A spectral bias-error stepwise correction method of plasma image-spectrum fusion based on deep learning for improving the performance of LIBS.

Poor spectral stability seriously hinders the wide application of laser-induced breakdown spectroscopy (LIBS), so how to improve its stability is the focus, hotspot, and difficulty of current research. In this study, to achieve high precision quantitative analysis under complex detection conditions, utilizing the fusion of multi-dimensional plasma information and the integration of physical models and algorithmic models, a spectral bias-error stepwise correction method of plasma image-spectrum fusion based on deep learning (SBESC-PISF) was proposed. In this method, based on the statistical properties of LIBS spectra, the actual obtained spectra were decomposed into three parts: the ideal spectral intensity related only to the element concentration, and the spectral bias and spectral error caused by the fluctuation of complex high-dimensional plasma parameters. Further, the deep learning methods were used to fully excavate all the effective features in the plasma images and spectra to invert the complex high-dimensional plasma parameters according to the physical models. Finally, the estimation models of spectral bias and spectral error were established based on these features, to realize the high-precision correction of spectral intensity. To verify the feasibility of SBESC-PISF, the spectra of aluminum alloy samples obtained under three complex detection conditions were used for analysis. Under the experimental condition of laser energy fluctuation, after correction by SBESC-PISF, R2 of the three calibration curves was all increased to 0.999, RMSE and STD of the validation set (RMSEV, STDV) were reduced by 55.246 % and 50.167 %, respectively. Under the experimental condition of defocusing amount fluctuation, R2 was also all increased to 0.999, RMSEV and STDV were decreased by 58.201 % and 51.006 %, respectively. When the laser energy and defocusing amount fluctuate simultaneously, R2 was increased to 0.999, 0.996 and 0.988, RMSEV and STDV were reduced by 58.776 % and 54.397 %, respectively. These experimental results demonstrate that the spectral fluctuation correction of SBESC-PISF under complex detection conditions is effective and has wide applicability.

Ultra-sensitive detection of heavy metal aerosols: A hybrid LIBS-LIF and gated PMT enhancement approach.

Laser-induced breakdown spectroscopy (LIBS) has great potential for rapid and in-situ detection in various applications, including environmental monitoring, mining, and metallurgy. However, its application is constrained by low sensitivity, particularly in the field of heavy metal aerosol detection due to little mass ablation and significant spectral fluctuations. For the first time, we achieved an outstanding limit of detection (LOD) of 0.0035 µg/m³ for lead aerosols using our hybrid LIBS assisted with laser-induced fluorescence (LIBS-LIF) and gated photomultiplier tube (PMT) approach. Compared to traditional LIBS, the sensitivity was improved by 4 orders of magnitude, and the spectral fluctuations were reduced by 90 %. This work represents a significant advancement in rapid and in-situ pollution monitoring, providing an efficient and ultra-sensitive alternative to existing methods.

Enhancement of spectral model transferability in LIBS systems through LIBS-LIPAS fusion technique.

BACKGROUND: Laser-induced breakdown spectroscopy (LIBS) is extensively utilized a range of scientific and industrial detection applications owing to its capability for rapid, in-situ detection. However, conventional LIBS models are often tailored to specific LIBS systems, hindering their transferability between LIBS subsystems. Transfer algorithms can adapt spectral models to subsystems, but require access to the datasets of each subsystem beforehand, followed by making individual adjustments for the dataset of each subsystem. It is clear that a method to enhance the inherent transferability of spectral original models is urgently needed. RESULTS: We proposed an innovative fusion methodology, named laser-induced breakdown spectroscopy fusion laser-induced plasma acoustic spectroscopy (LIBS-LIPAS), to enhance the transferability of support vector machine (SVM) original models across LIBS systems with varying laser beams. The methodology was demonstrated using nickel-based high-temperature alloy samples. Here, the area-full width at half maximum (AFCEI) Composite Evaluation Index was proposed for extracting critical features from LIBS. Further enhancing the transferability of the model, the laser-induced plasma acoustic signal was transformed from the time domain to the frequency domain. Subsequently, the feature-level fusion method was employed to improve the classification accuracy of the transferred LIBS system to 97.8 %. A decision-level fusion approach (amalgamating LIBS, LIPAS, and feature-level fusion models) achieved an exemplary accuracy of 99 %. Finally, the adaptability of the method was demonstrated using titanium alloy samples. SIGNIFICANCE AND NOVELTY: In this work, based on plasma radiation models, we simultaneously captured LIBS and LIPAS, and proposed the fusion of these two distinct yet origin-consistent signals, significantly enhancing the transferability of the LIBS original model. The methodology proposed holds significant potential to advance LIBS technology and broaden its applicability in analytical chemistry research and industrial applications.

