The auxin-responsive CsSPL9-CsGH3.4 module finely regulates auxin levels to suppress the development of adventitious roots in tea (Camellia sinensis).

The cutting technique is extensively used in tea breeding, with key emphasis on promoting the growth of adventitious roots (ARs). Despite its importance in tea cultivation, the mechanisms underlying AR development in tea remain unclear. In this study, we demonstrated the essential role of auxins in the initiation and progression of AR and established that the application of exogenous 1-naphthaleneacetic acid-enhanced AR formation in tissue-cultured seedlings and cuttings. Then, we found that the auxin-responsive transcription factor CsSPL9 acted as a negative regulator of AR development by reducing the levels of free indole-3-acetic acid (IAA) in tea plants. Furthermore, we identified CsGH3.4 as a downstream target of CsSPL9, which was activated by direct binding to its promoter. CsGH3.4 also inhibited AR development and maintained low levels of free IAA. Thus, these results revealed the inhibitory effect of the auxin-responsive CsSPL9-CsGH3.4 module on AR development by reducing free IAA levels in tea. These findings have significant theoretical and practical value for enhancing tea breeding practices.

KGANSynergy: knowledge graph attention network for drug synergy prediction.

Combination therapy is widely used to treat complex diseases, particularly in patients who respond poorly to monotherapy. For example, compared with the use of a single drug, drug combinations can reduce drug resistance and improve the efficacy of cancer treatment. Thus, it is vital for researchers and society to help develop effective combination therapies through clinical trials. However, high-throughput synergistic drug combination screening remains challenging and expensive in the large combinational space, where an array of compounds are used. To solve this problem, various computational approaches have been proposed to effectively identify drug combinations by utilizing drug-related biomedical information. In this study, considering the implications of various types of neighbor information of drug entities, we propose a novel end-to-end Knowledge Graph Attention Network to predict drug synergy (KGANSynergy), which utilizes neighbor information of known drugs/cell lines effectively. KGANSynergy uses knowledge graph (KG) hierarchical propagation to find multi-source neighbor nodes for drugs and cell lines. The knowledge graph attention network is designed to distinguish the importance of neighbors in a KG through a multi-attention mechanism and then aggregate the entity's neighbor node information to enrich the entity. Finally, the learned drug and cell line embeddings can be utilized to predict the synergy of drug combinations. Experiments demonstrated that our method outperformed several other competing methods, indicating that our method is effective in identifying drug combinations.

Maternal tobacco exposure during pregnancy and atopic dermatitis in offspring: A systematic review and meta-analysis.

The main purpose of this review was to examine the evidence of the relationship between active smoking or passive smoking during pregnancy and atopic dermatitis in offspring. The protocol was written following the PRISMA Checklist and was registered in the PROSPERO database (registration number CRD42022381136). We implemented a comprehensive search in PubMed, Embase and Web of Science databases to identify all potentially related articles from inception through 1 December 2022. We assessed cohort studies and case-control studies using the Newcastle-Ottawa Scale (NOS), and the Joanna Briggs Institute (JBI) critical appraisal tool to assess the quality of cross-sectional studies. Heterogeneity was investigated by using Cochrane Q tests and I2 statistics. In addition, according to the research design, population source and population size, the reasons for the heterogeneity were analysed. A total of 15 observational studies were included in this analysis. Our meta-analysis suggests that atopic dermatitis in offspring is not associated with active smoking during pregnancy (pooled OR, 0.96 [95% CI 0.86-1.07]); however, it is related to passive smoking (OR, 1.52 [95% CI 1.36-1.70]). Passive smoking during pregnancy is associated with an increased risk of eczema development in offspring. More research is needed to explore the risk of active smoking and eczema development in offspring, especially the association between measurements of pregnancy cotinine levels in maternal body fluids and AD in offspring.

Estimating the tuberculosis incidence using a SARIMAX-NNARX hybrid model by integrating meteorological factors in Qinghai Province, China.

