Mechanism exploration of Osteoking in the treatment of lumbar disc herniation based on network pharmacology and molecular docking.

OBJECTIVE: Lumbar disc herniation (LDH) is a common spinal surgical disease. Low back and leg pain caused by LDH is the main factor leading to functional disability, which has caused a serious burden to patients and society. Osteoking can delay the progression of osteoporosis and osteoarthritis, and even has a significant effect on the prevention of deep vein thrombosis after fracture surgery. In recent years, it has been gradually used in the treatment of LDH and has received significant results. However, the underlying mechanism remains unclear. The aim of this study was to predict the mechanism of Osteoking in the treatment of LDH through network pharmacology and verify it by molecular docking method. METHODS: The TCMSP database was used to collect the relevant active components and targets of Osteoking, while the GeneCards, OMIM and DisGeNET databases were utilized to collect the relevant disease targets of LDH. The Venny 2.1.0 software was employed to obtain the intersecting gene targets of Osteoking and LDH. PPI network construction and core target selection were performed using Cytoscape 3.9.0 software. The Metascape database was used for GO and KEGG enrichment analysis of the relevant targets. Finally, molecular docking was conducted using AutoDock software. RESULTS: The study identified 116 potential targets and 26 core targets for the treatment of LDH with Osteoking. Pathways in cancer, Alzheimer's disease, microRNAs in cancer and the IL-17 signalling pathway were among the main involved signalling pathways. Molecular docking results demonstrated that the key targets AKT1, IL-6, ALB, TNF and IL-1ß exhibited relatively stable binding activities with the main active components of Osteoking. CONCLUSIONS: Osteoking can alleviate the symptoms of lumbar disc herniation through the modulation of multiple targets and signalling pathways.

DEA-Net: Single Image Dehazing Based on Detail-Enhanced Convolution and Content-Guided Attention.

Single image dehazing is a challenging ill-posed problem which estimates latent haze-free images from observed hazy images. Some existing deep learning based methods are devoted to improving the model performance via increasing the depth or width of convolution. The learning ability of Convolutional Neural Network (CNN) structure is still under-explored. In this paper, a Detail-Enhanced Attention Block (DEAB) consisting of Detail-Enhanced Convolution (DEConv) and Content-Guided Attention (CGA) is proposed to boost the feature learning for improving the dehazing performance. Specifically, the DEConv contains difference convolutions which can integrate prior information to complement the vanilla one and enhance the representation capacity. Then by using the re-parameterization technique, DEConv is equivalently converted into a vanilla convolution to reduce parameters and computational cost. By assigning the unique Spatial Importance Map (SIM) to every channel, CGA can attend more useful information encoded in features. In addition, a CGA-based mixup fusion scheme is presented to effectively fuse the features and aid the gradient flow. By combining above mentioned components, we propose our Detail-Enhanced Attention Network (DEA-Net) for recovering high-quality haze-free images. Extensive experimental results demonstrate the effectiveness of our DEA-Net, outperforming the state-of-the-art (SOTA) methods by boosting the PSNR index over 41 dB with only 3.653 M parameters. (The source code of our DEA-Net is available at https://github.com/cecret3350/DEA-Net.).

The complete mitochondrial genome of Choristoneura metasequoiacola Liu,1983 (Lepidoptera: Tortricidae).

Choristoneura metasequoiacola Liu, 1983 is an important caterpillar species that specifically infests the leaves and branches of Metasequoia glyptostroboides Hu & W. C. Cheng 1948 with short larval infestations, long-term dormancy, and has a limited distribution in Lichuan, Hubei, China. The complete mitochondria genome of C. metasequoiacola was determined by using Illumina NovaSeq, and analyzed based on previously annotated sibling species. In total, we obtained mitochondria genome with 15,128 bp in length, circular in shape with a double-stranded closed ring structure, including 13 protein-coding genes, 2 rRNA genes, 22 tRNA genes, and an AT-rich region. Of which the nucleotide composition was highly A + T biased, accounting for 81.98% of the whole mitogenome. Thirteen protein-coding genes (PCGs) were 11,142 bp; Twenty-two tRNA genes and AT-rich region were 1,472 and 199 bp, respectively. Phylogenetically, the relationship between Choristoneura spp. (containing C. metasequoiacola) and Adoxophyes spp. was closer than any other two genera from Tortricidae, and the relationship between C. metasequoiacola and C.murinana was the closest among nine sibling species from that genus, which helps to explain species evolution within the family Tortricidae.