Selective impact of three homogenous polysaccharides with different structural characteristics from Grifola frondosa on human gut microbial composition and the structure-activity relationship.

Natural polysaccharides interact with gut microbes to enhance human well-being. Grifola frondosa is a polysaccharides-rich edible and medicinal mushroom. The prebiotic potential of G. frondosa polysaccharides has been explored in recent years, however, the relationship between their various structural features and prebiotic activities is poorly understood. In this study, three homogenous polysaccharides GFP10, GFP21 and GFP22 having different molecular weights (Mw), monosaccharide compositions and glycosidic linkages were purified from G. frondosa, and their effects on intestinal microbial composition were compared. GFP10 was a fucomannogalactan with an Mw of 23.0 kDa, and it selectively inhibited Enterobacter, while GFP21 was a fucomannogalactoglucan with an Mw of 18.6 kDa, and it stimulated Catenibacterium. GFP22 was a 4.9 kDa mannoglucan that selectively inhibited Klebsiella and boosted Bifidobacterium, Catenibacterium and Phascolarctobacterium, and prominently promoted the production of short-chain fatty acids (SCFAs). The selective modulation of gut microbiota by polysaccharides was structure-dependent. A relatively lower Mw and a high proportion of glycosidic linkages like T-Glcp, 1,3-Glcp, 1,3,6-Glcp and 1,4-Glcp might be more easily utilized to produce SCFAs and beneficial for the proliferation of Catenibacterium and Phascolarctobacterium. This research provided a valuable resource for further exploring the structure-activity relationship and prebiotic activity of G. frondosa polysaccharides.

Sensitivity and stability improvement on slippery surface-aggregated substrate for trace heavy metals detection using NELIBS.

The sensitive and stable detection of trace heavy metals in liquid is crucial given its profound impact on various aspects of human life. Currently, nanoparticle-enhanced laser-induced breakdown spectroscopy (NELIBS) with dried droplet method (DDM) is widely applied for heavy metals detection. Nevertheless, the coffee ring effect (CRE) in DDM affects the stability, accuracy, and sensitivity of NELIBS. Here, we developed a slippery surface-aggregated substrate (SS substrate) to suppress the CRE and enrich analytes, and form a plasmonic platform for NELIBS detection. The SS substrate was prepared by infiltrating perfluorinated lubricant into the pores of PTFE membrane. The droplet, with targeted elements and gold nanoparticles, was dried on the SS substate to form the plasmonic platform for NELIBS analysis. Then, trace heavy metal elements copper (Cu) and manganese (Mn) were analyzed by NELIBS. The results of Cu (RSD = 5.60%, LoD = 3.72 µg/L) and Mn (RSD = 7.42%, LoD = 6.37 µg/L), illustrated the CRE suppression and analytes enrichment by the SS substrate. The results verified the realization of stable, accurate and sensitive NELIBS detection. And the LoDs succeeded to reach the standard limit of China (GB/T 14848-2017). Furthermore, the results for groundwater detection (relative error: 5.92% (Cu) and 4.74% (Mn)), comparing NELIBS and inductively coupled plasma mass spectrometry (ICP-MS), validated the feasibility of the SS substrate in practical applications. In summary, the SS substrate exhibits immense potential for practical application such as water quality detection and supervision.

Synergizing Fe2O3 Nanoparticles on Single Atom Fe-N-C for Nitrate Reduction to Ammonia at Industrial Current Densities.

