In silico study of androgen receptor N-terminal domain and exploration of its modulators

The androgen receptor (AR, Uniprot: P10275) signaling plays a key role in the progression of prostate cancer, various AR-related ligands have been reported to treat prostate cancer. However, some resistance mechanisms limited the treating effect of these ligands. Since DBD binding or the allosteric binding sites in LBD of AR may allow the circumvention of some drug resistance mechanisms, anti-resistance is expected especially through the NTD (N-terminal domain) targeting. What's more, studies have shown that compounds including EPI-001 and its derivatives which bind to the Tau-5 region on NTD could be promising molecules for AR-based therapeutics. Herein, we employed aMD (accelerated molecular dynamics) simulation to fold Tau-5 unit proteins into native structure correctly. Subsequently, based on the predicted structural features of Tau-5, the virtual screening was conducted to discover new compounds targeting AR-NTD. We picked up 8 compounds (according to their docking scores and partly similar structural consists as known AR ligands) and analyzed their interaction with Tau-5, compared with the positive control EPI-001, four of the pick-up compounds showed better glide scores. Interestingly, although compound 8 had a lower docking score, it consisted of a similar component as the ligand EIQPN and the amide derivatives, this predicts that compound 8 has also the potential to be modified into an excellent AR-NTD binding molecule. These 8 compounds were all commercially available and could be tested to check whether there was a hit compound to bind the AR-NTD and to regulate its bio-activities. Together, this study described an in silico VLS approach to discover AR-NTD ligands and provided more choices for developing AR-targeted therapies.Communicated by Ramaswamy H. Sarma.

Efficient residual network using hyperspectral images for corn variety identification

Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures.

Adaptive sequential design for phase II single-arm oncology trials: an expansion of Simon's design

Single-arm phase II trials are very common in oncology. A fixed sample trial may lack sufficient power if the true efficacy is less than the assumed one. Adaptive designs have been proposed in the literature. We propose a Simon's design based, adaptive sequential design. Simon's design is the most used fixed sample design for single-arm phase II oncology trials. A prominent feature of Simon's design is that it minimizes the sample size when there is no clinically meaningful efficacy. We identify Simon's trial as a special group sequential design. Established methods for sample size re-estimation (SSR) can be readily applied to Simon's design. Simulations show that simply adding SSR to Simon's design may still not provide desirable power. We propose some expansions to Simon's design. The expanded design with SSR can provide even more power.

Molecularly Imprinted Heterostructure-Assisted Laser Desorption Ionization Mass Spectrometry Analysis and Imaging of Quinolones

Quinolone residues resulting from body metabolism and waste discharge pose a significant threat to the ecological environment and to human health. Therefore, it is essential to monitor quinolone residues in the environment. Herein, an efficient and sensitive matrix-assisted laser desorption/ionization mass spectrometry (MALDI/MS) method was devised by using a novel molecularly imprinted heterojunction (MIP-TNs@GCNs) as the matrix. Molecularly imprinted titanium dioxide nanosheets (MIP-TNs) and graphene-like carbon nitrides (GCNs) were associated at the heterojunction interface, allowing for the specific, rapid, and high-throughput ionization of quinolones. The mechanism of MIP-TNs@GCNs was clarified using their adsorption properties and laser desorption/ionization capability. The prepared oxygen-vacancy-rich MIP-TNs@GCNs heterojunction exhibited higher light absorption and ionization efficiencies than TNs and GCNs. The good linearity (in the quinolone concentration range of 0.5-50 pg/μL, R2 > 0.99), low limit of detection (0.1 pg/μL), good reproducibility (n = 8, relative standard deviation [RSD] < 15%), and high salt and protein resistance for quinolones in groundwater samples were achieved using the established MIP-TNs@GCNs-MALDI/MS method. Moreover, the spatial distributions of endogenous compounds (e.g., amino acids, organic acids, and flavonoids) and xenobiotic quinolones from Rhizoma Phragmitis and Rhizoma Nelumbinis were visualized using the MIP-TNs@GCNs film as the MALDI/MS imaging matrix. Because of its superior advantages, the MIP-TNs@GCNs-MALDI/MS method is promising for the analysis and imaging of quinolones and small molecules.

