ADAR promotes USP38 auto-deubiquitylation and stabilization in an RNA editing-independent manner in esophageal squamous cell carcinoma.

Esophageal cancer is mainly divided into esophageal adenocarcinoma and esophageal squamous cell carcinoma (ESCC). China is one of the high-incidence areas of esophageal cancer, of which about 90% are ESCC. The deubiquitinase USP38 has been reported to play significant roles in several biological processes, including inflammatory responses, antiviral infection, cell proliferation, migration, invasion, DNA damage repair, and chemotherapy resistance. However, the role and mechanisms of USP38 in ESCC development remain still unclear. Furthermore, although many substrates of USP38 have been identified, few upstream regulatory factors of USP38 have been identified. In this study, we found that USP38 was significantly upregulated in esophageal cancer tissues. Knockdown of USP38 inhibited ESCC growth. USP38 stabilized itself through auto-deubiquitylation. In addition, we demonstrate that adenosine deaminase acting on RNA (ADAR) could enhance the stability of USP38 protein and facilitate USP38 auto-deubiquitylation by interacting with USP38 in an RNA editing-independent manner. ADAR inhibition of ESCC cell proliferation depended on USP38. In summary, these results highlight that the potential of targeting the ADAR-USP38 axis for ESCC treatment.

A structure-based virtual screening identifies a novel MDM2 antagonist in the activation of the p53 signaling and inhibition of tumor growth.

p53, a tumor suppressor protein, has a vital role in the regulation of the cell cycle, apoptosis, and DNA damage repair. The degradation of p53 is predominantly controlled by the murine double minute 2 (MDM2) protein, a ubiquitin E3 ligase. The overexpression or amplification of MDM2 is commonly observed in various human cancers bearing wild-type p53 alleles, leading to the rapid degradation of the p53 protein and the attenuation of p53 tumor suppression functions. Thus, a major effort in p53-based cancer therapy has been to research MDM2 antagonists that specifically stabilize and activate p53, leading to the suppression of tumor growth. However, despite numerous efforts to develop MDM2 antagonists, to date they have failed to reach clinical use, largely because of the cytotoxicity associated with these small molecules. This study used our newly designed structure-based virtual screening approach on a commercial compound library to identify a novel compound, CGMA-Q18, which directly binds to MDM2, leading to the activation of p53, the induction of apoptosis, and cell cycle arrest in cancer cells. Notably, CGMA-Q18 significantly inhibited tumor xenograft growth in nude mice without observable toxicity. These findings highlight our useful virtual screening protocol and CGMA-Q18 as a putative MDM2 antagonist.

Spatial profiles of the bacterial microbiota throughout the gastrointestinal tract of dairy goats.

The gastrointestinal tract (GIT) is stationed by a dynamic and complex microbial community with functions in digestion, metabolism, immunomodulation, and reproduction. However, there is relatively little research on the composition and function of microorganisms in different GIT segments in dairy goats. Herein, 80 chyme samples were taken from ten GIT sites of eight Xinong Saanen dairy goats and then analyzed and identified the microbial composition via 16S rRNA V1-V9 amplicon sequencing. A total of 6669 different operational taxonomic units (OTUs) were clustered, and 187 OTUs were shared by ten GIT segments. We observed 264 species belonging to 23 different phyla scattered across ten GITs, with Firmicutes (52.42%) and Bacteroidetes (22.88%) predominating. The results revealed obvious location differences in the composition, diversity, and function of the GIT microbiota. In LEfSe analysis, unidentified_Lachnospiraceae and unidentified_Succinniclassicum were significantly enriched in the four chambers of stomach, with functions in carbohydrate fermentation to compose short-chain fatty acids. Aeriscardovia, Candidatus_Saccharimonas, and Romboutsia were significantly higher in the foregut, playing an important role in synthesizing enzymes, amino acids, and vitamins and immunomodulation. Akkermansia, Bacteroides, and Alistipes were significantly abundant in the hindgut to degrade polysaccharides and oligosaccharides, etc. From rumen to rectum, α-diversity decreased first and then increased, while ß-diversity showed the opposite trend. Metabolism was the major function of the GIT microbiome predicted by PICRUSt2, but with variation in target substrates along the regions. In summary, GIT segments play a decisive role in the composition and functions of microorganisms. KEY POINTS: • The jejunum and ileum were harsh for microorganisms to colonize due to the presence of bile acids, enzymes, faster chyme circulation, etc., exhibiting the lowest α-diversity and the highest ß-diversity. • Variability in microbial profiles between the three foregut segments was greater than four chambers of stomach and hindgut, with a higher abundance of Firmicutes dominating than others. • Dairy goats dominated a higher abundance of Kiritimatiellaeota than cows, which was reported to be associated with fatty acid synthesis.

