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Source apportionment of potentially toxic elements in soils is a critical step for devising soil sustainable management strategies. However, misjudgment or imprecision can occur when traditional statistical methods are applied to identify and apportion the sources. The main objective of the study was to develop a robust approach composed of the absolute principal component score/multiple linear regression (APCS/MLR) receptor model, positive matrix factorization (PMF) receptor model and geostatistics to identify and apportion sources of soil potentially toxic elements in typical industrial and mining city, eastern China. APCS/MLR and PMF were applied to provide robust factors with contribution rates. The geostatistics coupled with the variography and kriging methods was used to present factors derived from these two receptor models. The results indicated that mean concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn exceeded the local background levels. Based on multivariate receptor models and geostatistics, we determined four sources of eight potentially toxic elements including natural source (parent material), agricultural practices, pollutant emissions (industrial, mining and traffic) and the atmospheric deposition of coal combustion, which accounted for 68%, 12%, 12% and 9% of the observed potentially toxic element concentrations, respectively. This study provides a reliable and robust approach for potentially toxic elements source apportionment in this particular industrial and mining city with a clear potential for future application in other regions.

In this paper we report a facile method for preparing co-immobilized enzyme and magnetic nanoparticles (MNPs) using metal coordinated hydrogel nanofibers. Candida rugosa lipase (CRL) was selected as guest protein. For good aqueous dispersity, low price and other unique properties, citric acid-modified magnetic iron oxide nanoparticles (CA-Fe3O4 NPs) have been widely used for immobilizing enzymes. As a result, the relative activity of CA-Fe3O4@Zn/AMP nanofiber-immobilized CRL increased by 8-fold at pH 10.0 and nearly 1-fold in a 50 °C water bath after 30 min, compared to free CRL. Moreover, the immobilized CRL had excellent long-term storage stability (nearly 80% releative activity after storage for 13 days). This work indicated that metal-nucleotide nanofibers could efficiently co-immobilize enzymes and MNPs simultaneously, and improve the stability of biocatalysts.

The citrate reduction method for the synthesis of gold nanoparticles (GNPs) has known advantages but usually provides the products with low nanoparticle concentration and limits its application. Herein, we report a facile method to synthesize GNPs from concentrated chloroauric acid (2.5 mM) via adding sodium hydroxide and controlling the temperature. It was found that adding a proper amount of sodium hydroxide can produce uniform concentrated GNPs with low size distribution; otherwise, the largely distributed nanoparticles or instable colloids were obtained. The low reaction temperature is helpful to control the nanoparticle formation rate, and uniform GNPs can be obtained in presence of optimized NaOH concentrations. The pH values of the obtained uniform GNPs were found to be very near to neutral, and the pH influence on the particle size distribution may reveal the different formation mechanism of GNPs at high or low pH condition. Moreover, this modified synthesis method can save more than 90% energy in the heating step. Such environmental-friendly synthesis method for gold nanoparticles may have a great potential in large-scale manufacturing for commercial and industrial demand.

ObjectiveThe benefits of a low-salt diet for patients with chronic kidney disease (CKD) are controversial. We conducted a systematic review and meta-analysis of the effect of a low-salt diet on major clinical outcomes.DesignSystematic review and meta-analysis.Data sourcesMEDLINE by Ovid, EMBASE and the Cochrane Library databases.Eligibility criteria for selecting studiesWe included randomised controlled trials (RCTs) and cohort studies that assessed the effect of a low-salt diet on the renal composite outcomes (more than 50% decline in estimated glomerular filtration rate (eGFR) during follow-up, doubling of serum creatinine or end-stage renal disease), rate of eGFR decline, change in proteinuria, all-cause mortality events, cardiovascular (CV) events, and changes in systolic blood pressure and diastolic blood pressure.Data extraction and synthesisTwo independent researchers extracted data and evaluated their quality. Relative risks (RRs) with 95% CIs were used for dichotomous data. Differences in means (MDs) or standardised mean differences (SMDs) with 95% CIs were used to pool continuous data. We used the Cochrane Collaboration risk-of-bias tool to evaluate the quality of RCTs, and Newcastle–Ottawa Scale to evaluate the quality of cohort studies.ResultsWe found 9948 potential research records. After removing duplicates, we reviewed the titles and abstracts, and screened the full text of 230 publications. Thirty-three studies with 101 077 participants were included. A low-salt diet produced a 28% reduction in renal composite outcome events (RR: 0.72; 95% CI: 0.58 to 0.89). No significant effects were found in terms of changes in proteinuria (SMD: −0.71; 95% CI: −1.66 to 0.24), rate of eGFR (decline MD: 1.16; 95% CI: −2.02 to 4.33), risk of all-cause mortality (RR: 0.92; 95% CI: 0.58 to 1.46) and CV events (RR: 1.01; 95% CI: 0.46 to 2.22).ConclusionA low-salt diet seems to reduce the risk for renal composite outcome events in patients with CKD. However, no compelling evidence indicated that such a diet would reduce the eGFR decline rate, proteinuria, incidence of all-cause mortality and CV events. Further, more definitive studies are needed.PROSPERO registration numberCRD42017072395.

