Colorectal cancer is a highly lethal gastrointestinal tract malignancy whose pathogenesis and molecular drivers are not fully understood. This study focused on searching for genes that are differentially expressed in cancer versus normal mucosa, with the goal of identifying molecular patterns of expression that are mechanistically linked to colorectal cancer pathogenesis. We analyzed 585 colorectal cancer samples and 329 normal samples from the Gene Expression Omnibus database, creating a weighted gene coexpression network analysis across 24,069 genes. Through this approach, five modules associated with colorectal cancer were identified, which were enriched in MAPK signaling and cholesterol metabolism pathways. Using least absolute shrinkage and selection operator (LASSO) regression, we selected 13 hub genes [ABCB5, AOC1, ARHGAP44, CACNG3, dysbindin domain-containing protein 1 (DBNDD1), GAS7, GTF2IRD1, PRSS22, SCN4A, TTC22, DLX6, PDK4, and SLC13A2] from these modules. Survival analysis indicated that higher expression of DBNDD1 correlated with worse overall survival in patients with colorectal cancer. Machine learning validation confirmed the stability of these genetic markers. Experimental validation demonstrated increased levels of DBNDD1 and growth differentiation factor 15 (GDF15) in colorectal cancer, promoting constant NF-κB (RELA) activation via DBNDD1-dependent GDF15 induction. Knocking down DBNDD1 inhibited cell proliferation, migration, and invasion in vitro (DLD1/HCT116 cells), alongside decreased GDF15 expression and reduced p-NF-κB p65-p-I-κB signaling. Additionally, DBNDD1 knockdown resulted in reduced tumor growth in vivo, highlighting that the DBNDD1-GDF15-NF-κB signaling pathway drives colorectal cancer pathogenesis. IMPLICATIONS:This study highlights the crucial role of the DBNDD1-GDF15-NF-κB signaling pathway in colorectal cancer development, positioning DBNDD1 as a promising target for precision medicine strategies aimed at enhancing patient outcomes.

Reversible disruption of the blood-brain barrier (BBB) occurs within hours after the onset of ischemic stroke (IS), offering a critical window for therapeutic intervention. However, the molecular characteristics and their potential as circulating biomarkers associated with this transient phase of BBB dysfunction remain poorly defined. To elucidate these mechanisms, we employed an oxygen-glucose deprivation (OGD) model in human cerebral microvascular endothelial cells (hCMEC/D3) to simulate early ischemic stress, and systematically profiled their secreted proteome and metabolome. By comparing with non-brain-derived human umbilical vein endothelial cells (HUVECs), we identified brain endothelium-specific hypoxic response signatures. These molecules were significantly enriched in pathways related to metabolic reprogramming, antioxidant defense, and epigenetic regulation pathways, indicating a coordinated adaptive response to preserve BBB homeostasis. Furthermore, integrative multi-omics analysis revealed 14 protein-metabolite pairs with potential functional synergy. Based on a multi-criteria screening strategy including brain specificity, functional relevance, and secretory potential, we prioritized 10 candidate circulating biomarkers: ALDH2, ITGA5, KYNU, TFRC, CD44, COL1A2, HEXB, HSPG2, THBS4, and DLD. Preliminary validation using serum from acute IS (AIS) patients and healthy controls showed significantly altered levels of ALDH2, ITGA5, KYNU, and TFRC, with TFRC exhibiting promising diagnostic performance both individually (AUC = 0.816) and in combination with the other three biomarkers (AUC = 0.876). Moreover, multivariate logistic regression analysis revealed that elevated TFRC was independently associated with poor 90-day outcomes (OR = 1.02, 95

