Key mechanistic features of the trade-off between antibody escape and host cell binding in the SARS-CoV-2 Omicron variant spike proteins.

Since SARS-CoV-2 Omicron variant emerged, it is constantly evolving into multiple sub-variants, including BF.7, BQ.1, BQ.1.1, XBB, XBB.1.5 and the recently emerged BA.2.86 and JN.1. Receptor binding and immune evasion are recognized as two major drivers for evolution of the receptor binding domain (RBD) of the SARS-CoV-2 spike (S) protein. However, the underlying mechanism of interplay between two factors remains incompletely understood. Herein, we determined the structures of human ACE2 complexed with BF.7, BQ.1, BQ.1.1, XBB and XBB.1.5 RBDs. Based on the ACE2/RBD structures of these sub-variants and a comparison with the known complex structures, we found that R346T substitution in the RBD enhanced ACE2 binding upon an interaction with the residue R493, but not Q493, via a mechanism involving long-range conformation changes. Furthermore, we found that R493Q and F486V exert a balanced impact, through which immune evasion capability was somewhat compromised to achieve an optimal receptor binding. We propose a "two-steps-forward and one-step-backward" model to describe such a compromise between receptor binding affinity and immune evasion during RBD evolution of Omicron sub-variants.

Deep enhanced constraint clustering based on contrastive learning for scRNA-seq data.

Single-cell RNA sequencing (scRNA-seq) measures transcriptome-wide gene expression at single-cell resolution. Clustering analysis of scRNA-seq data enables researchers to characterize cell types and states, shedding new light on cell-to-cell heterogeneity in complex tissues. Recently, self-supervised contrastive learning has become a prominent technique for underlying feature representation learning. However, for the noisy, high-dimensional and sparse scRNA-seq data, existing methods still encounter difficulties in capturing the intrinsic patterns and structures of cells, and seldom utilize prior knowledge, resulting in clusters that mismatch with the real situation. To this end, we propose scDECL, a novel deep enhanced constraint clustering algorithm for scRNA-seq data analysis based on contrastive learning and pairwise constraints. Specifically, based on interpolated contrastive learning, a pre-training model is trained to learn the feature embedding, and then perform clustering according to the constructed enhanced pairwise constraint. In the pre-training stage, a mixup data augmentation strategy and interpolation loss is introduced to improve the diversity of the dataset and the robustness of the model. In the clustering stage, the prior information is converted into enhanced pairwise constraints to guide the clustering. To validate the performance of scDECL, we compare it with six state-of-the-art algorithms on six real scRNA-seq datasets. The experimental results demonstrate the proposed algorithm outperforms the six competing methods. In addition, the ablation studies on each module of the algorithm indicate that these modules are complementary to each other and effective in improving the performance of the proposed algorithm. Our method scDECL is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DBLABDHU/scDECL.

Entropy-based inference of transition states and cellular trajectory for single-cell transcriptomics.

The development of single-cell RNA-seq (scRNA-seq) technology allows researchers to characterize the cell types, states and transitions during dynamic biological processes at single-cell resolution. One of the critical tasks is to infer pseudo-time trajectory. However, the existence of transition cells in the intermediate state of complex biological processes poses a challenge for the trajectory inference. Here, we propose a new single-cell trajectory inference method based on transition entropy, named scTite, to identify transitional states and reconstruct cell trajectory from scRNA-seq data. Taking into account the continuity of cellular processes, we introduce a new metric called transition entropy to measure the uncertainty of a cell belonging to different cell clusters, and then identify cell states and transition cells. Specifically, we adopt different strategies to infer the trajectory for the identified cell states and transition cells, and combine them to obtain a detailed cell trajectory. For the identified cell clusters, we utilize the Wasserstein distance based on the probability distribution to calculate distance between clusters, and construct the minimum spanning tree. Meanwhile, we adopt the signaling entropy and partial correlation coefficient to determine transition paths, which contain a group of transition cells with the largest similarity. Then the transitional paths and the MST are combined to infer a refined cell trajectory. We apply scTite to four real scRNA-seq datasets and an integrated dataset, and conduct extensive performance comparison with nine existing trajectory inference methods. The experimental results demonstrate that the proposed method can reconstruct the cell trajectory more accurately than the compared algorithms. The scTite software package is available at https://github.com/dblab2022/scTite.

