Visualization of Microcirculation at Acupoints in vivo of Alzheimer's Disease Animal Model with Photoacoustic Microscope: A Pilot Study.

Background: Alzheimer's disease may be effectively treated with acupoint-based acupuncture, which is acknowledged globally. However, more research is needed to understand the alterations in acupoints that occur throughout the illness and acupuncture treatment. Objective: This research investigated the differences in acupoint microcirculation between normal mice and AD animals in vivo. This research also examined how acupuncture affected AD animal models and acupoint microcirculation. Methods: 6-month-old SAMP8 mice were divided into two groups: the AD group and the acupuncture group. Additionally, SAMR1 mice of the same month were included as the normal group. The study involved subjecting a group of mice to 28 consecutive days of acupuncture at the ST36 (Zusanli) and CV12 (Zhongwan) acupoints. Following this treatment, the Morris water maze test was conducted to assess the mice's learning and memory abilities; the acoustic-resolution photoacoustic microscope (AR-PAM) imaging system was utilized to observe the microcirculation in CV12 acupoint region and head-specific region of each group of mice. Results: In comparison to the control group, the mice in the AD group exhibited a considerable decline in their learning and memory capabilities (p < 0.01). In comparison to the control group, the vascular in the CV12 region and head-specific region in mice from the AD group exhibited a considerable reduction in length, distance, and diameter r (p < 0.01). The implementation of acupuncture treatment had the potential to enhance the aforementioned condition to a certain degree. Conclusions: These findings offered tangible visual evidence that supports the ongoing investigation into the underlying mechanisms of acupuncture's therapeutic effects.

Immunotherapeutic value of NUSAP1 associated with bladder cancer through a comprehensive analysis of 33 human cancer cases.

To investigate the correlation between nucleolar spindle-associated protein 1 (NUSAP1) and cancer immunotherapy across 33 different types of human cancers. We conducted an analysis of The Cancer Genome Atlas (TCGA) database to retrieve gene expression data and clinical characteristics for 33 different cancer types. The immunotherapy cohorts encompassed GSE67501, GSE78220, and IMvigor210. Relevant information was extracted from the gene expression repository. We assessed the prognostic significance of NUSAP1 by examining various clinical parameters. The single-sample gene-set enrichment analysis (ssGSEA) method was utilized to gauge NUSAP1 activity and to contrast NUSAP1 transcriptome and protein levels. We delved into the correlation between NUSAP1 and various immune processes and components to gain insights into NUSAP1's role. We also discussed coherent pathways associated with NUSAP1 signal transduction and its impact on immunotherapy biomarkers. To authenticate and validate the differential expression patterns of NUSAP1 in bladder tumor tissues versus normal bladder counterparts, we utilized Western blotting (WB), real-time quantitative polymerase chain reaction (RT-qPCR), and immunohistochemistry (IHC) techniques. NUSAP1 exhibits overexpression across a spectrum of malignancies, and its expression levels correlate with overall survival (OS), disease-specific survival, and tumor stage in specific cancer types. Furthermore, NUSAP1 expression is linked to mutations, methylation patterns, and immunotherapy responses in human cancers. Meanwhile, our experiments, involving WB, RT-qPCR, and IHC, consistently demonstrated significantly higher NUSAP1 expression in bladder tumor tissues compared to normal controls. Our study underscores the potential of NUSAP1 as a promising prognostic indicator and immunotherapeutic target for a range of malignant tumors.

Sophora flavescens alcohol extract ameliorates insomnia and promotes PI3K/AKT/BDNF signaling transduction in insomnia model rats.

