Effect of flaxseed oil cyclolinopeptides on lipid oxidation, protein oxidation, and lipid profile during in vitro digestion of high-fat beef.

This study investigated the influence of flaxseed oil cyclolinopeptides (CLs) on lipid and protein oxidation during high-fat meat digestion. Fourteen CLs were identified in flaxseed oil through UHPLC-ESI-QTOF-MS/MS, with dominant CLA, CLB, CLE, and CLM. During in vitro digestion, CLs inhibited lipid oxidation products (lipid hydroperoxide, Malondialdehyde, and 4-hydroxynonenal) and protein carbonylation. Compared to other groups, the lipid (16.28 ± 0.35) and protein (17.5 ± 0.6) oxidation was significantly inhibited, and antioxidant activity was remarkably increased when the CLs content reached 200 mg/kg. Through untargeted lipidomic analysis using Q-Exactive, it was observed that CLs mitigated the formation of oxidized triglycerides (OxTG) products and enhanced the hydrolysis of lipids to generate sphingolipid and polyunsaturated fatty-acids enriched glycerophospholipids imparting nutritional value to meat. Electron spin-resonance and fluorescence quenching showed that primary radicals such as alkyl and alkoxy radicals during high-fat meat digestion with flaxseed oil CLs significantly mitigate their formation. These findings collectively indicate that consuming CLs enriched flaxseed oil could reduce lipid oxidation and enhance the nutritional value of high-fat meat during digestion.

Propylene glycol alginate sodium sulfate suppressed lung metastasis by blocking P-selectin to recruit CD4 regulatory T cells.

P-selectin has been shown to enhance growth and metastasis of mouse tumors by promoting regulatory T cell (Treg) infiltration into the tumors. Theoretically, a P-selectin antagonist could suppress the process. Popylene glycol alginate sodium sulfate (PSS) is a heparin-like marine drug, which was originally approved to treat cardiovascular disease in China. Previously, we reported that PSS was an effective P-selectin antagonist in vitro. However, it is unknown whether PSS can regulate Treg infiltration and its effect on lung metastasis in vivo. Our results showed that PSS at 30 mg/kg significantly suppressed lung metastasis and improved overall survival, with potency comparable to the positive control LMWH. Mechanistic study indicated that PSS blocked tumor cells adhesion and activated platelets by directly binding with activated platelet's P-selectin. Compared to the model group, PSS decreased the percent of Tregs by 63 % in lungs after treating for 21 days while increasing CD8+ T cells (1.59-fold) and Granzyme B+ CD8 T cells (2.08-fold)' percentage for generating an adaptive response for systemic tumor suppression. The study indicated that the P-selectin antagonist, PSS, suppressed lung metastasis by inhibiting the infiltration of regulatory T cells (Treg) into the tumors.

Research into super-resolution in medical imaging from 2000 to 2023: bibliometric analysis and visualization.

