The objective of this study was to examine the chemical composition and oxidative stability of microalgae oil, also to explore in vitro bioaccessibility and antioxidant activity of microalgae oil after simulated gastrointestinal digestion. In total, more than 50 fatty acids were identified by GC–MS analysis, with both palmitic acid (38.3%) and DHA (34.5%) being identified as major fatty acids. The contents of total phenolics and flavonoids in the various solvent extracts were measured spectrometrically, and their amounts were 39.33 ± 0.34 µg gallic acid/g and 16.08 ± 4.3 µg rutin/g, respectively. HPLC analysis showed that the contents of β‐carotene, α‐tocopherols, β‐ and γ‐tocopherols (not separated) and δ‐tocopherols were 136 µg/100 g, 164.4 µg/g, 317.3 µg/g, and 43.2 µg/g, respectively. Concerning sterols, cholesterol was the principal sterol at 4210.5 mg/kg and the other six main sterols were campesterol (121.4 mg/kg), 24‐methylene cholesterol (192.8 mg/kg), 24‐methyl‐colest‐7‐en‐3β‐ol (144.6 mg/kg), ergosterol (144.8 mg/kg), stigmasterol (260.1 mg/kg) and Δ7,24‐stigmastadienol (150.5 mg/kg), respectively. The overall chemical properties of the tested oils indicated that microalgae oil had a great oil quality. A Schaal oven test was used to evaluate the oxidative stability of microalgae oil. Furthermore, in vitro simulated gastrointestinal digestion was performed, and the antioxidant ability of digestion oil was determined by using a 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) radical‐scavenging assay, a 3‐ethylbenzothiazoline‐6‐sulfonic acid (ABTS) radical cation decolourisation activity assay, a reducing power assay, a β‐carotene bleaching assay and an oxygen radical absorbance capacity (ORAC) antioxidant assay. The results showed that following simulated gastrointestinal digestion, microalgae oil displayed a good in vitro bioaccessibility and moderate antioxidant capacity. Thus, the antioxidant activity of the microalgae oil was mainly contributed by its abundant antioxidant constituents. Practical applications: Schizochytrium aggregatum oil is a good source of DHA and of effective, bioaccessible antioxidants. The DPPH radical is rather stable, and our study used a relatively fast method for measuring both the hydrophilic and lipophilic substances during in vitro antioxidant activity. The change of violet colour to yellow during the DPPH assay is thought to be due to the attribution of hydrogen atoms by antioxidant substances. The extent of decolourisation is a significant indicator of the sample's antioxidant ability. As shown in figure that the scavenging abilities of all the samples were well correlated with increasing concentrations, and the scavenging activity of BHT was higher than that of microalgae oil. At concentrations ranging from 0.5 to 10 mg/mL, the DPPH radical scavenging ability of digestion oil was determined at 6.8–64.9%. Lower IC 50 values indicated a higher radical scavenging activity. The IC 50 value of microalgae oil in our DPPH radical scavenging study was 5.76 mg/mL, and solutions of BHT had an IC 50 value of 0.56 mg/mL. Although microalgae oil was shown to be weaker than BHT, it demonstrated a fairly good scavenging ability. The DPPH radical scavenging abilities of digestion oil and BHT.
The main advantage of tail-sitter unmanned aerial vehicle (UAV) are introduced. Three design solutions of rotor tail-sitter lift system of UAV have been presented and the respective control strategies and characteristics of three solutions are also analyzed in the paper, through the related experiments the design of twin-rotor lift system is verified, and its feasibility is proved. The characteristics and the applying background of the twin-rotor tail-sitter UAV are described in detail. Some useful conclusions of the lift system for tail-sitter UAV are obtained.
