3D spheroid HepaRG and fluorescent biphasic tracer for CYP3A4-mediated antibiotic interaction monitoring in sepsis.

Cytochrome P450 3A4 (CYP3A4) is a crucial enzyme in the metabolism of xenobiotics, particularly in drug metabolism interactions (DDIs), making it a significant factor in clinical drug use. However, current assay techniques are both laborious and costly, making it difficult to construct a high-throughput monitoring method that can be used in conjunction with the clinic. This poses certain safety hazards for drug combination. Therefore, it is crucial to develop a synchronized monitoring method for the inhibition and induction of CYP3A4. In this study, we utilized 3D culture technology to develop a HepaRG cells spheroid model. The CYP450 and transporter expression, the albumin secretion, and urea synthesis capacity characteristics were analyzed. The NEN probe was utilized as a tracer molecule for CYP3A4. The fluorescence intensity of metabolites was characterized by laser confocal technique to determine the inhibition and expression of CYP3A4 in the HepaRG cell spheroid model by the antibiotics for sepsis. The results indicate that the HepaRG sphere model successfully possessed the physiological phenotype of the liver, which could be used for drug interaction monitoring. Through positive drug testing, NEN probe was able to achieve bidirectional characterization of CYP3A4 induction and inhibition. The monitoring method described in this paper was successfully applied to drug interaction monitoring of commonly used antibiotics in sepsis patients, which is a convenient and rapid monitoring method. The proposed method offers a new strategy for monitoring CYP3A4-mediated drug-drug interactions with a high-throughput assay, which will help to improve the safety of clinical drug combination.

A nano-platform harnessing synergistic amino acid browning for biomedical applications.

Amino acids show promise as versatile biomolecules for creating a variety of functional biomaterials. Previously, we discovered a novel amino acid reaction, in which a single amino acid can form browning species in a simple solvent mixture comprising DMSO and acetone at room temperature. In the present study, we initially conducted a comprehensive analysis of 190 pairs of binary amino acids (i.e., all the possible pairwise combinations out of 20 amino acids) and identified several surprising combinations that exhibited synergistic browning effects. Particularly, cysteine-lysine and cysteine-arginine pairs exhibited pronounced browning in DMSO/acetone cosolvent solutions. We hypothesize that the coloured species result from the formation of extended, hydrophobic molecules with highly conjugated systems, arising from extensive condensation reactions between amino acids. Subsequently, we aimed at developing a nano-platform based on this newly discovered amino acid reaction. We demonstrate that through a nanoprecipitation process (solvent-shifting), spherical nanoparticles with sizes ranging from 100 to 200 nm can be produced, in the presence of ferric ions added to the water phase. Through systematic optimization and comprehensive characterization, the final product is a zwitterionic, charge-reversible nanoparticle featuring three functional groups on its surface: carboxylates, amines, and thiols. Furthermore, it possesses mild antioxidant activity, making it a new type of nano-antioxidant. Finally, we present preliminary results highlighting the potential of using this new nanomaterial as a delivery system for polynucleotides. In conclusion, the paper introduces a novel class of amino acid-derived nanoparticles with significant promise for future biomedical applications.

Effect of iodized oil embolization on temperature change during cryoablation for renal cell carcinoma.

INTRODUCTION: We aimed to evaluate the effect of transcatheter arterial embolization (TAE) with iodized oil (Lipiodol) on temperature change during cryoablation (CA) for renal cell carcinoma (RCC). MATERIAL AND METHODS: We retrospectively reviewed patients receiving CA for RCC from February 2020 to July 2021, including those who received Lipiodol TAE prior to CA (TAE group) and those who underwent only CA with comparable clinical and tumor characteristics (non-TAE group). Clinical data and tumor characteristics of both groups were recorded. The temperature readings of each cryoprobe at every 15 s and 'time to -100 °C' were compared between the groups. RESULTS: A total of 17 patients with 18 RCCs were recruited (seven in the TAE group and 11 in the non-TAE group). The 'time to -100 °C' was significantly longer in the TAE group than in the non-TAE group (64.5 ± 24.3 s vs. 48.8 ± 9.7 s, p = 0.018). Positive correlation between 'time to -100 °C' and tumor maximal diameter, RENAL nephrometry and PADUA score were observed in the non-TAE group, while no corresponding correlation was found in the TAE group. CONCLUSIONS: Pre-embolization with iodized oil influences the temporal temperature changes during cryoablation by disrupting the positive correlation between the time to reach the target temperature and tumor characteristics.