The global burden of ischemic stroke (IS) continues to rise annually. This study aims to develop a machine learning prediction model that integrates blood biomarkers and carotid color Doppler ultrasound features to identify high-risk patients with large-artery atherosclerosis (LAA)-type IS among those with atherosclerosis (AS). A retrospective analysis was conducted involving 166 patients with LAA-type IS and 71 patients with AS. The baseline characteristics, blood biomarkers results and carotid color Doppler ultrasonic imaging features of the patients at admission were collected. Multivariate binary Logistic regression was used to identify the independent influencing factors of LAA-type IS, and logistic regression (LR), random forest (RF) and support vector machine (SVM) prediction models were established. Receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and Delong test were used to evaluate and compare the models. Internal validation of the optimal model was performed using the Bootstrap method; no external validation was carried out. Eight independent predictors of LAA-type IS were identified, which were carotid plaque (CP) presence, CP echogenicity, thrombomodulin (TM), platelet distribution width (PDW), glucose (GLU), apolipoprotein A (APA), homocysteine (HCY), and hydroxybutyric dehydrogenase (HBDH). The area under the ROC curve (AUC) of LR, SVM and RF model were 0.846, 0.901 and 0.955, respectively. The Delong test showed statistically significant differences among the three models (p < 0.05). The calibration curve and DCA results further showed good validity and clinical feasibility of all models. Among them, the RF model exhibited the best performance, with a C-index of 0.955 upon internal validation after 200 bootstrap iterations. The RF model shows promising potential for predicting LAA-type IS and may serves as a reference for clinicians to rapidly identify high-risk patients with this stroke subtype.

This study aimed to develop a hepatocellular carcinoma (HCC) risk prediction model based on clinlabomics and develop an online prediction tool to provide a novel approach for early HCC diagnosis. We retrospectively collected clinical and laboratory data from 1,017 patients (576 HCC cases, 358 cirrhosis cases, and 83 chronic viral hepatitis cases), randomly dividing them into a training set (798 cases) and a validation set (219 cases). Key variables were selected using LASSO logistic regression and variable importance scoring, followed by the construction of seven machine learning models. Model performance was comprehensively evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The optimal model was interpreted using SHapley Additive exPlanations (SHAP). Subsequently, we developed an online prediction tool for real-time risk prediction and validated its predictive performance. A total of 16 variables significantly related to HCC were selected for model construction, including: age, liver nodules (≥ 1 cm), CRP, RBC, PDW, AFP, PIVKA-II, IBIL, TBA, GASR, AAAR, ALBI, CK, INR, FIB, and FDP. Among the seven machine learning models developed, the SVM model demonstrated optimal performance, achieving AUC values of 0.962 (training set) and 0.877 (validation set). In prospective validation, the SVM-based online prediction tool demonstrated 85

Objective: This study aims to investigate the diagnostic potential of bile acid profiles combined with lipid parameters in metabolic dysfunction-associated steatotic liver disease (MASLD). Method: This study employed LC-MS technology to analyze the serum bile acid profiles of 186 male patients with MASLD and 80 male non-MASLD patients. According to whether the serum samples exhibit dyslipidemia, the subjects were divided into four groups. Subsequently, a series of analyses were conducted, including univariate analysis, the Spearman correlation test, multinomial logistic regression, restricted cubic spline regression, and ROC curve analysis. Result: The bile acid profiles of serum samples with dyslipidemia exhibit significant differences compared to those of normal serum samples. The bile acids, which include total deoxycholic acid (DCA), secondary bile acids (SBA), unconjugated bile acids (UCBA), 12 alpha-hydroxylated bile acids (12HBA), total bile acids (TBA), primary bile acids (PBA)/SBA, and glycine-conjugated bile acids (GCBA)/taurine-conjugated bile acids (TCBA), demonstrate a non-linear correlation with the logarithmic ratio of triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C). The ROC analysis indicates that, in populations with normal lipid levels, bile acid indicators such as GLCA and 12HBA demonstrate a superior ability to distinguish between MASLD and non-MASLD compared to populations with abnormal lipid levels and the overall population. Their diagnostic performance significantly surpasses that of existing MASLD diagnostic models. Conclusion: The combination of bile acid profiles and lipid indicators holds significant diagnostic potential in MASLD.

