Leaching of per- and polyfluoroalkyl substances (PFAS) from food contact materials with implications for waste disposal.

Leaching of per- and polyfluoroalkyl substances (PFAS) during the post-consumer disposal of food contact materials (FCMs) poses a potential environmental threat but has seldom been evaluated. This study characterized the leaching behavior of PFAS and unidentified precursors from six common FCMs and assessed the impact of environmental conditions on PFAS release during disposal. The total concentration of 21 PFAS ranged from 3.2 to 377 ng/g in FCMs, with PFAS leachability into water varying between 1.1-42.8 %. Increasing temperature promoted PFAS leaching, with leached nine primary PFAS (∑9PFAS) reaching 46.3, 70.4, and 102 ng/L at 35, 45, and 55 â„ƒ, respectively. Thermodynamic analysis (∆G>0, ∆H>0, and ∆S<0) indicated hydrophobic interactions control PFAS leaching. The presence of dissolved organic matter in synthetic leachate increased the leached ∑9PFAS from 47.1 to 103 ng/L but decreased PFBS, PFOS, and 6:2 FTS leaching. The total release of seven perfluorocarboxylic acids (∑7PFCAs) from takeaway food packaging waste was estimated to be 0.3-8.2 kg/y to landfill leachate and 0.6-15.4 kg/y to incineration plant leachate, contributing 0.2-4.8 % and 0.1-3.2 % of total ∑7PFCAs in each leachate type. While the study presents a refined methodology for estimating PFAS release during disposal, future research is needed on the indirect contribution from precursors.

Heterotrophic nitrification processes driven by glucose and sodium acetate: New insights into microbial communities, functional genes and nitrogen metabolism from metagenomics and metabolomics.

Heterotrophic nitrification (HN) bacteria use organic carbon sources to remove ammonia nitrogen (NH4+-N); however, the mechanisms of carbon and nitrogen metabolism are unknown. To understand this mechanism, HN functional microbial communities named MG and MA were enriched with glucose and sodium acetate, respectively. The NH4+-N removal efficiencies were 98.87 % and 98.91 %, with 88.06 % and 69.77 % nitrogen assimilation for MG and MA at 22 h and 10 h, respectively. Fungi (52.86 %) were more competitive in MG, and bacteria (99.99 %) were dominant in MA. Metagenomic and metabolomic analyses indicated that HN might be a signaling molecule (NO) in the production and detoxification processes when MG metabolizes glucose (amo, hao, and nosZ were not detected). MA metabolizes sodium acetate to produce less energy and promotes nitrogen oxidation reduction; however, genes (hao, hox, and NOS2) were not detected. These results suggest that NO and energy requirements induce microbial HN.

Intensified effect of nitrogen forms on dominant phytoplankton species succession by climate change.

Nutrient proportion, light intensity, and temperature affect the succession of dominant phytoplankton species. Despite these insights, this transformation mechanism in highly turbid lakes remains a research gap, especially in response to climate change. To fill this gap, we investigated the mechanism by which multi-environmental factors influence the succession of dominant phytoplankton species in Lake Chagan. This investigation deployed the structural equation model (SEM) and the hydrodynamic-water quality-water ecology mechanism model. Results demonstrated that the dominant phytoplankton species in Lake Chagan transformed from diatom to cyanobacteria during 2012 and 2022. Notably, Microcystis was detected in 2022. SEM revealed the primary environment variables for this succession, including water temperature (Tw), nutrients (total nitrogen (TN), total phosphorus (TP), and ammonia nitrogen (NH4N)), and total suspended solids (TSS). Moreover, this event was not the consequence of zooplankton grazing. An integrated hydrodynamic-water quality-bloom mechanism model was built to explore the mechanism driving phytoplankton succession and its response to climate change. Nutrients determined the phytoplankton biomass and dominant species succession based on various proportions. High NH4N:NO3N ratios favored cyanobacteria and inhibited diatom under high TSS. Additionally, the biomass proportions of diatom (30.77 % vs. 22.28 %) and green (30.56 % vs. 23.30 %) decreased dramatically. In contrast, cyanobacteria abundance remarkably increased (35.78 % to 51.71 %) with the increasing NH4-N:NO3-N ratios. In addition, the proportion of non-nitrogen-fixing cyanobacteria was higher than that of the nitrogen-fixing cyanobacteria counterparts when TN:TP≥20 and NH4N:NO3N ≥ 10. Light-limitation phenotypes also experienced an increase with the rising NH4N:NO3N ratios. Notably, the cyanobacterial biomass reached 3-6 times that in the baseline scenario when the air temperature escalated by 3.0 °C until 2061 under the SSP585 scenario. We highlighted the effect of nitrogen forms on the succession of dominant phytoplankton species. Climate warming will increase nitrogen proportion, providing an insightful reference for controlling cyanobacterial blooms.

