Antimony (Sb) is a typical environmental pollutant. With the development of industrialization, antimony is widely used in daily life and enters the human body through the food chain, water source, ...air pollution, and other channels. The risk of antimony exposure has emerged as one of the public's major health concerns. Current research on antimony shows that antimony has certain biological toxicity, and antimony exposure may be one of the carcinogenic risk factors for bladder cancer, prostate cancer (PCa), and other cancers. But the molecular mechanism of antimony exposure in PCa is still unclear. Our results showed that serum antimony levels were significantly higher in PCa patients than in benign prostatic hyperplasia (BPH), and high levels of serum antimony were associated with poorer prognosis in PCa. We demonstrate that antimony exposure promotes PCa progression in vivo and in vitro. In addition, our results also showed that low-dose antimony exposure resulted in increased GSH, increased GPX4 expression, and decreased Fe2+. Since GPX4 and Fe2+ are important molecular features in the mechanism of ferroptosis, we further found that low-dose antimony exposure can inhibit RSL3-induced ferroptosis and promote PCa proliferation. Finally, our study demonstrates that low-dose antimony exposure promotes Nrf2 expression, increases the expression level of SLC7A11, and then increases the expression of GPX4, inhibits ferroptosis, and promotes PCa progression. Taken together, our experimental results suggest that low-dose antimony exposure promotes PCa cell proliferation by inhibiting ferroptosis through activation of the Nrf2-SLC7A11-GPX4 pathway. These findings highlight the link between low-dose antimony exposure and the Nrf2-SLC7A11-GPX4 ferroptosis pathway, providing a new potential direction for the prevention and treatment of PCa.
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•Low-dose antimony exposure promotes proliferation of prostate cancer cells.•Low-dose antimony exposure increased GSH content and GPX4 expression level.•Low-dose antimony exposure inhibists ferroptosis by Nrf2-SLC7A11-GPX4 pathway.
The androgen receptor signaling inhibitor (ARSI) enzalutamide (Enz) has shown critical efficacy in the treatment of advanced prostate cancer (PCa). However, the development of drug resistance is a ...significant factor contributing to mortality in PCa patients. We aimed to explore the key mechanisms of Enz-resistance. Through analysis of GEO databases, we identified SLC4A4 as a novel driver in Enz resistance. Long-term Enz treatment leads to the up-regulation of SLC4A4, which in turn mediates P53 lactylation via the NF-κB/STAT3/SLC4A4 axis, ultimately leading to the development of Enz resistance and progression of PCa. SLC4A4 knockdown overcomes Enz resistance both in vitro and in vivo. Hence, our results suggest that targeting SLC4A4 could be a promising therapeutic strategy for Enz resistance.
SLC4A4 is a novel driver of enzalutamide resistance.
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•Drug resistance is one of the critical contributors to PCa mortality.•We performed an analysis of GEO databases and identified SLC4A4 as a major contributor to enza resistance.•Enz-treatment activates SLC4A4 and mediates P53 lactylation via the NF-κB/STAT3/SLC4A4 axis, followed by drug resistance.
Noninvasive, radiological image-based detection and stratification of Gleason patterns can impact clinical outcomes, treatment selection, and the determination of disease status at diagnosis without ...subjecting patients to surgical biopsies. We present machine learning-based automatic classification of prostate cancer aggressiveness by combining apparent diffusion coefficient (ADC) and T2-weighted (T2-w) MRI-based texture features. Our approach achieved reasonably accurate classification of Gleason scores (GS) 6(3 + 3) vs. ≥7 and 7(3 + 4) vs. 7(4 + 3) despite the presence of highly unbalanced samples by using two different sample augmentation techniques followed by feature selection-based classification. Our method distinguished between GS 6(3 + 3) and ≥7 cancers with 93% accuracy for cancers occurring in both peripheral (PZ) and transition (TZ) zones and 92% for cancers occurring in the PZ alone. Our approach distinguished the GS 7(3 + 4) from GS 7(4 + 3) with 92% accuracy for cancers occurring in both the PZ and TZ and with 93% for cancers occurring in the PZ alone. In comparison, a classifier using only the ADC mean achieved a top accuracy of 58% for distinguishing GS 6(3 + 3) vs. GS ≥7 for cancers occurring in PZ and TZ and 63% for cancers occurring in PZ alone. The same classifier achieved an accuracy of 59% for distinguishing GS 7(3 + 4) from GS 7(4 + 3) occurring in the PZ and TZ and 60% for cancers occurring in PZ alone. Separate analysis of the cancers occurring in TZ alone was not performed owing to the limited number of samples. Our results suggest that texture features derived from ADC and T2-w MRI together with sample augmentation can help to obtain reasonably accurate classification of Gleason patterns.
