Desorption Separation Ionization Mass Spectrometry (DSI-MS) for Rapid Analysis of COVID-19.

During the coronavirus disease 2019 (COVID-19) pandemic, which has witnessed over 772 million confirmed cases and over 6 million deaths globally, the outbreak of COVID-19 has emerged as a significant medical challenge affecting both affluent and impoverished nations. Therefore, there is an urgent need to explore the disease mechanism and to implement rapid detection methods. To address this, we employed the desorption separation ionization (DSI) device in conjunction with a mass spectrometer for the efficient detection and screening of COVID-19 urine samples. The study encompassed patients with COVID-19, healthy controls (HC), and patients with other types of pneumonia (OP) to evaluate their urine metabolomic profiles. Subsequently, we identified the differentially expressed metabolites in the COVID-19 patients and recognized amino acid metabolism as the predominant metabolic pathway involved. Furthermore, multiple established machine learning algorithms validated the exceptional performance of the metabolites in discriminating the COVID-19 group from healthy subjects, with an area under the curve of 0.932 in the blind test set. This study collectively suggests that the small-molecule metabolites detected from urine using the DSI device allow for rapid screening of COVID-19, taking just three minutes per sample. This approach has the potential to expand our understanding of the pathophysiological mechanisms of COVID-19 and offers a way to rapidly screen patients with COVID-19 through the utilization of machine learning algorithms.

Construction of yield loss indicators for cold vortex, light-temperature-water combined stress during the flowering period of rice in Northeast China.

The construction of a yield loss evaluation index for the cold vortex type light-temperature-water composite adversity during rice flowering period in Northeast China is important for elucidating the impacts of cold vortex type composite disasters on rice yield loss in middle and high latitude areas. Moreover, it can provide meteorological support to ensure safe production of high-quality japonica rice in China and contribute to regional disaster reduction and efficiency improvement. By combining growth period data, meteorological data, and yield data, we delineated and constructed the composite stress occurrence index of cold vortex type light-temperature-water at the flowering stage of japonica. We analyzed the relationship between factors causing disasters and yield structure, as well as the relationship between different yield structures and yield by employing BP neural network method. We further dissected the processes involved in the causation of combined disasters. Based on the K-means clustering method and historical typical disaster years, we quantified the critical thresholds and disaster grades, and established an evaluation index and model for assessing yield loss caused by combined stress from cold vortex type light-temperature-water. Finally, we examined the spatial and temporal variations of low temperature, abundant rainfall, and reduced sunlight during the flowering period in the three provinces of Northeast China. Results showed that the critical thresholds for light, temperature, and water stress index during the flowering stage of mild, moderate, and severe cold vortex types were [0, 0.21), [0.21, 0.32), and [0.32, 0.64], respectively. The rates of yield loss were [0, 0.03), [0.03, 0.08), and [0.08, 0.096], respectively. Based on the verification results of a total of 751 samples in 11 random years from 1961 to 2020, the percentage of stations for which the production reduction grade, as calculated by the composite index developed in this study, aligning with the actual production reduction grade was 63.7%, consistently exceeding 58.0% annually. Moreover, the proportion of sites with a similarity or difference level of 1 stood at 88.3%, surpassing 85.0% in each year. The index could effectively assess the extent of rice yield loss caused by cold vortex disasters in Northeast China.

Design of a hemispherical shell shaped natural ventilation temperature observation instrument.

Atmospheric temperature is fundamental information for various industries, such as production, life, and scientific research. The temperature error induced by the solar rays can reach 1 °C or even higher. A hemispherical shell-shaped atmospheric temperature measuring instrument that can reduce heat pollution and increase air velocity was designed. First, the instrument was optimized using computational fluid dynamics (CFD) software packages. Then, the CFD software packages were employed to quantify the temperature errors of the instrument with varying situations. A neural network model was employed to develop a temperature error correction model that can be targeted for multi-variable changes. This model provides accurate correction data when the influencing factors change continuously. Finally, field experiments were performed. The experimental data analysis indicates that the mean temperature error and the maximum error of the instrument before correction are 0.08 and 0.25 °C, respectively. The root mean square error, the mean absolute error, and the correlation coefficient between measured temperature errors from experiments and corrected temperature errors from the correction model are 0.099, 0.016, and 0.952 °C, respectively. By utilizing a temperature error correction model, the measuring error of the instrument can be minimized to a range between -0.05 and 0.04 °C. Consequently, the instrument is anticipated to enhance temperature measurement accuracy to ∼0.1 °C.

