Current situation and prospects for the clean utilization of gold tailings.

Gold tailings are characterized by low-grade, complex composition, fine embedded particle size, environmental pollution, and large land occupation. This paper describes the mineralogical properties of gold tailings, including chemical composition, phase composition, particle size distribution, and microstructure; summarizes the recycling and utilization of components such as mica, feldspar, and valuable metals in gold tailings; reviews harmless treatment measures for harmful elements in gold tailings; and adumbrated the research progress of gold tailings in the application fields of building materials, ceramics, and glass materials. Based on these discussions, a new technology roadmap that combines multistage magnetic separation and cemented filling is proposed for the clean utilization of all components of gold tailings.

Synergistic chemotherapy/PTT/oxygen enrichment by multifunctional liposomal polydopamine nanoparticles for rheumatoid arthritis treatment.

Amultifunctional liposomal polydopamine nanoparticle (MPM@Lipo) was designed in this study, to combine chemotherapy, photothermal therapy (PTT) and oxygen enrichment to clear hyperproliferating inflammatory cells and improve the hypoxic microenvironment for rheumatoid arthritis (RA) treatment. MPM@Lipo significantly scavenged intracellular reactive oxygen species and relieved joint hypoxia, thus contributing to the repolarization of M1 macrophages into M2 phenotype. Furthermore, MPM@Lipo could accumulate at inflammatory joints, inhibit the production of inflammatory factors, and protect cartilage in vivo, effectively alleviating RA progression in a rat adjuvant-induced arthritis model. Moreover, upon laser irradiation, MPM@Lipo can elevate the temperature to not only significantly obliterate excessively proliferating inflammatory cells but also accelerate the production of methotrexate and oxygen, resulting in excellent RA treatment effects. Overall, the use of synergistic chemotherapy/PTT/oxygen enrichment therapy to treat RA is a powerful potential strategy.

Co-delivery of Paclitaxel/Atovaquone/Quercetin to regulate energy metabolism to reverse multidrug resistance in ovarian cancer by PLGA-PEG nanoparticles.

Ovarian cancer is a malignant tumor that seriously endangers the lives of women, with chemotherapy being the primary clinical treatment. However, chemotherapy encounters the problem of generating multidrug resistance (MDR), mainly due to drug efflux induced by P-glycoprotein (P-gp), which decreases intracellular accumulation of chemotherapeutic drugs. The drugs efflux mediated by P-gp requires adenosine triphosphate (ATP) hydrolysis to provide energy. Therefore, modulating energy metabolism pathways and inhibiting ATP production may be a potential strategy to reverse MDR. Herein, we developed a PTX-ATO-QUE nanoparticle (PAQNPs) based on a PLGA-PEG nanoplatform capable of loading the mitochondrial oxidative phosphorylation (OXPHOS) inhibitor atovaquone (ATO), the glycolysis inhibitor quercetin (QUE), and the chemotherapeutic drug paclitaxel (PTX) to reverse MDR by inhibiting energy metabolism through multiple pathways. Mechanistically, PAQNPs could effectively inhibit the OXPHOS and glycolytic pathways of A2780/Taxol cells by suppressing the activities of mitochondrial complex III and hexokinase II (HK II), respectively, ultimately decreasing intracellular ATP levels in tumor cells. Energy depletion can effectively inhibit cell proliferation and reduce P-gp activity, increasing the chemotherapeutic drug PTX accumulation in the cells. Moreover, intracellular reactive oxygen species (ROS) is increased with PTX accumulation and leads to chemotherapy-resistant cell apoptosis. Furthermore, PAQNPs significantly inhibited tumor growth in the A2780/Taxol tumor-bearing NCG mice model. Immunohistochemical (IHC) analysis of tumor tissues revealed that P-gp expression was suppressed, demonstrating that PAQNPs are effective in reversing MDR in tumors by inducing energy depletion. In addition, the safety study results, including blood biochemical indices, major organ weights, and H&E staining images, showed that PAQNPs have a favorable in vivo safety profile. In summary, the results suggest that the combined inhibition of the two energy pathways, OXPHOS and glycolysis, can enhance chemotherapy efficacy and reverse MDR in ovarian cancer.

A comparative study on the preparation and evaluation of solubilizing systems for silymarin.

