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ObjectiveTo systemically assess the relationship between serum osteocalcin levels and the progression of diabetic kidney disease (DKD) in the Chinese population.MethodsThe PubMed, Web of Science, CNKI, Wanfang Database, VIP and Chinese Medical Journal full-text Database were searched. Two investigators independently reviewed the literature and extracted data based on predetermined inclusion and exclusion criteria. The Newcastle-Ottawa scale was used to assess the quality of the literature. The statistical analysis was performed using Stata16 software.ResultsA total of 20 case-control studies encompassed 4 565 cases, consisting of 643 healthy controls (CN), 1 649 individuals with simple diabetes mellitus (DM), 1 305 with microalbuminuria (MI), and 968 with macroalbuminuria (MA). The meta-analysis results indicated that the serum osteocalcin levels in MI group were significantly lower than those in CN group and DM group [SMD = -1.15, 95% CI (-1.46, -0.85), P < 0.01; and SMD = -0.53, 95% CI (-0.69, -0.37), P < 0.01, respectively], and lower in the MA group compared to the CN group [SMD = -1.28, 95% CI (-1.79, -0.76), P < 0.01]. In the MA group, the serum osteocalcin levels were considerably lower compared to those in DM group and MI group [SMD = -0.93, 95% CI (-1.28, -0.58), P < 0.01; and SMD = -0.41, 95% CI (-0.65, -0.17), P < 0.01, respectively].ConclusionThe serum osteocalcin levels are typically reduced and show a negative correlation with the severity of proteinuria in Chinese patients with DKD. This indicates a decline in bone formation at early-stage in DKD patients, which worsens as the disease progresses.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/,identifier CRD42024580324.

PurposeThe present study explores and investigates the efficiency of deep learning models in identifying discourse structure and functional features and explores the potential application of natural language processing (NLP) techniques in text mining, information measurement, and scientific communication.MethodThe PLOS literature series has been utilized to obtain full-text data, and four deep learning models, including BERT, RoBERTa, SciBERT, and SsciBERT, have been employed for structure-function recognition.ResultThe experimental findings reveal that the SciBERT model performs outstandingly, surpassing the other models, with an F1 score. Additionally, the performance of different paragraph structures has been analyzed, and it has been found that the model performs well in paragraphs such as method and result.ConclusionThe study's outcomes suggest that deep learning models can recognize the structure and functional elements at the discourse level, particularly for scientific literature, where the SciBERT model performs remarkably. Moreover, the NLP techniques have extensive prospects in various fields, including text mining, information measurement, and scientific communication. By automatically parsing and identifying structural and functional information in text, the efficiency of literature management and retrieval can be improved, thereby expediting scientific research progress. Therefore, deep learning and NLP technologies hold significant value in scientific research.

Resistive random-access memory (RRAM) devices have drawn increasing interest for the simplicity of its structure, low power consumption and applicability to neuromorphic computing. By combining analog computing and data storage at the device level, neuromorphic computing system has the potential to meet the demand of computing power in applications such as artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT). Monolayer rhenium diselenide (ReSe2), as a two-dimensional (2D) material, has been reported to exhibit non-volatile resistive switching (NVRS) behavior in RRAM devices with sub-nanometer active layer thickness. In this paper, we demonstrate stable multiple-step RESET in ReSe2 RRAM devices by applying different levels of DC electrical bias. Pulse measurement has been conducted to study the neuromorphic characteristics. Under different height of stimuli, the ReSe2 RRAM devices have been found to switch to different resistance states, which shows the potentiation of synaptic applications. Long-term potentiation (LTP) and depression (LTD) have been demonstrated with the gradual resistance switching behaviors observed in long-term plasticity programming. A Verilog-A model is proposed based on the multiple-step resistive switching behavior. By implementing the LTP/LTD parameters, an artificial neural network (ANN) is constructed for the demonstration of handwriting classification using Modified National Institute of Standards and Technology (MNIST) dataset.

Porous boron nitride (BN) is structurally analogous to activated carbon. This material is gaining increasing attention for its potential in a range of adsorption and chemical separation applications, with a number of recent proof-of-concept studies on the removal of organics from water. Today though, the properties of porous BN—i.e., surface area, pore network, chemistry—that dictate adsorption of specific organics remain vastly unknown. Yet, they will need to be optimized to realize the full potential of the material in the envisioned applications. Here, a selection of porous BN materials with varied pore structures and chemistries were studied for the adsorption of different organic molecules, either directly, through vapor sorption analyses or as part of a water/organic mixture in the liquid phase. These separations are relevant to the industrial and environmental sectors and are envisioned to take advantage of the hydrophobic character of the BN sheets. The materials were tested and regenerated and their physical and chemical features were characterized before and after testing. This study allowed identifying the adsorption mechanisms, assessing the performance of porous BN compared to benchmarks in the field and outlining ways to improve the adsorption performance further.

Objective: Cancer stem cell marker CD44 and its variant isoforms (CD44v) may be correlated with tumor growth, metastasis, and chemo-radiotherapy resistance. However, the prognostic power of CD44 and CD44v in advanced cancer remains controversial. Therefore, the purpose of our study was to generalize the prognostic significance of these cancer stem cell markers in advanced cancer patients.Methods: Hazard ratios (HRs) with 95% confidence intervals (95% CIs) were calculated from multivariable analysis to assess the associations among CD44, CD44v6, and CD44v9 positivity and overall survival (OS), disease-free survival (DFS), progression-free survival (PFS), cancer-specific survival (CSS), and recurrence-free survival (RFS). Trial sequential analysis (TSA) was also conducted.Results: We included 15 articles that reported on 1,201 patients with advanced cancer (CD44: nine studies with 796 cases, CD44v6: three studies with 143 cases, and CD44v9: three studies with 262 cases). CD44 expression was slightly linked to worse OS (HR = 2.03, P = 0.027), but there was no correlation between CD44 expression and DFS, RFS, or PFS. Stratified analysis showed that CD44 expression was not correlated with OS at ≥5 years or OS in patients receiving adjuvant therapy. CD44v6 expression was not associated with OS. CD44v9 expression was closely associated with poor 5-years CSS in patients treated with chemo/radiotherapy (HR = 3.62, P < 0.001). However, TSA suggested that additional trials were needed to confirm these conclusions.Conclusions: CD44 or CD44v9 might be novel therapeutic targets for improving the treatment of advanced cancer patients. Additional prospective clinical trials are strongly needed across different cancer types.