ASB6 as an Independent Prognostic Biomarker for Colorectal Cancer Progression Involves Lymphatic Invasion and Immune Infiltration.

Background: ASB6, an E3 ubiquitin ligase, mediates the proteasomal degradation of its substrate proteins via the ubiquitin-proteasome pathway. ASB6 has been reported to play significant roles in several biological processes, including tumor stemness and endoplasmic reticulum stress. However, the underlying role and mechanism of ASB6 in colorectal cancer, particularly its association with immune infiltration levels and its prognostic significance, remain to be fully elucidated. Methods: We identified key prognostic genes in CRC patients through LASSO-penalized Cox regression, Univariate and Multivariate Cox regression analyses. Subsequently, we comprehensively analyzed the prognostic value of hub genes and constructed a prognostic nomogram. Finally, we identified ASB6 interacting proteins through immunoprecipitation-mass spectrometry (IP-MS) and constructed protein-protein interaction (PPI) networks and performed pathway enrichment analysis to explore the potential mechanisms of ASB6. Meanwhile, we evaluated the functions of ASB6 in CRC cells through in vitro cell experiments. Results: We identified ASB6 as a hub gene in CRC. ASB6 was highly expressed in CRC, and patients with high ASB6 expression had worse Disease-Free Interval (DFI), Disease-Specific Survival (DSS), Overall Survival (OS), and Progression-Free Interval (PFI). Correlation analysis showed that ASB6 expression were positively correlated with lymph node invasion and distal metastasis. Overexpression of ASB6 enhanced the migration ability of CRC cells. Multivariate Cox regression analysis revealed that ASB6 was an independent prognostic factor for OS and DSS in CRC. The nomogram model constructed based on multivariate analysis results had good predictive effects, with C-indexes of 0.811 and 0.934 for OS and DSS, respectively. Furthermore, analysis of immune infiltration levels showed that ASB6 expression were positively correlated with M2-type macrophage infiltration levels in CRC, and patients with high levels of both ASB6 and M2-type macrophages had a worse prognosis. Furthermore, pathway enrichment analysis of ASB6 interacting proteins identified by IP-MS suggested that ASB6 may play a crucial role through the response to unfolded protein pathway and protein processing in the endoplasmic reticulum pathway. Conclusions: ASB6 is significantly upregulated in CRC tissues and is a risk factor for prognosis in CRC patients. ASB6 enhances the migration ability of CRC cells. Therefore, ASB6 may be an independent prognostic biomarker and potential therapeutic target for CRC patients.

Adaptive Sparse Memory Networks for Efficient and Robust Video Object Segmentation.

Recently, memory-based networks have achieved promising performance for video object segmentation (VOS). However, existing methods still suffer from unsatisfactory segmentation accuracy and inferior efficiency. The reasons are mainly twofold: 1) during memory construction, the inflexible memory storage mechanism results in a weak discriminative ability for similar appearances in complex scenarios, leading to video-level temporal redundancy, and 2) during memory reading, matching robustness and memory retrieval accuracy decrease as the number of video frames increases. To address these challenges, we propose an adaptive sparse memory network (ASM) that efficiently and effectively performs VOS by sparsely leveraging previous guidance while attending to key information. Specifically, we design an adaptive sparse memory constructor (ASMC) to adaptively memorize informative past frames according to dynamic temporal changes in video frames. Furthermore, we introduce an attentive local memory reader (ALMR) to quickly retrieve relevant information using a subset of memory, thereby reducing frame-level redundant computation and noise in a simpler and more convenient manner. To prevent key features from being discarded by the subset of memory, we further propose a novel attentive local feature aggregation (ALFA) module, which preserves useful cues by selectively aggregating discriminative spatial dependence from adjacent frames, thereby effectively increasing the receptive field of each memory frame. Extensive experiments demonstrate that our model achieves state-of-the-art performance with real-time speed on six popular VOS benchmarks. Furthermore, our ASM can be applied to existing memory-based methods as generic plugins to achieve significant performance improvements. More importantly, our method exhibits robustness in handling sparse videos with low frame rates.

