Abietane diterpenoids from Isodon amethystoides and their biological activities.

Seven undescribed abietane diterpenoids [abietamethinols A-G (1-7)] were isolated from the twigs and leaves of Isodon amethystoides. Their structures were elucidated on the basis of spectroscopic methods including 2D NMR, and they were further confirmed by X-ray crystallographic data. Lophanic acid was considered as the precursor of 1-7 in the biosynthesis pathway hypothesis. These compounds were evaluated for their cytotoxic, anti-bacterial and anti-AIV (avian influenza virus) activities. Compound 5 showed 42.9% inhibitory activity against the cancer cell line SMMC-7721 at the concentration of 40 µM, 3 and 4 could inhibit the bacterial growth of Streptococcus sobrinus by 55.3% and 63.2% at the concentrations of 148.6 and 141.9 µM, respectively, and 4 was demonstrated with antiviral activity against AIV with the inhibitory effect of 68.4% at 25 µM.

External Validation of the International IgA Nephropathy Prediction Tool in Older Adult Patients.

Purpose: The International IgA Nephropathy Prediction Tool (IIgAN-PT) can predict the risk of End-stage renal disease (ESRD) or estimated glomerular filtration rate (eGFR) decline ≥ 50% for adult IgAN patients. Considering the differential progression between older adult and adult patients, this study aims to externally validate its performance in the older adult cohort. Patients and Methods: We analyzed 165 IgAN patients aged 60 and above from six medical centers, categorizing them by their predicted risk. The primary outcome was a ≥50% reduction in estimated glomerular filtration rate (eGFR) or kidney failure. Evaluation of both models involved concordance statistics (C-statistics), time-dependent receiver operating characteristic (ROC) curves, Kaplan-Meier survival curves, and calibration plots. Comparative reclassification was conducted using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results: The study included 165 Chinese patients (median age 64, 60% male), with a median follow-up of 5.1 years. Of these, 21% reached the primary outcome. Both models with or without race demonstrated good discrimination (C-statistics 0.788 and 0.790, respectively). Survival curves for risk groups were well-separated. The full model without race more accurately predicted 5-year risks, whereas the full model with race tended to overestimate risks after 3 years. No significant reclassification improvement was noted in the full model without race (NRI 0.09, 95% CI: -0.27 to 0.34; IDI 0.003, 95% CI: -0.009 to 0.019). Conclusion: : Both models exhibited excellent discrimination among older adult IgAN patients. The full model without race demonstrated superior calibration in predicting the 5-year risk.

Design and optimization of an octuple-electrode array for micro-particle chain rotation via electrorotation integrated with machine vision technology.

Microparticle rotation is an important process in biomedical engineering, such as biosensors, cell injection or cell morphology. Single particle rotation has been widely investigated, while rotation of particle chains has gained rare attention. In this paper, we utilize a noncontact manipulation method to rotate microparticle chains via electrorotation by designing an octuple-electrode array (OEA). Finite element simulations were conducted for analyzing the desired electrode field and optimizing the structure of microelectrode pairs. The direction of the electric field in the workspace is investigated with different voltage signal inputs through specially designed circuits. In the experiment, microparticles are driven to form several chains in the proposed electrode fields. With the rotation of the electric field, particle chains could be rotated synchronously. Automated rotation and detection of polystyrene microspheres and yeast cell chains are achieved using machine vision technology. Results show that the proposed method could be utilized to rotate ordered microparticles with an appropriate input signal.

Crosstalk between brassinosteroids and other phytohormones during plant development and stress adaption.

Brassinosteroids (BRs) are a group of polyhydroxylated phytosterols that play essential roles in regulating plant growth and development as well as stress adaptation. It is worth noting that BRs do not function alone, but rather they crosstalk with other endogenous signaling molecules, including the phytohormones auxin, cytokinins (CKs), gibberellins (GAs), abscisic acid (ABA), ethylene (ET), jasmonates (JAs), salicylic acid (SA), and strigolactones (SLs), forming elaborate signaling networks to modulate plant growth and development. BRs interact with other phytohormones mainly by regulating each others' homeostasis, transport, or signaling pathway at the transcriptional and posttranslational levels. In this review, we focus our attention on current research progress in BR signal transduction and the crosstalk between BRs and other phytohormones.