Safety-Preserving Lyapunov-Based Model Predictive Rendezvous Control for Heterogeneous Marine Vehicles Subject to External Disturbances

This article investigates the cooperative rendezvous control problem for perturbed heterogeneous marine systems composed of an autonomous underwater vehicle (AUV) and an autonomous surface vehicle (ASV). A novel Lyapunov-based model predictive control (LMPC) framework is presented to accomplish safe and precise rendezvous under input limitations and external disturbances. First, by incorporating the prescribed performance control (PPC) technique into the LMPC framework, we transform the original ascending state of the AUV into a self-constrained state, which serves as the decision variable of the model predictive control (MPC) optimization problem. Then, PPC-aided auxiliary control laws based on disturbance observers (DOBs) are designed to establish a robust contractive constraint to provide stability margins. Combining the LMPC with the PPC technique makes the original state-constrained problem an equivalent state-constraint-free problem. By addressing the MPC problem for the equivalent unconstrained system, the proposed method preserves the rendezvous safety. With the robust contractive constraint, the proposed safety-preserving LMPC (SP-LMPC) controller can inherit robustness and stability from the robust auxiliary control laws. Furthermore, theoretical analyses are conducted to assess recursive feasibility and closed-loop stability. With comprehensive theoretical support, the proposed method provides a new framework to simultaneously address state constraints and disturbances for highly nonlinear marine systems. Finally, simulations and comparisons are conducted to demonstrate the effectiveness and advantages of the proposed algorithm.

The effect of delay time after injecting gadobutrol on the diagnosis of endolymphatic hydrops

OBJECTIVES

The aim of this study was to reduce the time delay between gadolinium injection and 3D-FLAIR (three-dimensional fluid-attenuated inversion recovery) MRI by using a single dose of intravenous gadobutrol in Menière's disease patients.

METHODS

17 patients diagnosed with definite unilateral Meniere's disease underwent 3D-FLAIR MRI scans at 2, 4, and 6 h post-intravenous administration of a single-dose of gadobutrol. The signal intensity ratio of bilateral inner ear, cochlear and vestibular hydrops was measured at 2 h, 4 h and 6 h, while the differences in signal intensity ratio and endolymphatic hydrops were evaluated at three time points.

RESULTS

The cochlea, vestibule, and semicircular canal exhibit clear structural features with distinct perilymph-endolymph boundaries at 2 h, 4 h, and 6 h. The signal intensity ratio of the affected ear was significantly higher than that of the unaffected ear at 2 h, 4 h, and 6 h. The signal intensity ratio at 4 h and 6 h in both the affected and unaffected ears was significantly higher than that at 2 h, but there was no significant difference between 4 h and 6 h. Cochlear hydrops and vestibular hydrops show no significant differences at these time points, demonstrating excellent consistency.

CONCLUSIONS

We have demonstrated that 3D-FLAIR images acquired 2 h after intravenous administration of a single-dose gadobutrol are of high quality and equally effective as those obtained at the conventional 4-h time point for diagnosing endolymphatic hydrops in Menière's disease. In clinical practice, the delay time can be safely shortened to 2 h.

Exponential Synchronization of Markovian Jump Neural Networks Based on Asynchronous Delayed-Feedback Controller With Uncertain Hidden Information

Due to the complex network environment, the feedback information cannot be timely received by the controller. This article proposes a method on the exponential synchronization for the Markovian jump neural networks, which is achieved by designing a new asynchronous delayed-feedback controller, with its feedback delay taken into account. The quantized relationship between the exponential synchronization and the feedback delay is derived from a new designed Lyapunov functional, to acquire delay boundaries. With the help of a hidden-Markov process, the designed controller shows asynchrony, which allows controller modes to run free. In particular, the detection probability is assumed to be bounded known, marking a breakthrough over existing results. Moreover, the proposed method proves to be applicable in both synchronous and asynchronous cases. By using the proposed method, the computation freedom of the controller gain matrix can be substantially augmented. Further, comparative numerical studies are implemented to validate the effectiveness and superiority of the proposed method.

