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IntroductionRapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status.MethodsIn this study, 20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods extreme learning machine (ELM), random forest (RF), back propagation (BP), multivariable linear regression (MLR), support vector machine (SVM)], and the optimal modeling strategy (RF) was selected. Finally, the vegetation indices with a high correlation with LAI were fused to construct the VI-fusion RF model, to explore the potential of multi-vegetation index fusion in the estimation of cotton LAI.ResultsThe RF model had the highest estimation accuracy among the LAI estimation models, and the estimation accuracy of models constructed by fusing multiple VIs was higher than that of models constructed based on single VIs. Among the multi-VI fusion models, the RF model constructed based on the fusion of seven vegetation indices (MNDSI, SRI, GRVI, REP, CIred-edge, MSR, and NVI) had the highest estimation accuracy, with coefficient of determination (R2), rootmean square error (RMSE), normalized rootmean square error (NRMSE), and mean absolute error (MAE) of 0.90, 0.50, 0.14, and 0.26, respectively. DiscussionAppropriate fusion of vegetation indices can include more spectral features in modeling and significantly improve the cotton LAI estimation accuracy. This study will provide a technical reference for improving the cotton LAI estimation accuracy, and the proposed method has great potential for crop growth monitoring applications.

Resumo Fundamento: O antibiótico quimioterápico antraciclina doxorrubicina (DOX) pode induzir cardiotoxicidade cumulativa e levar à disfunção cardíaca. RNAs não codificantes longos (lncRNAs) podem funcionar como importantes reguladores na lesão miocárdica induzida por DOX. Objetivo: Este estudo tem como objetivo investigar o papel funcional e o mecanismo molecular do RNA antisense lncRNA OXCT1 1 (OXCT1-AS1) na lesão celular miocárdica induzida por DOX in vitro. Métodos: Cardiomiócitos humanos (AC16) foram estimulados com DOX para induzir um modelo de lesão celular miocárdica. A expressão de OXCT1-AS1, miR-874-3p e BDH1 em células AC16 foi determinada por RT-qPCR. A viabilidade das células AC16 foi medida pelo ensaio XTT. A citometria de fluxo foi empregada para avaliar a apoptose de células AC16. Western blotting foi utilizado para avaliar os níveis proteicos de marcadores relacionados à apoptose. O ensaio repórter de luciferase dupla foi conduzido para verificar a capacidade de ligação entre miR-874-3p e OXCT1-AS1 e entre miR-874-3p e BDH1. O valor de p

IntroductionCotton yield estimation is crucial in the agricultural process, where the accuracy of boll detection during the flocculation period significantly influences yield estimations in cotton fields. Unmanned Aerial Vehicles (UAVs) are frequently employed for plant detection and counting due to their cost-effectiveness and adaptability.MethodsAddressing the challenges of small target cotton bolls and low resolution of UAVs, this paper introduces a method based on the YOLO v8 framework for transfer learning, named YOLO small-scale pyramid depth-aware detection (SSPD). The method combines space-to-depth and non-strided convolution (SPD-Conv) and a small target detector head, and also integrates a simple, parameter-free attentional mechanism (SimAM) that significantly improves target boll detection accuracy.ResultsThe YOLO SSPD achieved a boll detection accuracy of 0.874 on UAV-scale imagery. It also recorded a coefficient of determination (R2) of 0.86, with a root mean square error (RMSE) of 12.38 and a relative root mean square error (RRMSE) of 11.19% for boll counts.DiscussionThe findings indicate that YOLO SSPD can significantly improve the accuracy of cotton boll detection on UAV imagery, thereby supporting the cotton production process. This method offers a robust solution for high-precision cotton monitoring, enhancing the reliability of cotton yield estimates.

