FDA-approved disulfiram inhibits pyroptosis by blocking gasdermin D pore formation.

Cytosolic sensing of pathogens and damage by myeloid and barrier epithelial cells assembles large complexes called inflammasomes, which activate inflammatory caspases to process cytokines (IL-1ß) and gasdermin D (GSDMD). Cleaved GSDMD forms membrane pores, leading to cytokine release and inflammatory cell death (pyroptosis). Inhibiting GSDMD is an attractive strategy to curb inflammation. Here we identify disulfiram, a drug for treating alcohol addiction, as an inhibitor of pore formation by GSDMD but not other members of the GSDM family. Disulfiram blocks pyroptosis and cytokine release in cells and lipopolysaccharide-induced septic death in mice. At nanomolar concentration, disulfiram covalently modifies human/mouse Cys191/Cys192 in GSDMD to block pore formation. Disulfiram still allows IL-1ß and GSDMD processing, but abrogates pore formation, thereby preventing IL-1ß release and pyroptosis. The role of disulfiram in inhibiting GSDMD provides new therapeutic indications for repurposing this safe drug to counteract inflammation, which contributes to many human diseases.

Single-Cell Transcriptional Analysis of Lamina Propria Lymphocytes in the Jejunum Reveals Innate Lymphoid Cell-like Cells in Pigs.

Pigs are the most suitable model to study various therapeutic strategies and drugs for human beings, although knowledge about cell type-specific transcriptomes and heterogeneity is poorly available. Through single-cell RNA sequencing and flow cytometry analysis of the types in the jejunum of pigs, we found that innate lymphoid cells (ILCs) existed in the lamina propria lymphocytes (LPLs) of the jejunum. Then, through flow sorting of live/dead-lineage (Lin)-CD45+ cells and single-cell RNA sequencing, we found that ILCs in the porcine jejunum were mainly ILC3s, with a small number of NK cells, ILC1s, and ILC2s. ILCs coexpressed IL-7Rα, ID2, and other genes and differentially expressed RORC, GATA3, and other genes but did not express the CD3 gene. ILC3s can be divided into four subgroups, and genes such as CXCL8, CXCL2, IL-22, IL-17, and NCR2 are differentially expressed. To further detect and identify ILC3s, we verified the classification of ILCs in the porcine jejunum subgroup and the expression of related hallmark genes at the protein level by flow cytometry. For systematically characterizing ILCs in the porcine intestines, we combined our pig ILC dataset with publicly available human and mice ILC data and identified that the human and pig ILCs shared more common features than did those mouse ILCs in gene signatures and cell states. Our results showed in detail for the first time (to our knowledge) the gene expression of porcine jejunal ILCs, the subtype classification of ILCs, and the markers of various ILCs, which provide a basis for an in-depth exploration of porcine intestinal mucosal immunity.

AhR ligands from LGG metabolites promote piglet intestinal ILC3 activation and IL-22 secretion to inhibit PEDV infection.

In maintaining organismal homeostasis, gut immunity plays a crucial role. The coordination between the microbiota and the immune system through bidirectional interactions regulates the impact of microorganisms on the host. Our research focused on understanding the relationships between substantial changes in jejunal intestinal flora and metabolites and intestinal immunity during porcine epidemic diarrhea virus (PEDV) infection in piglets. We discovered that Lactobacillus rhamnosus GG (LGG) could effectively prevent PEDV infection in piglets. Further investigation revealed that LGG metabolites interact with type 3 innate lymphoid cells (ILC3s) in the jejunum of piglets through the aryl hydrocarbon receptor (AhR). This interaction promotes the activation of ILC3s and the production of interleukin-22 (IL-22). Subsequently, IL-22 facilitates the proliferation of IPEC-J2 cells and activates the STAT3 signaling pathway, thereby preventing PEDV infection. Moreover, the AhR receptor influences various cell types within organoids, including intestinal stem cells (ISCs), Paneth cells, and enterocytes, to promote their growth and development, suggesting that AhR has a broad impact on intestinal health. In conclusion, our study demonstrated the ability of LGG to modulate intestinal immunity and effectively prevent PEDV infection in piglets. These findings highlight the potential application of LGG as a preventive measure against viral infections in livestock.IMPORTANCEWe observed high expression of the AhR receptor on pig and human ILC3s, although its expression was negligible in mouse ILC3s. ILC3s are closely related to the gut microbiota, particularly the secretion of IL-22 stimulated by microbial signals, which plays a crucial regulatory role in intestinal immunity. In our study, we found that metabolites produced by beneficial gut bacteria interact with ILC3s through AhR, thereby maintaining intestinal immune homeostasis in pigs. Moreover, LGG feeding can enhance the activation of ILC3s and promote IL-22 secretion in the intestines of piglets, ultimately preventing PEDV infection.