Tuberculosis (TB) is recognized as being a major public health concern owing to its increase in Qinghai, China. In this study, we aimed to estimate the long-term effects of meteorological variables on TB incidence and construct an advanced hybrid model with seasonal autoregressive integrated moving average (SARIMA) and a neural network nonlinear autoregression (SARIMAX-NNARX) by integrating meteorological factors and evaluating the model fitting and prediction effect. During 2005-2017, TB experienced an upward trend with obvious periodic and seasonal characteristics, peaking in spring and winter. The results showed that TB incidence was positively correlated with average relative humidity (ARH) with a 2-month lag (ß = 1.889, p = 0.003), but negatively correlated with average atmospheric pressure (AAP) with a 1-month lag (ß = - 1.633, p = 0.012), average temperature (AT) with a 2-month lag (ß = - 0.093, p = 0.027), and average wind speed (AWS) with a 0-month lag (ß = - 13.221, p = 0.033), respectively. The SARIMA (3,1,0)(1,1,1)12, SARIMAX(3,1,0)(1,1,1)12, and SARIMAX(3,1,0)(1,1,1)12-NNARX(15,3) were considered preferred models based on the evaluation criteria. Of them, the SARIMAX-NNARX technique had smaller error values than the SARIMA and SARIMAX models in both fitting and forecasting aspects. The sensitivity analysis also revealed the robustness of the mixture forecasting model. Therefore, the SARIMAX-NNARX model by integrating meteorological variables can be used as an accurate method for forecasting the epidemic trends which would be great importance for TB prevention and control in the coming periods in Qinghai.

Correction: Structural diversity, bioactivities, and biosynthesis of natural diterpenoid alkaloids.

Correction for 'Structural diversity, bioactivities, and biosynthesis of natural diterpenoid alkaloids' by Yong Shen et al., Nat. Prod. Rep., 2020, 37, 763-796, https://doi.org/10.1039/D0NP00002G.

Clinical impact of the PAI-1 4G/5G polymorphism in Chinese patients with venous thromboembolism.

BACKGROUND: Venous thromboembolism (VTE) is a life-threatening cardiovascular syndrome that characterized by the imbalance of hemostasis and thrombosis and the formation of thrombi in the blood vessels. The aim of this study was to elucidate the clinical impact of the PAI-1 4G/5G polymorphism in Chinese patients with VTE. METHODS: A total of 169 subjects (89 VTE, 10 hyperbilirubinemia, 10 hyperlipidemia and 60 healthy controls) were recruited at Peking Union Medical College Hospital. The accuracy of the TaqMan-MGB RT-PCR method for detecting F5 G1691A (FVL) and PAI-1 4G/5G polymorphisms was evaluated by using sequencing method as the gold standard. Besides, the association of the PAI-1 4G/5G polymorphism with susceptibility, treatment efficacy and recurrence status of VTE in Chinese population were explored. Eventually, the plasma PAI-1 antigen levels and PAI-1 4G/5G polymorphisms were determined on additional 64 subjects (32 VTE and 32 healthy controls) simultaneously. RESULTS: The TaqMan-MGB RT-PCR method was proven to be highly accurate in determining the FVL and PAI-1 4G/5G polymorphisms without interference from bilirubin and lipids in the samples. No obvious correlation of the PAI-1 4G/5G polymorphism with VTE was observed in our study by using five genetic models (allele, genotype, dominant, recessive and additive). Additionally, we also observed that individuals with the 4G/5G genotype had lower neutrophil counts and neutrophil-to-lymphocyte ratio (NLR) than the 5G/5G genotype. Furthermore, we found that the patients with the 5G/5G genotype were more likely to achieve complete recanalization compared to the 4G/4G genotype. In addition, individuals carrying the 5G/5G genotype were more likely to develop a recurrence-free status as compared to individuals with the 4G/4G or 4G/5G genotypes. PAI-1 antigen levels in the VTE group were significantly higher than those in the HC group. However, there was no significant difference in the antigen levels of PAI-1 among subjects carrying various genotypes in the VTE group or HC group. CONCLUSION: The PAI-1 4G/5G polymorphism has potential value in assessing the prognosis of Chinese patients with VTE. Our study has laid the foundation for the application of PAI-1 4G/5G polymorphism in the personalized management and monitoring of patients with VTE.

Two new dammarane-type triterpenoids from the steamed roots of Panax notoginseng.