Background: The subtype classification of lung adenocarcinoma is of paramount importance for preoperative adjuvant therapy. The precise evaluation in histopathologic classification of lung adenocarcinoma depends on full nodule lesion resection. This study aimed to investigate the deep learning and radiomics networks for predicting histologic subtype classification and survival of lung adenocarcinoma diagnosed with small biopsy specimen through computed tomography (CT) images. Methods: A dataset of 1222 lung adenocarcinoma patients including the clinicopathological data were retrospectively enrolled from three institutions. The anonymised preoperative CT images and pathological labels of atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IAC) with predominant components classified into lepidic (pLAC), acinar(pAAC), papillary(pPAC), micropapillary(pMAC), and solid (pSAC) ones were obtained. These pathological labels were divided into 2-category classification (IAC; non-IAC), 3-category (Non-MAC/SAC, Non-pMAC/pSAC and pMAC/pSAC) and 8-category as aforementioned. We modeled the classification task of histological subtypes based on modified ResNet-34 deep learning network, radiomics strategies and deep radiomics combined algorithm. Then we established the prognostic models in lung adenocarcinoma patients with survival outcomes. The accuracy (ACC), area under ROC curves (AUCs) and C-index were primarily performed to evaluate the algorithms. Findings: This study included the a training set (n=802) and two validation cohorts (internal, n=196; external, n=224). The ACC of deep radiomics algorithm in internal validation achieved 0.8776, 0.8061 in the 2-category, 3-category classification, respectively. Even in 8 classifications, the AUC ranged from 0.739 to 0.940 in internal test. Further, we constructed a prognosis model that C-index was 0.892(95% Confidence Intervals: 0.846-0.937) in internal validation set. Interpretation: Our results reveal that the automated deep radiomics based triage system has achieved the great performance in the subtype classification and survival predictability in patients with CT-detected lung adenocarcinoma nodules, providing the clinical guide for preoperative adjuvant therapy.Funding Statement: National Natural Science Foundation of China, Science and TechnologyProject of Chengdu, and Science and Technology Project of SichuanDeclaration of Interests: The authors declare no conflict of interest.Ethics Approval Statement: This study was approved by the institutional ethics committee of participating institutions.
Most older patients with esophageal cancer cannot complete the standard concurrent chemoradiotherapy (CCRT). An effective and tolerable chemoradiotherapy regimen for older patients is needed.
Objective
To evaluate the efficacy and toxic effects of CCRT with S-1 vs radiotherapy (RT) alone in older patients with esophageal cancer.
Design, Setting, and Participants
A randomized, open-label, phase 3 clinical trial was conducted at 23 Chinese centers between June 1, 2016, and August 31, 2018. The study enrolled 298 patients aged 70 to 85 years. Eligible participants had histologically confirmed esophageal cancer, stage IB to IVB disease based on the 6th edition of the American Joint Committee on Cancer (stage IVB: only metastasis to the supraclavicular/celiac lymph nodes) and an Eastern Cooperative Oncology Group performance status of 0 to 1. Data analysis was performed from August 1, 2020, to March 10, 2021.
Interventions
Patients were stratified according to age (<80 vs ≥80 years) and tumor length (<5 vs ≥5 cm) and randomly assigned (1:1) to receive either CCRT with S-1 or RT alone.
Main Outcomes and Measures
The primary end point was the 2-year overall survival rate using intention-to-treat analysis.
Results
Of the 298 patients enrolled, 180 (60.4%) were men. The median age was 77 (interquartile range, 74-79) years in the CCRT group and 77 (interquartile range, 74-80) years in the RT alone group. A total of 151 patients (50.7%) had stage III or IV disease. The CCRT group had a significantly higher complete response rate than the RT group (41.6% vs 26.8%;P = .007). Surviving patients had a median follow-up of 33.9 months (interquartile range: 28.5-38.2 months), and the CCRT group had a significantly higher 2-year overall survival rate (53.2% vs 35.8%; hazard ratio, 0.63; 95% CI, 0.47-0.85;P = .002). There were no significant differences in the incidence of grade 3 or higher toxic effects between the CCRT and RT groups except that grade 3 or higher leukopenia occurred in more patients in the CCRT group (9.5% vs 2.7%;P = .01). Treatment-related deaths were observed in 3 patients (2.0%) in the CCRT group and 4 patients (2.7%) in the RT group.
Conclusions and Relevance
In this phase 3 randomized clinical trial, CCRT with S-1 was tolerable and provided significant benefits over RT alone in older patients with esophageal cancer.
