Insight into the sorption and desorption pattern of pyrrolizidine alkaloids and their N-oxides in acidic tea (Camellia sinensis) plantation soils.

Pyrrolizidine alkaloids (PAs) and their N-oxides (PANOs) are phytotoxins produced by various plant species and have been emerged as environmental pollutants. The sorption/desorption behaviors of PAs/PANOs in soil are crucial due to the horizontal transfer of these natural products from PA-producing plants to soil and subsequently absorbed by plant roots. This study firstly investigated the sorption/desorption behaviors of PAs/PANOs in tea plantation soils with distinct characteristics. Sorption amounts for seneciphylline (Sp) and seneciphylline-N-oxide (SpNO) in three acidic soils ranged from 2.9 to 5.9 µg/g and 1.7 to 2.8 µg/g, respectively. Desorption percentages for Sp and SpNO were from 22.2% to 30.5% and 36.1% to 43.9%. In the mixed PAs/PANOs systems, stronger sorption of PAs over PANOs was occurred in tested soils. Additionally, the Freundlich models more precisely described the sorption/desorption isotherms. Cation exchange capacity, sand content and total nitrogen were identified as major influencing factors by linear regression models. Overall, the soils exhibiting higher sorption capacities for compounds with greater hydrophobicity. PANOs were more likely to migrate within soils and be absorbed by tea plants. It contributes to the understanding of environmental fate of PAs/PANOs in tea plantations and provides basic data and clues for the development of PAs/PANOs reduction technology.

Effects of Weizhuan'an on rats with precancerous lesions of gastric cancer based on regulating gastric mucosal microflora and inflammatory factors.

Objectives: This study aimed to observe the intervention of Weizhuan'an prescription on rats with precancerous lesions of gastric cancer (PLGC) as well as its regulation on gastric mucosal microflora and inflammatory factors and explore the pharmacodynamic mechanisms of Weizhuan'an Formula. Methods: The rats were classified into the blank control group (BCG); low-, medium-, and high-dose groups of Weizhuan'an prescription (LDG, MDG, and HDG, respectively); and natural recovery group (NRG) at random. The rats in the traditional Chinese medicine (TCM) group were given corresponding doses of Weizhuan'an formula, while the rats in the NRG and BCG were given an equivalent volume of distilled water for 12 weeks. After that, gastric mucosa samples of rats were collected to observe the general and pathological changes in the gastric mucosa; the changes in gastric mucosal microflora were detected by 16S rDNA amplicon sequencing, and the inflammatory factors were analyzed by cytokine antibody microarray and Western blotting. Results: The results suggest that compared with the BCG, the pathology of gastric mucosa and gastric mucosal microflora and inflammatory factors in rats with PLGC have changed significantly, while Weizhuan'an formula effectively improved them, especially in the MDG and HDG (p < 0.05). Compared with the NRG, the abundance of probiotics such as Lactobacillus and Veillonella were increased, while the abundance of pathogens such as Proteobacteria and Pseudomonas was decreased (p < 0.05, p < 0.01), and the relative contents of IL-2, IL-4, IL-13, and MCP-1 in gastric mucosa were decreased (p < 0.05). Moreover, it can upregulate the DNA-binding transcriptional regulator, ABC type multidrug transport system, and related enzymes and affect the signaling pathways such as viral protein interaction with cytokine and cytokine receptor and T cell receptor signaling pathway significantly (p < 0.05, p < 0.01), which can promote drug absorption and utilization and repair damaged gastric mucosa. Conclusion: The study confirmed that Weizhuan'an prescription can treat rats with PLGC by regulating gastric mucosal microflora and inflammatory factors.

LAMAIS: A library-aided approach for efficient 1D 1H NMR qualitative analysis in plant metabolomics.