Background Circulating cell-free RNA (cfRNA) is gaining recognition as an effective biomarker for the early detection of preeclampsia (PE). However, the current methods for selecting disease-specific biomarkers are often inefficient and typically one-dimensional. Purpose This study introduces a Pyramid Scene Parsing Network (PSPNet) model to predict PE, aiming to improve early risk assessment using cfRNA profiles. Methods The theoretical maximum Preeclamptic Risk Index (PRI) of patients clinically diagnosed with PE is defined as “1”, and the control group (NP) is defined as “0”, referred to as the clinical PRI. A data preprocessing algorithm was used to screen relevant cfRNA indicators for PE. The cfRNA expression profiles were obtained from the Gene Expression Omnibus (GSE192902), consisting of 180 normal pregnancies (NP) and 69 preeclamptic (PE) samples, collected at two gestational time points: ≤ 12 weeks and 13–20 weeks. Based on the differences in cfRNA expression profiles, the Calculated Ground Truth values of the NP and PE groups in the sequencing data were acquired (Calculated PRI). The differential algorithm was embedded in the PSPNet neural network and the network was then trained using the generated dataset. Subsequently, the real-world sequencing dataset was used to validate and optimize the network, ultimately outputting the PRI values of the healthy control group and the PE group (PSPNet-based PRI). The model’s predictive ability for PE was evaluated by comparing the fit between Calculated PRI (Calculated Ground Truth) and PSPNet-based PRI. Results The mean absolute error (MAE) between the Calculated Ground Truth the PSPNet-based PRI was 0.0178 for cfRNA data sampled at ≤ 12 gws and 0.0195 for data sampled at 13–20 gws. For cfRNA data sequenced at ≤ 12 gws and 13–20 gws, the corresponding loss values, maximum absolute errors, peak-to-valley error values, mean absolute errors, and average prediction times per sample were 0.0178 (0.0195). Conclusions The present PSPNet model is reliable and fast for cfRNA-based PE prediction and its PRI output allows for continuous PE risk monitoring, introducing an innovative and effective method for early PE prediction. This model enables timely interventions and better management of pregnancy complications, particularly benefiting densely populated developing countries with high PE incidence and limited access to routine prenatal care.

Purple tea plant cultivars, enrich with flavonoids and anthocyanins, are valuable materials for manufacturing tea with unique color and flavor. Researchers found that ‘Zijuan’ leaves changed from purple to green mainly caused by the decreased flavonoids and anthocyanins concentrations. The mechanism of flavonoids and anthocyanin biosynthesis has been studied in many purple tea plant cultivars and the key genes which regulated the biosynthesis of flavonoid and anthocyanins in different purple tea plant cultivars were quite different. Also, the molecular regulation mechanism underlying the flavonoids and anthocyanins biosynthesis during leaves development and color changes is less-thoroughly understood. In this study, an integrative analysis of transcriptome and metabolome was performed on the purple leaves and green leaves of ‘Zijuan’ tea plant to reveal the regulatory networks correlated to flavonoid biosynthesis and to identify key regulatory genes. Our results indicated that the ‘Zijuan’ new shoots leaves were purple might be due to the copigmentation of quercetin and kaempferol derivatives. In ‘Zijuan’ tea plant cultivar, flavonoids metabolites concentrations in purple leaves and green leaves were significantly influenced by the genes involved in flavonoid biosynthesis, transcriptional regulation, transport, and hormone response. Transcription factors including NAC008, MYB23, and bHLH96 and transporters such as ABC transporter I might be responsible for the flavonoid and anthocyanins accumulation in purple leaves. This study provides a new insight into the metabolism and molecular mechanisms underlying flavonoid and anthocyanin biosynthesis in tea plant.

