Enhancing Reversibility and Stability of Mg Metal Anodes: High-Exposure (002) Facets and Nanosheet Arrays for Superior Mg Plating/Stripping.

Magnesium metal batteries (MMBs), recognized as promising contenders for post-lithium battery technologies, face challenges such as uneven magnesium (Mg) plating and stripping behaviors, leading to uncontrollable dendrite growth and irreversible structural damage. Herein, we have developed a Mg foil featuring prominently exposed (002) facets and an architecture of nanosheet arrays (termed (002)-Mg), created through a one-step acid etching method. Specifically, the prominent exposure of Mg (002) facets, known for their inherently low surface and adsorption energies with Mg atoms, not only facilitates smooth nucleation and dense deposition but also significantly mitigates side reactions on the Mg anode. Moreover, the nanosheet arrays on the surface evenly distribute the electric field and Mg ion flux, enhancing Mg ion transfer kinetics. As a result, the fabricated (002)-Mg electrodes exhibit unprecedented long-cycle performance, lasting over 6000 h (> 8 months) at a current density of 3 mA cm-2 for a capacity of 3 mAh cm-2. Furthermore, the corresponding pouch cells equipped with various electrolytes and cathodes demonstrate remarkable capacity and cycling stability, highlighting the superior electrochemical compatibility of the (002)-Mg electrode. This study provides new insights into the advancement of durable MMBs by modifying the crystal structure and morphology of Mg.

Qualitative and Quantitative Analysis of Three-Dimensional Fluorescence Spectra by Improved Parallel Factor Analysis with Internal Standard Sample Embedding.

The strategy of parallel factor analysis, combined with the internal standard method, has been increasingly applied to the qualitative and quantitative analysis of three-dimensional fluorescence spectra of unknown mixed fluorophores. Nevertheless, the disparity in the number of fluorophores included in the internal standard sample set and the number included in test samples may impact the qualitative and quantitative outcomes of parallel factor analysis. In this work, we systematically established the framework of the parallel factor analysis with internal standard sample embedding (ISSE-PARAFAC) strategy. We applied this framework to six datasets representing two scenarios and a real dataset and conducted a detailed discussion on the effects of the disparity between the number of fluorophores in the internal standard sample set and the number in the test set on both qualitative and quantitative results. Additionally, we introduced an enhancement to PARAFAC by aggregating fluorophores with similar emission wavelengths, corresponding to the peaks of emission loadings (spectra) obtained from PARAFAC, as a single fluorophore. This aggregation aimed to mitigate the strong correlation between similar fluorophores. The results imply that the presence of irrelevant fluorophores in the internal standard sample set, whether increased or decreased, does not significantly affect the qualitative and quantitative analysis of target fluorophores in the test set. Moreover, we demonstrated that the improved parallel factor analysis with internal standard sample embedding not only fully decomposes the uncorrelated mixed fluorophores for qualitative analysis but also allows the established linear concentration model for fluorescent components to predict the corresponding fluorophore concentration of test samples, enabling quantitative analysis at the ppm level (mg/L).

MMSSC-Net: multi-stage sequence cognitive networks for drug molecule recognition.

In the growing body of scientific literature, the structure and information of drugs are usually represented in two-dimensional vector graphics. Drug compound structures in vector graphics form are difficult to recognize and utilize by computers. Although the current OCSR paradigm has shown good performance, most existing work treats it as a single isolated whole. This paper proposes a multi-stage cognitive neural network model that predicts molecular vector graphics more finely. Based on cognitive methods, we construct a model for fine-grained perceptual representation of molecular images from bottom to top, and in stages, the primary representation of atoms and bonds is potential discrete label sequence (atom type, bond type, functional group, etc.). The second stage represents the molecular graph according to the label sequence, and the final stage evolves in an extensible manner from the molecular graph to a machine-readable sequence. Experimental results show that MMSSC-Net outperforms current advanced methods on multiple public datasets. It achieved an accuracy rate of 75-94% on cognitive recognition at different resolutions. MMSSC-Net uses a sequence cognitive method to make it more reliable in interpretability and transferability, and provides new ideas for drug information discovery and exploring the unknown chemical space.