Antibiotic combinations prediction based on machine learning to multicentre clinical data and drug interaction correlation.

BACKGROUND: With increasing antibiotic resistance and regulation, the issue of antibiotic combination has been emphasised. However, antibiotic combination prescribing lacks a rapid identification of feasibility, while its risk of drug interactions is unclear. METHODS: We conducted statistical descriptions on 16 101 antibiotic coprescriptions for inpatients with bacterial infections from 2015 to 2023. By integrating the frequency and effectiveness of prescriptions, we formulated recommendations for the feasibility of antibiotic combinations. Initially, a machine learning algorithm was utilised to optimise grading thresholds and habits for antibiotic combinations. A feedforward neural network (FNN) algorithm was employed to develop antibiotic combination recommendation model (ACRM). To enhance interpretability, we combined sequential methods and DrugBank to explore the correlation between antibiotic combinations and drug interactions. RESULTS: A total of 55 antibiotics, covering 657 empirical clinical antibiotic combinations were used for ACRM construction. Model performance on the test dataset showed AUROCs of 0.589-0.895 for various antibiotic recommendation classes. The ACRM showed satisfactory clinical relevance with 61.54-73.33% prediction accuracy in a new independent retrospective cohort. Antibiotic interaction detection showed that the risk of drug interactions was 29.2% for strongly recommended and 43.5% for not recommended. A positive correlation was identified between the level of clinical recommendation and the risk of drug interactions. CONCLUSIONS: Machine learning modelling of retrospective antibiotic prescriptions habits has the potential to predict antibiotic combination recommendations. The ACRM plays a supporting role in reducing the incidence of drug interactions. Clinicians are encouraged to adopt such systems to improve the management of antibiotic usage and medication safety.

Intermolecular Non-Bonded Interactions from Machine Learning Datasets.

Accurate determination of intermolecular non-covalent-bonded or non-bonded interactions is the key to potentially useful molecular dynamics simulations of polymer systems. However, it is challenging to balance both the accuracy and computational cost in force field modelling. One of the main difficulties is properly representing the calculated energy data as a continuous force function. In this paper, we employ well-developed machine learning techniques to construct a general purpose intermolecular non-bonded interaction force field for organic polymers. The original ab initio dataset SOFG-31 was calculated by us and has been well documented, and here we use it as our training set. The CLIFF kernel type machine learning scheme is used for predicting the interaction energies of heterodimers selected from the SOFG-31 dataset. Our test results show that the overall errors are well below the chemical accuracy of about 1 kcal/mol, thus demonstrating the promising feasibility of machine learning techniques in force field modelling.

Reduction in the duration of postoperative fever following NUSS surgery during the COVID-19 pandemic.

BACKGROUND: Our study aimed to compare the prevalence of postoperative fever during the COVID-19 pandemic period with that of the preceding non-pandemic period. METHODS: A retrospective analysis was conducted on patients with pectus excavatum (PE) undergoing minimally invasive repair (also called NUSS procedure) at Nanjing Children's Hospital from January 1, 2017 to March 1, 2019 (Group 2019), and from January 1, 2020 to March 1, 2021 (Group 2021). Data from a total of 284 patients, consisting of 200 (70.4%) males and 84 (29.6%) females with an average age of 9.73 ± 3.41 (range, 4 to 17) years, were collected. The presence of post-operative fever (defined as a forehead temperature of 37.5℃ or above within 72 h post-surgery), as well as the time of operation, duration of postoperative mechanical ventilator and urinary catheter use, and length of hospitalization were all assessed in admitted patients from Group 2019 (n = 144) and Group 2021 (n = 140). Postoperative white blood cell (WBC), C-reactive protein (CRP) levels, and prevalence of postoperative complications (i.e., pneumothorax, pulmonary atelectasis, pneumonia, wound infection, and dehiscence) were also determined. RESULT: Our results showed a statistically significant decrease in the incidence of postoperative fever within 24 to 72 h of surgery in patients admitted from Group 2019 as compared to Group 2021 (p < 0.001), as well as a decrease in peak body temperature within 72 h (p < 0.05). Meanwhile, no significant differences were observed in age and body mass index (BMI), time of operation, or duration of postoperative mechanical ventilator and urinary catheter use between the two groups (p > 0.05). The average hospitalization length of Group 2021 was significantly shorter than Group 2019 (12.49 ± 2.57 vs. 11.85 ± 2.19 days, p < 0.05). Furthermore, while the WBC count between the two groups 24 h after surgery showed a statistical difference (p < 0.05), no differences in CRP levels or the incidence of postoperative complications were observed (p > 0.05). CONCLUSION: The prevalence of postoperative fever within 72 h of surgery and the length of hospital stay for patients with PE undergoing NUSS surgery were both decreased in Group 2021. We propose that the above phenomenon may be related to increased used of personal protection equipment (such as surgical masks and filtering facepiece respirators (FFRs)) by physicians, nurses, and the patients themselves.