Background: Super-resolution (SR) refers to the use of hardware or software methods to enhance the resolution of low-resolution (LR) images and produce high-resolution (HR) images. SR is applied frequently across a variety of medical imaging contexts, particularly in the enhancement of neuroimaging, with specific techniques including SR microscopy-used for diagnostic biomarkers-and functional magnetic resonance imaging (fMRI)-a neuroimaging method for the measurement and mapping of brain activity. This bibliometric analysis of the literature related to SR in medical imaging was conducted to identify the global trends in this field, and visualization via graphs was completed to offer insights into future research prospects. Methods: In order to perform a bibliometric analysis of the SR literature, this study sourced all publications from the Web of Science Core Collection (WoSCC) database published from January 1, 2000, to October 11, 2023. A total of 3,262 articles on SR in medical imaging were evaluated. VOSviewer was used to perform co-occurrence and co-authorship analysis, and network visualization of the literature data, including author, journal, publication year, institution, and keywords, was completed. Results: From 2000 to 2023, the annual publication volume surged from 13 to 366. The top three journals in this field in terms of publication volume were as follows: (I) Scientific Reports (86 publications), (II) IEEE Transactions on Medical Imaging (74 publications), and (III) IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control (56 publications). The most prolific country, institution, and author were the United States (1,017 publications; 31,301 citations), the Chinese Academy of Sciences (124 publications; 2,758 citations), and Dinggang Shen (20 publications; 671 citations), respectively. A cluster analysis of the top 100 keywords was conducted, which revealed the presence of five co-occurrence clusters: (I) SR and artificial intelligence (AI) for medical image enhancement, (II) SR and inverse problem processing concepts for positron emission tomography (PET) image processing, (III) SR ultrasound through microbubbles, (IV) SR microscopy for Alzheimer and Parkinson diseases, and (V) SR in brain fMRI: rapid acquisition and precise imaging. The most recent high-frequency keywords were deep learning (DL), magnetic resonance imaging (MRI), and convolutional neural networks (CNNs). Conclusions: Over the past two decades, the output of publications by countries, institutions, and authors in the field of SR in medical imaging has steadily increased. Based on bibliometric analysis of international trends, the resurgence of SR in medical imaging has been facilitated by advancements in AI. The increasing need for multi-center and multi-modal medical images has further incentivized global collaboration, leading to the diverse research paths in SR medical imaging among prominent scientists.

Cervical cancer segmentation based on medical images: a literature review.

Background and Objective: Cervical cancer clinical target volume (CTV) outlining and organs at risk segmentation are crucial steps in the diagnosis and treatment of cervical cancer. Manual segmentation is inefficient and subjective, leading to the development of automated or semi-automated methods. However, limitation of image quality, organ motion, and individual differences still pose significant challenges. Apart from numbers of studies on the medical images' segmentation, a comprehensive review within the field is lacking. The purpose of this paper is to comprehensively review the literatures on different types of medical image segmentation regarding cervical cancer and discuss the current level and challenges in segmentation process. Methods: As of May 31, 2023, we conducted a comprehensive literature search on Google Scholar, PubMed, and Web of Science using the following term combinations: "cervical cancer images", "segmentation", and "outline". The included studies focused on the segmentation of cervical cancer utilizing computed tomography (CT), magnetic resonance (MR), and positron emission tomography (PET) images, with screening for eligibility by two independent investigators. Key Content and Findings: This paper reviews representative papers on CTV and organs at risk segmentation in cervical cancer and classifies the methods into three categories based on image modalities. The traditional or deep learning methods are comprehensively described. The similarities and differences of related methods are analyzed, and their advantages and limitations are discussed in-depth. We have also included experimental results by using our private datasets to verify the performance of selected methods. The results indicate that the residual module and squeeze-and-excitation blocks module can significantly improve the performance of the model. Additionally, the segmentation method based on improved level set demonstrates better segmentation accuracy than other methods. Conclusions: The paper provides valuable insights into the current state-of-the-art in cervical cancer CTV outlining and organs at risk segmentation, highlighting areas for future research.

Benign and malignant classification of breast tumor ultrasound images using conventional radiomics and transfer learning features: A multicenter retrospective study.

