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COVID-19 has recently become the most serious threat to public health, and its prevalence has been increasing at an alarming rate. The incubation period for the virus is ~1-14 days and all age groups may be susceptible to a fatality rate of about 5.9%. COVID-19 is caused by a novel single-stranded, positive (+) sense RNA beta coronavirus. The development of a vaccine for SARS-CoV-2 is an urgent need worldwide. Immunoinformatics approaches are both cost-effective and convenient, as predictions can reduce the number of experiments needed. In this study, with the aid of immunoinformatics tools, we tried to design a multi-epitope vaccine that can be used for the prevention and treatment of COVID-19. The epitopes were computed by using B cells, cytotoxic T lymphocytes (CTL), and helper T lymphocytes (HTL) base on the proteins of SARS-CoV-2. A vaccine was devised by fusing together the B cell, HTL, and CTL epitopes with linkers. To enhance the immunogenicity, the β-defensin (45 mer) amino acid sequence, and pan-HLA DR binding epitopes (13aa) were adjoined to the N-terminal of the vaccine with the help of the EAAAK linker. To enable the intracellular delivery of the modeled vaccine, a TAT sequence (11aa) was appended to C-terminal. Linkers play vital roles in producing an extended conformation (flexibility), protein folding, and separation of functional domains, and therefore, make the protein structure more stable. The secondary and three-dimensional (3D) structure of the final vaccine was then predicted. Furthermore, the complex between the final vaccine and immune receptors (toll-like receptor-3 (TLR-3), major histocompatibility complex (MHC-I), and MHC-II) were evaluated by molecular docking. Lastly, to confirm the expression of the designed vaccine, the mRNA of the vaccine was enhanced with the aid of the Java Codon Adaptation Tool, and the secondary structure was generated from Mfold. Then we performed cloning. The final vaccine requires experimental validation to determine its safety and efficacy in controlling SARS-CoV-2 infections.

Acteoside (Act), a phenylethanoid compound that was first isolated from mullein, has been widely used for the investigation of anti-inflammatory and anti-fibrotic effect. However, the mechanism of Act against unilateral ureteral obstruction (UUO)-mediated renal injury is largely unknown. Therefore, this study aimed to explore the effects of Act on UUO rats and possible mechanisms. A total of 20 Sprague-Dawley (SD) rats were divided randomly into three groups (  ≥ 6): (i) sham-operated group (Sham); (ii) UUO group (UUO+Saline); and (iii) UUO + Act 40 mg/kg/day, (UUO+Act); Continuous gavage administration for 2 weeks postoperatively, while the rats in Sham and UUO+saline groups were given equal amounts of saline. All rats were sacrificed after 14 days, the urine and blood samples were collected for biochemical analysis, the renal tissues were collected for pathological staining and immunohistochemistry. Correlations between individual proteins were analyzed by Pearson correlation analysis. The results of renal function indexes and histopathological staining showed that Act could improve renal function by reducing serum creatinine, blood urea nitrogen and urine protein at the same time, Act could alleviate renal inflammation and fibrosis. In addition, the results of immunohistochemistry showed that Act could reduce the expression of inflammation and kidney injury-related proteins F4/80, Mcp-1, KIM-1 proteins, as well as the expression of fibrosis-related protein -SMA and -catenin. More importantly, Act can also reduce the expression of HMGN1, TLR4 and TREM-1 proteins. These data demonstrate that Act can ameliorate UUO-induced renal inflammation and fibrosis in rats probably through triggering HMGN1/TLR4/TREM-1 pathway.