Abstract Background The oncogenic protein HOXA9 plays a critical role in leukemia transformation and maintenance, and its aberrant expression is a hallmark of most aggressive acute leukemia. Although inhibiting the upstream regulators of HOXA9 has been proven as a significant therapeutic intervention, the comprehensive regulation network controlling HOXA9 expression in leukemia has not been systematically investigated. Results Here, we perform genome-wide CRISPR/Cas9 screening in the HOXA9-driven reporter acute leukemia cells. We identify a poorly characterized RNA-binding protein, RBM5, as the top candidate gene required to maintain leukemia cell fitness. RBM5 is highly overexpressed in acute myeloid leukemia (AML) patients compared to healthy individuals. RBM5 loss triggered by CRISPR knockout and shRNA knockdown significantly impairs leukemia maintenance in vitro and in vivo. Through domain CRISPR screening, we reveal that RBM5 functions through a noncanonical transcriptional regulation circuitry rather than RNA splicing, such an effect depending on DNA-binding domains. By integrative analysis and functional assays, we identify HOXA9 as the downstream target of RBM5. Ectopic expression of HOXA9 rescues impaired leukemia cell proliferation upon RBM5 loss. Importantly, acute protein degradation of RBM5 through auxin-inducible degron system immediately reduces HOXA9 transcription. Conclusions We identify RBM5 as a new upstream regulator of HOXA9 and reveal its essential role in controlling the survival of AML. These functional and molecular mechanisms further support RBM5 as a promising therapeutic target for myeloid leukemia treatment.

Abstract This paper proposes the design of a helical hedge flow channel with a high energy loss, which shows promising potential for application in fruit tree root emitters. The aim is to investigate the relationship between the energy loss form in the channel and its influencing factors. The hydraulic performance testing method is employed to analyze the factors that affect energy loss. The main influencing factors are determined using the response surface methodology (RSM) for experimental design. Based on the obtained experimental results, the energy loss form and influencing factors are analyzed, and a prediction model for the energy loss coefficient (ξ) is established. The results indicate that the ξ exhibits a decreasing trend with an increase in the diversion angle (α), a trend of first increasing and then decreasing with an increase in the channel width (b), and an increasing trend with an increase in the number of channel units (n). The effects of the straight section length (l 1), convergence section length (l 2), and bend radius (r) on the ξ can be neglected. The ranking of the geometric parameters' influence on the ξ is as follows: n > b > α > l 1 > r > l 2. The experimental results reveal that the ξ ranges from 19.2 to 234.3. Furthermore, the head loss along the flow channel constitutes merely 0.06–0.47% of the local head loss, The main form of energy loss in the spiral counter flow channel is local head loss. There is a significant linear relationship between α, b, n and the ξ, The established prediction model (R2 = 0.9691) can accurately predict the ξ of the channel.

Ensuring the optimal operating temperature is imperative for achieving efficient performance in proton exchange membrane fuel cells. Consequently, this study introduces a dual-model predictive control strategy to regulate the water pump and cooling fan in a cooling system. Initially, we establish an electrochemical and thermal model for fuel cell stacks and validate the model’s accuracy through experimental data. The system model is linearized, and the model predictive control (MPC) controller is formulated using the MATLAB/Simulink toolbox. Subsequently, it is collaboratively simulated with the electrochemical model of the fuel cell stack and the temperature model. To evaluate the effectiveness of the MPC controller, we conducted a comparative analysis with the traditional proportional–integral–derivative (PID) control and water pump MPC under step load, uniform load increase, and variable target scenarios. The findings indicate that in contrast to the PID control, the MPC controller significantly decreases the stack temperature difference fluctuation by more than 50%, maintaining the stack temperature within ±0.6 K of the set value. Furthermore, we independently assessed the performance of the MPC controller under varying ambient temperatures. The findings illustrate that the dual MPC method proficiently adapts cooling parameters across different ambient temperature ranges (288.15 K–308.15 K), ensuring the stable performance of the fuel cell. The model is linearized, and the simulation work is explained mainly on the MATLAB/Simulink platform. In order to compare the effectiveness of the MPC controller, the comparison with the MPC controller strategy of the water pump is added, which can better reflect the effectiveness of the proposed collaborative MPC controller strategy.

