A novel and low-cost cross-priming amplification assay for rapid detection of Babesia duncani infection.

Babesia duncani, responsible for human babesiosis, is one of the most important tick-borne intraerythrocytic pathogens. Traditionally, babesiosis is definitively diagnosed by detecting parasite DNA in blood samples and examining Babesia parasites in Giemsa-stained peripheral blood smears. Although these techniques are valuable for determining Babesia duncani, they are often time-consuming and laborious. Therefore, developing rapid and reliable B. duncani identification assays is essential for subsequent epidemiological investigations and prevention and control. In this study, a cross-priming amplification (CPA) assay was developed, combined with a vertical flow visualization strip, to rapidly and accurately detect B. duncani infection. The detection limit of this method was as low as 0.98 pg/µl of genomic DNA from B. duncani merozoites per reaction at 59 °C for 60 min. There were no cross-reactions between B. duncani and other piroplasms infective to humans and mammals. A total of 592 blood samples from patients bitten by ticks and experimental infected hamsters were accurately assessed using CPA assay. The average cost of the CPA assay is as low as approximately $ 0.2 per person. These findings indicate that the CPA assay may therefore be a rapid screening tool for detection B. duncani infection, based on its accuracy, speed, and cost-effectiveness, particularly in resource-limited regions with a high prevalence of human babesiosis.

Mesenchymal Stem Cells Alleviate Mouse Sepsis-Induced Cardiomyopathy by Inhibiting the NR1D2/LCN2 Pathway.

ABSTRACT: Sepsis is characterized as a systemic inflammatory response syndrome resulting from infection, leading to the development of multiple organ dysfunction syndrome. Sepsis-induced cardiomyopathy (SICM) is a frequently encountered condition in clinical settings. Mesenchymal stem cells (MSCs) possess inherent immunomodulatory and anti-inflammatory attributes, rendering them a promising therapeutic approach to reestablish the equilibrium between anti-inflammatory and proinflammatory systems in septic patients. Consequently, MSCs are frequently employed in clinical investigations. In this study, the author established a mouse SICM model through cecal ligation and puncture and administered MSCs through the tail vein. Following successful modeling, the myocardial function and histopathological changes were detected by echocardiography, hematoxylin-eosin staining, terminal deoxynucleotidyl transferase dUTP nick end labeling staining, enzyme-linked immunosorbent assay,, and other experiments. As a result, MSCs demonstrated the ability to enhance myocardial function, promote cardiac tissue repair, suppress inflammatory response, reduce levels of myocardial injury markers, and mitigate oxidative stress. In addition, transcriptome and proteome analyses were conducted. Through differential expression analysis, functional enrichment analysis, and multiomics association analysis, it was revealed that the transcriptional factors nuclear receptor subfamily 1 (NR1D2) and target gene lipocalin 2 (LCN2) played key roles in mediating the effects of MSCs on SICM. JASPAR website and ChIP-qPCR experiment were used to predict and confirm the targeting relationship between them. Subsequent cell coculture experiments and a series of experiments confirmed that MSCs attenuated cardiomyocyte injury by downregulating the expression of NR1D2 and its downstream target gene LCN2. In conclusion, MSCs alleviate mice SICM through inhibiting NR1D2/LCN2 pathway.

Construction of the prediction model for multiple myeloma based on machine learning.