In the ongoing battle against breast cancer, a leading cause of cancer-related mortality among women globally, the urgent need for innovative prognostic markers and therapeutic targets is undeniable. This study pioneers an advanced methodology by integrating machine learning techniques to unveil a vascular mimicry signature, offering predictive insights into breast cancer outcomes. Vascular mimicry refers to the phenomenon where cancer cells mimic blood vessel formation absent of endothelial cells, a trait associated with heightened tumor aggression and diminished response to conventional treatments. The study's comprehensive analysis spanned data from over 6,000 breast cancer patients across 12 distinct datasets, incorporating both proprietary clinical data and single-cell data from 7 patients, accounting for a total of 43,095 cells. By employing an integrative strategy that utilized 10 machine learning algorithms across 108 unique combinations, the research scrutinized 100 existing breast cancer signatures. Empirical validation was sought through immunohistochemistry assays, alongside explorations into potential immunotherapeutic and chemotherapeutic avenues. The investigation successfully identified six genes related to vascular mimicry from multi-center cohorts, laying the groundwork for a novel predictive model. This model outstripped the prognostic accuracy of traditional clinical and molecular indicators in forecasting recurrence and mortality risks. High-risk individuals identified by our model faced worse outcomes. Further validation through IHC assays in 30 patients underscored the model's extensive applicability. Notably, the model unveiled varying therapeutic responses; low-risk patients might achieve greater benefits from immunotherapy, whereas high-risk patients demonstrated a particular sensitivity to certain chemotherapies, such as ispinesib. This model marks a significant step forward in the precise evaluation of breast cancer prognosis and therapeutic responses across different patient groups. It heralds the possibility of refining patient outcomes through tailored treatment strategies, accentuating the potential of machine learning in revolutionizing cancer prognosis and management.

This study aims to identify precise biomarkers for breast cancer to improve patient outcomes, addressing the limitations of traditional staging in predicting treatment responses. Our analysis encompassed data from over 7,000 breast cancer patients across 14 datasets, which included in-house clinical data and single-cell data from 8 patients (totaling 43,766 cells). We utilized an integrative approach, applying 10 machine learning algorithms in 54 unique combinations to analyze 100 existing breast cancer signatures. Immunohistochemistry assays were performed for empirical validation. The study also investigated potential immunotherapies and chemotherapies. Our research identified five consistent glutamine metabolic reprogramming (GMR)-related genes from multi-center cohorts, forming the foundation of a novel GMR-model. This model demonstrated superior accuracy in predicting recurrence and mortality risks compared to existing clinical and molecular features. Patients classified as high-risk by the model exhibited poorer outcomes. IHC validation in 30 patients reinforced these findings, suggesting the model's broad applicability. Intriguingly, the model indicates a differential therapeutic response: low-risk patients may benefit more from immunotherapy, whereas high-risk patients showed sensitivity to specific chemotherapies like BI-2536 and ispinesib. The GMR-model marks a significant leap forward in breast cancer prognosis and the personalization of treatment strategies, offering vital insights for the effective management of diverse breast cancer patient populations.

Breast cancer (BC) is a leading cause of mortality among women, underscoring the urgent need for improved therapeutic predictio. Developing a precise prognostic model is crucial. The role of Endoplasmic Reticulum Stress (ERS) in cancer suggests its potential as a critical factor in BC development and progression, highlighting the importance of precise prognostic models for tailored treatment strategies. Through comprehensive analysis of ERS-related gene expression in BC, utilizing both single-cell and bulk sequencing data from varied BC subtypes, we identified eight key ERS-related genes. LASSO regression and machine learning techniques were employed to construct a prognostic model, validated across multiple datasets and compared with existing models for its predictive accuracy. The developed ERS-model categorizes BC patients into distinct risk groups with significant differences in clinical prognosis, confirmed by robust ROC, DCA, and KM analyses. The model forecasts survival rates with high precision, revealing distinct immune infiltration patterns and treatment responsiveness between risk groups. Notably, we discovered six druggable targets and validated Methotrexate and Gemcitabine as effective agents for high-risk BC treatment, based on their sensitivity profiles and potential for addressing the lack of active targets in BC. Our study advances BC research by establishing a significant link between ERS and BC prognosis at both the molecular and cellular levels. By stratifying patients into risk-defined groups, we unveil disparities in immune cell infiltration and drug response, guiding personalized treatment. The identification of potential drug targets and therapeutic agents opens new avenues for targeted interventions, promising to enhance outcomes for high-risk BC patients and paving the way for personalized cancer therapy.