The high heterogeneity of hepatocellular carcinoma (HCC) poses challenges for precision treatment strategies. This study aims to use multi-omics methodologies to better understand its pathogenesis and discover biomarkers. Quantitative proteomics was used to investigate hepatocellular carcinoma tissues (HCT) and their corresponding adjacent non-tumor tissues (DNT), obtained from six HCC patients. Untargeted metabolomics was applied to analyze the metabolic profiles of HCT and DNT of ten HCC patients. Statistical analyses, such as the Student’s t-test, were performed to identify differentially expressed proteins (DEPs) and metabolites (DEMs) between the two groups. The functions and metabolic pathways involving DEPs and DEMs were annotated and enriched using the gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) databases. Bioinformatics methods were then utilized to analyze consistency between proteomics and metabolomics results, leading to identification of potential biomarkers along with key altered pathways associated with HCC. This study identified 1556 DEPs between HCT and DNT samples. These DEPs were primarily enriched in crucial biological pathways such as amino acid degradation, fatty acid metabolism, and DNA replication. Subsequently, the analysis of metabolomics identified 500 DEMs that mainly participated in glycerophospholipid metabolism, the phospholipase D signaling pathway, and choline metabolism related to cancer. Integrated analysis of proteomics and metabolomics data unveiled significant dysfunctions in bile secretion, multiple amino acid and fatty acid metabolic pathways among HCC patients. Further investigation revealed that five proteins (PTP4A3, B4GALT5, GAB1, ME2, and PKM) along with seven metabolites (PI(6 keto-PGF1alpha/16:0), 13, 16, 19-docosatrienoic acid, PA(18:2(9Z, 12Z)/20:1(11Z)), Citric Acid, PG(20:3(6, 8, 11)-OH(5)/18:2(9Z, 12Z)), Spermidine, and N2-Acetylornithine) exhibited excellent diagnostic efficiency for HCC and could serve as its potential biomarkers. Our integrated proteome and metabolome analysis revealed 10 key HCC-related pathways and proposed 12 potential biomarkers, which may enhance our understanding of HCC pathophysiology and be helpful in facilitating early diagnosis and treatment strategies.

OBJECTIVES:Rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are two systemic autoimmune diseases with significant comorbidity, and the shared molecular mechanisms remain unclear. METHODS:This study integrates four transcriptomic datasets (GSE17755, GSE110169, GSE93272, GSE110174), combining differential expression gene (DEG) screening, protein-protein interaction (PPI) network analysis, machine learning (LASSO and random forest), and single-sample gene set enrichment analysis (ssGSEA) to systematically analyse the comorbidity mechanisms of RA and SLE. RESULTS:It was found that RA and SLE share immune regulatory genes such as IFIT3, TNFSF13B, and ZCCHC2, which are significantly upregulated in both diseases and have high diagnostic efficacy. Functional analysis shows that IFIT3 is associated with type I interferon and cGAS-STING pathways, TNFSF13B (BAFF) is associated with B cell activation and TLR signalling pathways, and ZCCHC2 is associated with cell cycle regulation and neurodegenerative diseases. Immune cell analysis indicates that in RA, macrophages are positively correlated with TNFSF13B, while in SLE, plasmacytoid dendritic cells (pDCs) are negatively correlated with IFIT3, suggesting disease-specific differences in the immune microenvironment. CONCLUSIONS:This study elucidates the molecular mechanisms of RA and SLE comorbidity, identifies cross-disease biomarkers and their interactions with immune cells and drugs, providing theoretical basis for precision diagnosis and treatment. The study limitations include data heterogeneity and the lack of Rhupus patient samples, which need to be validated through functional experiments and multicentre cohorts in the future.