It has become commonplace to employ principal component analysis to reveal the most important motions in proteins. This method is more commonly known by its acronym, PCA. While most popular molecular ...dynamics packages inevitably provide PCA tools to analyze protein trajectories, researchers often make inferences of their results without having insight into how to make interpretations, and they are often unaware of limitations and generalizations of such analysis. Here we review best practices for applying standard PCA, describe useful variants, discuss why one may wish to make comparison studies, and describe a set of metrics that make comparisons possible. In practice, one will be forced to make inferences about the essential dynamics of a protein without having the desired amount of samples. Therefore, considerable time is spent on describing how to judge the significance of results, highlighting pitfalls. The topic of PCA is reviewed from the perspective of many practical considerations, and useful recipes are provided.
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•Direct production of bioplastics from grocery wastes without the need for long extraction processes.•Investigation of the solvent effect on water and TFA based bioplastic by using ...Synchrotron FTIR Microspectroscopy (SR-µFTIR).•Analyzing the structural properties of Hemp-PVA composite bioplastics.
Lignocellulosic bioplastics were produced using four different green wastes: hemp, parsley stem, pineapple leaves and walnut shell. Two different solutions were used to dissolve the green wastes: trifluoroacetic acid (TFA) and pure water. The changes in their natural structures and the solvent effect during the regeneration in biofilm formation were investigated by using Synchrotron FTIR Microspectroscopy (SR-µFTIR). The presence of cellulose, hemicellulose and lignin components in the water-based biofilms was confirmed. After dissolving in TFA, the spectra demonstrated some additional bands especially in the hemicellulose region. This is due to the hydrolysis of ester bonds and conversion to carboxylic acids. Principal component analysis showed grouping due to different solvents and polymer addition. Hemp-PVA (Polyvinyl Alcohol) composite biofilms were obtained by adding polyvinyl alcohol to the hemp solution to give extra strength to the hemp biofilms. It has been shown that water-based hemp-PVA biofilms do not cause any significant spectral changes, comparing with pure hemp and PVA spectra. However, after dissolving in TFA, unlike water-based biofilms, it appears that TFA molecules are retained by PVA through hydrogen bonds of TFA’s carboxylic acid and hydroxyl groups and distinct spectral regions belong to TFA bands are clearly identified.
This study investigated the role of present vegetation in improving air quality in Bucharest (Romania) by analyzing six years of air quality data (PM10 and NO2 ) from multiple monitoring stations. ...The target value for human health protection is regularly exceeded for PM10 and not for NO2 over time. Road traffic has substantially contributed (over 70%) to ambient PM10 and NO2 levels. The results showed high seasonal variations in pollutant concentrations, with a pronounced effect of vegetation in reducing PM10 and NO2 levels. Indeed, air quality improvements of 7% for PM10 and 25% for NO2 during the growing season were reported. By using Principal Component Analysis and pollution data subtraction methodology, we have disentangled the impact of vegetation on air pollution and observed distinct annual patterns, particularly higher differences in PM10 and NO2 concentrations during the warm season. Despite limitations such as a lack of full tree inventory for Bucharest and a limited number of monitoring stations, the study highlighted the efficiency of urban vegetation to mitigate air pollution.
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•Role of vegetation for improving the air quality (PM10 &NO2) in the city of Bucharest from 2017 to 2022 is investigated.•The target value is regularly exceeded for PM10 and not for NO2•Important road traffic contribution (over 70%) to ambient air pollution is found in Bucharest.•Air quality improvements of 7% for PM10 and 25% for NO2 during the growing season is found.