This study aims to establish an effective benign and malignant classification model for breast tumor ultrasound images by using conventional radiomics and transfer learning features. We collaborated with a local hospital and collected a base dataset (Dataset A) consisting of 1050 cases of single lesion 2D ultrasound images from patients, with a total of 593 benign and 357 malignant tumor cases. The experimental approach comprises three main parts: conventional radiomics, transfer learning, and feature fusion. Furthermore, we assessed the model's generalizability by utilizing multicenter data obtained from Datasets B and C. The results from conventional radiomics indicated that the SVM classifier achieved the highest balanced accuracy of 0.791, while XGBoost obtained the highest AUC of 0.854. For transfer learning, we extracted deep features from ResNet50, Inception-v3, DenseNet121, MNASNet, and MobileNet. Among these models, MNASNet, with 640-dimensional deep features, yielded the optimal performance, with a balanced accuracy of 0.866, AUC of 0.937, sensitivity of 0.819, and specificity of 0.913. In the feature fusion phase, we trained SVM, ExtraTrees, XGBoost, and LightGBM with early fusion features and evaluated them with weighted voting. This approach achieved the highest balanced accuracy of 0.964 and AUC of 0.981. Combining conventional radiomics and transfer learning features demonstrated clear advantages over using individual features for breast tumor ultrasound image classification. This automated diagnostic model can ease patient burden and provide additional diagnostic support to radiologists. The performance of this model encourages future prospective research in this domain.

SRT: Swin-residual transformer for benign and malignant nodules classification in thyroid ultrasound images.

With the advancement of deep learning technology, computer-aided diagnosis (CAD) is playing an increasing role in the field of medical diagnosis. In particular, the emergence of Transformer-based models has led to a wider application of computer vision technology in the field of medical image processing. In the diagnosis of thyroid diseases, the diagnosis of benign and malignant thyroid nodules based on the TI-RADS classification is greatly influenced by the subjective judgment of ultrasonographers, and at the same time, it also brings an extremely heavy workload to ultrasonographers. To address this, we propose Swin-Residual Transformer (SRT) in this paper, which incorporates residual blocks and triplet loss into Swin Transformer (SwinT). It improves the sensitivity to global and localized features of thyroid nodules and better distinguishes small feature differences. In our exploratory experiments, SRT model achieves an accuracy of 0.8832 with an AUC of 0.8660, outperforming state-of-the-art convolutional neural network (CNN) and Transformer models. Also, ablation experiments have demonstrated the improved performance in the thyroid nodule classification task after introducing residual blocks and triple loss. These results validate the potential of the proposed SRT model to improve the diagnosis of thyroid nodules' ultrasound images. It also provides a feasible guarantee to avoid excessive puncture sampling of thyroid nodules in future clinical diagnosis.

Attention gate and dilation U-shaped network (GDUNet): an efficient breast ultrasound image segmentation network with multiscale information extraction.

Background: In recent years, computer-aided diagnosis (CAD) systems have played an important role in breast cancer screening and diagnosis. The image segmentation task is the key step in a CAD system for the rapid identification of lesions. Therefore, an efficient breast image segmentation network is necessary for improving the diagnostic accuracy in breast cancer screening. However, due to the characteristics of blurred boundaries, low contrast, and speckle noise in breast ultrasound images, breast lesion segmentation is challenging. In addition, many of the proposed breast tumor segmentation networks are too complex to be applied in practice. Methods: We developed the attention gate and dilation U-shaped network (GDUNet), a lightweight, breast lesion segmentation model. This model improves the inverted bottleneck, integrating it with tokenized multilayer perceptron (MLP) to construct the encoder. Additionally, we introduce the lightweight attention gate (AG) within the skip connection, which effectively filters noise in low-level semantic information across spatial and channel dimensions, thus attenuating irrelevant features. To further improve performance, we innovated the AG dilation (AGDT) block and embedded it between the encoder and decoder in order to capture critical multiscale contextual information. Results: We conducted experiments on two breast cancer datasets. The experiment's results show that compared to UNet, GDUNet could reduce the number of parameters by 10 times and the computational complexity by 58 times while providing a double of the inference speed. Moreover, the GDUNet achieved a better segmentation performance than did the state-of-the-art medical image segmentation architecture. Conclusions: Our proposed GDUNet method can achieve advanced segmentation performance on different breast ultrasound image datasets with high efficiency.

Constructing the Optimal Classification Model for Benign and Malignant Breast Tumors Based on Multifeature Analysis from Multimodal Images.