The chromosome-level genome and functional database accelerate research about biosynthesis of secondary metabolites in Rosa roxburghii.

Rosa roxburghii Tratt, a valuable plant in China with long history, is famous for its fruit. It possesses various secondary metabolites, such as L-ascorbic acid (vitamin C), alkaloids and poly saccharides, which make it a high nutritional and medicinal value. Here we characterized the chromosome-level genome sequence of R. roxburghii, comprising seven pseudo-chromosomes with a total size of 531 Mb and a heterozygosity of 0.25%. We also annotated 45,226 coding gene loci after masking repeat elements. Orthologs for 90.1% of the Complete Single-Copy BUSCOs were found in the R. roxburghii annotation. By aligning with protein sequences from public platform, we annotated 85.89% genes from R. roxburghii. Comparative genomic analysis revealed that R. roxburghii diverged from Rosa chinensis approximately 5.58 to 13.17 million years ago, and no whole-genome duplication event occurred after the divergence from eudicots. To fully utilize this genomic resource, we constructed a genomic database RroFGD with various analysis tools. Otherwise, 69 enzyme genes involved in L-ascorbate biosynthesis were identified and a key enzyme in the biosynthesis of vitamin C, GDH (L-Gal-1-dehydrogenase), is used as an example to introduce the functions of the database. This genome and database will facilitate the future investigations into gene function and molecular breeding in R. roxburghii.

Predicting Antibiotic Resistance and Assessing the Risk Burden from Antibiotics: A Holistic Modeling Framework in a Tropical Reservoir.

Predicting the hotspots of antimicrobial resistance (AMR) in aquatics is crucial for managing associated risks. We developed an integrated modeling framework toward predicting the spatiotemporal abundance of antibiotics, indicator bacteria, and their corresponding antibiotic-resistant bacteria (ARB), as well as assessing the potential AMR risks to the aquatic ecosystem in a tropical reservoir. Our focus was on two antibiotics, sulfamethoxazole (SMX) and trimethoprim (TMP), and on Escherichia coli (E. coli) and its variant resistant to sulfamethoxazole-trimethoprim (EC_SXT). We validated the predictive model using withheld data, with all Nash-Sutcliffe efficiency (NSE) values above 0.79, absolute relative difference (ARD) less than 25%, and coefficient of determination (R2) greater than 0.800 for the modeled targets. Predictions indicated concentrations of 1-15 ng/L for SMX, 0.5-5 ng/L for TMP, and 0 to 5 (log10 MPN/100 mL) for E. coli and -1.1 to 3.5 (log10 CFU/100 mL) for EC_SXT. Risk assessment suggested that the predicted TMP could pose a higher risk of AMR development than SMX, but SMX could possess a higher ecological risk. The study lays down a hybrid modeling framework for integrating a statistic model with a process-based model to predict AMR in a holistic manner, thus facilitating the development of a better risk management framework.

Diagnosis of poorly differentiated adenocarcinoma of the stomach by confocal laser endomicroscopy: A case report.

BACKGROUND: In recent years, confocal laser endomicroscopy (CLE) has become a new endoscopic imaging technology at the microscopic level, which is extensively performed for real-time in vivo histological examination. CLE can be performed to distinguish benign from malignant lesions. In this study, we diagnosed using CLE an asymptomatic patient with poorly differentiated gastric adenocarcinoma. CASE SUMMARY: A 63-year-old woman was diagnosed with gastric mucosal lesions, which may be gastric cancer, in the small curvature of the stomach by gastroscopy. She consented to undergo CLE for morphological observation of the gastric mucosa. Through the combination of CLE diagnosis and postoperative pathology, the intraoperative CLE diagnosis was considered to be reliable. According to our experience, CLE can be performed as the first choice for the diagnosis of gastric cancer. CONCLUSION: CLE has several advantages over pathological diagnosis. We believe that CLE has great potential in the diagnosis of benign and malignant gastric lesions.