Abnormal upregulation of NUBP2 contributes to cancer progression in colorectal cancer.

Colorectal cancer (CRC), a digestive tract malignancy with high mortality and morbidity, lacks effective biomarkers for clinical prognosis due to its complex molecular pathogenesis. Nucleotide binding protein 2 (NUBP2) plays a vital role in the assembly of cytosolic Fe/S protein and has been implicated in cancer progression. In this study, we found that NUBP2 was highly expressed in CRC by TCGA database analysis. Subsequently, we verified the expression of NUBP2 in CRC tumor tissues and para-carcinoma tissues using IHC staining, and further investigated its association with clinicopathological parameters. In vitro cell experiments were conducted to assess the role of NUBP2 in CRC by evaluating cell proliferation, migration, and apoptosis upon NUBP2 dysregulation. Furthermore, we established a subcutaneous CRC model to evaluate the impact of NUBP2 on tumor growth in vivo. Additionally, we performed mechanistic exploration using a Human Phospho-Kinase Array-Membrane. Our results showed higher expression of NUBP2 in CRC tissues, which positively correlated with the pathological stage, indicating its involvement in tumor malignancy. Functional studies demonstrated that NUBP2 knockdown reduced cell proliferation, increased apoptosis, and impaired migration ability. Moreover, NUBP2 knockdown inhibited tumor growth in mice. We also observed significant changes in the phosphorylation level of GSK3ß upon NUBP2 knockdown or overexpression. Additionally, treatment with CHIR-99021 HCl, an inhibitor of GSK3ß, reversed the malignant phenotype induced by NUBP2 overexpression. Overall, this study elucidated the functional role of NUBP2 in CRC progression both in vitro and in vivo, providing insights into the molecular mechanisms underlying CRC and potential implications for targeted therapeutic strategies.

Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO3-Assisted Laser Desorption/Ionization Mass Spectrometry and Machine Learning.

Lung cancer ranks among the cancers with the highest global incidence rates and mortality. Swift and extensive screening is crucial for the early-stage diagnosis of lung cancer. Laser desorption/ionization mass spectrometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample processing, rapid speed, and high-throughput performance. As n-type semiconductors, titanate-based perovskite materials can generate charge carriers under ultraviolet light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO3 (STO/Rh)-assisted LDI-MS combined with machine learning to establish a method for urine-based lung cancer screening. We directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without employing any pretreatment. Through the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related pathways through metabolic enrichment analysis. These results suggest the potential of this method for rapidly distinguishing LCs in clinical applications and promoting precision medicine.

ZIP14 Affects the Proliferation, Apoptosis, and Migration of Cervical Cancer Cells by Regulating the P38 MAPK Pathway.

BACKGROUND: Cervical cancer (CC) remains a major public health concern and is a leading cause of female mortality worldwide. Understanding the molecular basis of its pathogenesis is essential for the development of novel therapeutic strategies. In this study, we aimed to dissect the role of a specific molecule, ZIP14, in the initiation and progression of CC. METHOD: We used Gene Expression Omnibus for target gene identification, while KEGG was used to delineate CC-related pathways. Proliferation, migration, and apoptosis levels in CC cells were assessed using CCK8, Transwell, and flow cytometry, respectively. The effect of the target genes on the in vivo tumorigenesis of CC cells was evaluated using the subcutaneous tumorigenesis assay. RESULTS: ZIP14 (SLC39A14) was found to be underexpressed in CC samples. Our KEGG pathway analysis revealed the potential involvement of the P38 mitogen-activated protein kinase (MAPK) pathway in CC pathogenesis. Overexpression of ZIP14 in HeLa and Caski cells increased p38 phosphorylation, inhibited cell growth and migration, and enhanced apoptosis. Conversely, ZIP14 knockdown produced the opposite effects. Importantly, the bioeffects induced by ZIP14 overexpression could be counteracted by the p38 MAPK pathway inhibitor SB203580. In vivo experiments further confirmed the influence of ZIP14 on CC cell migration. CONCLUSION: Our study is the first to elucidate the pivotal role of ZIP14 in the pathogenesis of CC, revealing its inhibitory effects through the activation of the p38 MAPK signaling pathway. The discovery not only provides a deeper understanding of CC's molecular underpinnings, but also highlights ZIP14 as a promising therapeutic target. As ZIP14 holds significant potential for therapeutic interventions, our findings lay a robust foundation for further studies and pave the way for the exploration of novel treatment modalities for cervical cancer.

