Molecular epidemiology and antimicrobial resistance profiles of Klebsiella pneumoniae isolates from hospitalized patients in different regions of China.

Introduction: The increasing incidence of Klebsiella pneumoniae and carbapenem-resistant Klebsiella pneumoniae (CRKP) has posed great challenges for the clinical anti-infective treatment. Here, we describe the molecular epidemiology and antimicrobial resistance profiles of K. pneumoniae and CRKP isolates from hospitalized patients in different regions of China. Methods: A total of 219 K. pneumoniae isolates from 26 hospitals in 19 provinces of China were collected during 2019-2020. Antimicrobial susceptibility tests, multilocus sequence typing were performed, antimicrobial resistance genes were detected by polymerase chain reaction (PCR). Antimicrobial resistance profiles were compared between different groups. Results: The resistance rates of K. pneumoniae isolates to imipenem, meropenem, and ertapenem were 20.1%, 20.1%, and 22.4%, respectively. A total of 45 CRKP isolates were identified. There was a significant difference in antimicrobial resistance between 45 CRKP and 174 carbapenem-sensitive Klebsiella pneumoniae (CSKP) strains, and the CRKP isolates were characterized by the multiple-drug resistance phenotype.There were regional differences among antimicrobial resistance rates of K. pneumoniae to cefazolin, chloramphenicol, and sulfamethoxazole,which were lower in the northwest than those in north and south of China.The mostcommon sequence type (ST) was ST11 (66.7% of the strains). In addition, we detected 13 other STs. There were differences between ST11 and non-ST11 isolates in the resistance rate to amikacin, gentamicin, latamoxef, ciprofloxacin, levofloxacin, aztreonam, nitrofurantoin, fosfomycin, and ceftazidime/avibactam. In terms of molecular resistance mechanisms, the majority of the CRKP strains (71.1%, 32/45) harbored blaKPC-2, followed by blaNDM (22.2%, 10/45). Strains harboring blaKPC or blaNDM genes showed different sensitivities to some antibiotics. Conclusion: Our analysis emphasizes the importance of surveilling carbapenem-resistant determinants and analyzing their molecular characteristics for better management of antimicrobial agents in clinical use.

sRNA expression profile of KPC-2-producing carbapenem-resistant Klebsiella pneumoniae: Functional role of sRNA51.

The emergence of carbapenem-resistant Klebsiella pneumoniae (CRKP) has significant challenges to human health and clinical treatment, with KPC-2-producing CRKP being the predominant epidemic strain. Therefore, there is an urgent need to identify new therapeutic targets and strategies. Non-coding small RNA (sRNA) is a post-transcriptional regulator of genes involved in important biological processes in bacteria and represents an emerging therapeutic strategy for antibiotic-resistant bacteria. In this study, we analyzed the transcription profile of KPC-2-producing CRKP using RNA-seq. Of the 4693 known genes detected, the expression of 307 genes was significantly different from that of carbapenem-sensitive Klebsiella pneumoniae (CSKP), including 133 up-regulated and 174 down-regulated genes. Both the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology (GO) analysis showed that these differentially expressed genes (DEGs) were mainly related to metabolism. In addition, we identified the sRNA expression profile of KPC-2-producing CRKP for the first time and detected 115 sRNAs, including 112 newly discovered sRNAs. Compared to CSKP, 43 sRNAs were differentially expressed in KPC-2-producing CRKP, including 39 up-regulated and 4 down-regulated sRNAs. We chose sRNA51, the most significantly differentially expressed sRNA in KPC-2-producing CRKP, as our research subject. By constructing sRNA51-overexpressing KPC-2-producing CRKP strains, we found that sRNA51 overexpression down-regulated the expression of acrA and alleviated resistance to meropenem and ertapenem in KPC-2-producing CRKP, while overexpression of acrA in sRNA51-overexpressing strains restored the reduction of resistance. Therefore, we speculated that sRNA51 could affect the resistance of KPC-2-producing CRKP by inhibiting acrA expression and affecting the formation of efflux pumps. This provides a new approach for developing antibiotic adjuvants to restore the sensitivity of CRKP.

