Phylogenomic analysis uncovers an unexpected capacity for the biosynthesis of secondary metabolites in Pseudoalteromonas.

Pseudoalteromonas is a genus of marine bacteria and a promising source of natural products with antibacterial, antifungal, and antifouling bioactivities. To accelerate the exploration of new compounds from this genus, we applied the gene-first approach to study 632 public Pseudoalteromonas genomes. We identified 3968 biosynthetic gene clusters (BGCs) involved in the biosynthesis of secondary metabolites and classified them into 995 gene cluster families (GCFs). Surprisingly, only 9 GCFs (0.9 %) included an experimentally identified reference biosynthetic gene cluster from the Minimum Information about a Biosynthetic Gene cluster database (MIBiG), suggesting a striking novelty of secondary metabolites in Pseudoalteromonas. Bioinformatic analysis of the biosynthetic diversity encoded in the identified BGCs uncovered six dominant species of this genus, P. citrea, P. flavipulchra, P. luteoviolacea, P. maricaloris, P. piscicida, and P. rubra, that encoded more than 17 BGCs on average. Moreover, each species exhibited a species-specific distribution of BGC. However, a deep analysis revealed two BGCs conserved across five of the six dominant species. These BGCS encoded an unknown lanthipeptide and the siderophore myxochelin B implying an essential role of antibiotics for Pseudoalteromonas. We chemically profiled 11 strains from the 6 dominant species and identified four new antibiotics, korormicins L-O (1-4), from P. citrea WJX-3. Our results highlight the unexplored biosynthetic potential for bioactive compounds in Pseudoalteromonas and provide an important guideline for targeting exploration.

Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms.

Schizophrenia is a disastrous mental disorder. Identification of diagnostic biomarkers and therapeutic targets is of significant importance. In this study, five datasets of schizophrenia post-mortem prefrontal cortex samples were downloaded from the GEO database and then merged and de-batched for the analyses of differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA). The WGCNA analysis showed the six schizophrenia-related modules containing 12,888 genes. The functional enrichment analyses indicated that the DEGs were highly involved in immune-related processes and functions. The immune cell infiltration analysis with the CIBERSORT algorithm revealed 12 types of immune cells that were significantly different between schizophrenia subjects and controls. Additionally, by intersecting DEGs, WGCNA module genes, and an immune gene set obtained from online databases, 151 schizophrenia-associated immune-related genes were obtained. Moreover, machine learning algorithms including LASSO and Random Forest were employed to further screen out 17 signature genes, including GRIN1, P2RX7, CYBB, PTPN4, UBR4, LTF, THBS1, PLXNB3, PLXNB1, PI15, RNF213, CXCL11, IL7, ARHGAP10, TTR, TYROBP, and EIF4A2. Then, SVM-RFE was added, and together with LASSO and Random Forest, a hub gene (EIF4A2) out of the 17 signature genes was revealed. Lastly, in a schizophrenia rat model, the EIF4A2 expression levels were reduced in the model rat brains in a brain-regional dependent manner, but can be reversed by risperidone. In conclusion, by using various bioinformatic and biological methods, this study found 17 immune-related signature genes and a hub gene of schizophrenia that might be potential diagnostic biomarkers and therapeutic targets of schizophrenia.

Andrographolide suppresses SARS-CoV-2 infection by downregulating ACE2 expression: A mechanistic study.

Angiotensin-converting enzyme 2 (ACE2) is the receptor that enables SARS-CoV-2 to invade host cells. Previous studies have reported that reducing ACE2 expression may have an anti-SARS-CoV-2 effect. In this study, we constructed a pGL4.10-F2-ACE2 vector with double luciferase genes (firefly and Renilla luciferase) under the control of the ACE2 promoter and used it to screen compounds from Chinese traditional medicinal herbs (CTMHs) that can inhibit ACE2 transcription in human cells. We transfected HEK293T cells with pGL4.10-F2-ACE2 and treated them with CTMH compounds and then measured fluorescence to evaluate the indirect inhibition of ACE2 transcription. Out of 37 compounds tested, andrographolide demonstrated a dose-dependent inhibition of ACE2 transcription. We further confirmed by RT-qPCR and Western blot assays that andrographolide also reduced ACE2 expression in BEAS-2B cells in a dose-dependent manner. Moreover, pseudovirus infection assays in BEAS-2B cells demonstrated that andrographolide can inhibit SARS-CoV-2 infection in a dose-dependent manner. These results suggest that andrographolide has potential anti-SARS-CoV-2 activity and could be a candidate drug for COVID-19 prevention and treatment.