BACKGROUND: One-dimensional proton nuclear magnetic resonance (1D 1H NMR) spectroscopy is a non-destructive, non-targeted analytical technique providing both qualitative and quantitative insights, particularly beneficial for mixture analysis. However, the qualitative analysis of 1D 1H NMR spectra for mixture samples is laborious and time-consuming, involving extensive database searches and verification experiments like spiking. This process heavily relies on the analyst's expertise, leading to efficiency discrepancies. There is a pressing need for a reliable method to streamline operations and enhance the efficiency of qualitative analysis in complex mixtures. RESULTS: We introduce a library-aided method for spectral profiling, named LAMAIS. This method achieves compound identification through similarity assessment between samples and template data, allowing rapid, automatic compound identification and full-spectrum peak assignment without the need for fitting. LAMAIS correctly identifies over 90 % of components in synthetic mixtures and more than 75 % in experimental mixtures, surpassing other representative methods with a higher F2 score. Our reference library, which currently includes 71 compounds, is tailored to capture the commonality of primary metabolites across diverse plant species. The analysis of real-world samples yielded encouraging results, underscoring LAMAIS's versatility as an auxiliary tool suitable for a variety of botanical sources. For analyst convenience, interactive graphics are utilized as the output format. SIGNIFICANCE: LAMAIS excels, demonstrating competitiveness and reliability. The approach minimizes repetitive tasks and sample wastage, improving the efficiency of 1D 1H NMR qualitative analysis. Constructing a reference library effectively preserves knowledge, mitigates reliance on human experience, and addresses gaps in the analysis of plant source samples.

A controllability reinforcement learning method for pancreatic cancer biomarker identification.

Pancreatic cancer is one of the most malignant cancers with rapid progression and poor prognosis. The use of transcriptional data can be effective in finding new biomarkers for pancreatic cancer. Many network-based methods used to identify cancer biomarkers are proposed, among which the combination of network controllability appears. However, most of the existing methods do not study RNA, rely on priori and mutations information, or can only achieve classification tasks. In this study, we propose a method combined Relational Graph Convolutional Network and Deep Q-Network called RDDriver to identify pancreatic cancer biomarkers based on multi-layer heterogeneous transcriptional regulation network. Firstly, we construct a regulation network containing long non-coding RNA, microRNA, and messenger RNA. Secondly, Relational Graph Convolutional Network is used to learn the node representation. Finally, we use the idea of Deep Q-Network to build a model, which score and prioritize each RNA with the Popov-Belevitch-Hautus criterion. We train RDDriver on three small simulated networks, and calculate the average score after applying the model parameters to the regulation networks separately. To demonstrate the effectiveness of the method, we perform experiments for comparison between RDDriver and other eight methods based on the approximate benchmark of three types cancer drivers RNAs.

Inhibition of glycosphingolipid synthesis with eliglustat in combination with immune checkpoint inhibitors in advanced cancers: preclinical evidence and phase I clinical trial.

Glycosphingolipids (GSLs) are abundantly expressed in cancer cells. The effects of GSL-targeted immunotherapies are not fully understood. Here, we show that the inhibition of GSL synthesis with the UDP-glucose ceramide glucosyltransferase inhibitor eliglustat can increase the exposure of the major histocompatibility complex (MHC) and tumour antigen peptides, enhancing the antitumour response of CD8+ T cells in a range of tumour models. We therefore conducted a proof-of-concept phase I trial on the combination of eliglustat and an anti-PD-1 antibody for the treatment of advanced cancers (NCT04944888). The primary endpoints were safety and feasibility, and the secondary endpoint was antitumor activity. All prespecified endpoints were met. Among the 31 enrolled patients, only 1 patient experienced a grade 3 adverse event (AE), and no grade 4 AEs were observed. The objective response rate was 22.6% and the disease control rate reached 71%. Of the 8 patients with proficient mismatch repair/microsatellite stable (pMMR/MSS) colorectal cancer, one achieved complete response and two each had partial response and stable disease. In summary, inhibiting the synthesis of GSLs might represent an effective immunotherapy approach.

Zirconium and cerium dioxide fabricated activated carbon-based nanocomposites for enhanced adsorption and photocatalytic removal of methylene blue and tetracycline hydrochloride.