Predicting synergistic anticancer drug combination based on low-rank global attention mechanism and bilinear predictor.

MOTIVATION: Drug combination therapy has exhibited remarkable therapeutic efficacy and has gradually become a promising clinical treatment strategy of complex diseases such as cancers. As the related databases keep expanding, computational methods based on deep learning model have become powerful tools to predict synergistic drug combinations. However, predicting effective synergistic drug combinations is still a challenge due to the high complexity of drug combinations, the lack of biological interpretability, and the large discrepancy in the response of drug combinations in vivo and in vitro biological systems. RESULTS: Here, we propose DGSSynADR, a new deep learning method based on global structured features of drugs and targets for predicting synergistic anticancer drug combinations. DGSSynADR constructs a heterogeneous graph by integrating the drug-drug, drug-target, protein-protein interactions and multi-omics data, utilizes a low-rank global attention (LRGA) model to perform global weighted aggregation of graph nodes and learn the global structured features of drugs and targets, and then feeds the embedded features into a bilinear predictor to predict the synergy scores of drug combinations in different cancer cell lines. Specifically, LRGA network brings better model generalization ability, and effectively reduces the complexity of graph computation. The bilinear predictor facilitates the dimension transformation of the features and fuses the feature representation of the two drugs to improve the prediction performance. The loss function Smooth L1 effectively avoids gradient explosion, contributing to better model convergence. To validate the performance of DGSSynADR, we compare it with seven competitive methods. The comparison results demonstrate that DGSSynADR achieves better performance. Meanwhile, the prediction of DGSSynADR is validated by previous findings in case studies. Furthermore, detailed ablation studies indicate that the one-hot coding drug feature, LRGA model and bilinear predictor play a key role in improving the prediction performance. AVAILABILITY AND IMPLEMENTATION: DGSSynADR is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDBlab/DGSSynADR.

A Metal-Free Molecular Ferroelectric [4-Me-cyclohexylamine]ClO4 Introduced by Boat and Chair Conformations of Cyclohexylamine.

Organic ferroelectrics have received a great deal of interest due to their exclusive properties. However, organic ferroelectrics have not been fully explored, which hinders their practical application. Here, we presented a novel metal-free organic molecular ferroelectric [4-MCHA][ClO4 ] (1) (4-MCHA=trans-4-methylcyclohexylamine), which exhibits an above-room-temperature of 328 K. Strikingly, the single crystal structure analysis of 1 shows that the driving force of phase transition is related to the interesting chair-boat conformation change of 4-MCHA cation, in addition to the order-disorder transition of ClO4 - anion. Using piezoelectric response force microscopy (PFM), the presence of domains and the implemented polarization switching were clearly observed, which explicitly determined the presence of room-temperature ferroelectricity of 1. As far as we know, the ferroelectric phase transition mechanism attributed to the conformational change in a trans isomeric cation is very rare. This research enriched the path of designing ferroelectric materials and smart materials.

Antimony Bromide Organic-Inorganic Hybrid Compound with a Long-Chain Diamine Showing Switchable Phase Transition and Second-Harmonic Generation Properties.

Organic-inorganic hybrid metal halides have attracted significant attention in recent years due to their excellent optoelectronic properties and potential applications in solar cells. Herein, the organic-inorganic hybrid molecule [N,N-dimethyl-1,3-propanediamine]SbBr5 (1) was synthesized by reacting a long-chain organic diamine N,N-dimethyl-1,3-propanediamine with SbBr3 as a metal halide precursor in HBr aqueous solution. Compound 1 possesses a one-dimensional chainlike structure with the second-harmonic generation switch and two continuous phase transitions above room temperature. The band gap of compound 1 is about 2.62 eV, exhibiting a semiconductive property, which may have important implications for the development of new optoelectronic devices.

Alkali Metal Organic-Inorganic Hybrid Compounds with Different Crystal Dimensions Show Phase-Transition, Dielectric, and SHG Properties Based on a Quasi-Spherical Amine (1S,4S)-2,5-Diazabicyclo[2.2.1]heptane.