Background This study aimed to analyze the clinical characteristics and identify predictive factors associated with small segment molded sputum plugs in pediatric patients with Mycoplasma pneumoniae pneumonia (MPP). Methods A retrospective analysis was performed on the clinical data of pediatric patients diagnosed with MPP who underwent bronchoscopy at our hospital between December 2021 and April 2024. Multivariate logistic regression was employed to determine independent predictors of small segment molded sputum plug formation. Results Among 116 pediatric patients with MPP who met the study criteria, 48 (41.38%) were found to have small segment molded sputum plugs, while 68 (58.62%) did not. Patients with a history of recurrent respiratory infections, diminished breath sounds, and radiological evidence of atelectasis were significantly more likely to develop molded sputum plugs ( P  < 0.05). Furthermore, the group with small segment molded sputum plugs exhibited a longer duration of fever (P  = 0.025) and elevated levels of procalcitonin (PCT; P  = 0.024), alanine aminotransferase (ALT; P  = 0.048) and prothrombin time (PT; P  = 0.021). Multivariate logistic regression analysis identified a history of recurrent respiratory infections and diminished breath sounds as independent predictors for the development of small segment molded sputum plugs. Conclusions A history of recurrent respiratory infections and diminished breath sounds emerged as independent predictors of small segment molded sputum plugs in pediatric MPP. These findings highlight the importance of considering these clinical features during the evaluation of MPP patients to inform diagnostic and therapeutic decision-making. Future studies are warranted to investigate the potential benefits of early detection and intervention on clinical outcomes in pediatric MPP.

Background The cultivation of bananas encounters substantial obstacles, particularly due to the detrimental effects of cold stress on their growth and productivity. A potential remedy that has gained attention is the utilization of ethyl mesylate (EMS)-induced mutagenesis technology, which enables the creation of a genetically varied group of banana mutants. This complex procedure entails subjecting the mutants to further stress screening utilizing L-Hyp in order to identify those exhibiting improved resistance to cold. This study conducted a comprehensive optimization of the screening conditions for EMS mutagenesis and L-Hyp, resulting in the identification of the mutant cm784 , which exhibited remarkable cold resistance. Subsequent investigations further elucidated the physiological and transcriptomic responses of cm784 to low-temperature stress. Results EMS mutagenesis had a substantial effect on banana seedlings, resulting in modifications in shoot and root traits, wherein a majority of seedlings exhibited delayed differentiation and limited elongation. Notably, mutant leaves displayed altered biomass composition, with starch content exhibiting the most pronounced variation. The application of L-Hyp pressure selection aided in the identification of cold-resistant mutants among seedling-lethal phenotypes. The mutant cm784 demonstrated enhanced cold resistance, as evidenced by improved survival rates and reduced symptoms of chilling injury. Physiological analyses demonstrated heightened activities of antioxidant enzymes and increased proline production in cm784 when subjected to cold stress. Transcriptome analysis unveiled 946 genes that were differentially expressed in cm784 , with a notable enrichment in categories related to ‘Carbohydrate transport and metabolism’ and ‘Secondary metabolites biosynthesis, transport, and catabolism’. Conclusion The present findings provide insights into the molecular mechanisms that contribute to the heightened cold resistance observed in banana mutants. These mechanisms encompass enhanced carbohydrate metabolism and secondary metabolite biosynthesis, thereby emphasizing the adaptive strategies employed to mitigate the detrimental effects induced by cold stress.

Bearings, as core components of mechanical equipment, play a critical role in ensuring equipment safety and reliability. Early fault detection holds significant importance. Addressing the challenges of insufficient robustness in bearing fault diagnosis under industrial high-noise conditions and the difficulty of extracting fault features from a single modality, this study proposes a three-channel multimodal fault diagnosis method that integrates a Convolutional Auto-Encoder (CAE) with a dual attention mechanism (M-CNNBiAM). This approach provides an effective technical solution for the precise diagnosis of bearing faults in high-noise environments. To suppress substantial noise interference, a CAE denoising module was designed to filter out intense noise, providing high-quality input for subsequent diagnostic networks. To address the limitations of single-modal feature extraction and restricted generalization capabilities, a three-channel time–frequency signal joint diagnosis model combining the Continuous Wavelet Transform (CWT) with an attention mechanism was proposed. This approach enables deep mining and efficient fusion of multi-domain features, thereby enhancing fault diagnosis accuracy and generalization capabilities. Experimental results demonstrate that the designed CAE module maintains excellent noise reduction performance even under −10 dB strong noise conditions. When combined with the proposed diagnostic model, it achieves an average diagnostic accuracy of 98% across both the CWRU and self-test datasets, demonstrating outstanding diagnostic precision. Furthermore, under −4 dB noise conditions, it achieves a 94% diagnostic accuracy even without relying on the CAE denoising module. With a single training cycle taking only 6.8 s, it balances training efficiency and diagnostic performance, making it well-suited for real-time, reliable bearing fault diagnosis in industrial environments with high noise levels.