Correction: Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis.

Correction for 'Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis' by Qiuyao Du et al., Analyst, 2023, https://doi.org/10.1039/d3an01051a.

Machine learning encodes urine and serum metabolic patterns for autoimmune disease discrimination, classification and metabolic dysregulation analysis.

There is a wide variety of autoimmune diseases (ADs) with complex pathogenesis and their accurate diagnosis is difficult to achieve because of their vague symptoms. Metabolomics has been proven to be an efficient tool in the analysis of metabolic disorders to provide clues about the mechanism and diagnosis of diseases. Previous studies of the metabolomics analysis of ADs were not competent in their discrimination. Herein, a liquid chromatography tandem mass spectrometry (LC-MS) strategy combined with machine learning is proposed for the discrimination and classification of ADs. Urine and serum samples were collected from 267 subjects consisting of 127 healthy controls (HC) and 140 AD patients, including those with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), sicca syndrome (SS), ankylosing spondylitis (AS), systemic scleroderma (SSc) and connective tissue disease (CTD). Machine learning algorithms were encoded for the discrimination and classification of ADs with metabolomic patterns obtained by LC-MS, and satisfactory results were achieved. Notably, urine samples exhibited higher accuracy for disease differentiation and triage than serum samples. Apart from that, differential metabolites were selected and metabolite panels were evaluated to demonstrate their representativeness. Metabolic dysregulations were also investigated to gain more knowledge about the pathogenesis of ADs. This research provides a promising method for the application of metabolomics combined with machine learning in precision medicine.

Urine and serum metabolic profiling combined with machine learning for autoimmune disease discrimination and classification.

Precision diagnosis and classification of autoimmune diseases (ADs) is challenging due to the obscure symptoms and pathological causes. Biofluid metabolic analysis has the potential for disease screening, in which high throughput, rapid analysis and minimum sample consumption must be addressed. Herein, we performed metabolomic profiling by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) in urine and serum samples. Combined with machine learning (ML), metabolomic patterns from urine achieved the discrimination and classification of ADs with high accuracy. Furthermore, metabolic disturbances among different ADs were also investigated, and provided information of etiology. These results demonstrated that urine metabolic patterns based on MALDI-MS and ML manifest substantial potential in precision medicine.

Disassembling ability of lipopeptide promotes the antibacterial activity.

Lipopeptides have become one of the most potent antibacterial agents, however, there is so far no consensus about the link between their physic-chemical properties and biological activity, in particular their inherent aggregation propensity and antibacterial potency. To this end, we here de novo design a series of lipopeptides (CnH(2n-1)O-(VVKK)2V-NH2), in which an alkyl chain is covalently attached onto the N-terminus of a short cationic peptide sequence with an alternating pattern of hydrophobic VV (Val) and positively charged KK (Lys) motifs. By varying the alkyl chain length (ortho-octanoic acid (C8), lauric acid (C12), and palmitic acid (C16)), the lipopeptides show distinct physicochemical properties and self-assembly behaviors, which have great effect on their antibacterial activities. C8H15O-(VVKK)2V-NH2, which contains the lowest hydrophobicity and surface activity has the lowest antibacterial activity. C12H23O-(VVKK)2V-NH2 and C16H31O-(VVKK)2V-NH2 both have high hydrophobicity and surface activity, and self-assembled into long nanofibers. However, the nanofibers formed by C12H23O-(VVKK)2V-NH2 disassembled by dilution, resulting in its high antibacterial activity via bacterial membrane disruption. Comparatively, the nanofibers formed by C16H31O-(VVKK)2V-NH2 were very stable, which can closely attach on bacterial surface but not permeate bacterial membrane, leading to its low antibacterial activity. Thus, the stability other than the morphologies of lipopeptides' nanostructures contribute to their antibacterial ability. Importantly, this study enhances our understanding of the antibacterial mechanisms of self-assembling lipopeptides that will be helpful in exploring their biomedical applications.

The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles.

A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research.

Chilling injury monitoring and intensity identification of dryland maize in Heilongjiang.

