Early Predicting Osteogenic Differentiation of Mesenchymal Stem Cells Based on Deep Learning Within One Day.

Osteogenic differentiation of mesenchymal stem cells (MSCs) is proposed to be critical for bone tissue engineering and regenerative medicine. However, the current approach for evaluating osteogenic differentiation mainly involves immunohistochemical staining of specific markers which often can be detected at day 5-7 of osteogenic inducing. Deep learning (DL) is a significant technology for realizing artificial intelligence (AI). Computer vision, a branch of AI, has been proved to achieve high-precision image recognition using convolutional neural networks (CNNs). Our goal was to train CNNs to quantitatively measure the osteogenic differentiation of MSCs. To this end, bright-field images of MSCs during early osteogenic differentiation (day 0, 1, 3, 5, and 7) were captured using a simple optical phase contrast microscope to train CNNs. The results showed that the CNNs could be trained to recognize undifferentiated cells and differentiating cells with an accuracy of 0.961 on the independent test set. In addition, we found that CNNs successfully distinguished differentiated cells at a very early stage (only 1 day). Further analysis showed that overall morphological features of MSCs were the main basis for the CNN classification. In conclusion, MSCs differentiation detection can be achieved early and accurately through simple bright-field images and DL networks, which may also provide a potential and novel method for the field of cell detection in the near future.

The first A-to-I RNA editome of hemipteran species Coridius chinensis reveals overrepresented recoding and prevalent intron editing in early-diverging insects.

BACKGROUND: Metazoan adenosine-to-inosine (A-to-I) RNA editing resembles A-to-G mutation and increases proteomic diversity in a temporal-spatial manner, allowing organisms adapting to changeable environment. The RNA editomes in many major animal clades remain unexplored, hampering the understanding on the evolution and adaptation of this essential post-transcriptional modification. METHODS: We assembled the chromosome-level genome of Coridius chinensis belonging to Hemiptera, the fifth largest insect order where RNA editing has not been studied yet. We generated ten head RNA-Seq libraries with DNA-Seq from the matched individuals. RESULTS: We identified thousands of high-confidence RNA editing sites in C. chinensis. Overrepresentation of nonsynonymous editing was observed, but conserved recoding across different orders was very rare. Under cold stress, the global editing efficiency was down-regulated and the general transcriptional processes were shut down. Nevertheless, we found an interesting site with "conserved editing but non-conserved recoding" in potassium channel Shab which was significantly up-regulated in cold, serving as a candidate functional site in response to temperature stress. CONCLUSIONS: RNA editing in C. chinensis largely recodes the proteome. The first RNA editome in Hemiptera indicates independent origin of beneficial recoding during insect evolution, which advances our understanding on the evolution, conservation, and adaptation of RNA editing.

Comparative genomic analyses reveal evidence for adaptive A-to-I RNA editing in insect Adar gene.

Although A-to-I RNA editing leads to similar effects to A-to-G DNA mutation, nonsynonymous RNA editing (recoding) is believed to confer its adaptiveness by 'epigenetically' regulating proteomic diversity in a temporospatial manner, avoiding the pleiotropic effect of genomic mutations. Recent discoveries on the evolutionary trajectory of Ser>Gly auto-editing site in insect Adar gene demonstrated a selective advantage to having an editable codon compared to uneditable ones. However, apart from pure observations, quantitative approaches for justifying the adaptiveness of individual RNA editing sites are still lacking. We performed a comparative genomic analysis on 113 Diptera species, focusing on the Adar Ser>Gly auto-recoding site in Drosophila. We only found one species having a derived Gly at the corresponding site, and this occurrence was significantly lower than genome-wide random expectation. This suggests that the Adar Ser>Gly site is unlikely to be genomically replaced with G during evolution, and thus indicating the advantage of editable status over hardwired genomic alleles. Similar trends were observed for the conserved Ile>Met recoding in gene Syt1. In the light of evolution, we established a comparative genomic approach for quantitatively justifying the adaptiveness of individual editing sites. Priority should be given to such adaptive editing sites in future functional studies.

Matrix metalloproteinases as attractive therapeutic targets for chronic pain: A narrative review.

