Molecular recognition between volatile molecules and odorant binding proteins 7 by homology modeling, molecular docking and molecular dynamics simulation.

BACKGROUND: Odorant-binding proteins (OBPs) in insects are key to detection and recognition of external chemical signals associated with survival. OBP7 in Spodoptera frugiperda's larval stage (SfruOBP7) may search for host plants by sensing plant volatiles, which are important sources of pest attractants and repellents. However, the atomic-level basis of binding modes remains elusive. RESULTS: SfruOBP7 structure was constructed through homology modeling, and complex models of six plant volatiles ((E)-2-hexenol, α-pinene, (Z)-3-hexenyl acetate, lauric acid, O-cymene and 1-octanol) and SfruOBP7 were obtained through molecular docking. To study the detailed interactions between the six plant volatile molecules and SfruOBP7, we conducted three 300 ns molecular dynamics simulations for each study object. The correlation coefficients between binding free energy obtained by molecular mechanics/generalized Born surface area together with solvated interaction energy methods and experimental values are 0.90 and 0.88, respectively, showing a good correlation. By comparing binding free energy along with interaction patterns between SfruOBP7 and the six volatile molecules, hotspot residues of SfruOBP7 when binding with different volatile molecules were determined. Hydrophobic interactions stemming from van der Waals interactions play a significant role in SfruOBP7 and these plant volatile systems. CONCLUSION: The optimized three-dimensional structure of SfruOBP7 and its binding modes with six plant volatiles revealed their interactions, thus providing a means for estimating the binding energies of other plant volatiles. Our study will help to guide the rational design of effective and selective insect attractants. © 2024 Society of Chemical Industry.

Calibrate the Inter-observer Segmentation Uncertainty via Diagnosis-first Principle.

Many of the tissues/lesions in the medical images may be ambiguous. Therefore, medical segmentation is typically annotated by a group of clinical experts to mitigate personal bias. A common solution to fuse different annotations is the majority vote, e.g., taking the average of multiple labels. However, such a strategy ignores the difference between the grader expertness. Inspired by the observation that medical image segmentation is usually used to assist the disease diagnosis in clinical practice, we propose the diagnosis-first principle, which is to take disease diagnosis as the criterion to calibrate the inter-observer segmentation uncertainty. Following this idea, a framework named Diagnosis-First segmentation Framework (DiFF) is proposed. Specifically, DiFF will first learn to fuse the multi-rater segmentation labels to a single ground-truth which could maximize the disease diagnosis performance. We dubbed the fused ground-truth as Diagnosis-First Ground-truth (DF-GT). Then, the Take and Give Model (T&G Model) to segment DF-GT from the raw image is proposed. With the T&G Model, DiFF can learn the segmentation with the calibrated uncertainty that facilitate the disease diagnosis. We verify the effectiveness of DiFF on three different medical segmentation tasks: optic-disc/optic-cup (OD/OC) segmentation on fundus images, thyroid nodule segmentation on ultrasound images, and skin lesion segmentation on dermoscopic images. Experimental results show that the proposed DiFF can effectively calibrate the segmentation uncertainty, and thus significantly facilitate the corresponding disease diagnosis, which outperforms previous state-of-the-art multi-rater learning methods.

Adjusting Catalytic Activity of ß-Amyrin Synthase GgBAS by Utilizing the Plasticity Residues of an Active Site.

ß-Amyrin synthase (bAS) is a representative plant oxidosqualene cyclase (OSC), and previous studies have identified many functional residues and mutants that can alter its catalytic activity. However, the regulatory mechanism of the active site architecture for adjusting the catalytic activity remains unclear. In this study, we investigate the function of key residues and their regulatory effects on the catalytic activity of Glycyrrhiza glabra ß-amyrin synthase (GgbAS) through molecular dynamics simulations and site-directed mutagenesis experiments. We identified the plasticity residues located in two active site regions and explored the interactions between these residues and tetracyclic/pentacyclic intermediates. Based on computational and experimental results, we further categorize these plasticity residues into three types: effector, adjuster, and supporter residues, according to their functions in the catalytic process. This study provides valuable insights into the catalytic mechanism and active site plasticity of GgbAS, offering important references for the rational enzyme engineering of other OSC enzyme.

Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy.