Computational framework for steady-state NLOS localization under changing ambient illumination conditions.

Non-line-of-sight (NLOS) imaging of hidden objects is a challenging yet vital task, facilitating important applications such as rescue operations, medical imaging, and autonomous driving. In this paper, we attempt to develop a computational steady-state NLOS localization framework that works accurately and robustly under various illumination conditions. For this purpose, we build a physical NLOS image acquisition hardware system and a corresponding virtual setup to obtain real-captured and simulated steady-state NLOS images under different ambient illuminations. Then, we utilize the captured NLOS images to train/fine-tune a multi-task convolutional neural network (CNN) architecture to perform simultaneous background illumination correction and NLOS object localization. Evaluation results on both stimulated and real-captured NLOS images demonstrate that the proposed method can effectively suppress severe disturbance caused by the variation of ambient light, significantly improving the accuracy and stability of steady-state NLOS localization using consumer-grade RGB cameras. The proposed method potentially paves the way to develop practical steady-state NLOS imaging solutions for around-the-clock and all-weather operations.

A biocompatible two-photon absorbing fluorescent mitochondrial probe for deep in vivo bioimaging.

Mitochondria, key organelles which keep in tune with energy demands for eukaryotic cells, are firmly associated with neurological conditions and post-traumatic rehabilitation. In vivo fluorescence imaging of mitochondria, especially with deep tissue penetration, would open a window to investigate the actual context of the brain. However, the depth of traditional two-photon mitochondrial fluorescence imaging is still limited due to the poor biological compatibility or low two-photon absorption cross-sections. A biocompatible mitochondria-targeted two-photon fluorescent dye (FO2) with an excellent two-photon absorption cross-section (the maximum of 1184 GM at 790 nm) and low cellular toxicity was designed and synthesized to overcome this problem. With this dye, we reached an imaging depth of ca. 640 µm during mitochondrial imaging of cortical cells in live animals. FO2 could be an excellent mitochondrial probe for live animal neural imaging to investigate the function and dysfunction of mitochondria in the brain.

Correction: A biocompatible two-photon absorbing fluorescent mitochondrial probe for deep in vivo bioimaging.

Correction for 'A biocompatible two-photon absorbing fluorescent mitochondrial probe for deep in vivo bioimaging' by Lingmin Lin et al., J. Mater. Chem. B, 2022, DOI: 10.1039/d1tb02040d.

Multi-channel residual network model for accurate estimation of spatially-varying and depth-dependent defocus kernels.

Digital projectors have been increasingly utilized in various commercial and scientific applications. However, they are prone to the out-of-focus blurring problem since their depth-of-fields are typically limited. In this paper, we explore the feasibility of utilizing a deep learning-based approach to analyze the spatially-varying and depth-dependent defocus properties of digital projectors. A multimodal displaying/imaging system is built for capturing images projected at various depths. Based on the constructed dataset containing well-aligned in-focus, out-of-focus, and depth images, we propose a novel multi-channel residual deep network model to learn the end-to-end mapping function between the in-focus and out-of-focus image patches captured at different spatial locations and depths. To the best of our knowledge, it is the first research work revealing that the complex spatially-varying and depth-dependent blurring effects can be accurately learned from a number of real-captured image pairs instead of being hand-crafted as before. Experimental results demonstrate that our proposed deep learning-based method significantly outperforms the state-of-the-art defocus kernel estimation techniques and thus leads to better out-of-focus compensation for extending the dynamic ranges of digital projectors.