Proteolysis improves the foaming properties of rice protein fibrils: Structure, physicochemical properties changes, and application in angel food cake

Proteolysis prior to fibrillation can change the functional properties of protein fibrils. This study aimed to investigate the effects of proteolysis pretreatment by alkaline protease on formation, structure, and foaming properties of rice protein fibrils. Thioflavin T fluorescence and conversion assays showed that proteolysis reduced the fibril formation capacity of rice protein. After 12 h of heating, the percent conversion of rice protein and rice protein hydrolysates to fibrils reached 60.32 ± 1.07% and 30.43 ± 2.01%, respectively. Transmission electron microscopy images showed that fibrils derived from rice protein hydrolysates had a longer contour length than rice protein fibrils. The foaming capacity and stability of rice protein hydrolysate fibrils increased by 16.70% and 11.27%, respectively, compared with those of rice protein fibrils. The addition of rice protein hydrolysate fibrils improved the texture of cakes. These results suggested that rice protein hydrolysate fibrils could be a promising plant-based foaming agent.

High expression of KIFC1 is a poor prognostic biomarker and correlates with TP53 mutation in lung cancer

The Kinesin Family Member C1 (KIFC1) is highly expressed in a variety of tumors. Since it is linked with tumorigenesis and progression, KIFC1 has emerged as a promising candidate for targeted chemotherapies. Thus, this study aims to find out the association between KIFC1 and lung cancer. The original data were assessed from The Cancer Genome Atlas and Gene Expression Omnibus databases. Compared to normal lung tissues, both mRNA and protein levels of KIFC1 were significantly increased in lung cancer tissues. The upregulation of KIFC1 was significantly correlated with sex, pathological stage, and TMN stage. Survival analysis revealed that increased KIFC1 expression was associated with poor overall survival, first-progression survival and post-progression survival in lung cancer. Based on the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis, we observed that KIFC1 upregulation was linked to enrichment of the cell cycle and TP53 signaling pathway. Additionally, the overexpression of KIFC1 was positively correlated with TP53 mutations in lung cancer. Based on real-world cohort results, western blotting and RT-qPCR showed high-KIFC1 expression in lung cancer, which may be related to the malignancy of lung cancer. Finally, experiments in vitro showed that KIFC1 inhibitor could significantly inhibit the proliferation and invasion of lung cancer cells. In conclusion, KIFC1 is a poor prognostic biomarker, and patients with high-KIFC1 levels may benefit from targeted therapy.

Data Augmentation of a Corrosion Dataset for Defect Growth Prediction of Pipelines Using Conditional Tabular Generative Adversarial Networks

Due to corrosion characteristics, there are data scarcity and uneven distribution in corrosion datasets, and collecting high-quality data is time-consuming and sometimes difficult. Therefore, this work introduces a novel data augmentation strategy using a conditional tabular generative adversarial network (CTGAN) for enhancing corrosion datasets of pipelines. Firstly, the corrosion dataset is subjected to data cleaning and variable correlation analysis. The CTGAN is then used to generate external environmental factors as input variables for corrosion growth prediction, and a hybrid model based on machine learning is employed to generate corrosion depth as an output variable. The fake data are merged with the original data to form the synthetic dataset. Finally, the proposed data augmentation strategy is verified by analyzing the synthetic dataset using different visualization methods and evaluation indicators. The results show that the synthetic and original datasets have similar distributions, and the data augmentation strategy can learn the distribution of real corrosion data and sample fake data that are highly similar to the real data. Predictive models trained on the synthetic dataset perform better than predictive models trained using only the original dataset. In comparative tests, the proposed strategy outperformed other data generation methods.