INTRODUCTION: The global burden of multiple myeloma (MM) is increasing every year. Here, we have developed machine learning models to provide a reference for the early detection of MM. METHODS: A total of 465 patients and 150 healthy controls were enrolled in this retrospective study. Based on the variable screening strategy of least absolute shrinkage and selection operator (LASSO), three prediction models, logistic regression (LR), support vector machine (SVM), and random forest (RF), were established combining complete blood count (CBC) and cell population data (CPD) parameters in the training set (210 cases), and were verified in the validation set (90 cases) and test set (165 cases). The performance of each model was analyzed using receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA). Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC curve (AUC) were applied to evaluate the models. Delong test was used to compare the AUC of the models. RESULTS: Six parameters including RBC (1012/L), RDW-CV (%), IG (%), NE-WZ, LY-WX, and LY-WZ were screened out by LASSO to construct the model. Among the three models, the AUC of RF model in the training set, validation set, and test set were 0.956, 0.892, and 0.875, which were higher than those of LR model (0.901, 0.849, and 0.858) and SVM model (0.929, 0.868, and 0.846). Delong test showed that there were significant differences among the models in the training set, no significant differences in the validation set, and significant differences only between SVM and RF models in the test set. The calibration curve and DCA showed that the three models had good validity and feasibility, and the RF model performed best. CONCLUSION: The proposed RF model may be a useful auxiliary tool for rapid screening of MM patients.

CLIP170 inhibits the metastasis and EMT of papillary thyroid cancer through the TGF-ß pathway.

Metastasis poses a significant challenge in combating tumors. Even in papillary thyroid cancer (PTC), which typically exhibits a favorable prognosis, high recurrence rates are attributed to metastasis. Cytoplasmic linker protein 170 (CLIP170) functions as a classical microtubule plus-end tracking protein (+TIP) and has shown close association with cell migration. Nevertheless, the specific impact of CLIP170 on PTC cells remains to be elucidated. Our analysis of the GEO and TCGA databases unveiled an association between CLIP170 and the progression of PTC. To explore the impact of CLIP170 on PTC cells, we conducted various assays. We evaluated its effects through CCK-8, wound healing assay, and transwell assay after knocking down CLIP170. Additionally, the influence of CLIP170 on the cellular actin structure was examined via immunofluorescence; we further investigated the molecular expressions of epithelial-mesenchymal transition (EMT) and the transforming growth factor-ß (TGF-ß) signaling pathways through Western blotting and RT-qPCR. These findings were substantiated through an in vivo nude mouse model of lung metastasis. We observed a decreased expression of CLIP170 in PTC in contrast to normal thyroid tissue. Functionally, the knockdown of CLIP170 (CLIP170KD) notably enhanced the metastatic potential and EMT of PTC cells, both in vitro and in vivo. Mechanistically, CLIP170KD triggered the activation of the TGF-ß pathway, subsequently promoting tumor cell migration, invasion, and EMT. Remarkably, the TGF-ß inhibitor LY2157299 effectively countered TGF-ß activity and significantly reversed tumor metastasis and EMT induced by CLIP170 knockdown. In summary, these findings collectively propose CLIP170 as a promising therapeutic target to mitigate metastatic tendencies in PTC.

Using a k-means clustering to identify novel phenotypes of acute ischemic stroke and development of its Clinlabomics models.

Objective: Acute ischemic stroke (AIS) is a heterogeneous condition. To stratify the heterogeneity, identify novel phenotypes, and develop Clinlabomics models of phenotypes that can conduct more personalized treatments for AIS. Methods: In a retrospective analysis, consecutive AIS and non-AIS inpatients were enrolled. An unsupervised k-means clustering algorithm was used to classify AIS patients into distinct novel phenotypes. Besides, the intergroup comparisons across the phenotypes were performed in clinical and laboratory data. Next, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select essential variables. In addition, Clinlabomics predictive models of phenotypes were established by a support vector machines (SVM) classifier. We used the area under curve (AUC), accuracy, sensitivity, and specificity to evaluate the performance of the models. Results: Of the three derived phenotypes in 909 AIS patients [median age 64 (IQR: 17) years, 69% male], in phenotype 1 (N = 401), patients were relatively young and obese and had significantly elevated levels of lipids. Phenotype 2 (N = 463) was associated with abnormal ion levels. Phenotype 3 (N = 45) was characterized by the highest level of inflammation, accompanied by mild multiple-organ dysfunction. The external validation cohort prospectively collected 507 AIS patients [median age 60 (IQR: 18) years, 70% male]. Phenotype characteristics were similar in the validation cohort. After LASSO analysis, Clinlabomics models of phenotype 1 and 2 were constructed by the SVM algorithm, yielding high AUC (0.977, 95% CI: 0.961-0.993 and 0.984, 95% CI: 0.971-0.997), accuracy (0.936, 95% CI: 0.922-0.956 and 0.952, 95% CI: 0.938-0.972), sensitivity (0.984, 95% CI: 0.968-0.998 and 0.958, 95% CI: 0.939-0.984), and specificity (0.892, 95% CI: 0.874-0.926 and 0.945, 95% CI: 0.923-0.969). Conclusion: In this study, three novel phenotypes that reflected the abnormal variables of AIS patients were identified, and the Clinlabomics models of phenotypes were established, which are conducive to individualized treatments.