Two new dammarane-type triterpenoids, notoginsenoside SY3 (1) and notoginsenoside SY4 (2), were isolated from the steamed roots of Panax notoginseng. Their structures were determined to be 3ß, 12ß, 20(S)-trihydroxy-27-anordammar-23(24)(E)-ene-3-O-ß-D-glucopyranosyl-(1→2)-ß-D-glucopyranoside (1) and 3ß, 12ß, 20(S)-trihydroxy-25-methoxyldammar-23(24)(E)-ene-3-O-ß-D-glucopyranosyl-(1→2)-ß-D-glucopyranoside (2) by IR, HRESIMS and NMR experiments.

Structural diversity, bioactivities, and biosynthesis of natural diterpenoid alkaloids.

Covering: 2009 to 2018. Diterpenoid alkaloids, originating from the amination of natural tetracyclic diterpenes, are a diverse class of compounds having complex structural features with many stereocenters. The important pharmacological activities and structural complexity of the diterpenoid alkaloids have long interested scientists due to their medicinal uses, infamous toxicity, and unique biosynthesis. Since 2009, 373 diterpenoid alkaloids, assigned to 46 skeletons, have been isolated and identified from plants mostly in the Ranunculaceae family. The names, classes, molecular weight, molecular formula, NMR data, and plant sources of these diterpene alkaloids are collated here. This review will be a detailed update of the naturally occurring diterpene alkaloids reported from the plant kingdom from 2009-2018, providing an in-depth discussion of their diversity, biological activities, pharmacokinetics, toxicity, application, evolution, and biosynthesis.

[Role of CRISPR/Cas systems in drugresistance and virulence and the effect of IS600 on the expression of cse2 in Shigella].

Objective: To analyze the relationship between CRISPR/Cas system and drug-resistance, virulence. To investigate the effect of IS600 on the expression of CRISPR associated gene cse2 in Shigella. Methods: CRISPR loci, CRISPR associated gene cse2, drug-resistant genes and virulent genes were detected by PCR in 33 Shigella strains; Trypan Blue counting test was used to detect bacterial virulence; Real-time PCR was used to detect relative mRNA expression of cse2; susceptibilities of Shigella strains were tested by agar diffusion method. Furthermore, we analyzed the relationship between CRISPR loci and drug-resistant genes, virulent genes. The effect of the IS600 on the expression of CRISPR associated gene cse2 was investigated. Results: The mortality of Hela cells infected by Shigella with CRISPR1 loci was significantly lower (P<0.05) than those infected by Shigella without CRISPR1. The mRNA expression level of cse2 in group with IS600 was significantly (P<0.05) lower than that in group without IS600. Conclusions: CRISPR loci were widely present in Shigella. Shigella without CRISPR1 has a higher pathogenicity. Due to the insertion of IS600, the mRNA expression level of cse2 was decreased in Shigella.

[Chemical constituents of Lepidium meyenii].

To study the chemical constituents of Lepidium meyenii, the air-dried rhizome of L. meyenii was extracted with 70% EtOH. The extract was condensed to a small amount of volume and extracted with petroleum ether, EtOAc and n-BuOH, successively. The compounds were isolated and purified by column chromatography, and identified based on spectral analyses (1H-NMR, 13C-NMR, HRESIMS). Eighteen compounds were isolated from L. meyenii, including 7 alkaloids and 4 fatty acids and 7 other compounds. They were characterized as (3-hydroxybenzyl) carbamic acid(1), phenylmethanamine(2), N-benzylformamide (3), N-benzylacetamide (4), pyridin-4-ylmethanamine(5), n-(4-methoxybenzyl) aniline(6), uracil(7), succininc acid(8), decanedioic acid(9), n-hexa- decanoic acid methyl ester(10), heptanoic acid(11), solerole(12), pyromucic acid methyl ester(13), 5-hydroxymethyl-2-furancar- boxadehyde(14), 5-(methoxymethyl)-1H-pyrrole-2-carbaldehyde(15), 1,7-dihydroxy-2,3, 4-trimethoxyxanthone (16), 1,7-di- hydroxy-3,4- dimethoxy-xanthone(17), (+)-pinoresinol(18). Meanwhile, compounds 1-18 were obtained from L. neyenii for the first time.

