GLP-1 in the Hypothalamic Paraventricular Nucleus Promotes Sympathetic Activation and Hypertension.

Glucagon-like peptide-1 (GLP-1) and its analogs are widely used for diabetes treatment. The paraventricular nucleus (PVN) is crucial for regulating cardiovascular activity. This study aims to determine the roles of GLP-1 and its receptors (GLP-1R) in the PVN in regulating sympathetic outflow and blood pressure. Experiments were carried out in male normotensive rats and spontaneously hypertensive rats (SHR). Renal sympathetic nerve activity (RSNA) and mean arterial pressure (MAP) were recorded. GLP-1 and GLP-1R expressions were present in the PVN. PVN microinjection of GLP-1R agonist recombinant human GLP-1 (rhGLP-1) or EX-4 increased RSNA and MAP, which were prevented by GLP-1R antagonist exendin 9-39 (EX9-39) or GLP-1R antagonist 1, superoxide scavenger tempol, antioxidant N-acetylcysteine, NADPH oxidase (NOX) inhibitor apocynin, adenylyl cyclase (AC) inhibitor SQ22536 or protein kinase A (PKA) inhibitor H89. PVN microinjection of rhGLP-1 increased superoxide production, NADPH oxidase activity, cAMP level, AC, and PKA activity, which were prevented by SQ22536 or H89. GLP-1 and GLP-1R were upregulated in the PVN of SHR. PVN microinjection of GLP-1 agonist increased RSNA and MAP in both WKY and SHR, but GLP-1 antagonists caused greater effects in reducing RSNA and MAP in SHR than in WKY. The increased superoxide production and NADPH oxidase activity in the PVN of SHR were augmented by GLP-1R agonists but attenuated by GLP-1R antagonists. These results indicate that activation of GLP-1R in the PVN increased sympathetic outflow and blood pressure via cAMP-PKA-mediated NADPH oxidase activation and subsequent superoxide production. GLP-1 and GLP-1R upregulation in the PVN partially contributes to sympathetic overactivity and hypertension.

Chemerin in caudal division of nucleus tractus solitarius increases sympathetic activity and blood pressure.

Chemerin is an adipokine that contributes to metabolism regulation. Nucleus tractus solitarius (NTS) is the first relay station in the brain for accepting various visceral afferent activities for regulating cardiovascular activity. However, the roles of chemerin in the NTS in regulating sympathetic activity and blood pressure are almost unknown. This study aimed to determine the role and potential mechanism of chemerin in the NTS in modulating sympathetic outflow and blood pressure. Bilateral NTS microinjections were performed in anaesthetized adult male Sprague-Dawley rats. Renal sympathetic nerve activity (RSNA), mean arterial pressure (MAP) and heart rate (HR) were continuously recorded. Chemerin and its receptor chemokine-like receptor 1 (CMKLR1) were highly expressed in caudal NTS (cNTS). Microinjection of chemerin-9 to the cNTS increased RSNA, MAP and HR, which were prevented by CMKLR1 antagonist α-NETA, superoxide scavenger tempol or N-acetyl cysteine, nicotinamide adenine dinucleotide phosphate (NADPH) oxidase inhibitors diphenyleneiodonium or apocynin. Chemerin-9 increased superoxide production and NADPH oxidase activity in the cNTS. The increased superoxide production induced by chemerin-9 was inhibited by α-NETA. The effects of cNTS microinjection of chemerin-9 on the RSNA, MAP and HR were attenuated by the pretreatment with paraventricular nucleus (PVN) microinjection of NMDA receptor antagonist MK-801 rather than AMPA/kainate receptor antagonist CNQX. These results indicate that chemerin-9 in the NTS increases sympathetic outflow, blood pressure and HR via CMKLR1-mediated NADPH oxidase activation and subsequent superoxide production in anaesthetized normotensive rats. Glutamatergic inputs in the PVN are needed for the chemerin-9-induced responses.

IFIT1 + neutrophil is a causative factor of immunosuppressive features of poorly cohesive carcinoma (PCC).