Abstract Background Ferroptosis plays an essential role in lipopolysaccharide (LPS)-induced acute lung injury (ALI). Meteorin-like/Meteorin-β (Metrnβ) is a protein secreted by skeletal muscle and adipose tissue and plays a role in cardiovascular diseases. However, its role in acute lung injury has not been elucidated. Methods In this study, we used an adenovirus (Ad) delivery system to overexpress or knockdown Metrnβ in lung tissue to examine the role of Metrnβ in LPS-induced acute lung injury. Results We found that ferroptosis was increased during LPS-induced ALI. The expression of Metrnβ was reduced in ALI lung tissue. Overexpression of Metrnβ in lung tissue alleviated LPS-induced lung injury, inflammation, and ferroptosis. Moreover, Metrnβ knockout in lung tissue accelerated LPS-induced ALI, inflammation, and ferroptosis. We also cultured MLE-12 cells and transfected the cells with Ad-Metrnβ or Metrnβ siRNA. Metrnβ overexpression ameliorated LPS-induced MLE cell death, inflammation and ferroptosis, while Metrnβ knockdown aggregated cell survival and decreased inflammation and ferroptosis. Moreover, we found that Metrnβ enhanced ferroptosis-related Gpx4 expression and reduced ferroportin and ferritin levels. Furthermore, we found that Metrnβ positively regulated SIRT1 transcription thus inhibited P53, increased SLC7A11 expression. When we used the ferroptosis inhibitor ferrostatin-1, the deteriorating effects of Metrnβ knockout were abolished in ALI mice. Moreover, SIRT1 knockout also abolished the protective effects of Metrnβ overexpression in vivo. Conclusions Taken together, Metrnβ could protect LPS-induced ALI by activating SIRT1-P53- SLC7A11 mediated ferroptosis inhibition.

Nonribosomal peptides (NRPs) are biosynthesized by nonribosomal peptide synthetases (NRPSs) and are widely distributed in both terrestrial and marine organisms. Many NRPs and their analogs are biologically active and serve as therapeutic agents. The adenylation (A) domain is a key catalytic domain that primarily controls the sequence of a product during the assembling of NRPs and thus plays a predominant role in the structural diversity of NRPs. Engineering of the A domain to alter substrate specificity is a potential strategy for obtaining novel NRPs for pharmaceutical studies. On the basis of introducing the catalytic mechanism and multiple functions of the A domains, this article systematically describes several representative NRPS engineering strategies targeting the A domain, including mutagenesis of substrate-specificity codes, substitution of condensation-adenylation bidomains, the entire A domain or its subdomains, domain insertion, and whole-module rearrangements.

Abstract Expression of the long non-coding RNA (lncRNA) keratin-7 antisense (KRT7-AS) is downregulated in various types of cancer; however, the impact of KRT7-AS deficiency on tumorigenesis and apoptosis is enigmatic. We aim to explore the influence of KRT7-AS in carcinogenesis and apoptosis. We found that KRT7-AS was deficient in breast and lung cancers, and low levels of KRT7-AS were a poor prognostic factor in breast cancer. Cellular studies showed that silencing of KRT7-AS in lung cancer cells increased oncogenic Keratin-7 levels and enhanced tumorigenesis, but diminished cancer apoptosis of the cancer cells; by contrast, overexpression of KRT7-AS inhibited lung cancer cell tumorigenesis. Additionally, KRT7-AS sensitized cancer cells to the anti-cancer drug cisplatin, consequently enhancing cancer cell apoptosis. In vivo, KRT7-AS overexpression significantly suppressed tumor growth in xenograft mice, while silencing of KRT7-AS promoted tumor growth. Mechanistically, KRT7-AS reduced the levels of oncogenic Keratin-7 and significantly elevated amounts of the key tumor suppressor PTEN in cancer cells through directly binding to PTEN protein via its core nucleic acid motif GGCAAUGGCGG. This inhibited the ubiquitination-proteasomal degradation of PTEN protein, therefore elevating PTEN levels in cancer cells. We also found that KRT7-AS gene transcription was driven by the transcription factor RXRα; intriguingly, the small molecule berberine enhanced KRT7-AS expression, reduced tumorigenesis, and promoted apoptosis of cancer cells. Collectively, KRT7-AS functions as a new tumor suppressor and an apoptosis enhancer in lung and breast cancers, and we unraveled that the RXRα-KRT7-AS-PTEN signaling axis controls carcinogenesis and apoptosis. Our findings highlight a tumor suppressive role of endogenous KRT7-AS in cancers and an important effect the RXRα-KRT7-AS-PTEN axis on control of cancer cell tumorigenesis and apoptosis, and offer a new platform for developing novel therapeutics against cancers.

