A case report and literature review on nephrogenic alveolar soft part sarcoma: clinicopathological manifestations and genetic features.

BACKGROUND: Alveolar soft part sarcoma (ASPS) is a rare kind of malignant soft tissue tumor with undefined differentiation, of which the incidence rate accounts for only 0.5-1.0% among all kinds of soft tissue tumors. An even rarer ASPS occurs in kidney. CASE PRESENTATION: Here we reported a case of a 7-year-old girl diagnosed with nephrogenic ASPS, regarding the analyses of the incidence, clinical manifestation, pathology and genetic diagnosis, in order to deepen the recognition of the disease. CONCLUSIONS: ASPS is very rare, and tends to occur to young patients. It is very significant to precisely diagnose ASPS at an early stage, which will be the key point for the following treatment choices and prognosis.

Comparison between spleen and liver stiffness measurements by sound touch elastography for diagnosing cirrhosis at different aminotransferase levels: a prospective multicenter study.

OBJECTIVES: To compare the performance of spleen stiffness measurement (SSM) and liver stiffness measurement (LSM) by sound touch elastography (STE) for the diagnosis of cirrhosis at different alanine aminotransferase (ALT) levels, and to compare the applicability and repeatability of SSM with LSM performed by STE, a new two-dimensional shear wave elastography technology. METHODS: This prospective multicenter study included 25 centers and recruited chronic hepatitis B (CHB) patients with liver biopsy between May 2018 and November 2019. All patients underwent LSM and SSM by STE. Success and reliability rates were calculated and compared. Intra-observer agreement was assessed using intraclass correlation coefficients (ICCs). Differences between areas under the receiver operating characteristic curves (AUCs) of LSMs and SSMs at different ALT levels were compared using the Delong test. RESULTS: Among 603 CHB patients, the success and reliability rates of SSM were 94.53% (570/603) and 85.74% (517/603), respectively, which were similar to those of LSM (p > 0.05), respectively. The ICC for intra-observer agreements of SSM was 0.964 (p < 0.001). In the total cohort, ALT ≤ 2 × upper limit of normal (ULN) group, and A0-1 group, the AUCs of SSMs were significantly lower than those of LSMs for the diagnosis of cirrhosis (p < 0.001). In the ALT > 2 × ULN group and A2-3 group, the AUC of SSM improved and was not significantly different from that of LSM (p = 0.342, p = 0.510, respectively). CONCLUSIONS: SSM by STE achieved applicability and repeatability equivalent to those of LSM. SSM might be a good substitute to LSM in patients with high ALT levels. KEY POINTS: • Spleen stiffness measurement performed by sound touch elastography was proven to have similar applicability and repeatability to liver stiffness measurement in this prospective multicenter study. • Spleen stiffness measurement demonstrated a poorer diagnostic performance for cirrhosis compared with liver stiffness measurement in the total cohort and low ALT level group, yet it showed a similar diagnostic performance to liver stiffness measurement in patients with high ALT levels.

Microbial community succession in soils under long-term heavy metal stress from community diversity-structure to KEGG function pathways.

Currently, understanding the structure and function of the microbial community is the key step in artificially constructing microbial communities to control soil heavy metal pollution. Abundant/rare microbial communities play different roles in different levels of concentrations. However, the correlation between heavy metals and rare/abundant subgroups is poorly understood. In this study, we used a metagenomics approach to comprehensively investigate the evolutionary changes in microbial diversity, structure, and function under different heavy metal concentration stress in soils surrounding gold tailings. The results show that the main pollutants were Pb, As, and Zn. Indigenous microorganisms have different responses to heavy metal concentrations. Bacteria are the main components of indigenous microorganisms, mainly including Actinobacteria, Proteobacteria, Chloroflexi, and Acidobacteria. With the increase of heavy metal pollution, the relative abundance of Proteobacteria increased, and that of Actinobacteria decreased. Archaea was significantly inhibited by heavy metal stress and was more sensitive to heavy metal concentration. The response of fungi to heavy metal concentration was not obvious. The results of KEGG pathways showed that carbon fixation was inhibited with increasing heavy metal concentrations, while nitrogen metabolism was in contrast. Abundant subcommunity had a greater correlation mainly with metal resistance mechanisms, and rare subcommunity plays a key role for soil nutrient cycling such as N, S cycling in soils contaminated. Overall, this study provides a comprehensive analysis of the effects of heavy metal stress at different concentrations on microorganisms in farmland around gold tailings and reveals the relationship between heavy metals on KEGG pathways.