The electrochemical reduction of nitrates (NO3 -) enables a pathway for the carbon neutral synthesis of ammonia (NH3), via the nitrate reduction reaction (NO3RR), which has been demonstrated at high selectivity. However, to make NH3 synthesis cost-competitive with current technologies, high NH3 partial current densities (jNH3) must be achieved to reduce the levelized cost of NH3. Here, the high NO3RR activity of Fe-based materials is leveraged to synthesize a novel active particle-active support system with Fe2O3 nanoparticles supported on atomically dispersed Fe-N-C. The optimized 3×Fe2O3/Fe-N-C catalyst demonstrates an ultrahigh NO3RR activity, reaching a maximum jNH3 of 1.95 A cm-2 at a Faradaic efficiency (FE) for NH3 of 100% and an NH3 yield rate over 9 mmol hr-1 cm-2. Operando XANES and post-mortem XPS reveal the importance of a pre-reduction activation step, reducing the surface Fe2O3 (Fe3+) to highly active Fe0 sites, which are maintained during electrolysis. Durability studies demonstrate the robustness of both the Fe2O3 particles and Fe-Nx sites at highly cathodic potentials, maintaining a current of -1.3 A cm-2 over 24 hours. This work exhibits an effective and durable active particle-active support system enhancing the performance of the NO3RR, enabling industrially relevant current densities and near 100% selectivity.

Polysaccharide isolated from Grifola frondosa eliminates myeloid-derived suppressor cells and inhibits tumor growth by enhancing T cells responses.

Myeloid derived suppressor cells (MDSCs) are known to accumulate in cancer patients and tumor-bearing mice, playing a significant role in promoting tumor growth. Depleting MDSCs has emerged as a potential therapeutic strategy for cancer. Here, we demonstrated that a fungal polysaccharide, extracted from Grifola frondosa, can effectively suppress breast tumorigenesis in mice by reducing the accumulation of MDSCs. Treatment with Grifola frondosa polysaccharide (GFI) leads to a substantial decrease in MDSCs in the blood and tumor tissue, and a potent inhibition of tumor growth. GFI treatment significantly reduces the number and proportion of MDSCs in the spleen, although this effect is not observed in the bone marrow. Further analysis reveals that GFI treatment primarily targets PMN-MDSCs, sparing M-MDSCs. Our research also highlights that GFI treatment has the dual effect of restoring and activating CD8+T cells, achieved through the downregulation of TIGIT expression and the upregulation of Granzyme B. Taken together, our findings suggest that GFI treatment effectively eliminates PMN-MDSCs in the spleen, leading to a reduction in MDSC numbers in circulation and tumor tissues, ultimately enhancing the antitumor immune response of CD8+T cells and inhibiting tumor growth. This study introduces a promising therapeutic agent for breast cancer.

Study on the inhibitory effect of fermentation extract of Microporus vernicipes on Candida albicans.

Candida albicans is one of the most common species of Candida, which cause various mucosal and systemic infectious diseases. However, the resistance rate to existing clinical antifungal drugs gradually increases in C. albicans. Therefore, new antifungal drugs must be developed to solve the current problem. This study discovered that the solid fermented ethyl acetate crude extract of Microporus vernicipes had inhibitory activity on C. albicans. This study determined that the Mv5 components had significantly inhibited the activity of C. albicans using column chromatography separation component screening. The components included 23 compounds of fatty acids and their derivatives, alkaloids, phenols, and other classes using ultra-high performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC-HR-MS) analysis, with fatty acids constituting the primary components. The mechanism of action showed that the minimum inhibitory concentration (MIC) of Mv5 components against C. albicans was 15.63 µg/mL, while minimum fungicidal concentration (MFC) was 31.25 µg/mL. Mv5 components can inhibit the early biofilm formation and destroy the mature biofilm structure. It can inhibit the germ tube growth of C. albicans, thereby inhibiting the transformation of yeast morphology to hyphae. We detected 193 differentially expressed genes, including 156 upregulated and 37 downregulated genes in the Mv5 components of the MIC concentration group. We detected 391 differentially expressed genes, including 334 upregulated and 57 downregulated expression genes in the MFC concentration group. Among these differentially expressed genes, the genes related to mycelium and biofilm formation were significantly downregulated. GO enrichment analysis presented that single-organism process metabolic process, and cellular processes were the biological processes with the most gene enrichment. Kyoto Encyclopedia of Genes and Genomes (KEGG)of Mv5 components were mainly enriched in metabolic pathways, such as meiosis yeast and amino acid metabolism. Therefore, it is believed that the fermentation extract of M. vernicipes inhibits C. albicans, which can provide clues for developing effective antifungal drugs.