Hirudin delays the progression of diabetic kidney disease by inhibiting glomerular endothelial cell migration and abnormal angiogenesis.

BACKGROUND: In the early stages of diabetic kidney disease (DKD), the pathogenesis involves abnormal angiogenesis in the glomerulus. Hirudin, as a natural specific inhibitor of thrombin, has been shown in previous studies to inhibit the migration of various tumor endothelial cells and abnormal angiogenesis. However, its role in DKD remains unclear. METHODS: The effects of hirudin in DKD were studied using spontaneous type 2 diabetic db/db mice (which develop kidney damage at 8 weeks). Network pharmacology was utilized to identify relevant targets. An in vitro high glucose model was established using mouse glomerular endothelial cells (MGECs) to investigate the effects of hirudin on the migration and angiogenic capacity of MGECs. RESULTS: Hirudin can ameliorate kidney damage in db/db mice. Network pharmacology suggests its potential association with the VEGFA/VEGFR2 pathway. Western blot and immunohistochemistry demonstrated elevated protein expression levels of VEGFA, VEGFR2, AQP1, and CD31 in db/db mice, while hirudin treatment reduced their expression. In the MGECs high glucose model, hirudin may reverse the enhanced migration and angiogenic capacity of MGECs in a high glucose environment by altering the expression of VEGFA, VEGFR2, AQP1, and CD31. Moreover, the drug effect gradually increases with higher concentrations of hirudin. CONCLUSIONS: This study suggests that hirudin can improve early-stage diabetic kidney disease kidney damage by inhibiting the migration and angiogenesis of glomerular endothelial cells, thereby further expanding the application scope of hirudin. Additionally, the study found increased expression of AQP1 in DKD, providing a new perspective for further research on the potential pathogenesis of DKD.

Olanzapine Modulate Lipid Metabolism and Adipose Tissue Accumulation via Hepatic Muscarinic M3 Receptor-Mediated Alk-Related Signaling.

Olanzapine is an atypical antipsychotic drug and a potent muscarinic M3 receptor (M3R) antagonist. Olanzapine has been reported to cause metabolic disorders, including dyslipidemia. Anaplastic lymphoma kinase (Alk), a tyrosine kinase receptor well known in the pathogenesis of cancer, has been recently identified as a key gene in the regulation of thinness via the regulation of adipose tissue lipolysis. This project aimed to investigate whether Olanzapine could modulate the hepatic Alk pathway and lipid metabolism via M3R. Female rats were treated with Olanzapine and/or Cevimeline (an M3R agonist) for 9 weeks. Lipid metabolism and hepatic Alk signaling were analyzed. Nine weeks' treatment of Olanzapine caused metabolic disturbance including increased body mass index (BMI), fat mass accumulation, and abnormal lipid metabolism. Olanzapine treatment also led to an upregulation of Chrm3, Alk, and its regulator Ptprz1, and a downregulation of Lmo4, a transcriptional repressor of Alk in the liver. Moreover, there were positive correlations between Alk and Chrm3, Alk and Ptprz1, and a negative correlation between Alk and Lmo4. However, cotreatment with Cevimeline significantly reversed the lipid metabolic disturbance and adipose tissue accumulation, as well as the upregulation of the hepatic Alk signaling caused by Olanzapine. This study demonstrates evidence that Olanzapine may cause metabolic disturbance by modulating hepatic Alk signaling via M3R, which provides novel insight for modulating the hepatic Alk signaling and potential interventions for targeting metabolic disorders.

Constructing a prognostic model for hepatocellular carcinoma based on bioinformatics analysis of inflammation-related genes.

