Impact of Oil Bodies on Structure, Rheology and Function of Acid-Mediated Soy Protein Isolate Gels.

Oil bodies (OBs) are naturally occurring pre-emulsified oil droplets that have broad application prospects in emulsions and gels. The main purpose of this research was to examine the impact of the OB content on the structure and functional aspects of acid-mediated soy protein isolate (SPI) gel filled with OBs. The results indicated that the peanut oil body (POBs) content significantly affected the water holding capacity of the gel. The rheological and textural analyses showed that POBs reduced the gel strength and hardness. The scanning electron and confocal laser scanning microscopy analyses revealed that POBs aggregated during gel formation and reduced the gel network density. The Fourier transform infrared spectrum (FTIR) analysis demonstrated that POBs participated in protein gels through hydrogen bonds, steric hindrance and hydrophobic interactions. Therefore, OBs served as inactive filler in the acid-mediated protein gel, replaced traditional oils and provided alternative ingredients for the development of new emulsion-filled gels.

High-Flow Nasal Oxygen versus Conventional Nasal Cannula in Preventing Hypoxemia in Elderly Patients Undergoing Gastroscopy with Sedation: A Randomized Controlled Trial.

Background: We aimed to compare the prevention of hypoxemia using High-flow nasal oxygen (HFNO) or regular nasal tubing (CNC) in elderly patients undergoing gastroscopy with sedation. Methods: This study was a prospective, randomized, controlled trial conducted at a single center. We included elective patients aged 65 and above who were undergoing gastroscopy with sedation. In the intervention group (HFNO), we set the oxygen flow rate to 60 liters per minute with an oxygen fraction (FiO2) of 0.6, while in the control group (CNC), it was 6 liters per minute. The primary outcome was the occurrence of hypoxemia (defined as Spo2 < 90%). Results: A total of 125 participants were enrolled (HFNO group: n = 63; CNC group: n = 62). The occurrence of hypoxemia was found to be significantly lower in the HFNO group compared to the CNC group (3.2% vs. 22.6%, p = 0.001). Additionally, a significantly shorter duration of low oxygen levels was observed in the HFNO group [0.0 seconds (0.0-13.0)] compared to the CNC group [0.0 seconds (0.0-124.0), p<0.001]. Moreover, a higher minimum Spo2 value was achieved in the HFNO group [99.0% (98.0-100.0) vs. 96.5% (91.0-99.0), p < 0.001], and a shorter recovery time was recorded [0.5 minutes (0.0-0.5) vs. 0.5 minutes (0.0-1.0), p = 0.016] in comparison to the CNC group. There were no differences in terms of comfort level [0 (0-4) vs. 0 (0-5), p = 0.268] between the two groups. Conclusions: The HFNO system was determined to be a safe and highly effective method for oxygen delivery, leading to a reduction in the occurrence of hypoxemia in elderly patients undergoing gastroscopy with sedation. It is recommended that HFNO be considered as the standard approach for management in this population.

The potential mechanism of Aidi injection against neuroblastoma-an investigation based on network pharmacology analysis.

Background: Aidi injection, a classic traditional Chinese medicine (TCM) formula, has been used on a broader scale in treating a variety of cancers. In this study, we aimed to explore the potential anti-tumor effects of Aidi injection in the treatment of neuroblastoma (NB) using network pharmacology (NP). Methods: To elucidate the anti-NB mechanism of Aidi injection, an NP-based approach and molecular docking validation were employed. The compounds and target genes were collected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (BATMAN-TCM) database. The protein-protein interaction network was constructed using the STRING database. clusterProfiler (R package) was utilized to annotate the bioinformatics of hub target genes. The gene survival analysis was performed on R2, a web-based genomic analysis application. iGEMDOCK was used for molecular docking validation, and GROMACS was utilized to validate molecular docking results. Furthermore, we investigated the anticancer effects of gomisin B and ginsenoside Rh2 on human NB cells using a cell viability assay. The Western blot assay was used to validate the protein levels of target genes in gomisin B- and ginsenoside Rh2-treated NB cells. Results: A total of 2 critical compounds with 16 hub target genes were identified for treating NB. All 16 hub genes could potentially influence the survival of NB patients. The top three genes (EGFR, ESR1, and MAPK1) were considered the central hub genes from the drug-compound-hub target gene-pathway network. The endocrine resistance and estrogen signaling pathways were identified as the therapeutic pathways using the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Gomisin B and ginsenoside Rh2 showed a good binding ability to the target protein in molecular docking. The results of cell experiments showed the anti-NB effect of gomisin B and ginsenoside Rh2. In addition, the administration of gomisin B over-regulated the expression of ESR1 protein in MYCN-amplified NB cells. Conclusion: In the present study, we investigated the potential pharmacological mechanisms of Aidi against NB and revealed the anti-NB effect of gomisin B, providing clinical evidence of Aidi in treating NB and establishing baselines for further research.