The importance of the immune microenvironment in poorly cohesive carcinoma (PCC) has been highlighted due to its limited response rate to conventional therapy and emerging treatment resistance. A combination of clinical cohorts, bioinformatics analyses, and functional/molecular experiments revealed that high infiltration of Interferon Induced Protein with Tetratricopeptide Repeats 1 (IFIT1) + tumor-associated neutrophils (TANs) is a distinguishing feature of PCC patients. Upregulation of IFIT1 + TANs promote migration and invasion of gastric cancer (GC) cell lines (MKN45 and MKN74) and stimulates the growth of cell-derived xenograft models. Besides, by promoting macrophage secreted phosphoprotein 1 (SPP1) expression and facilitating cancer-associated fibroblast and endothelial cell recruitment and activation through TANs, IFIT1 promotes a mesenchymal phenotype, which is associated with a poor prognosis. Importantly, compared to non-PCC (NPCC), PCC tumors is more immunosuppressive. Mechanistically, IFIT1 can be stimulated by IFN-γ and contributes to the expression of Programmed Cell Death 1 Ligand (PDL1) in TANs. We demonstrated in mouse models that IFIT1 + PDL1 + TANs can induce acquired resistance to anti-PD-1 immunotherapy, which may be responsible for the difficulty of PCC patients to benefit from immunotherapy. This work highlights the role of IFIT1 + TANs in mediating the remodeling of the tumor immune microenvironment and immunotherapeutic resistance and introduces IFIT1 + TANs as a promising target for precision therapy of PCC.

Correlation between hemoglobin and the risk of common malignant tumors: a 1999-2020 retrospective analysis and causal association analysis.

BACKGROUND: The role of hemoglobin (HGB) in common malignant tumors remains unclear. METHODS: A retrospective analysis was conducted to identify the correlation between HGB levels and risk of 15 malignant tumors using 50,085 samples from the National Health and Nutrition Examination Survey. Mendelian Randomization analyses (MRAs) were performed based on genome-wide association study data to assess the causal relationship between HGB levels and these malignant tumors using more than 700,000 samples. The robustness of the MRA results was confirmed through various analytical methods. Fifty-six in-house samples were used to investigate the correlation between HGB levels and the prognosis in prostate cancer (PRCA) using the Kaplan-Meier curve. RESULTS: High HGB levels were associated with a higher risk for patients with cervix cancer, melanoma, and non-melanoma skin cancer (OR > 1.000, p < 0.05). It served as a protective factor for colon cancer, esophagus cancer, stomach cancer, bone cancer, lung cancer, renal cancer, and PRCA (OR < 1.000, p < 0.05). Furthermore, MRAs suggested that elevated HGB levels were correlated with a reduced risk of PRCA (OR = 0.869, p < 0.05), with no significant association observed between this marker and the remaining 14 malignant tumors. No pleiotropy or heterogeneity was found in the ultimate results for MRAs (p-values > 0.05), suggesting the robustness of the results. The results derived from the in-house data revealed a relationship between higher HGB values and a more favorable prognosis in PRCA (p < 0.05). CONCLUSION: High circulating HGB levels may play a protective prognostic role for PRCA and serve as a protective factor against the occurrence of PRCA.

Understanding Variations in Ferrate Detection through the ABTS Method in the Presence of Electron-Rich Organic Compounds.

The chromogenic reaction between 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonate) (ABTS) and ferrate [Fe(VI)] has long been utilized for Fe(VI) content measurement. However, the presence of electron-rich organic compounds has been found to significantly impact Fe(VI) detection using the ABTS method, leading to relative errors ranging from ∼88 to 100%. Reducing substances consumed ABTS•+ and resulted in underestimated Fe(VI) levels. Moreover, the oxidation of electron-rich organics containing hydroxyl groups by Fe(VI) could generate a phenoxyl radical (Ph•), promoting the transformation of Fe(VI) → Fe(V) → Fe(IV). The in situ formation of Fe(IV) can then contribute to ABTS oxidation, altering the ABTS•+:Fe(VI) stoichiometry from 1:1 to 2:1. To overcome these challenges, we introduced Mn(II) as an activator and 3,3',5,5'-tetramethylbenzidine (TMB) as a chromogenic agent for Fe(VI) detection. This Mn(II)/TMB method enables rapid completion of the chromogenic reaction within 2 s, with a low detection limit of approximately 4 nM and a wide detection range (0.01-10 µM). Importantly, the Mn(II)/TMB method exhibits superior resistance to reductive interference and effectively eliminates the impact of phenoxyl-radical-mediated intermediate valence iron transfer processes associated with electron-rich organic compounds. Furthermore, this method is resilient to particle interference and demonstrates practical applicability in authentic waters.