BACKGROUND: Accurate and timely access to large-scale crop damage information provides an essential reference for responding to agricultural disaster prevention and mitigation needs and ensuring food production security. The present study aimed to reveal the new characteristics of low-temperature cold damage to maize in the context of climate warming. Heilongjiang, one of the provinces with the highest latitude, the most significant climate change and the largest maize production in China, was taken as the study area. We combined meteorological stations and MODIS remote sensing data to spatially identify the occurrence and intensity of cold damage to maize based on the growing season temperature distance level index, as well as to assess the extent of cold damage. RESULTS: The main findings are: (i) The frequency and intensity range of cold damage in the growing season (May to September) in Heilongjiang Province from 1991 to 2020 against climate warming showed a decreasing trend. The average temperature from 1991 to 2000 was 17.777 °C, with seven occurrences of maize cold damage years, of which 5 years comprised widespread cold damage and 2 years comprised regional cold damage. The average temperature from 2000 to 2010 was 18.137 °C, with cold damage three times, of which 2 years comprised regional cold damage and 1 year comprised widespread cold damage. The average temperature from 2010 to 2020 was 18.130 °C, with one maize cold damage year occurring, which comprised regional cold damage. The frequency of maize chilling injury decreased significantly from 1991 to 2020, from 0.23 in 1991-2000 to 0.1 in 2000-2010 and, finally, to 0.03 in 2010-2020. (ii) The good consistency between MODIS_LST data and temperature data from meteorological stations suggests that MODIS_LST data can be used to build a temperature remote sensing estimation model for spatially extensive cold damage monitoring and intensity discrimination. (iii) Taking 2009 as an example of a large-scale cold damage year, the spatial discrimination of maize cold damage intensity shows that the spatial distribution of chilling injury intensity has no obvious geographical features. The intensity of cold damage was mainly mild cold damage. According to administrative regions, the scope of chilling injury was the largest in Mudanjiang City, Heihe City, and Jixi City, accounting for 91.56%, 86.25%, and 84.91%, respectively. The areas with the most extensive range of severe chilling injuries were the Great Khingan Mountains region, Heihe City, Mudanjiang City, Yichun City, and Jixi City. CONCLUSION: In the context of climate warming, the frequency and intensity range of maize cold damage showed a decreasing trend from 1991 to 2020 in Heilongjiang Province. The results of cold damage identification based on MODIS_LST data are accurate and can improve the spatial accuracy. The results of the present study provide a reference and guidance for dealing with the occurrence and defence of spatially refined cold damage. © 2023 Society of Chemical Industry.

Drug Resistance Genes and Molecular Epidemiological Characteristics of Carbapenem-Resistant Klebsiella pneumonia.

Background: The global prevalence of carbapenem-resistant Klebsiella pneumoniae (CRKP) has become a serious challenge for nosocomial infection and attracted worldwide attention. This study explored the drug resistance genes and molecular characteristics for CRKP, providing a reference for nosocomial prevention and control. Methods: A total of 42 CRKP isolates were collected from the First Affiliated Hospital of Gannan Medical University (Ganzhou, China) from January 2018 to February 2021. The drug resistance of CRKP was tested by the VitekII Compact system. Drug resistance gene expression was detected by poly-merase chain reaction (PCR), and molecular typing was performed by pulsed-field gel electrophoresis (PFGE) and multi-locus sequence typing (MLST). Results: All the 42 CRKP isolates were multi-drug resistant. Among them, 35 isolates (83.3%) produced blaKPC-2 and 12 isolates (28.6%) produced blaNDM-1. The detection rate of blaIMP-4 and blaOXA-48 was 2.4% (1/42), respectively. Twelve isolates (28.6%) carried both blaKPC-2 and blaNDM-1, one isolate (2.4%) carried both blaKPC-2 and blaIMP-4, and one isolate (2.4%) carried blaKPC-2, blaNDM-1 and blaOXA-48. A variety of other extended-spectrum beta-lactamases (ESBLs) were also detected. All 42 isolates carried blaSHV and blaCTX-M-1, 27 isolates (64.3%) carried blaTEM and 12 isolates (28.6%) carried blaCTX-M-9. The MLST data classified the 42 CRKP isolates into 11 sequence types, mainly ST11, accounting for 61.9% (26/42), of which 92.3% of isolates (24/26) carrying blaKPC-2. The PFGE results demonstrated that the 42 CRKP isolates could be divided into 20 clusters A-T, with cluster A (26.2%, 11/42) and cluster H (21.4%, 9/42) dominating, which were all ST11. Conclusion: The CRKP isolates were severely multi-drug resistant, and the main resistant gene was blaKPC-2 production, carrying multiple ESBLs genes simultaneously. The MLST and PFGE revealed that the ST11-blaKPC-2 Klebsiella pneumoniae was the main clonotype. Our findings may offer help to antibiotics selection and nosocomial infection prevention and control.

