graph Transformer Network to Predict the Stability Constants of Chelating Agents and Metal Ions.

The latest advancements in nuclear medicine indicate that radioactive isotopes and associated metal chelators play crucial roles in the diagnosis and treatment of diseases. The development of metal chelators mainly relies on traditional trial-and-error methods, lacking rational guidance and design. In this study, we propose the structure-aware transformer (SAT) combined with molecular fingerprint (SATCMF), a novel graph transformer network framework that incorporates prior chemical knowledge to construct coordination edges and learns the interactions between chelating agents and metal ions. SATCMF is trained on stability data collected from metal ion-ligand complexes, leveraging the SAT network to extract structural features relevant to the binding of ligands with metal ions. It further integrates molecular fingerprint features to refine the prediction of the stability constants of the chelating agents and metal ions. The experimental results on benchmark data set demonstrate that SATCMF achieves state-of-the-art performance based on four different graph neural network architectures. Additionally, visualizing the learned molecular attention distribution provides interpretable insights from the prediction results, offering valuable guidance for the development of novel metal chelators.

Nonlinear relationship between oxidative balance score and hyperuricemia: analyses of NHANES 2007-2018.

BACKGROUND: Limited data regarding the correlation between oxidative balance score (OBS) and hyperuricemia highlights the necessity for thorough investigations. This study aims to examine the link between OBS, which incorporates dietary and lifestyle factors, and the occurrence of hyperuricemia. METHODS: We conducted a cross-sectional study involving 13,636 participants from the 2007-2018 National Health and Nutrition Examination Survey (NHANES). The oxidative balance score (OBS) was determined based on four lifestyle factors and sixteen dietary nutrients. We assessed the levels of serum uric acid (SUA) and the occurrence of hyperuricemia as outcomes. Weighted logistic regression and linear models were used for statistical analysis, using Restricted Cubic Splines (RCS) to examine potential nonlinear associations. Subgroup analysis and sensitivity assessments were performed to identify any variations and ensure the robustness of the findings. RESULTS: Higher OBS was consistently correlated with decreased SUA levels and a reduced prevalence of hyperuricemia. RCS highlighted a significant negative nonlinear association, particularly in females. Subgroup analysis revealed gender-based differences and interactive correlation, providing additional insights regarding OBS and hyperuricemia relationship. CONCLUSION: This study underscores a robust negative correlation between OBS and SUA levels as well as the incidence of hyperuricemia, emphasizing the importance of dietary and lifestyle factors. Incorporating RCS, subgroup analysis, and sensitivity assessments enhances the depth of our findings, providing valuable insights for further research.

Hypertensive disorders of pregnancy, neonatal outcomes and offspring developmental delay in Japan: The Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study.

INTRODUCTION: Developmental delay at an early age indicates the probability of continued problems after school age. Hypertensive disorders of pregnancy (HDP) are associated with developmental delays in offspring, with inconsistent outcomes. Neonatal outcomes vary according to HDP exposure and are relevant to development in later years. Here we aimed to clarify the relationship between HDP and developmental delay in offspring and whether neonatal outcomes mediate this association. MATERIAL AND METHODS: We used data from 5934 mother-child pairs from the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study, a prospective cohort study conducted in Japan between July 2013 and March 2017. The Ages and Stages Questionnaires, third edition, at 24 and 42 months of age, measured developmental delay in five areas. We performed multivariate quasi-Poisson regression and causal mediation analysis by neonatal outcomes. RESULTS: At 24 months of age, compared to offspring born from normotensive mothers, offspring born from HDP-affected mothers were more likely to experience developmental delay (risk ratio [RR] 1.29, 95% confidence interval [CI]: 1.09-1.52) in the areas of communication (RR 1.21, 95% CI: 1.00-1.45) and personal-social (RR 1.15, 95% CI: 1.03-1.28). This association was mediated by neonatal outcomes: preterm birth, neonatal asphyxia, NICU admission, and neonatal small head circumference. No association was observed between HDP and developmental delay at 42 months of age. CONCLUSIONS: Exposure to HDP during fetal life is associated with offspring developmental delay. This association is partly mediated by neonatal outcomes.

Magneto-optical fiber-based orbital angular momentum mode converters.

Orbital angular momentum (OAM) mode division multiplexing (MDM) systems can support large-capacity and high-speed rate information transmission, in which the OAM mode conversion devices play an important role. In this paper, the mode conversion principle of magneto-optical fiber-based long-period grating (MOF-LPG) is analyzed for further developing new magneto-optical (MO) OAM mode converters, including three types of C P 01 to O A M ±1,1, O A M ±1,1 to O A M ±2,1, and O A M ±1,1 to C P 02. It is shown that the magnetic tunability of the mode converters through the propagation constants of the eigenmodes is useful for compensating for process errors and increasing the operating wavelength range. The implementation of MOF-LPGs is also discussed from the aspect of the prospective experiments.