Background: Immunoglobulin A nephropathy (IgAN) is one of the leading causes of end-stage kidney disease (ESKD). Many studies have shown the significance of pathological manifestations in predicting the outcome of patients with IgAN, especially T-score of Oxford classification. Evaluating prognosis may be hampered in patients without renal biopsy. Methods: A baseline dataset of 690 patients with IgAN and an independent follow-up dataset of 1,168 patients were used as training and testing sets to develop the pathology T-score prediction (T pre) model based on the stacking algorithm, respectively. The 5-year ESKD prediction models using clinical variables (base model), clinical variables and real pathological T-score (base model plus T bio), and clinical variables and T pre (base model plus T pre) were developed separately in 1,168 patients with regular follow-up to evaluate whether T pre could assist in predicting ESKD. In addition, an external validation set consisting of 355 patients was used to evaluate the performance of the 5-year ESKD prediction model using T pre. Results: The features selected by AUCRF for the T pre model included age, systolic arterial pressure, diastolic arterial pressure, proteinuria, eGFR, serum IgA, and uric acid. The AUC of the T pre was 0.82 (95% CI: 0.80-0.85) in an independent testing set. For the 5-year ESKD prediction model, the AUC of the base model was 0.86 (95% CI: 0.75-0.97). When the T bio was added to the base model, there was an increase in AUC [from 0.86 (95% CI: 0.75-0.97) to 0.92 (95% CI: 0.85-0.98); P = 0.03]. There was no difference in AUC between the base model plus T pre and the base model plus T bio [0.90 (95% CI: 0.82-0.99) vs. 0.92 (95% CI: 0.85-0.98), P = 0.52]. The AUC of the 5-year ESKD prediction model using T pre was 0.93 (95% CI: 0.87-0.99) in the external validation set. Conclusion: A pathology T-score prediction (T pre) model using routine clinical characteristics was constructed, which could predict the pathological severity and assist clinicians to predict the prognosis of IgAN patients lacking kidney pathology scores.

Noisy Label Learning With Provable Consistency for a Wider Family of Losses.

Deep models have achieved state-of-the-art performance on a broad range of visual recognition tasks. Nevertheless, the generalization ability of deep models is seriously affected by noisy labels. Though deep learning packages have different losses, this is not transparent for users to choose consistent losses. This paper addresses the problem of how to use abundant loss functions designed for the traditional classification problem in the presence of label noise. We present a dynamic label learning (DLL) algorithm for noisy label learning and then prove that any surrogate loss function can be used for classification with noisy labels by using our proposed algorithm, with a consistency guarantee that the label noise does not ultimately hinder the search for the optimal classifier of the noise-free sample. In addition, we provide a depth theoretical analysis of our algorithm to verify the justifies' correctness and explain the powerful robustness. Finally, experimental results on synthetic and real datasets confirm the efficiency of our algorithm and the correctness of our justifies and show that our proposed algorithm significantly outperforms or is comparable to current state-of-the-art counterparts.

Phloroglucinol derivatives, coumarins and an alkaloid from the roots of Evodia lepta Merr.

Two previously undescribed phloroglucinol derivatives [(±) evolephloroglucinols A and B], five unusual coumarins [evolecoumarins A and B and (±) evolecoumarins C-E], and one novel enantiomeric quinoline-type alkaloid [(±) evolealkaloid A], along with 20 known compounds, were isolated from the EtOH extract of the roots of Evodia lepta Merr. Their structures were elucidated by extensive spectroscopic analyses. The absolute configurations of the undescribed compounds were determined by X-ray diffraction or computational calculations. Their anti-neuroinflammatory effects were assayed. Among the identified compounds, compound 5a effectively reduced nitric oxide (NO) production with an EC50 value of 22.08 ± 0.46 µM. Hence, it could indeed inhibit the lipopolysaccharide (LPS)-induced Nod-like receptor family, pyrin domain containing 3 (NLRP3) inflammasome.

Functional characterization of triterpene synthases in Cibotium barometz.