A twin enrichment method based on dispersive liquid-liquid microextraction and field-amplified sample injection for the simultaneous determination of sulfonamides.

A twin enrichment method based on offline dispersive liquid-liquid microextraction (DLLME) coupled with online field-amplified sample injection (FASI) was developed for the simultaneous determination of four sulfonamide (SA) antibiotics, including sulfamethazine (SMZ), sulfamerazine (SMR), sulfadizine (SDZ) and sulfacetamide (SFA), in different environmental waters, followed by capillary electrophoresis (CE). Various parameters that affected the separation performance of CE and the enrichment efficiencies of DLLME and FASI were optimized in detail, and excellent CE separation was attained within 6 min. The DLLME-FASI-CE offered high sensitivity enrichment factors of 206, 166, 185 and 150 for SMZ, SMR, SDZ and SFA, respectively. Highly sensitive detection was realized with low limits of detection (LODs), which ranged from 2.0-23.0, 2.2-26.0 and 4.3-63.0 ng mL-1 in tap water, lake water and seawater, respectively, as well as limits of quantification (LOQs) within 6.0-63.0, 7.4-96.0 and 14.0-201.0 ng mL-1, respectively. Satisfactory recoveries in the range of 91-108% were obtained with the three spiked environmental water samples, and the relative standard deviations were from 1.09-7.45%. The simple effective twin enrichment method provided promising perspective for CE determination of SAs in complicated aqueous matrices, with rapidity, sensitivity, and accuracy.

Dispersive liquid-liquid microextraction coupled with pressure-assisted electrokinetic injection for simultaneous enrichment of seven phenolic compounds in water samples followed by determination using capillary electrophoresis.

Offline dispersive liquid-liquid microextraction combined with online pressure-assisted electrokinetic injection was developed to simultaneously enrich seven phenolic compounds in water samples, followed by determination using capillary electrophoresis, namely phenol, 4-chlorophenol, pentachlorophenol, 2,4,6-trichlorophenol, 2,4-dichlorophenol, 2-chlorophenol, and 2,6-dichlorophenol. Several parameters affecting separation performance of capillary electrophoresis and the enrichment efficiency of pressure-assisted electrokinetic injection and dispersive liquid-liquid microextraction were systematically investigated. Under the optimal conditions, seven phenolic compounds were completely separated within 14 min and good enrichment factors were obtained of 61, 236, 3705, 3288, 920, 86, and 1807 for phenol, 4-chlorophenol, pentachlorophenol, 2,4,6-trichlorophenol, 2,4-dichlorophenol, 2-chlorophenol, and 2,6-dichlorophenol, respectively. Good linearity was attained in the range of 0.1-200 µg/L for 2,4-dichlorophenol, 0.5-200 µg/L for 4-chlorophenol, pentachlorophenol, 2,4,6-trichlorophenol, 2-chlorophenol, and 2,6-dichlorophenol, as well as 1-200 µg/L for phenol, with correlation coefficients (r) over 0.9905. The limits of detection and quantification ranging from 0.03-0.28 and 0.07-0.94 µg/L were attained. This two step enrichment method was potentially applicable for the rapid and simultaneous determination of phenolic compounds in water samples.

Tiansi Liquid Modulates Gut Microbiota Composition and Tryptophan⁻Kynurenine Metabolism in Rats with Hydrocortisone-Induced Depression.

Tiansi Liquid is a traditional Chinese herbal medicine used to treat depression; however, the underlying mechanisms remain unclear. Here, we examined the effect of Tiansi Liquid in a rat model of hydrocortisone-induced depression using behavioral testing, 16S rRNA high-throughput pyrosequencing and high-performance liquid chromatography-mass spectrometry-based metabolomics of the tryptophan (TRP)⁻kynurenine (KYN) pathway. Tiansi Liquid significantly improved the sucrose preference and exploratory behavior of the depressive rats. The richness of intestinal mucosa samples from the model (depressive) group tended to be higher than that from the control group, while the richness was higher in the Tiansi Liquid-treated group than in the model group. Tiansi Liquid increased the relative abundance of some microbiota (Ruminococcaceae, Lactococcus, Lactobacillus, Lachnospiraceae_NK4A136_group). Metabolomics showed that Tiansi Liquid reduced the levels of tryptophan 2,3 dioxygenase, indoleamine 2,3-dioxygenase, quinoline and the KYN/TRP ratio, while increasing kynurenic acid and 5-HT levels. Correlation analysis revealed a negative relationship between the relative abundance of the Lachnospiraceae_NK4A136_group and quinoline content. Collectively, these findings suggest that Tiansi Liquid ameliorates depressive symptoms in rats by modulating the gut microbiota composition and metabolites in the TRP⁻KYN pathway.