CircRNAs in cancer therapy tolerance.

The rapidly expanding field of circular RNA (circ-RNA) research has opened new avenues in cancer diagnostics and treatment, highlighting the role of serum circRNAs as potential biomarkers for assessing tumor therapy resistance. This review comprehensively compiles existing knowledge regarding the biogenesis, function, and clinical relevance of circRNAs, emphasizing their stability, abundance, and cell type-specific expression profiles, which make them ideal candidates for noninvasive early biomarkers in cancer treatment. We explored the roles of circRNAs in oncogenesis and tumor progression and their complex interactions with patient responses to various cancer treatments, such as chemotherapy, radiotherapy, targeted therapy, and immunotherapy. Through the analysis of data from recent studies and clinical trials, we underscore the prognostic significance of serum circRNAs in predicting therapeutic outcomes, their involvement in resistance mechanisms, and their capacity to inform personalized treatment approaches. Additionally, this review addresses the obstacles inherent in circRNA research, including the need for standardized protocols for circRNA extraction and quantification and the elucidation of the clinical significance of circRNAs. Furthermore, our investigation extends to future prospects, including embedding circRNA profiling into regular clinical workflows and pioneering circRNA-based therapeutic approaches. We underscore the transformative potential of serum circRNAs in enhancing cancer diagnosis, improving the accuracy of therapy tolerance predictions, and ultimately fostering the advent of precision oncology.

DNA Methylation-Based Testing in Peripheral Blood Mononuclear Cells Enables Accurate and Early Detection of Colorectal Cancer.

An effective blood-based method for the diagnosis of colorectal cancer has not yet been developed. Molecular alterations of immune cells occur early in tumorigenesis, providing the theoretical underpinning for early cancer diagnosis based on immune cell profiling. Therefore, we aimed to develop an effective detection method based on peripheral blood mononuclear cells (PBMC) to improve the diagnosis of colorectal cancer. Analysis of the genome-wide methylation landscape of PBMCs from patients with colorectal cancer and healthy controls by microarray, pyrosequencing, and targeted bisulfite sequencing revealed five DNA methylation markers for colorectal cancer diagnosis, especially early-stage colorectal cancer. A single-tube multiple methylation-specific quantitative PCR assay (multi-msqPCR) for simultaneous detection of five methylation markers was established, which allowed quantitative analysis of samples with as little as 0.1% PBMC DNA and had better discriminative performance than single-molecule detection. Then, a colorectal cancer diagnostic model (CDM) based on methylation markers and the multi-msqPCR method was constructed that achieved high accuracy for early-stage colorectal cancer (AUC = 0.91; sensitivity = 81.18%; specificity = 89.39%), which was improved compared with CEA (AUC = 0.79). The CDM also enabled a high degree of discrimination for advanced adenoma cases (AUC = 0.85; sensitivity = 63.04%). Follow-up data also demonstrated that the CDM could identify colorectal cancer potential up to 2 years before currently used diagnostic methods. In conclusion, the approach constructed in this study based on PBMC-derived DNA methylation markers and a multi-msqPCR method is a promising and easily implementable diagnostic method for early-stage colorectal cancer. SIGNIFICANCE: Development of a diagnostic model for early colorectal cancer based on epigenetic analysis of PBMCs supports the utility of altered DNA methylation in immune cells for cancer diagnosis.