Engineered Exosomes as Theranostic Platforms for Cancer Treatment.

Tremendous progress in nanotechnology and nanomedicine has made a significant positive effect on cancer treatment by integrating multicomponents into a single multifunctional nanosized delivery system for combinatorial therapies. Although numerous nanocarriers developed so far have achieved excellent therapeutic performance in mouse models via elegant integration of chemotherapy, photothermal therapy, photodynamic therapy, sonodynamic therapy, and immunotherapy, their synthetic origin may still cause systemic toxicity, immunogenicity, and preferential detection or elimination by the immune system. Exosomes, endogenous nanosized particles secreted by multiple biological cells, could be absorbed by recipient cells to facilitate intercellular communication and content delivery. Therefore, exosomes have emerged as novel cargo delivery tools and attracted considerable attention for cancer diagnosis and treatment due to their innate stability, biological compatibility, and biomembrane penetration capacity. Exosome-related properties and functions have been well-documented; however, there are few reviews, to our knowledge, with a focus on the combination of exosomes and nanotechnology for the development of exosome-based theranostic platforms. To make a timely review on this hot subject of research, we summarize the basic information, isolation and functionalization methodologies, diagnostic and therapeutic potential of exosomes in various cancers with an emphasis on the description of exosome-related nanomedicine for cancer theranostics. The existing appealing challenges and outlook in exosome clinical translation are finally introduced. Advanced biotechnology and nanotechnology will definitely not only promote the integration of intrinsic advantages of natural nanosized exosomes with traditional synthetic nanomaterials for modulated precise cancer treatment but also contribute to the clinical translations of exosome-based nanomedicine as theranostic nanoplatforms.

Spherical nucleic acids-based nanoplatforms for tumor precision medicine and immunotherapy.

Precise diagnosis and treatment of tumors currently still face considerable challenges due to the development of highly degreed heterogeneity in the dynamic evolution of tumors. With the rapid development of genomics, personalized diagnosis and treatment using specific genes may be a robust strategy to break through the bottleneck of traditional tumor treatment. Nevertheless, efficient in vivo gene delivery has been frequently hampered by the inherent defects of vectors and various biological barriers. Encouragingly, spherical nucleic acids (SNAs) with good modularity and programmability are excellent candidates capable of addressing traditional gene transfer-associated issues, which enables SNAs a precision nanoplatform with great potential for diverse biomedical applications. In this regard, there have been detailed reviews of SNA in drug delivery, gene regulation, and dermatology treatment. Still, to the best of our knowledge, there is no published systematic review summarizing the use of SNAs in oncology precision medicine and immunotherapy, which are considered new guidelines for oncology treatment. To this end, we summarized the notable advances in SNAs-based precision therapy and immunotherapy for tumors following a classification standard of different types of precise spatiotemporal control on active species by SNAs. Specifically, we focus on the structural diversity and programmability of SNAs. Finally, the challenges and possible solutions were discussed in the concluding remarks. This review will promote the rational design and development of SNAs for tumor-precise medicine and immunotherapy.