Background: This study aims to screen inflammation-related genes closely associated with the prognosis of hepatocellular carcinoma (HCC) to accurately forecast the prognosis of HCC patients. Methods: Gene expression matrices and clinical information for liver cancer samples were obtained from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). An intersection of differentially expressed genes of HCC and normal and GeneCards yielded inflammation-related genes associated with HCC. Cox regression and the minor absolute shrinkage and selection operator (LASSO) regression analysis to filter genes associated with HCC prognosis. The prognostic value of the model was confirmed by drawing Kaplan-Meier and ROC curves. Select differentially expressed genes between the high-risk and low-risk groups and perform GO and KEGG pathways analyses. CIBERSORT analysis was conducted to assess associations of risk models with immune cells and verified using real-time qPCR. Results: A total of six hub genes (C3, CTNNB1, CYBC1, DNASE1L3, IRAK1, and SERPINE1) were selected using multivariate Cox regression to construct a prognostic model. The validation evaluation of the prognostic model showed that it has an excellent ability to predict prognosis. A line plot was drawn to indicate the HCC patients' survival, and the calibration curve revealed satisfactory predictability. Among the six hub genes, C3 and DNASE1L3 are relatively low expressed in HCCLM3 and 97H liver cancer cell lines, while CTNNB1, CYBC1, IRAK1, and SERPINE1 are relatively overexpressed in liver cancer cell lines. Conclusion: One new inflammatory factor-associated prognostic model was constructed in this study. The risk score can be an independent predictor for judging the prognosis of HCC patients' survival.

Inflammatory microenvironment of moderate pulpitis enhances the osteo-/odontogenic potential of dental pulp stem cells by autophagy.

AIM: This study investigated the effects of the inflammatory microenvironment of moderate pulpitis on biological properties of human dental pulp stem cells (DPSCs) and further explored the mechanism involved in osteo-/odontogenic induction of the inflammatory microenvironment. METHODOLOGY: Healthy DPSCs (hDPSCs) and inflammatory DPSCs (iDPSCs) were isolated from human-impacted third molars free of caries and clinically diagnosed with moderate pulpitis, respectively. Healthy DPSCs were treated with lipopolysaccharides (LPS) to mimic iDPSCs in vitro. The surface markers expressed on hDPSCs and iDPSCs were detected by flow cytometry. A CCK-8 assay was performed to determine cell proliferation. Flow cytometric analysis was used to evaluate cell apoptosis. The osteo-/odontogenic differentiation of DPSCs was evaluated by western blot, alkaline phosphatase staining, and Alizarin Red S staining. The functions of the genes of differentially expressed mRNAs of hDPSCs and iDPSCs were analysed using gene set enrichment analysis. Transmission electron microscopy and western blot were used to evaluate the autophagy changes of LPS-treated DPSCs. RESULTS: Compared with hDPSCs, iDPSCs showed no significant difference in proliferative capacity but had stronger osteo-/odontogenic potential. In addition, the mRNAs differentially expressed between iDPSCs and hDPSCs were considerably enriched in autophagosome formation and assembly-related molecules. In vitro mechanism studies further found that low concentrations of LPS could upregulate DPSC autophagy-related protein expression and autophagosome formation and promote its odontogenic/osteogenic differentiation, whereas the inhibition of DPSC autophagy led to the weakening of the odontogenic/osteogenic differentiation induced by LPS. CONCLUSIONS: This explorative study showed that DPSCs isolated from teeth with moderate pulpitis possessed higher osteo-/odontogenic differentiation capacity, and the mechanism involved was related to the inflammatory microenvironment-mediated autophagy of DPSCs. This helps to better understand the repair potential of inflamed dental pulp and provides the biological basis for pulp preservation and hard tissue formation in minimally invasive endodontics.

Low-Dose Interleukin-2 Reverses Traumatic Brain Injury-Induced Cognitive Deficit and Pain in a Murine Model.