Enhanced Catalytic Soot Oxidation over Co-Based Metal Oxides: Effects of Transition Metal Doping.

A series of Co-M (M = Fe, Cr, and Mn) catalysts were synthesized by the sol-gel method for soot oxidation in a loose contact mode. The Co-Fe catalyst exhibited the best catalytic activity among the tested samples, with the characteristic temperatures (T10, T50, and T90) of 470 °C, 557 °C, and 602 °C, respectively, which were 57 °C, 51 °C, and 51 °C lower than those of the CoOx catalyst. Catalyst characterizations of N2 adsorption-desorption, X-ray diffraction (XRD), X-ray photo-electron spectrometry (XPS), and the temperature programmed desorption of O2 (O2-TPD) were performed to gain insights into the relationships between the activity of catalytic soot oxidation and the catalyst properties. The content of Co2+ (68.6%) increased due to the interactions between Co and Fe, while the redox properties and the relative concentration of surface oxygen adsorption (51.7%) were all improved, which could significantly boost the activity of catalytic soot oxidation. The effects of NO and contact mode on soot oxidation were investigated over the Co-Fe catalyst. The addition of 1000 ppm of NO led to significant reductions in T10, T50, and T90 by 92 °C, 106 °C, and 104 °C, respectively, compared to the case without the NO addition. In the tight contact mode, the soot oxidation was accelerated over the Co-Fe catalyst, resulting in 46 °C, 50 °C, and 50 °C reductions in T10, T50, and T90 compared to the loose contact mode. The comparison between real soot and model Printex-U showed that the T50 value of real soot (455 °C) was 102 °C lower than the model Printex-U soot.

One model, two brains: Automatic fetal brain extraction from MR images of twins.

Fetal brain extraction from magnetic resonance (MR) images is of great importance for both clinical applications and neuroscience studies. However, it is a challenging task, especially when dealing with twins, which are commonly existing in pregnancy. Currently, there is no brain extraction method dedicated to twins, raising significant demand to develop an effective twin fetal brain extraction method. To this end, we propose the first twin fetal brain extraction framework, which possesses three novel features. First, to narrow down the region of interest and preserve structural information between the two brains in twin fetal MR images, we take advantage of an advanced object detector to locate all the brains in twin fetal MR images at once. Second, we propose a Twin Fetal Brain Extraction Network (TFBE-Net) to further suppress insignificant features for segmenting brain regions. Finally, we propose a Two-step Training Strategy (TTS) to learn correlation features of the single fetal brain for further improving the performance of TFBE-Net. We validate the proposed framework on a twin fetal brain dataset. The experiments show that our framework achieves promising performance on both quantitative and qualitative evaluations, and outperforms state-of-the-art methods for fetal brain extraction.

Exploratory Training for Universal Lesion Detection: Enhancing Lesion Mining Quality Through Temporal Verification.

Universal lesion detection (ULD) has great value in clinical practice as it can detect various lesions across multiple organs. Deep learning-based detectors have great potential but require high-quality annotated training data. In practice, due to cost, expertise requirements, and the diverse nature of lesions, incomplete annotations are often encountered. Directly training ULD detectors under this condition can yield suboptimal results. Leading pseudo-label methods rely on a dynamic lesion-mining mechanism operating at the mini-batch level to address the issue of incomplete annotations. However, the quality of mined lesions in this approach is inconsistent across different iterations, potentially limiting performance enhancement. Inspired by the observation that deep models learn concepts with increasing complexity, we propose an innovative exploratory-training-based ULD (ET-ULD) method to assess the reliability of mined lesions over time. Specifically, we employ a teacher-student detection model, the teacher model is used to mine suspicious lesions, which are combined with incomplete annotations to train the student model. On top of that, we design a bounding-box bank to record the mining timestamps. Each image is trained in several rounds, allowing us to get a sequence of timestamps for the mined lesions. If a mined lesion consistently appears in the timestamp sequence, it is likely to be a true lesion, otherwise, it may just be a noise. This serves as a crucial criterion for selecting reliable mined lesions for subsequent retraining. Our experimental results confirm the effectiveness of ET-ULD, showcasing its ability to surpass existing state-of-the-art methods on two distinct lesion image datasets. Notably, on the DeepLesion dataset, ET-ULD achieved a significant enhancement, outperforming the previous leading method by 5.4% in Average Precision (AP), thus demonstrating its superior performance.

Fast analysis of straw proximates based on partial least squares using near-infrared spectroscopy.