A multiplex blood-based assay targeting DNA methylation in PBMCs enables early detection of breast cancer.

The immune system can monitor tumor development, and DNA methylation is involved in the body's immune response to tumors. In this work, we investigate whether DNA methylation alterations in peripheral blood mononuclear cells (PBMCs) could be used as markers for early detection of breast cancer (BC) from the perspective of tumor immune alterations. We identify four BC-specific methylation markers by combining Infinium 850 K BeadChips, pyrosequencing and targeted bisulfite sequencing. Based on the four methylation markers in PBMCs of BC, we develop an efficient and convenient multiplex methylation-specific quantitative PCR assay for the detection of BC and validate its diagnostic performance in a multicenter cohort. This assay was able to distinguish early-stage BC patients from normal controls, with an AUC of 0.940, sensitivity of 93.2%, and specificity of 90.4%. More importantly, this assay outperformed existing clinical diagnostic methods, especially in the detection of early-stage and minimal tumors.

PROTACs: Emerging Targeted Protein Degradation Approaches for Advanced Druggable Strategies.

A potential therapeutic strategy to treat conditions brought on by the aberrant production of a disease-causing protein is emerging for targeted protein breakdown using the PROTACs technology. Few medications now in use are tiny, component-based and utilize occupancy-driven pharmacology (MOA), which inhibits protein function for a short period of time to temporarily alter it. By utilizing an event-driven MOA, the proteolysis-targeting chimeras (PROTACs) technology introduces a revolutionary tactic. Small-molecule-based heterobifunctional PROTACs hijack the ubiquitin-proteasome system to trigger the degradation of the target protein. The main challenge PROTAC's development facing now is to find potent, tissue- and cell-specific PROTAC compounds with favorable drug-likeness and standard safety measures. The ways to increase the efficacy and selectivity of PROTACs are the main focus of this review. In this review, we have highlighted the most important discoveries related to the degradation of proteins by PROTACs, new targeted approaches to boost proteolysis' effectiveness and development, and promising future directions in medicine.

Babeisa duncani infection alters gut microbiota profile in hamsters.

The genus Babesia includes parasites that can induce human and animal babesiosis, which are common in tropical and subtropical regions of the world. The gut microbiota has not been examined in hamsters infected by Babesia duncani. Red blood cells infected with B. duncani were injected into hamsters through intraperitoneal route. To evaluate the changes in gut microbiota, DNAs were extracted from small intestinal contents, acquired from hamsters during disease development. Then, the V4 region of the 16S rRNA gene of bacteria was sequenced using the Illumina sequencing platform. Gut microbiota alternation and composition were assessed according to the sequencing data, which were clustered with >97.0% sequence similarity to create amplicon sequence variants (ASVs). Bacteroidetes and Firmicutes were made up of the major components of the gut microbiota in all samples. The abundance of Bacteroidetes elevated after B. duncani infection than the B. duncani-free group, while Firmicutes and Desulfobacterota declined. Alpha diversity analysis demonstrated that the shown ASVs were substantially decreased in the highest parasitemia group than B. duncani-free and lower parasitemia groups. Potential biomarkers were discovered by Linear discriminant analysis Effect Size (LEfSe) analysis, which demonstrated that several bacterial families (including Muribaculaceae, Desulfovibrionaceae, Oscillospiraceae, Helicobacteraceae, Clostridia UGG014, Desulfovibrionaceae, and Lachnospiraceae) were potential biomarkers in B. duncani-infected hamsters. This research demonstrated that B. duncani infectious can modify the gut microbiota of hamsters.