Staging classification of omicron variant SARS-CoV-2 infection based on dual-spectrometer LIBS (DS-LIBS) combined with machine learning.

Effective differentiation of the infection stages of omicron can provide significant assistance in transmission control and treatment strategies. The combination of LIBS serum detection and machine learning methods, as a novel disease auxiliary diagnostic approach, has a high potential for rapid and accurate staging classification of Omicron infection. However, conventional single-spectrometer LIBS serum detection methods focus on detecting the spectra of major elements, while trace elements are more closely related to the progression of COVID-19. Here, we proposed a rapid analytical method with dual-spectrometer LIBS (DS-LIBS) assisted with machine learning to classify different infection stages of omicron. The DS-LIBS, including a broadband spectrometer and a narrowband spectrometer, enables synchronous collection of major and trace elemental spectra in serum, respectively. By employing the RF machine learning models, the classification accuracy using the spectra data collected from DS-LIBS can reach 0.92, compared to 0.84 and 0.73 when using spectra data collected from single-spectrometer LIBS. This significant improvement in classification accuracy highlights the efficacy of the DS-LIBS approach. Then, the performance of four different models, SVM, RF, IGBT, and ETree, is compared. ETree demonstrates the best, with cross-validation and test set accuracies of 0.94 and 0.93, respectively. Additionally, it achieves classification accuracies of 1.00, 0.92, 0.92, and 0.89 for the four stages B1-acute, B1-post, B2, and B3. Overall, the results demonstrate that DS-LIBS combined with the ETree machine learning model enables effective staging classification of omicron infection.

BIR: Biomedical Information Retrieval System for Cancer Treatment in Electronic Health Record Using Transformers.

The rapid growth of electronic health records (EHRs) has led to unprecedented biomedical data. Clinician access to the latest patient information can improve the quality of healthcare. However, clinicians have difficulty finding information quickly and easily due to the sheer data mining volume. Biomedical information retrieval (BIR) systems can help clinicians find the information required by automatically searching EHRs and returning relevant results. However, traditional BIR systems cannot understand the complex relationships between EHR entities. Transformers are a new type of neural network that is very effective for natural language processing (NLP) tasks. As a result, transformers are well suited for tasks such as machine translation and text summarization. In this paper, we propose a new BIR system for EHRs that uses transformers for predicting cancer treatment from EHR. Our system can understand the complex relationships between the different entities in an EHR, which allows it to return more relevant results to clinicians. We evaluated our system on a dataset of EHRs and found that it outperformed state-of-the-art BIR systems on various tasks, including medical question answering and information extraction. Our results show that Transformers are a promising approach for BIR in EHRs, reaching an accuracy and an F1-score of 86.46%, and 0.8157, respectively. We believe that our system can help clinicians find the information they need more quickly and easily, leading to improved patient care.

Elucidating electrochemical nitrate and nitrite reduction over atomically-dispersed transition metal sites.

Electrocatalytic reduction of waste nitrates (NO3-) enables the synthesis of ammonia (NH3) in a carbon neutral and decentralized manner. Atomically dispersed metal-nitrogen-carbon (M-N-C) catalysts demonstrate a high catalytic activity and uniquely favor mono-nitrogen products. However, the reaction fundamentals remain largely underexplored. Herein, we report a set of 14; 3d-, 4d-, 5d- and f-block M-N-C catalysts. The selectivity and activity of NO3- reduction to NH3 in neutral media, with a specific focus on deciphering the role of the NO2- intermediate in the reaction cascade, reveals strong correlations (R=0.9) between the NO2- reduction activity and NO3- reduction selectivity for NH3. Moreover, theoretical computations reveal the associative/dissociative adsorption pathways for NO2- evolution, over the normal M-N4 sites and their oxo-form (O-M-N4) for oxyphilic metals. This work provides a platform for designing multi-element NO3RR cascades with single-atom sites or their hybridization with extended catalytic surfaces.

Drying Acoustically Levitated Droplets as Signal-Amplifying Platforms for Ultrasensitive and Multimode Laser Sensing.