Effects of 30% vs. 60% inspired oxygen fraction during mechanical ventilation on postoperative atelectasis: a randomised controlled trial.

BACKGROUND: There is the ongoing debate over the effect of inspired oxygen fraction (FiO2) during mechanical ventilation on postoperative atelectasis. We aimed to compare the effects of low (30%) and moderate (60%) FiO2 on postoperative atelectasis. The hypothesis of the study was that 30% FiO2 during mechanical ventilation could reduce postoperative atelectasis volume compared with 60% FiO2. METHODS: We performed a randomized controlled trial with 120 patients. Subjects were randomly assigned to receive 30% or 60% FiO2 during mechanical ventilation in a 1:1 ratio. The primary outcome was the percentage of postoperative atelectasis volume in the total lung measured using chest CT within 30 min after extubation. The secondary outcomes included different aeration region volumes, incidence of clinically significant atelectasis, and oxygenation index. RESULTS: In total, 113 subjects completed the trial, including 55 and 58 subjects in the 30% and 60% FiO2 groups, respectively. The percentage of the postoperative atelectasis volume in the 30% FiO2 group did not differ from that in the 60% FiO2 group. Furthermore, there was no significant difference in the atelectasis volume between the two groups after the missing data were imputed by multiple imputation. Additionally, there were no significant differences in the volumes of the over-aeration, normal-aeration, and poor-aeration regions between the groups. No significant differences in the incidence of clinically significant atelectasis or oxygenation index at the end of surgery were observed between the groups. CONCLUSIONS: Compared with 60% FiO2, the use of 30% FiO2 during mechanical ventilation does not reduce the postoperative atelectasis volume. TRIAL REGISTRATION: Chinese Clinical Trial Registry ( http://www.chictr.org.cn ). Identifier: ChiCTR1900021635. Date: 2 March 2019. Principal invetigator: Weidong Gu.

Comparison of 6-min walk test distance vs. estimated maximum oxygen consumption for predicting postoperative pulmonary complications in patients undergoing upper abdominal surgery: a prospective cohort study.

OBJECTIVE: The present study aims to evaluate the predictive ability of estimated maximum oxygen consumption (e[Formula: see text]O2max) and 6-min walk distance (6MWD) for postoperative pulmonary complications (PPCs) in adult surgical patients undergoing major upper abdominal surgery. METHOD: This study was conducted by collecting data prospectively from a single center. The two predictive variables in the study were defined as 6MWD and e[Formula: see text]O2max. Patients scheduled for elective major upper abdominal surgery from March 2019 to May 2021 were included. The 6MWD was measured for all patients before surgery. e[Formula: see text]O2max was calculated using the regression model of Burr, which uses 6MWD, age, gender, weight, and resting heart rate (HR) to predict aerobic fitness. The patients were categorized into PPC and non-PPC group. The sensitivity, specificity, and optimum cutoff values for 6MWD and e[Formula: see text]O2max were calculated to predict PPCs. The area under the receiver operating characteristic curve (AUC) of 6MWD or e[Formula: see text]O2max was constructed and compared using the Z test. The primary outcome measure was the AUC of 6MWD and e[Formula: see text]O2max in predicting PPCs. In addition, the net reclassification index (NRI) was calculated to assess ability of e[Formula: see text]O2max compared with 6MWT in predicting PPCs. RESULTS: A total of 308 patients were included 71/308 developed PPCs. Patients unable to complete the 6-min walk test (6MWT) due to contraindications or restrictions, or those taking beta-blockers, were excluded. The optimum cutoff point for 6MWD in predicting PPCs was 372.5 m with a sensitivity of 63.4% and specificity of 79.3%. The optimum cutoff point for e[Formula: see text]O2max was 30.8 ml/kg/min with a sensitivity of 91.6% and specificity of 79.3%. The AUC for 6MWD in predicting PPCs was 0.758 (95% confidence interval (CI): 0.694-0.822), and the AUC for e[Formula: see text]O2max was 0.912 (95%CI: 0.875-0.949). A significantly increased AUC was observed in e[Formula: see text]O2max compared to 6MWD in predicting PPCs (P < 0.001, Z = 4.713). And compared with 6MWT, the NRI of e[Formula: see text]O2max was 0.272 (95%CI: 0.130, 0.406). CONCLUSION: The results suggested that e[Formula: see text]O2max calculated from the 6MWT is a better predictor of PPCs than 6MWD in patients undergoing upper abdominal surgery and can be used as a tool to screen patients at risk of PPCs.