OBJECTIVE: Despite the high prevalence, mild traumatic brain injury (mTBI)-induced chronic headache and cognitive deficits are poorly understood and lack effective treatments. Low-dose interleukin-2 (LD-IL-2) treatment soon after mTBI or overexpressing IL-2 in brain astrocytes prior to injury protects mice from developing post-traumatic headache (PTH)-related behaviors and cognitive decline. The present study addresses a clinically relevant knowledge gap: whether LD-IL-2 treatment long after the initial injury is still effective for chronic PTH and cognitive deficits. METHODS: mTBI was induced by a noninvasive closed-head weight drop method. LD-IL-2 was administered 4-6 weeks post-mTBI to assess its effects on chronic PTH-related facial mechanical hypersensitivity as well as mTBI-induced impairment in novel object recognition and object location tests. Endogenous regulatory T (Treg) cells were depleted to investigate the mechanism of action of LD-IL-2. RESULTS: Delayed LD-IL-2 treatment abolished chronic PTH-related behaviors. It also completely reversed mTBI-induced cognitive impairment in both male and female mice. Treg cell depletion not only prolonged PTH-related behaviors but also abolished the effects of LD-IL-2. Interestingly, LD-IL-2 treatment significantly increased the number of Treg cells in dura but not in brain tissues. INTERPRETATION: These results suggest that the beneficial effects of LD-IL-2 treatment are mediated through the expansion of meningeal Treg cells. Collectively, our study identifies Treg as a cellular target and LD-IL-2 as a promising therapy for both chronic PTH and mTBI-induced cognitive impairment for both males and females, with a wide therapeutic time window and the potential of reducing polypharmacy in mTBI treatment. ANN NEUROL 2024;96:508-525.

A preliminary probabilistic nomogram model for predicting renal arteriolar damage in IgA nephropathy from clinical parameters.

Background: IgA nephropathy (IgAN) is a significant contributor to chronic kidney disease (CKD). Renal arteriolar damage is associated with IgAN prognosis. However, simple tools for predicting arteriolar damage of IgAN remain limited. We aim to develop and validate a nomogram model for predicting renal arteriolar damage in IgAN patients. Methods: We retrospectively analyzed 547 cases of biopsy-proven IgAN patients. Least absolute shrinkage and selection operator (LASSO) regression and logistic regression were applied to screen for factors associated with renal arteriolar damage in patients with IgAN. A nomogram was developed to evaluate the renal arteriolar damage in patients with IgAN. The performance of the proposed nomogram was evaluated based on a calibration plot, ROC curve (AUC) and Harrell's concordance index (C-index). Results: In this study, patients in the arteriolar damage group had higher levels of age, mean arterial pressure (MAP), serum creatinine, serum urea nitrogen, serum uric acid, triglycerides, proteinuria, tubular atrophy/interstitial fibrosis (T1-2) and decreased eGFR than those without arteriolar damage. Predictors contained in the prediction nomogram included age, MAP, eGFR and serum uric acid. Then, a nomogram model for predicting renal arteriolar damage was established combining the above indicators. Our model achieved well-fitted calibration curves and the C-indices of this model were 0.722 (95%CI 0.670-0.774) and 0.784 (95%CI 0.716-0.852) in the development and validation groups, respectively. Conclusion: With excellent predictive abilities, the nomogram may be a simple and reliable tool to predict the risk of renal arteriolar damage in patients with IgAN.

Distinct roles of H3K27me3 and H3K36me3 in vernalization response, maintenance, and resetting in winter wheat.

Winter plants rely on vernalization, a crucial process for adapting to cold conditions and ensuring successful reproduction. However, understanding the role of histone modifications in guiding the vernalization process in winter wheat remains limited. In this study, we investigated the transcriptome and chromatin dynamics in the shoot apex throughout the life cycle of winter wheat in the field. Two core histone modifications, H3K27me3 and H3K36me3, exhibited opposite patterns on the key vernalization gene VERNALIZATION1 (VRN1), correlating with its induction during cold exposure. Moreover, the H3K36me3 level remained high at VRN1 after cold exposure, which may maintain its active state. Mutations in FERTILIZATION-INDEPENDENT ENDOSPERM (TaFIE) and SET DOMAIN GROUP 8/EARLY FLOWERING IN SHORT DAYS (TaSDG8/TaEFS), components of the writer complex for H3K27me3 and H3K36me3, respectively, affected flowering time. Intriguingly, VRN1 lost its high expression after the cold exposure memory in the absence of H3K36me3. During embryo development, VRN1 was silenced with the removal of active histone modifications in both winter and spring wheat, with selective restoration of H3K27me3 in winter wheat. The mutant of Tafie-cr-87, a component of H3K27me3 "writer" complex, did not influence the silence of VRN1 during embryo development, but rather attenuated the cold exposure requirement of winter wheat. Integrating gene expression with H3K27me3 and H3K36me3 patterns identified potential regulators of flowering. This study unveils distinct roles of H3K27me3 and H3K36me3 in controlling vernalization response, maintenance, and resetting in winter wheat.