Simulation study of a new racetrack FAIMS analyzer to achieve both high-resolution and high-sensitivity.

A new racetrack field-asymmetric waveform ion mobility spectrometry (r-FAIMS) analyzer was developed in this study by combining the existing planar FAIMS (p-FAIMS) and cylindrical FAIMS (c-FAIMS). The ion inlet and outlet regions of r-FAIMS were consisted of a half of c-FAIMS, respectively, and these c-FAIMS were further connected by two p-FAIMS to form a racetrack shaped FAIMS. With such FAIMS working electrode configuration, the ions entering the r-FAIMS can be focused and separated in the first c-FAIMS section, be further separated in the p-FAIMS section with high-resolution, be focused and separated again in the final c-FAIMS section and eventually enter the mass spectrometer or other analyzers for analysis. Detailed simulation by using SIMION software with the default FAIMS user program showed that the ion focusing effect in the first c-FAIMS section ensures the ions entering the following p-FAIMS section as a compact ion packet. This effectively decreases the ion loss caused by Coulomb repulsion and thermal diffusion in p-FAIMS section as compared to the ions being introduced into the p-FAIMS gap randomly in the conventional design. As a result, the ion transmission efficiency of r-FAIMS is at least 3.3-fold higher than the single p-FAIMS under the operating conditions used in this study. The ion trajectory simulation results also showed that the resolving power of r-FAIMS is about the sum of the resolving powers for its c-FAIMS and p-FAIMS sections. The resolving power of r-FAIMS is at least 3.6-fold higher than the single c-FAIMS under the operation conditions used in this study. Therefore, the r-FAIMS can realize both high-resolution and high-sensitive ion mobility separation.

Electron ionization mass spectrometry feature peak relationships combined with deep classification model to assist similarity algorithm for fast and accurate identification of compounds.

RATIONALE: Gas chromatography-mass spectrometry (GC-MS) combines chromatography and MS, providing full play to the advantages of high separation efficiency of GC, strong qualitative ability of MS, and high sensitivity of detector. In GC-MS data processing, determining the experimental compounds is one of the most important analytical steps, which is usually realized by one-to-one similarity calculations between the experimental mass spectrum and the standard mass spectrum library. Although the accuracy of the algorithm has been improved in recent years, it is still difficult to distinguish structurally similar mass spectra, especially isomers. At the same time, the library capacity is very large and increasing every year, and the algorithm needs to perform large numbers of calculations with irrelevant compounds in the library to recognize unknown compounds, which leads to a significant reduction in efficiency. METHODS: This work proposed to exclude a large number of irrelevant mass spectra by presearching, perform preliminary similarity calculations using similarity algorithms, and finally improve the accuracy of similarity calculations using deep classification models. The replica library of NIST17 is used as the query data, and the master library is used as the reference database. RESULTS: Compared with the traditional recognition algorithm, the preprocessing algorithm has reduced the time by 4.2 h, and by adding the deep learning models 1 and 2 as the final determination, the recognition accuracy has been improved by 1.9% and 6.5%, respectively, based on the original algorithm. CONCLUSIONS: This method improves the recognition efficiency compared to conventional algorithms and at the same time has better recognition accuracy for structurally similar mass spectra and isomers.

Using blood routine indicators to establish a machine learning model for predicting liver fibrosis in patients with Schistosoma japonicum.

