Racetrack FAIMS for High-Resolution and High-Sensitivity Characterization of Peptide Conformers.

A racetrack field asymmetric waveform ion mobility spectrometry (r-FAIMS) device, which consists of both cylindrical FAIMS (c-FAIMS) and planar FAIMS (p-FAIMS) sections with a 1 mm gap width, was developed and applied for high-resolution and high-sensitivity exploration of conformational diversity for peptides. The optimal operating conditions of r-FAIMS were systemically studied, and the performance of the fully optimized r-FAIMS was compared to a previously developed p-FAIMS in detail by using pure nitrogen as the FAIMS carrier gas. Relying on the ion focusing effect in the c-FAIMS section, the intensity of the FAIMS spectrum for doubly charged bradykinin ions acquired by using r-FAIMS is ∼8.5-fold higher than that acquired by using p-FAIMS under the same resolving power/resolution condition, implying about an order of magnitude better sensitivity of r-FAIMS. In addition, the peak separation resolution of r-FAIMS was ∼1.70-fold higher than p-FAIMS under a similar sensitivity condition for doubly charged bradykinin ions. Due to a reduced gap width of the newly designed r-FAIMS (1 mm) as compared to the previously developed p-FAIMS (1.88 mm), r-FAIMS can operate at a much higher separation field with a similar FAIMS dispersion voltage (DV) to gain significantly higher resolving power. For triply charged syntide 2 ions, the resolving power of r-FAIMS can easily exceed 120 at -3.5 kV DV by using pure nitrogen as the FAIMS carrier gas as compared to 44.2 resolving power obtained by using p-FAIMS at -4.0 kV DV. All of the experimental results have confirmed that r-FAIMS can perform structural characterization of biomolecules with both high resolution and high sensitivity.

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.

2.09†µm high-gain, high-energy sub-nanosecond laser amplification system and its application to a millijoule-level mid-infrared ZnGeP2 optical parametric generator.

In this Letter, we report for the first time to our knowledge a 2 mJ-level 2.09 µm Ho:YAG regenerative amplifier (RA) seeded by the first-stage Ho-doped fiber (HDF) preamplifier of a gain-switched laser diode (GSLD). After the single-pass power amplifier (SPPA), the output of a 2.09 µm pulse laser with 1 kHz, 570 ps, and >10 mJ was achieved. The overall gain of the whole amplifier system was greater than 90 dB, providing a novel, stable, and reliable sub-nanosecond (sub-ns) pump source operating at a pulse repetition frequency (PRF) of 1 kHz for an optical parametric generator (OPG) based on ZnGeP2 (ZGP). Specifically, for the ZGP OPG structure, a maximum pulse energy of 1.82 mJ at 3-5 µm had been achieved with an injected pump pulse energy of 5.47 mJ, corresponding to a slope efficiency of 39.5% and an optical-to-optical conversion efficiency (OOCE) of 33.27%.

Narrowband mid-infrared optical parametric generator based on a type-II BaGa4Se7 crystal.

In this Letter, we first reported on a mid-infrared double-pass optical parametric generator (OPG) based on a single type-II phase-matching BaGa4Se7 (BGSe) crystal, pumped at 2.1 µm. The OPG achieved a maximum pulse energy of 55 µJ for generating narrowband mid-infrared laser pulses. The signal and idler lights exhibited center wavelengths of 4.04 and 4.33 µm, respectively, with bandwidths of 18.6 nm (11.4 cm-1) and 20.4 nm (10.9 cm-1). To improve the output performance, we utilized a cascaded scheme of type-I ZnGeP2 (ZGP) and type-II BGSe crystals. The spectral bandwidths of the signal and idler lights, nearing 4 µm, were narrower than 170 nm (90 cm-1), representing a significant improvement over the ZGP OPG. The cascaded OPG achieved a remarkable total optical-to-optical conversion efficiency (OOCE) of 14.9% and a maximum pulse energy of 0.329 mJ.

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.

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

OBJECTIVES: Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC). METHODS: In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression. RESULTS: A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group. CONCLUSION: The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.

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.

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.

An efficient strategy with a synergistic effect of hydrophilic and electrostatic interactions for simultaneous enrichment of N- and O-glycopeptides.