Active ingredient of Sophora flavescens is reported to promote non-rapid eye movement (NREM) sleep. However, the role of Sophora flavescens alcohol extract in insomnia is elusive, which is addressed in this study, together with the exploration on its potential mechanism. An insomnia model of rats was established by para-chlorophenylalanine induction and further treated with SFAE or Zaoren Anshen capsule (ZRAS; positive control drug). Sleep quality and sleep architecture of rats were evaluated by the sleep test, electroencephalogram and electromyogram. The levels of monoamine neurotransmitters in rat hypothalamus were determined using ELISA, and the transduction of the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/brain-derived neurotrophic factor (BDNF) signaling in the brain tissues of rats was examined by Western blot. SFAE and ZRAS increased the sleeping time and decreased the sleep latency of insomnia rats. SFAE reduced waking time and increased NREM and REM time, while changing power density of wakefulness, NREM sleep, and REM sleep in insomnia rats. SFAE and ZRAS upregulated levels of 5-hydroxytryptamine and 5-hydroxyindoleacetic acid, and downregulated those of norepinephrine and dopamine in insomnia rats. Besides, SFAE and ZRAS elevated BDNF expression as well as the ratios of phosphorylated (p)-PI3K/PI3K and p-AKT/AKT. The role of SFAE in insomnia model rats was similar with that of ZRAS. SFAE reduces insomnia and enhances the PI3K/AKT/BDNF signaling transduction in insomnia model rats, which can function as a drug candidate for insomnia.

Gut-brain communication mediates the impact of dietary lipids on cognitive capacity.

Cognitive impairment, as a prevalent symptom of nervous system disorders, poses one of the most challenging aspects in the management of brain diseases. Lipids present in the cell membranes of all neurons within the brain and dietary lipids can regulate the cognition and memory function. In recent years, the advancements in gut microbiome research have enabled the exploration of dietary lipids targeting the gut-brain axis as a strategy for regulating cognition. This present review provides an in-depth overview of how lipids modulate cognition via the gut-brain axis depending on metabolic, immune, neural and endocrine pathways. It also comprehensively analyzes the effects of diverse lipids on the gut microbiota and intestinal barrier function, thereby affecting the central nervous system and cognitive capacity. Moreover, comparative analysis of the positive and negative effects is presented between beneficial and detrimental lipids. The former encompass monounsaturated fatty acids, short-chain fatty acids, omega-3 polyunsaturated fatty acids, phospholipids, phytosterols, fungal sterols and bioactive lipid-soluble vitamins, as well as lipid-derived gut metabolites, whereas the latter (detrimental lipids) include medium- or long-chain fatty acids, excessive proportions of n-6 polyunsaturated fatty acids, industrial trans fatty acids, and zoosterols. To sum up, the focus of this review is on how gut-brain communication mediates the impact of dietary lipids on cognitive capacity, providing a novel theoretical foundation for promoting brain cognitive health and scientific lipid consumption patterns.

TriFNet: A triple-branch feature fusion network for pH determination by surface-enhanced Raman spectroscopy.

Due to the acidic tumor microenvironment caused by metabolic changes in tumor cells, the accurate pH detection of extracellular fluid is helpful for doctors in precise tumor resection. The combination of Raman spectroscopy and deep learning provides a solution for pH detection. However, most existing studies use one-dimensional convolutional neural networks (1D-CNNs) for spectral analysis, which limits the performance due to insufficient feature extraction. In this work, we propose a 2D triple-branch feature fusion network (TriFNet) for accurate pH determination using surface-enhanced Raman spectra (SERS). Specifically, we design a triple-branch network structure by converting Raman spectra into three types of images to extensively extract complex patterns in spectra. In addition, an attention fusion module, which leverages the complementarity among features in both space and channel, is designed to obtain the valuable information, achieving further accurate pH determination. On our Raman spectral dataset containing 14,137 samples, we achieved mean absolute error (MAE) of 0.059, standard deviation of the absolute error (SD) of 0.07, root mean squared error (RMSE) of 0.092, and coefficient of determination (R2) of 0.991 on the test set. Compared with other published methods, the four metrics showed an average improvement of 47%, 39%, 43%, and 6%, respectively. In addition, visualization validates the diagnostic capability of our model to correlate with biomolecular signatures. Meanwhile, our model has robustness to different SERS chips. These results prove the potential of our method to develop an effective technology based on Raman spectroscopy for accurate pH determination to guide surgery.