In Silico Prediction of the Anti-Depression Mechanism of a Herbal Formula (Tiansi Liquid) Containing Morinda officinalis and Cuscuta chinensis.

Purpose: Depression is a sickening psychiatric condition that is prevalent worldwide. To manage depression, the underlying modes of antidepressant effect of herbals are important to be explored for the development of natural drugs. Tiansi Liquid is a traditional Chinese medicine (TCM) that is prescribed for the management of depression, however its underlying mechanism of action is still uncertain. The purpose of this study was to systematically investigate the pharmacological mode of action of a herbal formula used in TCM for the treatment of depression. Methods: Based on literature search, an ingredients-targets database was developed for Tiansi Liquid, followed by the identification of targets related to depression. The interaction between these targets was evaluated on the basis of protein-protein interaction network constructed by STITCH and gene ontology (GO) enrichment analysis using ClueGO plugin. Results: As a result of literature search, 57 components in Tiansi Liquid formula and 106 potential targets of these ingredients were retrieved. A careful screening of these targets led to the identification of 42 potential targets associated with depression. Ultimately, 327 GO terms were found by analysis of gene functional annotation clusters and abundance value of these targets. Most of these terms were found to be closely related to depression. A significant number of protein targets such as IL10, MAPK1, PTGS2, AKT1, APOE, PPARA, MAPK1, MIF, NOS3 and TNF-α were found to be involved in the functioning of Tiansi Liquid against depression. Conclusions: The findings elaborate that Tiansi Liquid can be utilized to manage depression, however, multiple molecular mechanisms of action could be proposed for this effect. The observed core mechanisms could be the sensory perception of pain, regulation of lipid transport and lipopolysaccharide-mediated signaling pathway.

Genomic characterization of a novel ureolytic bacteria, Lysinibacillus capsici TSBLM, and its application to the remediation of acidic heavy metal-contaminated soil.

Soil heavy metal contamination is an essential challenge in ecological and environmental management, especially for acidic soils. Microbially induced carbonate precipitation (MICP) is an effective and environmentally friendly remediation technology for heavy metal contaminated sites, and one of the key factors for its realization lies in the microorganisms. In this study, Lysinibacillus capsici TSBLM was isolated from heavy metal contaminated soil around a gold mine, and inferred to be a novel ureolytic bacteria after phylogenomic inference and genome characterization. The urease of L. capsici TSBLM was analyzed by genetic analysis and molecular docking, and further applied this bacteria to the remediation of Cu and Pb in solution and acidic soils to investigate its biomineralization mechanism and practical application. The results revealed L. capsici TSBLM possessed a comprehensive urease gene cluster ureABCEFGD, and the encoded urease docked with urea at the lowest binding energy site (ΔG = -3.43 kcal/mol) connected to three amino acids threonine, aspartic, and alanine. The urease of L. capsici TSBLM is synthesized intracellularly but mainly functions extracellularly. L. capsici TSBLM removes Cu/Pb from the solution by generating heavy metal carbonates or co-precipitating with CaCO3 vaterite. For acidic heavy metal-contaminated soil, the carbonate-bound states of Cu and Pb increased significantly from 7 % to 16 % and from 23 % to 35 % after 30 days by L. capsici TSBLM. Soil pH improved additionally. L. capsici TSBLM maintained the dominant status in the remediated soil after 30 days, demonstrating good environmental adaptability and curing persistence. The results provided new strain resources and practical application references for the remediation of acidic heavy metal contaminated soil based on MICP.

Mechanisms of microbial-driven changes in soil ecological stoichiometry around gold mines.

In this study, we used soils with different pollution and nutrient levels (non-polluted S1, highly polluted low-nutrient S2, and highly polluted high nutrient S3) around the gold mine tailing ponds, and combined with metabolic limitation modeling and macro-genomics approaches, aiming to investigate the relationship between soil microbial composition and soil eco-chemometrics characteristics under heavy metal stress. The results showed that heavy pollution resulted in reduced SOC, TN, microbial biomass, and with C- and P- acquisition (BG, CBH, ALP) as well as nitrogen limitation of soil microbial metabolism in soils (S2, S3). Further analysis by macrogenomics showed that heavy metal contamination led to an increase in α-microbial diversity and altered the composition of microbial communities in the soil. The cycling of C, N, and P nutrients was altered by affecting the relative abundance of Anaeromyxobacter, Steroidobacter, Bradyrhizobium, Acidobacterium, Limnochorda (predominantly in the Ascomycetes and Acidobacteria phyla), with the most pronounced effect on the composition of microorganisms synthesizing C-acquiring enzymes, and heavy metals and pH were the main influences on ecological stoichiometry. The results of this study are useful for understanding the sustainability of ecological remediation in heavy metal contaminated areas and for developing ecological restoration strategies.