Development and validation of a coagulation-related genes prognostic model for hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) has a high incidence and mortality worldwide, which seriously threatens people's physical and mental health. Coagulation is closely related to the occurrence and development of HCC. Whether coagulation-related genes (CRGs) can be used as prognostic markers for HCC remains to be investigated. METHODS: Firstly, we identified differentially expressed coagulation-related genes of HCC and control samples in the datasets GSE54236, GSE102079, TCGA-LIHC, and Genecards database. Then, univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis were used to determine the key CRGs and establish the coagulation-related risk score (CRRS) prognostic model in the TCGA-LIHC dataset. The predictive capability of the CRRS model was evaluated by Kaplan-Meier survival analysis and ROC analysis. External validation was performed in the ICGC-LIRI-JP dataset. Besides, combining risk score and age, gender, grade, and stage, a nomogram was constructed to quantify the survival probability. We further analyzed the correlation between risk score and functional enrichment, pathway, and tumor immune microenvironment. RESULTS: We identified 5 key CRGs (FLVCR1, CENPE, LCAT, CYP2C9, and NQO1) and constructed the CRRS prognostic model. The overall survival (OS) of the high-risk group was shorter than that of the low-risk group. The AUC values for 1 -, 3 -, and 5-year OS in the TCGA dataset were 0.769, 0.691, and 0.674, respectively. The Cox analysis showed that CRRS was an independent prognostic factor for HCC. A nomogram established with risk score, age, gender, grade, and stage, has a better prognostic value for HCC patients. In the high-risk group, CD4+T cells memory resting, NK cells activated, and B cells naive were significantly lower. The expression levels of immune checkpoint genes in the high-risk group were generally higher than that in the low-risk group. CONCLUSIONS: The CRRS model has reliable predictive value for the prognosis of HCC patients.

Effect of plasma thrombin-antithrombin complex on ischemic stroke: a systematic review and meta-analysis.

BACKGROUND AND OBJECTIVE: Thrombin-antithrombin complex (TAT) is a prethrombotic marker, and its application in ischemic stroke is still uncertain. The purpose of this systematic review and meta-analysis is to evaluate the relationship between plasma TAT and ischemic stroke base on the current evidence. METHODS: A systematic literature search was conducted for searching the relative studies that investigated the association of TAT and ischemic stroke in PubMed, EMBASE, and Cochrane library databases. Mean difference and 95% confidence interval as the effect sizes were synthesized by random effects model in Review Manager (RevMan) Version 5.4. The heterogeneity was investigated using the chi-square test and the possible sources of heterogeneity were explored by sensitivity analysis and meta-regression. The publication bias was estimated by Egger's tests. RESULTS: A total of 12 eligible studies were included involving 1431 stroke cases and 532 healthy controls, of which six studies were eventually included in the meta-analysis. Plasma TAT in patients with ischemic stroke was significantly higher than that in healthy controls (MD 5.31, 95% CI = 4.12-6.51, P < 0.0001, I2 = 97.8%). There is a difference of TAT level in the same period among cardioembolic, lacunar, and atherothrombotic stroke (all P < 0.0001), in which the cardioembolic stroke with the highest level. Meanwhile, it is significant of TAT levels among various phases of cardioembolic stroke and the acute phase are markedly elevated (MD 7.75, 95CI%, 6.07-9.43, P < 0.001). However, no difference was found in the atherothrombotic (P = 0.13) and lacunar stroke (P = 0.34). Besides, the higher TAT level is closely related to the poor prognosis of patients with ischemic stroke, including higher recurrence, mortality, unfavorable recovery (modified Rankin scale > 2), and poor revascularization. CONCLUSIONS: This study suggested that plasma TAT levels are different in ischemic stroke subtypes, which are closely associated with the progression and might have an effect on the prognosis. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD: 42021248787.

The biomarkers discovery of hyperuricemia and gout: proteomics and metabolomics.