Silymarin (SM) exhibits clinical efficacy in treating liver injuries, cirrhosis, and chronic hepatitis. However, its limited water solubility and low bioavailability hinder its therapeutic potential. The primary objective of this study was to compare the in vitro and in vivo characteristics of the four distinct SM solubilization systems, namely SM solid dispersion (SM-SD), SM phospholipid complex (SM-PC), SM sulfobutyl ether-ß-cyclodextrin inclusion complex (SM-SBE-ß-CDIC) and SM self-microemulsifying drug delivery system (SM-SMEDDS) to provide further insights into their potential for enhancing the solubility and bioavailability of SM. The formation of SM-SD, SM-PC, and SM-SBE-ß-CDIC was thoroughly characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and powder X-ray diffractometry (PXRD) techniques to analyze the changes in their microscopic structure, molecular structure, and crystalline state. The particle size and polydispersity index (PDI) of SM-SMEDDS were 71.6 ± 1.57 nm, and 0.13 ± 0.03, respectively. The self-emulsifying time of SM-SMEDDS was 3.0 ± 0.3 min. SM-SMEDDS exhibited an improved in vitro dissolution rate and demonstrated the highest relative bioavailability compared to pure SM, SM-SD, SM-PC, SM-SBE-ß-CDIC, and Legalon®. Consequently, SMEDDS shows promise as a drug delivery system for orally administered SM, offering enhanced solubility and bioavailability.

Scientific discovery in the age of artificial intelligence.

Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

Hydrochemical characteristics and driving factors of travertine deposition in Huanglong, Sichuan, SW, China.

The hydrochemical characteristics were analyzed by mathematical statistics, the hydrochemical types were analyzed by Piper three line diagram, and the sources and influencing factors of main ions in surface water were discussed by Gibbs diagram and ion correlation analysis. The results show that the TDS of surface water in the study area is 109-559 mg·L-1, and the average value is 318.67 mg·L-1; The pH value is 6.81-8.62, and the average value is 7.85. Most of them belong to weakly alkaline water. The surface water cation is mainly Ca2+ and Mg2+, the anion is mainly HCO3- and the hydrochemical type is HCO3-Ca. Through the correlation analysis of the main ions, it can be seen that TDS has a significant positive correlation with Na+, K+, Mg2+, Ca2+ and HCO3-, and these ions contribute to TDS. HCO3- has a significant correlation with Na+, K+ and Mg2+ and comes from carbonate rocks. According to the analysis of water-rock model, the hydrochemical genesis of surface water in the study area is mainly controlled by rock weathering, most ions are weathered and dissolved by carbonate rock and evaporated salt rock and a few cations are affected by water ion exchange.

Genomic insights into the genetic basis of cotton breeding in China.

The excellent Upland cotton (Gossypium hirsutum) cultivars developed since 1949 have made a huge contribution to cotton production in China, the world's largest producer and consumer of cotton. However, the genetic and genomic basis for the improvements of these cotton cultivars remains largely unclear. In this study, we selected 16 Upland cotton cultivars with important historical status in Chinese cotton breeding and constructed a multiparent, advanced generation, intercross (MAGIC) population comprising 920 recombinant inbred lines. A genome-wide association study using the MAGIC population identified 54 genomic loci associated with lint yield and fiber quality. Of them, 25 (46.30%) pleiotropic genomic loci cause simultaneous changes of lint yield and/or fiber quality traits, revealing complex trade-offs and linkage drags in Upland cotton agronomic traits. Deep sequencing data of 11 introduced ancestor cultivars and publicly available resequencing datasets of 839 cultivars developed in China during the past 70 years were integrated to explore the historical distribution and origin of the elite or selected alleles. Interestingly, 85% of these elite alleles were selected and fixed from different American ancestors, consistent with cotton breeding practices in China. However, seven elite alleles of native origin that are responsible for Fusarium wilt resistance, early maturing, good-quality fiber, and other characteristics were not found in American ancestors but have greatly contributed to Chinese cotton breeding and wide cultivation. Taken together, these results provide a genetic basis for further improving cotton cultivars and reveal that the genetic composition of Chinese cotton cultivars is narrow and mainly derived from early introduced American varieties.

Enhanced cleaner flotation behavior of non-metallic particles in waste printed circuit boards: From the perspective of particle size.