REIA: A database for cancer A-to-I RNA editing with interactive analysis.

Epitranscriptomic changes caused by adenosine-to-inosine (A-to-I) RNA editing contribute to the pathogenesis of human cancers; however, only a small fraction of the millions editing sites detected so far has clear functionality. To facilitate more in-depth studies on the editing, this paper offers REIA (http://bioinfo-sysu.com/reia), an interactive web server that analyses and visualizes the association between human cancers and A-to-I RNA editing sites (RESs). As a comprehensive database, REIA curates not only 8,447,588 RESs from 9,895 patients across 34 cancers, where 33 are from TCGA and 1 from GEO, but also 13 different types of multi-omic data for the cancers. As an interactive server, REIA provides various options for the user to specify the interested sites, to browse their annotation/editing level/profile in cancer, and to compare the difference in multi-omic features between editing and non-editing groups. From the editing profiles, REIA further detects 658 peptides that are supported by mass spectrum data but not yet covered in any prior works.

Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis.

Objectives: The abnormal functional connectivity (FC) pattern of default mode network (DMN) may be key markers for early identification of various cognitive disorders. However, the whole-brain FC changes of DMN in delayed neurocognitive recovery (DNR) are still unclear. Our study was aimed at exploring the whole-brain FC patterns of all regions in DMN and the potential features as biomarkers for the prediction of DNR using machine-learning algorithms. Methods: Resting-state functional magnetic resonance imaging (fMRI) was conducted before surgery on 74 patients undergoing non-cardiac surgery. Seed-based whole-brain FC with 18 core regions located in the DMN was performed, and FC features that were statistically different between the DNR and non-DNR patients after false discovery correction were extracted. Afterward, based on the extracted FC features, machine-learning algorithms such as support vector machine, logistic regression, decision tree, and random forest were established to recognize DNR. The machine learning experiment procedure mainly included three following steps: feature standardization, parameter adjustment, and performance comparison. Finally, independent testing was conducted to validate the established prediction model. The algorithm performance was evaluated by a permutation test. Results: We found significantly decreased DMN connectivity with the brain regions involved in visual processing in DNR patients than in non-DNR patients. The best result was obtained from the random forest algorithm based on the 20 decision trees (estimators). The random forest model achieved the accuracy, sensitivity, and specificity of 84.0, 63.1, and 89.5%, respectively. The area under the receiver operating characteristic curve of the classifier reached 86.4%. The feature that contributed the most to the random forest model was the FC between the left retrosplenial cortex/posterior cingulate cortex and left precuneus. Conclusion: The decreased FC of DMN with regions involved in visual processing might be effective markers for the prediction of DNR and could provide new insights into the neural mechanisms of DNR. Clinical Trial Registration: : Chinese Clinical Trial Registry, ChiCTR-DCD-15006096.

Predicting Delayed Neurocognitive Recovery After Non-cardiac Surgery Using Resting-State Brain Network Patterns Combined With Machine Learning.