N- and O-glycosylation modifications of proteins are closely linked to the onset and development of many diseases and have gained widespread attention as potential targets for therapy and diagnosis. However, the low abundance and low ionization efficiency of glycopeptides as well as the high heterogeneity make glycosylation analysis challenging. Here, an enrichment strategy, using Knoevenagel copolymers modified with polydopamine-adenosine (denoted as PDA-ADE@KCP), was firstly proposed for simultaneous enrichment of N- and O-glycopeptides through the synergistic effects of hydrophilic and electrostatic interactions. The adjustable charged surface and hydrophilic properties endow the material with the capability to achieve effective enrichment of intact N- and O-glycopeptides. The experimental results exhibited excellent selectivity (1 : 5000) and sensitivity (0.1 fmol µL-1) of the prepared material for N-glycopeptides from standard protein digest samples. Moreover, it was further applied to simultaneous capturing of N- and O-glycopeptides from mouse liver protein digests. Compared to the commercially available zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) material, the number of glycoproteins corresponding to all N- and O-glycopeptides enriched with PDA-ADE@KCP was much more than that with ZIC-HILIC. Furthermore, PDA-ADE@KCP captured more O-glycopeptides than ZIC-HILIC, revealing its superior performance in O-glycopeptide enrichment. All these results indicated that the strategy holds immense potential in characterizing N- and O-intact glycopeptides in the field of proteomics.

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.

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).

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.

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.

Development of a fully packaged passive thermal regulator.

Thermal regulators are devices that can adopt either the role of a thermal insulator or a thermal conductor, depending on the thermal input conditions, and play an increasingly important role in thermal management systems. In this study, we developed and tested a new passive thermal regulator design that operates around room temperature and achieves high switching ratios. Our regulator is structurally integer, scalable, orientation-independent, resistant to vibration, and can be easily integrated into existing thermal management solutions. The working principle of the passive regulator is simple yet effective, whereby an aluminum plug attached to a bimetallic strip enters and exists a wedge-shaped gap between two conductors. We demonstrate a switching ratio of ≈ 50 ( - 8 + 34 ) :1 for a fully packaged prototype (≈ 320 (± 200):1 for a non-packaged regulator) operated in the open laboratory environment. Through geometric optimization using numerical simulations, we show that a switching ratio of ≈ 100 ( - 15 + 18 ) :1 can be easily obtained, which can be further increased by increasing the cross-sectional area of the input conductor, hence increasing the ON-state heat transfer rate. The OFF-state thermal performance is much less sensitive to the size of the conductor, making the device highly scalable.

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.

Metabolomics analysis of patients with Schistosoma japonicum infection based on UPLC-MS method.

BACKGROUND: Schistosomiasis is still one of the most serious parasitic diseases. Evidence showed that the metabolite profile in serum can potentially act as a marker for parasitic disease diagnosis and evaluate disease progression and prognosis. However, the serum metabolome in patients with Schistosoma japonicum infection is not well defined. In this study, we investigated the metabolite profiles of patients with chronic and with advanced S. japonicum infection. METHODS: The sera of 33 chronic S. japonicum patients, 15 patients with advanced schistosomiasis and 17 healthy volunteers were collected. Samples were extracted for metabolites and analyzed with ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). RESULTS: We observed significant differences in metabolite profiles in positive and negative ion modes between patients with advanced and chronic S. japonicum infection. In patients with chronic S. japonicum infection, 199 metabolites were significantly upregulated while 207 metabolites were downregulated in advanced infection. These differential metabolites were mainly concentrated in steroid hormone biosynthesis, cholesterol metabolism and bile secretion pathways. We also found that certain bile acid levels were significantly upregulated in the progression from chronic to advanced S. japonicum infection. In receiver operator characteristic (ROC) analysis, we identified three metabolites with area under the curve (AUC) > 0.8, including glycocholic (GCA), glycochenodeoxycholate (GCDCA) and taurochenodeoxycholic acid (TCDCA) concentrated in cholesterol metabolism, biliary secretion and primary bile acid biosynthesis. CONCLUSIONS: This study provides evidence that GCA, GCDCA and TCDCA can potentially act as novel metabolite biomarkers to distinguish patients in different stages of S. japonicum infection. This study will contribute to the understanding of the metabolite mechanisms of the transition from chronic to advanced S. japonicum infection, although more studies are needed to validate this potential role and explore the underlying mechanisms.

An optimize adaptable method for determining the monosaccharide composition of pectic polysaccharides.

Pectic polysaccharides are considered the highly complex natural plant polysaccharides which plays a vital role in plant tissue structure and human health. Detailed characterization of the monosaccharide composition can provide insights into the pectic polysaccharide structure. Nevertheless, when analyzing the monosaccharides of pectic polysaccharide, it is crucial to address the issue of incomplete hydrolysis that can occur due to the formation of acid-induced precipitates. Based on above, the main purpose of this article is to provide an optimized method for monosaccharide analysis of pectic polysaccharides through acid hydrolysis optimization using high-performance anion exchange chromatography (HPAEC) The results indicate that reducing the sample concentration to 0.5 mg/mL effectively reduces the acid gelling phenomenon and promotes the complete hydrolysis of pectin polysaccharides. The optimized parameters for acid hydrolysis involve 110 °C for 6 h in 2 M TFA. Furthermore, the consistency of this method is assessed, along with its ability to analyze pectin polysaccharides from various fruits. This hydrolysis approach holds promise for enabling accurate quantification of monosaccharide composition in pectic polysaccharides.

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.