Activated carbon (AC) is a porous, amorphous form of carbon known for its strong adsorption capacity, making it highly effective for use in wastewater treatment. In this investigation, AC-based nanocomposites (NCs) loaded with zirconium dioxide and cerium dioxide nanoparticles (ZrO2/CeO2 NPs) were successfully synthesized for the effective elimination of methylene blue (MB) and tetracycline hydrochloride (TCH). The AC-ZrO2/CeO2 NCs have a size of 231.83 nm, a zeta potential of -20.07 mV, and a PDI value of 0.160. The adsorption capacities of AC-ZrO2/CeO2 NCs for MB and TCH were proved in agreement with the Langmuir isotherm and pseudo 1st order kinetic model, respectively. The maximum adsorption capacities were determined to be 75.54 mg/g for MB and 26.75 mg/g for TCH. Notably, AC-ZrO2/CeO2 NCs exhibited superior photocatalytic degradation efficiency for MB and TCH under sunlight irradiation with removal efficiencies reaching up to 97.91% and 82.40% within 90 min, respectively. The t1/2 for the photo-degradation process of MB and TCH were 11.55 min and 44.37 min. Analysis of active species trapping confirmed the involvement of hole (h+), superoxide anion (•O2-), and hydroxyl radical (•OH) in the degradation mechanism. Furthermore, the residual solution post-contaminant removal exhibited minimal toxicity towards Artemia salina and NIH3T3 cells. Importantly, the NCs did not exhibit antibacterial activity against tested pathogens post-absorption/degradation of TCH. Thus, AC-ZrO2/CeO2 NCs could be a promising nanomaterial for wastewater treatment applications.

Associations between first-trimester screening biomarkers and maternal characteristics with gestational diabetes mellitus in Chinese women.

Background: Gestational diabetes mellitus (GDM) can result in adverse maternal and neonatal outcomes. Predicting those at high risk of GDM and early interventions can reduce the development of GDM. The aim of this study was to examine the associations between first-trimester prenatal screening biomarkers and maternal characteristics in relation to GDM in Chinese women. Methods: We conducted a retrospective cohort study of singleton pregnant women who received first-trimester aneuploidy and preeclampsia screening between January 2019 and May 2021. First-trimester prenatal screening biomarkers, including pregnancy-associated plasma protein A (PAPP-A), free beta-human chorionic gonadotropin, and placental growth factor (PLGF), along with maternal characteristics, were collected for analysis in relation to GDM. Receiver operating characteristic (ROC) curve and logistic regression analyses were used to evaluate variables associated with GDM. Results: Of the 1452 pregnant women enrolled, 96 developed GDM. PAPP-A (5.01 vs. 5.73 IU/L, P < 0.001) and PLGF (39.88 vs. 41.81 pg/mL, P = 0.044) were significantly lower in the GDM group than in the non-GDM group. The area under the ROC curve of combined maternal characteristics and biomarkers was 0.73 (95% confidence interval [CI] 0.68-0.79, P < 0.001). The formula for predicting GDM was as follows: P = 1/[1 + exp (-8.148 + 0.057 x age + 0.011 x pregestational body mass index + 1.752 x previous GDM history + 0.95 x previous preeclampsia history + 0.756 x family history of diabetes + 0.025 x chronic hypertension + 0.036 x mean arterial pressure - 0.09 x PAPP-A - 0.001 x PLGF)]. Logistic regression analysis revealed that higher pregestational body mass index (adjusted odds ratio [aOR] 1.03, 95% CI 1.01 - 1.06, P = 0.012), previous GDM history (aOR 9.97, 95% CI 3.92 - 25.37, P < 0.001), family history of diabetes (aOR 2.36, 95% CI 1.39 - 4.02, P = 0.001), higher mean arterial pressure (aOR 1.17, 95% CI 1.07 - 1.27, P < 0.001), and lower PAPP-A level (aOR 0.91, 95% CI 0.83 - 1.00, P = 0.040) were independently associated with the development of GDM. The Hosmer-Lemeshow test demonstrated that the model exhibited an excellent discrimination ability (chi-square = 3.089, df = 8, P = 0.929). Conclusion: Downregulation of first-trimester PAPP-A and PLGF was associated with the development of GDM. Combining first-trimester biomarkers with maternal characteristics could be valuable for predicting the risk of GDM.