This study intends to use the basic information and blood routine of schistosomiasis patients to establish a machine learning model for predicting liver fibrosis. We collected medical records of Schistosoma japonicum patients admitted to a hospital in China from June 2019 to June 2022. The method was to screen out the key variables and six different machine learning algorithms were used to establish prediction models. Finally, the optimal model was compared based on AUC, specificity, sensitivity and other indicators for further modeling. The interpretation of the model was shown by using the SHAP package. A total of 1049 patients' medical records were collected, and 10 key variables were screened for modeling using lasso method, including red cell distribution width-standard deviation (RDW-SD), Mean corpuscular hemoglobin concentration (MCHC), Mean corpuscular volume (MCV), hematocrit (HCT), Red blood cells, Eosinophils, Monocytes, Lymphocytes, Neutrophils, Age. Among the 6 different machine learning algorithms, LightGBM performed the best, and its AUCs in the training set and validation set were 1 and 0.818, respectively. This study established a machine learning model for predicting liver fibrosis in patients with Schistosoma japonicum. The model could help improve the early diagnosis and provide early intervention for schistosomiasis patients with liver fibrosis.

Calcitonin gene­related peptide alleviates hyperoxia­induced human alveolar cell injury via the CGRPR/TRPV1/Ca2+ axis.

Although exogenous calcitonin gene­related peptide (CGRP) protects against hyperoxia­induced lung injury (HILI), the underlying mechanisms remain unclear. The present study attempted to elucidate the molecular mechanism by which CGRP protects against hyperoxia­induced alveolar cell injury. Human alveolar A549 cells were treated with 95% hyperoxia to establish a hyperoxic cell injury model. ELISA was performed to detect the CGRP secretion. Immunofluorescence, quantitative (q)PCR, and western blotting were used to detect the expression and localization of CGRP receptor (CGRPR) and transient receptor potential vanilloid 1 (TRPV1). Cell counting kit­8 and flow cytometry were used to examine the proliferation and apoptosis of treated cells. Digital calcium imaging and patch clamp were used to analyze the changes in intracellular Ca2+ signaling and membrane currents induced by CGRP in A549 cells. The mRNA and protein expression levels of Cyclin D1, proliferating cell nuclear antigen (PCNA), Bcl­2 and Bax were detected by qPCR and western blotting. The expression levels of CGRPR and TRPV1 in A549 cells were significantly downregulated by hyperoxic treatment, but there was no significant difference in CGRP release between cells cultured under normal air and hyperoxic conditions. CGRP promoted cell proliferation and inhibited apoptosis in hyperoxia, but selective inhibitors of CGRPR and TRPV1 channels could effectively attenuate these effects; TRPV1 knockdown also attenuated this effect. CGRP induced Ca2+ entry via the TRPV1 channels and enhanced the membrane non­selective currents through TRPV1 channels. The CGRP­induced increase in intracellular Ca2+ was reduced by inhibiting the phospholipase C (PLC)/protein kinase C (PKC) pathway. Moreover, PLC and PKC inhibitors attenuated the effects of CGRP in promoting cell proliferation and inhibiting apoptosis. In conclusion, exogenous CGRP acted by inversely regulating the function of TRPV1 channels in alveolar cells. Importantly, CGRP protected alveolar cells from hyperoxia­induced injury via the CGRPR/TRPV1/Ca2+ axis, which may be a potential target for the prevention and treatment of the HILI.

A Fully Automated Online Enrichment and Separation System for Highly Reproducible and In-Depth Analysis of Intact Glycopeptide.

A fully automated online enrichment and separation system for intact glycopeptides, named AutoGP, was developed in this study by integrating three different columns in a nano-LC system. Specifically, the peptide mixture from the enzymatic digestion of a complex biological sample was first loaded on a hydrophilic interaction chromatography (HILIC) column. The nonglycopeptides in the sample were washed off the column, and the glycopeptides retained by the HILIC column were eluted to a C18 trap column to achieve an automated glycopeptide enrichment. The enriched glycopeptides were further eluted to a C18 column for separation, and the separated glycopeptides were eventually analyzed by using an orbitrap mass spectrometer (MS). The optimal operating conditions for AutoGP were systemically studied, and the performance of the fully optimized AutoGP was compared with a conventional manual system used for glycopeptide analysis. The experimental evaluation shows that the total number of glycopeptides identified is at least 1.5-fold higher, and the median coefficient of variation for the analyses is at least 50% lower by using AutoGP, as compared to the results acquired by using the manual system. In addition, AutoGP can perform effective analysis even with a 1-µg sample amount, while a 10-µg sample at least will be needed by the manual system, implying an order of magnitude better sensitivity of AutoGP. All the experimental results have consistently proven that AutoGP can be used for much better characterization of intact glycopeptides.