Flotation is an attractive method for separating the different components of waste printed circuit boards (WPCBs) due to its cleanliness and efficiency. Non-metallic particles (NMPs) with good floatability usually need to be floated, however, it is difficult to achieve complete removal. The effect of particle size on the flotation behavior of NMPs, which is usually ignored in previous studies, is concerned in this paper. Flotation tests and kinetic analysis were carried out to reveal the effect of reagent dosage on flotation characteristics of particles in narrow size fractions. As the fineness decreases, the particles are more likely to be floated. Equally, the finer the particle size, the lower the reagent dosage required to achieve the maximum recovery. For 1-0.5 mm and -0.045 mm, the maximum recovery increased from 42.16% (1500 g/t MIBC) to 97.31% (100 g/t MIBC). Therefore, the feasibility of reducing particle size by grinding to improve floatability was verified. The results show that the reduction of particle size can significantly promote its efficiency of being floated. After grinding treatment, -0.045 mm yields in each size fraction (1-0.5, 0.5-0.25, 0.25-0.125, 0.125-0.074, 0.074-0.045 mm) increased by 22.10%, 28.42%, 30.90%, 64.56%, 89.32%, resulting in an increase of 37.71%, 13.12%, 2.82%, 7.82% and 2.00% in maximum recovery, respectively. It is also proved that the particle size, rather than the resin content, has a more significant effect on the floatability of NMPs.

Development of the engineered "glanded plant and glandless seed" cotton.

After fiber, cottonseed is the second most important by-product of cotton production. However, high concentrations of toxic free gossypol deposited in the glands of the cottonseed greatly hamper its effective usage as food or feed. Here, we developed a cotton line with edible cottonseed by specifically silencing the endogenous expression of GoPGF in the seeds, which led to a glandless phenotype with an ultra-low gossypol content in the seeds and nearly normal gossypol in other parts of the plants. This engineered cotton maintains normal resistance to insect pests, but the gossypol content in the seeds dropped by 98%, and thus, it can be consumed directly as food. The trait of a low gossypol content in the cottonseeds was stable and heritable, while the protein, oil content, and fiber yield or quality were nearly unchanged compared to the transgenic receptor W0. In addition, comparative transcriptome analysis showed that down-regulated genes in the ovules of the glandless cotton were enriched in terpenoid biosynthesis, indicating the underlying relationship between gland formation and gossypol biosynthesis. These results pave the way for the comprehensive utilization of cotton as a fiber, oil, and feed crop in the future.

Analysis and comparison of lipids in Saanen goat milk from different geographic regions in China based on UHPLC-QTOF-MS lipidomics.

The lipids in goat milk from Guangdong Province (GGM), Shaanxi Province (SGM), and Inner Mongolia Province (NGM) were analyzed and compared using untargeted lipidomics. A total of 16 lipid sub-classes and 638 lipid molecules were identified in the three groups. The main lipids were diacylglycerol (DG), triacylglycerol (TG), and glycerophosphatidylethanolamine (PE). The contents of glycerophosphatidylcholine (PC), PE, glycerophosphatidylinositol (PI), sphingomyelin (SM), glucosylceramide (GlcCer), lactosylceramide (LacCer), DG, and TG were significantly different (P < 0.05) in three groups. Moreover, 173 significantly different lipids were screened out, and 13 lipid molecules could be applied as potential lipid markers for identifying the geographic region of goat milk. In addition, the related metabolic pathways were also analyzed. A hypothetical scheme was drawn by linking the most relevant metabolic pathways. This work will provide basics for the establishment of the Saanen goat milk traceability system and provide comprehensive lipid information for the goat milk of different regions.

UHPLC-Q-Exactive Orbitrap mass spectrometry reveals the lipidomics of bovine milk and yogurt.

Compared with milk intake, yogurt intake appears to cause a lower risk of cardiovascular disease (CVD) and type 2 diabetes (T2D). The molecular components responsible for the phenomenon are elusive. We hypothesized that the fermentation would change the lipid profile and fatty acid composition of milk. Untargeted analysis of lipids in milk and yogurt was performed using ultra-high-performance liquid chromatography (UHPLC) coupled with Q-Exactive Orbitrap mass spectrometry and gas chromatography (GC) with a flame ionization detector (FID). The results showed that yogurt had increased C4:0-C10:0 fatty acid, rumenic acid (cis-9 and trans-11-18:2), vaccenic acid (trans-11-18:1), linoleic acid (LA), and polyunsaturated fatty acid (PUFA) contents, and decreased triglyceride (TG), C16:0 and C18:0 fatty acids, and saturated fatty acid (SFA) contents compared with milk. These results advance the understanding of the difference between yogurt and milk regarding reduced risk of CVD and T2D.

Drug self-delivery nanorods enhance photodynamic therapy of triple-negative breast cancer by inhibiting oxidative phosphorylation.