Cibotium barometz (Linn.) J. Sm., a tree fern in the Dicksoniaceae family, is an economically important industrial exported plant in China and widely used in Traditional Chinese Medicine. C. barometz produces a range of bioactive triterpenes and their metabolites. However, the biosynthetic pathway of triterpenes in C. barometz remains unknown. To clarify the origin of diverse triterpenes in C. barometz, we conducted de novo transcriptome sequencing and analysis of C. barometz rhizomes and leaves to identify the candidate genes involved in C. barometz triterpene biosynthesis. Three C. barometz triterpene synthases (CbTSs) candidate genes were obtained. All of them were highly expressed in C. barometz rhizomes, consisting of the accumulation pattern of triterpenes in C. barometz. To characterize the function of these CbTSs, we constructed a squalene- and oxidosqualene-overproducing yeast chassis by overexpressing all the enzymes in the MVA pathway under the control of GAL-regulated promoter and disrupted the GAL80 gene in Saccharomyces cerevisiae simultaneously. Heterologous expressing CbTS1, CbTS2, and CbTS3 in engineering yeast strain produced cycloartenol, dammaradiene, and diploptene, respectively. Phylogenetic analysis revealed that CbTS1 belongs to oxidosqualene cyclase, while CbTS2 and CbTS3 belong to squalene cyclase. These results decipher enzymatic mechanisms underlying the origin of diverse triterpene in C. barometz.

Discovering a uniform functional trade-off of the CBC-type 2,3-oxidosqualene cyclases and deciphering its chemical logic.

Many functionally promiscuous plant 2,3-oxidosqualene cyclases (OSCs) have been found, but complete functional reshaping is rarely reported. In this study, we have identified two new plant OSCs: a unique protostadienol synthase (AoPDS) and a common cycloartenol synthase (AoCAS) from Alisma orientale (Sam.) Juzep. Multiscale simulations and mutagenesis experiments revealed that threonine-727 is an essential residue responsible for protosta-13 (17),24-dienol biosynthesis in AoPDS and that the F726T mutant completely reshapes the native function of AoCAS into a PDS function to yield almost exclusively protosta-13 (17),24-dienol. Unexpectedly, various native functions were uniformly reshaped into a PDS function by introducing the phenylalanine → threonine substitution at this conserved position in other plant and non-plant chair-boat-chair-type OSCs. Further computational modeling elaborated the trade-off mechanisms of the phenylalanine → threonine substitution that leads to the PDS activity. This study demonstrates a general strategy for functional reshaping by using a plastic residue based on the decipherment of the catalytic mechanism.

Characterization of a Group of 2,3-Oxidosqualene Cyclase Genes Involved in the Biosynthesis of Diverse Triterpenoids of Perilla frutescens.

Perilla frutescens (L.), a traditional edible and medicinal crop, contains diverse triterpenes with multiple pharmacological properties. However, the biosynthesis of triterpenes in perilla remains rarely revelation. In this study, nine putative 2,3-oxidosqualene cyclase (OSC) genes (PfOSC1-9) were screened from the P. frutescens genome and functionally characterized by heterologous expression. Camelliol C, a triterpenol with pharmacological effect, was first identified as abundant in perilla seeds, and the camelliol C synthase (PfOSC7) was first identified in P. frutescens utilizing a yeast system. In addition, PfOSC2, PfOSC4, and PfOSC9 were identified as cycloartenol, lupeol, and ß-amyrin synthase, respectively. Molecular docking and site-directed mutagenesis revealed that changes in Leu253 of PfOSC4, Ala480 of PfOSC7, and Trp257 of PfOSC9 might lead to variations of catalytic specificity or efficiency. These results will provide key insights into the biosynthetic pathways of triterpenoids and have great significance for germplasm breeding in P. frutescens.

Open Set Domain Adaptation With Soft Unknown-Class Rejection.

The goal of domain adaptation (DA) is to train a good model for a target domain, with a large amount of labeled data in a source domain but only limited labeled data in the target domain. Conventional closed set domain adaptation (CSDA) assumes source and target label spaces are the same. However, this is not quite practical in real-world applications. In this work, we study the problem of open set domain adaptation (OSDA), which only requires the target label space to partially overlap with the source label space. Consequently, the solution to OSDA requires unknown classes detection and separation, which is normally achieved by introducing a threshold for the prediction of target unknown classes; however, the performance can be quite sensitive to that threshold. In this article, we tackle the above issues by proposing a novel OSDA method to perform soft rejection of unknown target classes and simultaneously match the source and target domains. Extensive experiments on three standard datasets validate the effectiveness of the proposed method over the state-of-the-art competitors.