African Swine Fever Virus L83L Negatively Regulates the cGAS-STING-Mediated IFN-I Pathway by Recruiting Tollip To Promote STING Autophagic Degradation.

African swine fever (ASF) is a devastating infectious disease of pigs caused by the African swine fever virus (ASFV), which poses a great danger to the global pig industry. Many viral proteins can suppress with interferon signaling to evade the host's innate immune responses. Therefore, the development of an effective vaccine against ASFV has been dampened. Recent studies have suggested that the L83L gene may be integrated into the host genome, weakening the host immune system, but the underlying mechanism is unknown. Our study found that L83L negatively regulates the cGAS-STING-mediated type I interferon (IFN-I) signaling pathway. Overexpression of L83L inhibited IFN-ß promoter and ISRE activity, and knockdown of L83L induced higher transcriptional levels of interferon-stimulated genes (ISGs) and phosphorylation levels of IRF3 in primary porcine alveolar macrophages. Mechanistically, L83L interacted with cGAS and STING to promote autophagy-lysosomal degradation of STING by recruiting Tollip, thereby blocking the phosphorylation of the downstream signaling molecules TBK1, IRF3, and IκBα and reducing IFN-I production. Altogether, our study reveals a negative regulatory mechanism involving the L83L-cGAS-STING-IFN-I axis and provides insights into an evasion strategy involving autophagy and innate signaling pathways employed by ASFV. IMPORTANCE African swine fever virus (ASFV) is a large double-stranded DNA virus that primarily infects porcine macrophages. The ASFV genome encodes a large number of immunosuppressive proteins. Current options for the prevention and control of this pathogen remain pretty limited. Our study showed that overexpression of L83L inhibited the cGAS-STING-mediated type I interferon (IFN-I) signaling pathway. In contrast, the knockdown of L83L during ASFV infection enhanced IFN-I production in porcine alveolar macrophages. Additional analysis revealed that L83L protein downregulated IFN-I signaling by recruiting Tollip to promote STING autophagic degradation. Although L83L deletion has been reported to have little effect on viral replication, its immune evade mechanism has not been elucidated. The present study extends our understanding of the functions of ASFV-encoded pL83L and its immune evasion strategy, which may provide a new basis for developing a live attenuated vaccine for ASF.

Integration of bioinformatics and cellular experiments unveils the role of SYT12 in gastric cancer.

OBJECTIVE: This study employs integrated bioinformatics analysis and in vitro cellular experiments to elucidate the role of Synaptotagmin-12 (SYT12) in the progression of gastric cancer. METHODS: We utilized databases and platforms such as Xiantao Academic Tools, UALCAN, Kaplan-Meier plotter analysis, and The Cancer Genome Atlas (TCGA) to extract datasets on SYT12 in gastric cancer. We analyzed the relationship between SYT12 expression and the clinicopathological features, prognosis, diagnosis, and immune infiltration of stomach adenocarcinoma (STAD) patients. Verification was conducted using samples from 31 gastric cancer patients. Additionally, in vitro cellular experiments were performed to determine the role and potential mechanisms of SYT12 in the malignant behavior of gastric cancer cells. RESULTS: Comprehensive bioinformatics analysis indicated that SYT12 is highly expressed in most cancers and is associated with promoter DeoxyriboNucleic Acid (DNA) methylation levels. SYT12 expression correlated with clinicopathological features, immune cell infiltration, immune checkpoint gene expression, and poor prognosis in STAD patients. In vitro experiments suggest that SYT12 may promote the proliferation and migration of gastric cancer cells by inducing epithelial-mesenchymal transition (EMT). CONCLUSIONS: This study highlights the significant role of SYT12 in gastric cancer, suggesting its potential as a new target for early diagnosis, treatment, immunological, and prognostic evaluation in gastric cancer, offering new insights for precision medicine in this disease.