Background: Hyperuricemia and gout are a group of disorders of purine metabolism. In recent years, the incidence of hyperuricemia and gout has been increasing, which is a severe threat to people's health. Several studies on hyperuricemia and gout in proteomics and metabolomics have been conducted recently. Some literature has identified biomarkers that distinguish asymptomatic hyperuricemia from acute gout or remission of gout. We summarize the physiological processes in which these biomarkers may be involved and their role in disease progression. Methodology: We used professional databases including PubMed, Web of Science to conduct the literature review. This review addresses the current landscape of hyperuricemia and gout biomarkers with a focus on proteomics and metabolomics. Results: Proteomic methods are used to identify differentially expressed proteins to find specific biomarkers. These findings may be suggestive for the diagnosis and treatment of hyperuricemia and gout to explore the disease pathogenesis. The identified biomarkers may be mediators of the link between hyperuricemia, gout and kidney disease, metabolic syndrome, diabetes and hypertriglyceridemia. Metabolomics reveals the main influential pathways through small molecule metabolites, such as amino acid metabolism, lipid metabolism, or other characteristic metabolic pathways. These studies have contributed to the discovery of Chinese medicine. Some traditional Chinese medicine compounds can improve the metabolic disorders of the disease. Conclusions: We suggest some possible relationships of potential biomarkers with inflammatory episodes, complement activation, and metabolic pathways. These biomarkers are able to distinguish between different stages of disease development. However, there are relatively few proteomic as well as metabolomic studies on hyperuricemia and gout, and some experiments are only primary screening tests, which need further in-depth study.

Lipoxin A4 attenuates MSU-crystal-induced NLRP3 inflammasome activation through suppressing Nrf2 thereby increasing TXNRD2.

Gout is a common inflammatory disease. The activation of NLRP3 inflammasome induced by monosodium urate (MSU) crystals has a critical role in gout, and its prevention is beneficial for patients. Lipoxin A4 (LXA4) is an endogenous lipoxygenase-derived eicosanoid mediator with powerful anti-inflammatory properties. However, whether LXA4 can suppress NLRP3 inflammasome activation induced by MSU crystals remains unclear. This study aimed to investigate the protective effect of LXA4 on MSU-crystal-induced NLRP3 inflammasome activation and its underlying molecular mechanisms. We found that LXA4 inhibited MSU-crystal-induced NLRP3 inflammasome activation, interleukin (IL)-1ß maturation, and pyroptosis. More specifically, LXA4 suppressed the assembly of the NLRP3 inflammasome, including oligomerization and speck formation of ASC, and ASC-NLRP3 interaction. Furthermore, LXA4 suppressed oxidative stress, the upstream events for NLRP3 inflammasome activation, as evidenced by the fact that LXA4 eliminated total reactive oxygen species (ROS) generation and alleviated nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activation and mitochondrial dysfunction. However, LXA4 also depressed the Nrf2 activation, a critical molecule in the antioxidant pathway, and then exerted an inhibitory impact on Klf9 expression and promotional impact on TXNRD2 expression, two molecules located downstream of Nrf2 in sequence. Knockdown of TXNRD2 reversed the LXA4-induced depression of ROS and NLRP3 inflammasome. Moreover, LXA4 alleviated joint inflammation and decreased the production of cleaved caspase-1 and matured IL-1ß in gouty arthritis rats. Taken together, our findings demonstrate that LXA4 can attenuate MSU-crystal-induced NLRP3 inflammasome activation, probably through suppressing Nrf2 activation to increase TXNRD2 expression. The present study highlights the potential of LXA4 as an attractive new gout treatment candidate.

Comparison of ischemic stroke diagnosis models based on machine learning.