Divide and Conquer: Improving Multi-Camera 3D Perception With 2D Semantic-Depth Priors and Input-Dependent Queries.

3D perception tasks, such as 3D object detection and Bird's-Eye-View (BEV) segmentation using multi-camera images, have drawn significant attention recently. Despite the fact that accurately estimating both semantic and 3D scene layouts are crucial for this task, existing techniques often neglect the synergistic effects of semantic and depth cues, leading to the occurrence of classification and position estimation errors. Additionally, the input-independent nature of initial queries also limits the learning capacity of Transformer-based models. To tackle these challenges, we propose an input-aware Transformer framework that leverages Semantics and Depth as priors (named SDTR). Our approach involves the use of an S-D Encoder that explicitly models semantic and depth priors, thereby disentangling the learning process of object categorization and position estimation. Moreover, we introduce a Prior-guided Query Builder that incorporates the semantic prior into the initial queries of the Transformer, resulting in more effective input-aware queries. Extensive experiments on the nuScenes and Lyft benchmarks demonstrate the state-of-the-art performance of our method in both 3D object detection and BEV segmentation tasks.

Effects of reduced levels of organic trace minerals in proteinate forms and selenium yeast in the mineral mix on lactation performance, milk fatty acid composition, nutrient digestibility, and antioxidant status in dairy goats.

The objective was to evaluate the effects of replacing inorganic trace minerals (ITM) with reduced levels of organic trace minerals (OTM) in proteinate forms and selenium yeast (Se-yeast) in the mineral premix of prepartal and lactating dairy goats on lactation performance, milk fatty acid (FA) composition, nutrient digestibility, and antioxidant status. Xinong Saanen dairy goats (n = 40) were blocked by parity and body weight, and randomly assigned to either ITM or OTM treatments from 4 wk prepartum to 8 mo of lactation. Both groups received the same basal diet except for the trace mineral supplement. The ITM supplement included Fe, Cu, Zn, and Mn as sulfates, and Se as selenite to meet the recommendations. The OTM supplement included Fe, Cu, Zn, and Mn as proteinates at 50% of ITM supplement levels, and Se as Se-yeast at 100% of ITM supplement level. Sampling and measurements were performed in the first, second, fourth, and eighth month of lactation. Data were summarized by month and treatment, and analyzed using the Mixed Model of SPSS with repeated measures. OTM group showed lower milk fat (P = 0.02) and higher milk Se (P = 0.03) with no compromised effects on milk yield and milk protein compared to ITM group. Furthermore, OTM decreased the content of C6:0, C8:0, and C10:0 (P < 0.05) and increased the content of odd- and branched-chain FAs in milk fat due to greater content of C15:0 (P = 0.01) and anteiso C15:0 (P = 0.07). OTM led to greater total tract digestibility of dry matter (P = 0.03), crude protein (P = 0.07), ether extract (P = 0.03), and acid detergent fiber (P = 0.05). OTM goats showed less fecal excretion of Fe (P = 0.01), Cu (P < 0.01), and Zn (P = 0.08) compared to ITM goats. There was a tendency for greater serum GSH-Px activity (P = 0.09) with OTM. Overall, the long-term substitution of reduced levels of OTM for ITM can change milk fat and FA composition while maintaining milk yield, digestibility, and antioxidant status.

Lipids play important roles in the physiochemical properties of milk and dairy products. For example, specific milk fatty acids (FAs), such as those with 8- and 10-carbon chains, influence the flavor of goat milk. Additionally, certain odd- and branched-chain (OBCFA) exhibit anticarcinogenic effects in vitro. Studies in dairy cows have demonstrated organic trace minerals (OTM) can enhance lactation performance, nutrient digestibility, and antioxidant status. In this study, substituting OTM for inorganic trace minerals (ITM) in the diet of dairy goats decreased milk fat without negatively impacting milk yield, nutrient digestibility, and serum antioxidant status. Feeding OTM reduced the content of C6:0, C8:0, and C10:0 FAs while increasing the content of OBCFA in milk fat. The data suggest that replacing ITM with reduced levels of OTM in proteinates and selenium yeast can alter milk FA composition without compromising milk yield, nutrient digestibility, and antioxidant status in dairy goats.

You Only Train Once: Learning General and Distinctive 3D Local Descriptors.