A prospective cohort-based artificial intelligence evaluation system for the protective efficacy and immune response of SARS-CoV-2 inactivated vaccines.

BACKGROUND: Novel coronaviruses constitute a significant health threat, prompting the adoption of vaccination as the primary preventive measure. However, current evaluations of immune response and vaccine efficacy are deemed inadequate. OBJECTIVES: The study sought to explore the evolving dynamics of immune response at various vaccination time points and during breakthrough infections. It aimed to elucidate the synergistic effects of epidemiological factors, humoral immunity, and cellular immunity. Additionally, regression curves were used to determine the correlation between the protective efficacy of the vaccine and the stimulated immune response. METHODS: Employing LASSO for high-dimensional data analysis, the study utilised four machine learning algorithms-logistical regression, random forest, LGBM classifier, and AdaBoost classifier-to comprehensively assess the immune response following booster vaccination. RESULTS: Neutralising antibody levels exhibited a rapid surge post-booster, escalating to 102.38 AU/mL at one week and peaking at 298.02 AU/mL at two weeks. Influential factors such as sex, age, disease history, and smoking status significantly impacted post-booster antibody levels. The study further constructed regression curves for neutralising antibodies, non-switched memory B cells, CD4+T cells, and CD8+T cells using LASSO combined with the random forest algorithm. CONCLUSION: The establishment of an artificial intelligence evaluation system emerges as pivotal for predicting breakthrough infection prognosis after the COVID-19 booster vaccination. This research underscores the intricate interplay between various components of immunity and external factors, elucidating key insights to enhance vaccine effectiveness. 3D modelling discerned distinctive interactions between humoral and cellular immunity within prognostic groups (Class 0-2). This underscores the critical role of the synergistic effect of humoral immunity, cellular immunity, and epidemiological factors in determining the protective efficacy of COVID-19 vaccines post-booster administration.

A Patient of Spontaneous Bacterial Peritonitis in Hepatitis C Cirrhosis Caused by Gordonia terrae: A Case Report.

Background: Gordonia terrae is an opportunistic pathogen that rarely causes clinical infections. Here, we first report a case of spontaneous bacterial peritonitis in patients with hepatitis C cirrhosis caused by Gordonia terrea. Case Presentation: A 71-year-old male patient was diagnosed with spontaneous bacteria peritonitis secondary to hepatitis C cirrhosis. The result of bacterial culture in ascites was positive, and the pathogenic bacteria was preliminarily identified as the Gordonia genus by matrix-assisted laser desorption ionization-time of flight mass spectrometry. After 16S rRNA sequencing analysis, it was determined to be the Gordonia terrea. Symptoms relieved after treatment with ceftazidime. Conclusion: This case indicates that the clinical infections caused by Gordonia terrea should be brought to the forefront. Accurate and rapid bacterial identification results are highly beneficial to the diagnosis and therapeutic regime.

A prognostic model for SARS-CoV-2 breakthrough infection: Analyzing a prospective cellular immunity cohort.

BACKGROUND: Following the COVID-19 pandemic, studies have identified several prevalent characteristics, especially related to lymphocyte subsets. However, limited research is available on the focus of this study, namely, the specific memory cell subsets among individuals who received COVID-19 vaccine boosters and subsequently experienced a SARS-CoV-2 breakthrough infection. METHODS: Flow cytometry (FCM) was employed to investigate the early and longitudinal pattern changes of cellular immunity in patients with SARS-CoV-2 breakthrough infections following COVID-19 vaccine boosters. XGBoost (a machine learning algorithm) was employed to analyze cellular immunity prior to SARS-CoV-2 breakthrough, aiming to establish a prognostic model for SARS-CoV-2 breakthrough infections. RESULTS: Following SARS-CoV-2 breakthrough infection, naïve T cells and TEMRA subsets increased while the percentage of TCM and TEM cells decreased. Naïve and non-switched memory B cells increased while switched and double-negative memory B cells decreased. The XGBoost model achieved an area under the curve (AUC) of 0.78, with an accuracy rate of 81.8 %, a sensitivity of 75 %, and specificity of 85.7 %. TNF-α, CD27-CD19+cells, and TEMRA subsets were identified as high predictors. An increase in TNF-α, cTfh, double-negative memory B cells, IL-6, IL-10, and IFN-γ prior to SARS-CoV-2 infection was associated with enduring clinical symptoms; conversely, an increase in CD3+ T cells, CD4+ T cells, and IL-2 was associated with clinical with non-enduring clinical symptoms. CONCLUSION: SARS-CoV-2 breakthrough infection leads to disturbances in cellular immunity. Assessing cellular immunity prior to breakthrough infection serves as a valuable prognostic tool for SARS-CoV-2 infection, which facilitates clinical decision-making.