The purpose of this study was to fuse conventional radiomic and deep features from digital breast tomosynthesis craniocaudal projection (DBT-CC) and ultrasound (US) images to establish a multimodal benign-malignant classification model and evaluate its clinical value. Data were obtained from a total of 487 patients at three centers, each of whom underwent DBT-CC and US examinations. A total of 322 patients from dataset 1 were used to construct the model, while 165 patients from datasets 2 and 3 formed the prospective testing cohort. Two radiologists with 10-20 years of work experience and three sonographers with 12-20 years of work experience semiautomatically segmented the lesions using ITK-SNAP software while considering the surrounding tissue. For the experiments, we extracted conventional radiomic and deep features from tumors from DBT-CCs and US images using PyRadiomics and Inception-v3. Additionally, we extracted conventional radiomic features from four peritumoral layers around the tumors via DBT-CC and US images. Features were fused separately from the intratumoral and peritumoral regions. For the models, we tested the SVM, KNN, decision tree, RF, XGBoost, and LightGBM classifiers. Early fusion and late fusion (ensemble and stacking) strategies were employed for feature fusion. Using the SVM classifier, stacking fusion of deep features and three peritumoral radiomic features from tumors in DBT-CC and US images achieved the optimal performance, with an accuracy and AUC of 0.953 and 0.959 [CI: 0.886-0.996], a sensitivity and specificity of 0.952 [CI: 0.888-0.992] and 0.955 [0.868-0.985], and a precision of 0.976. The experimental results indicate that the fusion model of deep features and peritumoral radiomic features from tumors in DBT-CC and US images shows promise in differentiating benign and malignant breast tumors.

Aryl-Modified Pentamethyl Cyanine Dyes at the C2' Position: A Tunable Platform for Activatable Photosensitizers.

Pentamethyl cyanine dyes are promising fluorophores for fluorescence sensing and imaging. However, advanced biomedical applications require enhanced control of their excited-state properties. Herein, a synthetic approach for attaching aryl substituents at the C2' position of the thio-pentamethine cyanine (TCy5) dye structure is reported for the first time. C2'-aryl substitution enables the regulation of both the twisted intramolecular charge transfer (TICT) and photoinduced electron transfer (PET) mechanisms to be regulated in the excited state. Modulation of these mechanisms allows the design of a nitroreductase-activatable TCy5 fluorophore for hypoxic tumor photodynamic therapy and fluorescence imaging. These C2'-aryl TCy5 dyes provide a tunable platform for engineering cyanine dyes tailored to sophisticated biological applications, such as photodynamic therapy.

Cascaded-TOARNet: A cascaded framework based on mixed attention and multiscale information for thoracic OARs segmentation.

BACKGROUND: Accurate and automated segmentation of thoracic organs-at-risk (OARs) is critical for radiotherapy treatment planning of thoracic cancers. However, this has remained a challenging task for four major reasons: (1) thoracic OARs have diverse morphologies; (2) thoracic OARs have low contrast with the background; (3) boundaries of thoracic OARs are blurry; (4) class imbalance issue caused by small organs. PURPOSE: To overcome the above challenges and achieve accurate and automated segmentation of thoracic OARs on thoracic CT. METHODS: A novel cascaded framework based on mixed attention and multiscale information for thoracic OARs segmentation, called Cascaded-TOARNet. This cascaded framework comprises two stages: localization and segmentation. During the localization stage, TOARNet locates each organ to crop the regions of interest (ROIs). During the segmentation stage, TOARNet accurately segments the ROIs, and the segmentation results are merged into a complete result. RESULTS: We evaluated our proposed method and other common segmentation methods on two public datasets: the AAPM Thoracic Auto-Segmentation Challenge dataset and the Segmentation of Thoracic Organs at Risk (SegTHOR) dataset. Our method demonstrated superior performance, achieving a mean Dice score of 92.6% on the SegTHOR dataset and 90.8% on the AAPM dataset. CONCLUSIONS: This segmentation method holds great promise as an essential tool for enhancing the efficiency of thoracic radiotherapy planning.