Extracting distinctive, robust, and general 3D local features is essential to downstream tasks such as point cloud registration. However, existing methods either rely on noise-sensitive handcrafted features, or depend on rotation-variant neural architectures. It remains challenging to learn robust and general local feature descriptors for surface matching. In this paper, we propose a new, simple yet effective neural network, termed SpinNet, to extract local surface descriptors which are rotation-invariant whilst sufficiently distinctive and general. A Spatial Point Transformer is first introduced to embed the input local surface into an elaborate cylindrical representation (SO(2) rotation-equivariant), further enabling end-to-end optimization of the entire framework. A Neural Feature Extractor, composed of point-based and 3D cylindrical convolutional layers, is then presented to learn representative and general geometric patterns. An invariant layer is finally used to generate rotation-invariant feature descriptors. Extensive experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability across unseen scenarios with different sensor modalities.

Goat FADS2 controlling fatty acid metabolism is directly regulated by SREBP1 in mammary epithelial cells.

Goat milk provides benefits to human health due to its richness in bioactive components, such as polyunsaturated fatty acids (PUFAs). The fatty acid desaturase 2 (FADS2) is the first rate-limiting enzyme in PUFAs biosynthesis. However, its role and transcriptional regulation mechanisms in fatty acid metabolism in dairy goat remain unclear. Here, our study revealed that the FADS2 gene was highly expressed during the peak lactation compared with the dry period, early lactation, and late lactation. The content of triacylglycerol (TAG) was enhanced with the increasing mRNA expression of TAG synthesis genes (diacylglycerol acyltransferase 1/2, DGAT1/2) in FADS2-overexpressed goat mammary epithelial cells (GMECs). Overexpression of FADS2 was positively correlated with the elevated concentrations of dihomo-gamma-linolenic acid (DGLA) and docosahexaenoic acid (DHA) in GMECs. BODIPY staining showed that FADS2 promoted lipid droplet accumulation in GMECs. To clarify the transcriptional regulatory mechanisms of FADS2, 2,226 bp length of FADS2 promoter was obtained. Deletion mutation assays revealed that the core region of FADS2 promoter was located between the -375 and -26 region, which contained SRE1 (-361/-351) and SRE2 (-191/-181) cis-acting elements of transcription factor sterol regulatory element-binding protein 1 (SREBP1). Overexpression of SREBP1 enhanced relative luciferase activity of the single mutant of SRE1 or SRE2, vice versa, and failed to alter the relative luciferase activity of the joint mutant of SRE1 and SRE2. Chromatin immunoprecipitation (ChIP) and site-directed mutation assays further demonstrated that SREBP1 regulated the transcription of the FADS2 gene by binding to SRE sites in vivo and in vitro. In addition, the mRNA levels of FADS2 were significantly decreased by targeting SRE1 and SRE2 sites in the genome via the CRISPR interference (CRISPRi) system. These findings establish a direct role for FADS2 regulating TAG and fatty acid synthesis by SREBP1 transcriptional regulation in dairy goat, providing new insights into fatty acid metabolism in mammary gland of ruminants.

The fatty acid desaturase 2 (FADS2) is the first rate-limiting enzyme in polyunsaturated fatty acids (PUFAs) biosynthesis in mammals. This study aimed to investigate the function and transcriptional regulation mechanism of FADS2 in goat mammary epithelial cells (GMECs). The content of triacylglycerol (TAG) was enhanced with lipid droplet accumulation in FADS2-overexpressed GMECs. Overexpression of FADS2 was positively correlated with elevated concentrations of docosahexaenoic acid (DHA) in GMECs. Furthermore, site-directed mutation and chromatin immunoprecipitation (ChIP) assays simultaneously demonstrated that FADS2 was directly regulated by SREBP1 transcriptional factor binding to sterol regulatory element (SRE) in vitro and in vivo. In addition, genetic ablation of SRE1 and SRE2 in the genome resulted in a significant reduction in the mRNA levels of FADS2 via the CRISPR interference (CRISPRi) system. Altogether, this study discovered that the SREBP1 exerts control on FADS2 to regulate milk fatty acids, and provides a theoretical approach for improving milk quality via genetic approaches.

RoReg: Pairwise Point Cloud Registration With Oriented Descriptors and Local Rotations.