Novel Polystyrene-Binding Nanobody for Enhancing Immunoassays: Insights into Affinity, Immobilization, and Application Potential.

Nanobodies, which represent the next generation of antibodies due to their unique properties, face a significant limitation in their poor physical adsorption on solid supports. In this study, we successfully discovered polystyrene binding nanobodies from a synthetic nanobody library. Notably, bivalent nanobody B2 exhibited high affinity for polystyrene (0.7 nM for ELISA saturation binding analysis and 15.6 nM for isothermal titration calorimetry), displaying a pH-dependent behavior. Remarkably, hydrophobic and electrostatic interactions contribute minimally to the binding process. Molecular modeling provided insights into the interaction between B2 and polystyrene, revealing that the Trp51 residue within the CDR2 loop formed an aromatic H-bond with polystyrene at a distance of 2.74 Å, thus explaining the observed reduction in B2 affinity caused by Trp51 mutations. To explore B2's potential in protein immobilization, we constructed a bispecific nanobody by fusing B2 to an anticarcinoembryonic antigen nanobody 11C12, which cannot be immobilized on polystyrene through passive adsorption. Remarkably, the fusion construct achieved effective immobilization on polystyrene within 5 min by passing the need for periplasmic protein purification despite its low expression level. Moreover, the fusion construct demonstrated excellent linearity in the chemiluminescent enzyme immunoassay. For the first time, this study reports a simplified and seamless platform for the oriented immobilization of nanobody. Importantly, the entire process eliminated the need for protein purification, enabling efficient and rapid immobilization of fusion proteins directly from crude cell extracts, even when the expression level was low. Our developed process dramatically reduced the processing time from 2.5 days to just 5 min.

Biological effects of IL-21 on immune cells and its potential for cancer treatment.

Interleukin-21 (IL-21), a member of the IL-2 cytokine family, is one of the most important effector and messenger molecules in the immune system. Produced by various immune cells, IL-21 has pleiotropic effects on innate and adaptive immune responses via regulation of natural killer, T, and B cells. An anti-tumor role of IL-21 has also been reported in the literature, as it may support cell proliferation or on the contrary induce growth arrest or apoptosis of the tumor cell. Anti-tumor effect of IL-21 enhances when combined with other agents that target tumor cells, immune regulatory circuits, or other immune-enhancing molecules. Therefore, understanding the biology of IL-21 in the tumor microenvironment (TME) and reducing its systemic toxic and side effects is crucial to ensure the maximum benefits of anti-tumor treatment strategies. In this review, we provide a comprehensive overview on the biological functions, roles in tumors, and the recent advances in preclinical and clinical research of IL-21 in tumor immunotherapy.

Proteome microarray identifies autoantibody biomarkers for diagnosis of hepatitis B-related hepatocellular carcinoma.

BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) has the highest mortality rate among malignant tumors worldwide. This study aimed to analyze the biological characteristics of serum proteins in hepatitis B (HBV)-related liver diseases, identify diagnostic biomarkers for HBV-infected HCC, and provide a scientific basis for its prevention and treatment. MATERIALS AND METHODS: We used HuProt arrays to identify candidate biomarkers for HBV-related liver diseases and verified the differential biomarkers by using an HCC-focused array. The biological characteristics of serum proteins were analyzed via bioinformatics. Serum biomarkers levels were validated by ELISA. RESULTS: We identified 547 differentially expressed proteins from HBV-infected HCC in a screening cohort. After analyzing the biological characteristics of serum proteins, we identified 10 potential differential autoantibodies against tumor-associated antigens (TAAbs) and a candidate biomarker panel (APEX2, RCSD1, and TP53) for the diagnosis of HBV-associated HCC with 61.9% sensitivity and 81.7% specificity in an HCC-focused array validation cohort. Finally, the protein levels and diagnostic capability of the biomarker panel were confirmed in a large-sample validation cohort, and this panel was found to be superior to alpha-fetoprotein, the standard hallmark for the diagnosis of HCC. CONCLUSION: The APEX2, RCSD1, and TP53 biomarker panels could be used for the diagnosis of HBV-associated HCC, providing a scientific basis for clinical practice.

An update on our understanding of Gram-positive bacterial membrane vesicles: discovery, functions, and applications.

Extracellular vesicles (EVs) are nano-sized particles released from cells into the extracellular environment, and are separated from eukaryotic cells, bacteria, and other organisms with cellular structures. EVs alter cell communication by delivering their contents and performing various functions depending on their cargo and release into certain environments or other cells. The cell walls of Gram-positive bacteria have a thick peptidoglycan layer and were previously thought to be unable to produce EVs. However, recent studies have demonstrated that Gram-positive bacterial EVs are crucial for health and disease. In this review, we have summarized the formation, composition, and characteristics of the contents, resistance to external stress, participation in immune regulation, and other functions of Gram-positive bacterial EVs, as well as their application in clinical diagnosis and treatment, to provide a new perspective to further our understanding of Gram-positive bacterial EVs.

Metformin combined with rapamycin ameliorates podocyte injury in idiopathic membranous nephropathy through the AMPK/mTOR signaling pathway.

Autophagy activation protects against podocyte injury in idiopathic membranous nephropathy (IMN). The AMPK/mTOR signaling pathway is a vital autophagy regulatory pathway. Metformin promotes autophagy, whereas rapamycin is an autophagy agonist. However, the therapeutic mechanisms of metformin and rapamycin in IMN remain unclear. Thus, we examined the mechanisms of action of metformin and rapamycin in IMN by regulating the AMPK/mTOR autophagy signaling pathway. Female Sprague-Dawley (SD) rats were treated with cationic bovine serum albumin (C-BSA) to establish an IMN model and were randomly divided into IMN model, metformin, rapamycin, and metformin + rapamycin groups. A control group was also established. Metformin and rapamycin were used as treatments. Renal histological changes, urinary protein excretion, the protein expression levels of key AMPK/mTOR signaling pathway proteins, renal tissue cell apoptosis, and autophagy-associated proteins (Beclin 1 and LC3) were examined. In addition, a C5b-9 sublysis model using the MPC-5 mouse podocyte cell line was established to verify the effect of metformin combined with rapamycin on podocytes. Metformin combined with rapamycin improved urinary protein excretion in IMN rats. Metformin combined with rapamycin attenuated the inflammatory response, renal fibrosis, and podocyte foot process fusion. In addition, it improved autophagy in podocytes as demonstrated by the enhanced expression of Beclin-1, p-AMPK/AMPK, LC3-II/I, and autophagosomes in podocytes and decreased p-mTOR/mTOR expression. In conclusion, metformin combined with rapamycin decreased proteinuria, improved renal fibrosis and podocyte autophagy via AMPK/mTOR pathway in IMN rats. The metformin and rapamycin decreased proteinuria and inproved renal fibrosis in IMN model rats.

Altered profile of glycosylated proteins in serum samples obtained from patients with Hashimoto's thyroiditis following depletion of highly abundant proteins.