Quality Assessment of TikTok as a Source of Information About Mitral Valve Regurgitation in China: Cross-Sectional Study.

BACKGROUND: In China, mitral valve regurgitation (MR) is the most common cardiovascular valve disease. However, patients in China typically experience a high incidence of this condition, coupled with a low level of health knowledge and a relatively low rate of surgical treatment. TikTok hosts a vast amount of content related to diseases and health knowledge, providing viewers with access to relevant information. However, there has been no investigation or evaluation of the quality of videos specifically addressing MR. OBJECTIVE: This study aims to assess the quality of videos about MR on TikTok in China. METHODS: A cross-sectional study was conducted on the Chinese version of TikTok on September 9, 2023. The top 100 videos on MR were included and evaluated using quantitative scoring tools such as the modified DISCERN (mDISCERN), the Journal of the American Medical Association (JAMA) benchmark criteria, the Global Quality Score (GQS), and the Patient Education Materials Assessment Tool for Audio-Visual Content (PEMAT-A/V). Correlation and stepwise regression analyses were performed to examine the relationships between video quality and various characteristics. RESULTS: We obtained 88 valid video files, of which most (n=81, 92%) were uploaded by certified physicians, primarily cardiac surgeons, and cardiologists. News agencies/organizations and physicians had higher GQS scores compared with individuals (news agencies/organizations vs individuals, P=.001; physicians vs individuals, P=.03). Additionally, news agencies/organizations had higher PEMAT understandability scores than individuals (P=.01). Videos focused on disease knowledge scored higher in GQS (P<.001), PEMAT understandability (P<.001), and PEMAT actionability (P<.001) compared with videos covering surgical cases. PEMAT actionability scores were higher for outpatient cases compared with surgical cases (P<.001). Additionally, videos focused on surgical techniques had lower PEMAT actionability scores than those about disease knowledge (P=.04). The strongest correlations observed were between thumbs up and comments (r=0.92, P<.001), thumbs up and favorites (r=0.89, P<.001), thumbs up and shares (r=0.87, P<.001), comments and favorites (r=0.81, P<.001), comments and shares (r=0.87, P<.001), and favorites and shares (r=0.83, P<.001). Stepwise regression analysis identified "length (P<.001)," "content (P<.001)," and "physicians (P=.004)" as significant predictors of GQS. The final model (model 3) explained 50.1% of the variance in GQSs. The predictive equation for GQS is as follows: GQS = 3.230 - 0.294 × content - 0.274 × physicians + 0.005 × length. This model was statistically significant (P=.004) and showed no issues with multicollinearity or autocorrelation. CONCLUSIONS: Our study reveals that while most MR-related videos on TikTok were uploaded by certified physicians, ensuring professional and scientific content, the overall quality scores were suboptimal. Despite the educational value of these videos, the guidance provided was often insufficient. The predictive equation for GQS developed from our analysis offers valuable insights but should be applied with caution beyond the study context. It suggests that creators should focus on improving both the content and presentation of their videos to enhance the quality of health information shared on social media.

Recent advances in photocatalytic nanomaterials for environmental remediation: Strategies, mechanisms, and future directions.

Innovative and efficient strategies are urgently needed for wastewater treatment and environmental remediation. The photocatalytic degradation properties of photo-responsive nanomaterials (NMs) have become a prime candidate due to their low negative impact and photo-adjustability. Photocatalytic NMs vary in their degradation of different pollutants depending on the type of synthetic material, excitation light source, and physicochemical properties. Essentially, photocatalytic NMs excited by light produce reactive oxygen species (ROS) or metal ions that can degrade complex structure pollutants. Therefore, this review summarises the recent advances of photocatalytic NMs in the environmental application within the last 3 years, focusing on the development schemes, structural analyses, photocatalytic mechanisms, and the degradation effects of dyes, antibiotics, pesticides, phenolic compounds, metals, hormones, and other contaminants. The limitations and future directions are also explained. This review hopes to provide a possible pathway for the subsequent development of novel and efficient photocatalytic NMs to cope with complex and variable polluted environments.