Near-infrared spectroscopy (NIRS) is a rapid measurement technique based on the spectroscopic absorption bands of specific functional groups within biomass. Its main advantages include simple preparation, precise analysis, and the ability to analyze multiple components simultaneously. Fast analysis of straw proximates (moisture, ash, and fixed carbon) has been investigated by means of NIRS. A total of 144 samples were collected, the spectral data were analyzed by partial least squares (PLS) regression and support vector regression (SVR) with four wavelength selection methods. PLS combined with competitive adaptive reweighted sampling (CARS) provided excellent predictive performance for moisture, ash, and fixed carbon. For moisture prediction, the values of RP2, RMSEP and RPD were 0.7202, 0.8196, and 2.11, respectively. For ash prediction, the values of RP2, RMSEP and RPD were 0.9307, 0.5901, and 3.69, respectively. For fixed carbon prediction, the values of RP2, RMSEP and RPD were 0.8504, 0.2735, and 2.76, respectively. Fast analysis of proximates of corn stover was possible using this NIRS system.

Boosting broadband short-wave infrared emission to achieve near-unity quantum efficiency via bridging Cr3+-Ni2+ in spinel solid-solutions towards light-emitting diode applications.

Recently, short-wave infrared (SWIR) phosphor-converted light-emitting diodes (pc-LEDs) have garnered increased attention due to their widespread applications in night vision, biological imaging, and non-destructive testing. Nevertheless, the currently used SWIR phosphors suffer from poor thermal stability and low quantum efficiency. In this study, a finely tuned spinel-based solid solution, Mg0.5Zn0.5Ga2O4, is prepared to host Ni2+ to induce SWIR emission. Cr3+ is codoped as a sensitizer to bridge Cr3+ and Ni2+ ions, significantly enhancing blue light absorption and facilitating energy transfer (ET) to Ni2+ ions. The champion SWIR phosphor exhibits a broadband emission centered at 1304 nm with a full width at half maximum (FWHM) of 250 nm, achieving a near-unity internal quantum efficiency (IQE = 97.7%) and a good thermal stability (70.7%@423 K). The fabricated SWIR pc-LED device delivers a high SWIR output power of 39.9 mW at 360 mA, enabling its application in non-destructive imaging and testing.

Hepatotoxicity and lipid metabolism disorders of 8:2 polyfluoroalkyl phosphate diester in zebrafish: In vivo and in silico evidence.

8:2 polyfluoroalkyl phosphate diester (8:2 diPAP) has been shown to accumulate in the liver, but whether it induces hepatotoxicity and lipid metabolism disorders remains largely unknown. In this study, zebrafish embryos were exposed to 8:2 diPAP for 7 d. Hepatocellular hypertrophy and karyolysis were noted after exposure to 0.5 ng/L 8:2 diPAP, suggesting suppressed liver development. Compared to the water control, 8:2 diPAP led to significantly higher triglyceride and total cholesterol levels, but markedly lower levels of low-density lipoprotein, implying disturbed lipid homeostasis. The levels of two peroxisome proliferator activated receptor (PPAR) subtypes (pparα and pparγ) involved in hepatotoxicity and lipid metabolism were significantly upregulated by 8:2 diPAP, consistent with their overexpression as determined by immunohistochemistry. In silico results showed that 8:2 diPAP formed hydrogen bonds with PPARα and PPARγ. Among seven machine learning models, Adaptive Boosting performed the best in predicting the binding affinities of PPARα and PPARγ on the test set. The predicted binding affinity of 8:2 diPAP to PPARα (7.12) was higher than that to PPARγ (6.97) by Adaptive Boosting, which matched well with the experimental results. Our results revealed PPAR - mediated adverse effects of 8:2 diPAP on the liver and lipid metabolism of zebrafish larvae.

Development and Evaluation of Refreshable Braille Display and Active Touch-Reading System for Digital Reading of the Visually Impaired.

The traditional way of reading through Braille books is constraining the reading experience of blind or visually impaired (BVI) in the digital age. In order to improve the reading convenience of BVI, this paper proposes a low-cost and refreshable Braille display device, and solves the problems of high energy consumption and low latching force existing in existing devices. Further, the Braille display device was combined with the 3D Systems Touch device to develop an active Braille touch-reading system for digital reading of BVI with the help of the CHAI3D virtual environment. Firstly, according to the actual needs of BVI to touch and read the Braille dots, this paper utilizes the beam structure to provide a full latching function for the raised Braille dot without energy consumption. Through theoretical derivation and finite element analysis, the performance of the Braille dot actuator is optimized to provide sufficient feedback force and latching force for finger's touch-reading. Then, this paper designs a virtual Braille interactive environment based on the CHAI3D, and combines the sense of touch with audio to effectively improve the recognition accuracy and reading efficiency of BVI for Braille through the multi-modal presentation of Braille information. The performance test results of the device show that the average lifting force of the Braille dot actuator is 101.67 mN, the latching force is over 5 N, and the average refresh frequency is 17.1 Hz, which meets the touch-reading needs of BVI. User experiments show that the average accuracy rate of BVI subjects in identifying digitized Braille is 95.5%, and subjects have a high subjective evaluation of the system.