This study aimed to identify metabolic footprints associated with distinct phenotypes of acute ischemic stroke (AIS) using untargeted metabolomics. We included 20 samples each from AIS phenotype A (n = 251), B (n = 213), and C (n = 43) groups, along with 20 age- and gender-matched healthy controls (HCs). Plasma metabolic profiles were analyzed using liquid chromatography-mass spectrometry (LC-MS). Weighted gene correlation network analysis (WGCNA) evaluated associations between metabolite clusters and clinical traits, including the National Institutes of Health Stroke Scale (NIHSS) and the modified Rankin Scale (mRS). We identified three, five, and six key differential metabolites for diagnosing phenotypes A, B, and C, respectively, demonstrating high diagnostic performance. These metabolites were focused on fatty acids, sex hormones, amino acids, and their derivatives. WGCNA identified 12 core metabolites involved in phenotype progression. Notably, phenylalanylphenylalanine and phenylalanylleucine were inversely correlated with disease severity and disability. Metabolites related to energy supply and inflammation were common across phenotypes, with additional changes in ionic homeostasis in phenotype A and decreased neurotransmitter release in phenotype C. Biosynthesis of unsaturated fatty acids and the pentose phosphate pathway (PPP) were relevant across all phenotypes, while the folate biosynthesis pathway was linked to phenotype C and clinical scales. Key metabolites, including phenylalanylphenylalanine and phenylalanylleucine, and pathways such as folate biosynthesis, significantly contribute to AIS severity and differentiation of phenotypes. These findings offer new insights into the pathogenesis and mechanisms underlying AIS phenotypes.

Epigenetic inhibitors targeting histone methyltransferases (HMTs) have been proven to be promising for blood protozoan treatment. However, little is known about the effects of HMT inhibitors on Babesia parasites. Here, in vitro and in vivo drug tests were performed to evaluate the efficacy of several compounds targeting various HMTs for Babesia treatment. Their cytotoxicity to MDOK cells was also assessed. Among these compounds, furamidine demonstrated outstanding activity in vitro at the nanomolar level (IC50s of 0.03 ± 0.55, 0.02 ± 0.50, and0.02 ± 0.76 μM at 48, 72, and 96 h, respectively). Furthermore, the IC50 of furamidine on MDOK cells was 100 μM after 24 h, 45 μM after 48 h and 40 μM after 72 h. The therapeutic index of furamidine was greater than 1,500. In addition, furamidine effectively inhibited the growth of B. duncani and B. microti in hamsters and BALB/c mice. Furthermore, furamidine was demonstrated high in vivo safety. These findings suggest that furamidine could be an effective alternative drug for treating babesiosis.

The emergence of acquired resistance to osimertinib represents a formidable therapeutic challenge in the management of non-small cell lung cancer (NSCLC). While circular RNAs (circRNAs) have been increasingly recognized as crucial modulators of chemotherapeutic resistance, their specific involvement in osimertinib resistance mechanisms remains poorly elucidated. We established osimertinib-resistant NSCLC cell lines (HCC4006OR) through prolonged drug exposure and conducted comprehensive transcriptome sequencing to identify differentially expressed circRNAs. The molecular characteristics and functional implications of circROBO2 were systematically investigated utilizing an array of cellular and molecular biological methodologies. Advanced molecular dynamics simulations were implemented to elucidate the potential molecular interactions between PDGFB and osimertinib. We identified circROBO2 as significantly upregulated in osimertinib-resistant HCC4006OR cells. Functional studies revealed that circROBO2 enhances cell survival, proliferation, and invasion while suppressing apoptosis under osimertinib treatment. Mechanistically, circROBO2 functions as a molecular sponge for miR-625-5p, resulting in elevated PDGFB expression and subsequent activation of the MAPK pathway, particularly the RAF/MEK/ERK cascade. Targeting this pathway through circROBO2 knockdown or miR-625-5p overexpression partially restored osimertinib sensitivity in resistant cells. Molecular dynamics simulations suggested potential direct interactions between PDGFB and osimertinib, providing additional insights into the resistance mechanism. Our study identifies a novel circROBO2/miR-625-5p/PDGFB regulatory axis in osimertinib resistance and positions circROBO2 as a potential therapeutic target and biomarker for NSCLC treatment.

Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, with early diagnosis remaining a significant challenge. Available serum biomarkers lack specificity, making it difficult to accurately identify early non-metastatic GC cases. Reliable diagnostic biomarkers that can detect early GC are critical to improve prognosis. We employed serum proteomics combined with bioinformatics to identify genes differentially expressed in the serum of non-metastatic GC patients. Single-cell RNA sequencing (ScRNA-seq) and immune infiltration analysis were performed to evaluate the relationship between gene expression and immune cell function. Then we evaluated 107 machine learning models for biomarker-based early GC diagnosis and develops a nomogram validated for accuracy and clinical utility, subsequently comparing the performance of potential biomarkers with traditional tumor markers in diagnosing early gastric cancer. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) and immunohistochemical staining using the Human Protein Atlas (HPA) database were used to validate the differential expression of candidate genes in GC tissues and adjacent non-cancerous tissues. The proteomic analysis identified several genes upregulated in the serum of GC patients compared to healthy controls. Single-cell RNA sequencing analysis further revealed that these upregulated genes were associated with altered immune cell infiltration in the tumor microenvironment. The glmBoost + XGBoost model incorporating B2M, CFL1, CTSD, and HSP90AB1 demonstrated strong diagnostic performance (mean AUC = 0.792), with 101 algorithm combinations achieving an average AUC > 0.7. A nomogram integrating gene expression and clinical data was developed, validated through calibration and decision curve analyses, highlighting its potential for early GC diagnosis. Additionally, four genes—TAGLN2, HSP90AB1, SH3BGRL3, and CFL1—were found to be highly expressed in non-metastatic GC tissues and were significantly correlated with immune infiltration, including CD8 + T cells, monocytes, and myeloid-derived suppressor cells. These findings were validated by qRT-PCR and immunohistochemical analyses, confirming their elevated expression in GC tissues. TAGLN2, HSP90AB1, SH3BGRL3 and CFL1 are potential diagnostic biomarkers for early-stage GC, with strong associations with immune cell infiltration. Machine learning model shows excellent diagnostic performance. These results provide a foundation for future studies to improve early diagnosis and individualized treatment strategies for GC. Serum proteomics identified genes upregulated in non-metastatic gastric cancer. Single-cell RNA sequencing analysis revealed gene expression correlates with immune cell infiltration in gastric cancer. Machine learning model and nomograph show that potential markers have good diagnostic performance. Gastric cancer tissues validated TAGLN2, HSP90AB1, SH3BGRL3, and CFL1 as upregulated. Identified biomarkers are associated with immune infiltration, indicating a role in gastric cancer progression. Potential biomarkers could improve early diagnosis and personalized management of gastric cancer.

BACKGROUND:This study investigates the relationships between folate intake, RBC folate, serum folate levels, and stroke risk, with an emphasis on the mediating roles of the dietary inflammatory index (DII) and systemic immune-inflammation index (SII). METHODS:A cross-sectional analysis was conducted using 24,106 participants from NHANES (2007-2018). Associations were assessed with weighted multivariate logistic regression, adjusting for key confounders. Propensity score matching (PSM) was applied, yielding 1,838 matched participants, respectively. Nonlinear relationships were analyzed with restricted cubic splines, and mediation analysis was performed for DII and SII. RESULTS:Post-PSM, folate intake in Q2 (252-350 μg/day), Q3 (350-484 μg/day), and Q4 (> 484 μg/day) was significantly inversely associated with stroke risk (trend P < 0.05), with adjusted ORs of 0.62 (95 % CI: 0.45-0.85), 0.65 (95 % CI: 0.46-0.90), and 0.60 (95 % CI: 0.42-0.86), respectively. Serum folate levels in Q3 (37.0 - 54.8 nmol/L) were also protective (OR: 0.47, 95 % CI: 0.32-0.68, trend P < 0.05). Serum folate levels exhibited a biphasic effect, with the lowest stroke risk at 41.9 nmol/L before PSM and 43.3 nmol/L after PSM. Mediation analysis showed DII mediated 45.2 % of the relationship between folate intake and stroke risk (P = 0.018), while SII's mediation effect was minimal (0.412 %, P = 0.016). No significant interactions were observed between folate intake, serum folate and stratified variables (P > 0.05) after PSM. CONCLUSION:Higher folate intake lowers stroke risk, with DII playing a significant mediating role, while serum folate presents a biphasic risk pattern. Personalized dietary strategies addressing folate intake and inflammation may be crucial for stroke prevention.