Chronic pain constitutes an abnormal pain state that detrimentally affects the quality of life, daily activities, occupational performance, and stability of mood. Despite the prevalence of chronic pain, effective drugs with potent abirritation and minimal side effects remain elusive. Substantial studies have revealed aberrant activation of the matrix metalloproteinases (MMPs) in multiple chronic pain models. Additionally, emerging evidence has demonstrated that the downregulation of MMPs can alleviate chronic pain in diverse animal models, underscoring the unique and crucial role of MMPs in different stages and types of chronic pain. This review delves into the mechanistic insights and roles of MMPs in modulating chronic pain. The aberrant activation of MMPs has been linked to neuropathic pain through mechanisms involving myelin abnormalities in peripheral nerve and spinal dorsal horn (SDH), hyperexcitability of dorsal root ganglion (DRG) neurons, activation of N-methyl-d-aspartate receptors (NMDAR) and Ca2+-dependent signals, glial cell activation, and proinflammatory cytokines release. Different MMPs also contribute significantly to inflammatory pain and cancer pain. Furthermore, we summarized the substantial therapeutic potential of MMP pharmacological inhibitors across different types of chronic pain. Overall, our findings underscore the promising therapeutic prospects of MMPs targeting for managing chronic pain.

SI-ViT: Shuffle instance-based Vision Transformer for pancreatic cancer ROSE image classification.

BACKGROUND AND OBJECTIVE: The rapid on-site evaluation (ROSE) technique improves pancreatic cancer diagnosis by enabling immediate analysis of fast-stained cytopathological images. Automating ROSE classification could not only reduce the burden on pathologists but also broaden the application of this increasingly popular technique. However, this approach faces substantial challenges due to complex perturbations in color distribution, brightness, and contrast, which are influenced by various staining environments and devices. Additionally, the pronounced variability in cancerous patterns across samples further complicates classification, underscoring the difficulty in precisely identifying local cells and establishing their global relationships. METHODS: To address these challenges, we propose an instance-aware approach that enhances the Vision Transformer with a novel shuffle instance strategy (SI-ViT). Our approach presents a shuffle step to generate bags of shuffled instances and corresponding bag-level soft-labels, allowing the model to understand relationships and distributions beyond the limited original distributions. Simultaneously, combined with an un-shuffle step, the traditional ViT can model the relationships corresponding to the sample labels. This dual-step approach helps the model to focus on inner-sample and cross-sample instance relationships, making it potent in extracting diverse image patterns and reducing complicated perturbations. RESULTS: Compared to state-of-the-art methods, significant improvements in ROSE classification have been achieved. Aiming for interpretability, equipped with instance shuffling, SI-ViT yields precise attention regions that identifying cancer and normal cells in various scenarios. Additionally, the approach shows excellent potential in pathological image analysis through generalization validation on other datasets. CONCLUSIONS: By proposing instance relationship modeling through shuffling, we introduce a new insight in pathological image analysis. The significant improvements in ROSE classification leads to protential AI-on-site applications in pancreatic cancer diagnosis. The code and results are publicly available at https://github.com/sagizty/MIL-SI.

Interleukin-17: A Putative Novel Pharmacological Target for Pathological Pain.

Pathological pain imposes a huge burden on the economy and the lives of patients. At present, drugs used for the treatment of pathological pain have only modest efficacy and are also plagued by adverse effects and risk for misuse and abuse. Therefore, understanding the mechanisms of pathological pain is essential for the development of novel analgesics. Several lines of evidence indicate that interleukin-17 (IL-17) is upregulated in rodent models of pathological pain in the periphery and central nervous system. Besides, the administration of IL-17 antibody alleviated pathological pain. Moreover, IL-17 administration led to mechanical allodynia which was alleviated by the IL-17 antibody. In this review, we summarized and discussed the therapeutic potential of targeting IL-17 for pathological pain. The upregulation of IL-17 promoted the development of pathological pain by promoting neuroinflammation, enhancing the excitability of dorsal root ganglion neurons, and promoting the communication of glial cells and neurons in the spinal cord. In general, the existing research shows that IL-17 is an attractive therapeutic target for pathologic pain, but the underlying mechanisms still need to be investigated.

Identification and Interpretation of A-to-I RNA Editing Events in Insect Transcriptomes.