Synthesis, structure-activity relationships and biological evaluation of dehydroandrographolide and andrographolide derivatives as novel anti-hepatitis B virus agents.

Dehydroandrographolide and andrographolide, two natural diterpenoids isolated from Andrographis paniculata possessed activity against HBV DNA replication with IC50 values of 22.58 and 54.07µM and low SI values of 8.7 and 3.7 in our random assay. Consequently, 48 derivatives of dehydroandrographolide and andrographolide were synthesized and evaluated for their anti-HBV properties to yield a series of active derivatives with lower cytotoxicity, including 14 derivatives against HBsAg secretion, 19 derivatives against HBeAg secretion and 38 derivatives against HBV DNA replication. Interestingly, compound 4e could inhibit not only HBsAg and HBeAg secretions but also HBV DNA replication with SI values of 20.3, 125.0 and 104.9. Furthermore, the most active compound 2c with SI value higher than 165.1 inhibiting HBV DNA replication was revealed with the optimal logP value of 1.78 and logD values. Structure-activity relationships (SARs) of the derivatives were disclosed for guiding the future research toward the discovery of new anti-HBV drugs.

DP-SSLoRA: A privacy-preserving medical classification model combining differential privacy with self-supervised low-rank adaptation.

BACKGROUND AND OBJECTIVE: Concerns about patient privacy issues have limited the application of medical deep learning models in certain real-world scenarios. Differential privacy (DP) can alleviate this problem by injecting random noise into the model. However, naively applying DP to medical models will not achieve a satisfactory balance between privacy and utility due to the high dimensionality of medical models and the limited labeled samples. METHODS: This work proposed the DP-SSLoRA model, a privacy-preserving classification model for medical images combining differential privacy with self-supervised low-rank adaptation. In this work, a self-supervised pre-training method is used to obtain enhanced representations from unlabeled publicly available medical data. Then, a low-rank decomposition method is employed to mitigate the impact of differentially private noise and combined with pre-trained features to conduct the classification task on private datasets. RESULTS: In the classification experiments using three real chest-X ray datasets, DP-SSLoRA achieves good performance with strong privacy guarantees. Under the premise of ɛ=2, with the AUC of 0.942 in RSNA, the AUC of 0.9658 in Covid-QU-mini, and the AUC of 0.9886 in Chest X-ray 15k. CONCLUSION: Extensive experiments on real chest X-ray datasets show that DP-SSLoRA can achieve satisfactory performance with stronger privacy guarantees. This study provides guidance for studying privacy-preserving in the medical field. Source code is publicly available online. https://github.com/oneheartforone/DP-SSLoRA.

Rapid detection of markers in green coffee beans with different primary processing treatments of Coffea arabica L. from Yunnan.

The characteristic flavor of Coffea arabica from Yunnan is largely attributed to the primary processing treatments through affecting the VOCs accumulation. Therefore, a rapid and comprehensive detection technique is needed to accurately recognize VOCs in green coffee beans with different pretreatment methods. Hence, we conducted volatile profiles and identified nine markers of three different primary processed green coffee beans from the major production areas in Yunnan with the combined of HS-SPME-GC-MS and PTR-TOF-MS. The relationships between the chemical composition and the content of VOCs in green coffee beans were elucidated. Among the markers, palmitic acid (F3), linoleic acid (F6), α-ethylidene phenylacetaldehyde (T4), and phytane (T8) contributed to the antioxidant activity of sun-exposed green coffee beans. In conclusion, the analytical technology presented here provided a general tool for an overall and rapid understanding of a detailed volatile profiles of green coffee beans in Yunnan.

The characteristic VOCs of different parts of Artocarpus heterophyllus fruit based on HS-SPME-GC-MS and PTR-TOF-MS.