Construction of a Cuproptosis-Related Gene Signature for Predicting Prognosis in Gastric Cancer.

This study aimed to develop and validate a cuproptosis-related gene signature for the prognosis of gastric cancer. The data in TCGA GC TPM format from UCSC were extracted for analysis, and GC samples were randomly divided into training and validation groups. Pearson correlation analysis was used to obtain cuproptosis-related genes co-expressed with 19 Cuproptosis genes. Univariate Cox and Lasso regression analyses were used to obtain cuproptosis-related prognostic genes. Multivariate Cox regression analysis was used to construct the final prognostic risk model. The risk score curve, Kaplan-Meier survival curves, and ROC curve were used to evaluate the predictive ability of Cox risk model. Finally, the functional annotation of the risk model was obtained through enrichment analysis. Then, a six-gene signature was identified in the training cohort and verified among all cohorts using Cox regression analyses and Kaplan-Meier plots, demonstrating its independent prognostic significance for gastric cancer. In addition, ROC analysis confirmed the significant predictive potential of this signature for the prognosis of gastric cancer. Functional enrichment analysis was mainly related to cell-matrix function. Therefore, a new cuproptosis-related six-gene signature (ACLY, FGD6, SERPINE1, SPATA13, RANGAP1, and ADGRE5) was constructed for the prognosis of gastric cancer, allowing for tailored prediction of outcome and the formulation of novel therapeutics for gastric cancer patients.

Clinical features and outcomes of pediatric intracranial gliomas: results from single center's 226 cases and corroborated with SEER database.

BACKGROUND: Pediatric gliomas are the most common central nervous system (CNS) tumors in children and adolescents and show different clinical and histopathological characteristics from the adult. The prognostic factors were poorly defined in pediatric intracranial gliomas. METHODS: We collected pediatric intracranial glioma cases in our institution between February 2011 and June 2022. The patient clinical data, tumor growth characteristics, treatments, and follow-up data were analyzed by Cox regression analysis to identify impact factors on the prognosis of pediatric intracranial glioma patients. To corroborate our data, an independent cohort of pediatric intracranial glioma from the Surveillance, Epidemiology, and End Results Program (SEER) database was analyzed. RESULTS: A total of 181 cases of pediatric low-grade glioma (PLGG) and 45 cases of pediatric high-grade glioma (PHGG) were included. In multivariate Cox regression analysis, tumor size > 59.5 mm (p = 0.006) and non-gross total resection (non-GTR; subtotal resection, STR, p = 0.042; biopsy, p = 0.012) were associated with decreased overall survival (OS) in PLGG patients. In PHGG patients, only chemotherapy (p = 0.023) was associated with OS while tumor size was marginally prognostic for OS (p = 0.051). Additional independent analysis of 2734 PLGG and 741 PHGG from the SEER database corroborated that larger tumor size was associated with decreased OS in LGG (p = 0.001) and HGG (p < 0.001) patients, separately. CONCLUSION: In this study, we found that tumor size was a significant prognostic factor for the OS of PLGG patients in our series. Besides the tumor size, the extent of resection also independently impacted the prognosis of PLGG patients. While in PHGG patients, only chemotherapy was associated with improved OS and tumor size was marginally prognostic.

Bioactive Constituents of Verbena officinalis Alleviate Inflammation and Enhance Killing Efficiency of Natural Killer Cells.

Natural killer (NK) cells play key roles in eliminating pathogen-infected cells. Verbena officinalis (V. officinalis) has been used as a medical plant in traditional and modern medicine for its anti-tumor and anti-inflammatory activities, but its effects on immune responses remain largely elusive. This study aimed to investigate the potential of V. officinalis extract (VO extract) to regulate inflammation and NK cell functions. We examined the effects of VO extract on lung injury in a mouse model of influenza virus infection. We also investigated the impact of five bioactive components of VO extract on NK killing functions using primary human NK cells. Our results showed that oral administration of VO extract reduced lung injury, promoted the maturation and activation of NK cells in the lung, and decreased the levels of inflammatory cytokines (IL-6, TNF-α and IL-1ß) in the serum. Among five bioactive components of VO extract, Verbenalin significantly enhanced NK killing efficiency in vitro, as determined by real-time killing assays based on plate-reader or high-content live-cell imaging in 3D using primary human NK cells. Further investigation showed that treatment of Verbenalin accelerated the killing process by reducing the contact time of NK cells with their target cells without affecting NK cell proliferation, expression of cytotoxic proteins, or lytic granule degranulation. Together, our findings suggest that VO extract has a satisfactory anti-inflammatory effect against viral infection in vivo, and regulates the activation, maturation, and killing functions of NK cells. Verbenalin from V. officinalis enhances NK killing efficiency, suggesting its potential as a promising therapeutic to fight viral infection.