Relationship between eutrophication and greenhouse gases emission in shallow freshwater lakes.

In shallow lakes, there are complex relationships between lake eutrophication and greenhouse gas emissions that deserve to be studied, which are important for solving lake eutrophication, slowing down climate warming, and reducing carbon emissions. In order to explore the relationship and mechanism between eutrophication and greenhouse gases (GHGs), the net GHGs emission flux and transformation of carbon, and nitrogen in 45 shallow freshwater lakes were investigated from May to September 2022. Eutrophication facilitated potential denitrification rate (Dt) without increasing nitrous oxide (N2O) production based on the significantly positive relationship between eutrophication and Dt. This should be attributed to the shift from incomplete (N2O producing process) to complete denitrification (N2 producing process). Compared to NarG mediating nitrate (NO3-) to nitrite (NO2-), fewer eutrophication indicators showed a positive relationship with NosZ mediating N2O to N2, suggesting that more stringent conditions are required for complete denitrification, which was achieved in the lakes we investigated. Optimal reduction in net carbon dioxide (CO2) emissions occurs at high levels of primary productivity, as indicated by the V-shaped relationship between chlorophyll a (Chl a) and CO2 emissions. However, in hyper-eutrophic lakes, there is an upward trend in CO2 production. The possible explanations should include CO2 production and fixation as well as methane (CH4) oxidation. The bell-shaped relationship between the net flux of CH4 emission and Chl a could be explained that CH4 was heavily oxidized due to sufficient oxygen caused by algal bloom. This fact gave evidence for the increase of the net flux of CO2 emission in high primary productivity lakes. Therefore, the relationship and mechanism between net GHGs emission flux and eutrophication remained complex and various.

Gut microbiota non-convergence and adaptations in sympatric Tibetan and Przewalski's gazelles.

Unraveling the connection between gut microbiota and adaptability in wild species in natural habitats is imperative yet challenging. We studied the gut microbiota of sympatric and allopatric populations of two closely related species, the Procapra picticaudata and P. przewalskii, with the latter showing lower adaptability and adaptive potential than the former. Despite shared habitat, sympatric populations showed no convergence in gut microbiota, revealing distinct microbiota-environment relationships between the two gazelle species. Furthermore, the gut microbiota assembly process of the P. przewalskii was shifted toward homogeneous selection processes relative to that of the P. picticaudata. Those taxa which contributed to the shift were mainly from the phyla Firmicutes and Verrucomicrobiota, with functions highly related to micronutrient and macronutrient metabolism. Our study provides new insights into the complex dynamics between gut microbiota, host adaptability, and environment in wildlife adaptation and highlights the need to consider host adaptability when examining wildlife host-microbiome interplay.

A two-stage exacerbated hypoxia nanoengineering strategy induced amplifying activation of tirapazamine for microwave hyperthermia-chemotherapy of breast cancer.

Microwave hyperthermia (MH) is an emerging treatment for solid tumors, such as breast cancer, due to its advantages of minimally invasive and deep tissue penetration. However, MH induced tumor hypoxia is still an obstacle to breast tumor treatment failure. Therefore, an original nanoengineering strategy was proposed to exacerbate hypoxia in two stages, thereby amplifying the efficiency of activating tirapazamine (TPZ). And a novel microwave-sensitized nanomaterial (GdEuMOF@TPZ, GEMT) is designed. GdEuMOF (GEM) nanoparticles are certified excellent microwave (MW) sensitization performance, thus improving tumor selectivity to achieve MH. Meanwhile MW can aggravate the generation of thrombus and caused local circulatory disturbance of tumor, resulting in the Stage I exacerbated hypoxia environment passively. Due to tumor heterogeneity and uneven hypoxia, GEMT nanoparticles under microwave could actively deplete residual oxygen through the chemical reaction, exacerbating hypoxia level more evenly, thus forming the Stage II of exacerbated hypoxia environment. Consequently, a two-stage exacerbated hypoxia GEMT nanoparticles realize amplifying activation of TPZ, significantly enhance the efficacy of microwave hyperthermia and chemotherapy, and effectively inhibit breast cancer. This research provides insights into the development of progressive nanoengineering strategies for effective breast tumor therapy.