We present RoReg, a novel point cloud registration framework that fully exploits oriented descriptors and estimated local rotations in the whole registration pipeline. Previous methods mainly focus on extracting rotation-invariant descriptors for registration but unanimously neglect the orientations of descriptors. In this paper, we show that the oriented descriptors and the estimated local rotations are very useful in the whole registration pipeline, including feature description, feature detection, feature matching, and transformation estimation. Consequently, we design a novel oriented descriptor RoReg-Desc and apply RoReg-Desc to estimate the local rotations. Such estimated local rotations enable us to develop a rotation-guided detector, a rotation coherence matcher, and a one-shot-estimation RANSAC, all of which greatly improve the registration performance. Extensive experiments demonstrate that RoReg achieves state-of-the-art performance on the widely-used 3DMatch and 3DLoMatch datasets, and also generalizes well to the outdoor ETH dataset. In particular, we also provide in-depth analysis on each component of RoReg, validating the improvements brought by oriented descriptors and the estimated local rotations. Source code and supplementary material are available at https://github.com/HpWang-whu/RoReg.

Nrf3 promotes the proliferation and migration of triple­negative breast cancer by activating PI3K/AKT/mTOR and epithelial­mesenchymal transition.

Nuclear factor erythroid 2-related factor 3 (Nrf3) is increasingly implicated in multiple types of cancer; however, its function in triple-negative breast cancer (TNBC) remains unclear. This study aimed to examine the role of Nrf3 in TNBC. Compared with adjacent normal tissues, TNBC tissues expressed higher levels of Nrf3, and its expression was negatively correlated with survival time. Additionally, Nrf3 knockdown reduced the proliferation and migration of TNBC cells, whereas overexpression of Nrf3 had the opposite effects in vitro and in vivo. Moreover, functional enrichment of TNBC cells overexpressing Nrf3 allowed for the identification of numerous genes and pathways that were altered following Nrf3 overexpression. Further study showed that overexpression of Nrf3 activated the PI3K/AKT/mTOR signaling pathway and regulated the expression of proteins associated with epithelial-mesenchymal transition. Nrf3 was found to directly bind to p110α promoter regions, as evidenced by luciferase reporter and chromatin immunoprecipitation assays. Furthermore, PI3K inhibitors partially decreased the proliferation and migration of the Nrf3 overexpressing TNBC cells. In conclusion, Nrf3 enhances cellular proliferation and migration by activating PI3K/AKT/mTOR signaling pathways, highlighting a novel therapeutic target for TNBC.

Learning Semantic Segmentation of Large-Scale Point Clouds With Random Sampling.

We study the problem of efficient semantic segmentation of large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing steps, most existing approaches are only able to be trained and operate over small-scale point clouds. In this paper, we introduce RandLA-Net, an efficient and lightweight neural architecture to directly infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Although remarkably computation and memory efficient, random sampling can discard key features by chance. To overcome this, we introduce a novel local feature aggregation module to progressively increase the receptive field for each 3D point, thereby effectively preserving geometric details. Comparative experiments show that our RandLA-Net can process 1 million points in a single pass up to 200× faster than existing approaches. Moreover, extensive experiments on five large-scale point cloud datasets, including Semantic3D, SemanticKITTI, Toronto3D, NPM3D and S3DIS, demonstrate the state-of-the-art semantic segmentation performance of our RandLA-Net.

USP5-Beclin 1 axis overrides p53-dependent senescence and drives Kras-induced tumorigenicity.

Non-small cell lung cancers (NSCLC) frequently contain KRAS mutation but retain wild-type TP53. Abundant senescent cells are observed in premalignant but not in malignant tumors derived from the Kras-driven mouse model, suggesting that KRAS oncogenic signaling would have to overcome the intrinsic senescence burden for cancer progression. Here, we show that the nuclear Beclin 1-mediated inhibition of p53-dependent senescence drives Kras-mediated tumorigenesis. KRAS activates USP5 to stabilize nuclear Beclin 1, leading to MDM2-mediated p53 protein instability. KrasG12D mice lacking Beclin 1 display retarded lung tumor growth. Knockdown of USP5 or knockout of Becn1 leads to increased senescence and reduced autophagy. Mechanistically, KRAS elevates ROS to induce USP5 homodimer formation by forming the C195 disulfide bond, resulting in stabilization and activation of USP5. Together, these results demonstrate that activation of the USP5-Beclin 1 axis is pivotal in overriding intrinsic p53-dependent senescence in Kras-driven lung cancer development.

Evaluation of the prognostic values of solute carrier (SLC) family 39 genes for patients with lung adenocarcinoma.