Network analysis to explore the anti-senescence mechanism of Jinchan Yishen Tongluo Formula (JCYSTLF) in diabetic kidneys.

Background: The Jinchan Yishen Tongluo Formula (JCYSTLF) has the effect of delaying senescence in diabetic kidneys. However, the mechanism is not clear. Purpose: Combination methods to investigate the anti-senescence mechanism of JCYSTLF in diabetic kidneys. Methods: The main compounds of JCYSTLF were characterized by LC-MS/MS, and the anti-senescence targets of JCYSTLF were screened via network analysis. Then, we performed in vivo and in vitro experiments to validate the results. Results: The target profiles of compounds were obtained by LC-MS/MS to characterize the primary function of JCYSTLF. Senescence was identified as a key biological functional module of JCYSTLF in the treatment of DN via constructing compounds-target-biological network analysis. Further analysis of senescence-related targets recognized the HIF-1α/autophagy pathway as the core anti-senescence mechanism of JCYSTLF in diabetic kidneys. Animal experiments showed, in comparison with valsartan, JCYSTLF showed an improvement in urinary albumin and renal pathological damage. JCYSTLF enhanced the ability of diabetic kidneys to clear senescence-related proteins via regulating autophagy confirmed by autophagy inhibitor CQ. However, HIF-1α inhibitor 2-ME weakened the role of JCYSLTF in regulating autophagy in diabetic kidneys. Meanwhile, over-expressed HIF-1α in HK-2 cells decreased the levels of SA-ß-gal, p21 and p53 induced by AGEs. Upregulated HIF-1α could reverse the blocking of autophagy induced by AGEs in HK-2 cells evaluated by ptfLC3. Conclusion: We provided in vitro and in vivo evidence for the anti-senescence role of JCYSTLF in regulating the HIF-1α/autophagy pathway.

Women's experiences with Centering-Based Group Care in Zhejiang China: A pilot study.

BACKGROUND: The group prenatal care model, which caters to women with low medical needs but high support needs, has become a highly prevalent and innovative approach implemented globally. For Centering-Based Group Care (CBGC) to remain effective, women's evaluations of the quality of care and perspectives about the model are crucial. AIM: This study aimed to describe women's appraisal of CBGC quality and explore the experiences of women in the mixed-methods pilot study conducted in Zhejiang, China. METHODS: From August 2021 to December 2022, 20 women provided complete quantitative data using the Quality of Prenatal Care Questionnaire before hospital discharge. Semi-structured interviews were conducted at 6 months postpartum. Qualitative data were analysed using Colaizzi's method. FINDINGS: The mean (standard deviation) total score (of the 5) of the questionnaire was 4.43 (0.1) with a good quality of CBGC. Qualitative research identified five themes: motivations and concerns for participation, the appeal of interactive learning, the development of community ties and social support, healing from psychological trauma with CBGC, and suggestions for CBGC enhancement. DISCUSSION: Women rated CBGC quality as good and benefited significantly from it in the study. As a new alternative option, the women's accounts suggested that CBGC performed excellently in enhancing knowledge, strengthening social bonds, and providing psychological support. CONCLUSION: CBGC quality cannot be determined based on limited the sample size. This pilot study provides evidence regarding the beneficial effects of knowledge, socialization, and psychological healing on CBGC. Further research is suggested to measure CBGC effectiveness and quality.

GCMSFormer: A Fully Automatic Method for the Resolution of Overlapping Peaks in Gas Chromatography-Mass Spectrometry.