Ultrasensitive sensing to trace atomic and molecular analytes has gained interest for its intimate relation to industrial sectors and human lives. One of the keys to ultrasensitive sensing for many analytical techniques lies in enriching trace analytes onto well-designed substrates. However, the coffee ring effect, nonuniform distribution of analytes onto substrates, in the droplet drying process hinders the ultrasensitive and stable sensing onto the substrates. Here, we propose a substrate-free strategy to suppress the coffee ring effect, enrich analytes, and self-assemble a signal-amplifying (SA) platform for multimode laser sensing. The strategy involves acoustically levitating and drying a droplet, mixed with analytes and core-shell Au@SiO2 nanoparticles, to self-assemble an SA platform. The SA platform with a plasmonic nanostructure can dramatically enrich analytes, enabling enormous spectroscopic signal amplification. Specifically, the SA platform can promote atomic detection (cadmium and chromium) to the 10-3 mg/L level by nanoparticle-enhanced laser-induced breakdown spectroscopy and can promote molecule detection (rhodamine 6G) to the 10-11 mol/L level by surface-enhanced Raman scattering. All in all, the SA platform, self-assembled by acoustic levitation, can intrinsically suppress the coffee ring effect and enrich trace analytes, enabling ultrasensitive multimode laser sensing.

Comparative proteomic analysis of two divergent strains provides insights into thermotolerance mechanisms of Ganoderma lingzhi.

Heat stress (HS) is a major abiotic factor influencing fungal growth and metabolism. However, the genetic basis of thermotolerance in Ganoderma lingzhi (G. lingzhi) remains largely unknown. In this study, we investigated the thermotolerance capacities of 21 G. lingzhi strains and screened the thermo-tolerant (S566) and heat-sensitive (Z381) strains. The mycelia of S566 and Z381 were collected and subjected to a tandem mass tag (TMT)-based proteome assay. We identified 1493 differentially expressed proteins (DEPs), with 376 and 395 DEPs specific to the heat-tolerant and heat-susceptible genotypes, respectively. In the heat-tolerant genotype, upregulated proteins were linked to stimulus regulation and response. Proteins related to oxidative phosphorylation, glycosylphosphatidylinositol-anchor biosynthesis, and cell wall macromolecule metabolism were downregulated in susceptible genotypes. After HS, the mycelial growth of the heat-sensitive Z381 strain was inhibited, and mitochondrial cristae and cell wall integrity of this strain were severely impaired, suggesting that HS may inhibit mycelial growth of Z381 by damaging the cell wall and mitochondrial structure. Furthermore, thermotolerance-related regulatory pathways were explored by analyzing the protein-protein interaction network of DEPs considered to participate in the controlling the thermotolerance capacity. This study provides insights into G. lingzhi thermotolerance mechanisms and a basis for breeding a thermotolerant germplasm bank for G. lingzhi and other fungi.

Self-absorption correction method for one-point calibration laser-induced breakdown spectroscopy.

As an important variant of calibration-free laser-induced breakdown spectroscopy (CF-LIBS), one-point calibration LIBS (OPC-LIBS) corrects the Boltzmann plot of the unknown sample by using one known sample and obtains higher quantitative accuracy than CF-LIBS. However, the self-absorption effect restricts its accuracy. In this work, a new self-absorption correction (SAC) method for OPC-LIBS is proposed to solve this problem. This method uses an algorithm to correct the self-absorption and does not require the calculation of the self-absorption coefficient. To verify the effectiveness of this SAC method, Ti, V, and Al elements in two titanium alloys were determined by classical OPC-LIBS and OPC-LIBS with SAC. The average relative errors (AREs) of all elements in the two samples were decreased from 8.78% and 9.28% to 8.07% and 7.56%, respectively. The results demonstrated the effectiveness of this SAC method for OPC-LIBS.

Revealing the role of ionic liquids in promoting fuel cell catalysts reactivity and durability.

Ionic liquids (ILs) have shown to be promising additives to the catalyst layer to enhance oxygen reduction reaction in polymer electrolyte fuel cells. However, fundamental understanding of their role in complex catalyst layers in practically relevant membrane electrode assembly environment is needed for rational design of highly durable and active platinum-based catalysts. Here we explore three imidazolium-derived ionic liquids, selected for their high proton conductivity and oxygen solubility, and incorporate them into high surface area carbon black support. Further, we establish a correlation between the physical properties and electrochemical performance of the ionic liquid-modified catalysts by providing direct evidence of ionic liquids role in altering hydrophilic/hydrophobic interactions within the catalyst layer interface. The resulting catalyst with optimized interface design achieved a high mass activity of 347 A g-1Pt at 0.9 V under H2/O2, power density of 0.909 W cm-2 under H2/air and 1.5 bar, and had only 0.11 V potential decrease at 0.8 A cm-2 after 30 k accelerated stress test cycles. This performance stems from substantial enhancement in Pt utilization, which is buried inside the mesopores and is now accessible due to ILs addition.