Abstract To develop a random forest prediction model for the and short- and long-term survival of ovarian cancer patients with lung metastasis. This retrospective cohort study enrolled primary ovarian cancer patients with lung metastasis from the surveillance, epidemiology and end results (SEER) database (2010–2015). All eligible women were randomly divided into the training (n = 1357) and testing set (n = 582). The outcomes were 1-, 3- and 5-year survival. Predictive factors were screened by random forest analysis. The prediction models for predicting the 1-, 3- and 5-year survival were conducted using the training set, and the internal validation was carried out by the testing set. The performance of the models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). The subgroups based on the pathological classification further assessed the model’s performance. Totally 1345 patients suffered from death within 5 years. The median follow-up was 7.00 (1.00, 21.00) months. Age at diagnosis, race, marital status, tumor size, tumor grade, TNM stage, brain metastasis, liver metastasis, bone metastasis, etc. were predictors. The AUCs of the prediction model for the 1-, 3-, 5-year survival in the testing set were 0.849 [95% confidence interval (CI): 0.820–0.884], 0.789 (95% CI 0.753–0.826) and 0.763 (95% CI 0.723–0.802), respectively. The results of subgroups on different pathological classifications showed that the AUCs of the model were over 0.7. This random forest model performed well predictive ability for the short- and long-term survival of ovarian cancer patients with lung metastasis, which may be beneficial to identify high-risk individuals for intelligent medical services.

The Heat Shock Transcription Factor (Hsf) serves as a critical transcription factor in plants, exerting its function by binding to specific promoter regions in response to heat stress. This activation leads to the up-regulation of heat shock proteins. Taraxacum kok-saghyz Rodin (TKS) is a plant species that produces high quality natural rubber in its root latex. During the growth of TKS, various stresses can affect its defense mechanisms and overall growth. However, studies investigating the Hsf transcription factors in TKS remain limited. In this study, we identified and named 31 Hsf transcription factors in TKS and performed a comparative analysis of their core motifs and gene structures. Based on phylogenetic analysis, these transcription factors were classified into three distinct classes. By cis-element analysis of the TKS promoter genes, we gained valuable insights into the evolutionary characteristics of the TKS Hsf gene family. Transcriptome expression profiling data revealed different expression of various genes within the same tissues. Several TKS Hsf genes exhibited responsiveness to various abiotic stresses and hormonal treatments, indicating their involvement in regulating the plant's response to different environmental conditions. Overall, the Hsf gene family in TKS plays a critical role in plant growth, development, and adaptation to abiotic stresses. This study provides a basis for further functional validation of TKS Hsf genes and elucidation of the regulatory mechanisms of target genes.