Gas chromatography-mass spectrometry (GC-MS) is one of the most important instruments for analyzing volatile organic compounds. However, the complexity of real samples and the limitations of chromatographic separation capabilities lead to coeluting compounds without ideal separation. In this study, a Transformer-based automatic resolution method (GCMSFormer) is proposed to resolve mass spectra from GC-MS peaks in an end-to-end manner, predicting the mass spectra of components directly from the raw overlapping peaks data. Furthermore, orthogonal projection resolution (OPR) was integrated into GCMSFormer to resolve minor components. The GCMSFormer model was trained, validated, and tested using 100,000 augmented data. It achieves 99.88% of the bilingual evaluation understudy (BLEU) value on the test set, significantly higher than the 97.68% BLEU value of the baseline sequence-to-sequence model long short-term memory (LSTM). GCMSFormer was also compared with two nondeep learning resolution tools (MZmine and AMDIS) and two deep learning resolution tools (PARAFAC2 with DL and MSHub/GNPS) on a real plant essential oil GC-MS data set. Their resolution results were compared on evaluation metrics, including the number of compounds resolved, mass spectral match score, correlation coefficient, explained variance, and resolution speed. The results demonstrate that GCMSFormer has better resolution performance, higher automation, and faster resolution speed. In summary, GCMSFormer is an end-to-end, fast, fully automatic, and accurate method for analyzing GC-MS data of complex samples.

A novel lncRNA TCONS_00071187 upregulated by activated GSK3ß promotes high glucose-induced mesangial cell proliferation in the diabetic nephropathy.

Inhibiting mesangial cell proliferation is one of the strategies to control the early progression of diabetic nephropathy (DN). GSK3ß is closely related to cell apoptosis as well as the development of DN, but whether it acts on the proliferation of mesangial cells is unclear. This study aimed to elucidate the role and mechanism of GSK3ß-mediated lncRNA in high glucose-induced mesangial cell proliferation. HBZY-1 cells were used to establish the cell model of DN. The automatic cell counter was applied to assess cell proliferation. Flow cytometry was used to detect cell apoptosis and intracellular ROS levels. High-throughput transcriptomics sequencing was performed to detect the different expressions of long noncoding RNAs (lncRNAs) in the cell model of DN after knocking down the expression of GSK3ß by the transfection of siRNA. The expression of RNA was detected by real-time PCR. In the cell model of DN using HBZY-1 cells, cell proliferation was enhanced accompanied by GSK3ß activation and elevated apoptosis rate and reactive oxygen species (ROS) levels. A panel of novel lncRNAs, which were differentially expressed after GSK3ß knockdown in the cell model of DN, were identified by high-throughput transcriptomics sequencing. Among them, the expression of TCONS_00071187 was upregulated under high glucose conditions while the knockdown of the GSK3ß expression led to the downregulation of TCONS_00071187. The knockdown of TCONS_00071187 resulted in reduced mesangial cell proliferation, and decreased apoptosis rates and ROS levels. In conclusion, GSK3ß promoted mesangial cell proliferation by upregulating TCONS_00071187, which led to enhanced ROS production under high glucose conditions in the cell model of DN. This study revealed the role of GSK3ß medicated lncRNAs in the development of DN.

Large-Scale Glass-Transition Temperature Prediction with an Equivariant Neural Network for Screening Polymers.

The practically infinite chemical and morphological space of polymers makes them pervasive with applications in materials science but challenges the rational discovery of new materials with favorable properties. Polymer informatics aims to accelerate materials design through property prediction and large-scale virtual screening. In this study, a new method (Lieconv-Tg) has been developed to predict glass-transition temperature (Tg) values from repeating units of polymers based on Lieconv, which is equivariant with transformations from any specified Lie group. The introduction of equivariance allows the prediction of molecular properties from their 3D structures, independent of orientation and position. A total of 27,659 homopolymers with Tg values were collected from PolyInfo, and a standard data set containing 7166 polymers (named data set_Tg) was created for training a robust Lieconv-Tg model. Using the 3D coordinates as input, Lieconv-Tg performs better than Edge-Conditioned Convolution (ECC), and the mean absolute error (MAE) is significantly reduced by ∼6 from ∼30 to ∼24 on both the validation set and the test set, and the R2 value for both the validation set and the test set can reach 0.90. Lieconv-Tg is thus used to screen promising candidates from a benchmark database named PI1M with 995,800 generated polymers. However, there are some implausible repeating units in PI1M. To get more reasonable candidates from PI1M, a new filtering method has been accomplished by utilizing Morgan fingerprints at the polymerization points (MF@PP) of repeating units in data set_Tg. The combination of a standard data set, Lieconv-Tg, and a more reasonable screening strategy provides new directions in materials design for polymers.