Metagenome-assembled genomes from enrichment cultures grown on xenobiotic solvents.

Microbes play a significant role in the cleanup of xenobiotic contaminants. Based on metagenomes derived from long-term enrichment cultures grown on xenobiotic solvents, we report 166 metagenome-assembled genomes, of which 137 are predicted to be more than 90% complete. These genomes broaden the representation of xenobiotic degraders.

Unrelated umbilical cord blood transplantation with low-dose anti-thymocyte globulin for children with severe aplastic anemia: A case series.

OBJECTIVE: This study aimed to investigate the prognosis of unrelated umbilical cord blood transplantation (UCBT) using low-dose anti-thymocyte globulin (ATG) in children diagnosed with severe aplastic anemia (SAA). METHODS: This retrospective case series study was conducted involving pediatric SAA patients treated at the Capital Institute of Pediatrics from January 2020 to February 2023. All patients underwent a reduced-intensity conditioning (RIC) regimen alongside low-dose ATG. RESULTS: The study comprised nine patients (five males) with a median age of 5 years (range: 1.7 to 7 years). The median follow-up duration was 799 days (range: 367 to 1481 days), during which all patients survived. The median time interval from diagnosis to transplantation was 3 months (range: 1 to 9 months). The median dosage of ATG administered was 5 mg/kg (range: 2.5 to 7.5 mg/kg). The median durations for granulocyte and platelet engraftment were 15 days (range: 12 to 23 days) and 26 days (range: 12 to 41 days), respectively. Three patients experienced grade 2-4 acute graft-versus-host disease (aGVHD). Epstein-Barr virus (EBV) reactivation was observed in three patients, while cytomegalovirus (CMV) reactivation occurred in seven patients, with no cases of CMV disease or post-transplant lymphoproliferative disorder (PTLD). One patient experienced recurrence 15 months after transplantation due to influenza A infection. CONCLUSION: These findings indicate that SAA patients may attain a favorable prognosis following UCBT with a RIC regimen combined with low-dose ATG.

Prediction of neoplastic gallbladder polyps in patients with different age level based on preoperative ultrasound: a multi-center retrospective real-world study.

BACKGROUND: The prevalence of neoplastic polyps in gallbladder polyps (GPs) increases sharply with age and is associated with gallbladder carcinoma (GBC). This study aims to predict neoplastic polyps and provide appropriate treatment strategies based on preoperative ultrasound features in patients with different age level. METHODS: According to the age classification of WHO, 1523 patients with GPs who underwent cholecystectomy from January 2015 to December 2019 at 11 tertiary hospitals in China were divided into young adults group (n=622), middle-aged group (n=665) and elderly group (n=236). Linear scoring models were established based on independent risk variables screened by the Logistic regression model in different age groups. The area under ROC (AUC) to evaluate the predictive ability of linear scoring models, long- and short- diameter of GPs. RESULTS: Independent risk factors for neoplastic polyps included the number of polyps, polyp size (long diameter), and fundus in the young adults and elderly groups, while the number of polyps, polyp size (long diameter), and polyp size (short diameter) in the middle-aged groups. In different age groups, the AUCs of its linear scoring model were higher than the AUCs of the long- and short- diameter of GPs for differentiating neoplastic and non-neoplastic polyps (all P<0.05), and Hosmer-Lemeshow goodness of fit test showed that the prediction accuracy of the linear scoring models was higher than the long- and short- diameter of GPs (all P>0.05). CONCLUSION: The linear scoring models of the young adults, middle-aged and elderly groups can effectively distinguish neoplastic polyps from non-neoplastic polyps based on preoperative ultrasound features.