Reactions of a chiral and quasi-spherical molecule [1S,4S-2,5-2.2.1-H2dabch]I2 (1) with alkali metal halide MX (M = Na, K, Cs; X = Cl, Br) at room temperature produced a series of organic-inorganic hybrid (OIH) materials [1S,4S-2,5-2.2.1-H2dabch]NaBr3 (2), [1S,4S-2,5-2.2.1-H2dabch]CsCl3·H2O (3) and [1S,4S-2,5-2.2.1-H2dabch]KBr3·H2O (4). The single-crystal X-ray diffraction analysis revealed that the organic-inorganic framework structures comprised of the templating ligand and alkali metal halides (NaBr, CsCl, KBr) displayed dimensions spanning from one-dimensional (1D) to three-dimensional (3D). Moreover, the results of both differential scanning calorimetry (DSC) and dielectric measurements demonstrated that compounds 1-4 displayed reversible, high-temperature phase transitions and noticeable dielectric anomalies. In addition, the temperature-dependent second harmonic generation (SHG) results revealed crystals 1 and 3 can switch from the SHG-ON to the SHG-OFF state, which was proved by the variable-temperature X-ray diffraction. This research aims to streamline the exploration of multifunctional second harmonic generation (SHG) and dielectric materials that have been synthesized using chiral ligands and alkali metals. This will provide researchers with enhanced opportunities to delve further into this specific research domain.

A systematic review on aquaculture wastewater: Pollutants, impacts, and treatment technology.

Aquaculture is the major way to solve the global food sacrcity. As the global population increases, the demand for aquaculture increases. Fish feed, drugs and chemicals, and metabolic waste or mortalities of aquatic organisms also increase, eventually resulting in the production of a large amount of aquaculture wastewater. These aquaculture discharges contain a variety of pollutants, such as conventional pollutants, organic compounds, heavy metals, and biological contaminants, inducing occupational hazards and risks, food security, the environment pollution. Proper wastewater treatment technologies are required to remove hazardous pollutants for minimizing their impacts on environmental and human health. Recirculating aquaculture systems, some biological and physicochemical methods have been applied to remove some pollutants from the aquaculture wastewater, but their efficiency in removing pollutants still requires to be further improved for achieving zero-waste discharge and ensuring sustainable aquaculture development. Meanwhile, sound regulation and legislation needs to be established for ensuring the normal operation of aquaculture industries and the standard discharge of wastewater. This review aims to provide comprehensive information of aquaculture wastewater for the researchers and promote the healthy development of aquaculture.

Treatment of neuropathic pain by traditional Chinese medicine: An updated review on their effect and putative mechanisms of action.

Neuropathic pain (NP) is a common chronic pain with heterogeneous clinical features, and consequent lowering of quality of life. Currently, although conventional chemical drugs can effectively manage NP symptoms in the short term, their long-term efficacy is limited, and they come with significant side effects. In this regard, traditional Chinese medicine (TCM) provides a promising avenue for treating NP. Numerous pharmacological and clinical studies have substantiated the effectiveness of TCM with multiple targets and mechanisms. We aimed to outline the characteristics of TCM, including compound prescriptions, single Chinese herbs, active ingredients, and TCM physical therapy, for NP treatment and discussed their efficacy by analyzing the pathogenesis of NP. Various databases, such as PubMed, Web of Science, China National Knowledge Infrastructure, and Wanfang database, were searched. We focused on recent research progress in NP treatment by TCM. Finally, we proposed the future challenges and emerging trends in the treatment of NP. TCM demonstrates significant clinical efficacy in NP treatment, employing multi-mechanisms. Drawing from the theory of syndrome differentiation, four types of dialectical treatments for NP by compound TCM prescriptions were introduced: promoting blood circulation and removing blood stasis; promoting blood circulation and promote Qi flow; warming Yang and benefiting Qi; soothing the liver and regulating Qi. Meanwhile, 33 single Chinese herbs and 25 active ingredients were included. In addition, TCM physical therapy (e.g., acupuncture, massage, acupoint injection, and fumigation) also showed good efficacy in NP treatment. TCM, particularly through the use of compound prescriptions and acupuncture, holds bright prospects in treating NP owing to its diverse holistic effects. Nonetheless, the multi-targets of TCM may result in possible disadvantages to NP treatment, and the pharmacological mechanisms of TCM need further evaluation. Here, we provide an overview of NP treatment via TCM, based on the pathogenesis and the potential therapeutic mechanisms, thus providing a reference for further studies.