Progress in RAS-targeted therapeutic strategies: From small molecule inhibitors to proteolysis targeting chimeras

As a widely considerable target in chemical biology and pharmacological research, rat sarcoma (RAS) gene mutations play a critical driving factor in several fatal cancers. Despite the great progress of RAS subtype-specific inhibitors, rapid acquired drug resistance could limit their further clinical applications. Proteolysis targeting chimera (PROTAC) has emerged as a powerful tool to handle "undruggable" targets and exhibited significant therapeutic benefit for the combat of drug resistance. Owing to unique molecular mechanism and binding kinetics, PROTAC is expected to become a feasible strategy to break the bottleneck of classical RAS inhibitors. This review aims to discuss the current advances of RAS inhibitors and especially focus on PROTAC strategy targeting RAS mutations and their downstream effectors for relevant cancer treatment.

Benzosceptrin C induces lysosomal degradation of PD-L1 and promotes antitumor immunity by targeting DHHC3

Programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) blockade has become a mainstay of cancer immunotherapy. Targeting the PD-1/PD-L1 axis with small molecules is an attractive approach to enhance antitumor immunity. Here, we identified a natural marine product, benzosceptrin C (BC), that enhances the cytotoxicity of T cells to cancer cells by reducing the abundance of PD-L1. Furthermore, BC exerts its antitumor effect in mice bearing MC38 tumors by activating tumor-infiltrating T cell immunity. Mechanistic studies suggest that BC can prevent palmitoylation of PD-L1 by inhibiting DHHC3 enzymatic activity. Subsequently, PD-L1 is transferred from the membrane to the cytoplasm and cannot return to the membrane via recycling endosomes, triggering lysosome-mediated degradation of PD-L1. Moreover, the combination of BC and anti-CTLA4 effectively enhances antitumor T cell immunity. Our findings reveal a previously unrecognized antitumor mechanism of BC and represent an alternative immune checkpoint blockade (ICB) therapeutic strategy to enhance the efficacy of cancer immunotherapy.

ALKBH5 promotes the development of lung adenocarcinoma by regulating the polarization of M2 macrophages through CDCA4

OBJECTIVE

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, with high morbidity and mortality. N6-methyladenosine (m6A) is an important regulator of LUAD progression. Here, we investigated the potential biological functions of ALKBH5 (a m6A demethylated enzyme) and cell division cycle associated protein 4 (CDCA4) in the progression of LUAD.

METHODS

The expressions of CDCA4, METTL3, ALKBH5, FTO, YTHDC2 and YTHDC1 mRNA and proteins in LUAD and adjacent tissues, as well as NCI-H1299 and NCI-H157 cells were detected by RT-qPCR and western blot. Meanwhile, the role of ALKBH5 and CDCA4 in macrophage polarization was explored through tumor formation in Lewis lung carcinoma (LLC) mice and the co-culture system of NCI-H1299 and NCI-H157/THP-1 cells. Cell characterization was further analyzed. The expression of Ki-67 in tumor tissue was tested by immunohistochemistry. The scale of M1 and M2 macrophages was determined by flow cytometry.

RESULTS

CDCA4 was significantly overexpressed in NCI-H1299 and NCI-H157 cell lines compared with BEAS-2B cells. The fold enrichment of CDCA4 m6A level in the overexpression (oe)-METTL3 or short hairpin (sh)-ALKBH5 cells was enhanced. Overexpression of CDCA4 promoted the cell viability, proliferation and migration, and inhibited apoptosis, which was reversed by sh-ALKBH5 intervention. Overexpression of YTHDC2 (not YTHDC1) inhibited the effect of CDCA4 on sh-ALKBH5 cells. sh-CDCA4 inhibited tumor growth and weight of LLC cells in mice, and promoted M1/M2 ratio in LLC mice and NCI-H1299/THP-1 and NCI-H157/THP-1 co-culture systems. Oe-CDCA4 promoted the volume and weight of tumor and inhibited the M1/M2 ratio of tumor tissue in LLC mice, but was reversed by sh-ALKBH5 intervention.