Delayed neurocognitive recovery (DNR) is a common subtype of postoperative neurocognitive disorders. An objective approach for identifying subjects at high risk of DNR is yet lacking. The present study aimed to predict DNR using the machine learning method based on multiple cognitive-related brain network features. A total of 74 elderly patients (≥ 60-years-old) undergoing non-cardiac surgery were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) before the surgery. Seed-based whole-brain functional connectivity (FC) was analyzed with 18 regions of interest (ROIs) located in the default mode network (DMN), limbic network, salience network (SN), and central executive network (CEN). Multiple machine learning models (support vector machine, decision tree, and random forest) were constructed to recognize the DNR based on FC network features. The experiment has three parts, including performance comparison, feature screening, and parameter adjustment. Then, the model with the best predictive efficacy for DNR was identified. Finally, independent testing was conducted to validate the established predictive model. Compared to the non-DNR group, the DNR group exhibited aberrant whole-brain FC in seven ROIs, including the right posterior cingulate cortex, right medial prefrontal cortex, and left lateral parietal cortex in the DMN, the right insula in the SN, the left anterior prefrontal cortex in the CEN, and the left ventral hippocampus and left amygdala in the limbic network. The machine learning experimental results identified a random forest model combined with FC features of DMN and CEN as the best prediction model. The area under the curve was 0.958 (accuracy = 0.935, precision = 0.899, recall = 0.900, F1 = 0.890) on the test set. Thus, the current study indicated that the random forest machine learning model based on rs-FC features of DMN and CEN predicts the DNR following non-cardiac surgery, which could be beneficial to the early prevention of DNR. Clinical Trial Registration: The study was registered at the Chinese Clinical Trial Registry (Identification number: ChiCTR-DCD-15006096).

Multi-Order Brain Functional Connectivity Network-Based Machine Learning Method for Recognition of Delayed Neurocognitive Recovery in Older Adults Undergoing Non-cardiac Surgery.

Objectives: Delayed neurocognitive recovery (DNR) seriously affects the post-operative recovery of elderly surgical patients, but there is still a lack of effective methods to recognize high-risk patients with DNR. This study proposed a machine learning method based on a multi-order brain functional connectivity (FC) network to recognize DNR. Method: Seventy-four patients who completed assessments were included in this study, in which 16/74 (21.6%) had DNR following surgery. Based on resting-state functional magnetic resonance imaging (rs-fMRI), we first constructed low-order FC networks of 90 brain regions by calculating the correlation of brain region signal changing in the time dimension. Then, we established high-order FC networks by calculating correlations among each pair of brain regions. Afterward, we built sparse representation-based machine learning model to recognize DNR on the extracted multi-order FC network features. Finally, an independent testing was conducted to validate the established recognition model. Results: Three hundred ninety features of FC networks were finally extracted to identify DNR. After performing the independent-sample T test between these features and the categories, 15 features showed statistical differences (P < 0.05) and 3 features had significant statistical differences (P < 0.01). By comparing DNR and non-DNR patients' brain region connection matrices, it is found that there are more connections among brain regions in DNR patients than in non-DNR patients. For the machine learning recognition model based on multi-feature combination, the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the classifier reached 95.61, 92.00, 66.67, and 100.00%, respectively. Conclusion: This study not only reveals the significance of preoperative rs-fMRI in recognizing post-operative DNR in elderly patients but also establishes a promising machine learning method to recognize DNR.

Anti-chronic myeloid leukemia activity and quantitative structure-activity relationship of novel thiazole aminobenzamide derivatives.

The anti-chronic myeloid leukemia activity of thiazole aminobenzamide derivatives in vitro was tested by a methanethiosulfonate (MTS)-based viability assay method, and the result showed that some compounds exhibited good inhibitory activities against human chronic myeloid leukemia cell line K562, imatinib-resistant strain K562/R and T135I mutant cell line BaF3-ABL-BCR-T315I. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods were used to analyze the relationship between the structure of thiazole aminobenzamide derivatives and the inhibition of K562/R cell activity. In CoMFA, Q2 was 0.899 and R2 was 0.963; in CoMSIA, Q2 and R2 were 0.840 and 0.903, respectively. These data indicated that the selected test set showed suitable external predictive ability. Combined with the contour map results, we further analyzed the three-dimensional quantitative structure (3D-QSAR) model. The results demonstrated that in the backbone of the thiazole aminobenzamide derivative, the substitution of a small group at R1 position, or the introduction of a hydrophilic group at R2 position, or the introduction of a large-volume amino acid at R3 position may be beneficial to improve the anti-CML activity of the compound.