DHHC2 regulates fear memory formation, LTP, and AKAP150 signaling in the hippocampus.

Palmitoyl acyltransferases (PATs) have been suggested to be involved in learning and memory. However, the underlying mechanisms have not yet been fully elucidated. Here, we found that the activity of DHHC2 was upregulated in the hippocampus after fear conditioning, and DHHC2 knockdown impaired fear induced memory and long-term potentiation (LTP). Additionally, the activity of DHHC2 and its synaptic expression were increased after high frequency stimulation (HFS) or glycine treatment. Importantly, fear learning selectively augmented the palmitoylation level of AKAP150, not PSD-95, and this effect was abolished by DHHC2 knockdown. Furthermore, 2-bromopalmitic acid (2-BP), a palmitoylation inhibitor, attenuated the increased palmitoylation level of AKAP150 and the interaction between AKAP150 and PSD-95 induced by HFS. Lastly, DHHC2 knockdown reduced the phosphorylation level of GluA1 at Ser845, and also induced an impairment of LTP in the hippocampus. Our results suggest that DHHC2 plays a critical role in regulating fear memory via AKAP150 signaling.

High-Photovoltage Silicon Nanowire for Biological Cofactor Production.

Photocathodic conversion of NAD+ to NADH cofactor is a promising platform for activating redox biological catalysts and enzymatic synthesis using renewable solar energy. However, many photocathodes suffer from low photovoltage, consequently requiring a high cathodic bias for NADH production. Here, we report an n+p-type silicon nanowire (n+p-SiNW) photocathode having a photovoltage of 435 mV to drive energy-efficient NADH production. The enhanced band bending at the n+/p interface accounts for the high photovoltage, which conduces to a benchmark onset potential [0.393 V vs the reversible hydrogen electrode (VRHE)] for SiNW-based photocathodic NADH generation. In addition, the n+p-SiNW nanomaterial exhibits a Faradaic efficiency of 84.7% and a conversion rate of 1.63 µmol h-1 cm-1 at 0.2 VRHE, which is the lowest cathodic potential to achieve the maximum productivity among SiNW-sensitized cofactor production.

Quantitative analysis of contrast-enhanced ultrasonography in rat models of hepatic acute graft-versus-host disease.

Background: Hepatic acute graft-versus-host disease (aGVHD) is a major life-threatening complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT). We hypothesized that contrast-enhanced ultrasound (CEUS) could serve as a new imaging biomarker in early diagnosis of hepatic aGVHD by detecting liver microcirculation. Methods: Thirty Wistar rats received allo-HSCT were finally included after excluding 9 rats, and they were randomly divided into 5 groups (1- to 5-week groups, 6 per group). Six rats were used for the control group without any intervention. We observed the clinical scores, serum liver enzyme levels and liver CEUS parameters of rats in each group. Hepatic aGVHD was finally confirmed by histopathologic analysis. The diagnostic performance of CEUS parameters in detecting GVHD was evaluated by comparing the area under the receiver operating curve (AUC) values. Results: After HSCT, the rats developed ruffling of fur, maculopapular rash, weight loss, accompanied by increased clinical scores. Serum liver enzymes were significantly higher than those in the control group from the third week, especially alkaline phosphatase, while CEUS parameters, peak intensity (PI) and mean transit time (MTT), changed in the second week (P<0.001). Compared with non-aGVHD group, the PI was significantly decreased while time to peak and MTT were prolonged in aGVHD group. CEUS parameters were more strongly correlated with pathological grade than serology. PI was an independent predictor for hepatic aGVHD. The AUC of CEUS parameters for diagnosing hepatic aGVHD was 0.933 (95% CI: 0.779-0.992), which was higher than that of clinical scores (AUC =0.748, 95% CI: 0.557-0.888, P=0.032) and serological markers (AUC =0.902, 95% CI: 0.737-0.980, P=0.694). Conclusions: CEUS exhibits promising applications as a quantitative method to detect hepatic aGVHD and early liver damage.

