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BACKGROUND: Since the 2016 WHO guidelines, glioma diagnosis has entered an era of integrated diagnosis, combining tissue pathology and molecular pathology. The WHO has focused on promoting the application of molecular diagnosis in the classification of central nervous system tumors. Genetic information such as IDH1 and 1p/19q are important molecular markers, and pathological grading is also a key clinical indicator. However, obtaining genetic pathology labels is more costly than conventional MRI images, resulting in a large number of missing labels in realistic modeling. METHOD: We propose a training strategy based on label encoding and a corresponding loss function to enable the model to effectively utilize data with missing labels. Additionally, we integrate a graph model with genes and pathology-related clinical prior knowledge into the ResNet backbone to further improve the efficacy of diagnosis. Ten-fold cross-validation experiments were conducted on a large dataset of 1072 patients. RESULTS: The classification area under the curve (AUC) values are 0.93, 0.91, and 0.90 for IDH1, 1p/19q status, and grade (LGG/HGG), respectively. When the label miss rate reached 59.3 %, the method improved the AUC by 0.09, 0.10, and 0.04 for IDH1, 1p/19q, and pathological grade, respectively, compared to the same backbone without the missing label strategy. CONCLUSIONS: Our method effectively utilizes data with missing labels and integrates clinical prior knowledge, resulting in improved diagnostic performance for glioma genetic and pathological markers, even with high rates of missing labels.
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Neoplasias Encefálicas , Glioma , Imageamento por Ressonância Magnética , Humanos , Glioma/diagnóstico por imagem , Glioma/genética , Imageamento por Ressonância Magnética/métodos , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/patologia , Interpretação de Imagem Assistida por Computador/métodos , Feminino , MasculinoRESUMO
Segmentation of complex medical images such as vascular network and pulmonary tracheal network requires segmentation of many tiny targets on each tomographic section of the 3-D medical image volume. Although semantic segmentation of medical images based on deep learning has made great progress, fully supervised models require a great amount of annotations, making such complex medical image segmentation a difficult problem. In this article, we propose a semi-supervised model for complex medical image segmentation, which innovatively proposes a bidirectional self-training paradigm, through dynamically exchanging the roles of teacher and student by estimating the reliability at the model level. The direction of information and knowledge transfer between the two networks can be controlled, and the probability distribution of the roles of teacher and student in the next stage will be jointly determined by the model's uncertainty and instability in the training process. We also resolve the problem that loosely coupled networks are prone to collapse when training on small-scale annotated data by proposing asymmetric supervision (AS) strategy and hierarchical dual student (HDS) structure. In particular, a bidirectional distillation loss combined with the role exchange (RE) strategy and a global-local-aware consistency loss are introduced to obtain stable mutual promotion and achieve matching of global and local features, respectively. We conduct detailed experiments on two public datasets and one private dataset and lead existing semi-supervised methods by a large margin, while achieving fully supervised performance at a labeling cost of 5%.
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Objective: SWI image signal is related to venous reflux disorder and perfusion defect. Computed tomography perfusion (CTP) contains perfusion information in space and time. There is a complementary basis between them to affect the prognosis of cerebral infarction. Methods: Sixty-six patients included in the retrospective study were designated as the training set. Effective perfusion indicator features and imaging radiomic features of the peri-infarction area on Susceptibility weighted imaging (SWI) and CTP modality images were extracted from each case. Thirty-three patients from the prospectively included group were designated as the test set of the machine learning model based on a sparse representation method. The predicted results were compared with the DWI results of the patients' 7-10 days review to assess the validity and accuracy of the prediction. Results: The AUC of the SWI + CTP integrated model was 0.952, the ACC was 0.909, the SEN was 0.889, and the SPE was 0.933. The prediction performance is the highest. Compared with the value of AUC: the SWI model is 0.874, inferior to the performance of the SWI + CTP model, and the CTP model is 0.715. Conclusion: The prediction efficiency of the changing trend of infarction volume is further improved by the correlation between the combination of the two image features.
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Drug local delivery system that directly supply anti-cancer drugs to the tumor microenvironment (TME) results in excellent tumor control and minimizes side effects associated with the anti-cancer drugs. Immune checkpoint inhibitors (ICIs) have been the mainstay of cancer immunotherapy. However, the systemic administration of ICIs is accompanied by considerable immunotherapy-related toxicity. To explore whether an anti-PD-L1 antibody administered locally via a sustained-release gel-forming carrier retains its effective anticancer function while causing fewer colitis-like side effects, CT, a previously reported depot system, was used to locally deliver an anti-PD-L1 antibody together with curcumin to the TME in bladder cancer-bearing ulcerative colitis model mice. We showed that CT-mediated intratumoral coinjection of an anti-PD-L1 antibody and curcumin enabled sustained release of both the loaded anti-PD-L1 antibody and curcumin, which contributed to substantial anticancer effects with negligible side effects on the colons of the UC model mice. However, although the anti-PD-L1 antibody administered systemically synergized with the CT-mediated intratumoral delivery of curcumin in inhibiting tumour growth, colitis was significantly worsened by intraperitoneal administration of anti-PD-L1 antibody. These findings suggested that CT is a promising agent for the local delivery of anticancer drugs, as it can allow effective anticancer functions to be retained while sharply reducing the adverse side effects associated with the systemic administration of these drugs.