Exploring the synergistic interplay of sulfur metabolism and electron transfer in Cr(VI) and Cd(II) removal by Clostridium thiosulfatireducens: Genomic and mechanistic insights.

In this study, a sulfate-reducing bacterium, Clostridium thiosulfatireducens (CT) was reported and the performance and removal mechanism of Cr(VI) and Cd(II) removal were investigated. It is noteworthy that the dsrAB gene is absent in this strain, but the strain is capable of producing sulfide. The conversion rate of Cr(VI) by CT was 84.24 % at a concentration of 25 mg/L, and the conversion rate of Cd(II) was 94.19 % at a concentration of 28 mg/L. The complete genome is 6,106,624 bp and the genome consisted of a single chromosome. The GC content of the chromosomes was 29.65 %. The mechanism of heavy metal removal by CT bacteria mainly includes biosorption, electron transfer and redox, with reduction combined with S2- precipitation as the main pathway. The product characterization results showed that the formation of mainly ionic crystals and precipitates (CdS, Cd(OH)2, Cr(OH)3, Cr2O3) after adsorption. Genome-wide techniques have shown that the clearance of Cr(VI) and Cd(II) by CT is largely dependent on sulfate transport, sulfur metabolism, and energy metabolism to some extent. In addition, genes related to ATP binding, electron carrier activity, transporter protein genes, and DNA repair are also important factors to improve the heavy metal resistance and transformation ability of CT strains. Both the Fe-S cycle and the ROS-resistant system can enhance the electron transfer activity and thus slow down the damage of heavy metals to microorganisms. This study fills the gap in the understanding of the basic properties and heavy metal transformation mechanism of CT.

Diagnostic performance of artificial intelligence-based computer-aided diagnosis system in longitudinal and transverse ultrasonic views for differentiating thyroid nodules.

Objective: To evaluate the diagnostic performance of different ultrasound sections of thyroid nodule (TN) using computer-aided diagnosis system based on artificial intelligence (AI-CADS) in predicting thyroid malignancy. Materials and methods: This is a retrospective study. From January 2019 to July 2019, patients with preoperative thyroid ultrasound data and postoperative pathological results were enrolled, which were divided into two groups: lower risk group (ACR TI-RADS 1, 2 and 3) and higher risk group (ACR TI-RADS 4 and 5). The malignant risk scores (MRS) of TNs were obtained from longitudinal and transverse sections using AI-CADS. The diagnostic performance of AI-CADS and the consistency of each US characteristic were evaluated between these sections. The receiver operating characteristic (ROC) curve and the Cohen κ-statistic were performed. Results: A total of 203 patients (45.61 ± 11.59 years, 163 female) with 221 TNs were enrolled. The area under the ROC curve (AUC) of criterion 3 [0.86 (95%CI: 0.80~0.91)] was lower than criterion 1 [0.94 (95%CI: 0.90~ 0.99)], 2 [0.93 (95%CI: 0.89~0.97)] and 4 [0.94 (95%CI: 0.90, 0.99)] significantly (P<0.001, P=0.01, P<0.001, respectively). In the higher risk group, the MRS of transverse section was higher than longitudinal section (P<0.001), and the agreement of extrathyroidal extension and shape was moderate and fair (κ =0.48, 0.31 respectively). The diagnostic agreement of other ultrasonic features was substantial or almost perfect (κ >0.60). Conclusion: The diagnostic performance of computer-aided diagnosis system based on artificial intelligence (AI-CADS) in longitudinal and transverse ultrasonic views for differentiating thyroid nodules (TN) was different, which was higher in the transverse section. It was more dependent on the section for the AI-CADS diagnosis of suspected malignant TNs.

Machine learning in TCM with natural products and molecules: current status and future perspectives.

Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory.

Integration of Network Pharmacology and Molecular Docking Technology Reveals the Mechanism of the Therapeutic Effect of Xixin Decoction on Alzheimer's Disease.