Copper hexacyanoferrate/carbon sheet combination with high selectivity and capacity for copper removal by pseudocapacitance.

The efficient capture of copper ions (Cu2+) in wastewater has dual significance in pollution control and resource recovery. Prussian blue analog (PBA)-based pseudocapacitive materials with open frameworks and abundant metal sites have attracted considerable attention as capacitive deionization (CDI) electrodes for copper removal. In this study, the efficiency of copper hexacyanoferrate (CuHCF) as CDI electrode for Cu2+ treating was evaluated for the first time upon the successful synthesis of copper hexacyanoferrate/carbon sheet combination (CuHCF/C) by introducing carbon sheet as conductive substrate. CuHCF/C exhibited significant pseudocapacitance and high specific capacitance (52.92 F g-1) through the intercalation, deintercalation, and coupling of Cu+/Cu2+ and Fe2+/Fe3+ redox pairs. At 0.8 an applied voltage and CuSO4 feed liquid concentration of 100 mg L-1, the salt adsorption capacity was 134.47 mg g-1 higher than those of most reported electrodes. Moreover, CuHCF/C demonstrated excellent Cu2+ selectivity in multi-ion coexisting solutions and in actual wastewater experiments. Density functional theory (DFT) calculations were employed to elucidate the mechanism. This study not only reveals the essence of Cu2+ deionization by PBAs pseudocapacitance with promising potential applications but also provides a new strategy for selecting efficient CDI electrodes for Cu2+ removal.

Rapid intraoperative multi-molecular diagnosis of glioma with ultrasound radio frequency signals and deep learning.

BACKGROUND: Molecular diagnosis is crucial for biomarker-assisted glioma resection and management. However, some limitations of current molecular diagnostic techniques prevent their widespread use intraoperatively. With the unique advantages of ultrasound, this study developed a rapid intraoperative molecular diagnostic method based on ultrasound radio-frequency signals. METHODS: We built a brain tumor ultrasound bank with 169 cases enrolled since July 2020, of which 43483 RF signal patches from 67 cases with a pathological diagnosis of glioma were a retrospective cohort for model training and validation. IDH1 and TERT promoter (TERTp) mutations and 1p/19q co-deletion were detected by next-generation sequencing. We designed a spatial-temporal integration model (STIM) to diagnose the three molecular biomarkers, thus establishing a rapid intraoperative molecular diagnostic system for glioma, and further analysed its consistency with the fifth edition of the WHO Classification of Tumors of the Central Nervous System (WHO CNS5). We tested STIM in 16-case prospective cohorts, which contained a total of 10384 RF signal patches. Two other RF-based classical models were used for comparison. Further, we included 20 cases additional prospective data for robustness test (ClinicalTrials.govNCT05656053). FINDINGS: In the retrospective cohort, STIM achieved a mean accuracy and AUC of 0.9190 and 0.9650 (95% CI, 0.94-0.99) respectively for the three molecular biomarkers, with a total time of 3 s and a 96% match to WHO CNS5. In the prospective cohort, the diagnostic accuracy of STIM is 0.85 ± 0.04 (mean ± SD) for IDH1, 0.84 ± 0.05 for TERTp, and 0.88 ± 0.04 for 1p/19q. The AUC is 0.89 ± 0.02 (95% CI, 0.84-0.94) for IDH1, 0.80 ± 0.04 (95% CI, 0.71-0.89) for TERTp, and 0.85 ± 0.06 (95% CI, 0.73-0.98) for 1p/19q. Compared to the second best available method based on RF signal, the diagnostic accuracy of STIM is improved by 16.70% and the AUC is improved by 19.23% on average. INTERPRETATION: STIM is a rapid, cost-effective, and easy-to-manipulate AI method to perform real-time intraoperative molecular diagnosis. In the future, it may help neurosurgeons designate personalized surgical plans and predict survival outcomes. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model.