Hollow glass microspheres embedded in porous network of chitosan aerogel used for thermal insulation and flame retardant materials.

In recent years, biopolymer aerogels as thermal insulation materials have received widespread attention due to natural abundance, cost-efficiency, and environment-friendly. However, the flammability and low strength hinder its practical application. Hollow glass microspheres (HGMs) as an inorganic thermal insulation filler have been filled in biopolymer aerogels to improve flame retardancy. However, the structure formed by HGMs embedded porous network of biopolymer aerogel has rarely been investigated, which not only reduce thermal conductivity through high porosity, but also adjust the filling volume of HGMs and achieve uniform distribution through chemical cross-linking. Herein, a biopolymer aerogel composite was assembled by chitosan aerogel (CSA) and different volume of HGMs by chemical cross-linking, freeze-drying, and silylation modification processes. When the filling volume fraction of HGMs reached 40 %, a skeleton structure was initially formed. The composites with HGMs volume of 40 %-60 % exhibited low density, high porosity, low thermal conductivity, good mechanical property, and excellent flame retardancy. According to GB 8624-2012 standard for classification, the composite with 60 % HGMs achieved class A1 non-combustible.

Effect of interface layer on the enhancement of thermal conductivity of SiC-Water nanofluids: Molecular dynamics simulation.

To investigate the impact of interfacial layer effects on the thermal conductivity of nanofluids and the microscopic mechanisms of enhanced thermal conductivity, this study employed non-equilibrium molecular dynamics to compute the thermal conductivity, number density, radial distribution function, and mean square displacement distribution of SiC nanofluids. The impact of nanoparticle volume fraction and particle size parameters on the thermal conductivity of nanofluids and the structure of interfacial adsorption layers was discussed. The simulation calculation results show that the coefficient of thermal conductivity of nanofluid is positively related to the volume fraction of nanoparticles, increasing from 0.6529 W/(m·K) to 0.8159 W/(m·K), and the enhancement of thermal conductivity by the volume fraction can be up to 33.97 %. The thermal conductivity is inversely correlated with the change in particle size, and the maximum improvement in thermal conductivity by particle size can reach up to 12.05 %. The simulated results of the thermal conductivity of nanofluid are almost consistent with the predicted results of the Yu&Choi model, and the error is controlled within 5 %. Simultaneously, the thickness of the interfacial adsorption layer decreases with an increase in particle size. This reduction arises due to larger particles having a smaller specific surface area, resulting in fewer particle surfaces covered by the interface layer. Moreover, the impact of particle size on the arrangement and affinity of molecules within the interface layer contributes to this decrease. Overall, interface layer effects exhibit a dual impact on the thermal conduction of nanofluids. The structured formation and high-density distribution of the adsorption layer contribute to enhanced heat transfer, while thermal resistance between nanoparticle surfaces and the fluid restricts heat transmission.

MolCAP: Molecular Chemical reActivity Pretraining and prompted-finetuning enhanced molecular representation learning.

Molecular representation learning (MRL) is a fundamental task for drug discovery. However, previous deep-learning (DL) methods focus excessively on learning robust inner-molecular representations by mask-dominated pretraining frameworks, neglecting abundant chemical reactivity molecular relationships that have been demonstrated as the determining factor for various molecular property prediction tasks. Here, we present MolCAP to promote MRL, a graph-pretraining Transformer based on chemical reactivity (IMR) knowledge with prompted finetuning. Results show that MolCAP outperforms comparative methods based on traditional molecular pretraining frameworks, in 13 publicly available molecular datasets across a diversity of biomedical tasks. Prompted by MolCAP, even basic graph neural networks are capable of achieving surprising performance that outperforms previous models, indicating the promising prospect of applying reactivity information to MRL. In addition, manually designed molecular templets are potential to uncover the dataset bias. All in all, we expect our MolCAP to gain more chemical meaningful insights for the entire process of drug discovery.