Objectives: Hashimoto's thyroiditis (HT) is one of the most common autoimmune disorders; however, its underlying pathological mechanisms remain unclear. Although aberrant glycosylation has been implicated in the N-glycome of immunoglobulin G (IgG), changes in serum proteins have not been comprehensively characterized. This study aimed to investigate glycosylation profiles in serum samples depleted of highly abundant proteins from patients with HT and propose the potential functions of glycoproteins for further studies on the pathological mechanisms of HT. Methods: A lectin microarray containing 70 lectins was used to detect and analyze glycosylation of serum proteins using serum samples (N=27 HT; N=26 healthy control [HC]) depleted of abundant proteins. Significant differences in glycosylation status between HT patients and the HC group were verified using lectin blot analysis. A lectin-based pull-down assay combined with mass spectrometry was used to investigate potential glycoproteins combined with differentially present lectins, and an enzyme-linked immunosorbent assay (ELISA) was used to identify the expression of targeted glycoproteins in 131 patients with papillary thyroid carcinoma (PTC), 131 patients with benign thyroid nodules (BTN) patients, 130 patients with HT, and 128 HCs. Results: Compared with the HC group, the majority of the lectin binding signals in HT group were weakened, while the Vicia villosa agglutinin (VVA) binding signal was increased. The difference in VVA binding signals verified by lectin blotting was consistent with the results of the lectin microarray. A total of 113 potential VVA-binding glycoproteins were identified by mass spectrometry and classified by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses. Using ELISA, we confirmed that lactoferrin (LTF) and mannan-binding lectin-associated serine protease 1 (MASP-1) levels were elevated in the serum of patients with HT and PTC. Conclusion: Following depletion of abundant proteins, remaining serum proteins in HT patients exhibited lower glycosylation levels than those observed in HCs. An increased level of potential VVA-binding glycoproteins may play an important role in HT development. LTF and MASP-1 expression was significantly higher in the serum of HT and PTC patients, providing novel insight into HT and PTC.

Immunology of SARS-CoV-2 infection and vaccination.

Comprehensive elucidation of humoral immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and vaccination is critical for understanding coronavirus disease 2019 (COVID-19) pathogenesis in general and developing antibody-based diagnostic and therapeutic strategies specifically. Following the emergence of SARS-CoV-2, significant scientific research has been conducted worldwide using omics, sequencing and immunologic approaches. These studies have been critical to the successful development of vaccines. Here, the current understanding of SARS-CoV-2 immunogenic epitopes, humoral immunity to SARS-CoV-2 structural proteins and non-structural proteins, SARS-CoV-2-specific antibodies, and T-cell responses in convalescents and vaccinated individuals are reviewed. Additionally, we explore the integrated analysis of proteomic and metabolomic data to examine mechanisms of organ injury and identify potential biomarkers. Insight into the immunologic diagnosis of COVID-19 and improvements of laboratory methods are highlighted.

Current status and challenges in establishing reference intervals based on real-world data.

Reference intervals (RIs) are the cornerstone for evaluation of test results in clinical practice and are invaluable in judging patient health and making clinical decisions. Establishing RIs based on clinical laboratory data is a branch of real-world data mining research. Compared to the traditional direct method, this indirect approach is highly practical, widely applicable, and low-cost. Improving the accuracy of RIs requires not only the collection of sufficient data and the use of correct statistical methods, but also proper stratification of heterogeneous subpopulations. This includes the establishment of age-specific RIs and taking into account other characteristics of reference individuals. Although there are many studies on establishing RIs by indirect methods, it is still very difficult for laboratories to select appropriate statistical methods due to the lack of formal guidelines. This review describes the application of real-world data and an approach for establishing indirect reference intervals (iRIs). We summarize the processes for establishing iRIs using real-world data and analyze the principle and applicable scope of the indirect method model in detail. Moreover, we compare different methods for constructing growth curves to establish age-specific RIs, in hopes of providing laboratories with a reference for establishing specific iRIs and giving new insight into clinical laboratory RI research. (201 words).

Meta-Analysis of the Efficacy of Low Molecular Weight Heparin and Aspirin in the Treatment of Thrombosis During Pregnancy and Effects on Coagulation Function.