OBJECTIVES: As a few types of glioma, young high-risk low-grade gliomas (HRLGGs) have higher requirements for postoperative quality of life. Although adjuvant chemotherapy with delayed radiotherapy is the first treatment strategy for HRLGGs, not all HRLGGs benefit from it. Accurate assessment of chemosensitivity in HRLGGs is vital for making treatment choices. This study developed a multimodal fusion radiomics (MFR) model to support radiochemotherapy decision-making for HRLGGs. METHODS: A MFR model combining macroscopic MRI and microscopic pathological images was proposed. Multiscale features including macroscopic tumor structure and microscopic histological layer and nuclear information were grabbed by unique paradigm, respectively. Then, these features were adaptively incorporated into the MFR model through attention mechanism to predict the chemosensitivity of temozolomide (TMZ) by means of objective response rate and progression free survival (PFS). RESULTS: Macroscopic tumor texture complexity and microscopic nuclear size showed significant statistical differences (p < 0.001) between sensitivity and insensitivity groups. The MFR model achieved stable prediction results, with an area under the curve of 0.950 (95% CI: 0.942-0.958), sensitivity of 0.833 (95% CI: 0.780-0.848), specificity of 0.929 (95% CI: 0.914-0.936), positive predictive value of 0.833 (95% CI: 0.811-0.860), and negative predictive value of 0.929 (95% CI: 0.914-0.934). The predictive efficacy of MFR was significantly higher than that of the reported molecular markers (p < 0.001). MFR was also demonstrated to be a predictor of PFS. CONCLUSIONS: A MFR model including radiomics and pathological features predicts accurately the response postoperative TMZ treatment. CLINICAL RELEVANCE STATEMENT: Our MFR model could identify young high-risk low-grade glioma patients who can have the most benefit from postoperative upfront temozolomide (TMZ) treatment. KEY POINTS: • Multimodal radiomics is proposed to support the radiochemotherapy of glioma. • Some macro and micro image markers related to tumor chemotherapy sensitivity are revealed. • The proposed model surpasses reported molecular markers, with a promising area under the curve (AUC) of 0.95.

A multicenter study on the application of artificial intelligence radiological characteristics to predict prognosis after percutaneous nephrolithotomy.

Background: A model to predict preoperative outcomes after percutaneous nephrolithotomy (PCNL) with renal staghorn stones is developed to be an essential preoperative consultation tool. Objective: In this study, we constructed a predictive model for one-time stone clearance after PCNL for renal staghorn calculi, so as to predict the stone clearance rate of patients in one operation, and provide a reference direction for patients and clinicians. Methods: According to the 175 patients with renal staghorn stones undergoing PCNL at two centers, preoperative/postoperative variables were collected. After identifying characteristic variables using PCA analysis to avoid overfitting. A predictive model was developed for preoperative outcomes after PCNL in patients with renal staghorn stones. In addition, we repeatedly cross-validated their model's predictive efficacy and clinical application using data from two different centers. Results: The study included 175 patients from two centers treated with PCNL. We used a training set and an external validation set. Radionics characteristics, deep migration learning, clinical characteristics, and DTL+Rad-signature were successfully constructed using machine learning based on patients' pre/postoperative imaging characteristics and clinical variables using minimum absolute shrinkage and selection operator algorithms. In this study, DTL-Rad signal was found to be the outstanding predictor of stone clearance in patients with renal deer antler-like stones treated by PCNL. The DTL+Rad signature showed good discriminatory ability in both the training and external validation groups with AUC values of 0.871 (95% CI, 0.800-0.942) and 0.744 (95% CI, 0.617-0.871). The decision curve demonstrated the radiographic model's clinical utility and illustrated specificities of 0.935 and 0.806, respectively. Conclusion: We found a prediction model combining imaging characteristics, neural networks, and clinical characteristics can be used as an effective preoperative prediction method.