The influence of resourcefulness on the family functioning of young- and middle-aged lymphoma patients in China: a cross-sectional study.

BACKGROUND: Lymphoma has become 1 of the 10 most common cancers with increased prevalence in young- and middle-aged adults in China. This poses a tremendous burden on patients and their families and brings great challenges to maintaining the balance of family functioning in young- and middle-aged patients. OBJECTIVE: This cross-sectional study aimed to analyse the influence of resourcefulness on the family functioning of Chinese young- and middle-aged lymphoma patients. METHODS: A total of 172 Chinese young- and middle-aged patients with lymphoma were recruited from the oncology departments of two tertiary hospitals in Zhengzhou, Henan, China. They were invited to complete a survey that included a demographic questionnaire, the Resourcefulness Scale and the Chinese Version Family Adaptability and Cohesion Scale II. Multiple linear regression was used to analyse the related factors for family functioning. RESULTS: The multiple regression analysis revealed that the main influencing factors of family cohesion were resourcefulness (ß = 0.338, 95% CI (0.072, 0.173)), spouse caregiver (ß = 0.376, 95% CI (1.938, 10.395)), and cancer stage (ß = -0.274, 95% CI (-3.219, -1.047)). Resourcefulness (ß = 0.438, 95% CI (0.096, 0.181)), spouse caregiver (ß = 0.340, 95% CI (1.348, 8.363)), and family per capita monthly income (ß = 0.157, 95% CI (0.066, 2.243)) were the influencing factors of family adaptability. CONCLUSIONS: Healthcare professionals and family scholars should value young- and middle-aged lymphoma patients' family functioning throughout the cancer treatment process, and family interventions should be designed by healthcare providers based on patients' resourcefulness. Moreover, healthcare providers need to pay attention to the risk factors of patients' family cohesion and adaptability, such as low family per capita monthly income, and consider employing corresponding measures to help them.

Lewis Acidic Metal-Organic Framework Assisted Ambient Liquid Extraction Mass Spectrometry Imaging for Enhancing the Coverage of Poorly Ionizable Lipids in Brain Tissue.

The spatial distribution of lipidomes in tissues is of great importance in studies of living processes, diseases, and therapies. Mass spectrometry imaging (MSI) has become a critical technique for spatial lipidomics. However, MSI of low-abundance or poorly ionizable lipids is still challenging because of the ion suppression from high-abundance lipids. Here, a metal-organic framework (MOF) Zr6O4(OH)4(1,3,5-Tris(4-carboxyphenyl) benzene)2(triflate)6(Zr6OTf-BTB) was prepared and used for selective on-tissue adsorption of phospholipids to reduce ion suppression from them to poorly ionizable lipids. The results show that Zr6OTf-BTB with strong Lewis acidic sites and a large specific surface area (647.9 m2·g-1) could selectively adsorb phospholipids under 1% FA-MeOH. Adsorption efficiencies of phospholipids are 88.4-144.9 times higher than those of other neutral lipids. Moreover, the adsorption capacity and the adsorption kinetic rate constant of the new material to phospholipids are higher than those of Zr6-BTB (242.72 vs 73.96 mg·g-1, 0.0442 vs 0.0220 g·mg-1·min-1). A Zr6OTf-BTB sheet was prepared by a lamination technique for on-tissue phospholipid adsorption from brain tissue. Then, the tissue section on the Zr6OTf-BTB sheet was directly imaged via ambient liquid extraction-MSI with 1% FA-MeOH as the sampling solvent. The results showed that phospholipids could be 100% removed directly on tissue, and the detection coverage of the Zr6OTf-BTB-enhanced MSI method to ceramides (Cers) and hexosylceramides (HexCers) was increased by 5-26 times compared with direct tissue MSI (26 vs 1 and 17 vs 3). The new method provides an efficient and convenient way to eliminate the ion suppression from phospholipids in MSI, largely improving the detection coverage of low-abundance and poorly ionizable lipids.