Adenosine-to-inosine (A-to-I) RNA editing is the most prevalent RNA modification in the nervous systems of metazoans. To study the biological significance of RNA editing, we first have to accurately identify these editing events from the transcriptome. The genome-wide identification of RNA editing sites remains a challenging task. In this review, we will first introduce the occurrence, regulation, and importance of A-to-I RNA editing and then describe the established bioinformatic procedures and difficulties in the accurate identification of these sit esespecially in small sized non-model insects. In brief, (1) to obtain an accurate profile of RNA editing sites, a transcriptome coupled with the DNA resequencing of a matched sample is favorable; (2) the single-cell sequencing technique is ready to be applied to RNA editing studies, but there are a few limitations to overcome; (3) during mapping and variant calling steps, various issues, like mapping and base quality, soft-clipping, and the positions of mismatches on reads, should be carefully considered; (4) Sanger sequencing of both RNA and the matched DNA is the best verification of RNA editing sites, but other auxiliary evidence, like the nonsynonymous-to-synonymous ratio or the linkage information, is also helpful for judging the reliability of editing sites. We have systematically reviewed the understanding of the biological significance of RNA editing and summarized the methodology for identifying such editing events. We also raised several promising aspects and challenges in this field. With insightful perspectives on both scientific and technical issues, our review will benefit the researchers in the broader RNA editing community.

FTO Sensitizes Oral Squamous Cell Carcinoma to Ferroptosis via Suppressing ACSL3 and GPX4.

Ferroptosis is a newly established form of regulated cell death characterized by intracellular lipid peroxidation and iron accumulation that may be a promising cancer treatment strategy. However, the function and therapeutic value of ferroptosis in oral squamous cell carcinoma (OSCC) remain inadequately understood. In the present study, we investigated the biological role of the fat mass and obesity-associated gene (FTO) in ferroptosis in the context of OSCC. We found that OSCC had greater potential for ferroptosis, and FTO is associated with ferroptosis. Furthermore, higher FTO expression sensitized OSCC cells to ferroptosis in vitro and in vivo. Mechanistically, FTO suppressed the expression of anti-ferroptotic factors, acyl-CoA synthetase long-chain family member 3 (ACSL3) and glutathione peroxidase 4 (GPX4), by demethylating the m6A modification on the mRNA of ACSL3 and GPX4 and decreasing their stability. Taken together, our findings revealed that FTO promotes ferroptosis through ACSL3 and GPX4 regulation. Thus, ferroptosis activation in OSCC with high FTO levels may serve as a potential therapeutic target.

Identification of CKS2 as a novel prognostic biomarker and potential therapeutic target for oral squamous cell carcinoma.

Background: The cyclin-dependent kinase subunit 2 (CKS2) is recognized to have a substantial impact on the pathogenesis and advancement of several malignant neoplasms. Nevertheless, its biological function and prognostic significance in oral squamous cell carcinoma (OSCC) have yet to be thoroughly investigated. Our primary objective was to clarify the contribution of CKS2 in the progression and prognosis of OSCC. Methods: We first conducted a thorough examination of online databases to investigate the expression of CKS2, and subsequently corroborated our discoveries by analyzing clinical specimens that we collected. According to the clinicopathological data, we then explored the prognostic significance of CKS2. Furthermore, we predicted the role of CKS2 in OSCC progression by employing weighted gene co-expression network analysis (WGCNA) in conjunction with functional enrichment analysis. We conducted functional experiments in vitro to confirm our speculations. Additionally, we explored other potential functions of CKS2 in immune infiltration, tumor mutation burden (TMB), and drug sensitivity. Finally, we established and validated a nomogram that effectively integrated CKS2-related genes and other relevant clinical factors. Results: Our findings indicated a significant upregulation of CKS2 expression in OSCC tissues compared to normal groups, which was positively associated with poor clinical outcomes. We also predicted and validated the role of CKS2 in promoting proliferation by regulating the cell cycle. Additionally, its upregulation was significantly correlated to enhanced immune cell infiltration, high TMB, and increased sensitivity of anti-tumor agents. Following verification, the nomogram was conducted to quantify an individual's survival probability. Conclusions: In general, our study indicates that CKS2 is a novel prognostic biomarker and potential therapeutic target in OSCC.