A real-world study of recombinant human endostatin combined with PD-1/PD-L1 blockade and chemotherapy for patients with advanced non-small cell lung cancer negative for actionable molecular biomarkers.

The ongoing ENPOWER study exploring the efficacy and safety of the recombinant human endostatin (endostar) combined with programmed cell death 1 antibody sintilimab and chemotherapy showed encouraging efficacy and safety in advanced non-squamous non-small cell lung cancer. To evaluate the real-world efficacy and safety of endostar combined with immune checkpoint inhibitor and chemotherapy (EIC) for advanced non-squamous non-small cell lung cancer patients negative for actionable molecular biomarkers (NSCLCnm), patients with advanced NSCLCnm hospitalized to Zhejiang Provincial People's Hospital from January 2020 to December 2022 were screened for eligibility. The included patients were analyzed for the objective response rate (ORR) and disease control rate (DCR). The pre- and posttreatment expression levels of serum tumor associated biomarkers, chemokines and subpopulations of immune cells in peripheral blood were compared. For the 31 patients with advanced NSCLCnm treated with EIC, the median follow-up and treatment cycles were 18.0 months and 4, respectively. The ORR and DCR were 38.7% and 90.3%, respectively. For those who received EIC as first-line treatment, the ORR and DCR were 63.2% and 94.7%, respectively. EIC significantly decreased expression levels of carcinoma antigen 125, carcinoma embryonic antigen and cytokeratin 19 (P<0.05) in patients who were partial remission or stable disease. Among the 31 patients, 27 (87.1%) experienced at least 1 treatment-related adverse events, and 13 (41.9%) had the treatment-related adverse events of grade 3 or higher. No antiangiogenesis-related adverse events were observed. The current study showed that EIC was potentially effective for patients with NSCLCnm, especially when used as first-line therapy, and well tolerated.

Neuroprotective effect of Kurarinone against corticosterone-induced cytotoxicity on rat hippocampal neurons by targeting BACE1 to activate P13K-AKT signaling - A potential treatment in insomnia disorder.

The hippocampus has been implicated in the pathogenesis of insomnia disorder (ID) and the purpose of this study was to investigate the neuroprotective mechanism of the natural flavone Kurarinone (Kur) on hippocampal neurotoxicity as a potential treatment of ID. The effect of Kur on hippocampal neuronal cell (HNC) viability and apoptosis were assessed by Cell counting kit-8 (CCK-8) assay and flow cytometry, respectively. Then, the effect of Kur on ß-site amyloid precursor protein-cleaving enzyme 1 (BACE1), brain-derived neurotrophic factor (BDNF), and phosphatidylinositol-3-kinase (PI3K)/protein kinase B (AKT) phosphorylation level were measured by Western blot. Further, SwissTargetPrediction analysis and molecular docking experiments were used to detect a potential target of Kur. Then, the p-chlorophenylalanine (PCPA) model was established in vivo to further study the effect of BACE1 expression on Kur and HNC. As a result, HNC viability was only significantly decreased by 2 µM of Kur. Kur reversed the impacts of corticosterone upon inhibiting viability (0.25-1 µM), PI3K (0.5-1 µM)/AKT phosphorylation, and BDNF (1 µM) level, and enhancing the apoptosis (0.25-1 µM) and BACE1 expression (1 µM) in HNCs. BACE1 was a potential target of Kur. Notably, Kur (150 mg/kg) attenuated PCPA-induced upregulation of BACE1 expression in rat hippocampal tissues as ZRAS (0.8 g/kg). The effects of Kur (1 µM) on corticosterone-treated HNCs were reversed by BACE1 overexpression. Collectively, Kur downregulates BACE1 level to activate PI3K/AKT, thereby attenuating corticosterone-induced toxicity in HNCs, indicating that Kur possibly exerted a neuroprotective effect, which providing a new perspective for the treatment of insomnia disorders.