BACKGROUND: Lung cancer is the first fatality rate of cancer-related death worldwide. This study aimed to evaluate the solute carrier family 39 (SLC39A) genes as biological markers associated with the prognosis of lung adenocarcinoma (LUAD). METHODS AND MATERIALS: MRNA expression of SLC39A genes in non-small cell lung cancer (NSCLC) was analyzed using UCSC database. We investigated the overall survival (OS) of SLC39A genes in patients with NSCLC as the only prognostic indicator using the Kaplan-Meier plotter. CERES score obtained from the Project Achilles was used to perform the survival analysis. Crystal violet-glutaraldehyde solution staining and CCK-8 assay were used to determine colony formation and cell viability, respectively. RESULTS: For patients with lung squamous cell carcinoma, only high expression of SLC39A3, SLC39A4 and SLC39A7 have significant affections to the prognosis. But for patients with LUAD, 11 out of 14 SLC39A genes were significantly associated with prognostic values. Additional analysis indicated that SLC39A7 played an essential role for cell survival of LUAD. Furthermore, SLC39A7 high expression in LUAD was associated with current smoking. CONCLUSIONS: Our findings indicated that SLC39A groups were significantly associated with prognosis of LUAD. The SLC39A7 gene was significantly linked with survival and growth of LUAD cells.

Deep Learning for 3D Point Clouds: A Survey.

Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI, deep learning has been successfully used to solve various 2D vision problems. However, deep learning on point clouds is still in its infancy due to the unique challenges faced by the processing of point clouds with deep neural networks. Recently, deep learning on point clouds has become even thriving, with numerous methods being proposed to address different problems in this area. To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds. It covers three major tasks, including 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It also presents comparative results on several publicly available datasets, together with insightful observations and inspiring future research directions.

Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI.

Background: It is often difficult to diagnose pituitary microadenoma (PM) by MRI alone, due to its relatively small size, variable anatomical structure, complex clinical symptoms, and signs among individuals. We develop and validate a deep learning -based system to diagnose PM from MRI. Methods: A total of 11,935 infertility participants were initially recruited for this project. After applying the exclusion criteria, 1,520 participants (556 PM patients and 964 controls subjects) were included for further stratified into 3 non-overlapping cohorts. The data used for the training set were derived from a retrospective study, and in the validation dataset, prospective temporal and geographical validation set were adopted. A total of 780 participants were used for training, 195 participants for testing, and 545 participants were used to validate the diagnosis performance. The PM-computer-aided diagnosis (PM-CAD) system consists of two parts: pituitary region detection and PM diagnosis. The diagnosis performance of the PM-CAD system was measured using the receiver operating characteristics (ROC) curve and area under the ROC curve (AUC), calibration curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. Results: Pituitary microadenoma-computer-aided diagnosis system showed 94.36% diagnostic accuracy and 98.13% AUC score in the testing dataset. We confirm the robustness and generalization of our PM-CAD system, the diagnostic accuracy in the internal dataset was 96.50% and in the external dataset was 92.26 and 92.36%, the AUC was 95.5, 94.7, and 93.7%, respectively. In human-computer competition, the diagnosis performance of our PM-CAD system was comparable to radiologists with >10 years of professional expertise (diagnosis accuracy of 94.0% vs. 95.0%, AUC of 95.6% vs. 95.0%). For the misdiagnosis cases from radiologists, our system showed a 100% accurate diagnosis. A browser-based software was designed to assist the PM diagnosis. Conclusions: This is the first report showing that the PM-CAD system is a viable tool for detecting PM. Our results suggest that the PM-CAD system is applicable to radiology departments, especially in primary health care institutions.

FBXL2 counteracts Grp94 to destabilize EGFR and inhibit EGFR-driven NSCLC growth.