Rice Origin Tracing Technology Based on Fluorescence Spectroscopy and Stoichiometry.

The origin of agricultural products is crucial to their quality and safety. This study explored the differences in chemical composition and structure of rice from different origins using fluorescence detection technology. These differences are mainly affected by climate, environment, geology and other factors. By identifying the fluorescence characteristic absorption peaks of the same rice seed varieties from different origins, and comparing them with known or standard samples, this study aims to authenticate rice, protect brands, and achieve traceability. The study selected the same variety of rice seed planted in different regions of Jilin Province in the same year as samples. Fluorescence spectroscopy was used to collect spectral data, which was preprocessed by normalization, smoothing, and wavelet transformation to remove noise, scattering, and burrs. The processed spectral data was used as input for the long short-term memory (LSTM) model. The study focused on the processing and analysis of rice spectra based on NZ-WT-processed data. To simplify the model, uninformative variable elimination (UVE) and successive projections algorithm (SPA) were used to screen the best wavelengths. These wavelengths were used as input for the support vector machine (SVM) prediction model to achieve efficient and accurate predictions. Within the fluorescence spectral range of 475-525 nm and 665-690 nm, absorption peaks of nicotinamide adenine dinucleotide (NADPH), riboflavin (B2), starch, and protein were observed. The origin tracing prediction model established using SVM exhibited stable performance with a classification accuracy of up to 99.5%.The experiment demonstrated that fluorescence spectroscopy technology has high discrimination accuracy in tracing the origin of rice, providing a new method for rapid identification of rice origin.

Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development.

The opioid crisis in the United States is a significant public health issue, with a nearly threefold increase in opioid-related fatalities between 1999 and 2014. In response to this crisis, society has made numerous efforts to mitigate its impact. Recent advancements in understanding the structural intricacies of the κ opioid receptor (KOR) have improved our knowledge of how opioids interact with their receptors, triggering downstream signaling pathways that lead to pain relief. This review concentrates on the KOR, offering crucial structural insights into the binding mechanisms of both agonists and antagonists to the receptor. Through comparative analysis of the atomic details of the binding site, distinct interactions specific to agonists and antagonists have been identified. These insights not only enhance our understanding of ligand binding mechanisms but also shed light on potential pathways for developing new opioid analgesics with an improved risk-benefit profile.

The effect of the interaction between intrinsic capacity and social support on the trajectories of activities of daily living in older adults.

OBJECTIVES: This study explored the effect of the interaction between intrinsic capacity and social support on the activities of daily living in the older adults. METHODS: A total of 3,124 older adults participated in the study. Data on the assessment of intrinsic capacity as well as activities of daily living (ADL) and social support were collected. The latent growth curve model (LGCM) was used to examine the effect of the interaction between social support and intrinsic capacity on the activities of daily living in the older adults. RESULTS: A total of 3,730 older adults were included at baseline, and 3,124 older adults completed the 4-year follow-up. The ADL score of the older adults showed a significant downward trend with time over 4 years (ß=-0.115, P<0.05). In the elderly population with high intrinsic capacity, the ADL of individuals with low levels of social participation decreased significantly over time (ß=-0.114, P=0.012). In the elderly group with low intrinsic capacity, the ADL of individuals with low levels of social participation (ß=-0.245, P=0.005) and high levels of life care (ß=-0.167, P=0.001) decreased significantly over time. CONCLUSION: This is the first longitudinal study to explore the effects of interactions among intrinsic capacity, social participation and family support on the trajectory of ADL in the older adults from the perspective of the life course. Social participation can effectively improve functional ability of older adults with low intrinsic capacity. Life care is beneficial for maintaining the functional ability of older adults with intact intrinsic capacity, but it accelerates the deterioration of the functional ability of older adults with low intrinsic capacity.

The First Discovery of Spherical Carborane Molecular Ferroelectric Crystals.