BACKGROUND: At present, there is no comprehensive evaluation of the efficacy and safety of low molecular weight heparin (LMWH) for the treatment of thrombophilia during pregnancy in clinical practice. This study aimed to systematically evaluate the efficacy of LMWH in the treatment of patients and its effects on coagulation function, thereby providing a reference for the clinical treatment and prognosis evaluation of thrombophilia during pregnancy. METHODS: Database PubMed, Web of Science and Embase as well as China National Knowledge Infrastructure and Wanfang Database were applied for the search of data. A comparative study on the efficacy of LMWH in the treatment of gestational thrombophilia was enrolled. Stata 16.0 software (Stata, College Station, TX, USA) was utilized to conduct the meta-analysis. RESULTS: A total of 487 relevant articles were retrieved and 14 studies were finally included. Patients in the LMWH combined with the low-dose aspirin group had a significantly higher live birth rate than those in the aspirin or LMWH treat group (OR (odds ratio) = 4.54, 95% CI (confidence interval): 2.76, 7.45). The adverse effects rate was lower in the LMWH combined with the low-dose aspirin group than in the aspirin or LMWH treatment group (OR = 0.40, 95% CI: 0.29, 0.56). After treatment, patients in the LMWH combined with the low-dose aspirin group had significantly lower D-dimer (SMD (standardized mean differences) = -1.50, 95% CI: -2.19, 0.80) and platelet count (PLT; SMD = -0.13, 95% CI: -0.35, 0.09) than those in the aspirin or LMWH treatment group. However, activated partial thromboplastin time (APTT; SMD = 0.16, 95% CI: -0.10, 0.42), thrombin time (TT; SMD = 0.60, 95% CI: -0.14, 1.34), plasma prothrombin time (PT; SMD = 0.42, 95% CI: -0.71, 1.56), and fibrin values (FIB; SMD = -0.92, 95% CI: -2.12, 0.28) were significantly higher in the LMWH combined with low-dose aspirin group than those in the aspirin or LMWH treatment group. CONCLUSIONS: LMWH heparin combined with low-dose aspirin can effectively correct coagulation function in pregnant women, improve prothrombotic state and increase the live birth rate, which has high clinical value.

Early warning signs of thyroid autoantibodies seroconversion: A retrospective cohort study.

BACKGROUND: Serum anti-thyroid peroxidase antibody (anti-TPO) and anti-thyroglobulin antibody (anti-Tg) levels are key indicators for the diagnosis of autoimmune diseases, especially autoimmune thyroiditis. Before the thyroid autoantibodies turn from negative to positive, it is unknown whether any clinical indicators in the body play a warning role. PURPOSE: To establish an early prediction model of seroconversion to positive thyroid autoantibodies. METHODS: This retrospective cohort study collected information based on clinical laboratory data. A logistic regression model was used to analyse the risk factors associated with a change in thyroid autoantibodies to an abnormal status. A machine-learning approach was employed to establish an early warning model, and a nomogram was used for model performance assessment and visualisation. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses were used for internal and external validation. RESULTS: Logistic regression analysis revealed that albumin to globulin ratio, triglyceride levels, and Glutamic acid levels among liver function and some metabolism-related indicators, high density lipoprotein C among metabolism-related indicators, and cystatin C among renal function indicators were all risk factors for thyroid antibody conversion (P < 0.05). In addition, several indicators in the blood count correlated with thyroid conversion (P < 0.05). Changes in the ratio of free thyroxine to free triiodothyronine were a risk factor for positive thyroid antibody conversion (ORfT4/fT3 = 1.763; 95% confidence interval 1.554-2.000). The area under the curve (AUC) of the early warning model based on the positive impact of clinical laboratory indicators, age, and sex was 0.85, which was validated by both internal (AUC 0.8515) and external (AUC 0.8378) validation. CONCLUSIONS: The early warning model of anti-TPO and anti-Tg conversion combined with some clinical laboratory indicators in routine physical examination has a stable warning efficiency.

Vitamin D status among residents of Molidawa Daur Autonomous Banner, Inner Mongolia, North China.