African swine fever virus MGF360-11L negatively regulates cGAS-STING-mediated inhibition of type I interferon production.

The type I interferon (IFN-I) signaling pathway is an important part of the innate immune response and plays a vital role in controlling and eliminating pathogens. African swine fever virus (ASFV) encodes various proteins to evade the host's natural immunity. However, the molecular mechanism by which the ASFV-encoded proteins inhibit interferon production remains poorly understood. In the present study, ASFV MGF360-11L inhibited cGAS, STING, TBK1, IKKε, IRF7 and IRF3-5D mediated activation of the IFN-ß and ISRE promoters, accompanied by decreases in IFN-ß, ISG15 and ISG56 mRNA expression. ASFV MGF360-11L interacted with TBK1 and IRF7, degrading TBK1 and IRF7 through the cysteine, ubiquitin-proteasome and autophagy pathways. Moreover, ASFV MGF360-11L also inhibited the phosphorylation of TBK1 and IRF3 stimulated by cGAS-STING overexpression. Truncation mutation analysis revealed that aa 167-353 of ASFV MGF360-11L could inhibit cGAS-STING-mediated activation of the IFN-ß and ISRE promoters. Finally, the results indicated that ASFV MGF360-11L plays a significant role in inhibiting IL-1ß, IL-6 and IFN-ß production in PAM cells (PAMs) infected with ASFV. In short, these results demonstrated that ASFV MGF360-11L was involved in regulating IFN-I expression by negatively regulating the cGAS signaling pathway. In summary, this study preliminarily clarified the molecular mechanism by which the ASFV MGF360-11L protein antagonizes IFN-I-mediated antiviral activity, which will help to provide new strategies for the treatment and prevention of ASF.

Anti-phytopathogenic-bacterial fatty acids from the mycelia of the edible mushroom Agaricus blazei.

Five compounds including a new compound (1) were isolated from mycelia of a mushroom-forming fungus Agaricus blazei. Compound 2 was isolated from nature for the first time. Their structures were determined by the interpretation of spectroscopic data. In the bioassay examining growth inhibitory activity against phytopathogenic bacteria Clavibacter michiganensis, Burkholderia glumae, and Peptobacterium carotovorum, all the compounds showed inhibition effects on C. michiganensis. Compounds 3 and 4 also showed weak inhibitory activity against growth of B. glumae.

A new lanostane triterpenoid from the mushroom Hypholoma fasciculare.

A novel compound (1) and 3 known compounds (2-4) were isolated from the fruiting bodies of Hypholoma fasciculare. The structure of 1 was determined by the interpretation of spectroscopic data. Compounds 2-4 were identified by comparing the spectra data of known compounds. In the bioassay examining growth inhibitory activity against phytopathogenic bacteria Clavibacter michiganensis, Burkholderia glumae, and Peptobacterium carotovorum, compounds 1, 2, and 4 showed inhibition effects on C. michiganensis only.

Heterologous expression and characterization of two novel glucanases derived from sheep rumen microbiota.