Photocatalytic degradation-based ambient mass spectrometry imaging for enhancing detection coverage of poorly-ionizable lipidomes.

Localization of lipidomes and tracking their spatial changes in tissues by mass spectrometry imaging (MSI) plays an important role in unveiling the mechanisms of living processes, diseases and therapeutic treatments. However, it is always challenging to achieve direct MSI of poorly-ionizable lipids, such as glycolipids and glycerolipids, due to the strong ion suppression and isobaric peaks interference from high-abundance phosphatidylcholines (PCs) in tissues. Here we developed a photocatalytic degradation-based ambient liquid extraction MSI method to largely enhance the detection coverage of poorly-ionizable lipids by rapid online removal of PCs in MSI. Phospholipids were found to be selectively photodegraded on TiO2 surface in acidic conditions in the presence of water under UV irradiation, while other poorly-ionizable lipids remained. Sulfate ion could largely improve the degradation efficiencies. Anatase nanoparticles-embedded TiO2 monolith was in-situ synthesized in the capillary of ambient liquid extraction system, and rapid online photodegradation of PCs was achieved during MSI with efficiency >80 %, largely reducing ion suppression. The pathway analysis showed that PC was oxidatively degraded starting from hydroxylation of C=C bonds. With intense UV irradiation, PCs were completely degraded into small molecules<200 Da without interference on the detection of endogenous lipids. With the new MSI method, detection coverage to cerebrosides, ceramides and diglycerides was enhanced by 2-9 times comparing with traditional MSI. Clearer localizations were observed for poorly-ionizable lipids via the new method than traditional method. Thus, this work provided a complementary MSI method for traditional MSI to address the issues on direct imaging of poorly ionizable lipids in ambient conditions.

Establishment of a model for predicting preterm birth based on the machine learning algorithm.

BACKGROUND: The purpose of this study was to construct a preterm birth prediction model based on electronic health records and to provide a reference for preterm birth prediction in the future. METHODS: This was a cross-sectional design. The risk factors for the outcomes of preterm birth were assessed by multifactor logistic regression analysis. In this study, a logical regression model, decision tree, Naive Bayes, support vector machine, and AdaBoost are used to construct the prediction model. Accuracy, recall, precision, F1 value, and receiver operating characteristic curve, were used to evaluate the prediction performance of the model, and the clinical application of the model was verified. RESULTS: A total of 5411 participants were included and were used for model construction. AdaBoost model has the best prediction ability among the five models. The accuracy of the model for the prediction of "non-preterm birth" was the highest, reaching 100%, and that of "preterm birth" was 72.73%. CONCLUSIONS: By constructing a preterm birth prediction model based on electronic health records, we believe that machine algorithms have great potential for preterm birth identification. However, more relevant studies are needed before its application in the clinic.

Pulled Flowprobe for Ambient Liquid Extraction-Based High Spatial Resolution Mass Spectrometry Imaging with Enhanced Sensitivity and Stability.