AXL, along with PROS1, is overexpressed in papillary thyroid carcinoma and regulates its biological behaviour.

BACKGROUND: AXL, a TAM tyrosine kinase receptor, plays an essential role in the pathogenesis of various solid tumours. This study explores the role of AXL and its ligand PROS1 in the generation and biological behaviour of papillary thyroid cancer (PTC). METHODS: The expression levels of AXL in PTC cancer tissue were analysed using immunohistochemistry (IHC) staining. The expression levels of AXL in PTC and normal thyroid cell lines were analysed using real-time quantitative polymerase chain reaction (RT-qPCR). CCK-8 was used to assess the proliferation of the PTC cell line with and without the effect of the AXL inhibitor (R428). Scratching assays played a role in evaluating the cell migration rate. RESULTS: PROS1 and AXL were expressed in TPC-1, B-CPAP, and Nthy-Ori 3-1 cells at different levels. Expression was significantly higher in PTC cell lines (TPC-1 and B-CPAP) than in the normal thyroid cell line (Nthy-Ori 3-1) (p < 0.05). In addition, AXL expression in PTC tissues was significantly higher than in adjacent normal tissues (p < 0.05). CCK-8 experiments confirmed that R428 suppresses the proliferation of PTC cell lines in a dose-dependent manner, with an increase in concentration from 0.5 to 4 µM, decreasing the inhibitory effect (p < 0.01). In addition, R428 inhibited PTC cell line migration to different degrees in a range of concentrations from 0.5 to 2 µM compared to control cells (p < 0.01). CONCLUSION: PROS1 and its downstream receptor AXL expression were significantly higher in PTC than in normal thyroid cells. AXL expression was also higher in human PTC tissues than in normal thyroid tissues. Inhibiting the PROS1-AXL-mediated TAM signaling pathway via the AXL blocker R428 suppressed the proliferation and migration of human PTC cells, highlighting the role of this cascade in human PTC development and progression.

Whole-Genome Sequencing and Transcriptome Analysis of Ganoderma lucidum Strain Yw-1-5 Provides New Insights into the Enhanced Effect of Tween80 on Exopolysaccharide Production.

Ganoderma lucidum is an important medicinal mushroom widely cultured in Asian countries. Exopolysaccharides are bioactive compounds of G. lucidum with health benefits. Limited exopolysaccharide content hinders its extraction from G. lucidum. The addition of Tween80 had an enhanced effect on G. lucidum exopolysaccharide production in submerged fermentation. However, the mechanism of this effect remains unclear. In this study, we report on a high-quality assembly of G. lucidum strain yw-1-5 to lay the foundation for further transcriptome analysis. The genome sequence was 58.16 Mb and consisted of 58 scaffolds with an N50 of 4.78 Mb. A total of 13,957 protein-coding genes were annotated and Hi-C data mapped to 12 pseudo-chromosomes. Genes encoding glycosyltransferases and glycoside hydrolases were also obtained. Furthermore, RNA-seq was performed in a Tween80-treated group and control group for revealing the enhanced effect of Tween80 on exopolysaccharide production. In total, 655 genes were identified as differentially expressed, including 341 up-regulated and 314 down-regulated. Further analysis of differentially expressed genes showed that groups of MAPK, amino sugar and nucleotide sugar metabolism, autophagy, ubiquitin-mediated proteolysis, peroxisome, starch and sucrose metabolism, TCA cycle, glycolysis/gluconeogenesis KEGG pathway, glycosyltransferases and glycoside hydrolases played important roles in the enhanced effect of Tween80 on exopolysaccharide production. This work provides a valuable resource for facilitating our understanding of the synthesis of polysaccharides and accelerating the breeding of new strains with a high content of exopolysaccharides.

Insertable, Scabbarded, and Nanoetched Silver Needle Sensor for Hazardous Element Depth Profiling by Laser-Induced Breakdown Spectroscopy.