Carborane compounds, known for their exceptional thermal stability and non-toxic attributes, have garnered widespread utility in medicine, supramolecular design, coordination/organometallic chemistry, and others. Although there is considerable interest among chemists, the integration of suitable carborane molecules into ferroelectric materials remains a formidable challenge. In this study, we employ the quasi-spherical design strategy to introduce functional groups at the boron vertices of the o-carborane cage, aiming to reduce molecular symmetry. This approach led to the successful synthesis of the pioneering ferroelectric crystals composed of cage-like carboranes: 9-OH-o-carborane (1) and 9-SH-o-carborane (2), which undergo above-room ferroelectric phase transitions (Tc) at approximately 367 K and 347 K. Interestingly, 1 and 2 represent uniaxial and multiaxial ferroelectrics respectively, with 2 exhibiting six polar axes and as many as twelve equivalent polarization directions. As the pioneering instance of carborane ferroelectric crystals, this study introduces a novel structural archetype for molecular ferroelectrics, thereby providing fresh insights into the exploration of molecular ferroelectric crystals with promising applications.

S-S Bond Strategy at Sulfide Heterointerface: Reversing Charge Transfer and Constructing Hydrogen Spillover for Boosted Hydrogen Evolution.

Developing efficient electrocatalyst in sulfides for hydrogen evolution reaction (HER) still poses challenges due to the lack of understanding the role of sulfide heterointerface. Here, we report a sulfide heterostructure RuSx/NbS2, which is composed of 3R-type NbS2 loaded by amorphous RuSx nanoparticles with S-S bonds formed at the interface. As HER electrocatalyst, the RuSx/NbS2 shows remarkable low overpotential of 38 mV to drive a current density of 10 mA cm-2 in acid, and also low Tafel slope of 51.05 mV dec-1. The intrinsic activity of RuSx/NbS2 is much higher than that of Ru/NbS2 reference as well as the commercial Pt/C. Both experiments and theoretical calculations unveil a reversed charge transfer at the interface from NbS2 to RuSx that driven by the formation of S-S bonds, resulting in electron-rich Ru configuration for strong hydrogen adsorption. Meanwhile, electronic redistribution induced by the sulfide heterostructure facilitates hydrogen spillover (HSo) effect in this system, leading to accelerated hydrogen desorption at the basal plane of NbS2. This study provides an effective S-S bond strategy in sulfide heterostructure to synergistically modulate the charge transfer and adsorption thermodynamics, which is very valuable for the development of efficient electrocatalysts in practical applications.

Loss of Protein Kinase D2 Activity Protects Against Bleomycin-Induced Dermal Fibrosis in Mice.

Protein kinase D (PKD) has been linked to inflammatory responses in various pathologic conditions; however, its role in inflammation-induced dermal fibrosis has not been evaluated. In this study, we aimed to investigate the roles and mechanisms of protein kinase D2 (PKD2) in inflammation-induced dermal fibrosis and evaluate the therapeutic potential of PKD inhibitors in this disease. Using homozygous kinase-dead PKD2 knock-in (KI) mice, we examined whether genetic ablation or pharmacologic inhibition of PKD2 activity affected dermal inflammation and fibrosis in a bleomycin (BLM)-induced skin fibrosis model. Our data showed that dermal thickness and collagen fibers were significantly reduced in BLM-treated PKD2 KI mice compared with that in wild-type mice, and so was the expression of α-smooth muscle actin and collagens and the mRNA levels of transforming growth factor-ß1 and interleukin-6 in the KI mice. Corroboratively, pharmacologic inhibition of PKD by CRT0066101 also significantly blocked BLM-induced dermal fibrosis and reduced α-smooth muscle actin, collagen, and interleukin-6 expression. Further analyses indicated that loss of PKD2 activity significantly blocked BLM-induced infiltration of monocytes/macrophages and neutrophils in the dermis. Moreover, using bone marrow-derived macrophages, we demonstrated that PKD activity was required for cytokine production and migration of macrophages. We have further identified Akt as a major downstream target of PKD2 in the early inflammatory phase of the fibrotic process. Taken together, our findings indicate that PKD2 promotes dermal fibrosis via regulating immune cell infiltration, cytokine production, and downstream activation of Akt in lesional skin, and targeted inhibition of PKD2 may benefit the treatment of this condition.