A full repertoire of Hemiptera genomes reveals a multi-step evolutionary trajectory of auto-RNA editing site in insect Adar gene.

Adenosine-to-inosine (A-to-I) RNA editing, mediated by metazoan ADAR enzymes, is a prevalent post-transcriptional modification that diversifies the proteome and promotes adaptive evolution of organisms. The Drosophila Adar gene has an auto-recoding site (termed S>G site) that forms a negative-feedback loop and stabilizes the global editing activity. However, the evolutionary trajectory of Adar S>G site in many other insects remains largely unknown, preventing us from a deeper understanding on the significance of this auto-editing mechanism. In this study, we retrieved the well-annotated genomes of 375 arthropod species including the five major insect orders (Lepidoptera, Diptera, Coleoptera, Hymenoptera and Hemiptera) and several outgroup species. We performed comparative genomic analysis on the Adar auto-recoding S>G site. We found that the ancestral state of insect S>G site was an uneditable serine codon (unSer) and that this state was largely maintained in Hymenoptera. The editable serine codon (edSer) appeared in the common ancestor of Lepidoptera, Diptera and Coleoptera and was almost fixed in the three orders. Interestingly, Hemiptera species possessed comparable numbers of unSer and edSer codons, and a few 'intermediate codons', demonstrating a multi-step evolutionary trace from unSer-to-edSer with non-synchronized mutations at three codon positions. We argue that the evolution of Adar S>G site is the best genomic evidence supporting the 'proteomic diversifying hypothesis' of RNA editing. Our work deepens our understanding on the evolutionary significance of Adar auto-recoding site which stabilizes the global editing activity and controls transcriptomic diversity.

METTL14 inhibits malignant progression of oral squamous cell carcinoma by targeting the autophagy-related gene RB1CC1 in an m6A-IGF2BP2-dependent manner.

N6-methyladenosine (m6A) plays crucial roles in tumorigenesis and autophagy. However, the underlying mechanisms mediated by m6A and autophagy in the malignant progression of oral squamous cell carcinoma (OSCC) remain unclear. In the present study, we revealed that down-regulated expression of METTL14 was correlated with advanced clinicopathological characteristics and poor prognosis in OSCC. METTL14 knockdown significantly inhibited autophagy and facilitated malignant progression in vitro, and promoted tumor growth and metastasis in vivo. A cell model of rapamycin-induced autophagy was established to identify RB1CC1 as a potential target gene involved in m6A-regulated autophagy in OSCC, through RNA sequencing and methylated RNA immunoprecipitation sequencing (meRIP-seq) analysis. Mechanistically, we confirmed that METTL14 posttranscriptionally enhanced RB1CC1 expression in an m6A-IGF2BP2-dependent manner, thereby affecting autophagy and progression in OSCC, through methylated RNA immunoprecipitation qRT-PCR (meRIP-qPCR), RNA stability assays, mutagenesis assays and dual-luciferase reporter. Collectively, our findings demonstrated that METTL14 serves as an OSCC suppressor by regulating the autophagy-related gene RB1CC1 through m6A modification, which may provide a new insight for the diagnosis and therapy of OSCC.

Autorecoding A-to-I RNA editing sites in the Adar gene underwent compensatory gains and losses in major insect clades.

As one of the most prevalent RNA modifications in animals, adenosine-to-inosine (A-to-I) RNA editing facilitates the environmental adaptation of organisms by diversifying the proteome in a temporal-spatial manner. In flies and bees, the editing enzyme Adar has independently gained two different autorecoding sites that form an autofeedback loop, stabilizing the overall editing efficiency. This ensures cellular homeostasis by keeping the normal function of target genes. However, in a broader range of insects, the evolutionary dynamics and significance of this Adar autoregulatory mechanism are unclear. We retrieved the genomes of 377 arthropod species covering the five major insect orders (Hemiptera, Hymenoptera, Coleoptera, Diptera, and Lepidoptera) and aligned the Adar autorecoding sites across all genomes. We found that the two autorecoding sites underwent compensatory gains and losses during the evolution of two orders with the most sequenced species (Diptera and Hymenoptera), and that the two editing sites were mutually exclusive among them: One editable site is significantly linked to another uneditable site. This autorecoding mechanism of Adar could flexibly diversify the proteome and stabilize global editing activity. Many insects independently selected different autorecoding sites to achieve a feedback loop and regulate the global RNA editome, revealing an interesting phenomenon during evolution. Our study reveals the evolutionary force acting on accurate regulation of RNA editing activity in insects and thus deepens our understanding of the functional importance of RNA editing in environmental adaptation and evolution.