Progress in the chemotherapeutic treatment of osteosarcoma.

Osteosarcoma (OS) is the most common type of primary bone tumor in children and adolescents and has been associated with a high degree of malignancy, early metastasis, rapid progression and poor prognosis. However, the use of adjuvant chemotherapy improves the prognosis of patients with OS. OS chemotherapy is based primarily on the use of adriamycin, cisplatin (DDP), methotrexate (MTX), ifosfamide (IFO), epirubicin (EPI) and other drugs. Previous studies have revealed that the survival rate for patients with OS appears to have plateaued: 5-year survival rates remain close to 60%, even with the use of combined chemotherapy. The most limiting factors include complications and fatal toxicity associated with chemotherapy agents, particularly high-dose MTX (HD-MTX), for which high toxicity and great individual variation in responses have been observed. Docetaxel (TXT) is a representative member of the relatively recently developed taxane class of drugs, which function to inhibit OS cell proliferation and induce apoptosis. Recently, more clinical studies have reported that TXT combined with gemcitabine (GEM) is effective in the treatment of OS (relapse/refractory and progressive), providing evidence in support of potential novel treatment strategies for this patient population. However, there is still no global consensus on this type of chemotherapy approach. The present review summarizes current studies surrounding progress in the chemotherapeutic treatment of OS and discusses the advantages and potential feasibility of TXT+GEM in the treatment of OS.

Single-image-based nonuniformity correction of uncooled long-wave infrared detectors: a deep-learning approach.

Fixed-pattern noise (FPN), which is caused by the nonuniform opto-electronic responses of microbolometer focal-plane-array (FPA) optoelectronics, imposes a challenging problem in infrared imaging systems. In this paper, we successfully demonstrate that a better single-image-based non-uniformity correction (NUC) operator can be directly learned from a large number of simulated training images instead of being handcrafted as before. Our proposed training scheme, which is based on convolutional neural networks (CNNs) and a column FPN simulation module, gives rise to a powerful technique to reconstruct the noise-free infrared image from its corresponding noisy observation. Specifically, a comprehensive column FPN model is utilized to depict the nonlinear characteristics of column amplifiers in the readout circuit of FPA. A large number of high-fidelity training images are simulated based on this model and the end-to-end residual deep network is capable of learning the intrinsic difference between undesirable FPN and original image details. Therefore, column FPN can be accurately estimated and further subtracted from the raw infrared images to obtain NUC results. Comparative results with state-of-the-art single-image-based NUC methods, using real-captured noisy infrared images, demonstrate that our proposed deep-learning-based approach delivers better performances of FPN removal, detail preservation, and artifact suppression.

Deep RNA sequencing reveals the dynamic regulation of miRNA, lncRNAs, and mRNAs in osteosarcoma tumorigenesis and pulmonary metastasis.

Osteosarcoma (OS) is the most common pediatric malignant bone tumor, and occurrence of pulmonary metastasis generally causes a rapid and fatal outcome. Here we aimed to provide clues for exploring the mechanism of tumorigenesis and pulmonary metastasis for OS by comprehensive analysis of microRNA (miRNA), long non-coding RNA (lncRNA), and mRNA expression in primary OS and OS pulmonary metastasis. In this study, deep sequencing with samples from primary OS (n = 3), pulmonary metastatic OS (n = 3), and normal controls (n = 3) was conducted and differentially expressed miRNAs (DEmiRNAs), lncRNAs (DElncRNAs), and mRNAs (DEmRNAs) between primary OS and normal controls as well as pulmonary metastatic and primary OS were identified. A total of 65 DEmiRNAs, 233 DElncRNAs, and 1405 DEmRNAs were obtained between primary OS and normal controls; 48 DEmiRNAs, 50 DElncRNAs, and 307 DEmRNAs were obtained between pulmonary metastatic and primary OS. Then, the target DEmRNAs and DElncRNAs regulated by the same DEmiRNAs were searched and the OS tumorigenesis-related and OS pulmonary metastasis-related competing endogenous RNA (ceRNA) networks were constructed, respectively. Based on these ceRNA networks and Venn diagram analysis, we obtained 3 DEmiRNAs, 15 DElncRNAs, and 100 DEmRNAs, and eight target pairs including miR-223-5p/(CLSTN2, AC009951.1, LINC01705, AC090673.1), miR-378b/(ALX4, IGSF3, SULF1), and miR-323b-3p/TGFBR3 were involved in both tumorigenesis and pulmonary metastasis of OS. The TGF-ß superfamily co-receptor TGFBR3, which is regulated by miR-323b-3p, acts as a tumor suppressor in OS tumorigenesis and acts as a tumor promoter in pulmonary metastatic OS via activation of the epithelial-mesenchymal transition (EMT) program.In conclusion, the OS transcriptome (miRNA, lncRNA, and mRNA) is dynamically regulated. These analyses might provide new clues to uncover the molecular mechanisms and signaling networks that contribute to OS progression, toward patient-tailored and novel-targeted treatments.