Risk factors for one-year mortality following discharge in patients with acute aortic dissection: development and validation of a predictive model in a cross-sectional study.

PURPOSE: This study was aimed to identify the risk factors that influence the mortality risk in patients with acute aortic dissection (AAD) within one year after discharge, and aimed to construct a predictive model for assessing mortality risk. METHODS: The study involved 320 adult patients obtained from the Medical Information Mart for Intensive Care (MIMIC) database. Logistic regression analysis was conducted to identify potential risk factors associated with mortality in AAD patients within one year after discharge and to develop a predictive model. The performance of the predictive model was assessed using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). To further validate the findings, patient data from the First Affiliated Hospital of Guangxi Medical University (157 patients) were analyzed. RESULTS: Univariate and multivariate logistic regression analyses revealed that gender, length of hospital stay, highest blood urea nitrogen (BUN_max), use of adrenaline, and use of amiodarone were significant risk factors for mortality within one year after discharge (p < 0.05). The constructed model exhibited a consistency index (C-index) and an area under the ROC curve of 0.738. The calibration curve and DCA demonstrated that these indicators had a good degree of agreement and utility. The external validation results of the model also indicated good predictability (AUC = 0.700, p < 0.05). CONCLUSION: The personalized scoring prediction model constructed by gender, length of hospital stays, BUN_max levels, as well as the use of adrenaline and amiodarone, can effectively identify AAD patients with high mortality risk within one year after discharge.

Effective Treatment of Anlotinib Combined With Chemotherapy in Children With Desmoplastic Small Round Cell Tumor: A Case Series in a Single-center and Literature Review.

INTRODUCTION: Desmoplastic small round cell tumor (DSRCT) is a highly aggressive primitive sarcoma with a 5-year survival rate estimated at only 15% to 30%. Although few curative treatment options exist, patients are most often treated with a combination of aggressive chemotherapy, radiation, and surgery. Targeted therapy inhibitors of platelet-derived growth factor A, insulin-like growth factor receptor 1, and vascular endothelial growth factor receptor-2, which are almost uniformly overexpressed in DSRCT, have largely failed in clinical trials. Anlotinib is a multitarget receptor tyrosine kinase inhibitor that inhibits vascular endothelial growth factor receptor 1-3, fibroblast growth factor receptor 1-4, platelet-derived growth factor receptor α/ß, c-Kit, and Met. In this study, we presented 3 cases of DSRCT treated effectively with anlotinib combined with chemotherapy. CASE PRESENTATION: Three children DSRCT patients were enrolled from September 2020 to December 2021 and monitored until August 30, 2022. The clinical data were prospectively studied. The peritoneal cancer index classified all 3 patients as stage IV. After surgery, all 3 patients received anlotinib in combination with chemotherapy and reacted to the medication. For all 3 patients, clinical symptoms were substantially eased, and the size of the masses was reduced. Patient 1 and patient 3's progression-free survival had been extended, and anlotinib was continued as a maintenance medication in the 2 patients who were in good health at the end of the follow-up. Patient 2 died of postoperative complications 1 month after second-stage surgery. The main side effects of anlotinib were fatigue and hypertension. However, its toxicity was controllable and tolerable in children patients. CONCLUSIONS: This is the first report that anlotinib is effective in children with DSRCT. This report may provide an additional option for the treatment of metastatic DSRCT.

Naringin attenuates Actinobacillus pleuropneumoniae-induced acute lung injury via MAPK/NF-κB and Keap1/Nrf2/HO-1 pathway.