BACKGROUND: So far, only a few researchers have systematically analyzed the constituents of the traditional Chinese medicine prescription Xixin Decoction (XXD) and its potential mechanism of action in treating Alzheimer's disease (AD). This study aimed to explore the potential mechanism of XXD in the treatment of AD using network pharmacology and molecular docking. METHODS: The compounds of XXD were searched within the Traditional Chinese Medicine System Pharmacology Database (TCMSP) and the Traditional Chinese Medicine Integrated Database (TCMID) databases. Overlapping AD-related targets obtained from the two databases and the predicted targets of XXD obtained from SwissTargetPrediction platform were imported into the STRING database to build PPI networks including hub targets; Cytoscape software was used to construct the herb-compound-target network while its plug-in CytoNCA was used to screen the main active compounds of XXD. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses explored the core biological mechanism and pathways via the Metascape platform. In addition, we used AutoDock Vina and PyMOL software to investigate the molecular docking of main compounds to hub targets. RESULTS: We determined 114 active compounds, 973 drug targets, and 973 disease targets. However, intersection analysis screened out 208 shared targets.Protein-protein interaction (PPI) network identified 9 hub targets. The hub targets were found to be majorly enriched in several biological processes (positive regulation of kinase activity, positive regulation of cell death, regulation of MAPK cascade, trans-synaptic signaling, synaptic signaling, etc.) and the relevant pathways of Alzheimer's disease, including neuroactive ligand-receptor interaction, dopaminergic synapse, serotonergic synapse, and the MAPK signaling pathway, etc. The pathway-target-compound network of XXD for treating AD was then constructed. 8 hub targets exhibited good binding activity with 9 main active compounds of XXD in molecular docking. CONCLUSION: In this study, we found multi-compound-multi-target-multi-pathway regulation to reveal the mechanism of XXD for treating AD based on network pharmacology and molecular docking. XXD may play a therapeutic role through regulating the Alzheimer's disease pathway, its downstream PI3K/Akt signaling pathway or the MAPK signaling pathway, thereby treating AD. This provides new insights for further experiments on the pharmacological effects of XXD.

Downregulation of GSK-3ß Expression via Ultrasound-Targeted Microbubble Destruction Enhances Atherosclerotic Plaque Stability in New Zealand Rabbits.

Accumulating evidence suggests that atherosclerosis (AS) is the underlying cause of vascular diseases, including heart disease and stroke. Ultrasound-targeted microbubble destruction (UTMD) technology provides a tolerable, efficient and effective system for drug delivery and gene transfection, which has broad application prospects in the treatment of AS. In addition, glycogen synthase kinase (GSK)-3ß has been implicated as a potentially valuable therapeutic agent for AS treatment; however, the specific molecular mechanisms remain unknown. Therefore, this study was conducted to explore the effect of downregulation of GSK-3ß expression via UTMD on atherosclerotic plaque stability. We established a THP-1 macrophage-derived foam cell model in vitro and an atherosclerotic plaque model in the right common carotid artery of New Zealand rabbits. We determined levels of the relevant vulnerable plaque stability elements. The results indicate that GSK-3ß was upregulated in the foam cells and in atherosclerotic rabbits. Downregulation of GSK-3ß expression by UTMD suppressed vulnerable plaque factors and inflammation in vitro and in vivo, changed the cytoskeleton of the foam cells in vitro, increased Young's modulus and decreased the peak intensity of atherosclerotic plaque in vivo. Moreover, GSK-3ß inhibition by UTMD did not influence the viability of the foam cells. Collectively, our results indicate that GSK-3ß could be a potential target for anti-atherogenic interventions and, in particular, can improve the stability of AS plaques in combination with UTMD.

Application of Ultrasound-Targeted Microbubble Destruction-Mediated Exogenous Gene Transfer in Treating Various Renal Diseases.

Chronic renal disease or acute renal injury could result in end-stage renal disease or renal failure. Sonoporation, induced by ultrasound-targeted microbubble destruction (UTMD), has evolved as a new technology for gene delivery. It increases the transfection efficiency of the genes into target kidney tissues. Moreover, UTMD-mediated gene delivery can directly repair the damaged tissues or improve the recruitment and homing of stem cells in the recovery of injured tissues, which has the potential to act as a non-viral and effective method to current gene therapy. This article reviews the mechanisms and applications of UTMD in terms of renal disease, including diabetic nephropathy, renal carcinoma, acute kidney injury, renal interstitial fibrosis, nephrotoxic nephritis, urinary stones, and acute rejection.