ObjectiveThis study aimed to assess the prevalence and distribution of respiratory pathogens in children under 18 years old with Acute Respiratory Infections (ARTIs) in Lanzhou, Northwest China, from July 2019 to January 2024.MethodsThe respiratory pathogens studied were FluA, FluB, PIV, RSV, ADV, MP, CP, CB, and LP, detected by indirect immunofluorescence assay (IIF). Data were obtained from the laboratory information system (LIS) of the Lanzhou University Second Hospital. As in Lanzhou, NPIs were implemented in January 2020, and were lifted in December 2022, data were divided into pre-NPIs (July 2019 to December 2019), NPIs (January 2020 to December 2022) and post-NPIs (January 2023 to January 2024) periods for analysis. Pearson’s chi-square test, ANOVA, and Fisher’s exact test were used to evaluate statistical significance in variable differences, with P < 0.05 considered significant.ResultsA total of 29,659 children diagnosed with ARTIs were included in the study, with 13030(43.93%) test positive for at least one pathogen. Single-pathogen infections predominated (33.10%), while co-detection of MP and PIV was the most common among multi-pathogen cases (52.96%). Pathogen detection rates were notably higher in female children (50.62%) and preschool-aged children (53.45%) and exhibited seasonal variations, with a pronounced increase in winter (47.61%) and a peak in November (48.92%). MP had the highest detection rate (38.59%), followed by PIV (10.18%). Detection rates significantly increased following the lifting of NPIs, rising from 33.82% (SD ± 13.13) during NPIs to 64.42% (SD ± 4.67) (P < 0.001), with 2023 showing the highest detection rate (64.61%) and largest participant count (9,591). In November 2023, detection rates reached their highest level at 73.09%. Post-NPI, most pathogens, except CB and LP, demonstrated significantly higher prevalence (P<0.001).ConclusionIn the Lanzhou region, MP and PIV were identified as the most prevalent respiratory pathogens among children with ARTIs, with peak detection rates during the winter season. Boys and school-age children exhibited higher susceptibility to these infections. NPIs played a critical role in reducing respiratory pathogen transmission. Once NPIs were lifted, a marked resurgence in pathogen incidence highlighted their impact on controlling infection spread.