Derivation and validation of the first web-based nomogram to predict the spontaneous pregnancy after reproductive surgery using machine learning models.

Objective: Infertility remains a significant global burden over the years. Reproductive surgery is an effective strategy for infertile women. Early prediction of spontaneous pregnancy after reproductive surgery is of high interest for the patients seeking the infertility treatment. However, there are no high-quality models and clinical applicable tools to predict the probability of natural conception after reproductive surgery. Methods: The eligible data involving 1013 patients who operated for infertility between June 2016 and June 2021 in Yantai Yuhuangding Hospital in China, were randomly divided into training and internal testing cohorts. 195 subjects from the Linyi People's Hospital in China were considered for external validation. Both univariate combining with multivariate logistic regression and the least absolute shrinkage and selection operator (LASSO) algorithm were performed to identify independent predictors. Multiple common machine learning algorithms, namely logistic regression, decision tree, random forest, support vector machine, k-nearest neighbor, and extreme gradient boosting, were employed to construct the predictive models. The optimal model was verified by evaluating the model performance in both the internal and external validation datasets. Results: Six clinical indicators, including female age, infertility type, duration of infertility, intraoperative diagnosis, ovulation monitoring, and anti-Müllerian hormone (AMH) level, were screened out. Based on the logistic regression model's superior clinical predictive value, as indicated by the area under the receiver operating characteristic curve (AUC) in both the internal (0.870) and external (0.880) validation sets, we ultimately selected it as the optimal model. Consequently, we utilized it to generate a web-based nomogram for predicting the probability of spontaneous pregnancy after reproductive surgery. Furthermore, the calibration curve, Hosmer-Lemeshow (H-L) test, the decision curve analysis (DCA) and clinical impact curve analysis (CIC) demonstrated that the model has superior calibration degree, clinical net benefit and generalization ability, which were confirmed by both internal and external validations. Conclusion: Overall, our developed first nomogram with online operation provides an early and accurate prediction for the probability of natural conception after reproductive surgery, which helps clinicians and infertile couples make sensible decision of choosing the mode of subsequent conception, natural or IVF, to further improve the clinical practices of infertility treatment.

A Prototype of Graphene E-Nose for Exhaled Breath Detection and Label-Free Diagnosis of Helicobacter Pylori Infection.

Helicobacter pylori (HP), a common microanaerobic bacteria that lives in the human mouth and stomach, is reported to infect ≈50% of the global population. The current diagnostic methods for HP are either invasive, time-consuming, or harmful. Therefore, a noninvasive and label-free HP diagnostic method needs to be developed urgently. Herein, reduced graphene oxide (rGO) is composited with different metal-based materials to construct a graphene-based electronic nose (e-nose), which exhibits excellent sensitivity and cross-reactive response to several gases in exhaled breath (EB). Principal component analysis (PCA) shows that four typical types of gases in EB can be well discriminated. Additionally, the potential of the e-nose in label-free detection of HP infection is demonstrated through the measurement and analysis of EB samples. Furthermore, a prototype of an e-nose device is designed and constructed for automatic EB detection and HP diagnosis. The accuracy of the prototype machine integrated with the graphene-based e-nose can reach 92% and 91% in the training and validation sets, respectively. These results demonstrate that the highly sensitive graphene-based e-nose has great potential for the label-free diagnosis of HP and may become a novel tool for non-invasive disease screening and diagnosis.

Improving denitrification estimation by joint inclusion of suspended particles and chlorophyll a in aquaculture ponds.