Microalgal proteins: Unveiling sustainable alternatives to address the protein challenge.

Recent breakthroughs emphasized the considerable potential of microalgae as a sustainable protein source. Microalgae are regarded as a substitute for protein-rich foods because of their high protein and amino acid content. However, despite their nutritional value, microalgae cannot be easily digested by humans due to the presence of cell walls. In the subsequent sections, protein extraction technology, the overview of the inherent challenges of the process, and the summary of the factors affecting protein extraction and utilization have been deliberated. Moreover, the review inspected the formation of proteolytic products, highlighting their diverse bioactivities, including antioxidant, antihypertensive, and immunomodulatory activities. Finally, the discussion extended to the emerging microalgal protein sourced foods, such as baked goods and nutritional supplements, as well as the sensory and marketing challenges encountered in the production of microalgal protein foods. The lack of consumer awareness about the health benefits of microalgae complicates its acceptance in the market. Long-standing challenges, such as high production costs, persist. Currently, multi-product utilization strategies are being developed to improve the economic viability of microalgae. By integrating economic, environmental, and social factors, microalgae protein can be sustainably developed to provide a reliable source of raw materials for the future food industry.

Silencing of transient receptor potential canonical channel 4 inhibits endothelial progenitor cell angiogenesis by suppressing VEGF and SDF-1.

OBJECTIVES: Endothelial progenitor cells (EPCs) play a crucial role in acquired angiogenesis and endothelial injury repair. Transient receptor potential canonical channel 4 (TRPC4), a key component of store-operated calcium channels, is essential for EPC function. While the role of TRPCs has been clarified in vascular diseases, the relationship between TRPC4 and EPC function, along with the underlying molecular mechanisms, remains unclear and requires further elucidation. METHODS: EPCs were isolated from canine bone marrow and identified by morphology and flow cytometry. TRPC4 was transfected into EPCs using lentivirus or negative control, and its expression was assessed using real-time polymerase chain reaction (RT-PCR). Proliferation, migration, and tube formation were evaluated using Cell Counting Kit-8 (CCK-8), Transwell, and Matrigel assays, respectively. Levels of vascular endothelial growth factor (VEGF) and stromal cell-derived factor-1 (SDF-1) were measured using enzyme-linked immunosorbent assay (ELISA). RESULTS: TRPC4 mRNA expression was significantly reduced in TRPC4-short hairpin RNA (shRNA) transfected EPCs compared to the normal control (NC)-shRNA groups. Migration and tube formation were significantly decreased after TRPC4 silencing, while proliferation showed no difference. Additionally, levels of SDF-1 and VEGF in EPCs were markedly reduced following TRPC4 silencing. CONCLUSION: TRPC4 plays a crucial role in regulating angiogenesis in EPCs. Silencing of TRPC4 can lead to decreased angiogenesis by inhibiting VEGF and SDF-1 expression, suggesting that TRPC4 knockdown might be a novel therapeutic strategy for vascular diseases.

ALDH1A3-acetaldehyde metabolism potentiates transcriptional heterogeneity in melanoma.

Cancer cellular heterogeneity and therapy resistance arise substantially from metabolic and transcriptional adaptations, but how these are interconnected is poorly understood. Here, we show that, in melanoma, the cancer stem cell marker aldehyde dehydrogenase 1A3 (ALDH1A3) forms an enzymatic partnership with acetyl-coenzyme A (CoA) synthetase 2 (ACSS2) in the nucleus to couple high glucose metabolic flux with acetyl-histone H3 modification of neural crest (NC) lineage and glucose metabolism genes. Importantly, we show that acetaldehyde is a metabolite source for acetyl-histone H3 modification in an ALDH1A3-dependent manner, providing a physiologic function for this highly volatile and toxic metabolite. In a zebrafish melanoma residual disease model, an ALDH1-high subpopulation emerges following BRAF inhibitor treatment, and targeting these with an ALDH1 suicide inhibitor, nifuroxazide, delays or prevents BRAF inhibitor drug-resistant relapse. Our work reveals that the ALDH1A3-ACSS2 couple directly coordinates nuclear acetaldehyde-acetyl-CoA metabolism with specific chromatin-based gene regulation and represents a potential therapeutic vulnerability in melanoma.