Donkey-like kirkovirus is associated with diarrhea in piglets.

Kirkovirus (kirV), a seemingly novel virus family, has been found in horses and donkeys. The study's objectives are to investigate the presence of the virus in swine. In this study, donkey-like kirV was detected in rectal swabs of piglets with diarrhea, and the positive rate was found to be 100% (149/149). However, this virus was detected in only one of 261 clinically healthy piglets, which suggested a strong relationship between the kirV and the diarrheic disease. We obtained the whole-genome sequences of three kirVs (Cj-D5, Cj-D32, and Cj-D43), with a length of 3750 nucleotides (nt) and sharing 99.9% nt identity with donkey kirVs. Furthermore, the three viruses shared 88.5-100% and 23-51% of the Rep protein sequence, similar to available reference strains of Kirkoviridae and Circoviridae, respectively. Moreover, like horse and donkey kirVs, the RCR domain and P-loop NTPase domains of Rep protein and nonanucleotide motif (CAATATTAC) of the three viruses were similar to those of Circoviruses and Cycloviruses. Phylogenetic analysis showed that these viruses could be grouped with members in the proposed family Kirkoviridae. This is the first report to describe that kirV can circulate in piglets with diarrhea, and future studies are needed to determine the pathogenesis of this virus.

Circulating cell-free DNA as a biomarker for diagnosis of Schistosomiasis japonica.

BACKGROUND: Schistosomiasis, a neglected tropical disease, remains an important public health problem. Although there are various methods for diagnosing schistosomiasis, many limitations still exist. Early diagnosis and treatment of schistosomiasis can significantly improve survival and prognosis of patients. METHODOLOGY: Circulating cell-free (cf)DNA has been widely used in the diagnosis of various diseases. In our study, we evaluated the diagnostic value of circulating cfDNA for schistosomiasis caused by Schistosoma japonicum. We focused on the tandem sequences and mitochondrial genes of S. japonicum to identify highly sensitive and specific targets for diagnosis of Schistosomiasis japonica. RESULTS: Through data screening and analysis, we ultimately identified four specific tandem sequences (TD-1, TD-2, TD-3. and TD-4) and six mitochondrial genes (COX1(1), COX1(2), CYTB, ATP6, COX3, and ND5). We designed specific primers to detect the amount of circulating cfDNA in S. japonicum-infected mouse and chronic schistosomiasis patients. Our results showed that the number of tandem sequences was significantly higher than that of the mitochondrial genes. A S. japonicum infection model in mice suggested that infection of S. japonicum can be diagnosed by detecting circulating cfDNA as early as the first week. We measured the expression levels of circulating cfDNA (TD-1, TD-2, and TD-3) at different time points and found that TD-3 expression was significantly higher than that of TD-1 or TD-2. We also infected mice with different quantities of cercariae (20 s and 80 s). The level of cfDNA (TD-3) in the 80 s infection group was significantly higher than in the 20 s infection group. Additionally, cfDNA (TD-3) levels increased after egg deposition. Meanwhile, we tested 42 patients with chronic Schistosomiasis japonica and circulating cfDNA (TD-3) was detected in nine patients. CONCLUSIONS: We have screened highly sensitive targets for the diagnosis of Schistosomiasis japonica, and the detection of circulating cfDNA is a rapid and effective method for the diagnosis of Schistosomiasis japonica. The levels of cfDNA is correlated with cercariae infection severity. Early detection and diagnosis of schistosomiasis is crucial for patient treatment and improving prognosis.

Radial basis function neural network optimization algorithm based on dynamic inertial weight particle swarm optimization for separating overlapping peaks in ion mobility spectrometry.