Seasonal Variation of Gut Microbial Composition and Metabolism in Tibetan antelopes in Hoh Xil National Nature Reserve.

The Tibetan antelope is an endangered species suffering from poaching and habitat fragmentation. The intestinal flora and metabolites play a crucial role in the physiological homeostasis of hosts, which are influenced by various environmental factors like seasonal variation. In this particular research, our main goal was to explore the alterations in the metabolism and gut microbiota of Tibetan antelopes between the cold season (XB) and warm season (DA), using untargeted metabolomics and 16S rRNA gene-sequencing analyses. The findings indicated that Tibetan antelopes had a higher alpha-diversity of intestinal microbes during the cold season than during the warm season. Principal co-ordinate analysis revealed notable seasonal discrepancies in the function and structure of intestinal microbes in Tibetan antelopes. The relative abundance of Firmicutes was significantly increased during the cold season compared to during the warm season. Furthermore, the Tibetan antelope's primary metabolic functions of the intestinal micro-organisms were significantly higher during the cold season. The untargeted metabolomics analysis results showed a total of 532 metabolites that were significantly different between the cold season and warm season groups. These metabolites were found to be enriched in a total of 62 metabolic pathways. Among the most significant pathways of enrichment were the purine metabolism and pyrimidine metabolism. The levels of related metabolites in those pathways were remarkably higher in the warm season compared to the cold season. The comprehensive analysis of 16S rRNA and the metabolome reveals there is a significant correlation between differential microbiota and differential metabolites. Therefore, the gut microbiota changes caused by seasonal changes influenced the metabolites as well. This research reveals the function of seasonal changes in the intestinal flora and metabolites in the adaptation of Tibetan antelopes to environmental fluctuations and supplies a theoretical basis for instructing the protection management of Tibetan antelopes.

Oat milk analogue versus traditional milk: Comprehensive evaluation of scientific evidence for processing techniques and health effects.

Milk, enriched with high-quality protein, is a healthy and nutritious food that meets people's needs. However, consumers are turning their attention to plant-based milk due to several concerns, such as lactose intolerance, allergies and some diseases caused by milk; carbon emission from cattle farming; economical aspects; and low access to vitamins and minerals. Oat milk, which is produced from whole grain oats, is lactose free and rich in a variety of nutrients and phytochemicals. With the significant development of food processing methods and advancement in milk simulation products, the production of plant-based milk, such as cereal milk, has greatly progressed. This review described some features of oat milk analogue versus traditional milk and compared the properties, processing technologies, health effects, environmental friendliness, and consumer acceptance of these products. It is expected to provide a reference for evaluating development trends and helping consumers choose between oat milk and traditional milk.

Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks.

Automating retrosynthesis with artificial intelligence expedites organic chemistry research in digital laboratories. However, most existing deep-learning approaches are hard to explain, like a "black box" with few insights. Here, we propose RetroExplainer, formulizing the retrosynthesis task into a molecular assembly process, containing several retrosynthetic actions guided by deep learning. To guarantee a robust performance of our model, we propose three units: a multi-sense and multi-scale Graph Transformer, structure-aware contrastive learning, and dynamic adaptive multi-task learning. The results on 12 large-scale benchmark datasets demonstrate the effectiveness of RetroExplainer, which outperforms the state-of-the-art single-step retrosynthesis approaches. In addition, the molecular assembly process renders our model with good interpretability, allowing for transparent decision-making and quantitative attribution. When extended to multi-step retrosynthesis planning, RetroExplainer has identified 101 pathways, in which 86.9% of the single reactions correspond to those already reported in the literature. As a result, RetroExplainer is expected to offer valuable insights for reliable, high-throughput, and high-quality organic synthesis in drug development.

Guidongnins I-J: Two New 6,7-seco-7,20-Olide-ent-kaurene Diterpenes with Unusual Structures from Isodon rubescens.