Insight into the Neuroprotective Effect of Genistein-3'-Sodium Sulfonate Against Neonatal Hypoxic-Ischaemic Brain Injury in Rats by Bioinformatics.

Therapeutic hypothermia (TH) is the only intervention approved for the treatment of neonatal hypoxic-ischaemic encephalopathy (HIE), but its treatment window is narrow (within 6 h after birth), and its efficacy is not ideal. Thus, alternative treatments are urgently needed. Our previous studies showed that genistein-3'-sodium sulfonate (GSS), a derivative of genistein (Gen), has a strong neuroprotective effect in rats with ischaemic stroke, but its role in HIE is unclear. A hypoxia-ischaemia (HI) brain injury model was established in neonatal male Sprague‒Dawley (SD) rats. Twenty-four hours after reperfusion, rats treated with GSS were assessed for cerebral infarction, neurological function, and neuronal damage. RNA-Seq and bioinformatics analysis were used to explore differentially expressed genes (DEGs) and regulated signalling pathways, which were subsequently validated by Western blotting and immunofluorescence. In this study, we found that GSS not only significantly reduced the size of brain infarcts and alleviated nerve damage in rats with HIE but also inhibited neuronal loss and degeneration in neonatal rats with HIE. A total of 2170 DEGs, of which 1102 were upregulated and 1068 were downregulated, were identified in the GSS group compared with the HI group. In an analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) categories, the downregulated DEGs were significantly enriched in the pathways "Phagosome", "NF-κB signalling", and "Complement and coagulation cascades", amongst others. Meanwhile, the upregulated DEGs were significantly enriched in the pathways "Neurodegeneration", "Glutamatergic synapse", and "Calcium signalling pathway", amongst others. These results indicate that GSS intervenes in the process of HIE-induced brain injury by participating in multiple pathways, which suggests potential candidate drugs for the treatment of HIE.

Influence of blended instruction on students' learning effectiveness: the role of Flow.

The rapid development and extensive application of information and communication technologies has facilitated blended instruction, which is regarded as the "new normal" in the field of modern education and has become the focus of academic research. This study thus explored the influencing mechanism of blended instruction on students' learning effectiveness from the perspective of complementarity and conflict with the support of flow. This study collected 349 survey data from universities in Southwest China that adopted a blended instruction mode and analyzed them using the structural equation model. The results demonstrated that complementary advantages and practical conflicts in blended instruction influenced students' flow experience during the learning process. Flow experience plays an important role in blended instruction and influences positively students' cognitive, emotional, and behavioral engagement. Learning engagement impacted positively students' learning effectiveness. In addition, self-efficacy positively moderated the relationship between students' learning engagement and learning effectiveness in blended instruction. These findings contribute to related research on blended instruction. The implications and limitations of this study are discussed.

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Establishment of indices for low temperature damage of soybeans is important for systematically analyzing the adaptation strategies to climate change and collaborative adaptation technology for disaster prevention and mitigation and other stresses in high cold region. Based on historical data of low temperature damage and phenophase of soybean from 1980 to 2020 and daily temperature data from 78 meteorological stations in Heilongjiang Province, we used GIS to match the phenophase and meteorological data, by considering the accumulated temperature anomaly in different growth stages, and constructed a comprehensive soybean low temperature damages index (CSCDI) in high cold region. Using K-S distribution fitting test and the lower limit value of confidence intervals, we constructed the level index of soybean low temperature damage. The results showed that the CSCDI lower limits of mild, moderate and severe low temperature damage were 0.061, 0.115 and 0.237 from sowing to emergence stage, were 0.072, 0.152 and 0.312 from emergence to flowering, and 0.133, 0.245 and 0.412 from flowering to maturity, respectively. The time distribution of soybean low temperature damage inversed by CSCDI in Heilongjiang Province was consistent with the historical disaster records. The spatial distribution showed obvious latitude characteristics, with the frequency of low temperature damage increasing gradually from south to north.

Profiling of Urine Carbonyl Metabolic Fingerprints in Bladder Cancer Based on Ambient Ionization Mass Spectrometry.