Coping with the concurrent heatwaves and ozone extremes in China under a warming climate.

Intensive human activity has brought about unprecedented climate and environmental crises, in which concurrent heatwaves and ozone extremes pose the most serious threats. However, a limited understanding of the comprehensive mechanism hinders our ability to mitigate such compound events, especially in densely populated regions like China. Here, based on field observations and climate-chemistry coupled modelling, we elucidate the linkage between human activities and the climate system in heat-related ozone pollution. In China, we have observed that both the frequency and intensity of heatwaves have almost tripled since the beginning of this century. Moreover, these heatwaves are becoming more common in urban clusters with serious ozone pollution. Persistent heatwaves during the extremely hot and dry summers of 2013 and 2022 accelerated photochemical ozone production by boosting anthropogenic and biogenic emissions, and aggravated ozone accumulation by suppressing dry deposition due to water-stressed vegetation, leading to a more than 30% increase in ozone pollution in China's urban areas. The sensitivity of ozone to heat is demonstrated to be substantially modulated by anthropogenic emissions, and China's clean air policy may have altered the relationship between ozone and temperature. Climate model projections further highlight that the high-emission climate-socioeconomic scenario tends to intensify the concurrent heat and ozone extremes in the next century. Our results underscore that the implementation of a strict emission strategy will significantly reduce the co-occurrence of heatwaves and ozone extremes, achieving climate and environmental co-benefits.

PneumoLLM: Harnessing the power of large language model for pneumoconiosis diagnosis.

The conventional pretraining-and-finetuning paradigm, while effective for common diseases with ample data, faces challenges in diagnosing data-scarce occupational diseases like pneumoconiosis. Recently, large language models (LLMs) have exhibits unprecedented ability when conducting multiple tasks in dialogue, bringing opportunities to diagnosis. A common strategy might involve using adapter layers for vision-language alignment and diagnosis in a dialogic manner. Yet, this approach often requires optimization of extensive learnable parameters in the text branch and the dialogue head, potentially diminishing the LLMs' efficacy, especially with limited training data. In our work, we innovate by eliminating the text branch and substituting the dialogue head with a classification head. This approach presents a more effective method for harnessing LLMs in diagnosis with fewer learnable parameters. Furthermore, to balance the retention of detailed image information with progression towards accurate diagnosis, we introduce the contextual multi-token engine. This engine is specialized in adaptively generating diagnostic tokens. Additionally, we propose the information emitter module, which unidirectionally emits information from image tokens to diagnosis tokens. Comprehensive experiments validate the superiority of our methods.

Exploring ALK fusion in colorectal cancer: a case series and comprehensive analysis.

Anaplastic lymphoma kinase (ALK) fusion-positive colorectal cancer (CRC) is a rare and chemotherapy-refractory subtype that lacks established and effective treatment strategies. Additionally, the efficacy and safety of ALK inhibitors (ALKi) in CRC remain undetermined. Herein, we examined a series of ALK-positive CRC patients who underwent various lines of ALKi treatment. Notably, we detected an ALK 1196M resistance mutation in a CRC patient who received multiple lines of chemotherapy and ALKi treatment. Importantly, we found that Brigatinib and Lorlatinib demonstrated some efficacy in managing this patient, although the observed effectiveness was not as pronounced as in non-small cell lung cancer cases. Furthermore, based on our preliminary analyses, we surmise that ALK-positive CRC patients are likely to exhibit inner resistance to Cetuximab. Taken together, our findings have important implications for the treatment of ALK-positive CRC patients.