Intra-Operative Definition of Glioma Infiltrative Margins by Visualizing Immunosuppressive Tumor-Associated Macrophages.

Accurate delineation of glioma infiltrative margins remains a challenge due to the low density of cancer cells in these regions. Here, a hierarchical imaging strategy to define glioma margins by locating the immunosuppressive tumor-associated macrophages (TAMs) is proposed. A pH ratiometric fluorescent probe CP2-M that targets immunosuppressive TAMs by binding to mannose receptor (CD206) is developed, and it subsequently senses the acidic phagosomal lumen, resulting in a remarkable fluorescence enhancement. With assistance of CP2-M, glioma xenografts in mouse models with a tumor-to-background ratio exceeding 3.0 for up to 6 h are successfully visualized. Furthermore, by intra-operatively mapping the pH distribution of exposed tissue after craniotomy, the glioma allograft in rat models is precisely excised. The overall survival of rat models significantly surpasses that achieved using clinically employed fluorescent probes. This work presents a novel strategy for locating glioma margins, thereby improving surgical outcomes for tumors with infiltrative characteristics.

OCIF: automatically learning the optimized clinical information fusion method for computer-aided diagnosis tasks.

PURPOSE: In computer-aided diagnosis, the fusion of image features extracted from neural networks and clinical information is crucial to improve diagnostic accuracy. How to integrate low-dimensional clinical information (LDCF) with high-dimensional network features (HDNF) is an urgent problem to be solved. We offer a new network search framework to address this problem, which can provide optimized LDCF fusion and efficient dimensionality reduction in HDNF. METHODS: OCIF innovatively uses Gaussian process optimization to explore the search space for the number of fully connected (FC) layers, the number of neurons in each FC layer, the activation function, the dropout factor, and whether to add clinical information to each FC layer. Moreover, OCIF employs transfer learning to reduce the training parameter space and improve search efficiency. To evaluate the effectiveness of the proposed OCIF, we utilized three popular end-to-end overall survival (OS) time prediction models to predict the three classes. RESULTS: Our experimental results show that applying OCIF to a classical computer-aided diagnosis neural network can improve classification accuracy. Experiments on the 2020 BRATS dataset prove that OCIF achieves satisfactory performance, with an accuracy of 0.684, precision of 0.735, recall of 0.684, and F1-score of 0.675 on the OS time prediction task. CONCLUSION: OCIF effectively and creatively combines clinical information and network features, leveraging both clinical information and image features to enhance the accuracy of the final diagnosis. Our experiments demonstrate that the use of OCIF can significantly improve computer-aided diagnosis accuracy, and the approach has the potential to be extended to other medical classification tasks as well.

Case Report: Life-threatening pancytopenia with tislelizumab followed by cerebral infarction in a patient with lung adenocarcinoma.

Immune checkpoint inhibitors (ICIs) are an integral antitumor therapy for many malignancies. Most patients show very good tolerability to ICIs; however, serious immune-related adverse events (irAEs) with ICIs have been well documented and prevent some patients from continuing ICIs or even become the direct cause of patient death. Cytopenia is a rare irAE but can be life-threatening. Here, we present the case of a 66-year-old male patient with metastatic lung adenocarcinoma who received two doses of chemotherapy + PD-1 antibody tislelizumab and developed pancytopenia after each dose. Although the first episode of pancytopenia resolved with a treatment regimen of granulocyte colony-stimulating factor (G-CSF), thrombopoietin (TPO), and red blood cell and platelet transfusion, the second episode showed extreme resistance to these treatments and improved only after the administration of steroids. His second pancytopenia episode resolved after a long course of treatment with methylprednisolone, G-CSF, TPO, hetrombopag and multiple red blood cell and platelet transfusions. However, he suffered a cerebral infarction when his platelet count was in the normal range and gradually recovered 1 week later. This case highlights the importance of the early recognition and management of hematological irAEs.

