Uptake, sequestration and tolerance of cadmium at cellular levels in the hyperaccumulator plant species Sedum alfredii.

Sedum alfredii is one of a few plant species known to hyperaccumulate cadmium (Cd). Uptake, localization, and tolerance of Cd at cellular levels in shoots were compared in hyperaccumulating (HE) and non-hyperaccumulating (NHE) ecotypes of Sedum alfredii. X-ray fluorescence images of Cd in stems and leaves showed only a slight Cd signal restricted within vascular bundles in the NHEs, while enhanced localization of Cd, with significant tissue- and age-dependent variations, was detected in HEs. In contrast to the vascular-enriched Cd in young stems, parenchyma cells in leaf mesophyll, stem pith and cortex tissues served as terminal storage sites for Cd sequestration in HEs. Kinetics of Cd transport into individual leaf protoplasts of the two ecotypes showed little difference in Cd accumulation. However, far more efficient storage of Cd in vacuoles was apparent in HEs. Subsequent analysis of cell viability and hydrogen peroxide levels suggested that HE protoplasts exhibited higher resistance to Cd than those of NHE protoplasts. These results suggest that efficient sequestration into vacuoles, as opposed to rapid transport into parenchyma cells, is a pivotal process in Cd accumulation and homeostasis in shoots of HE S. alfredii. This is in addition to its efficient root-to-shoot translocation of Cd

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient’s quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients.Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach.Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis.Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC

Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model

BackgroundPancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications.MethodsIn this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method.ResultsOur analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age.ConclusionOur study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease

Acupuncture for insomnia symptoms in hypertensive patients: a systematic review and meta-analysis

PurposeIn the realm of pain management, traditional Chinese medicine, specifically acupuncture, has garnered increasing attention. This meta-analysis pioneers the evaluation of acupuncture’s effectiveness in treating insomnia among hypertensive patients.MethodsWe conducted a comprehensive search across several databases—PubMed, Web of Science, Cochrane Library, WANFANG, China National Knowledge Infrastructure (CNKI), Sinomed, and the Chinese Journal of Science and Technology (VIP). Additionally, forward and backward articles of studies published from the inception of these databases until 10 September 2023, were reviewed. This systematic review and meta-analysis included all randomized controlled trials (RCTs) focusing on acupuncture for insomnia in hypertensive patients, without imposing language or date restrictions. We rigorously assessed all outcome measures reported in these trials. The evidence was synthesized by calculating the difference between mean differences (MD) in symptom change. The quality of the evidence was determined using the Cochrane Risk of Bias tool. This study is registered with PROSPERO under number CRD42023461760.ResultsOur analysis included 16 RCTs, comprising 1,309 patients. The findings revealed that acupuncture was significantly more effective than the control group in reducing insomnia symptoms, as indicated by a greater decrease in the PSQI score (MD = −3.1, 95% CI [−3.77 to −2.62], p < 0.00001). Additionally, improvements in both systolic and diastolic blood pressure were more pronounced in the acupuncture group compared to the control group (SBP: MD = −10.31, 95% CI [−16.98 to −3.64], p = 0.002; DBP: MD = −5.71, 95% CI [−8.19 to −3.23], p < 0.00001). These results suggest that acupuncture not only improves sleep quality but also lowers blood pressure in patients suffering from hypertension and insomnia. Further research is warranted to elucidate optimal acupuncture points and the duration of treatment for maximized therapeutic effect.Systematic review registration:https://www.crd.york.ac.uk/prospero, CRD42023461760

Distribution and Source of Polycyclic Aromatic Hydrocarbons (PAHs) in Water Dissolved Phase, Suspended Particulate Matter and Sediment from Weihe River in Northwest China

Weihe River is a typical river located in the arid and semi-arid regions of Northwest China. In this study, the distribution and sources of 16 polycyclic aromatic hydrocarbons (PAHs) in Weihe River were investigated. The concentrations of ∑PAHs ranged from 351 to 4427 ng/L with a mean value of 835.4 ng/L in water dissolved phase (WDP), from 3557 ng/L to 147,907 ng/L with a mean value of 20,780 ng /L in suspended particulate matter (SPM), and from 362 to 15,667 ng/g dry weight (dw) with a mean value of 2000 ng/g dw in sediment, respectively. The concentrations of PAHs in Weihe River were higher compared with other rivers in the world. In both WDP and sediment, the highest concentrations of ∑PAHs were observed in the middle reach, while the lowest concentrations of ∑PAHs were found in the lower reach. For SPM, however, the PAHs concentrations in the lower reach were highest and the PAHs concentrations in the upper reach were lowest. The ratios of anthracene/(anthracene + phenanthrene) and fluoranthene/ (fluoranthene + pyrene) reflected a pattern of both pyrolytic and petrogenic input of PAHs in Weihe River. The potential ecosystem risk assessment indicated that harmful biological impairments occur frequently in Weihe River

Concentrations, Distributions, and Risk Assessment of HBCD in Sediment in the Weihe River Basin in Northwest China

As one of the most widely used brominated flame retardants, hexabromocyclododecane (HBCD) is found widely in the environmental media. In this study, the content and spatial distribution of HBCD and risk posed by HBCD in surface sediment in the Weihe River Basin in Northwest China were investigated. The HBCD concentration ranged nd⁻4.04 ng/g dw with the mean was 0.45 ng/g dw. The major source of HBCD in surface sediment was local point discharge. The distribution profiles of α-, β-, γ-HBCD were 24.7⁻87.9%, 0⁻42.0%, and 0⁻67.1%, respectively. Specially, α-HBCD was the dominating isomer in most sample sites. This differed significantly from that in HBCD technical product, which might be attributed to the different degradation rates and inter-transformation of the three HBCD isomers. Risk quotient method was used to assess the potential risk posed by HBCD in sediment. HBCD do not pose strong risks to aquatic algae organisms in the Weihe River Basin

Deniable ring authentication based on projective hash functions

2017, Springer International Publishing AG. Deniable authentication allows the participants to deny an authentication process as there is no any evidence that it ever took place. It is quite suitable for the privacy-preserving scenario. Combining with the ring signature property, the deniable authentication can reach the source hiding. In other words, the receiver is only convinced that a member in a group is authenticating a message without knowing which one. It is also deniable, that is the receiver cannot convince anyone else that this message was indeed authenticated. We present a deniable ring authentication based on the projective hash function. With this building block the underlying (encryption) scheme is not required to be CCA secure, which results in a more realistic deniable authentication protocol