Sensing of hazardous metals is urgent in many areas (e.g., water pollution and meat products) as heavy metals threaten people's health. Laser-induced breakdown spectroscopy (LIBS), as a rapid, in situ, and multielemental analytical technique, has been widely utilized in rapid hazardous heavy metal sensing. However, loose and water-containing samples (e.g., meat, plant, and soil) are hard to analyze by LIBS directly, and heavy metal depth profiling for bulk samples remains suspenseful. Here, inspired by the Needle, the sword of Arya Stark in Game of Thrones, we propose an insertable, scabbarded, and nanoetched silver (NE-Ag) needle sensor for rapid hazardous element sensing and depth profiling. The NE-Ag needle sensor features a micro-nanostructure surface for inserting into the bulk sample and absorbing hazardous analytes. For accurate elemental depth profiling, we design a stainless-steel scabbard to wrap and protect the NE-Ag needle from pollution (unexpected contaminant absorption) during the needle insertion and extraction process. The results for cadmium (Cd) show that the relative standard deviation equals to 6.7% and the limit of detection reaches 0.8 mg/L (ppm). Furthermore, the correlations (Pearson correlation coefficient) for Cd and chromium (Cr) depth profiling results are no less than 0.96. Furthermore, the total testing time could be less than 1 h. All in all, the insertable and scabbarded NE-Ag needle senor has high potential in rapid hazardous heavy metal depth profiling in different industries.

Comparative transcriptome analysis of genes and metabolic pathways involved in sporulation in Ganoderma lingzhi.

Over the past decades, Ganoderma lingzhi spores have received considerable attention as a great potential pharmaceutical resource. However, the genetic regulation of sporulation is not well understood. In this study, a comparative transcriptome analysis of the low-sporing HZ203 and high-sporing YW-1 was performed to characterize the mechanism underlying sporulation. A total of 917 differentially expressed genes were identified in HZ203 and 1,450 differentially expressed genes in YW-1. Differentially expressed genes involved in sporulation were identified, which included HOP1, Mek1, MSH4, MSH5, and Spo5 in meiosis. Positive regulatory pathways of sporulation were proposed as 2 transcriptional factors had high connectivity with MSH4 and Spo5. Furthermore, we found that the pathways associated with energy production were enriched in the high-sporing genotype, such as the glyoxylate and dicarboxylate metabolism, starch and sucrose metabolism. Finally, we performed a weighted gene coexpression network analysis and found that the hub genes of the module which exhibit strong positive relationship with the high-sporing phase purportedly participate in signal transduction, carbohydrate transport and metabolism. The dissection of differentially expressed genes during sporulation extends our knowledge about the genetic and molecular networks mediating spore morphogenesis and sheds light on the importance of energy source during sporulation.

A polysaccharide isolated from Ganoderma lucidum ameliorates hyperglycemia through modulating gut microbiota in type 2 diabetic mice.

In this study, we reported a thermal stable and non-toxic heteropolysaccharide F31, which decreased the blood glucose of diabetic mice (21.75 mmol/L) induced by high-fat diet (HFD) and streptozotocin (STZ) to 12.56 and 15.18 mmol/L (P < 0.01) at 180 and 60 mg/kg, depicting remarkable hypoglycemic effects of 42.25 and 30.21%. Moreover, F31 repaired islet cells and increased insulin secretion, promoted the synthesis and storage of glycogen in liver and improved activities of antioxidant enzymes and insulin resistances, declining HOMA-IR (43.77 mmol/mU) of diabetic mice (P < 0.01) to 17.32 and 20.96 mmol/mU at both doses. 16S rRNA gene sequencing revealed that F31 significantly decreased Firmicutes (44.92%, P < 0.01) and enhanced Bacteroidetes (33.73%, P < 0.01) and then increased B/F ratio of diabetic mice to 0.6969 (P < 0.01), even being close to normal control (P = 0.9579). F31 enriched Lactobacillus, Bacteroides and Ruminococcaceae, which may relieve glucose, insulin resistance and inflammation through decreasing the release of endotoxins into the circulation from intestine, carbohydrate fermentation in gut and activation of the intestine-brain axis. Functionally, F31 improved metabolism of gut microbiota to a normal state. These results may provide novel insights into the beneficial effect of F31 against hyperglycemia.