Effectiveness of an online management platform (Joint Cloud) versus standard process for patients undergoing total knee arthroplasty: study protocol for a prospective randomised controlled trial.

INTRODUCTION: Osteoarthritis (OA) is one of the main causes of mobility impairment in the elderly worldwide. Therefore, total knee arthroplasty (TKA) is often performed and is one of the most successful surgery and has resulted in substantial quality-of-life gains for people with end-stage arthritis. There is still room for improvement in the standard treatment process in the preoperative, intraoperative and postoperative period of TKA. Telerehabilitation has the potential to become a positive alternative to face-to-face rehabilitation nowadays. But it remains unclear how well telemedicine interventions cover the entire surgical pathway (preoperation, intraoperation, postoperation). This study aims to explore the effectiveness of Joint Cloud (JC, an online management platform) compared with existing standard process in regulating functional recovery, pain management, muscle strength changes and other health-related outcomes in patients undergoing total knee arthroplasty preoperation, intraoperation and postoperation. METHODS AND ANALYSIS: A randomised controlled trial was designed to compare the online management platform (JC) with standard process (SP) in patients undergoing TKA. A total of 186 TKA patients will be randomly assigned to the intervention (n=93) or control (n=93) group. Patients in the intervention group will receive access to the 'JC' mini-program. This mini-program provides popular science information (eg, information about OA and TKA), functional exercise information and communication channels. Patients evaluate their condition and functional level through standardised digital questionnaires. The control group of patients will not accept any functions of this mini-program. The primary outcome is knee functional recovery, and the secondary outcomes are pain management, isometric knee extensor muscle strength, patient satisfaction and cost-benefit analysis. Assessments will be performed 1 month and 3 days before surgery (T0) and 1 month and 3 months after surgery. Data analysis will be performed according to the intent-to-treat (ITT) principle. Repeated measures of linear mixed models and parametric and non-parametric testing will be used for statistical analysis. ETHICS AND DISSEMINATION: The study was reviewed and approved by the Tianjin Hospital Medical Ethics Review Committee on 10 February 2023 (2022YLS155). Test data are considered highly sensitive but are available upon request. The findings will be disseminated in peer-reviewed publications. TRIAL REGISTRATION NUMBER: ChiCTR2300068486.

Synchronized activating therapeutic nano-agent: Enhancement and tracing for hypoxia-induced chemotherapy.

Prodrug is a potential regime to overcome serious adverse events and off-target effects of chemotherapy agents. Among various prodrug activators, hypoxia stands out owing to the generalizability and prominence in tumor micro-environment. However, existing hypoxia activating prodrugs generally face the limitations of stringent structural requirements, the lack of feedback and the singularity of therapeutic modality, which is imputed to the traditional paradigm that recognition groups must be located at the terminus of prodrugs. Herein, a multifunctional nano-prodrug Mal@Cy-NTR-CB has been designed. In this nano-prodrug, a self-destructive tether is introduced to break the mindset, and achieves the activation by hypoxia of chemotherapy based on Chlorambucil (CB), whose efficacy can be augmented and traced by photodynamic therapy (PDT) and fluorescence from Cyanine dyes (Cy). In addition, Maleimide (Mal) carried by the nano-shells can regulate glutathione (GSH) content, preventing 1O2 scavenging, so as to realize PDT sensitization. Experiments demonstrate that Mal@Cy-NTR-CB specifically responds to hypoxic tumors, and achieve synchronous activation, enhancement and feedback of chemotherapy and PDT, inhibiting the tumor growth effectively. This study broadens the design ideas of activatable prodrugs and provides the possibility of multifunctional nano-prodrugs to improve the generalization and prognosis in precision oncology.

Deep Learning Mammography Classification with a Small Set of Data.