Reproducibility of deep learning based scleral spur localisation and anterior chamber angle measurements from anterior segment optical coherence tomography images.

AIMS: To apply a deep learning model for automatic localisation of the scleral spur (SS) in anterior segment optical coherence tomography (AS-OCT) images and compare the reproducibility of anterior chamber angle (ACA) width between deep learning located SS (DLLSS) and manually plotted SS (MPSS). METHODS: In this multicentre, cross-sectional study, a test dataset comprising 5166 AS-OCT images from 287 eyes (116 healthy eyes with open angles and 171 eyes with primary angle-closure disease (PACD)) of 287 subjects were recruited from four ophthalmology clinics. Each eye was imaged twice by a swept-source AS-OCT (CASIA2, Tomey, Nagoya, Japan) in the same visit and one eye of each patient was randomly selected for measurements of ACA. The agreement between DLLSS and MPSS was assessed using the Euclidean distance (ED). The angle opening distance (AOD) of 750 µm (AOD750) and trabecular-iris space area (TISA) of 750 µm (TISA750) were calculated using the CASIA2 embedded software. The repeatability of ACA width was measured. RESULTS: The mean age was 60.8±12.3 years (range: 30-85 years) for the normal group and 63.4±10.6 years (range: 40-91 years) for the PACD group. The mean difference in ED for SS localisation between DLLSS and MPSS was 66.50±20.54 µm and 84.78±28.33 µm for the normal group and the PACD group, respectively. The span of 95% limits of agreement between DLLSS and MPSS was 0.064 mm for AOD750 and 0.034 mm2 for TISA750. The respective repeatability coefficients of AOD750 and TISA750 were 0.049 mm and 0.026 mm2 for DLLSS, and 0.058 mm and 0.030 mm2 for MPSS. CONCLUSION: DLLSS achieved comparable repeatability compared with MPSS for measurement of ACA.

Genomic characterization of bZIP transcription factors related to andrographolide biosynthesis in Andrographis paniculata.

The basic leucine zipper (bZIP) transcription factor family plays an important role in various biological processes in plants. Andrographis paniculata (Burm.f) Nees, belonging to the family Acanthaceae, has been widely used as an important traditional herb with a wide range of pharmacological activities, such as antivenom, antiretroviral, anticancer and so on. However, there was no comprehensive analysis of bZIP gene family in the Andrographis paniculata been reported. In this study, we identified 62 bZIPs in Andrographis paniculata and grouped them into 12 subfamilies through the phylogenetic tree analysis. The bZIPs in the same groups have similar motif composition, exon-intron structure and domain distribution. In addition, the RNA-seq data gave a reference for selecting candidate bZIPs to make further function verification. Lastly, qRT-PCR analyses revealed seven ApbZIPs (ApbZIP4, ApbZIP19, ApbZIP30, ApbZIP42, ApbZIP50, ApbZIP52, ApbZIP62) were the most highly expressed in leaf and significantly up-regulated with MeJA and ABA treatment which may be involved in biosynthesis regulation of andrographolide. These data pave the way for further revealing the function of the bZIPs in Andrographis paniculata.

Instantaneous High-Resolution Visual Imaging of Latent Fingerprints in Water Using Color-Tunable AIE Pincer Complexes.

Instant visualization of latent fingerprints is developed by using a series of water-soluble terpyridine zinc complexes as aggregation-induced emission probes in pure water, under UV light or ambient sunlight. By simply soaking, or spraying with an aqueous solution of the probe, bright yellow fluorescence images with high contrast and resolution are readily developed on various surfaces including tinfoil, glass, paper, steel, leather, and ceramic tile. Remarkably, latent fingerprints can be visualized within seconds including details of whorl and sweat pores. The color of emission can be tuned from blue to orange by modifying the pincer ligands, allowing direct imaging under sunlight. These inexpensive, water-resistant, and color-tunable probes provide a practical approach for latent fingerprints recording and analysis, security protection, as well as criminal investigation in different scenarios.

Oxidosqualene Cyclases Involved in the Biosynthesis of Diverse Triterpenes in Camellia sasanqua.