Actinobacillus pleuropneumoniae (APP) causes porcine pleuropneumonia (PCP), which is clinically characterized by acute hemorrhagic, necrotizing pneumonia, and chronic fibrinous pneumonia. Although many measures have been taken to prevent the disease, prevention and control of the disease are becoming increasingly difficult due to the abundance of APP sera, weak vaccine cross-protection, and increasing antibiotic resistance in APP. Therefore, there is an urgent need to develop novel drugs against APP infection to prevent the spread of APP. Naringin (NAR) has been reported to have an excellent therapeutic effect on pulmonary diseases, but its therapeutic effect on lung injury caused by APP is not apparent. Our research has shown that NAR was able to alleviate APP-induced weight loss and quantity of food taken and reduce the number of WBCs and NEs in peripheral blood in mice; pathological tissue sections showed that NAR was able to prevent and control APP-induced pathological lung injury effectively; based on the establishment of an in vivo/in vitro model of APP inflammation, it was found that NAR was able to play an anti-inflammatory role through inhibiting the MAPK/NF-κB signaling pathway and exerting anti-inflammatory effects; additionally, NAR activating the Nrf2 signalling pathway, increasing the secretion of antioxidant enzymes Nqo1, CAT, and SOD1, inhibiting the secretion of oxidative damage factors NOS2 and COX2, and enhancing the antioxidant stress ability, thus playing an antioxidant role. In summary, NAR can relieve severe lung injury caused by APP by reducing excessive inflammatory response and improving antioxidant capacity.

A Chinese adaptation of the Patient Health Questionnaire for Adolescents (PHQ-A): factor structure and psychometric properties.

BACKGROUND: To examine the factor structure and psychometric properties of the Patient Health Questionnaire for Adolescents (PHQ-A) in Chinese children and adolescents with major depressive disorder (MDD). METHODS: A total of 248 MDD patients aged between 12 and 18 years were recruited and evaluated by the Patient Health Questionnaire for Adolescents (PHQ-A), the Center for Epidemiological Survey Depression Scale (CES-D), the Mood and Feelings Questionnaire (MFQ), and the improved Clinical Global Impression Scale, Severity item (iCGI-S). Thirty-one patients were selected randomly to complete the PHQ-A again one week later. Confirmatory factor analysis (CFA) was used to test the construct validity of the scale. Reliability was evaluated by Macdonald Omega coefficient. Pearson correlation coefficient was used to assess the item-total correlation and the correlation of PHQ-A with CES-D and MFQ respectively. Spearman correlation coefficient was used to assess test-retest reliability. The optimal cut-off value, sensitivity, and specificity of the PHQ-A were achieved by estimating the Receiver Operating Characteristics (ROC) curve. RESULTS: CFA reported adequate loadings for all items, except for item 3. Macdonald Omega coefficient of the PHQ-A was 0.87. The Spearman correlation coefficient of the test-retest reliability was 0.70. The Pearson correlation coefficients of the PHQ-A with CES-D and MFQ were 0.87 and 0.85, respectively (p < 0.01). By taking the iCGI-S as the remission criteria for MDD, the optimal cut-off value, sensitivity and specificity of the PHQ-A were 7, 98.7%, 94.7% respectively. CONCLUSION: The PHQ-A presented as a unidimensional construct and demonstrated satisfactory reliability and validity among the Chinese children and adolescents with MDD. A cut-off value of 7 was suggested for remission.

Retracted: Marchantin M Induces Apoptosis of Prostate Cancer Cells Through Endoplasmic Reticulum Stress.

The Editors of Medical Science Monitor wish to inform you that the above manuscript has been retracted from publication due to concerns with the credibility and originality of the study, the manuscript content, and the Figure images. Reference: Tian-Wei Zhang, Li Xing, Jun-Long Tang, Jing-Xiao Lu, Chun-Xiao Liu. Marchantin M Induces Apoptosis of Prostate Cancer Cells Through Endoplasmic Reticulum Stress. Med Sci Monit, 2015; 21: 3570-3576. DOI: 10.12659/MSM.894476.

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OBJECTIVES: To evaluate the incidence rate of Duchenne muscular dystrophy (DMD) in the male newborns in the Ningxia region and establish a critical threshold for screening DMD in newborns to distinguish between the normal population and affected individuals. METHODS: A total of 10 000 male newborns were screened using immunofluorescence analysis of creatine kinase isoenzyme concentrations in heel spot dried blood specimens. Newborns with the concentrations higher than the critical threshold were recalled for serum creatine kinase measurements. Genetic testing was performed to confirm diagnosis in cases showing abnormalities. RESULTS: Among the screened 10 000 male newborns, two were confirmed to have DMD through genetic testing, resulting in a preliminary estimated incidence rate of 1/5 000 for male newborns in the Ningxia region. The critical threshold for creatine kinase isoenzyme concentration in newborns in this region was determined to be 468.57 ng/mL. CONCLUSIONS: Screening for DMD in newborns is feasible in the Ningxia region. Early screening, diagnosis, and treatment of DMD can improve the quality of life for affected individuals and help families make informed decisions regarding further pregnancies.