ß-Glucanases are a suite of glycoside hydrolases that depolymerize ß-glucan into cellooligosaccharides and/or monosaccharides and have been widely used as feed additives in livestock. In this study, two novel glucanase genes, IDSGluc5-26 and IDSGluc5-37, derived from sheep rumen microbiota, were expressed and functionally characterized. The optimal temperatures/pH of recombinant IDSGLUC5-26 and IDSGLUC5-37 were 50 °C/5.0 and 40 °C/6.0, respectively. Notably, IDSGLUC5-26 showed considerable stability under acidic conditions. Both IDSGLUC5-26 and IDSGLUC5-37 showed the highest activities toward barley ß-glucan, with Vmax values of 89.96 ± 9.19 µmol/min/mg and 459.50 ± 25.02 µmol/min/mg, respectively. Additionally, these two glucanases demonstrated hydrolysis of Icelandic moss lichenan and konjac gum, IDSGLUC5-26 releasing cellobiose (G2; occupying 17.37% of total reducing sugars), cellotriose (G3; 23.97%), and cellotetraose (G4; 30.93%) from barley ß-glucan and Icelandic moss lichenan after 10 min and suggestive of a typical endo-ß-1,4-glucanase (EC.3.2.1.4). In contrast, IDSGLUC5-37 was capable of liberating dominant G3 (64.11% or 67.55%) from barley ß-glucan or Icelandic moss lichenan, suggesting that the enzyme was likely an endo-ß-1,3 - 1,4-glucanases/lichenase (EC3.2.1.73). These findings describe the expression and characterization of two novel glucanase genes from sheep rumen microbiota. The two recombinant enzymes, particularly the acid-stable IDSGLUC5-26, will be of interest for potential application in food-/feed-additive development.

[Research on muscle fatigue recognition model based on improved wavelet denoising and long short-term memory].

The automatic recognition technology of muscle fatigue has widespread application in the field of kinesiology and rehabilitation medicine. In this paper, we used surface electromyography (sEMG) to study the recognition of leg muscle fatigue during circuit resistance training. The purpose of this study was to solve the problem that the sEMG signals have a lot of noise interference and the recognition accuracy of the existing muscle fatigue recognition model is not high enough. First, we proposed an improved wavelet threshold function denoising algorithm to denoise the sEMG signal. Then, we build a muscle fatigue state recognition model based on long short-term memory (LSTM), and used the Holdout method to evaluate the performance of the model. Finally, the denoising effect of the improved wavelet threshold function denoising method proposed in this paper was compared with the denoising effect of the traditional wavelet threshold denoising method. We compared the performance of the proposed muscle fatigue recognition model with that of particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN). The results showed that the new wavelet threshold function had better denoising performance than hard and soft threshold functions. The accuracy of LSTM network model in identifying muscle fatigue was 4.89% and 2.47% higher than that of PSO-SVM and CNN, respectively. The sEMG signal denoising method and muscle fatigue recognition model proposed in this paper have important implications for monitoring muscle fatigue during rehabilitation training and exercise.

Development and validation of a nomogram using on admission routine laboratory parameters to predict in-hospital survival of patients with COVID-19.

To develop and validate a nomogram using on admission data to predict in-hospital survival probabilities of coronavirus disease 2019 (COVID-19) patients. We analyzed 855 COVID-19 patients with 52 variables. The least absolute shrinkage and selection operator regression and multivariate Cox analyses were used to screen significant factors associated with in-hospital mortality. A nomogram was established based on the variables identified by Cox regression. The performance of the model was evaluated by C-index and calibration plots. Decision curve analysis was conducted to determine the clinical utility of the nomogram. Six variables, including neutrophil (hazard ratio [HR], 1.088; 95% confidence interval [CI], [1.0004-1.147]; p < .001), C-reactive protein (HR, 1.007; 95% CI, [1.0026-1.011]; p = .002), IL-6 (HR, 1.001; 95% CI, [1.0003-1.002]; p = .005), d-dimer (HR, 1.034; 95% CI, [1.0111-1.057]; p = .003), prothrombin time (HR 1.086, 95% CI [1.0369-1.139], p < .001), and myoglobin (HR, 1.001; 95% CI, [1.0007-1.002]; p < .001), were identified and applied to develop a nomogram. The nomogram predicted 14-day and 28-day survival probabilities with reasonable accuracy, as assessed by the C-index (0.912) and calibration plots. Decision curve analysis showed relatively wide ranges of threshold probability, suggesting a high clinical value of the nomogram. Neutrophil, C-reactive protein, IL-6, d-dimer, prothrombin time, and myoglobin levels were significantly correlated with in-hospital mortality of COVID-19 patients. Demonstrating satisfactory discrimination and calibration, this model could predict patient outcomes as early as on admission and might serve as a useful triage tool for clinical decision making.

Inflammation-associated factors for predicting in-hospital mortality in patients with COVID-19.