Camellia sasanqua is an important economic plant that is rich in lipophilic triterpenols with pharmacological activities including antiallergic, anti-inflammatory, and anticancer activities. However, the key enzymes related to triterpene biosynthesis have seldom been studied in C. sasanqua. Oxidosqualene cyclases (OSCs) are the rate-limiting enzymes related to triterpene biosynthesis. In this study, seven putative OSC genes (CsOSC1-7) were mined from the C. sasanqua transcriptome. Six CsOSCs were characterized for the biosynthesis of diverse triterpene skeletons, including α-amyrin, ß-amyrin, δ-amyrin, dammarenediol-II, ψ-taraxasterol, taraxasterol, and cycloartenol by the heterologous expression system. CsOSC3 was a multiple functional α-amyrin synthase. Three key residues, Trp260, Tyr262, and Phe415, are critical to the catalytic performance of CsOSC3 judging from the results of molecular docking and site-directed mutagenesis. These findings provide important insights into the biosynthesis pathway of triterpenes in C. sasanqua.

Transcriptome Analysis and Identification of the Cholesterol Side Chain Cleavage Enzyme BbgCYP11A1 From Bufo bufo gargarizans.

Bufo bufo gargarizans Cantor are precious medicinal animals in traditional Chinese medicine (TCM). Bufadienolides as the major pharmacological components are generated from the venomous glands of B. bufo gargarizans. Bufadienolides are one type of cardiac aglycone with a six-member lactone ring and have properties of antitumor, cardiotonic, tonsillitis, and anti-inflammatory. The biosynthesis of bufadienolides is complex and unclear. This study explored the transcriptome of three different tissues (skin glands, venom glands, and muscles) of B. bufo gargarizans by high-throughput sequencing. According to the gene tissue-specific expression profile, 389 candidate genes were predicted possibly participating in the bufadienolides biosynthesis pathway. Then, BbgCYP11A1 was identified as a cholesterol side chain cleaving the enzyme in engineering yeast producing cholesterol. Furthermore, the catalytic activity of BbgCYP11A1 was studied with various redox partners. Interestingly, a plant NADPH-cytochrome P450 reductase (CPR) from Anemarrhena asphodeloides showed notably higher production than BbgAdx-2A-BbgAdR from B. bufo gargarizans. These results will provide certainly molecular research to reveal the bufadienolides biosynthesis pathway in B. bufo gargarizans.

Six C21 steroidal glycosides from Cynanchum wallichii Wight roots and their multidrug resistance reversal activities.

Six unidentified C21 steroidal glycosides, cynwallosides A-F, as well as twenty-two known compounds, were isolated from the roots of Cynanchum wallichii Wight. The structures of cynwallosides A-F were determined by spectroscopic analysis and acidic hydrolysis. Most of these twenty-eight compounds were found to significantly reverse drug resistance in both the MCF-7/ADR and HepG2/ADM cell lines by suppressing P-gp protein expression. Further investigation revealed that three compounds suppressed P-gp expression by significantly inactivating the JNK and NF-κB pathways.

Feature Re-Representation and Reliable Pseudo Label Retraining for Cross-Domain Semantic Segmentation.

This paper presents a novel unsupervised domain adaptation method for semantic segmentation. We argue that a good representation of the target-domain data should keep both the knowledge from the source domain and the target-domain-specific information. To obtain the knowledge from the source domain, we first learn a set of bases to characterize the feature distribution of the source domain, then features from both the source and the target domain are re-represented as a weighted summation of the source bases. A discriminator is additionally introduced to make the re-representation responsibilities of both domain features under the same bases indistinguishable. In this way, the domain gap between the source re-representation and target re-representation is minimized, and the re-represented target domain features contain the source domain information. Then we combine the feature re-representation with the original domain-specific feature together for subsequent pixel-wise classification. To further make the re-represented target features semantically meaningful, a Reliable Pseudo Label Retraining (RPLR) strategy is proposed, which utilizes the consistency of the prediction by the networks trained with multi-view source images to select the clean pseudo labels on unlabeled target images for re-training. Extensive experiments demonstrate the competitive performance of our approach for unsupervised domain adaptation on the semantic segmentation benchmarks.

Exploring the catalytic function and active sites of a novel C-glycosyltransferase from Anemarrhena asphodeloides.