Does intensified chemotherapy increase survival outcomes of osteosarcoma patients? A meta-analysis.

STUDY DESIGN: Meta-analysis. BACKGROUND: Although some new insights have been offered for clinical and scientific relevance, minor progress has been made in osteosarcoma treatment after a dramatic survival improvement in the late 1980s with the addition of chemotherapy to surgery. Intensified chemotherapy strategies have been suggested to increase the survival rate of patients with osteosarcoma. We performed this study to access whether intensified chemotherapy strategiesincreased survival outcomes of osteosarcoma patients compared with conventional chemotherapy strategies. METHODS: MEDLINE/PubMed, EMBASE, BIOSIS Previews, and Cochrane Library were searched from database set up to October2016. Randomized controlled trials (RCTs) and comparative clinical trials (CCTs) on intensified versus conventional chemotherapy strategies for osteosarcoma patients met the inclusion criteria, and the methodological quality standard were retrieved and reviewed. Data on participant characteristics, interventions, follow-up period, and outcomes were extracted from the included studies and analyzed by Review Manager 5.3. RESULTS: 12 studies (8 RCTs and 4CCT) involving 4112 patients were selected. There were no significant differences between intensified and conventional chemotherapy strategies group in 3-year event-free survival (OR, 1.01; 95% CI, [0.74-1.37]; P = 0.97), 5-year event-free survival (OR, 1.00; 95% CI, [0.86-1.17]; P = 0.97), and 5-year overall survival (OR, 1.04; 95% CI, [0.87-1.26]; P = 0.64), and good histologic response to preoperative chemotherapy (OR, 1.12; 95% CI, [0.78-1.60]; P = 0.55). Pooled analysis of local recurrence rate showed that local recurrence rate was significantly decreased in the intensified group compared with that in the conventional group (OR, 0.60; 95% CI, [0.42-0.85]; P = 0.004). CONCLUSIONS: Intensified chemotherapy might not be a preferred treatment for all of the osteosarcoma patients.

Advances in limb salvage treatment of osteosarcoma.

Osteosarcoma is the most common primary malignant bone tumor; its standard treatment includes neoadjuvant chemotherapy combined with surgery. Neoadjuvant chemotherapy has significantly improved the 5-year survival and limb salvage rates in osteosarcoma since the 1870s. The survival rate of patients with limb salvage was not inferior to that of amputees, and therefore, limb salvage has become the main surgical option for patients with osteosarcoma. The 5-year survival rate for osteosarcoma has plateaued. However, new advances in limb salvage therapy in osteosarcoma, including adjuvant chemotherapy, ablation techniques, bone transport techniques, and computer navigation techniques, are now available. This report summarizes the recent advances in limb salvage therapy for osteosarcoma over the past decade.

Circular RNAs: Regulators of Cancer-Related Signaling Pathways and Potential Diagnostic Biomarkers for Human Cancers.