ABSTRACT Staphylococcus aureus is a pathogen responsible for diverse severe infections. The global spread of methicillin-resistant S. aureus (MRSA) necessitates innovative therapeutic approaches beyond traditional antibiotics. S. aureus virulence mechanisms remain a critical concern. Targeting histidinol dehydrogenase (HisD), a key enzyme in histidine biosynthesis, presents a novel anti-virulence strategy. We used the derivative strains of Newman strains (ΔhisD, ΔhisD::pRAB-hisD, and WT::pRAB-hisD) and clinical strains to study the role of HisD in the pathogenicity of S. aureus. HisD inhibition by pixantrone was further evaluated. The absence of hisD significantly reduced hemolytic activity and biofilm formation, accompanied by the downregulation of virulence genes (hla, coa, hlgA-C, lukE/S/F, and NWMN_1873) and the saeR/S two-component system (P < 0.05). The expression of biofilm-inhibiting proteases was elevated, notably Aur and ScpA. Murine challenge revealed that ΔhisD exhibited 5.6-fold higher LD50 (4.17 × 109 CFU/mL vs. WT 7.42 × 108 CFU/mL) and reduced organ colonization (P < 0.05). Through structure-based virtual screening and SPR affinity verification, we discovered pixantrone—a nitrogenated anthraquinone—as a potent HisD inhibitor binding via four hydrogen bonds and two salt bridges. Pixantrone dose-dependently (25–200 μM) suppressed virulence phenotypes in vitro, achieving hemolysis inhibition, virulence gene inhibition, and biofilm reduction. In vivo, pixantrone (30 mg/kg) decreased serum CRP, IL-6, and TNF-α levels while diminishing abscess sizes and splenic bacterial loads (P < 0.05 vs. untreated). These findings establish HisD as a pivotal virulence regulator in S. aureus through saeR/S-mediated pathways. Pixantrone demonstrates potent anti-virulence efficacy, positioning HisD inhibition as a promising therapeutic strategy against S. aureus infections. This study provides foundational insights for developing HisD-targeted agents to combat antibiotic-resistant staphylococcal pathogens.IMPORTANCEThe increase in drug-resistant Staphylococcus aureus (MRSA) demands therapies that block virulence without promoting resistance. We identify histidinol dehydrogenase (HisD), a histidine-synthesis enzyme, as a key controller of S. aureus pathogenicity. Disrupting HisD genetically or with pixantrone—a newly identified inhibitor—reduces bacterial toxicity, biofilm formation, and virulence gene activity while improving survival and reducing organ damage in infected mice. Pixantrone's dose-dependent suppression of infection severity and inflammation positions it as a therapeutic candidate. Unlike traditional antibiotics, this strategy disarms bacteria rather than killing them, reducing resistance risks. By uncovering HisD’s role in connecting metabolism to virulence through the saeR/S system, we reveal a druggable target for fighting multidrug-resistant infections. This work addresses the urgent need for innovative solutions to the global antibiotic resistance crisis, paving the way for therapies that outsmart evolving superbugs.

BACKGROUND:Human babesiosis caused by Babesia microti is an emerging tick-borne zoonosis, with a global pooled prevalence of 2.23% and regional peaks in Europe (4.17%) and North America (1.54%). Traditional diagnostics like microscopy and polymerase chain reaction (PCR) suffer from low sensitivity in low-parasitemia cases or high costs ($230/test), necessitating accessible, rapid assays for resource-limited regions. METHODS:A cross-priming amplification combined with vertical flow visualization (CPA-VF) assay, a straightforward molecular method targeting the 18S rRNA gene of B. microti, requires minimal equipment and facilitates rapid detection. RESULTS:Sensitivity/Specificity: The CPA-VF assay detected 2.56 fg/reaction (equivalent to 0.000004% parasitic red blood cells), with a sensitivity of 95.5% matching that of RT-PCR but at a 60-fold lower cost ($3.8/test). It showed no cross-reactivity with B. duncani, B. divergens, or Plasmodium. Clinical Validation: Testing 49 positive samples (19 experimentally infected mice +30 artificially spiked) and 492 field samples, CPA-VF demonstrated 95.5% sensitivity (95% CI: 88.2-98.7) and 95.5% specificity compared to nested PCR (nPCR). Intra-assay coefficients of variation (CV) was 2.1%-7.2% and inter-assay kappa coefficient was 0.94, confirming reliability. CONCLUSION:CPA-VF is a rapid, low-cost ($3.8/test), and instrument-free diagnostic tool for B. microti, particularly suitable for endemic regions where timely diagnosis reduces mortality risks from misdiagnosis as malaria. Its portability and visual readout address critical gaps in resource-constrained settings.