A Novel Feature Engineering Method Based on Latent Representation Learning for Radiomics: Application in NSCLC Subtype Classification.

Radiomics refers to the high-throughput extraction of quantitative features from medical images, and is widely used to construct machine learning models for the prediction of clinical outcomes, while feature engineering is the most important work in radiomics. However, current feature engineering methods fail to fully and effectively utilize the heterogeneity of features when dealing with different kinds of radiomics features. In this work, latent representation learning is first presented as a novel feature engineering approach to reconstruct a set of latent space features from original shape, intensity and texture features. This proposed method projects features into a subspace called latent space, in which the latent space features are obtained by minimizing a unique hybrid loss function including a clustering-like loss and a reconstruction loss. The former one ensures the separability among each class while the latter one narrows the gap between the original features and latent space features. Experiments were performed on a multi-center non-small cell lung cancer (NSCLC) subtype classification dataset from 8 international open databases. Results showed that compared with four traditional feature engineering methods (baseline, PCA, Lasso and L2,1-norm minimization), latent representation learning could significantly improve the classification performance of various machine learning classifiers on the independent test set (all p<0.001). Further on two additional test sets, latent representation learning also showed a significant improvement in generalization performance. Our research shows that latent representation learning is a more effective feature engineering method, which has the potential to be used as a general technology in a wide range of radiomics researches.

MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis of Pancreatic Cancer.

Pancreatic cancer is one of the most malignant cancers with high mortality. The rapid on-site evaluation (ROSE) technique can significantly accelerate the diagnostic workflow of pancreatic cancer by immediately analyzing the fast-stained cytopathological images with on-site pathologists. However, the broader expansion of ROSE diagnosis has been hindered by the shortage of experienced pathologists. Deep learning has great potential for the automatic classification of ROSE images in diagnosis. But it is challenging to model the complicated local and global image features. The traditional convolutional neural network (CNN) structure can effectively extract spatial features, while it tends to ignore global features when the prominent local features are misleading. In contrast, the Transformer structure has excellent advantages in capturing global features and long-range relations, while it has limited ability in utilizing local features. We propose a multi-stage hybrid Transformer (MSHT) to combine the strengths of both, where a CNN backbone robustly extracts multi-stage local features at different scales as the attention guidance, and a Transformer encodes them for sophisticated global modeling. Going beyond the strength of each single method, the MSHT can simultaneously enhance the Transformer global modeling ability with the local guidance from CNN features. To evaluate the method in this unexplored field, a dataset of 4240 ROSE images is collected where MSHT achieves 95.68% in classification accuracy with more accurate attention regions. The distinctively superior results compared to the state-of-the-art models make MSHT extremely promising for cytopathological image analysis. The codes and records are available at: https://github.com/sagizty/ Multi-Stage-Hybrid-Transformer.

Neuro death through autophagy via the acetylation of FoxO1 by SIRT2 in the hippocampus of mice in a autism spectrum disorder mice model.

Autism Spectrum Disorder (ASD) is a series of complex neurodevelopmental disorders, which can affect children's social, behavioral and communication abilities. A member of the Sirtuins family of NAD + dependent deacetylases called SIRT2 could regulate the inflammation progress during stress, but the relevant mechanism has not been clearly defined. In the present study, the ASD model of wild type and SIRT2 knock out mice was established to evaluate the impact on the homeostasis of neurons in the hippocampus using western blotting, immunofluorescence and Nissl staining. The results showed that the amplification of neuronal richness was significantly decreased and neuroinflammation increased in the hippocampus following ASD due to autophagy, caused by enhancing the acetylation of FoxO1 using SIRT2 gene deletion and indicating this should be the target for ASD or other psychological stress treatment.

Novel Genetic Subtypes of Urothelial Carcinoma With Differential Outcomes on Immune Checkpoint Blockade.