Photodynamic therapy (PDT) shows very high potential for the clinical treatment of triple-negative breast cancer. However, the efficacy of PDT is significantly weakened by tumor hypoxia, the relatively high intracellular glutathione levels and the active proliferation of cancer cells. To address these issues, we developed a novel drug self-delivery nanorod (defined as AINRs) through the hydrophobic interaction among the mitochondrial complex III inhibitor (atovaquone, ATO), the photosensitizer (indocyanine green, ICG) and the dispersion stabilizer (distearoyl phosphoethanolamine-polyethylene glycol 2000, DSPE-PEG 2000). The AINRs showed a rod-like morphology with a mean diameter of 120.6 ± 5.4 nm, a zeta potential of -26.35 ± 1.63 mV and a significantly high drug loading rate of 93.48%. The results of in vitro cell experiments involving triple-negative breast cancer cell lines (4T1 cells and MDA-MB-231 cells) indicated that the AINRs could effectively block the oxidative phosphorylation of cancer cells through the inhibition of mitochondrial complex III, which results in the reduction of endogenous oxygen consumption and the decrease of the intracellular ATP level. The reduction of ATP content further inhibited the glutathione synthesis and arrested the cell cycle at the S-phase, which results in enhanced in vitro PDT efficacy of ICG. The results of in vivo antitumor activity in 4T1-bearing mice showed that the tumor growth inhibition rate of the AINRs with near-infrared laser irradiation (NIR) was greater than 90%, whereas the tumor growth inhibition rates of the AINRs without NIR, ICG with NIR and doxorubicin (3 mg/kg) were only 31.68%, 61.15% and 24.59%, respectively. In addition, the results of safety studies, including body weights, biochemical indicators and H&E staining images of the main organs demonstrated the security of the AINRs. In summary, this study showed that the oxidative phosphorylation inhibition of triple-negative breast cancer was a safe and effective method to enhance its PDT efficacy.

Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search.

Machine learning (ML) plays a growing role in the design and discovery of chemicals, aiming to reduce the need to perform expensive experiments and simulations. ML for such applications is promising but difficult, as models must generalize to vast chemical spaces from small training sets and must have reliable uncertainty quantification metrics to identify and prioritize unexplored regions. Ab initio computational chemistry and chemical intuition alike often take advantage of differences between chemical conditions, rather than their absolute structure or state, to generate more reliable results. We have developed an analogous comparison-based approach for ML regression, called pairwise difference regression (PADRE), which is applicable to arbitrary underlying learning models and operates on pairs of input data points. During training, the model learns to predict differences between all possible pairs of input points. During prediction, the test points are paired with all training set points, giving rise to a set of predictions that can be treated as a distribution of which the mean is treated as a final prediction and the dispersion is treated as an uncertainty measure. Pairwise difference regression was shown to reliably improve the performance of the random forest algorithm across five chemical ML tasks. Additionally, the pair-derived dispersion is both well correlated with model error and performs well in active learning. We also show that this method is competitive with state-of-the-art neural network techniques. Thus, pairwise difference regression is a promising tool for candidate selection algorithms used in chemical discovery.

Deep Learning in Protein Structural Modeling and Design.

Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields, including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in protein structural modeling and design. We dissect the emerging approaches using deep learning techniques for protein structural modeling and discuss advances and challenges that must be addressed. We argue for the central importance of structure, following the "sequence → structure → function" paradigm. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.

The Synthesizability of Molecules Proposed by Generative Models.

The discovery of functional molecules is an expensive and time-consuming process, exemplified by the rising costs of small molecule therapeutic discovery. One class of techniques of growing interest for early stage drug discovery is de novo molecular generation and optimization, catalyzed by the development of new deep learning approaches. These techniques can suggest novel molecular structures intended to maximize a multiobjective function, e.g., suitability as a therapeutic against a particular target, without relying on brute-force exploration of a chemical space. However, the utility of these approaches is stymied by ignorance of synthesizability. To highlight the severity of this issue, we use a data-driven computer-aided synthesis planning program to quantify how often molecules proposed by state-of-the-art generative models cannot be readily synthesized. Our analysis demonstrates that there are several tasks for which these models generate unrealistic molecular structures despite performing well on popular quantitative benchmarks. Synthetic complexity heuristics can successfully bias generation toward synthetically tractable chemical space, although doing so necessarily detracts from the primary objective. This analysis suggests that to improve the utility of these models in real discovery workflows, new algorithm development is warranted.