The denitrification process in aquaculture systems plays a crucial role in nitrogen (N) cycle and N budget estimation. Reliable models are needed to rapidly quantify denitrification rates and assess nitrogen losses. This study conducted a comparative analysis of denitrification rates in fish, crabs, and natural ponds in the Taihu region from March to November 2021, covering a complete aquaculture cycle. The results revealed that aquaculture ponds exhibited higher denitrification rates compared to natural ponds. Key variables influencing denitrification rates were Nitrate nitrogen (NO3--N), Suspended particles (SPS), and chlorophyll a (Chla). There was a significant positive correlation between SPS concentration and denitrification rates. However, we observed that the denitrification rate initially rose with increasing Chla concentration, followed by a subsequent decline. To develop parsimonious models for denitrification rates in aquaculture ponds, we constructed five different statistical models to predict denitrification rates, among which the improved quadratic polynomial regression model (SQPR) that incorporated the three key parameters accounted for 80.7% of the variability in denitrification rates. Additionally, a remote sensing model (RSM) utilizing SPS and Chla explained 43.8% of the variability. The RSM model is particularly valuable for rapid estimation in large regions where remote sensing data are the only available source. This study enhances the understanding of denitrification processes in aquaculture systems, introduces a new model for estimating denitrification in aquaculture ponds, and offers valuable insights for environmental management.

Structural and functional insights into the 2'-O-methyltransferase of SARS-CoV-2.

A unique feature of coronaviruses is their utilization of self-encoded nonstructural protein 16 (nsp16), 2'-O-methyltransferase (2'-O-MTase), to cap their RNAs through ribose 2'-O-methylation modification. This process is crucial for maintaining viral genome stability, facilitating efficient translation, and enabling immune escape. Despite considerable advances in the ultrastructure of SARS-CoV-2 nsp16/nsp10, insights into its molecular mechanism have so far been limited. In this study, we systematically characterized the 2'-O-MTase activity of nsp16 in SARS-CoV-2, focusing on its dependence on nsp10 stimulation. We observed cross-reactivity between nsp16 and nsp10 in various coronaviruses due to a conserved interaction interface. However, a single residue substitution (K58T) in SARS-CoV-2 nsp10 restricted the functional activation of MERS-CoV nsp16. Furthermore, the cofactor nsp10 effectively enhanced the binding of nsp16 to the substrate RNA and the methyl donor S-adenosyl-l-methionine (SAM). Mechanistically, His-80, Lys-93, and Gly-94 of nsp10 interacted with Asp-102, Ser-105, and Asp-106 of nsp16, respectively, thereby effectively stabilizing the SAM binding pocket. Lys-43 of nsp10 interacted with Lys-38 and Gly-39 of nsp16 to dynamically regulate the RNA binding pocket and facilitate precise binding of RNA to the nsp16/nsp10 complex. By assessing the conformational epitopes of nsp16/nsp10 complex, we further determined the critical residues involved in 2'-O-MTase activity. Additionally, we utilized an in vitro biochemical platform to screen potential inhibitors targeting 2'-O-MTase activity. Overall, our results significantly enhance the understanding of viral 2'-O methylation process and mechanism, providing valuable targets for antiviral drug development.

High-precision Helicobacter pylori infection diagnosis using a dual-element multimodal gas sensor array.

Helicobacter pylori (H. pylori) is a globally widespread bacterial infection. Early diagnosis of this infection is vital for public and individual health. Prevalent diagnosis methods like the isotope 13C or 14C labelled urea breath test (UBT) are not convenient and may do harm to the human body. The use of cross-response gas sensor arrays (GSAs) is an alternative way for label-free detection of metabolite changes in exhaled breath (EB). However, conventional GSAs are complex to prepare, lack reliability, and fail to discriminate subtle changes in EB due to the use of numerous sensing elements and single dimensional signal. This work presents a dual-element multimodal GSA empowered with multimodal sensing signals including conductance (G), capacitance (C), and dissipation factor (DF) to improve the ability for gas recognition and H. pylori-infection diagnosis. Sensitized by poly(diallyldimethylammonium chloride) (PDDA) and the metal-organic framework material NH2-UiO66, the dual-element graphene oxide (GO)-composite GSAs exhibited a high specific surface area and abundant adsorption sites, resulting in high sensitivity, repeatability, and fast response/recovery speed in all three signals. The multimodal sensing signals with rich sensing features allowed the GSA to detect various physicochemical properties of gas analytes, such as charge transfer and polarization ability, enhancing the sensing capabilities for gas discrimination. The dual-element GSA could differentiate different typical standard gases and non-dehumidified EB samples, demonstrating the advantages in EB analysis. In a case-control clinical study on 52 clinical EB samples, the diagnosis model based on the multimodal GSA achieved an accuracy of 94.1%, a sensitivity of 100%, and a specificity of 90.9% for diagnosing H. pylori infection, offering a promising strategy for developing an accurate, non-invasive and label-free method for disease diagnosis.