PURPOSE: Immune checkpoint blockade (ICB) therapy has significantly improved clinical outcomes in bladder cancer. Identification of correlates of benefit is critical to select appropriate therapy for individual patients. METHODS: To reveal genetic variables associated with benefit from ICB, we performed whole-exome sequencing on tumor specimens from 88 patients with advanced bladder cancer treated with ICB. RESULTS: We identified several genetic factors that correlated with progression-free and overall survival after ICB therapy including ARID1A mutation, tumor mutational burden, intratumoral heterogeneity, the ratio of nonsynonymous to synonymous mutations in the immunopeptidome (immune dN/dS), and tumor cell purity. In addition, we noted that neutrophil-to-lymphocyte ratio and smoking history were negatively associated with overall survival. These genetic characteristics define four molecular subtypes demonstrating differential sensitivity to ICB. We validated the association of these four subtypes with clinical benefit from ICB in an independent cohort (IMvigor210). Finally, we showed that these molecular subtypes also correlate with outcome, although with distinct relationships, among patients not treated with ICB using The Cancer Genome Atlas (TCGA) bladder cancer cohort. Using parallel RNA sequencing data, the subtypes were also shown to correlate with immune infiltration and inflammation, respectively, in the IMvigor210 and TCGA cohorts. CONCLUSION: Together, our study defines molecular subgroups of bladder cancer that influence benefit from ICB.

Recent Progress in Nanomaterial-Based Biosensors and Theranostic Nanomedicine for Bladder Cancer.

Bladder cancer (BCa) is one of the most expensive and common malignancies in the urinary system due to its high progression and recurrence rate. Although there are various methods, including cystoscopy, biopsy, and cytology, that have become the standard diagnosis methods for BCa, their intrinsic invasive and inaccurate properties need to be overcome. The novel urine cancer biomarkers are assisted by nanomaterials-based biosensors, such as field-effect transistors (FETs) with high sensitivity and specificity, which may provide solutions to these problems. In addition, nanomaterials can be applied for the advancement of next-generation optical imaging techniques and the contrast agents of conventional techniques; for example, magnetic resonance imaging (MRI) for the diagnosis of BCa. Regarding BCa therapy, nanocarriers, including mucoadhesive nanoparticles and other polymeric nanoparticles, successfully overcome the disadvantages of conventional intravesical instillation and improve the efficacy and safety of intravesical chemotherapy for BCa. Aside from chemotherapy, nanomedicine-based novel therapies, including photodynamic therapy (PDT), photothermal therapy (PTT), chemodynamic therapy (CDT), sonodynamic therapy (SDT), and combination therapy, have afforded us new ways to provide BC therapy and hope, which can be translated into the clinic. In addition, nanomotors and the nanomaterials-based solid tumor disassociation strategy provide new ideas for future research. Here, the advances in BCa diagnosis and therapy mentioned above are reviewed in this paper.

Radiomics feature analysis and model research for predicting histopathological subtypes of non-small cell lung cancer on CT images: A multi-dataset study.

PURPOSE: Classifying the subtypes of non-small cell lung cancer (NSCLC) is essential for clinically adopting optimal treatment strategies and improving clinical outcomes, but the histological subtypes are confirmed by invasive biopsy or post-operative examination at present. Based on multi-center data, this study aimed to analyze the importance of extracted CT radiomics features and develop the model with good generalization performance for precisely distinguishing major NSCLC subtypes: adenocarcinoma (ADC) and squamous cell carcinoma (SCC). METHODS: We collected a multi-center CT dataset with 868 patients from eight international databases on the cancer imaging archive (TCIA). Among them, patients from five databases were mixed and split to training and test sets (560:140). The remaining three databases were used as independent test sets: TCGA set (n = 97) and lung3 set (n = 71). A total of 1409 features containing shape, intensity, and texture information were extracted from tumor volume of interest (VOI), then the ℓ2,1 -norm minimization was used for feature selection and the importance of selected features was analyzed. Next, the prediction and generalization performance of 130 radiomics models (10 common algorithms and 120 heterogeneous ensemble combinations) were compared by the average AUC value on three test sets. Finally, predictive results of the optimal model were shown. RESULTS: After feature selection, 401 features were obtained. Features of intensity, texture GLCM, GLRLM, and GLSZM had higher classification weight coefficients than other features (shape, texture GLDM, and NGTDM), and the filtered image features exhibited significant importance than original image features (p-value = 0.0210). Moreover, five ensemble learning algorithms (Bagging, AdaBoost, RF, XGBoost, GBDT) had better generalization performance (p-value = 0.00418) than other non-ensemble algorithms (MLP, LR, GNB, SVM, KNN). The Bagging-AdaBoost-SVM model had the highest AUC value (0.815 ± 0.010) on three test sets. It obtained AUC values of 0.819, 0.823, and 0.804 on test set, TCGA set and lung3 set, respectively. CONCLUSION: Our multi-dataset study showed that intensity features, texture features (GLCM, GLRLM, and GLSZM) and filtered image features were more important for distinguishing ADCs from SCCs. The method of ensemble learning can improve the prediction and generalization performance on the complicated multi-center data. The Bagging-AdaBoost-SVM model had the strongest generalization performance, and it showed promising clinical value for non-invasively predicting the histopathological subtypes of NSCLC.