Abstract The remarkable success of deep learning technologies has provided new ideas for solving complex tracking problems. It is difficult for traditional algorithms to directly estimate the trajectory vector and target class from the received signal due to the limitation of modelling ability, which causes inevitable information loss. Moreover, existing algorithms suffer severe performance degradation when dealing with problems that are difficult to mathematically model in advance, such as highly nonlinear observations and manoeuvring scenarios. To address these issues, we propose a deep learning algorithm for joint direct tracking and classification (DeepDTC), which is a novel direct tracking framework. Specifically, we construct a convolutional neural network (CNN)‐based signal processing component to capture observation features, and a Transformer‐based trajectory tracking component to capture the features of target state and identity. Meanwhile, in signal processing component, we design an attribute network to learn auxiliary knowledge features. Finally, we construct a multi‐task learning network to connect these components and estimate trajectory vectors and target classes simultaneously. Our algorithm takes the transformer with attention mechanism as the core network, which is highly scalable and suitable for introducing various auxiliary knowledge. The comparison experiments with traditional methods demonstrate the effectiveness and advancement of the proposed algorithm. The comparative experiments with the single‐task model show that DeepDTC can further improve the tracking accuracy by utilising the learnt classification information.

ObjectiveMucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1) modulates the inflammatory immune response and organ dysfunction, which are closely implicated in sepsis pathogenesis and progression. This study aimed to explore the role of MALT1 in sepsis-induced organ injury, immune cell dysregulation, and inflammatory storms.MethodsSeptic mice were constructed by intraperitoneal injection of lipopolysaccharide, followed by overexpression or knockdown of MALT1 by tail vein injection of the corresponding lentivirus. Mouse naïve CD4+ T cells and bone marrow-derived macrophages were treated with MALT1 overexpression/knockdown lentivirus plus lipopolysaccharide.ResultsIn the lungs, livers, and kidneys of septic mice, MALT1 overexpression exaggerated their injuries, as shown by hematoxylin and eosin staining (all p

tRNA methylations, including base modification and 2’-O-methylation of ribose moiety, play critical roles in the structural stabilization of tRNAs and the fidelity and efficiency of protein translation. These modifications are catalyzed by tRNA methyltransferases (TRMs). Some of the TRMs from yeast can fully function only by a single subunit. In this study, after performing the primary bioinformatic analyses, the progress of the studies of yeast single-subunit TRMs, as well as the studies of their homologues from yeast and other types of eukaryotes and the corresponding TRMs from other types of organisms was systematically reviewed, which will facilitate the understanding of the evolutionary origin of functional diversity of eukaryotic single-subunit TRM.

Accurate soil quality evaluation is an important prerequisite for improving soil management systems and remediating soil pollution. However, traditional soil quality evaluation methods are cumbersome to calculate, and suffer from low efficiency and low accuracy, which often lead to large deviations in the evaluation results. This study aims to provide a new and accurate soil quality evaluation method based on graph convolution network (GCN). In this study, soil organic matter (SOM), alkaline hydrolysable nitrogen (AN), available potassium (AK), salinity, and heavy metals (iron (Fe), copper (Cu), manganese (Mn), and zinc (Zn)) were determined and evaluated using the soil quality index (SQI). Then, the graph convolution network (GCN) was first introduced in the soil quality evaluation to construct an evaluation model, and its evaluation results were compared with those of the SQI. Finally, the spatial distribution of the evaluation results of the GCN model was displayed. The results showed that soil salinity had the largest coefficient of variation (86%), followed by soil heavy metals (67%) and nutrients (30.3%). The soil salinization and heavy metal pollution were at a low level in this area, and the soil nutrients and soil quality were at a high level. The evaluation accuracy of the GCN model for soil salinity/heavy metals, soil nutrients, and soil quality were 0.91, 0.84, and 0.90, respectively. Therefore, the GCN model has a high accuracy and is feasible to be applied in the soil quality evaluation. This study provides a new, simple, and highly accurate method for soil quality evaluation.