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Antígeno B7-H1 , Curcumina , Inibidores de Checkpoint Imunológico , Imunoterapia , Neoplasias da Bexiga Urinária , Animais , Neoplasias da Bexiga Urinária/tratamento farmacológico , Neoplasias da Bexiga Urinária/imunologia , Neoplasias da Bexiga Urinária/terapia , Antígeno B7-H1/antagonistas & inibidores , Antígeno B7-H1/imunologia , Curcumina/uso terapêutico , Curcumina/administração & dosagem , Camundongos , Imunoterapia/métodos , Inibidores de Checkpoint Imunológico/uso terapêutico , Humanos , Linhagem Celular Tumoral , Feminino , Colite/induzido quimicamente , Colite/imunologia , Colite/tratamento farmacológico , Microambiente Tumoral/efeitos dos fármacos , Microambiente Tumoral/imunologia , Sistemas de Liberação de Medicamentos , Modelos Animais de Doenças , Camundongos Endogâmicos C57BL , Antineoplásicos Imunológicos/administração & dosagem , Antineoplásicos Imunológicos/uso terapêutico , Colite Ulcerativa/induzido quimicamente , Colite Ulcerativa/tratamento farmacológico , Colite Ulcerativa/imunologiaRESUMO
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 (pâ<â0.01). 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 (pâ<â0.01). 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.
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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.
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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.
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Gorduras na Dieta , Ácidos Graxos Ômega-3 , Gorduras na Dieta/metabolismo , Ácidos Graxos/metabolismo , Encéfalo/metabolismo , Ácidos Graxos Ômega-3/metabolismo , CogniçãoRESUMO
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.
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Benchmarking , Análise Espectral Raman , Líquido Extracelular , Redes Neurais de Computação , Concentração de Íons de HidrogênioRESUMO
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.
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Proteínas Proto-Oncogênicas c-akt , Distúrbios do Início e da Manutenção do Sono , Ratos , Animais , Distúrbios do Início e da Manutenção do Sono/tratamento farmacológico , Sophora flavescens , Fator Neurotrófico Derivado do Encéfalo/metabolismo , Fosfatidilinositol 3-Quinases , EtanolRESUMO
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.
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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.
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Neoplasias Encefálicas , Aprendizado Profundo , Glioma , Humanos , Estudos Retrospectivos , Estudos Prospectivos , Mutação , Glioma/diagnóstico por imagem , Glioma/genética , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/genética , Biomarcadores Tumorais/genética , Isocitrato Desidrogenase/genética , Cromossomos Humanos Par 1RESUMO
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.
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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.
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Cervos , Cálculos Renais , Nefrolitotomia Percutânea , Nefrostomia Percutânea , Animais , Humanos , Nefrolitotomia Percutânea/métodos , Inteligência Artificial , Nefrostomia Percutânea/efeitos adversos , Nefrostomia Percutânea/métodos , Prognóstico , Cálculos Renais/diagnóstico por imagem , Cálculos Renais/cirurgia , Cálculos Renais/etiologiaRESUMO
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.
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Carcinoma Pulmonar de Células não Pequenas , Neoplasias Pulmonares , Humanos , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Antígeno B7-H1/uso terapêutico , Biomarcadores Tumorais , Carcinoma Pulmonar de Células não Pequenas/patologia , Endostatinas , Inibidores de Checkpoint Imunológico/uso terapêutico , Neoplasias Pulmonares/patologia , Receptor de Morte Celular Programada 1/uso terapêuticoRESUMO
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.
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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.
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Diagnóstico por Computador , Redes Neurais de Computação , Humanos , Diagnóstico por Computador/métodos , ComputadoresRESUMO
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.
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Adenocarcinoma de Pulmão , Neoplasias Pulmonares , Pancitopenia , Masculino , Humanos , Idoso , Pancitopenia/induzido quimicamente , Pancitopenia/diagnóstico , Adenocarcinoma de Pulmão/complicações , Adenocarcinoma de Pulmão/tratamento farmacológico , Fator Estimulador de Colônias de Granulócitos , Neoplasias Pulmonares/complicações , Neoplasias Pulmonares/tratamento farmacológico , Neoplasias Pulmonares/patologia , Infarto CerebralRESUMO
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.
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Glioma , Macrófagos Associados a Tumor , Camundongos , Humanos , Ratos , Animais , Glioma/metabolismo , Corantes Fluorescentes , Receptor de ManoseRESUMO
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.
Assuntos
Realidade Aumentada , Humanos , Iluminação , Máscaras , Tecnologia , Aprendizado de Máquina não SupervisionadoRESUMO
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.