Robust reconstruction of fluorescence molecular tomography based on adaptive adversarial learning strategy.

Objective.Fluorescence molecular tomography (FMT) is a promising molecular imaging modality for quantifying the three-dimensional (3D) distribution of tumor probes in small animals. However, traditional deep learning reconstruction methods that aim to minimize the mean squared error (MSE) and iterative regularization algorithms that rely on optimal parameters are typically influenced by strong noise, resulting in poor FMT reconstruction robustness.Approach.In this letter, we propose an adaptive adversarial learning strategy (3D-UR-WGAN) to achieve robust FMT reconstructions. Unlike the pixel-based MSE criterion in traditional CNNs or the regularization strategy in iterative solving schemes, the reconstruction strategy can greatly facilitate the performance of the network models through alternating loop training of the generator and the discriminator. Second, the reconstruction strategy combines the adversarial loss in GANs with the L1 loss to significantly enhance the robustness and preserve image details and textual information.Main results.Both numerical simulations and physical phantom experiments demonstrate that the 3D-UR-WGAN method can adaptively eliminate the effects of different noise levels on the reconstruction results, resulting in robust reconstructed images with reduced artifacts and enhanced image contrast. Compared with the state-of-the-art methods, the proposed method achieves better reconstruction performance in terms of target shape recovery and localization accuracy.Significance.This adaptive adversarial learning reconstruction strategy can provide a possible paradigm for robust reconstruction in complex environments, and also has great potential to provide an alternative solution for solving the problem of poor robustness encountered in other optical imaging modalities such as diffuse optical tomography, bioluminescence imaging, and Cherenkov luminescence imaging.

Targeting the nitric oxide/cGMP signaling pathway to treat chronic pain.

Nitric oxide (NO)/cyclic guanosine 3',5'-monophosphate (cGMP) signaling has been shown to act as a mediator involved in pain transmission and processing. In this review, we summarize and discuss the mechanisms of the NO/cGMP signaling pathway involved in chronic pain, including neuropathic pain, bone cancer pain, inflammatory pain, and morphine tolerance. The main process in the NO/cGMP signaling pathway in cells involves NO activating soluble guanylate cyclase, which leads to subsequent production of cGMP. cGMP then activates cGMP-dependent protein kinase (PKG), resulting in the activation of multiple targets such as the opening of ATP-sensitive K+ channels. The activation of NO/cGMP signaling in the spinal cord evidently induces upregulation of downstream molecules, as well as reactive astrogliosis and microglial polarization which participate in the process of chronic pain. In dorsal root ganglion neurons, natriuretic peptide binds to particulate guanylyl cyclase, generating and further activating the cGMP/PKG pathway, and it also contributes to the development of chronic pain. Upregulation of multiple receptors is involved in activation of the NO/cGMP signaling pathway in various pain models. Notably the NO/cGMP signaling pathway induces expression of downstream effectors, exerting both algesic and analgesic effects in neuropathic pain and inflammatory pain. These findings suggest that activation of NO/cGMP signaling plays a constituent role in the development of chronic pain, and this signaling pathway with dual effects is an interesting and promising target for chronic pain therapy.