Abstract The estimated number of outpatients with skin diseases in China is ~200 million per year, while the dermatologists are insufficient and the doctor-patient ratio remains low, which causes fewer patients receive effective diagnosis. Compared with others, the diagnosis of skin diseases, which is less reliant on laboratory tests, imaging and pathology, needs the assistance of large hardware devices. By contrast, dermatologic diagnosis requires a combination of visual inspection and interrogation frequently which is exactly what Artificial Intelligence (AI) specialises in — Computer Vision (CV), Natural Language Processing (NLP) and Speech Recognition (SR). This allows a simple image capturing tool embedded with an AI model to perform dermatological diagnosis at the primary level. Hence, based on the dataset, which from Asian, with more than 200,000 images and 220,000 medical records, we explored an AI skin diseases diagnosis model---DIET-AI to diagnose 31 skin diseases, covering the majority of common skin diseases. Ranging from 1st September to 1st December 2021, we prospectively collected case information from 15 hospitals in 7 provinces in China, using mobile devices to collect images and medical records of 6043 cases. Then, we compared the performance of the DIET-AI with 6 doctors of different seniority in the prospective clinical dataset, concluding the average performance of the DIET-AI in 31 diseases is no less than that of all different seniority doctors. By comparing the area under curve (AUC), sensitivity and specificity, we demonstrate that DIET-AI model is effective under the clinical scenario. It is further validated under more complex clinical scenarios, providing references for exploring the feasibility and performance evaluation of DIET-AI in clinical use afterwards
Objective: This study was aimed at investigating the effects of preoperative treatment with a loading dose of statins combined with a PCSK9 inhibitor on coronary blood perfusion and short-term cardiovascular adverse events in patients with ST-segment elevation myocardial infarction (STEMI). Method: Sixty-five patients with STEMI who had visited the Shanxi Cardiovascular Disease Hospital between May 2018 and May 2021 were enrolled in the study. The enrolled patients had no history of oral statins or antiplatelet therapy. The patients were divided into a combined treatment group (loading dose of statins combined with PCSK9 inhibitors, 35 patients) and a routine treatment group (loading dose of statins only, 30 patients). The primary endpoints were thrombolysis in myocardial infarction (TIMI) blood flow grading, corrected TIMI frame count (CTFC), and TIMI myocardial perfusion grading (TMPG), immediately after and 30 days after the operation. The secondary endpoint was a composite endpoint of cardiovascular death, nonfatal myocardial infarction, and target vessel revascularization 30 days after the operation. Results: The combined treatment group had significantly lower CTFC (14.09±8.42 vs 26±12.42, P=0.04) and better TMPG (2.74±0.61 vs 2.5±0.73, P=0.04) than the routine treatment group immediately after the operation. Similarly, the combined treatment group had a significantly lower CTFC (16.29±7.39 vs 26.23±11.53, P=0.04) and significantly better TMPG (2.94±0.24 vs 2.76±0.43, P=0.01) than the routine treatment group 1 month after the operation. Conclusion: Preoperative treatment with a loading dose of high-intensity statins combined with PCSK9 inhibitors increased coronary blood flow and myocardial perfusion after emergency thrombus aspiration in patients with STEMI. However, the treatment did not significantly decrease the incidence of cardiovascular death, nonfatal myocardial infarction, or target vessel revascularization.
Natural triterpenes represent a group of pharmacologically active and structurally diverse organic compounds. The focus on these phytochemicals has been enormous in the past few years, worldwide. Asiatic acid (AA), a naturally occurring pentacyclic triterpenoid, is found mainly in the traditional medicinal herb Centella asiatica. Triterpenoid saponins, which are the primary constituents of C. asiatica, are commonly believed to be responsible for their extensive therapeutic actions. Published research work has described the molecular mechanisms underlying the various biological activities of AA and its derivatives, which vary for each chronic disease. However, a compilation of the various pharmacological properties of AA has not yet been done. Herein, we describe in detail the pharmacological properties of AA and its derivatives that inhibit multiple pathways of intracellular signaling molecules and transcription factors that are involved in the various stages of chronic diseases. Furthermore, the pharmacological activities of AA were compared with two natural compounds: curcumin and resveratrol. This review summarizes the research on AA and its derivatives and helps to provide future directions in the area of drug development.
Background Due to the lower reliability of laboratory tests, skin diseases are more suitable for diagnosis with AI models. There are limited AI dermatology diagnostic models combining images and text; few of these are for Asian populations, and few cover the most common types of diseases. Methods Leveraging a dataset sourced from Asia comprising over 200,000 images and 220,000 medical records, we explored a deep learning-based system for Dual-channel images and extracted text for the diagnosis of skin diseases model DIET-AI to diagnose 31 skin diseases, which covers the majority of common skin diseases. From 1 September to 1 December 2021, we prospectively collected images from 6,043 cases and medical records from 15 hospitals in seven provinces in China. Then the performance of DIET-AI was compared with that of six doctors of different seniorities in the clinical dataset. Results The average performance of DIET-AI in 31 diseases was not less than that of all the doctors of different seniorities. By comparing the area under the curve, sensitivity, and specificity, we demonstrate that the DIET-AI model is effective in clinical scenarios. In addition, medical records affect the performance of DIET-AI and physicians to varying degrees. Conclusion This is the largest dermatological dataset for the Chinese demographic. For the first time, we built a Dual-channel image classification model on a non-cancer dermatitis dataset with both images and medical records and achieved comparable diagnostic performance to senior doctors about common skin diseases. It provides references for exploring the feasibility and performance evaluation of DIET-AI in clinical use afterward.
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