Ambient liquid extraction techniques enable direct mass spectrometry imaging (MSI) under ambient conditions with minimal sample preparation. However, currently an integrated probe for ambient liquid extraction-based MSI with high spatial resolution, high sensitivity, and stability is still lacking. In this work, we developed a new integrated probe made of pulled coaxial capillaries, named pulled flowprobe, and compared it with the previously reported single-probe. Mass transfer kinetics in probes was first investigated. The extraction kinetic curves during probe sampling indicate a narrower and higher peak shape for the pulled flowprobe than single-probe. Computational fluid dynamics analysis reveals that in the pulled flowprobe flow velocities are lower in liquid microjunction and higher in the transferring channels, resulting in higher extraction efficiencies and reduced band diffusion compared with single-probe and other probes with a similar flow route. Results of ambient liquid extraction-based MSI of lipids in rat cerebrum show that signals of low-abundance lipids were 2-5 times higher via a pulled flowprobe than via a single-probe, and 26 more lipid species were detected on brain tissue via a pulled flowprobe than via a single-probe. The stability of MSI with the pulled flowprobe was found to be higher than that with single-probe (averaged relative standard deviation = 18% vs 80%) by imaging a lab-made uniform ink coating. Moreover, in the pulled flowprobe, no retraction of the inner capillary from outer capillary is optimal for both sensitivity and stability. The spatial resolution of the pulled flowprobe (30-40 µm) was measured to be higher than that of a comparable size single-probe by calculation with the "80-20" rule. Finally, the new pulled flowprobe was applied to high-resolution MSI of lipids in the hippocampus, and localization of several lipids to the specific cell layers in the hippocampus region was observed. Thus, this work provides an alternative easily fabricated sampling probe with enhanced sensitivity, stability, and spatial resolution, promoting the use of ambient liquid extraction-based MSI in biological and clinical research.

Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors.

Nuclear magnetic resonance (NMR) is a crucial technique for analyzing mixtures consisting of small molecules, providing non-destructive, fast, reproducible, and unbiased benefits. However, it is challenging to perform mixture identification because of the offset of chemical shifts and peak overlaps that often exist in mixtures such as plant flavors. Here, we propose a deep-learning-based mixture identification method (DeepMID) that can be used to identify plant flavors (mixtures) in a formulated flavor (mixture consisting of several plant flavors) without the need to know the specific components in the plant flavors. A pseudo-Siamese convolutional neural network (pSCNN) and a spatial pyramid pooling (SPP) layer were used to solve the problems due to their high accuracy and robustness. The DeepMID model is trained, validated, and tested on an augmented data set containing 50,000 pairs of formulated and plant flavors. We demonstrate that DeepMID can achieve excellent prediction results in the augmented test set: ACC = 99.58%, TPR = 99.48%, FPR = 0.32%; and two experimentally obtained data sets: one shows ACC = 97.60%, TPR = 92.81%, FPR = 0.78% and the other shows ACC = 92.31%, TPR = 80.00%, FPR = 0.00%. In conclusion, DeepMID is a reliable method for identifying plant flavors in formulated flavors based on NMR spectroscopy, which can assist researchers in accelerating the design of flavor formulations.

Highly automatic and universal approach for pure ion chromatogram construction from liquid chromatography-mass spectrometry data using deep learning.

Feature extraction is the most fundamental step when analyzing liquid chromatography-mass spectrometry (LC-MS) datasets. However, traditional methods require optimal parameter selections and re-optimization for different datasets, thus hindering efficient and objective large-scale data analysis. Pure ion chromatogram (PIC) is widely used because it avoids the peak splitting problem of the extracted ion chromatogram (EIC) and regions of interest (ROIs). Here, we developed a deep learning-based pure ion chromatogram method (DeepPIC) to find PICs using a customized U-Net from centroid mode data of LC-MS directly and automatically. A model was trained, validated, and tested on the Arabidopsis thaliana dataset with 200 input-label pairs. DeepPIC was integrated into KPIC2. The combination enables the entire processing pipeline from raw data to discriminant models for metabolomics datasets. The KPIC2 with DeepPIC was compared against other competing methods (XCMS, FeatureFinderMetabo, and peakonly) on the MM48, simulated MM48, and quantitative datasets. These comparisons showed that DeepPIC outperforms XCMS, FeatureFinderMetabo, and peakonly in recall rates and correlation with sample concentrations. Five datasets of different instruments and samples were used to evaluate the quality of PICs and the universal applicability of DeepPIC, and 95.12% of the found PICs could precisely match their manually labeled PICs. Therefore, KPIC2+DeepPIC is an automatic, practical, and off-the-shelf method to extract features from raw data directly, exceeding traditional methods with careful parameter tuning. It is publicly available at https://github.com/yuxuanliao/DeepPIC.