Abstract Background Triple-negative breast cancer (TNBC) is highly metastatic and lethal. Due to a lack of druggable targets for this disease, there are no effective therapies in the clinic. Methods We used TNBC cells and xenografted mice as models to explore triptonide-mediated inhibition of TNBC metastasis and tumor growth. Colony formation assay was used to quantify the tumorigenesis of TNBC cells. Wound-healing and cell trans-well assays were utilized to measure cell migration and invasion. Tube formation assay was applied to access tumor cell-mediated vasculogenic mimicry. Western blot, quantitative-PCR, immunofluorescence imaging, and immunohistochemical staining were used to measure the expression levels of various tumorigenic genes in TNBC cells. Results Here, we showed that triptonide, a small molecule from the traditional Chinese medicinal herb Tripterygium wilfordii Hook F, potently inhibited TNBC cell migration, invasion, and vasculogenic mimicry, and effectively suppressed TNBC tumor growth and lung metastasis in xenografted mice with no observable toxicity. Molecular mechanistic studies revealed that triptonide strongly triggered the degradation of master epithelial-mesenchymal transition (EMT)-inducing protein Twist1 through the lysosomal system and reduced Notch1 expression and NF-κB phosphorylation, which consequently diminished the expression of pro-metastatic and angiogenic genes N-cadherin, VE-cadherin, and vascular endothelial cell growth factor receptor 2 (VEGFR2). Conclusions Triptonide effectively suppressed TNBC cell tumorigenesis, vasculogenic mimicry, and strongly inhibited the metastasis of TNBC via degradation of Twist1 and Notch1 oncoproteins, downregulation of metastatic and angiogenic gene expression, and reduction of NF-κB signaling pathway. Our findings provide a new strategy for treating highly lethal TNBC and offer a potential new drug candidate for combatting this aggressive disease.

To understand the formation process of dough with different hydration levels upon mixing and the response of dough rheology, the dynamic evolution of gluten protein was tracked and quantified at morphological, structural, and molecular levels. Both macroscopical and microscopic distribution images showed that partial and full hydration induced quick formation of a more compact gluten network compared with limited hydration. Gluten network in highly hydrated samples was more susceptible to the formation and collapse induced by mechanical force. SE-HPLC results indicated significant depolymerization of glutenin macropolymer (GMP) in fully and partially hydrated samples. Sufficient mixing was accompanied by the increase of ionic and hydrogen bonds, while excessive mixing increased exposure of free -SH. Higher hydration level induced more ordered secondary structure. Correlation and principal component analysis revealed the patterns and dynamics of gluten evolution during dough formation with different hydration levels, and their contribution to the changes in dough modulus.

Soil salinization seriously threatens agricultural production and ecological environments in arid areas. The accurate and rapid monitoring of soil salinity and its spatial variability is of great significance for the amelioration of saline soils. In this study, 191 soil samples were collected from cotton fields in southern Xinjiang, China, to obtain spectral reflectance and electrical conductivity (EC) indoors. Then, multi-granularity spectral segmentation (MGSS) and seven conventional spectral preprocessing methods were employed to preprocess the spectral data, followed by the construction of partial least squares regression (PLSR) models for soil EC estimation. Finally, the performance of the models was compared. The results showed that compared with conventional spectral preprocessing methods, MGSS could greatly improve the correlation between spectrum and soil EC, extract the weak spectral information of soil EC, and expand the spectral utilization range. The model validation results showed that the PLSR model based on the second-order derivative (2nd-der-PLSR) had the highest estimation accuracy among the models constructed by conventional methods. However, the PLSR model based on MGSS (MGSS-PLSR) had the highest estimation accuracy among all models, with Rp2 (0.901) and RPD (3.080) being 0.151 and 1.302 higher than those of the 2nd-der-PLSR model, respectively, and nRMSEP (5.857%) being 4.29% lower than that of the 2nd-der-PLSR model. The reason for the high accuracy of the MGSS-PLSR model is as follows: In the continuous segmentation of the raw spectrum by MGSS, the bands with strong and weak correlations with respect to soil EC were concentrated during low granularity segmentation. With the increase in granularity level, the spectral features decreased and were distributed discretely. In addition, the locations of spectral features were also different at different granularity levels. Therefore, the spectral features of soil EC can be effectively extracted by the MGSS, which significantly improves the spectral estimation accuracy of soil salinity. This study provides a new technical means for soil salinity estimation in arid areas.