BACKGROUND: Breast cancer is one of the leading causes of mortality among women. In addition, 1 in 8 women and 1 in 833 men will be diagnosed with breast cancer in 2022. The detection of breast cancer can not only lower treatment costs but also increase survival rates. Due to increased cancer awareness, more women are undergoing breast cancer screening, leading to more cases being diagnosed worldwide, but doctors' ability to analyze these images is limited. As a result, they get overloaded leading to misinterpretations. The advent of computer-aided diagnosis (CAD) minimized man's involvement and achieved good results. CAD helps medical doctors automatically detect and analyze abnormalities found in the breast. Such abnormalities may be benign or malignant tumors. OBJECTIVE: The goal of this study is to evaluate the effectiveness of using seven layers to classify breast cancer as either benign or malignant using mammograms. MATERIALS AND METHODS: The open-source MIAS dataset of 322 images was used for our study, of which 207 were normal images and 115 were abnormal images. The proposed CNN model convolves an image into seven layers that extract features from the input images, and these features are used to classify breast cancer as malignant or benign. RESULTS: The proposed CNN used a limited data set and achieved the best result compared to previous work. The method achieved results with a 0.39% loss, 99.89% accuracy, 99.85% precision, 99.89% recall, 99.87% F1-score, and an area under the curve noted to be 100.0%. CONCLUSION: CNN uses a small amount of data to determine abnormalities; the method will assist a medical doctor in determining whether or not a specific patient has cancer.

Recent Research Advances in Nano-Based Drug Delivery Systems for Local Anesthetics.

From a clinical perspective, local anesthetics have rather widespread application in regional blockade for surgery, postoperative analgesia, acute/chronic pain control, and even cancer treatments. However, a number of disadvantages are associated with traditional local anesthetic agents as well as routine drug delivery administration ways, such as neurotoxicity, short half-time, and non-sustained release, thereby limiting their application in clinical practice. Successful characterization of drug delivery systems (DDSs) for individual local anesthetic agents can support to achieve more efficient drug release and prolonged duration of action with reduced systemic toxicity. Different types of DDSs involving various carriers have been examined, including micromaterials, nanomaterials, and cyclodextrin. Among them, nanotechnology-based delivery approaches have significantly developed in the last decade due to the low systemic toxicity and the greater efficacy of non-conventional local anesthetics. Multiple nanosized materials, including polymeric, lipid (solid lipid nanoparticles, nanostructured lipid carriers, and nanoemulsions), metallic, inorganic non-metallic, and hybrid nanoparticles, offer a safe, localized, and long-acting solution for pain management and tumor therapy. This review provides a brief synopsis of different nano-based DDSs for local anesthetics with variable sizes and structural morphology, such as nanocapsules and nanospheres. Recent original research utilizing nanotechnology-based delivery systems is particularly discussed, and the progress and strengths of these DDSs are highlighted. A specific focus of this review is the comparison of various nano-based DDSs for local anesthetics, which can offer additional indications for their further improvement. All in all, nano-based DDSs with unique advantages provide a novel direction for the development of safer and more effective local anesthetic formulations.

Highly Efficient Photosensitizers with Molecular Vibrational Torsion for Cancer Photodynamic Therapy.

The development of highly effective photosensitizers (PSs) for photodynamic therapy remains a great challenge at present. Most PSs rely on the heavy-atom effect or the spin-orbit charge-transfer intersystem crossing (SOCT-ISC) effect to promote ISC, which brings about additional cytotoxicity, and the latter is susceptible to the interference of solvent environment. Herein, an immanent universal property named photoinduced molecular vibrational torsion (PVT)-enhanced spin-orbit coupling (PVT-SOC) in PSs has been first revealed. PVT is verified to be a widespread intrinsic property of quinoid cyanine (QCy) dyes that occurs on an extremely short time scale (10-10 s) and can be captured by transient spectra. The PVT property can provide reinforced SOC as the occurrence of ISC predicted by the El Sayed rules (1ππ*-3nπ*), which ensures efficient photosensitization ability for QCy dyes. Hence, QTCy7-Ac exhibited the highest singlet oxygen yield (13-fold higher than that of TCy7) and lossless fluorescence quantum yield (ΦF) under near-infrared (NIR) irradiation. The preeminent photochemical properties accompanied by high biosecurity enable it to effectively perform photoablation in solid tumors. The revelation of this property supplies a new route for constructing high-performance PSs for achieving enhanced cancer phototherapy.