CONCLUSION

m6A demethylase ALKBH5 promotes the development of LUAD through CDCA4 regulation of malignant characterization and M1/M2 macrophage polarization.

Astragalus polysaccharide promotes the regeneration of intestinal stem cells through HIF-1 signalling pathway

Ionizing radiation (IR)-induced intestinal injury is usually accompanied by high lethality. Intestinal stem cells (ISCs) are critical and responsible for the regeneration of the damaged intestine. Astragalus polysaccharide (APS), one of the main active ingredients of Astragalus membranaceus (AM), has a variety of biological functions. This study was aimed to investigate the potential effects of APS on IR-induced intestine injury via promoting the regeneration of ISCs. We have established models of IR-induced intestinal injury and our results showed that APS played great radioprotective effects on the intestine. APS improved the survival rate of irradiated mice, reversed the radiation damage of intestinal tissue, increased the survival rate of intestinal crypts, the number of ISCs and the expression of intestinal tight junction-related proteins after IR. Moreover, APS promoted the cell viability while inhibited the apoptosis of MODE-K. Through organoid experiments, we found that APS promoted the regeneration of ISCs. Remarkably, the results of network pharmacology, RNA sequencing and RT-PCR assays showed that APS significantly upregulated the HIF-1 signalling pathway, and HIF-1 inhibitor destroyed the radioprotection of APS. Our findings suggested that APS promotes the regeneration of ISCs through HIF-1 signalling pathway, and it may be an effective radioprotective agent for IR-induced intestinal injury.

Tumor-Agnostic Approvals: Insights and Practical Considerations

Since the first approval of a tumor-agnostic indication in 2017, a total of seven tumor-agnostic indications involving six drugs have received approval from the FDA. In this paper, the master protocol subteam of the Statistical Methods in Oncology Scientific Working Group, Biopharmaceutical Session, American Statistical Association, provides a comprehensive summary of these seven tumor-agnostic approvals, describing their mechanisms of action; biomarker prevalence; study design; companion diagnostics; regulatory aspects, including comparisons of global regulatory requirements; and health technology assessment approval. Also discussed are practical considerations relating to the regulatory approval of tumor-agnostic indications, specifically (i) recommendations for the design stage to mitigate the risk that exceptions may occur if a treatment is initially hypothesized to be effective for all tumor types and (ii) because drug development continues after approval of a tumor-agnostic indication, recommendations for further development of tumor-specific indications in first-line patients in the setting of a randomized confirmatory basket trial, acknowledging the challenges in this area. These recommendations and practical considerations may provide insights for the future development of drugs for tumor-agnostic indications.

Soil enzyme profile analysis for indicating decomposer micro-food web

Highly diverse exoenzymes mediate the energy flow from substrates to the multitrophic microbiota within the soil decomposer micro-food web. Here, we used a "soil enzyme profile analysis" approach to establish a series of enzyme profile indices; those indices were hypothesized to reflect micro-food web features. We systematically evaluated the shifts in enzyme profile indices in relation to the micro-food web features in the restoration of an abandoned cropland to a natural area. We found that enzymatic C:N stoichiometry and decomposability index were significantly associated with substrate availability. Furthermore, the higher Shannon diversity index in the exoenzyme profile, especially for the C-degrading hydrolase, corresponded to a greater microbiota community diversity. The increased complexity and stability of the exoenzyme network reflected similar changes with the micro-food web networks. In addition, the gross activity of the enzyme profile as a parameter for soil multifunctionality, effectively predicted the substrate content, microbiota community size, diversity, and network complexity. Ultimately, the proposed enzymic channel index was closely associated with the traditional decomposition channel indices derived from microorganisms and nematodes. Our results showed that soil enzyme profile analysis reflected very well the decomposer food web features. Our study has important implications for projecting future climate change or anthropogenic disturbance impacts on soil decomposer micro-food web features by using soil enzyme profile analysis.