The diagnosis of bladder cancer (BC) is currently based on cystoscopy, which is invasive and expensive. Here, we describe a noninvasive profiling method for carbonyl metabolic fingerprints in BC, which is based on a desorption, separation, and ionization mass spectrometry (DSI-MS) platform with N,N-dimethylethylenediamine (DMED) as a differential labeling reagent. The DSI-MS platform avoids the interferences from intra- and/or intersamples. Additionally, the DMED derivatization increases detection sensitivity and distinguishes carboxyl, aldehyde, and ketone groups in untreated urine samples. Carbonyl metabolic fingerprints of urine from 41 BC patients and 41 controls were portrayed and 9 potential biomarkers were identified. The mechanisms of the regulations of these biomarkers have been tentatively discussed. A logistic regression (LR) machine learning algorithm was applied to discriminate BC from controls, and an accuracy of 85% was achieved. We believe that the method proposed here may pave the way toward the point-of-care diagnosis of BC in a patient-friendly manner.

Alloy-Type Anodes for High-Performance Rechargeable Batteries.

Alloy-type anodes are one of the most promising classes of next-generation anode materials due to their ultrahigh theoretical capacity (2-10 times that of graphite). However, current alloy-type anodes have several limitations: huge volume expansion, high tendency to fracture and disintegrate, an unstable solid-electrolyte interphase (SEI) layer, and low Coulombic efficiency. Efforts to overcome these challenges are ongoing. This Review details recent progress in the research of batteries based on alloy-type anodes and discusses the direction of their future development. We conclude that improvements in structural design, the introduction of a protective interface, and the selection of suitable electrolytes are the most effective ways to improve the performance of alloy-type anodes. Furthermore, future studies should direct more attention toward analyzing their synergistic promoting effect.

The relationship between MHC-peptide interaction and resistance to virus in chickens.

INTRODUCTION: The MHC-peptide interaction has a subtle influence on host resistance to virus. This paper aims to study the relationship between MHC-peptide interaction and MHC-related virus-resistance. METHODS: By 3D homology modeling, the structure of chicken BF2 molecule BF2*0201 (PDB code: 4d0d) was studied and compared with the known structures of BF2 molecule BF2*0401 (PDB code: 4e0r) to elucidate the characteristics of BF2*0201-binding antigenic peptides. RESULTS: The results show that due to the amino acid difference between the two binding groove of 4e0r and 4d0d, the size of the binding groove of the two are 1130 ų and1380 ų respectively, indicating the amino acid species that 4e0r binding peptide has lower selectivity than 4d0d; and because of large side chain conformation of Arg (especially Arg111) of 4e0r replaced by small side chain Tyr111 of 4d0d, the volume of central part of the binding groove of 4d0d is obviously larger than that of 4e0r, indicating that the restrictive of binding antigenic peptides for 4d0d is narrower than that of 4e0r; and on account of the chargeability of the binding groove of the two are different, namely the binding groove chargeability of 4e0r (strong positive polarity) and 4d0d (weak negative polarity). CONCLUSION: There are generally more peptides presented by the BF2 of B2 haplotype than by that of B4 haplotype, leading to more resistance of B2 than that of B4 to virus.

Salidroside-pretreated mesenchymal stem cells contribute to neuroprotection in cerebral ischemic injury in vitro and in vivo.

Mesenchymal stem cells (MSCs) are considered a promising tool for treating cerebral ischemic injury. However, their poor survival after transplantation limits their therapeutic effect and applications. Salidroside has been reported to exert potent cytoprotective and neuroprotective effects. This study aimed to investigate whether salidroside could improve MSC survival under hypoxic-ischemic conditions and, subsequently, alleviate cerebral ischemic injury in a rat model. MSCs were pretreated by salidroside under hypoxic-ischemic conditions. The cell proliferation, migratory capacity, and apoptosis were evaluated by means of Cell Counting Kit-8, transwell assay, and flow cytometry. MSCs pretreated with salidroside were transplanted into the rats subsequent to middle cerebral artery occlusion. The grip strength, 2,3,5-triphenyltetrazolium chloride, and hematoxylin-eosin staining were used to analyze the therapeutic efficiency and pathological changes. The mature neuron marker NeuN and astrocyte marker GFAP in the focal area were detected by immunofluorescence. These results indicated that salidroside promoted the proliferation, migration and reduced apoptosis of MSCs under hypoxic-ischemic conditions. In vivo experiments revealed that transplantation of salidroside-pretreated MSCs strengthened the therapeutic efficiency by enhancing neurogenesis and inhibiting neuroinflammation in the hippocampal CA1 area after ischemia. Our results suggest that pretreatment with salidroside could be an effective strategy to enhance the cell survival rate and the therapeutic effect of MSCs in treating cerebral ischemic injury.