Medication burden in patients with dialysis-dependent CKD: a systematic review.

This systematic review aimed to statistically profile the medication burden and associated influencing factors, and outcomes in patients with dialysis-dependent chronic kidney disease (DD-CKD). Studies of medication burden in patients with DD-CKD in the last 10 years from 1 January 2013 to 31 March 2024 were searched from PubMed, Embase, and Cochrane databases. Newcastle-Ottawa Scale (NOS) or Agency for Healthcare Research and Quality (AHRQ) methodology checklist was used to evaluate quality and bias. Data extraction and combining from multiple groups of number (n), mean, and standard deviation (SD) were performed using R programming language (version4.3.1; R Core Team, Vienna, Austria). A total of 10 studies were included, and the results showed a higher drug burden in patients with DD-CKD. The combined pill burden was 14.57 ± 7.56 per day in hemodialysis (HD) patients and 14.63 ± 6.32 in peritoneal dialysis (PD) patients. The combined number of medications was 9.74 ± 3.37 in HD and 8 ± 3 in PD. Four studies described the various drug classes and their proportions, in general, antihypertensives and phosphate binders were the most commonly used drugs. Five studies mentioned factors associated with medication burden. A total of five studies mentioned medication burden-related outcomes, with one study finding that medication-related burden was associated with increased treatment burden, three studies finding that poor medication adherence was associated with medication burden, and another study finding that medication complexity was not associated with self-reported medication adherence. Limitations: meta-analysis was not possible due to the heterogeneity of studies.

Intelligent extraction of reservoir dispatching information integrating large language model and structured prompts.

Reservoir dispatching regulations are a crucial basis for reservoir operation, and using information extraction technology to extract entities and relationships from heterogeneous texts to form triples can provide structured knowledge support for professionals in making dispatch decisions and intelligent recommendations. Current information extraction technologies require manual data labeling, consuming a significant amount of time. As the number of dispatch rules increases, this method cannot meet the need for timely generation of dispatch plans during emergency flood control periods. Furthermore, utilizing natural language prompts to guide large language models in completing reservoir dispatch extraction tasks also presents challenges of cognitive load and instability in model output. Therefore, this paper proposes an entity and relationship extraction method for reservoir dispatch based on structured prompt language. Initially, a variety of labels are refined according to the extraction tasks, then organized and defined using the Backus-Naur Form (BNF) to create a structured format, thus better guiding large language models in the extraction work. Moreover, an AI agent based on this method has been developed to facilitate operation by dispatch professionals, allowing for the quick acquisition of structured data. Experimental verification has shown that, in the task of extracting entities and relationships for reservoir dispatch, this AI agent not only effectively reduces cognitive burden and the impact of instability in model output but also demonstrates high extraction performance (with F1 scores for extracting entities and relationships both above 80%), offering a new solution approach for knowledge extraction tasks in other water resource fields.

Mechanistic insights into zinc oxide nanoparticles induced embryotoxicity via H3K9me3 modulation.

The widespread application of nanoparticles (NPs) in various fields has raised health concerns, especially in reproductive health. Our research has shown zinc oxide nanoparticles (ZnONPs) exhibit the most significant toxicity to pre-implantation embryos in mice compared to other common NPs. In patients undergoing assisted reproduction technology (ART), a significant negative correlation was observed between Zn concentration and clinical outcomes. Therefore, this study explores the impact of ZnONPs exposure on pre-implantation embryonic development and its underlying mechanisms. We revealed that both in vivo and in vitro exposure to ZnONPs impairs pre-implantation embryonic development. Moreover, ZnONPs were found to reduce the pluripotency of mouse embryonic stem cells (mESCs), as evidenced by teratoma and diploid chimera assays. Employing multi-omics approaches, including RNA-Seq, CUT&Tag, and ATAC-seq, the embryotoxicity mechanisms of ZnONPs were elucidated. The findings indicate that ZnONPs elevate H3K9me3 levels, leading to increased heterochromatin and consequent inhibition of gene expression related to development and pluripotency. Notably, Chaetocin, a H3K9me3 inhibitor, sucessfully reversed the embryotoxicity effects induced by ZnONPs. Additionally, the direct interaction between ZnONPs and H3K9me3 was verified through pull-down and immunoprecipitation assays. Collectively, these findings offer new insights into the epigenetic mechanisms of ZnONPs toxicity, enhancing our understanding of their impact on human reproductive health.