Distributed estimation and control over mobile sensor networks with jointly connected topology: Event-triggered approach

This article deals with the problem of distributed event-triggered tracking control in mobile sensor networks (MSNs) with a jointly connected topology (JCT). Two schemes are proposed for linear and Lipschitz nonlinear MSNs to estimate and track a mobile target. The proposed schemes are established using an event-triggered method to avoid continuous exchange of information between sensor nodes. In comparison with the other research under event-triggered communication strategies where states of the target are available, this paper considers that the states of the target are not available and two event-triggered algorithms are established for sensor nodes to estimate and follow the states of the continuous-time targets that can be seen in various real-world applications. Also, the proposed schemes are designed for the JCT with disconnected graphs which means the communication topology of the MSN is not required to be connected for all time instants. By employing the Cauchy convergence criterion and a common Lyapunov function, sufficient conditions are also established to ensure event-based tracking control subject to JCT. The effectiveness of the proposed work is verified by presenting simulation examples.

Danshensu inhibits SARS-CoV-2 by targeting its main protease as a specific covalent inhibitor and discovery of bifunctional compounds eliciting antiviral and anti-inflammatory activity

The coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed a serious threat to human. Since there are still no effective treatment options against the new emerging variants of SARS-CoV-2, it is necessary to devote a continuous endeavor for more targeted drugs and the preparation for the next pandemic. Salvia miltiorrhiza and its active ingredients possess wide antiviral activities, including against SARS-CoV-2. Danshensu, as one of the most important active ingredients in Salvia miltiorrhiza, has been reported to inhibit the entry of SARS-CoV-2 into ACE2 (angiotensin-converting enzyme 2)-overexpressed HEK-293T cells and Vero-E6 cells. However, there is a paucity of information regarding its detailed target and mechanism against SARS-CoV-2. Here, we present Danshensu as a covalent inhibitor of 3-chymotrypsin-like protease (3CLpro) against SARS-CoV-2 by the time-dependent inhibition assay (TDI) and mass spectrometry analysis. Further molecular docking, site-directed mutagenesis, circular dichroism (CD) and fluorescence spectra revealed that Danshensu covalently binds to C145 of SARS-CoV-2 3CLpro, meanwhile forming the hydrogen bonds with S144, H163 and E166 in the S1 site. Structure-based optimization of Danshensu led to the discovery of the promising compounds with good inhibitory activity and microsomal stability in vitro. Due to Danshensu inhibiting lung inflammation in the mouse model, we found that Danshensu derivatives also showed better anti-inflammatory activity than Danshensu in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophage cells. Thus, our study provides not only the clue of the efficacy of Salvia miltiorrhiza against SARS-CoV-2, but also a detailed mechanistic insight into the covalent mode of action of Danshensu for design of covalent inhibitors against SARS-CoV-2 3CLpro, highlighting its potential as a bifunctional molecule with antivirus and anti-inflammation.

CVANet: Cascaded visual attention network for single image super-resolution

Deep convolutional neural networks (DCNNs) have exhibited excellent feature extraction and detail reconstruction capabilities for single image super-resolution (SISR). Nevertheless, most previous DCNN-based methods do not fully utilize the complementary strengths between feature maps, channels, and pixels. Therefore, it hinders the ability of DCNNs to represent abundant features. To tackle the aforementioned issues, we present a Cascaded Visual Attention Network for SISR called CVANet, which simulates the visual attention mechanism of the human eyes to focus on the reconstruction process of details. Specifically, we first designed a trainable feature attention module (FAM) for feature-level attention learning. Afterward, we introduce a channel attention module (CAM) to reinforce feature maps under channel-level attention learning. Meanwhile, we propose a pixel attention module (PAM) that adaptively selects representative features from the previous layers, which are utilized to generate a high-resolution image. Satisfactory, our CVANet can effectively improve the resolution of images by exploring the feature representation capabilities of different modules and the visual perception properties of the human eyes. Extensive experiments with different methods on four benchmarks demonstrate that our CVANet outperforms the state-of-the-art (SOTA) methods in subjective visual perception, PSNR, and SSIM.The code will be made available https://github.com/WilyZhao8/CVANet.