Background: The incidence, prevalence, and mortality of ischemic stroke (IS) continue to rise, resulting in a serious global disease burden. The prediction models have a great value in the early prediction and diagnosis of IS. Methods: The R software was used to screen the differentially expressed genes (DEGs) of IS and control samples in the datasets GSE16561, GSE58294, and GSE37587 and analyze DEGs for enrichment analysis. The feature genes of IS were obtained by several machine learning algorithms, including the least absolute shrinkage and selector operation (LASSO) logistic regression, the support vector machine-recursive feature elimination (SVM-RFE), and the Random Forest (RF). The IS diagnostic models were constructed based on transcriptomics by machine learning and artificial neural network (ANN). Results: A total of 69 DEGs, mainly involved in immune and inflammatory responses, were identified. The pathways enriched in the IS group were complement and coagulation cascades, lysosome, PPAR signaling pathway, regulation of autophagy, and toll-like receptor signaling pathway. The feature genes selected by LASSO, SVM-RFE, and RF were 17, 10, and 12, respectively. The area under the curve (AUC) of the LASSO model in the training dataset, GSE22255, and GSE195442 was 0.969, 0.890, and 1.000. The AUC of the SVM-RFE model was 0.957, 0.805, and 1.000, respectively. The AUC of the RF model was 0.947, 0.935, and 1.000, respectively. The models have good sensitivity, specificity, and accuracy. The AUC of the LASSO+ANN, SVM-RFE+ANN, and RF+ANN models was 1.000, 0.995, and 0.997, respectively, in the training dataset. However, the AUC of LASSO+ANN, SVM-RFE+ANN, and RF+ANN models was 0.688, 0.605, and 0.619, respectively, in the GSE22255 dataset. The AUC of the LASSO+ANN and RF+ANN models was 0.740 and 0.630, respectively, in the GSE195442 dataset. In the training dataset, the sensitivity, specificity, and accuracy of the LASSO+ANN model were 1.000, 1.000, and 1.000, respectively; of the SVM-RFE+ANN model were 0.946, 0.982, and 0.964, respectively; and of the RF+ANN model were 0.964, 1.000, and 0.982, respectively. In the test datasets, the sensitivity was very satisfactory; however, the specificity and accuracy were not good. Conclusion: The LASSO, SVM-RFE, and RF models have good prediction abilities. However, the ANN model is efficient at classifying positive samples and is unsuitable at classifying negative samples.

Susceptibility genes of hyperuricemia and gout.

Gout is a chronic metabolic disease that seriously affects human health. It is also a major challenge facing the world, which has brought a heavy burden to patients and society. Hyperuricemia (HUA) is the most important risk factor for gout. In recent years, with the improvement of living standards and the change of dietary habits, the incidence of gout in the world has increased dramatically, and gradually tends to be younger. An increasing number of studies have shown that gene mutations may play an important role in the development of HUA and gout. Therefore, we reviewed the existing literature and summarized the susceptibility genes and research status of HUA and gout, in order to provide reference for the early diagnosis, individualized treatment and the development of new targeted drugs of HUA and gout.

Lipoxin alleviates oxidative stress: a state-of-the-art review.

OBJECTIVE: This review aims to summarize the capability of lipoxin in regulating oxidative stress. BACKGROUND: Oxidative stress is defined as an imbalance between the production of free radicals and the antioxidant system, and it is associated with the existence of a large number of oxidation products, such as reactive oxygen species (ROS) and reaction nitrogen species (RNS), causing damage to human tissues through immunoinflammatory responses. Therefore, reducing oxidative stress is vital to alleviate pathological damage. Lipoxin, an acronym for lipoxygenase interaction product, is a bioactive autacoid metabolite of arachidonic acid made by various cell types. Previous studies have shown that lipoxin is associated with a variety of biological functions, including anti-inflammatory, regulating immune responses, promoting the repair of damaged cells, etc. The deficiency of lipoxin is a critical pathological mechanism in different diseases. Moreover, the ability of lipoxin to attenuate oxidative stress is noteworthy, thereby protecting the human body from diverse diseases. METHODS: We searched papers from PubMed database using search terms, such as lipoxin, lipoxin A4, oxidative stress, and other relevant terms. RESULTS: A total of 103 articles published over the past 20 years were identified for inclusion. We summarized the capability of lipoxin in regulating oxidative stress and mechanism. CONCLUSION: Lipoxin is provided with a protective role in attenuating oxidative stress.