Cancer survival prediction by learning comprehensive deep feature representation for multiple types of genetic data.

BACKGROUND: Cancer is one of the leading death causes around the world. Accurate prediction of its survival time is significant, which can help clinicians make appropriate therapeutic schemes. Cancer data can be characterized by varied molecular features, clinical behaviors and morphological appearances. However, the cancer heterogeneity problem usually makes patient samples with different risks (i.e., short and long survival time) inseparable, thereby causing unsatisfactory prediction results. Clinical studies have shown that genetic data tends to contain more molecular biomarkers associated with cancer, and hence integrating multi-type genetic data may be a feasible way to deal with cancer heterogeneity. Although multi-type gene data have been used in the existing work, how to learn more effective features for cancer survival prediction has not been well studied. RESULTS: To this end, we propose a deep learning approach to reduce the negative impact of cancer heterogeneity and improve the cancer survival prediction effect. It represents each type of genetic data as the shared and specific features, which can capture the consensus and complementary information among all types of data. We collect mRNA expression, DNA methylation and microRNA expression data for four cancers to conduct experiments. CONCLUSIONS: Experimental results demonstrate that our approach substantially outperforms established integrative methods and is effective for cancer survival prediction. AVAILABILITY AND IMPLEMENTATION: https://github.com/githyr/ComprehensiveSurvival .

A novel two-way rebalancing strategy for identifying carbonylation sites.

BACKGROUND: As an irreversible post-translational modification, protein carbonylation is closely related to many diseases and aging. Protein carbonylation prediction for related patients is significant, which can help clinicians make appropriate therapeutic schemes. Because carbonylation sites can be used to indicate change or loss of protein function, integrating these protein carbonylation site data has been a promising method in prediction. Based on these protein carbonylation site data, some protein carbonylation prediction methods have been proposed. However, most data is highly class imbalanced, and the number of un-carbonylation sites greatly exceeds that of carbonylation sites. Unfortunately, existing methods have not addressed this issue adequately. RESULTS: In this work, we propose a novel two-way rebalancing strategy based on the attention technique and generative adversarial network (Carsite_AGan) for identifying protein carbonylation sites. Specifically, Carsite_AGan proposes a novel undersampling method based on attention technology that allows sites with high importance value to be selected from un-carbonylation sites. The attention technique can obtain the value of each sample's importance. In the meanwhile, Carsite_AGan designs a generative adversarial network-based oversampling method to generate high-feasibility carbonylation sites. The generative adversarial network can generate high-feasibility samples through its generator and discriminator. Finally, we use a classifier like a nonlinear support vector machine to identify protein carbonylation sites. CONCLUSIONS: Experimental results demonstrate that our approach significantly outperforms other resampling methods. Using our approach to resampling carbonylation data can significantly improve the effect of identifying protein carbonylation sites.

CDK6 is a novel predictive and prognosis biomarker correlated with immune infiltrates in multiple human neoplasms, including small cell lung carcinoma.

The roles of cyclin-dependent kinase 6 (CDK6) in various cancers, including small cell lung carcinoma (SCLC), remain unclear. Here, 111,54 multi-center samples were investigated to determine the expression, clinical significance, and underlying mechanisms of CDK6 in 34 cancers. The area under the curve (AUC), Cox regression analysis, and the Kaplan-Meier curves were used to explore the clinical value of CDK6 in cancers. Gene set enrichment analysis and correlation analysis were performed to detect potential CDK6 mechanisms. CDK6 expression was essential in 24 cancer cell types. Abnormal CDK6 expression was observed in 14 cancer types (e.g., downregulated in breast invasive carcinoma; p < 0.05). CDK6 allowed six cancers to be distinguished from their controls (AUC > 0.750). CDK6 expression was a prognosis marker for 13 cancers (e.g., adrenocortical carcinoma; p < 0.05). CDK6 was correlated with several immune-related signaling pathways and the infiltration levels of certain immune cells (e.g., CD8+ T cells; p < 0.05). Downregulated CDK6 mRNA and protein levels were observed in SCLC (p < 0.05, SMD = - 0.90). CDK6 allowed the identification of SCLC status (AUC = 0.91) and predicted a favorable prognosis for SCLC patients (p < 0.05). CDK6 may be a novel biomarker for the prediction and prognosis of several cancers, including SCLC.