Unsupervised Monocular Depth and Camera Pose Estimation with Multiple Masks and Geometric Consistency Constraints.

This paper presents a novel unsupervised learning framework for estimating scene depth and camera pose from video sequences, fundamental to many high-level tasks such as 3D reconstruction, visual navigation, and augmented reality. Although existing unsupervised methods have achieved promising results, their performance suffers in challenging scenes such as those with dynamic objects and occluded regions. As a result, multiple mask technologies and geometric consistency constraints are adopted in this research to mitigate their negative impacts. Firstly, multiple mask technologies are used to identify numerous outliers in the scene, which are excluded from the loss computation. In addition, the identified outliers are employed as a supervised signal to train a mask estimation network. The estimated mask is then utilized to preprocess the input to the pose estimation network, mitigating the potential adverse effects of challenging scenes on pose estimation. Furthermore, we propose geometric consistency constraints to reduce the sensitivity of illumination changes, which act as additional supervised signals to train the network. Experimental results on the KITTI dataset demonstrate that our proposed strategies can effectively enhance the model's performance, outperforming other unsupervised methods.

Dietary Methionine Restriction Improves Gastrocnemius Muscle Glucose Metabolism through Improved Insulin Secretion and H19/IRS-1/Akt Pathway in Middle-Aged Mice.

Methionine restriction (MR) improves glucose metabolism. In skeletal muscle, H19 is a key regulator of insulin sensitivity and glucose metabolism. Therefore, this study aims to reveal the underlying mechanism of H19 upon MR on glucose metabolism in skeletal muscle. Middle-aged mice were fed MR diet for 25 weeks. Mouse islets ß cell line ß-TC6 cells and mouse myoblast cell line C2C12 cells were used to establish the apoptosis or insulin resistance model. Our findings showed that MR increased B-cell lymphoma-2 (Bcl-2) expression, deceased Bcl-2 associated X protein (Bax), cleaved cysteinyl aspartate-specific proteinase-3 (Caspase-3) expression in pancreas, and promoted insulin secretion of ß-TC6 cells. Meanwhile, MR increased H19 expression, insulin Receptor Substrate-1/insulin Receptor Substrate-2 (IRS-1/IRS-2) value, protein Kinase B (Akt) phosphorylation, glycogen synthase kinase-3ß (GSK3ß) phosphorylation, and hexokinase 2 (HK2) expression in gastrocnemius muscle and promoted glucose uptake in C2C12 cells. But these results were reversed after H19 knockdown in C2C12 cells. In conclusion, MR alleviates pancreatic apoptosis and promotes insulin secretion. And MR enhances gastrocnemius muscle insulin-dependent glucose uptake and utilization via the H19/IRS-1/Akt pathway, thereby ameliorating blood glucose disorders and insulin resistance in high-fat-diet (HFD) middle-aged mice.

Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis.

Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. Here we show a cost-efficient solution by designing a deep neural network to synthesize virtual EUS (V-EUS) from conventional B-mode images. A total of 4580 breast tumor cases were collected from 15 medical centers, including a main cohort with 2501 cases for model establishment, an external dataset with 1730 cases and a portable dataset with 349 cases for testing. In the task of differentiating benign and malignant breast tumors, there is no significant difference between V-EUS and real EUS on high-end ultrasound, while the diagnostic performance of pocket-sized ultrasound can be improved by about 5% after V-EUS is equipped.

Mechanisms and strategies to enhance penetration during intravesical drug therapy for bladder cancer.