RATIONALE: Ion mobility spectrometry (IMS), as a promising analytical tool, has been widely employed in the structural characterization of biomolecules. Nevertheless, the inherent limitation in the structural resolution of IMS frequently results in peak overlap during the analysis of isomers exhibiting comparable structures. METHODS: The radial basis function (RBF) neural network optimization algorithm based on dynamic inertial weight particle swarm optimization (DIWPSO) was proposed for separating overlapping peaks in IMS. The RBF network structure and parameters were optimized using the DIWPSO algorithm. By extensively training using a large dataset, an adaptive model was developed to effectively separate overlapping peaks in IMS data. This approach successfully overcomes issues related to local optima, ensuring efficient and precise separation of overlapping peaks. RESULTS: The method's performance was evaluated using experimental validation and analysis of overlapping peaks in the IMS spectra of two sets of isomers: 3'/6'-sialyllactose; fructose-6-phosphate, glucose-1-phosphate, and glucose-6-phosphate. A comparative analysis was conducted using other algorithms, including the sparrow search algorithm, DIWPSO algorithm, and multi-objective dynamic teaching-learning-based optimization algorithm. The comparison results show that the DIWPSO-RBF algorithm achieved remarkably low maximum relative errors of only 0.42%, 0.092%, and 0.41% for ion height, mobility, and half peak width, respectively. These error rates are significantly lower than those obtained using the other three algorithms. CONCLUSIONS: The experimental results convincingly demonstrate that this method can adaptively, rapidly, and accurately separate overlapping peaks of multiple components, improving the structural resolution of IMS.

Lessons on food security from the COVID-19 pandemic in Bermuda.

Compared with other OECD countries, Bermuda ranks third globally in terms of income inequality globally. During the COVID-19 pandemic, anecdotal evidence suggested, significant fluctuations in the food demand and supply. We aimed to examine the impact of the COVID-19 pandemic on food insecurity, with a focus on the availability and affordability of various foods in Bermuda. We utilized a cross-sectional study design to investigate potential drivers of food insecurity within the local population. To gauge the level of household food insecurity we relied on the Bermuda Omnibus survey (N = 400) undertaken by Total Research Associates Ltd via telephone. To assess changes in food availability and affordability we conducted semi-structured interviews with key stakeholders who played pivotal roles in shaping food accessibility availability and affordability of food in Bermuda. These interviews were systematically analysed using the framework method. We performed analyses of food retail and import data to evaluate fluctuations in food prices and their impact on food availability and affordability. We found statistically significant associations between changes in food consumption, household income, and government aid. Food aid beneficiaries ate fewer fruits and vegetables by 50% [95% CI:17%-83%] and less fresh meat and fish by 39% [95 CI:3%-75%] compared with residents who did not receive any aid during the COVID-19 period from March 2020 to March 2021. Although we did not identify statistically significant food price increases feeding programmes played a pivotal role in preventing food insecurity during the pandemic in Bermuda. However, a lack of monitoring regarding the nutritional quality within the programmes, allowed a wide availability of foods high in sugar, salts, and fats, disproportionately affected low-income populations. In conclusion, food availability in Bermuda remained largely unaffected during the pandemic. Nevertheless, the surge in demand for feeding programs underscores underlying food security challenges in Bermuda and warrants further attention.

C/EBPα alleviates hepatic ischemia-reperfusion injury by inhibiting endoplasmic reticulum stress via HDAC1-mediated deacetylation of ATF4.