The aim is to explore the relation between inflammation-associated factors and in-hospital mortality and investigate which factor is an independent predictor of in-hospital death in patients with coronavirus disease-2019. This study included patients with coronavirus disease-2019, who were hospitalized between February 9, 2020, and March 30, 2020. Univariate Cox regression analysis and least absolute shrinkage and selection operator regression (LASSO) were used to select variables. Multivariate Cox regression analysis was applied to identify independent risk factors in coronavirus disease-2019. A total of 1135 patients were analyzed during the study period. A total of 35 variables were considered to be risk factors after the univariate regression analysis of the clinical characteristics and laboratory parameters (p < .05), and LASSO regression analysis screened out seven risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were myoglobin (HR, 5.353; 95% CI, 2.633-10.882; p < .001), C-reactive protein (HR, 2.063; 95% CI, 1.036-4.109; p = .039), neutrophil count (HR, 2.015; 95% CI, 1.154-3.518; p = .014), interleukin 6 (Il-6; HR, 9.753; 95% CI, 2.952-32.218; p < .001), age (HR, 2.016; 95% CI, 1.077-3.773; p = .028), and international normalized ratio (HR, 2.595; 95% CI, 1.412-4.769; p = .002). Our results suggested that inflammation-associated factors were significantly associated with in-hospital mortality in coronavirus disease-2019 patients. C-reactive protein, neutrophil count, and interleukin 6 were independent factors for predicting in-hospital mortality and had a better independent predictive ability. We believe these findings may allow early identification of the patients at high risk for death, and can also assist in better management of these patients.

Diabetes, even newly defined by HbA1c testing, is associated with an increased risk of in-hospital death in adults with COVID-19.

BACKGROUND: Diabetes is associated with poor coronavirus disease 2019 (COVID-19) outcomes. However, little is known on the impact of undiagnosed diabetes in the COVID-19 population. We investigated whether diabetes, particularly undiagnosed diabetes, was associated with an increased risk of death from COVID-19. METHODS: This retrospective study identified adult patients with COVID-19 admitted to Tongji Hospital (Wuhan) from January 28 to April 4, 2020. Diabetes was determined using patients' past history (diagnosed) or was newly defined if the hemoglobin A1c (HbA1c) level at admission was ≥6.5% (48 mmol/mol) (undiagnosed). The in-hospital mortality rate and survival probability were compared between the non-diabetes and diabetes (overall, diagnosed, and undiagnosed diabetes) groups. Risk factors of mortality were explored using Cox regression analysis. RESULTS: Of 373 patients, 233 were included in the final analysis, among whom 80 (34.3%) had diabetes: 44 (55.0%) reported a diabetes history, and 36 (45.0%) were newly defined as having undiagnosed diabetes by HbA1c testing at admission. Compared with the non-diabetes group, the overall diabetes group had a significantly increased mortality rate (22.5% vs. 5.9%, p <  0.001). Moreover, the overall, diagnosed, and undiagnosed diabetes groups displayed lower survival probability in the Kaplan-Meier survival analysis (all p <  0.01). Using multivariate Cox regression, diabetes, age, quick sequential organ failure assessment score, and D-dimer ≥1.0 µg/mL were identified as independent risk factors for in-hospital death in patients with COVID-19. CONCLUSIONS: The prevalence of undiagnosed pre-existing diabetes among patients with COVID-19 is high in China. Diabetes, even newly defined by HbA1c testing at admission, is associated with increased mortality in patients with COVID-19. Screening for undiagnosed diabetes by HbA1c measurement should be considered in adult Chinese inpatients with COVID-19.

Biotransformation ability of endophytic fungi: from species evolution to industrial applications.

Increased understanding of the interactions between endophytic fungi and plants has led to the discovery of a new generation of chemical compounds and processes between endophytic fungi and plants. Due to the long-term co-evolution between fungal endophytes and host plants, endophytes have evolved special biotransformation abilities, which can have critical consequences on plant metabolic processes and their composition. Biotransformation or bioconversion can impact the synthesis and decomposition of hormones, sugars, amino acids, vitamins, lipids, proteins, and various secondary metabolites, including flavonoids, polysaccharides, and terpenes. Endophytic fungi produce enzymes and various bioactive secondary metabolites with industrial value and can degrade or sequester inorganic and organic small molecules and macromolecules (e.g., toxins, pollutants, heavy metals). These fungi also have the ability to cause highly selective catalytic conversion of high-value compounds in an environmentally friendly manner, which can be important for the production/innovation of bioactive molecules, food and nutrition, agriculture, and environment. This work mainly summarized recent research progress in this field, providing a reference for further research and application of fungal endophytes. KEY POINTS: •The industrial value of degradation of endophytes was summarized. • The commercial value for the pharmaceutical industry is reviewed.