Highly accurate and large-scale collision cross sections prediction with graph neural networks.

The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D conformers as inputs. A model was trained, evaluated, and tested with >5,000 experimental CCS values. It achieved a coefficient of determination of 0.9945 and a median relative error of 1.1751% on the test set. The model-agnostic interpretation method and the visualization of the learned representations were used to investigate the chemical rationality of SigmaCCS. An in-silico database with 282 million CCS values was generated for three different adduct types of 94 million compounds. Its source code is publicly available at https://github.com/zmzhang/SigmaCCS . Altogether, SigmaCCS is an accurate, rational, and off-the-shelf method to directly predict CCS values from molecular structures.

Combined SERS Microfluidic Chip with Gold Nanocone Array for Effective Early Lung Cancer Prognosis in Mice Model.

Introduction: As the most common malignant tumor in the world, the prognosis of patients with advanced lung cancer remains poor even after treatment. There are many prognostic marker assays available, but there is still more room for the development of high-throughput and sensitive detection of circulating tumor DNA (ctDNA). Surface-enhanced Raman spectroscopy (SERS), a spectroscopic detection method that has received wide attention in recent years, can achieve exponential amplification of Raman signals by using different metallic nanomaterials. Integrating SERS with signal amplification strategy into the microfluidic chip and applying it to ctDNA detection is expected to be an effective tool for the prognosis of lung cancer treatment effect in the future. Methods: To construct a high-throughput SERS microfluidic chip integrated with enzyme-assisted signal amplification (EASA) and catalytic hairpin self-assembly (CHA) signal amplification strategies, using hpDNA-functionalized Au nanocone arrays (AuNCAs) as capture substrates and cisplatin-treated lung cancer mice to simulate the detection environment for sensitive detection of ctDNA in serum of lung cancer patients after treatment. Results: The SERS microfluidic chip constructed by this scheme, with two reaction zones, can simultaneously and sensitively detect the concentrations of four prognostic ctDNAs in the serum of three lung cancer patients with a limit of detection (LOD) as low as the aM level. The results of the ELISA assay are consistent with this scheme, and its accuracy is guaranteed. Conclusion: This high-throughput SERS microfluidic chip has high sensitivity and specificity in the detection of ctDNA. This could be a potential tool for prognostic assessment of lung cancer treatment efficacy in future clinical applications.

Ultra-fast and accurate electron ionization mass spectrum matching for compound identification with million-scale in-silico library.

Spectrum matching is the most common method for compound identification in mass spectrometry (MS). However, some challenges limit its efficiency, including the coverage of spectral libraries, the accuracy, and the speed of matching. In this study, a million-scale in-silico EI-MS library is established. Furthermore, an ultra-fast and accurate spectrum matching (FastEI) method is proposed to substantially improve accuracy using Word2vec spectral embedding and boost the speed using the hierarchical navigable small-world graph (HNSW). It achieves 80.4% recall@10 accuracy (88.3% with 5 Da mass filter) with a speedup of two orders of magnitude compared with the weighted cosine similarity method (WCS). When FastEI is applied to identify the molecules beyond NIST 2017 library, it achieves 50% recall@1 accuracy. FastEI is packaged as a standalone and user-friendly software for common users with limited computational backgrounds. Overall, FastEI combined with a million-scale in-silico library facilitates compound identification as an accurate and ultra-fast tool.

Emodin prevents renal ischemia-reperfusion injury via suppression of p53-mediated cell apoptosis based on network pharmacology.