Diabetic kidney disease (DKD) is a major health burden closely related to lipid metabolism disorders. Leptin has lipid-lowering efficacy, but the specific mechanism of its local effects on kidney is still unclear. This study aims to investigate the role of ectopic lipid deposition (ELD) in DKD and evaluate the lipid-lowering efficacy of leptin in the palmitic acid (PA)-induced renal tubular epithelial cells (NRK-52E). DKD model was established in Sprague–Dawley (SD) rats by giving single intraperitoneal injection of streptozotocin (STZ, 30 mg/kg) after high-fat diet for 8 weeks. Then, the expression changes of lipid metabolism-related markers were observed. At week 12, the protein expression level of lipid-deposited marker adipose differentiation-related protein (ADRP) was significantly increased. Besides, the lipid synthesis marker sterol regulatory element-binding protein 1c (SREBP 1c) was highly expressed while the expression of insulin-induced gene 1 (Insig-1), a key molecular of inhibiting SREBP 1c, was decreased. Leptin and compound c were incubated with the PA-induced NRK-52E cells to investigate the lipid-lowering effects and whether this effect was mediated by the AMPK/Insig-1/SREBP 1c signaling pathways. mRNA and protein of ADRP and SREBP 1c were reduced after leptin treatment, while Insig-1 and phosphorylated AMP-activated protein kinase (AMPK) were increased. Conversely, inhibition of AMPK phosphorylation by compound c mostly eliminated lipid-lowering efficacy of leptin in PA-induced cells. Collectively, these results suggested that there was ELD of renal tubular epithelial cells in DKD rats. Leptin upregulated the expression level of Insig-1 by activating AMPK to attenuate ELD in PA-induced NRK-52E cells.

Chikungunya virus (CHIKV) is a mosquito-borne virus known to cause acute febrile illness associated with debilitating polyarthritis. In 2019, several institutions in Myanmar reported a CHIKV outbreak. There are no official reports of CHIKV cases between 2011 and 2018. Therefore, this study sought to determine the seroprevalence of CHIKV infection before the 2019 outbreak. A total of 1,544 serum samples were collected from healthy volunteers and patients with febrile illnesses in Yangon, Mandalay, and the Myeik district in 2013, 2015, and 2018. Participants ranged from one month to 65 years of age. Antibody screening was performed with in-house anti-CHIKV IgG and IgM ELISA. A neutralization assay was used as a confirmatory test. The seroprevalence of anti-CHIKV IgM and anti-CHIKV IgG was 8.9% and 28.6%, respectively, with an overall seropositivity rate of 34.5%. A focus reduction neutralization assay confirmed 32.5% seroprevalence of CHIKV in the study population. Age, health status, and region were significantly associated with neutralizing antibodies (NAbs) and CHIKV seropositivity (p < 0.05), while gender was not (p = 0.9). Seroprevalence in 2013, 2015, and 2018 was 32.1%, 28.8%, and 37.3%, respectively. Of the clinical symptoms observed in participants with fevers, arthralgia was mainly noted in CHIKV-seropositive patients. The findings in this study reveal the circulation of CHIKV in Myanmar's Mandalay, Yangon, and Myeik regions before the 2019 CHIKV outbreak. As no treatment or vaccine for CHIKV exists, the virus must be monitored through systematic surveillance in Myanmar.