Hepatic ischemia-reperfusion (IR) injury is a complex systemic process causing a series clinical problem. C/EBPα is a key transcription factor for hepatocyte function, but its role and mechanism in regulating hepatic IR injury are largely unknown. Occluding portal vein and hepatic artery was used to establish a mouse model of hepatic IR injury. C/EBPα expression was decreased in IR-injured liver compared with the sham, accompanied by increased contents of serum alanine transaminase (ALT), aspartate transaminase (AST), high mobility group box-1, and proportion of hepatic cells. Oxygen and glucose deprivation/recovery (OGD/R) was used to establish a cellular hepatic IR model in WRL-68 hepatocytes in vitro, and C/EBPα was overexpressed in the hepatocytes to evaluate its effect on hepatic IR injury. OGD/R promoted oxidative stress, cell apoptosis and endoplasmic reticulum (ER) stress in hepatocytes, which was reversed by C/EBPα overexpression. Then, we found that C/EBPα promoted histone deacetylase 1 (HDAC1) transcription through binding to HDAC1 promoter. Moreover, HDAC1 deacetylated the activating transcription factor 4 (ATF4), a key positive regulator of ER stress. Trichostatin-A (an HDAC inhibitor) or ATF4 overexpression reversed the improvement of C/EBPα on OGD/R-induced ER stress and hepatocyte dysfunction. 4-Phenylbutyric acid (an endoplasmic reticulum stress inhibitor) also reversed the hepatic IR injury induced by ATF4 overexpression. Finally, lentivirus-mediated C/EBPα overexpression vector was applied to administrate hepatic IR mice, and the results showed that C/EBPα overexpression ameliorated IR-induced hepatic injury, manifesting with reduced ALT/AST, oxidative stress and ER stress. Altogether, our findings suggested that C/EBPα ameliorated hepatic IR injury by inhibiting ER stress via HDAC1-mediated deacetylation of ATF4 promoter.

Structures, Biosynthesis, and Bioactivity of Oligomycins from the Marine-Derived Streptomyces sp. FXY-T5.

Oligomycins are potent antifungal and antitumor agents. Mass spectrometry (MS)- and nuclear magnetic resonance (NMR)-based metabolomic fingerprinting analysis of marine-derived actinomycetes in our in-house library provided an oligomycin-producing strain, Streptomyces sp. FXY-T5. Chemical investigation led to the discovery of five new oligomycins, 24-lumooligomycin B (1), 4-lumooligomycin B (2), 6-lumooligomycin B (3), 40-homooligomycin B (4), and 15-hydroxy-oligomycin B (5), together with seven biosynthetically related known derivatives. Their structures were assigned by MS, NMR, electronic circular dichroism (ECD), and single-crystal X-ray diffraction analyses. The biosynthesis pathway of oligomycins was first proposed based on the analysis of a type I modular polyketide synthase (PKS) system and targeted gene disruption. As expected, the isolated oligomycins showed significant antiagricultural fungal pathogen activity and antiproliferative properties from which the possible structure-activity relationships were first suggested. More importantly, oligomycins induced significant G1-phase cell cycle arrest on cancer cells and significantly attenuated their Cyclin D1 and PCNA expression through a ß-catenin signaling pathway.

A high-performance standalone planar FAIMS system for effective detection of chemical warfare agents via TSPSO algorithm.

A high-performance standalone planar field asymmetric waveform ion mobility spectrometry (p-FAIMS) system with a deconvolution algorithm (two-step particle swarm optimization algorithm, TSPSO) for overlapping peaks was developed to effectively detect chemical warfare agents (CWAs). Four CWA simulants were applied in this study to systemically evaluate the performance of the standalone p-FAIMS system. The experimental results showed that each CWA simulant in the mixture can be positively identified by carefully comparing the compensation voltage (CV) value of each peak in the FAIMS spectra for the mixture to the ones in the spectra acquired by using the same FAIMS system for the pure CWA simulant standards. The FAIMS spectrum of the CWA simulant mixture might consist of multiple overlapping peaks, which would be difficult to accurately determine the CV value for each CWA simulant peak. This problem has been effectively resolved in this study by deconvoluting the overlapping peaks via the TSPSO algorithm. As the effective peak deconvolution via TSPSO requires the degree of overlap between each FAIMS peak to be lower than a specific value, the flow rate of FAIMS carrier gas was decreased to further improve the resolution of the p-FAIMS system. After the accurate deconvolution, the resolution of original FAIMS spectrum can also be enhanced to achieve baseline separation by using TSPSO algorithm to narrow the peak width of each peak. The experimental results in this study demonstrated the possibility of using TSPSO algorithm to achieve high-resolution on a typically low-resolution standalone FAIMS. The concept in this study can potentially be applied to any low-resolution instruments to achieve high-resolution results.