A clinical-radiomics nomogram based on dual-layer spectral detector CT to predict cancer stage in pancreatic ductal adenocarcinoma.

BACKGROUND: This study aimed to evaluate the efficacy of radiomics signatures derived from polyenergetic images (PEIs) and virtual monoenergetic images (VMIs) obtained through dual-layer spectral detector CT (DLCT). Moreover, it sought to develop a clinical-radiomics nomogram based on DLCT for predicting cancer stage (early stage: stage I-II, advanced stage: stage III-IV) in pancreatic ductal adenocarcinoma (PDAC). METHODS: A total of 173 patients histopathologically diagnosed with PDAC and who underwent contrast-enhanced DLCT were enrolled in this study. Among them, 49 were in the early stage, and 124 were in the advanced stage. Patients were randomly categorized into training (n = 122) and test (n = 51) cohorts at a 7:3 ratio. Radiomics features were extracted from PEIs and 40-keV VMIs were reconstructed at both arterial and portal venous phases. Radiomics signatures were constructed based on both PEIs and 40-keV VMIs. A radiomics nomogram was developed by integrating the 40-keV VMI-based radiomics signature with selected clinical predictors. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curves analysis (DCA). RESULTS: The PEI-based radiomics signature demonstrated satisfactory diagnostic efficacy, with the areas under the ROC curves (AUCs) of 0.92 in both the training and test cohorts. The optimal radiomics signature was based on 40-keV VMIs, with AUCs of 0.96 and 0.94 in the training and test cohorts. The nomogram, which integrated a 40-keV VMI-based radiomics signature with two clinical parameters (tumour diameter and normalized iodine density at the portal venous phase), demonstrated promising calibration and discrimination in both the training and test cohorts (0.97 and 0.91, respectively). DCA indicated that the clinical-radiomics nomogram provided the most significant clinical benefit. CONCLUSIONS: The radiomics signature derived from 40-keV VMI and the clinical-radiomics nomogram based on DLCT both exhibited exceptional performance in distinguishing early from advanced stages in PDAC, aiding clinical decision-making for patients with this condition.

Characterisation and critical processes identification for production of herbal preparations using 1H-NMR and chemometrics: A case study of Trichosanthis Pericarpium injection.

INTRODUCTION: Herbal preparations are extensively utilised for the treatment of diseases in Asian countries. However, the variations in origin, climate, and production processes can lead to inconsistencies in the quality of herbal preparations. Existing quality control methods only target a few components in the finished product but ignore the control in the pharmaceutical process. Therefore, this study intends to develop a comprehensive component analysis method for intermediates in the pharmaceutical process to reveal the change patterns of substances and deepen the process understanding. OBJECTIVE: This study aims to develop a rapid and comprehensive process characterisation and critical process identification method for herbal preparations. METHODS: Six batches of Trichosanthis Pericarpium injection (TPI) intermediates were collected from the production process. Proton nuclear magnetic resonance (1H-NMR) spectra were acquired for qualitative and quantitative analysis of the se intermediates. Subsequently, chemometrics were used to identify critical processes and potential chemical markers. RESULTS: A total of 39 components in intermediates were identified, and the transfer of 25 components during the production process was investigated. Column chromatography was determined as the critical process. Nine components were identified as chemical markers. CONCLUSION: The application of 1H-NMR facilitated a comprehensive reflection of the chemical composition information of process intermediates, enabling investigations into the transfer of multi-component substances and accurate identification of critical processes and chemical markers.

Uptake, accumulation, translocation and transformation of seneciphylline (Sp) and seneciphylline-N-oxide (SpNO) by Camellia sinensis L.