Ambient fine particulate matter ≤ 2.5 µm (PM2.5) air pollution exposure has been identified as a global health threat, the epidemiological evidence suggests that PM2.5 increased the risk of chronic kidney disease (CKD) among the diabetes mellitus (DM) patients. Despite the growing body of research on PM2.5 exposure, there has been limited investigation into its impact on the kidneys and the underlying mechanisms. Past studies have demonstrated that PM2.5 exposure can lead to lipid metabolism disorder, which has been linked to the development and progression of diabetic kidney disease (DKD).BackgroundAmbient fine particulate matter ≤ 2.5 µm (PM2.5) air pollution exposure has been identified as a global health threat, the epidemiological evidence suggests that PM2.5 increased the risk of chronic kidney disease (CKD) among the diabetes mellitus (DM) patients. Despite the growing body of research on PM2.5 exposure, there has been limited investigation into its impact on the kidneys and the underlying mechanisms. Past studies have demonstrated that PM2.5 exposure can lead to lipid metabolism disorder, which has been linked to the development and progression of diabetic kidney disease (DKD).In this study, db/db mice were exposed to different dosage PM2.5 for 8 weeks. The effect of PM2.5 exposure was analysis by assessment of renal function, pathological staining, immunohistochemical (IHC), quantitative real-time PCR (qPCR) and liquid chromatography with tandem mass spectrometry (LC-MS/MS) based metabolomic analyses.MethodsIn this study, db/db mice were exposed to different dosage PM2.5 for 8 weeks. The effect of PM2.5 exposure was analysis by assessment of renal function, pathological staining, immunohistochemical (IHC), quantitative real-time PCR (qPCR) and liquid chromatography with tandem mass spectrometry (LC-MS/MS) based metabolomic analyses.The increasing of Oil Red staining area and adipose differentiation related protein (ADRP) expression detected by IHC staining indicated more ectopic lipid accumulation in kidney after PM2.5 exposure, and the increasing of SREBP-1 and the declining of ATGL detected by IHC staining and qPCR indicated the disorder of lipid synthesisandlipolysis in DKD mice kidney after PM2.5 exposure. The expressions of high mobility group nucleosome binding protein 1 (HMGN1) and kidney injury molecule 1 (KIM-1) that are associated with kidney damage increased in kidney after PM2.5 exposure. Correlation analysis indicated that there was a relationship between HMGN1-KIM-1 and lipid metabolic markers. In addition, kidneys of mice were analyzed using LC-MS/MS based metabolomic analyses. PM2.5 exposure altered metabolic profiles in the mice kidney, including 50 metabolites. In conclusion the results of this study show that PM2.5 exposure lead to abnormal renal function and further promotes renal injury by disturbance of renal lipid metabolism and alter metabolic profiles.ResultsThe increasing of Oil Red staining area and adipose differentiation related protein (ADRP) expression detected by IHC staining indicated more ectopic lipid accumulation in kidney after PM2.5 exposure, and the increasing of SREBP-1 and the declining of ATGL detected by IHC staining and qPCR indicated the disorder of lipid synthesisandlipolysis in DKD mice kidney after PM2.5 exposure. The expressions of high mobility group nucleosome binding protein 1 (HMGN1) and kidney injury molecule 1 (KIM-1) that are associated with kidney damage increased in kidney after PM2.5 exposure. Correlation analysis indicated that there was a relationship between HMGN1-KIM-1 and lipid metabolic markers. In addition, kidneys of mice were analyzed using LC-MS/MS based metabolomic analyses. PM2.5 exposure altered metabolic profiles in the mice kidney, including 50 metabolites. In conclusion the results of this study show that PM2.5 exposure lead to abnormal renal function and further promotes renal injury by disturbance of renal lipid metabolism and alter metabolic profiles.