Bladder cancer (BCa) is one of the most prevalent cancers worldwide. The effectiveness of intravesical therapy for bladder cancer, however, is limited due to the short dwell time and the presence of permeation barriers. Considering the histopathological features of BCa, the permeation barriers for drugs to transport across consist of a mucus layer and a nether tumor physiological barrier. Mucoadhesive delivery systems or mucus-penetrating delivery systems are developed to enhance their retention in or penetration across the mucus layer, but delivery systems that are capable of mucoadhesion-to-mucopenetration transition are more efficient to deliver drugs across the mucus layer. For the tumor physiological barrier, delivery systems mainly rely on four types of penetration mechanisms to cross it. This review summarizes the classical and latest approaches to intravesical drug delivery systems to penetrate BCa.

Deep learning and radiomics based automatic diagnosis of hippocampal sclerosis.

Accurate and rapid segmentation of the hippocampus can help doctors perform intractable temporal lobe epilepsy (TLE) preoperative evaluations to identify good surgical candidates. This study aims to establish a radiomics system for the automatic diagnosis of hippocampal sclerosis with the help of machine learning. A total of 240 cases were analysed to develop a diagnostic model. First, an automatic hippocampal segmentation process was established that exploits a priori knowledge of the relatively fixed location of the hippocampus in brain partitions, as well as a deep-learning segmentation network based on an Attention U-net. Then, we extracted 527 radiomics features from each side of the segmented hippocampus. The iterative sparse representation based on feature selection and a support vector machine classifier were finally used to establish the diagnostic model of hippocampal sclerosis. The diagnostic model consists of two consecutive steps: distinguish hippocampal sclerosis (HS) from normal control (NC) and detect whether the HS is located on the left or right side. When the automatic diagnosis model identified HS and NC, the sensitivity and specificity reached 0.941 and 0.917 in the 10-fold cross-validation set and 0.920 and 0.909 in the independent testing set. When the diagnostic model detected HS lateralization, the sensitivity and specificity reached 0.923 and 0.920 in cross-validation and 0.909 and 0.929 in independent testing. Our results show that the developed radiomics model can help detect TLE patients with hippocampal sclerosis and has the potential to simplify preoperative evaluations and select surgical candidates.

CD3 high and FoxP3 - tumor-infiltrating lymphocytes in the invasive margin as a favorable prognostic marker in patients with invasive urothelial carcinoma of the bladder.

Tumor-infiltrating lymphocytes (TILs) have been extensively explored as prognostic biomarkers and cellular immunotherapy methods in cancer patients. However, the prognostic significance of TILs in bladder cancer remains unresolved. We evaluated the prognostic effect of TILs in bladder cancer patients. Sixty-four bladder cancer patients who underwent surgical resection between 2018 and 2020 in Zhejiang Provincial People's Hospital were analyzed in this study. Immunohistochemistry was used to evaluate CD3, CD4, CD8, and FoxP3 expression on TILs in the invasive margin of tumor tissue, and the presence of TIL subsets was correlated with the disease-free survival (DFS) of bladder cancer patients. The relationship between clinical-pathological features and DFS were analyzed. A high level of CD3 + TILs (CD3 high TILs) ( P = 0.027) or negative expression of FoxP3 TILs (FoxP3 - TILs) ( P = 0.016) was significantly related to better DFS in bladder cancer patients. Those with CD3 high FoxP3 - TILs had the best prognosis compared to those with CD3 high FoxP3 + TILs or CD3 low FoxP3 - TILs ( P = 0.0035). Advanced age [HR 4.57, (1.86-11.25); P = 0.001], CD3 low TILs [HR 0.21, (0.06-0.71); P = 0.012], CD8 low TILs [HR 0.34, (0.12-0.94); P = 0.039], and FoxP3 + TILs [HR 10.11 (1.96-52.27); P = 0.006] in the invasive margin were associated with a worse prognosis (DFS) by multivariate analysis. In conclusion, we demonstrated that CD3 high , FoxP3 - , and CD3 high FoxP3 - TILs in the invasive margin were significantly associated with better DFS. CD8 high and CD4 high TILs in the invasive margin tended to predict better DFS in bladder cancer. Patients with CD4 high CD8 high TILs in the invasive margin were likely to have a better prognosis.