PCSK9 involves in the high-fat diet-induced abnormal testicular function of male mice.

In brief: Impaired spermatogenesis resulting from disturbed cholesterol metabolism due to intake of high-fat diet (HFD) has been widely recognized, however, the role of preprotein invertase subtilin 9 (PCSK9), which is a negative regulator of cholesterol metabolism, has never been reported. This study aims to reveal the role of PCSK9 on spermatogenesis induced by HFD in mice. Abstract: Long-term consumption of a high-fat diet (HFD) is an important factor that leads to impaired spermatogenesis exhibiting poor sperm quantity and quality. However, the mechanism of this is yet to be elucidated. Disrupted cholesterol homeostasis is one of many crucial pathological factors which could contribute to impaired spermatogenesis. As a negative regulator of cholesterol metabolism, preprotein invertase subtilin 9 (PCSK9) mediates low density lipoprotein receptor (LDLR) degradation to the lysosome, thereby reducing the expression of LDLR on the cell membrane and increasing serum low-density lipoprotein cholesterol level, resulting in lipid metabolism disorders. Here, we aim to study whether PCSK9 is a pathological factor for impaired spermatogenesis induced by HFD and the underlying mechanism. To meet the purpose of our study, we utilized wild-type C57BL/6 male mice and PCSK9 knockout mice with same background as experimental subjects and alirocumab, a PCSK9 inhibitor, was used for treatment. Results indicated that HFD induced higher PCSK9 expression in serum, liver, and testes, and serum PCSK9 is negatively correlated with spermatogenesis, while both PCSK9 inhibitor treatment and PCSK9 knockout methodologies ameliorated impaired lipid metabolism and spermatogenesis in mice fed a HFD. This could be due to the overexpression of PCSK9 induced by HFD leading to dyslipidemia, resulting in testicular lipotoxicity, thus activating the Bcl-2-Bax-Caspase3 apoptosis signaling pathway in testes, particularly in Leydig cells. Our study demonstrates that PCSK9 is an important pathological factor in the dysfunction of spermatogenesis in mice induced by HFD. This finding could provide innovative ideas for the diagnosis and treatment of male infertility.

Red-grouper nervous necrosis virus B1 protein inhibits fish IFN response by targeting Ser5-phosphorylated RNA polymerase II to promote viral replication.

Nervous necrosis virus (NNV) could infect more than 200 fish species worldwide, with almost 100% mortality in affected larvae and juvenile fish. Among different genotypes of NNV, the red-grouper nervous necrosis virus (RGNNV) genotype is the most widely reported with the highest number of susceptible species. Interferon (IFN) is a crucial antiviral cytokine and RGNNV needs to develop some efficient strategies to resist host IFN-stimulated antiviral immune. Although considerable researches on RGNNV, whether RGNNV B1 protein participates in regulating the host's IFN response remains unknown. Here, we reported that B1 protein acted as a transcript inhibition factor to suppress fish IFN production. We firstly found that ectopic expression of B1 protein significantly decreased IFN and IFN-stimulated genes (ISGs) mRNA levels and IFNφ1 promoter activity induced by polyinosinic:polycytidylic acid [poly (I:C)]. Further studies showed that B1 protein inhibited the IFNφ1 promoter activity stimulated by the key RIG-I-like receptors (RLRs) factors, including MDA5, MAVS, TBK1, IRF3, and IRF7 and decreased their protein levels. Moreover, B1 protein significantly inhibited the activity of constitutively active cytomegalovirus (CMV) promoter, which suggested that B1 protein was a transcription inhibitor. Western blot indicated that B1 protein decreased the Ser5 phosphorylation of RNA polymerase II (RNAP II) C-terminal domain (CTD). Together, our data demonstrated that RGNNV B1 protein was a host transcript antagonist, which intervened RNAP II Ser5-phosphorylation, inhibiting host IFN response and facilitating RGNNV replication.

The metabolite profiling of YR-1702 injection in human plasma, urine and feces by HPLC-Q-TOF-MS/MS.