Integrative Multi-Omics Analysis Identifies Transmembrane p24 Trafficking Protein 1 (TMED1) as a Potential Prognostic Marker in Colorectal Cancer

Several TMED protein family members are overexpressed in malignant tumors and associated with tumor progression. TMED1 belongs to the TMED protein family and is involved in protein vesicular trafficking. However, the expression level and biological role of TMED1 in colorectal cancer (CRC) have yet to be fully elucidated. In this study, the integration of patient survival and multi-omics data (immunohistochemical staining, transcriptomics, and proteomics) revealed that the highly expressed TMED1 was related to the poor prognosis in CRC. Crystal violet staining indicated the cell growth was reduced after knocking down TMED1. Moreover, the flow cytometry results showed that TMED1 knockdown could increase cell apoptosis. The expression of TMED1 was positively correlated with other TMED family members (TMED2, TMED4, TMED9, and TMED10) in CRC, and the protein-protein interaction network suggested its potential impact on immune regulation. Furthermore, TMED1 expression was positively associated with the infiltration levels of regulatory T cells (Tregs), cancer-associated fibroblasts (CAFs), and endothelial cells and negatively correlated with the infiltration levels of CD4+ T cells, CD8+ T cells, and B cells. At last, the CTRP and GDSC datasets on the GSCA platform were used to analyze the relationship between TMED1 expression and drug sensitivity (IC50). The result found that the elevation of TMED1 was positively correlated with IC50 and implied it could increase the drug resistance of cancer cells. This research revealed that TMED1 is a novel prognostic biomarker in CRC and provided a valuable strategy for analyzing potential therapeutic targets of malignant tumors.

Operando imaging in electrocatalysis: insights into microstructural materials design

Electrocatalysis plays a pivotal role in renewable energy conversion and associated chemical production, enabling a variety of emerging sustainability technologies with societal impacts. Achieving marked improvement in electrocatalytic performance relies on a deep understanding of catalyst microstructures and catalytic mechanisms, with a particular emphasis on the detailed, spatiotemporally resolved characterizations of the underlying fundamental electrocatalytic processes. This fundamental need drives the development of operando imaging techniques, which improve the ability to detect dynamic structural changes in electrocatalysts and establish clear structure-performance relationships for morphologically complex, hierarchically structured catalytic materials. This review aims to highlight significant advancements in the application of operando imaging techniques to develop a deeper understanding of important heterogeneous electrocatalytic reactions critical for emerging sustainability technologies. We summarize the up-to-date key mechanistic insights regarding these reactions achieved through a range of operando imaging techniques, including electron microscopies, X-ray imaging techniques, scanning probe microscopies, and optical microscopies. We conclude by pointing out emerging directions and future prospects within the field of operando imaging in electrocatalysis.

Considerations on Design and Analysis of External Control in Pediatric Oncology

Pediatric cancer consists of a diverse group of rare diseases. Due to limited patient populations, standard randomized and controlled trials are often infeasible. As a result, single-arm trials are common in pediatric oncology and the use of external controls is often desirable or necessary to help generate actionable evidence and contextualize trial results. In this paper, we illustrate unique features in pediatric oncology clinical trials and describe their impact on the use of external controls. Various types of relevant external control data sources are described in terms of their utility and drawbacks. Statistical methodologies and design implications with external control are discussed. Two recent case studies using external controls to support pediatric oncology drug development are described in detail.

SCGNet: efficient sparsely connected group convolution network for wheat grains classification

Introduction

Efficient and accurate varietal classification of wheat grains is crucial for maintaining varietal purity and reducing susceptibility to pests and diseases, thereby enhancing crop yield. Traditional manual and machine learning methods for wheat grain identification often suffer from inefficiencies and the use of large models. In this study, we propose a novel classification and recognition model called SCGNet, designed for rapid and efficient wheat grain classification.