Exosomes secreted by mesenchymal stem cells delay brain aging by upregulating SIRT1 expression.

The increase in the aging population has seriously affected our society. Neurodegenerative diseases caused by aging of the brain significantly impact the normal life of the elderly, and delaying brain aging is currently the focus of research. SIRT1 is a viable therapeutic target, and there is mounting evidence that it plays a significant role in the aging process. Mesenchymal stem cell-derived exosomes (MSC-Exos) have gained widespread interest as nanotherapeutic agents because of their ability to be injected at high doses to reduce the immune response. The present study focused on the ameliorative effect of MSC-Exos on aging mice and the potential mechanisms of this effect on cognitive impairment and brain aging. In this study, we first tested the neuroprotective effects of MSC-Exos in vitro on H2O2-induced oxidative damage in BV2 cells. An in vivo SAMP8 rapid senescence mouse model showed that MSC-Exos significantly increased SIRT1 gene expression in senescent mice. In addition, MSC-Exos also had an anti-apoptotic effect and reduced oxidative stress in the brains of SAMP8 senescent mice. In conclusion, MSC-Exos may exert neuroprotective effects and help prevent brain senescence in SAMP8 mice by activating the SIRT1 signaling pathway.

DPAM-PSPNet: ultrasonic image segmentation of thyroid nodule based on dual-path attention mechanism.

Objective.Deep learning has demonstrated its versatility in the medical field, particularly in medical image segmentation, image classification, and other forms of automated diagnostics. The clinical diagnosis of thyroid nodules requires radiologists to locate nodules, diagnose conditions based on nodule boundaries, textures and their experience. This task is labor-intensive and tiring; therefore, an automated system for accurate thyroid nodule segmentation is essential. In this study, a model named DPAM-PSPNet was proposed, which automatically segments nodules in thyroid ultrasound images and enables to segment malignant nodules precisely.Approach.In this paper, accurate segmentation of nodule edges is achieved by introducing the dual path attention mechanism (DPAM) in PSPNet. In one channel, it captures global information with a lightweight cross-channel interaction mechanism. In other channel, it focus on nodal margins and surrounding information through the residual bridge network. We also updated the integrated loss function to accommodate the DPAM-PSPNet.Main results.The DPAM-PSPNet was tested against the classical segmentation model. Ablation experiments were designed for the two-path attention mechanism and the new loss function, and generalization experiments were designed on the public dataset. Our experimental results demonstrate that DPAM-PSPNet outperforms other existing methods in various evaluation metrics. In the model comparison experiments, it achieved performance with an mIOU of 0.8675, mPA of 0.9357, mPrecision of 0.9202, and Dice coefficient of 0.9213.Significance.The DPAM-PSPNet model can segment thyroid nodules in ultrasound images with little training data and generate accurate boundary regions for these nodules.

Enzyme-triggered transcytosis of drug carrier system for deep penetration into hepatoma tumors.