YR-1702, a hybrid µ/κ/δ receptor agonist, is modified from the traditional opioid analgesic dezocine. It had shown both excellent analgesic effect and lower addiction in phase I clinical trial in China, however, the metabolic pathway of YR-1702 in humans remains unelucidated.The goals of this study are to characterise the metabolism of YR-1702 in human liver microsomes (HLMs) and patients with chronic non-cancer pain by high performance liquid chromatography-coupled with quadrupole-time-of-flight mass spectrometry (HPLC-Q-TOF-MS/MS).The results showed that a total of twelve metabolites were identified in HLMs, in which 7, 6 and 5 metabolites were also found in human plasma, urine and feces, respectively. And the major metabolic pathways include mono-hydroxylation, di-hydroxylation, dehydrogenation and glucuronidation. The locations of hydroxylation and dehydrogenation were identified by the signature fragments of the metabolites.The relative contents of the metabolites in human plasma were also evaluated, in which the main metabolite M1 notably accounting for more than 14% of the total drug exposure. This study would contribute to the understanding of the in vivo metabolite profile of YR-1702 injection for future use.

Exploring the association of dietary patterns with the risk of hypertension using principal balances analysis and principal component analysis.

OBJECTIVE: In the field of nutritional epidemiology, principal component analysis (PCA) has been used extensively in identifying dietary patterns. Recently, compositional data analysis (CoDA) has emerged as an alternative approach for obtaining dietary patterns. We aimed to directly compare and evaluate the ability of PCA and principal balances analysis (PBA), a data-driven method in CoDA, in identifying dietary patterns and their associations with the risk of hypertension. DESIGN: Cohort study. A 24-h dietary recall questionnaire was used to collect dietary data. Multivariate logistic regression analysis was used to analyse the association between dietary patterns and hypertension. SETTING: 2004 and 2009 China Health and Nutrition Survey. PARTICIPANTS: A total of 3892 study participants aged 18-60 years were included as the subjects. RESULTS: PCA and PBA identified five patterns each. PCA patterns comprised a linear combination of all food groups, whereas PBA patterns included several food groups with zero loadings. The coarse cereals pattern identified by PBA was inversely associated with hypertension risk (highest quintile: OR = 0·74 (95 % CI 0·57, 0·95); Pfor trend = 0·037). None of the five PCA patterns was associated with hypertension. Compared with the PCA patterns, the PBA patterns were clearly interpretable and accounted for a higher percentage of variance in food intake. CONCLUSIONS: Findings showed that PBA might be an appropriate and promising approach in dietary pattern analysis. Higher adherence to the coarse cereals dietary pattern was associated with a lower risk of hypertension. Nevertheless, the advantages of PBA over PCA should be confirmed in future studies.

Clinical value of prenatal ultrasound in diagnosis and classification of common arterial trunk.

OBJECTIVES: To investigate the clinical value of prenatal ultrasound in the diagnosis of the common arterial trunk (CAT) classification and associated malformations. MATERIALS AND METHODS: The 2D ultrasound images, spatiotemporal image correlations (STICs) and clinical data of 88 fetuses diagnosed with CAT malformations by prenatal ultrasound were retrospectively analyzed and classified. The correlation between different types, fetal malformation and pregnancy outcomes were analyzed. RESULTS: Among the 88 fetuses, there were 39 cases (44.32%) of type A1, 40 cases (45.45%) of type A2, 8 cases (9.09%) of type A3, and 1 case of type A4 (1.14%). There were 16 cases (18.18%) with isolated CAT, 48 cases (54.55%) with complex intra-cardiac structural abnormalities, and 24 cases (27.27%) with intra-cardiac and extra-cardiac structural abnormalities. In extra-cardiac structural malformations, 14 cases were associated with 1 other system abnormality, 4 cases with 2 other system abnormalities, 3 cases with 3 other system abnormalities, while 3 cases were combined with 4 other system abnormalities, among which the facial and physical abnormalities had the highest incidence (39.13%). The STIC images were completely displayed in all 88 cases. There was a statistical difference between isolated CAT and CAT combined with other abnormalities in fetal pregnancy outcomes. CONCLUSIONS: Prenatal ultrasound had a high clinical application value in CAT classification. Pregnancy outcomes were highly correlated with the classification and associated intra-cardiac and extra-cardiac structural malformations. The early evaluation of fetal prognosis before birth has important value for clinical intervention.