Prediction model for treatment response of primary membranous nephropathy with nephrotic syndrome.

OBJECTIVE: To investigate the predictors and establish a nomogram model for the prediction of the response to treatment in primary membranous nephropathy (PMN) with nephrotic syndrome (NS). METHODS: The clinical, laboratory, pathological and follow-up data of patients with biopsy-proven membranous nephropathy at the Affiliated Hospital of Qingdao University were collected. A total of 373 patients were randomly assigned into development group (n = 262) and validation group (n = 111). Logistic regression analysis was performed in the development group to determine the predictors of treatment response. A nomogram model was established based on the multivariate logistic regression analysis and validated in the validation group. The C-index and calibration plots were used for the evaluation of the discrimination and calibration performance, respectively. RESULTS: Serum albumin levels (OR = 1.151, 95% CI 1.078-1.229, P < 0.001) and glomerular C3 deposition (OR = 0.407, 95% CI 0.213-0.775, P = 0.004) were identified as independent predictive factors for treatment response in PMN with NS, then a nomogram was established combining the above indicators and treatment regimen. The C-indices of this model were 0.718 (95% CI 0.654-0.782) and 0.789 (95% CI 0.705-0.873) in the development and validation groups, respectively. The calibration plots showed that the predicted probabilities of the model were consistent with the actual probabilities (P > 0.05), which indicated favorable performance of this model in predicting the treatment response probability. CONCLUSIONS: Serum albumin levels and glomerular C3 deposition were predictors for treatment response of PMN with NS. A novel nomogram model with good discrimination and calibration was constructed to predict treatment response probability at an early stage.

Pantoprazole suppresses carcinogenesis and growth of hepatocellular carcinoma by inhibiting glycolysis and Na+/H+ exchange.

Hepatocellular carcinoma (HCC) is one of the deadliest cancers. The prevention and therapy for this deadly disease remain a global medical challenge. In this study, we investigated the effect of pantoprazole (PPZ) on the carcinogenesis and growth of HCC. Both diethylnitrosamine (DEN) plus CCl4-induced and DEN plus high fat diet (HFD)-induced HCC models in mice were established. Cytokines and cell proliferation-associated gene in the liver tissues of mice and HCC cells were analyzed. Cellular glycolysis and Na+/H+ exchange activity were measured. The preventive administration of pantoprazole (PPZ) at a clinically relevant low dose markedly suppressed HCC carcinogenesis in both DEN plus CCl4-induced and HFD-induced murine HCC models, whereas the therapeutic administration of PPZ at the dose suppressed the growth of HCC. In the liver tissues of PPZ-treated mice, inflammatory cytokines, IL1, CXCL1, CXCL5, CXCL9, CXCL10, CCL2, CCL5, CCL6, CCL7, CCL20, and CCL22, were reduced. The administration of CXCL1, CXCL5, CCL2, or CCL20 all reversed PPZ-suppressed DEN plus CCL4-induced HCC carcinogenesis in mice. PPZ inhibited the expressions of CCNA2, CCNB2, CCNE2, CDC25C, CDCA5, CDK1, CDK2, TOP2A, TTK, AURKA, and BIRC5 in HCC cells. Further results showed that PPZ reduced the production of these inflammatory cytokines and the expression of these cell proliferation-associated genes through the inhibition of glycolysis and Na+/H+ exchange. In conclusion, PPZ suppresses the carcinogenesis and growth of HCC, which is related to inhibiting the production of inflammatory cytokines and the expression of cell proliferation-associated genes in the liver through the inhibition of glycolysis and Na+/H+ exchange.