Background: Previous evidence indicated that emodin has significant advantages for preventing acute kidney injury (AKI). However, the mechanisms responsible for these effects of emodin have yet to be elucidated. Methods: We first used network pharmacology and molecular docking to identify the core targets of emodin for AKI and performed a range of experiments to validate this result. Pretreatment with emodin for 7 days, the rats were treated with bilateral renal artery clipping for 45 min to identify the prevention effect. Hypoxia/reoxygenation (H/R), and vancomycin - induced renal tubular epithelial cells (HK-2 cells) were treated with emodin to explore the related molecular mechanism. Results: Network pharmacology and molecular docking showed that anti-apoptosis might be the core mechanism responsible for the action of emodin on AKI; this anti-apoptotic effect appears to because by regulation p53-related signaling pathway. Our data showed that pretreatment with emodin significantly improved renal function and renal tubular injury in renal I/R model rats (P < 0.05. The prevention effect of emodin was proved to be related to anti - apoptosis of HK-2 cells, possibly by downregulating the levels of p53, cleaved-caspase-3, pro-caspase-9, and upregulated the levels of Bcl-2. The efficacy and mechanism of emodin on anti - apoptosis was also confirmed in vancomycin - induced HK-2 cells. Meanwhile, the data also showed that emodin promoted angiogenesis in I/R damaged kidneys and H/R-induced HK-2 cells, which was associated with decreasing HIF-1α levels and increasing VEGF levels. Conclusions: Our findings indicated that the preventive effect of emodin on AKI is probably attributable to anti-apoptosis response and promoting angiogenesis effect.

Gut microbiota-derived trimethylamine N-oxide is associated with the risk of all-cause and cardiovascular mortality in patients with chronic kidney disease: a systematic review and dose-response meta-analysis.

BACKGROUND: Trimethylamine N-oxide (TMAO) derived from gut microbiota causes kidney-heart damage in chronic kidney disease (CKD) patients. However, it is controversial whether CKD patients with higher TMAO are associated with a higher risk of death. We aimed to assess the correlation between circulating TMAO concentration and the risk of all-cause and cardiovascular death in CKD patients of different dialysis statuses and different races by dose-response analyses, and the underlying mechanisms were also explored by analyzing the correlations of TMAO with glomerular filtration rate (GFR) and inflammation. METHOD: PubMed, Web of Science, and EMBASE were systematically searched up to 1 July 2022. A total of 21 studies involving 15,637 individuals were included. Stata 15.0 was used to perform the meta-analyses and dose-response analyses with extracted data. Subgroup analyses were conducted to recognize possible sources of heterogeneity. RESULTS: The risk of all-cause mortality was increased in non-dialysis CKD patients (RR = 1.26, 95%CI = 1.03-1.54, p = 0.028) and non-black dialysis patients (RR = 1.62, 95%CI = 1.19-2.22, p = 0.002) with the highest circulating TMAO concentration, and the association was confirmed to be linear. In addition, an increased risk of cardiovascular mortality was also found in non-black dialysis patients with the highest circulating TMAO concentration (RR = 1.72, 95%CI = 1.19-2.47, p = 0.004), likewise, a linear association was identified. However, for dialysis patients including blacks with high TMAO concentrations, there was no significant increase in either all-cause mortality (RR = 0.98, 95%CI = 0.94-1.03, p = 0.542) or cardiovascular mortality (RR = 0.87, 95% CI = 0.65-1.17, p = 0.362). Meanwhile, we verified strong correlations between TMAO and both GFR (r= -0.49; 95% CI= -0.75, -0.24; p < 0.001) and inflammatory markers (r = 0.43; 95% CI= 0.03, 0.84; p = 0.036) in non-dialysis patients. CONCLUSIONS: Increased circulating TMAO concentrations increase the risk of all-cause mortality in non-dialysis and non-black dialysis CKD patients. Moreover, elevated TMAO levels raise the cardiovascular mortality risk in non-black dialysis patients.Key messagesNon-dialysis and non-black dialysis CKD patients with higher circulating TMAO concentrations are associated with an increased risk of all-cause mortality.Non-black dialysis patients with higher concentrations of TMAO are associated with an increased risk of cardiovascular mortality.Circulating TMAO concentrations have a strong negative correlation with GFR and a positive correlation with inflammation biomarkers in non-dialysis CKD patients.