Novel Deep Learning Network for Gait Recognition Using Multimodal Inertial Sensors.

Some recent studies use a convolutional neural network (CNN) or long short-term memory (LSTM) to extract gait features, but the methods based on the CNN and LSTM have a high loss rate of time-series and spatial information, respectively. Since gait has obvious time-series characteristics, while CNN only collects waveform characteristics, and only uses CNN for gait recognition, this leads to a certain lack of time-series characteristics. LSTM can collect time-series characteristics, but LSTM results in performance degradation when processing long sequences. However, using CNN can compress the length of feature vectors. In this paper, a sequential convolution LSTM network for gait recognition using multimodal wearable inertial sensors is proposed, which is called SConvLSTM. Based on 1D-CNN and a bidirectional LSTM network, the method can automatically extract features from the raw acceleration and gyroscope signals without a manual feature design. 1D-CNN is first used to extract the high-dimensional features of the inertial sensor signals. While retaining the time-series features of the data, the dimension of the features is expanded, and the length of the feature vectors is compressed. Then, the bidirectional LSTM network is used to extract the time-series features of the data. The proposed method uses fixed-length data frames as the input and does not require gait cycle detection, which avoids the impact of cycle detection errors on the recognition accuracy. We performed experiments on three public benchmark datasets: UCI-HAR, HuGaDB, and WISDM. The results show that SConvLSTM performs better than most of those reporting the best performance methods, at present, on the three datasets.

TMT-Based Proteomics Reveal the Mechanism of Action of Amygdalin against Rheumatoid Arthritis in a Rat Model through Regulation of Complement and Coagulation Cascades.

The limitations of current medications for treating rheumatoid arthritis (RA) emphasize the urgent need for the development of new drugs. This study aimed to investigate the potential anti-RA mechanism of amygdalin using tandem mass tag (TMT)-based quantitative proteomics technology. First, the anti-RA activity of amygdalin was evaluated in a Complete Freund's adjuvant (CFA)-induced rat model. Then, the roles and importance of proteins in the extracted rat joint tissue were evaluated using TMT-based quantitative proteomics technology. A bioinformatics analysis was used to analyze differentially abundant proteins (DAPs). A proteomics analysis identified 297 DAPs in the amygdalin group compared with the model group, of which 53 upregulated proteins and 51 downregulated proteins showed opposite regulatory trends to the DAPs produced after modeling. According to enrichment analyses of the DAPs, the signaling pathways with a high correlation degree were determined to be the complement and coagulation cascades. Furthermore, western blotting and molecular docking were used to further validate the key node proteins, e.g., complement C1s subcomponent (C1s), component C3 (C3) and kininogen 1 (Kng1). These results suggest that amygdalin may be a promising agent for treating RA by regulating the complement and coagulation cascades.

Synthesis and Biological Evaluation of Novel 2-Amino-1,4-Naphthoquinone Amide-Oxime Derivatives as Potent IDO1/STAT3 Dual Inhibitors with Prospective Antitumor Effects.

Indoleamine-2,3-dioxygenase 1 (IDO1) and signal transducer and activator of transcription 3 (STAT3) have emerged as significant targets in the tumor microenvironment for cancer therapy. In this study, we synthesized three novel 2-amino-1,4-naphthoquinone amide-oxime derivatives and identified them as dual inhibitors of IDO1 and STAT3. The representative compound NK3 demonstrated effective binding to IDO1 and exhibited good inhibitory activity (hIDO1 IC50 = 0.06 µM), leading to its selection for further investigation. The direct interactions between compound NK3 and IDO1 and STAT3 proteins were confirmed through surface plasmon resonance analysis. A molecular docking study of compound NK3 revealed key interactions between NK3 and IDO1, with the naphthoquinone-oxime moiety coordinating with the heme iron. In the in vitro anticancer assay, compound NK3 displayed potent antitumor activity against selected cancer cell lines and effectively suppressed nuclear translocation of STAT3. Moreover, in vivo assays conducted on CT26 tumor-bearing Balb/c mice and an athymic HepG2 xenograft model revealed that compound NK3 exhibited potent antitumor activity with low toxicity relative to 1-methyl-L-tryptophan (1-MT) and doxorubicin (DOX). Overall, these findings provided evidence that the dual inhibitors of IDO1 and STAT3 may offer a promising avenue for the development of highly effective drug candidates for cancer therapy.