Association of cigarette smoking with risk of colorectal cancer subtypes classified by gut microbiota.

INTRODUCTION: Both cigarette smoking and gut microbiota play important roles in colorectal carcinogenesis. We explored whether the association between smoking and colorectal cancer (CRC) risk varies by gut microbial enterotypes and how smoking-related enterotypes promote colorectal carcinogenesis. METHODS: A case-control study was conducted. Fecal microbiota was determined by 16S rDNA sequencing. The cases with CRC or adenoma were subclassified by gut microbiota enterotypes. Multivariate analyses were used to test associations between smoking and the odds of colorectal neoplasm subtypes. Mann-Whitney U tests were used to find differential genera, genes, and pathways between the subtypes. RESULTS: Included in the study were 130 CRC patients (type I: n=77; type II: n=53), 120 adenoma patients (type I: n=66; type II: n=54), and 130 healthy participants. Smoking increased the odds for type II tumors significantly (all p for trend <0.05) but not for type I tumors. The associations of smoking with increased odds of colorectal neoplasm significantly differed by gut microbiota enterotypes (p<0.05 for heterogeneity). An increase in carcinogenic bacteria (genus Escherichia shigella) and a decrease in probiotics (family Lachnospiraceae and Ruminococcaceae) in type II tumors may drive disease progression by upregulating oncogenic signaling pathways and inflammatory/oxidative stress response pathways, as well as protein phospholipase D1/2, cytochrome C, and prostaglandin-endoperoxide synthase 2 expression. CONCLUSIONS: Smoking was associated with a higher odds of type II colorectal neoplasms but not type I tumors, supporting a potential role for the gut microbiota in mediating the association between smoking and colorectal neoplasms.

High expression of IL4I1 is correlated with poor prognosis and immune infiltration in thyroid cancer.

BACKGROUND: Thyroid cancer-related deaths mostly result from metastasis. It was reported that the immunometabolism associated enzyme interleukin-4-induced-1 (IL4I1) was related to tumor metastasis. The present study was intended to investigate the effects of IL4I1 on thyroid cancer metastasis and its relationship with the prognosis. METHODS: Data from Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to find out the different mRNA expressions of IL4I1 between thyroid cancer and normal tissues. And Human Protein Atlas (HPA) was used to assess IL4I1 protein expression. To further differentiate thyroid cancer from normal tissues and estimate the impact of IL4I1 on the prognosis, the receiver operating characteristic curve (ROC) and Kaplan-Meier (KM) method was performed. The protein-protein interaction (PPI) network was established using STRING, and functional enrichment analyses were conducted by "clusterProfiler" package. Then, we assayed the correlation between IL4I1 and some related molecules. The relationship between IL4I1 and immune infiltration was performed using "Gene Set Variation Analysis (GSVA)" package in TCGA and tumor-immune system interaction database (TISIDB). Finally, we did in vitro experiments in order to further prove the bioeffects of IL4I1 on metastasis. RESULTS: The expression of IL4I1 mRNA and IL4I1 protein was significantly upregulated in thyroid cancer tissues. The increment of IL4I1 mRNA expression was related to high-grade malignancy, lymph node metastases and extrathyroidal extension. The ROC curve displayed the cutoff value of 0.782, with the sensitivity of 77.5% and the specificity of 77.8%. KM survival analysis showed that there was a worse PFS in patients with high IL4I1 expression than those with low IL4I1 expression (p = 0.013). Further study indicated that IL4I1 was associated with lactate, body fluid secretion, positive regulation of T cell differentiation, and cellular response to nutrients in Gene Ontology (GO) analysis. Moreover, IL4I1 was found correlated with immune infiltration. Finally, the in vitro experiments revealed the promotion of IL4I1 on cancer cell proliferation, migration and invasion. CONCLUSIONS: The increased IL4I1 expression is markedly correlated with the immune imbalance in the tumor microenvironment (TME) and predicts poor survival in thyroid cancer. This study reveals the potential clinical biomarker of poor prognosis and the target of immune therapy in thyroid cancer.

Gut Microbiota Enterotypes Mediate the Effects of Dietary Patterns on Colorectal Neoplasm Risk in a Chinese Population.