OBJECTIVES: To retrospectively analyze the vitamin D (VD) status of residents in northeastern Inner Mongolia and its relationship with the average monthly sunshine hours. METHODS: Serum 25-hydroxyvitamin D[s-25(OH)D] samples from 4982 outpatients (2092 males) in Moli Dawa Daur Autonomous Banner People's Hospital, Hulunbuir, China from July 2018 to January 2022 were included in this study. RESULTS: The overall median s-25(OH)D was 53.3 nmol/L, VD deficiency (<30 nmol/L), deficiency (30-50 nmol/L), sufficient (>50-250 nmol/L) and excess (>250 nmol/L) were 16% (796/4982), 30% (1495/4982), 53.4% (2658/4982) and 0.7% (33/4982). There were statistically significant differences in median s-25(OH)D by month, age-groups and gender (p<0.001). Low VD status (LVDS, including VD deficiency and insufficiency) in females was 54.6% and males was 33.9%, and the LVDS composition differed significantly by age-group and month (p<0.05). The changing trend of the median s-25(OH)D level was similar to the monthly average sunshine hours, with a slight lag. CONCLUSION: Nearly half of residents live in LVDS. LVDS is affected by month, gender, and age.

Progress in understanding primary glomerular disease: insights from urinary proteomics and in-depth analyses of potential biomarkers based on bioinformatics.

Chronic kidney disease (CKD) has become a global public health challenge. While primary glomerular disease (PGD) is one of the leading causes of CKD, the specific pathogenesis of PGD is still unclear. Accurate diagnosis relies largely on invasive renal biopsy, which carries risks of bleeding, pain, infection and kidney vein thrombosis. Problems with the biopsy procedure include lack of glomeruli in the tissue obtained, and the sampling site not being reflective of the overall lesion in the kidney. Repeated renal biopsies to monitor disease progression cannot be performed because of the significant risks of bleeding and kidney vein thrombosis. On the other hand, urine collection, a noninvasive method, can be performed repeatedly, and urinary proteins can reflect pathological changes in the urinary system. Advancements in proteomics technologies, especially mass spectrometry, have facilitated the identification of candidate biomarkers in different pathological types of PGD. Such biomarkers not only provide insights into the pathogenesis of PGD but also are important for diagnosis, monitoring treatment, and prognosis. In this review, we summarize the findings from studies that have used urinary proteomics, among other omics screens, to identify potential biomarkers for different types of PGD. Moreover, we performed an in-depth bioinformatic analysis to gain a deeper understanding of the biological processes and protein-protein interaction networks in which these candidate biomarkers may participate. This review, including a description of an integrated analysis method, is intended to provide insights into the pathogenesis, noninvasive diagnosis, and personalized treatment efforts of PGD and other associated diseases.

Identification of serum glycobiomarkers for Hepatocellular Carcinoma using lectin microarrays.

Objective: Hepatocellular carcinoma (HCC) is the sixth most commonly occurring cancer and ranks third in mortality among all malignant tumors; as a result, HCC represents a major human health issue. Although aberrant glycosylation is clearly implicated in HCC, changes in serum immunoglobulin (Ig)G and IgM glycosylation have not been comprehensively characterized. In this study, we used lectin microarrays to evaluate differences in serum IgG and IgM glycosylation among patients with HCC, hepatitis B cirrhosis (HBC), or chronic hepatitis B (CHB), and healthy normal controls (NC) and aimed to establish a model to improve the diagnostic accuracy of HCC. Methods: In total, 207 serum samples collected in 2019-2020 were used for lectin microarray analyses, including 97 cases of HCC, 50 cases of HBC, 30 cases of CHB, and 30 cases of NC. Samples were randomly divided into training and validation groups at a 2:1 ratio. Training group data were used to investigate the diagnostic value of the relative signal intensity for the lectin probe combined with alpha-fetoprotein (AFP). The efficacy of models for HCC diagnosis were analyzed by receiver operating characteristic (ROC) curves. Results: In terms of IgG, a model combining three lectins and AFP had good diagnostic accuracy for HCC. The area under the ROC curve was 0.96 (P < 0.05), the sensitivity was 82.54%, and the specificity was 100%. In terms of IgM, a model including one lectin combined with AFP had an area under the curve of 0.90 (P < 0.05), sensitivity of 75.41%, and specificity of 100%. Conclusion: Estimation of serum IgG and IgM glycosylation could act as complementary techniques to improve diagnosis and shed light on the occurrence and development of the HCC.