Methods

Specifically, our proposed model incorporates several modules that enhance information exchange and feature multiplexing between group convolutions. This mechanism enables the network to gather feature information from each subgroup of the previous layer, facilitating effective utilization of upper-layer features. Additionally, we introduce sparsity in channel connections between groups to further reduce computational complexity without compromising accuracy. Furthermore, we design a novel classification output layer based on 3-D convolution, replacing the traditional maximum pooling layer and fully connected layer in conventional convolutional neural networks (CNNs). This modification results in more efficient classification output generation.

Results

We conduct extensive experiments using a curated wheat grain dataset, demonstrating the superior performance of our proposed method. Our approach achieves an impressive accuracy of 99.56%, precision of 99.59%, recall of 99.55%, and an F 1-score of 99.57%.

Discussion

Notably, our method also exhibits the lowest number of Floating-Point Operations (FLOPs) and the number of parameters, making it a highly efficient solution for wheat grains classification.

The NERP-4-SNAT2 axis regulates pancreatic β-cell maintenance and function

Insulin secretion from pancreatic β cells is regulated by multiple stimuli, including nutrients, hormones, neuronal inputs, and local signalling. Amino acids modulate insulin secretion via amino acid transporters expressed on β cells. The granin protein VGF has dual roles in β cells: regulating secretory granule formation and functioning as a multiple peptide precursor. A VGF-derived peptide, neuroendocrine regulatory peptide-4 (NERP-4), increases Ca2+ influx in the pancreata of transgenic mice expressing apoaequorin, a Ca2+-induced bioluminescent protein complex. NERP-4 enhances glucose-stimulated insulin secretion from isolated human and mouse islets and β-cell-derived MIN6-K8 cells. NERP-4 administration reverses the impairment of β-cell maintenance and function in db/db mice by enhancing mitochondrial function and reducing metabolic stress. NERP-4 acts on sodium-coupled neutral amino acid transporter 2 (SNAT2), thereby increasing glutamine, alanine, and proline uptake into β cells and stimulating insulin secretion. SNAT2 deletion and inhibition abolish the protective effects of NERP-4 on β-cell maintenance. These findings demonstrate a novel autocrine mechanism of β-cell maintenance and function that is mediated by the peptide-amino acid transporter axis.

Analysis of clinical features of 7 cases of primary pleomorphic adenoma of lower respiratory tract and review of literature

To investigate the clinical characteristics of patients with primary pleomorphic adenoma of the lower respiratory tract. The clinical manifestations, laboratory results, pathological and imaging, treatment and prognosis of 7 patients with primary pleomorphic adenoma of the lower respiratory tract who were treated in Hunan Provincial People's Hospital from December 2015 to May 2020 were analyzed. Among the 7 patients, 5 patients had cough and expectoration as the main clinical manifestations, and the other 2 patients had no symptoms. Pleomorphic adenomas of the lower respiratory tract are mostly located in the trachea or bronchus, and the chest computed tomography findings are circular or ellipsoid masses, or flake high-density shadows and local thickening of the tracheobronchial wall. Tumor histological features and immunohistochemistry can diagnose pleomorphic adenoma and its origin. In this study, 2 asymptomatic patients refused further treatment, 1 survived for more than 3 years, and the other was lost to follow-up during follow-up. One patient with surgical resection was followed up for 5 years after surgery and had a good survival status. The other 4 patients with respiratory symptoms who could not undergo surgery were mainly treated with bronchoscopic interventional therapy on demand, and the survival time up to now was 2 to 5 years. Primary pleomorphic adenoma of the lower respiratory tract is rare in clinic. Its clinical symptoms are related to the location and size of the tumor. Both surgical resection and bronchoscopic intervention have a good clinical prognosis. The cause of death of patients with such diseases is mostly dyspnea caused by tumors in the airway. Early diagnosis, timely intervention and regular follow-up can make patients obtain better curative effect.