EM-Trans: Edge-Aware Multimodal Transformer for RGB-D Salient Object Detection.

RGB-D salient object detection (SOD) has gained tremendous attention in recent years. In particular, transformer has been employed and shown great potential. However, existing transformer models usually overlook the vital edge information, which is a major issue restricting the further improvement of SOD accuracy. To this end, we propose a novel edge-aware RGB-D SOD transformer, called, which explicitly models the edge information in a dual-band decomposition framework. Specifically, we employ two parallel decoder networks to learn the high-frequency edge and low-frequency body features from the low-and high-level features extracted from a two-steam multimodal backbone network, respectively. Next, we propose a cross-attention complementarity exploration module to enrich the edge/body features by exploiting the multimodal complementarity information. The refined features are then fed into our proposed color-hint guided fusion module for enhancing the depth feature and fusing the multimodal features. Finally, the resulting features are fused using our deeply supervised progressive fusion module, which progressively integrates edge and body features for predicting saliency maps. Our model explicitly considers the edge information for accurate RGB-D SOD, overcoming the limitations of existing methods and effectively improving the performance. Extensive experiments on benchmark datasets demonstrate that is an effective RGB-D SOD framework that outperforms the current state-of-the-art models, both quantitatively and qualitatively. A further extension to RGB-T SOD demonstrates the promising potential of our model in various kinds of multimodal SOD tasks.

Impact of the extent and location of liver split on future liver remnant hypertrophy after portal vein ligation in a rat model.

BACKGROUND: To investigate the role and mechanism of liver parenchyma transection in accelerating the regeneration of future liver remnants in rats with portal vein ligation (PVL). METHODS: Rats were randomly divided into the PVL group (90% PVL at the caudate lobe, right lobe , left lateral lobe and left median lobe), associating liver partition and portal vein ligation for staged hepatectomy (portal vein ligation with complete liver parenchyma transection [ALPPS]) group (90% PVL with 80 to 90% liver parenchyma transection), PVL + partial liver partition (PLP) group (90% PVL with 30 to 50% liver parenchyma transection), PVL + partition in the ligated lobe (PLL) group (90% PVL with 40 to 60% liver parenchyma transection in the portal vein ligated lobe), PVL + partition in the remnant lobe (PRL) group (90% PVL with 40 to 60% liver parenchyma transection in the remnant lobe), PVL + radiofrequency ablation (RFA) group (90% PVL with splenic ablation) and sham operation (sham) group. The animals were killed at 4 time points of postoperative days 1, 3, 5, and 7. Six rats were killed at each time point, with 24 rats in each group. The weights of the future liver remnant and whole liver were measured. Serum alanine aminotransferase, aspartate aminotransferase, and total bilirubin were analyzed by using an automatic biochemical analyzer. Serum tumor necrosis factor-α, interleukin-6, and hepatocyte growth factor were measured by enzyme-linked immunosorbent assay. The expression of cell proliferating nuclear antigen (Ki67) and phosphorylated histone H3 was detected by immunohistochemistry, and the positive rate was calculated. RESULTS: The ALPPS group displayed the highest FLR weight to body weight ratio compared with that of the other groups (P < .05), and the partial liver split (PVL + PLP) group also displayed higher remnant weight to body weight ratio than the ectopic liver split (PVL + PLL and PVL + PRL) groups (P < .05). During the first 7 days after surgery the cytokine levels of the ALPPS, PVL + PLP, PVL + PLL and PVL + PRL groups were comparable (P > .05). The PVL + PLP, PVL + PLL, PVL + PRL and PVL + RFA groups showed similar necrotic areas in the portal vein ligated lobe (P > .05). A hemodynamic study revealed that a liver split along the demarcation line could further increase the portal pressure of the FLR and both the split site and completeness were associated with portal hemodynamic alternations and liver hypertrophy. Extrahepatic organ injury (eg, spleen ablation) also has a significant impact on portal hemodynamics and liver regeneration. CONCLUSION: Complete liver splitting along the demarcation line induced higher portal vein pressure and more rapid FLR hypertrophy than partial or ectopic liver splitting after PVL. The portal hemodynamic alterations after liver split rather than inflammatory cytokine release may be the major cause of ALPPS-induced rapid liver hypertrophy.

Classification of lung cancer subtypes on CT images with synthetic pathological priors.

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case's CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the "gold standard" information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), positive predictive value (PPV) and F1-score.