Influencing factors of urban innovation and development: a grounded theory analysis.

Urban innovation and development are a core driver for promoting the industrial, economic, and social development of cities. However, the factors that affect the innovation and development of cities lack systematic analysis as well as interaction analysis. Based on a multidimensional perspective, this study suggests that natural, economic, and social factors are three major factors conditioning urban innovation and development. A grounded theoretical qualitative method is further adopted to code relevant research literatures, news reports and interview materials, resulting in an onion factors model. We find that natural factors-including environmental quality, geographic location, and city scale-are prerequisite for conditioning urban innovation and development. Economic factors are also key, including economic level, industrial structure, industrial agglomeration, and technological innovation. Social factors are guarantee factors, including administrative hierarchy, cultural environment, population structure, and government management and services, i.e., they are essential for cities to become adaptable in the current dynamic situation. The study provides theoretical support and practical directions for the formulation of policies for urban innovation development.

Joint learning sample similarity and correlation representation for cancer survival prediction.

BACKGROUND: As a highly aggressive disease, cancer has been becoming the leading death cause around the world. Accurate prediction of the survival expectancy for cancer patients is significant, which can help clinicians make appropriate therapeutic schemes. With the high-throughput sequencing technology becoming more and more cost-effective, integrating multi-type genome-wide data has been a promising method in cancer survival prediction. Based on these genomic data, some data-integration methods for cancer survival prediction have been proposed. However, existing methods fail to simultaneously utilize feature information and structure information of multi-type genome-wide data. RESULTS: We propose a Multi-type Data Joint Learning (MDJL) approach based on multi-type genome-wide data, which comprehensively exploits feature information and structure information. Specifically, MDJL exploits correlation representations between any two data types by cross-correlation calculation for learning discriminant features. Moreover, based on the learned multiple correlation representations, MDJL constructs sample similarity matrices for capturing global and local structures across different data types. With the learned discriminant representation matrix and fused similarity matrix, MDJL constructs graph convolutional network with Cox loss for survival prediction. CONCLUSIONS: Experimental results demonstrate that our approach substantially outperforms established integrative methods and is effective for cancer survival prediction.

The associations of serum valine with mild cognitive impairment and Alzheimer's disease.

BACKGROUND: The introduction of metabolomics makes it possible to study the characteristic changes of peripheral metabolism in Alzheimer's disease (AD). Recent studies have found that the levels of valine are related to mild cognitive impairment (MCI) and AD. AIMS: This study aimed to further clarify the characteristics of valine levels in MCI and AD. METHODS: A total of 786 participants from the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1) cohort were selected to evaluate the relationships between serum valine and cerebrospinal fluid (CSF) biomarkers, brain structure (magnetic resonance imaging, MRI), cerebral glucose metabolism (18F-fluorodeoxyglucose-positron emission tomography, FDG-PET), and cognitive declines, through different cognitive subgroups. RESULTS: Serum valine was decreased in patients with AD compared with cognitive normal (CN) and stable MCI (sMCI), and in progressive MCI (pMCI) compared with CN. Serum valine was negatively correlated with CSF total tau (t-tau) and phosphorylated tau (p-tau) in pMCI. Serum valine significantly predicted conversion from MCI to AD. In addition, serum valine was related to the rate of change of cerebral glucose metabolism during the follow-up period in pMCI. CONCLUSIONS: Serum valine may be a peripheral biomarker of pMCI and AD, and its level predicts the progression of MCI to AD. Our study may help to reveal the metabolic changes during AD disease trajectory and its relationship to clinical phenotype.