Aeruginosins, a family of nonribosomal linear tetrapeptides discovered from cyanobacteria and sponges, exhibit in vitro inhibitory activity on various types of serine proteases. This family is characterized by the existence of the 2-carboxy-6-hydroxy-octahydroindole (Choi) moiety occupied at the central position of the tetrapeptide. Aeruginosins have attracted much attention due to their special structures and unique bioactivities. Although many studies on aeruginosins have been published, there has not yet been a comprehensive review that summarizes the diverse research ranging from biogenesis, structural characterization and biosynthesis to bioactivity. In this review, we provide an overview of the source, chemical structure as well as spectrum of bioactivities of aeruginosins. Furthermore, possible opportunities for future research and development of aeruginosins were discussed.

Jigucao capsules (JGCC) have the effects of soothing the liver and gallbladder and clearing heat and detoxification. It is a good medicine for treating acute and chronic hepatitis cholecystitis with damp heat of the liver and gallbladder. However, the existing quality standard of JGCC does not have content determination items, which is not conducive to quality control. In this study, serum pharmacochemistry technology and UNIFI data processing software were used to identify the blood prototype components and metabolites under the condition of the obvious drug effects of JGCC, and the referenced literature reports and the results from in vitro analysis of JGCC in the early stage revealed a total of 43 prototype blood components and 33 metabolites in JGCC. Quality markers (Q-markers) were discovered, such as abrine, trigonelline, hypaphorine and isoschaftoside. In addition, ultra-high-performance liquid chromatography–triple quadrupole mass spectrometry (UPLC-QQQ-MS) was used to determine the active ingredients in JGCC. The components of quantitative analysis have good correlation in the linear range with R2 ≥ 0.9993. The recovery rate is 93.15%~108.92% and the relative standard deviation (RSD) is less than 9.48%. The established UPLC-MS/MS quantitative analysis method has high sensitivity and accuracy, and can be used for the quality evaluation of JGCC.

Ground-received solar radiation is affected by several meteorological and air pollution factors. Previous studies have mainly focused on the effects of meteorological factors on solar radiation, but research on the influence of air pollutants is limited. Therefore, this study aimed to analyse the effects of air pollution characteristics on solar radiation. Meteorological data, air quality index (AQI) data, and data on the concentrations of six air pollutants (O3, CO, SO2, PM10, PM2.5, and NO2) in nine cities in China were considered for analysis. A city model (model-C) based on the data of each city and a unified model (model-U) based on national data were established, and the key pollutants under these conditions were identified. Correlation analysis was performed between each pollutant and the daily global solar radiation. The correlation between O3 and daily global solar radiation was the highest (r = 0.575), while that between SO2 and daily global solar radiation was the lowest. Further, AQI and solar radiation were negatively correlated, while some pollution components (e.g., O3) were positively correlated with the daily global solar radiation. Different key pollutants affected the solar radiation in each city. In Shenyang and Guangzhou, the driving effect of particles on the daily global solar radiation was stronger than that of pollutants. However, there were no key pollutants that affect solar radiation in Shanghai. Furthermore, the prediction performance of model-U was not as good as that of model-C. The model-U showed a good performance for Urumqi (R2 = 0.803), while the difference between the two models was not particularly significant in other areas. This study provides significant insights to improve the accuracy of regional solar radiation prediction and fill the gap regarding the absence of long-term solar radiation monitoring data in some areas.