Two undescribed ent-kaurene diterpenes, named guidongnins I (1) and J (2), were isolated from the medicinal plant Isodon rubescens. Compound 1 was determined to contain an unprecedented 23 carbons in the skeleton by bearing an extra isopropyl group at C-17 out of the diterpenoid parent structure, and compound 2 was the first example of 6,7-seco-7,20-olide-ent-kaurenes with two fused-tetrahydrofuran rings formed between C-6 and C-19/C-20 through oxygen bridges. Their structures, including their absolute configurations, were determined using the analyses of the spectroscopic and X-ray diffraction data. Guidongnins I (1) and J (2) were assessed for their anti-cancer activities against the growth of various cancer cell lines, and 2 displayed cytotoxic potency against HepG2 at IC50 27.14 ± 3.43 µM.

MC design and FIB preparation of a YSZ biochemical material microstructure.

Aiming at the difficulty of traditional machining of Y2O3-ZrO2 (YSZ) inert ceramic materials, a different method using focused ion beam to selectively create nanoscale microscale structures on the surface of materials was proposed. The sputtering yield, surface damage, and the energy loss of YSZ materials was investigated using the SRIM software using the Monte Carlo method. It is shown that the sputtering yield increases with ion energy in the range 0-30 keV, reaching a maximum of 9.4 atoms/ion at 30 keV. At an ion beam voltage of 30 keV, the most severe damage to the material is 8 mm on the surface. At the same time, the main forms of energy loss in the treatment are phonon energy loss and ionization energy loss, of which phonon energy loss due to the recoil atoms is the largest. In addition, we continue to perform focused ion beam processing experiments on YSZ materials, combining previous MC modeling to optimize different operating conditions such as ion beam, voltage and processing mode. The optimized processing parameters are 30 keV and 2.5 nA. It is shown that the quality of the deep grooves gradually improves with decreasing ion beam current at the same ion beam voltage. However, an excessively small ion beam current leads to an excessively large depth of the deep grooves and lengthy processing times.

Lnc-LRRTM4 promotes proliferation, metastasis and EMT of colorectal cancer through activating LRRTM4 transcription.

Numerous mechanisms have shown that long noncoding RNAs (lncRNAs) promote the development of colorectal cancer (CRC), but the role of lnc-LRRTM4 in the progression of CRC remains unclear. In this article, we found that lnc-LRRTM4 was highly expressed in CRC tissues and cell lines and that lnc-LRRTM4 could promote the proliferation and metastasis of CRC cells. These consequences were achieved by lnc-LRRTM4 directly binding to the promoter of LRRTM4 to induce its transcription. Moreover, lnc-LRRTM4 enhanced the growth of CRC cells in vivo by promoting cell cycle progression and reducing apoptosis. Taken together, our results revealed that lnc-LRRTM4 promotes the proliferation and metastasis of CRC cells, suggesting that it may be a potential diagnostic and therapeutic target for CRC.

Jasmonates regulate apical hook development by repressing brassinosteroid biosynthesis and signaling.

An apical hook is a special structure formed during skotomorphogenesis in dicotyledonous plant species. It is critical for protecting the shoot apical meristem from mechanical damage during seed germination and hypocotyl elongation in soil. Brassinosteroid (BR) and jasmonate (JA) phytohormones antagonistically regulate apical hook formation. However, the interrelationship between BRs and JAs in this process has not been well elucidated. Here, we reveal that JAs repress BRs to regulate apical hook development in Arabidopsis (Arabidopsis thaliana). Exogenous application of methyl jasmonate (MeJA) repressed the expression of the rate-limiting BR biosynthetic gene DWARF4 (DWF4) in a process relying on 3 key JA-dependent transcription factors, MYC2, MYC3, and MYC4. We demonstrated that MYC2 interacts with the critical BR-activated transcription factor BRASSINAZOLE RESISTANT 1 (BZR1), disrupting the association of BZR1 with its partner transcription factors, such as those of the PHYTOCHROME INTERACTING FACTOR (PIF) family and downregulating the expression of their target genes, such as WAVY ROOT GROWTH 2 (WAG2), encoding a protein kinase essential for apical hook development. Our results indicate that JAs not only repress the expression of BR biosynthetic gene DWF4 but, more importantly, attenuate BR signaling by inhibiting the transcriptional activation of BZR1 by MYC2 during apical hook development.