Abnormal activation of epidermal growth factor receptor (EGFR) drives non-small cell lung cancer (NSCLC) development. EGFR mutations-mediated resistance to tyrosine-kinase inhibitors (TKIs) is a major hurdle for NSCLC treatment. Here, we show that F-box protein FBXL2 targets EGFR and EGFR TKI-resistant mutants for proteasome-mediated degradation, resulting in suppression of EGFR-driven NSCLC growth. Reduced FBXL2 expression is associated with poor clinical outcomes of NSCLC patients. Furthermore, we show that glucose-regulated protein 94 (Grp94) protects EGFR from degradation via blockage of FBXL2 binding to EGFR. Moreover, we have identified nebivolol, a clinically used small molecule inhibitor, that can upregulate FBXL2 expression to inhibit EGFR-driven NSCLC growth. Nebivolol in combination with osimertinib or Grp94-inhibitor-1 exhibits strong inhibitory effects on osimertinib-resistant NSCLC. Together, this study demonstrates that the FBXL2-Grp94-EGFR axis plays a critical role in NSCLC development and suggests that targeting FBXL2-Grp94 to destabilize EGFR may represent a putative therapeutic strategy for TKI-resistant NSCLC.

Production and characterization of functional wheat bran hydrolysate rich in reducing sugars, xylooligosaccharides and phenolic acids.

The aim was to enhance production of functional hydrolysate from wheat bran (WB). WB was hydrolyzed with 3000 U/mL ɑ-amylase and 1200 U/mL alkaline protease to prepare WB insoluble dietary fibre (WBIDF). Functional hydrolysate production from the extract containing crude xylan of WBIDF by xylanase was optimized by Taguchi method. The optimal condition for xylan degradation and functional substances production was 78.50 U/mL xylanase, pH 10.0, 50 °C, and reaction time 6 h. The maximum yield of reducing sugars was 614.0 µg/mL, xylobiose increased from 12.9 µg/mL to 213.3 µg/mL, xylotriose increased from 34.9 µg/mL to 174.0 µg/mL, ferulic acid 13.1 µg/mL made up 57.5 % of the total identifiable phenolic pool in the hydrolysate. The total antioxidant activity of hydrolysate was 141.8 mg ascorbic acid equivalents g-1 crude xylan, and the highest 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity reached 92.7 %. The hydrolysate exhibited great potential in agricultural and food industry application.

The Deubiquitinase USP4 Stabilizes Twist1 Protein to Promote Lung Cancer Cell Stemness.

Lung cancer stem cells (CSCs) play a pivotal role in tumor development, drug resistance, metastasis and recurrence of lung cancer. Thus, it is of great importance to study the mechanism by which CSCs are regulated. In this study, we demonstrate that the deubiquitinase USP4 is critically important in promoting lung cancer stemness. Silencing of USP4 leads to reduction of Oct4 and Sox2 expression, decreased CD133+ cell population and inhibition of tumorsphere formation. Conversely, ectopic expression of USP4 significantly enhances lung cancer cell stemness, which is effectively rescued by simultaneous silencing of Twist1. Mechanistically, we identified USP4 as a novel deubiquitinase of Twist1. USP4 binds to, deubiquitinates and stabilizes Twist1 protein. Furthermore, we show that USP4 expression is elevated in human lung cancer specimens and is positively correlated with Twist1 expression. High expression of USP4/Twist1 is associated with poor clinical outcomes of lung cancer patients. Together, this study highlights an important role for USP4 in lung cancer stemness and suggests USP4 as a potential target for lung cancer diagnosis and treatment.

A Hybrid Peptide DEFB-TP5 Expressed in Methylotrophic Yeast Neutralizes LPS With Potent Anti-inflammatory Activities.

DEFB-TP5 is a novel auspicious health-beneficial peptide derivative from two naturally occurring peptides, ß-Defensin (DEFB) and thymopentin (TP5), and shows strong anti-inflammatory activity and binds to LPS without cytotoxicity and hemolytic effect. Furthermore, the application of DEFB-TP5 peptide is inadequate by its high cost. In the current study, we developed a biocompatible mechanism for expression of the DEFB-TP5 peptide in Pichia pastoris. The transgenic strain of hybrid DEFB-TP5 peptide with a molecular weight of 6.7kDa as predictable was obtained. The recombinant DEFB-TP5 peptide was purified by Ni-NTA chromatography, estimated 30.41 mg/L was obtained from the cell culture medium with 98.2% purity. Additionally, The purified DEFB-TP5 peptide significantly (p< 0.05) diminished the release of nitric oxide (NO), TNF-α, IL-6, IL-1ß in LPS-stimulated RAW264.7 macrophages in a dose-dependent manner. This study will not only help to understand the molecular mechanism of expression that can potentially be used to develop an anti-endotoxin peptide but also to serve as the basis for the development of antimicrobial and anti-inflammatory agents as well, which also provides a potential source for the production of recombinant bioactive DEFB-TP5 at the industrial level.