Dampness-heat Jaundice Syndrome (DHJS) is a complex Chinese medicine syndrome, while Jigucao capsule (JGCC) is an effective compound preparation of Chinese medicine for the treatment of DHJS about liver and gallbladder, but its mechanism is not clear yet. The purpose of this study is to clarify the pathogenesis of DHJS and the treatment mechanism of JGCC. We used ultra-high performance liquid chromatography/mass spectrometry (UPLC/MS) combined with pattern recognition, accompanied the advanced software and online database for the urine metabolomics of rats. The potential biomarkers disturbing metabolism were identified and the metabolic pathway was analyzed. We investigated the callback of biomarkers after treatment with JGCC. Finally, A total of 25 potential urine biomarkers were identified, including Arachidonic acid, Phenylpyruvic acid, L-Urobilin and so on, and 14 related metabolic pathways were disturbed. After treatment with JGCC, the clinical biochemical indexes and histopathological were significantly improved, and the disturbed biomarkers were also obviously adjusted. It is proved that JGCC has remarkable effect on the treatment of DHJS.

A previous study found that transmembrane protein 43 (TMEM43) was highly associated with arrhythmogenic right ventricular dysplasia/cardiomyopathy. However, as a transmembrane protein, TMEM43 may be involved in ferroptosis in cardiovascular disease. In this study, we aimed to explore the role of TMEM43 in lipopolysaccharide (LPS)-induced cardiac injury and the underlying mechanism. Mice were injected with LPS (10 mg/kg) for 12 h to generate experimental sepsis. Mice were also subjected to AAV9-shTMEM43 to knock down TMEM43 or AAV9-TMEM43 to overexpress TMEM43 in hearts. H9c2 rat cardiomyocytes were also transfected with Ad-TMEM43 or TMEM43 siRNA to overexpress/knock down TMEM43. As a result, TMEM43 knockdown in hearts deteriorated LPS-induced mouse cardiac injury and dysfunction. LPS increased cardiac ferroptosis as assessed by malonaldehyde (MDA) and cardiac iron density, which were aggravated by TMEM43 knockdown. Moreover, TMEM43 overexpression alleviated LPS-induced cardiac injury, dysfunction, and ferroptosis. In vitro experiments showed that TMEM43 overexpression inhibited LPS-induced lipid peroxidation and cardiomyocyte injury while TMEM43 knockdown aggravated LPS-induced ferroptosis and injury in cardiomyocytes. Mechanistically, LPS increased the expression of P53 and ferritin but decreased the level of Gpx4 and SLC7A11. TMEM43 could inhibit the level of P53 and ferritin enhanced the level of Gpx4 and SLC7A11. Furthermore, ferrostatin-1 (Fer-1), a specific inhibitor of ferroptosis, could protect against LPS-induced cardiac injury and also counteracted the deteriorating effects of TMEM43 silencing in the heart. Based on these findings, we concluded that TMEM43 protects against sepsis-induced cardiac injury via inhibiting ferroptosis in mice. By targeting ferroptosis in cardiomyocytes, TMEM43 may be a therapeutic strategy for preventing sepsis in the future.

Lonicera japonica Thunb [...]

Nanozymes are nanomaterials with intrinsic natural enzyme-like catalytic properties. They have received extensive attention and have the potential to be an alternative to natural enzymes. Increasing the atom utilization rate of active centers in nanozymes has gradually become a concern of scientists. As the limit of designing nanozymes at the atomic level, single-atom nanozymes (SAzymes) have become the research frontier of the biomedical field recently because of their high atom utilization, well-defined active centers, and good natural enzyme mimicry. In this review, we first introduce the preparation of SAzymes through pyrolysis and defect engineering with regulated activity, then the characterization and surface modification methods of SAzymes are introduced. The possible influences of surface modification on the activity of SAzymes are discussed. Furthermore, we summarize the applications of SAzymes in the biomedical fields, especially in those of reactive oxygen species (ROS) scavenging and antibacterial. Finally, the challenges and opportunities of SAzymes are summarized and prospected.