Anemarrhena asphodeloides is an immensely popular medicinal herb in China, which contains an abundant of mangiferin. As an important bioactive xanthone C-glycoside, mangiferin possesses a variety of pharmacological activities and is derived from the cyclization reaction of a benzophenone C-glycoside (maclurin). Biosynthetically, C-glycosyltransferases are critical for the formation of benzophenone C-glycosides. However, the benzophenone C-glycosyltransferases from Anemarrhena asphodeloides have not been discovered. Herein, a promiscuous C-glycosyltransferase (AaCGT) was identified from Anemarrhena asphodeloides. It was able to catalyze efficiently mono-C-glycosylation of benzophenone, together with di-C-glycosylation of dihydrochalcone. It also exhibited the weak O-glycosylation or potent S-glycosylation capacities toward 12 other types of flavonoid scaffolds and a simple aromatic compound with -SH group. Homology modeling and mutagenesis experiments revealed that the glycosylation reaction of AaCGT was initiated by the conserved residue H23 as the catalytic base. Three critical residues H356, W359 and D380 were involved in the recognition of sugar donor through hydrogen-bonding interactions. In particular, the double mutant of F94W/L378M led to an unexpected enzymatic conversion of mono-C- to di-C-glycosylation. This study highlights the important value of AaCGT as a potential biocatalyst for efficiently synthesizing high-value C-glycosides.

Domain Adversarial Reinforcement Learning for Partial Domain Adaptation.

Partial domain adaptation aims to transfer knowledge from a label-rich source domain to a label-scarce target domain (i.e., the target categories are a subset of the source ones), which relaxes the common assumption in traditional domain adaptation that the label space is fully shared across different domains. In this more general and practical scenario on partial domain adaptation, a major challenge is how to select source instances from the shared categories to ensure positive transfer for the target domain. To address this problem, we propose a domain adversarial reinforcement learning (DARL) framework to progressively select source instances to learn transferable features between domains by reducing the domain shift. Specifically, we employ a deep Q-learning to learn policies for an agent to make selection decisions by approximating the action-value function. Moreover, domain adversarial learning is introduced to learn a common feature subspace for the selected source instances and the target instances, and also to contribute to the reward calculation for the agent that is based on the relevance of the selected source instances with respect to the target domain. Extensive experiments on several benchmark data sets clearly demonstrate the superior performance of our proposed DARL over existing state-of-the-art methods for partial domain adaptation.

Effect of single spinal anesthesia with two doses ropivacaine on urinary retention after hemorrhoidectomy in male patients.

Background: Anorectal diseases are common in the population and include internal, external, and mixed hemorrhoids. Although hemorrhoid surgery is a brief operation, anesthesia, anesthetic drugs, drug concentrations, and anesthesia level control are closely related to postoperative uroschesis. For hemorrhoid surgery, a single spinal block with ropivacaine is commonly used that blocks the S2-S4 parasympathetic nervous system, which in turn governs the voiding reflex, causing postoperative urinary retention; this affects the recovery of patients. This study was performed to investigate the effects of two doses ropivacaine that provided satisfactory analgesia and muscle relaxation and inhibited adverse reflexes on urinary retention after hemorrhoidectomy. Methods: The study included 200 male patients who underwent anorectal surgery with American Society of Anesthesiologists (ASA) grade I-II single elective spinal anesthesia between March 2021 and March 2022. Patients were randomly assigned to 2 groups using a random number table: Group A (n = 100) received 10 mg 0.5% ropivacaine (1.5 ml 1% ropivacaine + 1.5 ml 10% glucose = 3 ml), and Group B (n = 100) received 15 mg 0.5% ropivacaine (1.5 ml 1% ropivacaine + 1.5 ml 10% glucose = 3 ml). Results: The anal sphincter exhibited good relaxation, and no obvious traction pain or significant difference in the time of muscle strength recovery was observed between the 10 mg and 15 mg 0.5% ropivacaine groups (P > 0.05). The 10 mg 0.5% ropivacaine group had shorter time of micturition exceeding 100 ml and lower voiding International Prostate Symptom Score than the 15 mg 0.5% ropivacaine group (P < 0.01). Conclusions: Single spinal anesthesia with 10 mg 0.5% ropivacaine not only provides satisfactory anesthetic effect for hemorrhoidectomy but also has less influence on postoperative uroschesis and is worthy of clinical application. Trial registration: The study was registered in the Chinese Clinical Trial Registry (http://www.chictr.org.cn; identifier: ChiCTR2,100,043,686) on February 27, 2021.