Retraction: The lnc-SNHG1 Gene Is Overexpressed and Correlates with Increased White Blood Cell Count, Poor Induction Treatment Response, and Poor Survival Profile in Adult Acute Myeloid Leukemia

Objective: Long noncoding RNA small nucleolar RNA host gene 1 (lnc-SNHG1) is involved in leukemogenesis via mediating multiple pathways. The current study aimed to further explore its clinical roles in disease risk, clinical features, and prognosis in patients with acute myeloid leukemia (AML). Materials and Methods: A total of 161 adult AML patients, 50 patients as a disease control (DC) group, and 50 healthy individuals as a healthy control (HC) group were enrolled and bone marrow mononuclear cells were collected. Subsequently, reverse transcriptionquantitative polymerase chain reaction (RT-qPCR) was performed to measure lnc-SNHG1 expression. Results: Lnc-SNHG1 expression was higher in AML patients than in the DC and HC groups (both p<0.001), with good value in distinguishing AML patients from DC and HC individuals (area under the curve of 0.726 and 0.884, respectively). Moreover, lnc-SNHG1 expression was positively associated with white blood cell (WBC) count (p=0.008) but was not correlated with other clinical features such as cytogenetics, molecular genetics, and risk stratification (all p>0.05). Lnc-SNHG1 expression was also associated with a lower complete remission (CR) rate (p=0.001). Patients with lnc-SNHG1 expression in the fourth quantile had the worst CR rates compared to patients with lnc-SNHG1 expressions in the first, second, and third quantiles (all p<0.05). Furthermore, lnc-SNHG1 expression was correlated with unsatisfactory event-free survival (p<0.001) and overall survival (p=0.002), which were worst in patients with lnc-SNHG1 expression in the fourth quantile compared to patients with lnc-SNHG1 expressions in the first, second, and third quantiles (all p<0.05). Conclusion: Lnc-SNHG1 overexpression is associated with elevated WBC count, poor induction treatment response, and poor survival profile in cases of AML and it may serve as a potential indicator for AML.

The SUMO components in rheumatoid arthritis.

Small ubiquitin-like modifier (SUMO) proteins can reversibly attach covalently or non-covalently to lysine residues of various substrates. The processes are named SUMOylation and de-SUMOylation, which maintain a dynamic balance in the physiological state, and are regulated by SUMO components. However, the dysregulation of components disturbs the balance and alters the functions of target proteins, which causes the occurrence of diseases. To date, certain SUMO components, including SUMO-1, SUMO-2/3, SAE1/Uba2, Ubc9, PIASs (protein inhibitors of activated signal transducer and activator of transcription) and SENPs (SUMO-specific proteases), have been found to participate in the pathogenesis of RA and their potential value as therapeutic targets also have been highlighted. In addition, single nucleotide polymorphisms (SNPs) in the SUMO components have been reported to be associated with disease susceptibility. Until now, only the SNP site of SUMO-4 has been reported in RA. Here we provided a systematic overview of the general characteristics of SUMO components and highlighted a summary of their impact on RA.

Association of toll-like receptors single nucleotide polymorphisms with HBV and HCV infection: research status.

Background: Hepatitis B virus (HBV) and hepatitis C virus (HCV) infections have become increasingly severe worldwide and are a threat to public health. There have been a number of studies conducted recently on the relationship of single nucleotide polymorphisms (SNPs) to innate immune receptor genes such as toll-like receptors (TLRs). Some literature suggests that SNPs of TLRs are associated with HBV and HCV infection. We summarized the role of TLRs gene polymorphisms associated with HBV and HCV infections and explored their possible mechanisms of action. Methodology: PubMed and Web of Science were used to perform the literature review. Related articles and references were identified and used to analyze the role of TLRs gene polymorphism in HBV and HCV infection. Results: TLRs gene polymorphisms may have beneficial or detrimental effects in HBV and HCV infection, and some SNPs can affect disease progression or prognosis. They affect the disease state by altering gene expression or protein synthesis; however, the mechanism of action is not clearly understood. Conclusions: Single nucleotide polymorphisms of TLRs play a role in HBV and HCV infection, but the mechanism of action still needs to be explored in future studies.