Circular RNAs (circRNAs) are newly discovered endogenous non-coding RNAs featuring structural stability, high abundance, and tissue-specific expression. CircRNAs are prevalent and conserved in mammalian cells. They are involved in cellular processes and regulate gene expression at the transcriptional or post-transcriptional level by interacting with microRNAs (miRNAs) and other molecules. Recent studies have shown that circRNAs play an important role in the progression of various human diseases including atherosclerosis, nervous system disorders, diabetes, and cancer. In this review, we summarize the advances on endogenous circRNAs in eukaryotic cells and elucidate their diagnostic and prognostic significance in human cancers. Especially, we highlight the involvement of circRNAs in signal transduction pathways as well as their clinical potential to serve as biomarkers.

Fluvastatin Prevents Lung Adenocarcinoma Bone Metastasis by Triggering Autophagy.

Bone is one of the most preferred sites of metastasis in lung cancer. Currently, bisphosphonates and denosumab are major agents for controlling tumor-associated skeletal-related events (SREs). However, both bisphosphonates and denosumab significantly increase the risk for jaw osteonecrosis. Statins, 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors and the most frequently prescribed cholesterol-lowering agents, have been reported to inhibit tumor progression and induce autophagy in cancer cells. However, the effects of statin and role of autophagy by statin on bone metastasis are unknown. In this study, we report that fluvastatin effectively prevented lung adenocarcinoma bone metastasis in a nude mouse model. We further reveal that fluvastatin-induced anti-bone metastatic property was largely dependent on its ability to induce autophagy in lung adenocarcinoma cells. Atg5 or Atg7 deletion, or 3-methyadenine (3-MA) or Bafilomycin A1 (Baf A1) treatment prevented the fluvastatin-induced suppression of bone metastasis. Furthermore, we reveal that fluvastatin stimulation increased the nuclear p53 expression, and fluvastatin-induced autophagy and anti-bone metastatic activity were mostly dependent on p53.

Identification of differentially expressed genes in the development of osteosarcoma using RNA-seq.

OBJECTIVE: Osteosarcoma (OS) is a malignant bone tumor with high morbidity in young adults and adolescents. This study aimed to discover potential early diagnosis biomarkers in OS. RESULTS: In total, 111 differentially expressed genes (DEGs) were identified in primary OS compared with normal controls and 235 DEGs were identified in metastatic OS compared with primary OS. AURKB and PPP2R2B were the significantly up-regulated and down-regulated hub proteins, respectively, in the PPI protein-protein network (PPI) network of primary OS. ISG15 and BTRC were the significantly up-regulated and down-regulated hub proteins, respectively, in the network of metastatic OS. The DEGs in metastatic OS compared with primary OS were significantly enriched in the arachidonic acid metabolism, malaria, and chemokine signaling pathways. Finally, we employed quantitative real-time polymerase chain reaction (qRT-PCR) to validate the expression levels of candidate DEGs and the results indicated that our bioinformatics approach was acceptable. MATERIALS AND METHODS: The mRNA expression profiling of 20 subjects was obtained through high-throughput RNA-sequencing. DEGs were identified between primary OS and normal Control, and between primary OS and metastatic OS, respectively. Functional annotation and PPI networks were used to obtain insights into the functions of DEGs. qRT-PCR was performed to detect the expression levels of dysregulated genes in OS. CONCLUSIONS: Our work might provide groundwork for the further exploration of tumorigenesis and metastasis mechanisms of OS.

Long noncoding RNAs in the progression, metastasis, and prognosis of osteosarcoma.

Long noncoding RNAs (lncRNAs) are a class of non-protein-coding molecules longer than 200 nucleotides that are involved in the development and progression of many types of tumors. Numerous lncRNAs regulate cell proliferation, metastasis, and chemotherapeutic drug resistance. Osteosarcoma is one of the main bone tumor subtypes that poses a serious threat to adolescent health. We summarized how lncRNAs regulate osteosarcoma progression, invasion, and drug resistance, as well as how lncRNAs can function as biomarkers or independent prognostic indicators with respect to osteosarcoma therapy.