Background The 2019 novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2) is a current worldwide threat for which the immunological features after infection need to be investigated. The aim of this study was to establish a highly sensitive and quantitative detection method for SARS-CoV-2 IgG antibody and to compare the antibody reaction difference in patients with different disease severity. Results Recombinant SARS-CoV-2 nucleocapsid protein was expressed in Escherichia coli and purified to establish an indirect IgG ELISA detection system. The sensitivity of the ELISA was 100% with a specificity of 96.8% and a 98.3% concordance when compared to a colloidal gold kit, in addition, the sensitivity of the ELISA was 100% with a specificity of 98.9% and a 99.4% concordance when compared to a SARS-CoV-2 spike S1 protein IgG antibody ELISA kit. The increased sensitivity resulted in a higher rate of IgG antibody detection for COVID-19 patients. Moreover, the quantitative detection can be conducted with a much higher serum dilution (1:400 vs 1:10, 1:400 vs 1:100). The antibody titers of 88 patients with differing COVID-19 severity at their early convalescence ranged from 800 to 102,400, and the geometric mean titer for severe and critical cases, moderate cases, asymptomatic and mild cases was 51,203, 20,912, and 9590 respectively. Conclusion The development of a highly sensitive ELISA system for the detection of SARS-CoV-2 IgG antibodies is described herein. This system enabled a quantitative study of rSARS-CoV-2-N IgG antibody titers in COVID-19 patients, the occurrence of higher IgG antibody titers were found to be correlated with more severe cases.

Overproduction of cytokine interleukin-6 (IL-6) is a hallmark of severe COVID-19 and is believed to play a critical role in exacerbating the excessive inflammatory response. Polymorphisms in IL-6 account for the variability of IL-6 expression and disparities in infectious diseases, but its contribution to the clinical presentation of COVID-19 has not been reported. Interleukin6 (IL-6) is a key driver of hyperinflammation in COVID-19, and its level strongly correlates with disease progression. To investigate whether variability in COVID-19 severity partially results from differential IL-6 expression, functional single-nucleotide polymorphisms (SNPs) of IL-6 were determined in Chinese COVID-19 patients with mild or severe illness. An Asian-common IL-6 haplotype defined by promoter SNP rs1800796 and intronic SNPs rs1524107 and rs2066992 correlated with COVID-19 severity. Homozygote carriers of C-T-T variant haplotype were at lower risk of developing severe symptoms (odds ratio, 0.256; 95% confidence interval,  0.088 to 0.739; P  = 0.007). This protective haplotype was associated with lower levels of IL-6 and its antisense long noncoding RNA IL-6-AS1 by cis -expression quantitative trait loci analysis. The differences in expression resulted from the disturbance of stimulus-dependent bidirectional transcription of the IL-6 / IL-6-AS1 locus by the polymorphisms. The protective rs2066992- T allele disrupted a conserved CTCF-binding locus at the enhancer elements of IL-6-AS1 , which transcribed antisense to IL-6 and induces IL-6 expression in inflammatory responses. As a result, carriers of the protective allele had significantly reduced IL-6-AS1 expression and attenuated IL-6 induction in response to acute inflammatory stimuli and viral infection. Intriguingly, this low-producing variant that is endemic to present-day Asia was found in early humans who had inhabited mainland Asia since ∼40,000 years ago but not in other ancient humans, such as Neanderthals and Denisovans. The present study suggests that an individual's IL-6 genotype underlies COVID-19 outcome and may be used to guide IL-6 blockade therapy in Asian patients. IMPORTANCE Overproduction of cytokine interleukin-6 (IL-6) is a hallmark of severe COVID-19 and is believed to play a critical role in exacerbating the excessive inflammatory response. Polymorphisms in IL-6 account for the variability of IL-6 expression and disparities in infectious diseases, but its contribution to the clinical presentation of COVID-19 has not been reported. Here, we investigated IL-6 polymorphisms in severe and mild cases of COVID-19 in a Chinese population. The variant haplotype C-T-T , represented by rs1800796, rs1524107, and rs2066992 at the IL-6 locus, was reduced in patients with severe illness; in contrast, carriers of the wild-type haplotype G - C - G had higher risk of severe illness. Mechanistically, the protective variant haplotype lost CTCF binding at the IL-6 intron and responded poorly to inflammatory stimuli, which may protect the carriers from hyperinflammation in response to acute SARS-CoV-2 infection. These results point out the possibility that IL-6 genotypes underlie the differential viral virulence during the outbreak of COVID-19. The risk loci we identified may serve as a genetic marker to screen high-risk COVID-19 patients.