SNHG1, interacting with SND1, contributes to sorafenib resistance of liver cancer cells by increasing m6A-mediated SLC7A11 expression and promoting aerobic glycolysis.

Aerobic glycolysis plays an important role in multidrug resistance of cancer cells. Here, we screened different expressed lncRNAs associated with sorafenib resistance of liver cancer cells, by intersecting the bioinformatics analyses of TCGA and GEO (the GSE62813 dataset) databases. Our results revealed that the 18 upregulated lncRNAs in the intersection are associated with and enriched in metabolism of small molecule organic acids, suggesting their potential in glycolysis. The lncRNA small nucleolar RNA host gene 1 (Snhg1) was chosen as a potential regulator of aerobic glycolysis in liver cancer cells, for its significant promotion on lactate production. Gain- and loss-of-function experiments mediated by Crispr-Cas9 technique in HepG2 cells indicated that Snhg1 promoted cell proliferation, invasion, sorafenib resistance, and aerobic glycolysis. In the mechanism exploration, we found that Snhg1 can interact with SND1 protein, a famous RNA binding protein and recently identified "Reader" of N6-methyladenosine (m6A). SND1 was demonstrated to be positively regulated by Snhg1 and had similar promoting effects on proliferation, invasion, sorafenib resistance, and aerobic glycolysis of HepG2 cells. SND1 bound with and promoted the expression of SLC7A11, an aerobic glycolysis regulator. Furthermore, either silencing SLC7A11 or blocking aerobic glycolysis with 2-deoxy-d-glucose (2-DG) was able to reverse the promotion of Snhg1 overexpression on malignancy, sorafenib resistance, and aerobic glycolysis of HepG2 cells. Finally, in a liver cancer xenograft mouse model, we found that formed tumors with Snhg1-knocked-down HepG2 cells were more sensitive to sorafenib administration. Altogether, SNHG1 contributes to sorafenib resistance of liver cancer cells by promoting SND1-m6A-SLC7A11-mediated aerobic glycolysis.

Associations between host microbiome and inflammation suggest role for host microbiome in driving COVID-19 disease severity.

Systemic inflammation and innate immune activation are associated with COVID-19 disease severity. Knowledge gaps remain in the relationships between microbiome, inflammation and COVID-19 disease severity. To better characterise these associations, we performed 16SrDNA analysis of stool samples in COVID-19 subjects to explore diversity and taxanomic composition. We correlated these to host inflammatory profiles, derived from soluble plasma biomarkers measured by bead-based fluorescence and electrochemiluminescence immunoassays. Associations of microbial diversity and inflammatory biomarkers on maximal COVID-19 severity (mild, moderate v severe/critical) was explored using logistic regression and weighted gene correlation network analysis (WGCNA). Of 79 subjects, 58% were male and 88% were Caucasian with 36% experiencing mild disease, 22% moderate disease and 40% critical/severe COVID-19. Hierarchical clustering and principal component analysis (PCo) revealed distinct inflammatory clusters that were found to correlate with 4 modules of microbiome profiles. Modules 3 and 4 were associated with both older age and severe/critical disease outcomes. These modules were enriched in pathogenic and inflammatory bacteria that mapped to a pro-inflammatory biomarker cluster. In contrast, module 1 exhibited enrichment of anti-inflammatory bacteria, was associated with younger age and mild/moderate disease outcomes and mapped to a less-inflamed biomarker cluster. This study provides further insights into links between host microbiome, inflammatory responses to SARS-CoV-2 infection and clinical COVID-19 disease severity, suggesting a role for the microbiome in shaping distinct host inflammatory responses to infection.