Fluorescent nucleotide-lanthanide nanoparticles for highly selective determination of picric acid.

A new method based on coordination polymer nanoparticles (CPNs) derived from nucleotides and Tb3+ ions (GMP/Tb) for the selective and sensitive determination of aqueous 2,4,6-trinitrophenol (TNP) (picric acid) is established. The fluorescence of GMP/Tb nanoparticles is effectively quenched by TNP via photo-induced charge transfer (PCT), thus achieving its selectivity toward TNP over other nitroaromatic explosives. The decreased fluorescence of GMP/Tb shows a good linear relationship to the concentrations of TNP ranging from 5.0 to 40.0 µM, and the limit of detection is 26.0 nM (5.96 ppb). The proposed GMP/Tb probe also achieves satisfactory results in real samples. The obtained recoveries of this method in river water samples are in the range 93.15-106.10%. The relative standard deviation (RSD) are 0.57 to 1.01% based on three repeated determinations. This fabricated detector provides a feasible path for determination of ppb-level TNP in natural water samples, which can help humans to avoid TNP-contaminated drinking water. Graphical abstract.

A Muscle Fatigue Classification Model Based on LSTM and Improved Wavelet Packet Threshold.

Previous studies have used the anaerobic threshold (AT) to non-invasively predict muscle fatigue. This study proposes a novel method for the automatic classification of muscle fatigue based on surface electromyography (sEMG). The sEMG data were acquired from 20 participants during an incremental test on a cycle ergometer using sEMG sensors placed on the vastus rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), and gastrocnemius (GA) muscles of the left leg. The ventilation volume (VE), oxygen uptake (VO2), and carbon dioxide production (VCO2) data of each participant were collected during the test. Then, we extracted the time-domain and frequency-domain features of the sEMG signal denoised by the improved wavelet packet threshold denoising algorithm. In this study, we propose a new muscle fatigue recognition model based on the long short-term memory (LSTM) network. The LSTM network was trained to classify muscle fatigue using sEMG signal features. The results showed that the improved wavelet packet threshold function has better performance in denoising sEMG signals than hard threshold and soft threshold functions. The classification performance of the muscle fatigue recognition model proposed in this paper is better than that of CNN (convolutional neural network), SVM (support vector machine), and the classification models proposed by other scholars. The best performance of the LSTM network was achieved with 70% training, 10% validation, and 20% testing rates. Generally, the proposed model can be used to monitor muscle fatigue.

Motor style at rest and during locomotion in human.

Humans exhibit various motor styles that reflect their intra- and interindividual variability when implementing sensorimotor transformations. This opens important questions, such as, At what point should they be readjusted to maintain optimal motor control? Do changes in motor style reveal the onset of a pathological process and can these changes help rehabilitation and recovery? To further investigate the concept of motor style, tests were carried out to quantify posture at rest and motor control in 18 healthy subjects under four conditions: walking at three velocities (comfortable walking, walking at 4 km/h, and race walking) and running at maximum velocity. The results suggest that motor control can be conveniently decomposed into a static component (a stable configuration of the head and column with respect to the gravitational vertical) and dynamic components (head, trunk, and limb movements) in humans, as in quadrupeds, and both at rest and during locomotion. These skeletal configurations provide static markers to quantify the motor style of individuals because they exhibit large variability among subjects. Also, using four measurements (jerk, root mean square, sample entropy, and the two-thirds power law), it was shown that the dynamics were variable at both intra- and interindividual levels during locomotion. Variability increased following a head-to -toe gradient. These findings led us to select dynamic markers that could define, together with static markers, the motor style of a subject. Finally, our results support the view that postural and motor control are subserved by different neuronal networks in frontal, sagittal, and transversal planes.NEW & NOTEWORTHY During human locomotion, motor control can be conveniently decomposed into a static and dynamic components. Variable dynamics were observed at both the intra- and interindividual levels during locomotion. Variability increased following a head-to-toe gradient. Finally, our results support the view that postural and motor control are subserved by different neuronal networks in the frontal, sagittal, and transversal planes.