Pyrrolizidine alkaloids (PAs) and their N-oxide (PANOs), as emerging environmental pollutants and chemical hazards in food, have become the focus of global attention. PAs/PANOs enter crops from soil and reach edible parts, but knowledge about their uptake and transport behavior in crops is currently limited. In this study, we chose tea (Camellia sinensis L.) as a representative crop and Sp/SpNO as typical PAs/PANOs to analyze their root uptake and transport mechanism. Tea roots efficiently absorbed Sp/SpNO, utilizing both passive and active transmembrane pathways. Sp predominantly concentrated in roots and SpNO efficiently translocated to above-ground parts. The prevalence of SpNO in cell-soluble fractions facilitated its translocation from roots to stems and leaves. In soil experiment, tea plants exhibited weaker capabilities for the uptake and transport of Sp/SpNO compared to hydroponic conditions, likely due to the swift degradation of these compounds in the soil. Moreover, a noteworthy interconversion between Sp and SpNO in tea plants indicated a preference for reducing SpNO to Sp. These findings represent a significant stride in understanding the accumulation and movement mechanisms of Sp/SpNO in tea plants. The insights garnered from this study are pivotal for evaluating the associated risks of PAs/PANOs and formulating effective control strategies.

Enhanced properties of a positive-charged nanofiltration membrane containing quaternarized chitosan through second interfacial polymerization for the removal of salts and pharmaceuticals.

Nanofiltration (NF) membrane technology has been widely used in the removal of salts and trace organic pollutants, such as pharmaceuticals and personal care products (PPCPs), due to its superiority. A positive-charged composite NF membrane with an active skin layer was prepared by polyethyleneimine (PEI), trimethyl benzene chloride, and quaternate chitosan (HTCC) through second interfacial polymerization on the polyethersulfone ultrafiltration membrane. The physicochemical properties of the nanocomposite membrane were investigated using surface morphology, hydrophilicity, surface charge, and molecular weight cut-off (MWCO). The influence of the concentration and reaction time of PEI and HTCC was documented. The optimized membrane had a MWCO of about 481 Da and possessed a pure water permeability of 25.37 L·m-2·h-1·MPa-1. The results also exhibited salt rejection ability as MgCl2 > CaCl2 > MgSO4 > Na2SO4 > NaCl > KCl, showing a positive charge on the fabricated membrane. In addition, the membrane had higher rejection to atenolol, carbamazepine, amlodipine, and ibuprofen at 89.46, 86.02, 90.12, and 77.21%, respectively. Moreover, the anti-fouling performance and stability of the NF membrane were also improved.

Development of a Nomogram Model to Predict the Risk of Stricture Recurrence after Urethroplasty: A Retrospective Study.

OBJECTIVE: A retrospective study was performed to analyse the influencing factors of stricture recurrence after urethroplasty and to establish a predictive nomogram model. METHODS: The clinical data of patients who underwent urethroplasty in our hospital from January 2021 to June 2023 were retrospectively analysed. Depending on whether stenosis occurs six months after surgery, the patients were divided into recurrence and nonrecurrence groups. Logistic regression analysis was performed on the indicators with statistically significant differences between the two groups in single factor analysis to analyse the influencing factors of postoperative recurrence risk of stricture. X64.4.1.3 version R language and external source packages were used to build the nomogram model. The nomogram was internally validated through 10-fold cross-validation, and C-index was calculated. The area under the curve (AUC) of the receiver operating characteristic curve was employed to evaluate the results of the internal validation. RESULTS: Amongst 105 patients who underwent urethroplasty in our hospital, 15 patients with recurrence were included in the recurrence group, and 90 patients without recurrence were included in the nonrecurrence group. The length of stricture segment, history of urethroplasty and smoking history within 3 months before surgery were risk factors for stricture recurrence, with odds ratio (OR) values of 1.874 (95% CI: 1.103-5.725), 1.670 (95% CI: 1.105-2.904) and 1.740 (95% CI: 1.456-5.785), respectively. The constructed nomogram obtained an average AUC of 0.842 and an average C-index of 0.794, calculated after 200 times of 10-fold cross-validation. CONCLUSIONS: From the data of this study, it can be deduced that the influencing factors of stricture recurrence after urethroplasty include the length of stricture segment, history of urethroplasty and smoking history of 3 months before surgery. Using the above factors as a basis to construct a predictive nomogram model is helpful to screen high-risk patients with recurrence of stricture after urethroplasty.