The degree of association between yield and its components can be identified using correlation and Principal Component Analyses (PCA). PCA also reveals key characteristics that explain most of the ...differences between genotypes. A study was formulated to evaluate the relationship between yield and its contributing traits in cucumber. The experiment was conducted with 16 cucumber genotypes in a Randomized Complete Block Design, with three replications. The correlation analysis revealed a strong and statistically significant relationship in number of pistillate flowers (r = 0.58**), number of branches (r = 0.43**), vine length (r = 0.69**), number of leaves (r = 0.73**), leaf area (r = 0.70**), number of fruits (r = 0.91**), fruit length (r = 0.40**), fruit girth (r = 0.39**), and fruit weight (r = 0.74**) with fruit yield. PCA revealed that PC1 accounted for 51.53% of the total variation, while PC2 explained 13.91% of the total variability. This study demonstrated that choosing traits such as number of pistillate flowers, number of branches, vine length, number of leaves, leaf area, number of fruits, fruit length, fruit girth, and fruit weight that have a strong positive correlation with fruit yield could be given priority in selection for yield improvement.
Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal component analysis is widely used to simplify complex spectral datasets into comprehensible ...information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning principal component analysis is not well understood by many applied analytical scientists and spectroscopists who use principal component analysis. The meaning of features identified through principal component analysis is often unclear. This manuscript traces the journey of the spectra themselves through the operations behind principal component analysis, with each step illustrated by simulated spectra. Principal component analysis relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of principal component analysis , such as the scores representing “concentration” or “weights". The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a principal component analysis model shows how to interpret application specific chemical meaning of the principal component analysis loadings and how to analyze scores. A critical benefit of principal component analysis is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.
Agricultural productivity plays a key role in determining the economy of a country. Detection of plant leaf disease is a vital task as it greatly affects the agricultural productivity. If proper ...detection is not done, it may lead to serious damage in the quality and quantity of the agricultural yield. In this research we propose a novel scheme for the detection of plant leaf diseases using deep convolutional neural networks (DCNN). In the proposed framework, initially, the plant leaf images are preprocessed using filtering and enhancement techniques. In our work, image filtering is done using 2D Adaptive Anisotropic Diffusion Filter (2D AADF) for noise removal. Using these de-noised images, enhancement is done using Adaptive Mean Adjustment (AMA) technique. This step helps to intensify the region of interest in the image. Using the enhanced image, segmentation is performed by means of clustering and thresholding. Clustering is done using the Improved Fast Fuzzy C Means Clustering (IFFCMC) Algorithm and image thresholding is performed using the Adaptive Otsu (AO) thresholding algorithm. From the segmented images, features are extracted using grey level co-occurrence matrix (GLCM). Dimensionality reduction of features is performed using principle component analysis (PCA). Finally, classification is done using a novel DCNN architecture. The proposed architecture has four convolutional layers, two fully connected layers and one SoftMax layer. Experimental results show that the proposed framework is effective and achieves best classification results other classifiers.
Incipient faults in electrical drives can corrupt overall performance of high-speed trains; however, they are difficult to discover because of their slight fault symptoms. By sufficiently exploiting ...the distribution information of incipient faults, this paper presents the reason why incipient faults cannot be detected by the existing fault detection and diagnosis (FDD) methods. Under principal component analysis (PCA) framework, we propose a new data-driven FDD method, which is named probability-relevant PCA (PRPCA), for electrical drives in high-speed trains. The salient strengths of the PRPCA-based FDD method are: 1) it can greatly improve the fault detectability; it is suitable for non-Gaussian electrical drives; 2) based on the improved fault detectability, it can achieve accurate fault diagnosis via support vector machine; and 3) it can be easily applied to electrical drives even if neither physical models or parameters nor expert knowledge of drive systems is given; and it is of highly computational efficiency that can meet requirements on the real-time FDD. A set of experiments on a dSPACE platform-based traction system of the CRH2A-type high-speed train are carried out to demonstrate the effectiveness of the proposed method.
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