Insight investigation on both edible pulps and inedible parts involving inflorescence axis and shreds of Artocarpus heterophyllus Lam were carried out, a total of 98 VOCs and 201 masses were identified by the combination of HS-SPME-GC-MS and PTR-TOF-MS. Among them, according to the consistency of OAV and results of VIP > 1, p < 0.05, compounds methyl isovalerate (A2), 3-methylbutyl acetate (A5) and octanoic acid, ethyl ester (A21) were recognized as aroma markers to distinguish the pulps, shreds and inflorescence axis. Meanwhile, the inflorescence axis (IC50: 1.82 mg/mL) and shreds (IC50: 16.74 mg/mL) exhibited more excellent antioxidant potency than pulps (IC50: 17.43 mg/mL) in vitro. These findings validated the feasibility of coupling HS-SPME-GC-MS and PTR-TOF-MS for rapid detection of characteristic VOCs of this plant, and offered new prospect of fragrance utilization and waste management of the edible and inedible parts of A. heterophyllus fruit.

MSFN: a multi-omics stacked fusion network for breast cancer survival prediction.

Introduction: Developing effective breast cancer survival prediction models is critical to breast cancer prognosis. With the widespread use of next-generation sequencing technologies, numerous studies have focused on survival prediction. However, previous methods predominantly relied on single-omics data, and survival prediction using multi-omics data remains a significant challenge. Methods: In this study, considering the similarity of patients and the relevance of multi-omics data, we propose a novel multi-omics stacked fusion network (MSFN) based on a stacking strategy to predict the survival of breast cancer patients. MSFN first constructs a patient similarity network (PSN) and employs a residual graph neural network (ResGCN) to obtain correlative prognostic information from PSN. Simultaneously, it employs convolutional neural networks (CNNs) to obtain specificity prognostic information from multi-omics data. Finally, MSFN stacks the prognostic information from these networks and feeds into AdaboostRF for survival prediction. Results: Experiments results demonstrated that our method outperformed several state-of-the-art methods, and biologically validated by Kaplan-Meier and t-SNE.

Drug-drug interactions prediction based on deep learning and knowledge graph: A review.

Drug-drug interactions (DDIs) can produce unpredictable pharmacological effects and lead to adverse events that have the potential to cause irreversible damage to the organism. Traditional methods to detect DDIs through biological or pharmacological analysis are time-consuming and expensive, therefore, there is an urgent need to develop computational methods to effectively predict drug-drug interactions. Currently, deep learning and knowledge graph techniques which can effectively extract features of entities have been widely utilized to develop DDI prediction methods. In this research, we aim to systematically review DDI prediction researches applying deep learning and graph knowledge. The available biomedical data and public databases related to drugs are firstly summarized in this review. Then, we discuss the existing drug-drug interactions prediction methods which have utilized deep learning and knowledge graph techniques and group them into three main classes: deep learning-based methods, knowledge graph-based methods, and methods that combine deep learning with knowledge graph. We comprehensively analyze the commonly used drug related data and various DDI prediction methods, and compare these prediction methods on benchmark datasets. Finally, we briefly discuss the challenges related to drug-drug interactions prediction, including asymmetric DDIs prediction and high-order DDI prediction.

MPASL: multi-perspective learning knowledge graph attention network for synthetic lethality prediction in human cancer.

Synthetic lethality (SL) is widely used to discover the anti-cancer drug targets. However, the identification of SL interactions through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for SL interactions prediction is of great significance. In this study, we propose MPASL, a multi-perspective learning knowledge graph attention network to enhance synthetic lethality prediction. MPASL utilizes knowledge graph hierarchy propagation to explore multi-source neighbor nodes related to genes. The knowledge graph ripple propagation expands gene representations through existing gene SL preference sets. MPASL can learn the gene representations from both gene-entity perspective and entity-entity perspective. Specifically, based on the aggregation method, we learn to obtain gene-oriented entity embeddings. Then, the gene representations are refined by comparing the various layer-wise neighborhood features of entities using the discrepancy contrastive technique. Finally, the learned gene representation is applied in SL prediction. Experimental results demonstrated that MPASL outperforms several state-of-the-art methods. Additionally, case studies have validated the effectiveness of MPASL in identifying SL interactions between genes.

STAT transcription factor is indispensable for oogenesis in silkworm.