Colorectal cancer (CRC) risk is influenced by dietary patterns and gut microbiota enterotypes. However, the interaction between these factors remains unclear. This study examines this relationship, hypothesizing that different diets may affect colorectal tumor risk in individuals with varied gut microbiota enterotypes. We conducted a case-control study involving 410 Han Chinese individuals, using exploratory structural equation modeling to identify two dietary patterns, and a Dirichlet multinomial mixture model to classify 250 colorectal neoplasm cases into three gut microbiota enterotypes. We assessed the association between dietary patterns and the risk of each tumor subtype using logistic regression analysis. We found that a healthy diet, rich in vegetables, fruits, milk, and yogurt, lowers CRC risk, particularly in individuals with type I (dominated by Bacteroides and Lachnoclostridium) and type II (dominated by Bacteroides and Faecalibacterium) gut microbiota enterotypes, with adjusted odds ratios (ORs) of 0.66 (95% confidence interval [CI] = 0.48-0.89) and 0.42 (95% CI = 0.29-0.62), respectively. Fruit consumption was the main contributor to this protective effect. No association was found between a healthy dietary pattern and colorectal adenoma risk or between a high-fat diet and colorectal neoplasm risk. Different CRC subtypes associated with gut microbiota enterotypes displayed unique microbial compositions and functions. Our study suggests that specific gut microbiota enterotypes can modulate the effects of diet on CRC risk, offering new perspectives on the relationship between diet, gut microbiota, and colorectal neoplasm risk.

Photochemical Diodes for Simultaneous Bias-Free Glycerol Valorization and Hydrogen Evolution.

Artificial photosynthesis offers a route to producing clean fuel energy. However, the large thermodynamic requirement for water splitting along with the corresponding sluggish kinetics for the oxygen evolution reaction (OER) limits its current practical application. Here, we offer an alternative approach by replacing the OER with the glycerol oxidation reaction (GOR) for value-added chemicals. By using a Si photoanode, a low GOR onset potential of -0.05 V vs RHE and a photocurrent density of 10 mA/cm2 at 0.5 V vs RHE can be reached. Coupled with a Si nanowire photocathode for the hydrogen evolution reaction (HER), the integrated system yields a high photocurrent density of 6 mA/cm2 with no applied bias under 1 sun illumination and can run for over 4 days under diurnal illumination. The demonstration of the GOR-HER integrated system provides a framework for designing bias-free photoelectrochemical devices at appreciable currents and establishes a facile approach to artificial photosynthesis.

Bioinformatic analysis identifies GPR91 as a potential key gene in brain injury after deep hypothermic low flow.

Purpose: Explore the transcription change of brain ischemia and reperfusion injury after deep hypothermic low flow. Method: The data from PRJNA739516 and GSE104036 were obtained for the differentially expressed genes identification, functional enrichment analysis, gene set enrichment analysis, protein-protein interaction construction and hub gene identification. Oxygen and glucose deprivation model was set to validate the hub gene and explore the detailed brain injury mechanism. Result: Interleukin, immunological response, NF-κB signaling pathway, G protein-coupled receptor signaling pathway and NLRP inflammatory are functional pathway were enriched in differentially expressed genes analysis. Sucnr1, Casr, Cxcr4, C5ar1, Tas2r41, Tas2r60 and Hcar2 were identified and verified in the OGD model. Knocking down GPR91 reduces the inflammatory response after OGD and GPR91 may be involved in the inflammatory pre-reaction through the synergistic activation of NF-κB, NLRP3, and IL-1ß respectively. Conclusion: Our study found that Interleukin, immunological response, NF-κB signaling pathway, G protein-coupled receptor signaling pathway and NLRP inflammatory are all associated with brain ischemia and reperfusion injury after deep hypothermic low flow and GPR91 can activate NF-κB/NLRP3 pathway and trigger the release of IL-1ß in this progress.

Using Semantic Text Similarity calculation for question matching in a rheumatoid arthritis question-answering system.

Background: When users inquire about knowledge in a certain field using the internet, the intelligent question-answering system based on frequently asked questions (FAQs) provides numerous concise and accurate answers that have been manually verified. However, there are few specific question-answering systems for chronic diseases, such as rheumatoid arthritis, and the related technology to construct a question-answering system about chronic diseases is not sufficiently mature. Methods: Our research embedded the classification information of the question into the sentence vector based on the bidirectional encoder representations from transformers (BERT) language model. First of all, we calculated the similarity using edit distance to recall the candidate set of similar questions. Then, we took advantage of the BERT pretraining model to map the sentence information to the corresponding embedding representation. Finally, each dimensional feature of the sentence was obtained by passing a sentence vector through the multihead attention layer and the fully connected feedforward layer. The features that were stitched and fused were used for the semantic similarity calculation. Results: Our improved model achieved a Top-1 precision of 0.551, Top-3 precision of 0.767, and Top-5 precision of 0.813 on 176 testing question sentences. In the analysis of the actual application effect of the model, we found that our model performed well in understanding the actual intention of users. Conclusions: Our deep learning model takes into account the background and classifications of questions and combines the efficiency of deep learning technology and the comprehensibility of semantics. The model enables the deep meaning of the user's question to be better understood by the intelligent question answering system, and answers that are more relevant to the original query are provided.