In recent years, nano-drug delivery systems have made considerable progress in the direction of tumor treatment, but the low permeability of drugs has restricted the development of nano drugs. To solve this problem, we constructed a nano-drug delivery system with the dual effects of γ-glutamyltransferase (GGT) reaction and high nuclear targeting in tumor microenvironment to promote the deep penetration of drugs. Over-expression of GGT in tumor cells can specifically recognize γ-glutamyl substrate and release amino group from the hydrolysis reaction, which makes the whole system change from negative or neutral to positive charge system. The conjugated complex with positive charge rapidly endocytosis through electrostatic interaction, enhancing its permeability in tumor parenchyma. At the same time, the cell penetrating TAT contains a large amount of lysine, which can be identified by the nuclear pore complexes (NPCs) on the surface of the nuclear membrane, showing excellent nuclear localization function. The active DOX is released in the nucleus, which inhibits the mitosis of cancer cells and enhances the active transport ability of drugs in tumor cells. Therefore, this drug delivery system actively transports adriamycin into the tumor to achieve deep penetration of drugs through enzyme response and nuclear targeting, showing high anti-tumor activity and can be effectively applied to the treatment of liver cancer.

Contribution of immune cells to cancer-related neuropathic pain: An updated review.

Given that the incidence of cancer is dramatically increasing nowadays, cancer-related neuropathic pain including tumor-related and therapy-related pain gradually attracts more attention from researchers, which basically behaves as a metabolic-neuro-immune disorder with worse clinical outcomes and prognosis. Among various mechanisms of neuropathic pain, the common underlying one is the activation of inflammatory responses around the injured or affected nerve(s). Innate and adaptive immune reactions following nerve injury together contribute to the regulation of pain. On the other hand, the tumor immune microenvironment involving immune cells, as exemplified by lymphocytes, macrophages, neutrophils and dendritic cells, inflammatory mediators as well as tumor metastasis have added additional characteristics for studying the initiation and maintenance of cancer-related neuropathic pain. Of interest, these immune cells in tumor microenvironment exert potent functions in promoting neuropathic pain through different signaling pathways. To this end, this review mainly focuses on the contribution of different types of immune cells to cancer-related neuropathic pain, aims to provide a comprehensive summary of how these immune cells derived from the certain tumor microenvironment participate in the pathogenesis of neuropathic pain. Furthermore, the clarification of roles of various immune cells in different tumor immune microenvironments associated with certain cancers under neuropathic pain states constitutes innovative biology that takes the pain field in a different direction, and thereby provides more opportunities for novel approaches for the prevention and treatment of cancer-related neuropathic pain.

Expression Levels of MicroRNA-300/BCL2L11 in Papillary Thyroid Cancer and Their Clinical Diagnostic Values.

INTRODUCTION: This research aims to explore the expression levels of microRNA (miRNA)-300/BCL-2-like protein 11 (BCL2L11) and their values in the clinical diagnosis of papillary thyroid cancer (PTC). METHODS: Pathological tissues that were surgically removed for thyroid disease were selected. miR-300 and BCL2L11 expression levels in the samples were measured. Receiver operating characteristic (ROC) curves were plotted to analyze miR-300 and BCL2L11 predictive values for PTC. Upon silencing miR-300 and silencing BCL2L11 in PTC cells, the corresponding miR-300 and BCL2L11 expression levels were tested, followed by examining PTC cell activities. The targeting relationship of miR-300 and BCL2L11 was detected by the bioinformatics website and luciferase activity assay. RESULTS: miR-300 expression levels were elevated and BCL2L11 expression levels were reduced in PTC tissues. miR-300 and BCL2L11 expression levels in PTC tissues had a correlation with TNM stage and lymph node metastasis. The results of ROC curve revealed that both miR-300 and BCL2L11 had clinical predictive values for PTC. Mechanistically, miR-300 negatively regulated BCL2L11. The functional assays unveiled that silencing miR-300 impeded PTC cell activities, and silencing BCL2L11 induced PTC cell activities. In the rescue experiment, silencing BCL2L11 reversed the impacts of silencing miR-300 on PTC cell development. CONCLUSION: This study underlines that miR-300 expression is increased and BCL2L11 expression is declined in PTC. miR-300 and BCL2L11 both have clinical predictive values for diagnosing PTC.