Signal Transducer and Activator of Transcription (STAT) proteins represent a critical transcription factor family with multifaceted roles in diverse fundamental eukaryotic processes. In Drosophila, STAT exerts a pivotal regulatory influence on oogenesis, governing the early differentiation of follicular cells and ensuring proper encapsulation of germ-line cells. However, the role of STAT in egg development in silkworms remains unknown. In the present study, using CRISPR/Cas9 technology, we successfully generated a strain of silkworms with targeted deletion of the STAT-L gene, which resulted in significant reproductive abnormalities observed in female moths, including shortened fallopian tubes and reduced egg production. The ovaries dissected from STAT-L knockout silkworms during the pupal stage of silkworm exhibited varying degrees of fusion among egg chambers. Additionally, paraffin sections of prepupal ovaries also revealed evidence of egg chambers fusion. To elucidate the molecular mechanism underlying the role of the STAT-L gene regulation on egg development in silkworm, we performed ovarian transcriptomic analysis following STAT-L knockout. Our findings indicated that STAT-L gene can modulate Notch signaling pathway by down-regulating APH-1 gene expression. These results suggest that STAT-L gene plays a crucial role in normal egg chamber formation in silkworms, potentially through its influence on Notch signaling pathway expression.

Privately vertically mining of sequential patterns based on differential privacy with high efficiency and utility.

Sequential pattern mining is one of the fundamental tools for many important data analysis tasks, such as web browsing behavior analysis. Based on frequent patterns, decision-makers can obtain both economic gains and social values. Sequential data, on the other hand, frequently contain sensitive information, and directly analyzing these data will raise user concerns from a privacy perspective. Differential privacy (DP), as the most popular privacy model, has been employed to address this privacy concern. Most existing DP-Solutions are designed to combine horizontal sequence pattern mining algorithms with differential privacy. Due to the inefficiency of horizontal algorithms, their DP-Solutions cannot ensure high efficiency and accuracy while offering a high privacy guarantee. Therefore, we proposed privVertical, a new private sequence pattern mining scheme combining the vertical mining algorithm with differential privacy to achieve the above objective. Unlike DP-solutions based on horizontal algorithms, privVertical can promote efficiency by avoiding performing costly database scans or costly projection database constructions. Moreover, to promote accuracy, a differentially private hash MapList (called privHashMap) is designed to record frequent concurrency items and their noisy support based on the Sparse Vector Technique. PrivHashMap is used to pre-pruning excessive infrequent candidate sequences in private mining, and Sparse Vector Technique is used to promote the accuracy of PrivHashMap. After pruning these invalid candidate sequences, less noise is required to achieve the same level of privacy, increasing the accuracy of private mining. Theoretical privacy analysis proves privVertical satisfies [Formula: see text]-differential privacy. Experiments show that privVertical achieves higher accuracy and efficiency while achieving the same privacy level.

Effect of domestic COVID-19 vaccine on the plasma D-dimer levels of early pregnant women in China.

Objective: To investigate the effect of COVID-19 vaccination on plasma D-dimer levels in early pregnant women. Methods: A total of 834 early pregnant women(gestational age ≤ 13 weeks), who visited Northwest Women and Children's Hospital between December 2020 and April 2022, were selected. There were 696 women in the healthy group (group A) and 138 in the group with a history of adverse pregnancy and childbirth (group B). The plasma D-dimer levels of all participants were tested, and the COVID-19 vaccine history of all participants was collected using a survey questionnaire. Results: The plasma D-dimer levels did not differ between group A and the group B (p = 0.1327). In the group A, 470 were vaccinated and 226 were unvaccinated. The D-dimer levels of vaccinated individuals were lower than those of unvaccinated individuals (p = 0.0047). In the group B, 84 were vaccinated and 54 were unvaccinated; no difference in D-dimer levels was found between the vaccinated and unvaccinated individuals (p = 0.0542). In the group A, the D-dimer levels of the unvaccinated group were not different from those of women vaccinated with one dose (p = 0.208), but they were higher than those who received two doses (p = 0.019) or three doses (p = 0.003). And, no significant difference in D-dimer levels was found among women who received different vaccine brands and with different vaccination times. Conclusion: This study preliminarily indicates that COVID-19 vaccination does not increase D-dimer levels in early pregnant women.