Ultrafiltration versus Diuretics on Prognostic Cardiac and Renal Biomarkers in Acute Decompensated Heart Failure: A Systematic Review and Meta-Analysis.

Existing systematic reviews have insufficiently delineated the differing cardiac and renal profile of ultrafiltration compared to diuretics as a method of decongestion in acute decompensated heart failure. This meta-analysis will investigate the impact of ultrafiltration compared to diuretics on prognostic cardiac and renal biomarkers. We searched PubMed Central, Ovid MEDLINE®, Ovid Embase, all EBM reviews, and Web of Science Core Collection for randomised controlled trials published before 21 July 2022. Our main outcome measures were cardiac (brain natriuretic peptide and N-terminal pro-brain natriuretic peptide) and renal biomarkers (serum creatinine, serum sodium, and blood urea nitrogen). A total of 10 randomised trials were included in our analysis after screening. An inverse-variance random effects meta-analysis of the pooled results demonstrated no significant difference between ultrafiltration and diuretics for brain natriuretic peptide, N-terminal pro-brain natriuretic peptide, creatinine, sodium and long-term blood urea nitrogen. However, ultrafiltration produced statistically greater increases in blood urea nitrogen in the short-term (mean difference, 3.88; 95% confidence interval 0.59-7.17 mg/dL). Overall, ultrafiltration produces a similar impact on prognostic cardiac and renal biomarkers when compared to diuretic therapy. We highlight ultrafiltration's significant impact on short-term BUN and recommend further research to investigate more optimal protocols of ultrafiltration administration.

Genomic profiling of WRKY transcription factors and functional analysis of CcWRKY7, CcWRKY29, and CcWRKY32 related to protoberberine alkaloids biosynthesis in Coptis chinensis Franch.

Coptis chinensis Franch. (Huanglian in Chinese) is an important economic crop with medicinal value. Its rhizome has been used as a traditional herbal medicine for thousands of years in Asia. Protoberberine alkaloids, as the main bioactive component of Coptis chinensis, have a series of pharmacological activities. However, the protoberberine alkaloids content of C. chinensis is relatively low. Understanding the molecular mechanisms affecting the transcriptional regulation of protoberberine alkaloids would be crucial to increase their production via metabolic engineering. WRKY, one of the largest plant-specific gene families, regulates plant defense responses via the biosynthesis of specialized metabolites such as alkaloids. Totally, 41 WRKY transcription factors (TFs) related to protoberberine alkaloid biosynthesis were identified in the C. chinensis genome and classified into three groups based on phylogenetic and conserved motif analyses. Three WRKY genes (CcWRKY7, CcWRKY29, and CcWRKY32) may regulate protoberberine alkaloid biosynthesis, as suggested by gene-specific expression patterns, metabolic pathways, phylogenetic, and dual-luciferase analysis. Furthermore, the CcWRKY7, CcWRKY29, and CcWRKY32 proteins were specifically detected in the nucleus via subcellular localization. This study provides a basis for understanding the regulatory mechanisms of protoberberine alkaloid biosynthesis and valuable information for breeding C. chinensis varieties.

Efficient removal of phytochrome using rice straw-derived biochar: Adsorption performance, mechanisms, and practical applications.

Rice straw derived biochar was fabricated and applied as a purification agent. The adsorption kinetics, isotherms, and thermodynamics for adsorbates were determined using the biochar. Adsorption kinetics and isotherms were best fitted by the pseudo-second order and Langmuir models. Biochar could effectively remove chlorophyll in 9 different solutions. Biochar was employed as a clean-up reagent for 149 pesticides detection, which revealed that biochar had a higher phytochrome removal capacity than graphitized carbon black and 123 pesticides had satisfactory recovery values. The biochar was prepared into a sample pad by electrospinning and was then used for online sample clean-up in a test strip, and it showed high ability of removing phytochrome and improving detection sensitivity. Thus, biochar could be applied as a purification agent to remove pigmentation, making it a promising candidate not only for sample pretreatment but also in the fields of food, agriculture and environment.