BackgroundGastric cancer (GC) ranks among the most prevalent malignant neoplasms globally and is associated with a significant mortality rate. Despite the availability of various therapeutic interventions for GC, the overall prognosis for this disease remains unfavorable. This can be attributed to several factors, including delayed diagnosis and the inherent heterogeneity of the tumors. With the continuous enrichment of treatment methods, GC has entered an era of comprehensive treatment oriented toward precision and standardization.MethodsThrough the application of bioinformatics and assessments of tissue microarrays, this study has selected the histone chaperone Anti-Silencing Function 1B (ASF1B) for detailed analysis, including clinical specimens. We then constructed ASF1B knockout and overexpression cell lines, and conducted biological function tests on this basis, validated at mouse and organoid levels. Additionally, human immunereconstitution was performed in NOD-PrkdcscidIl2rgem1/Smoc (NSG) mice, followed by flow cytometry analysis of mouse blood. Mechanically, protein-protein interaction analyses were conducted utilizing Immunoprecipitation-Mass Spectrometry (IP-MS) and Tandem mass tagging (TMT) methodologies to identify protein clusters.ResultsThe analysis demonstrated that ASF1B is significantly upregulated in GC tissues and correlates with unfavorable prognostic outcomes. Biological function tests provided that ASF1B contributes to tumor cell proliferation, colony formation, invasion and migration, and plays an important role in the progression of GC in vivo. These findings were validated at both the mouse and organoid levels. Additionally, we observed that ASF1B is involved in the tumor microenvironment, where ASF1B knockdown increases CD8+ T cell infiltration, indicating a negative correlation with immune activation. Mechanically, our investigation revealed that ASF1B emerged as a promoter of GC progression by downregulating H2A clustered histone 20 (H2AC20), thereby influencing the activation of the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) and extracellular regulated protein kinases (ERK)1/2 signaling pathways.ConclusionASF1B, recognized as an oncogene, contributes to the initiation and progression of tumors, positioning it as a prospective target for therapeutic intervention in GC.

Metabolic-inflammatory syndrome represents a major global health challenge, closely linked to diabetes, cardiovascular disease, obesity, and non-alcoholic fatty liver disease. The core of these disorders involves remodeling of metabolic pathways under nutritional stress and the ensuing chronic inflammatory vicious cycle. This review systematically examines the mechanisms by which metabolic reprogramming drives metabolic-inflammatory syndrome, revealing the significant role of the gut microbiota in this process. Furthermore, it evaluates current metabolic-inflammatory syndrome early-warning systems and proposes innovative therapeutic strategies precisely targeting the metabolic-immune axis, establishing a foundation for enhancing early detection and clinical intervention.

BACKGROUND:Early diagnosis plays a crucial role in improving the survival rate of acute leukemia (AL) patients. This study aims to develop a warning model for the detection of acute leukemia (AL) using complete blood count (CBC) and cell population data (CPD), which could aid in clinical diagnosis. METHODS:In this study, CBC and CPD were utilized to develop a warning model for assisting clinical diagnosis of AL. Clinical characteristics and peripheral blood data were retrospectively collected from 262 AL patients and 280 non-AL patients at the Second Hospital of Lanzhou University; they were randomly divided into a training set and a test set in a ratio of 7:3. The training set was used to establish support vector machine (SVM), random forest (RF), and logistic regression (LR) models for AL. The validation set consisted of 357 cases (97 AL, 260 non-AL) collected from the General Hospital of Ningxia Medical University to verify the warning efficacy of the optimal model in conjunction with the test set. RESULTS:The comparative analysis revealed that the SVM model outperformed the RF and LR models in terms of diagnostic accuracy. In the training set, the accuracy was 92.93%; the area under the ROC curve (AUC) and 95% confidence interval (95% CI) were 0.981 (0.970, 0.992). For the test set, the accuracy was 89.66%; the AUC and 95% CI were 0.959 (0.931, 0.988). As for the validation set, the accuracy was 76.34%; the AUC and 95% CI were 0.841 (0.789, 0.893). Additionally, the calibration curve and decision curve analysis (DCA) demonstrated that the SVM model exhibited satisfactory effectiveness and feasibility. CONCLUSION:The SVM model shows significant potential as a clinical screening tool for AL.

OBJECTIVE:Endothelial dysfunction is implicated in the pathogenesis of ischemic stroke (IS), but its causal role remains unclear. This study systematically investigates the causal relationship between endothelial dysfunction proteins and IS and its subtypes through integrated observational and genetic evidence. METHODS:A two-stage study was conducted combining systematic meta-analysis and Mendelian randomization (MR). The meta-analysis integrated data from 29 observational studies to assess associations between endothelial dysfunction proteins (vWF, sE-selectin, sP-selectin, ICAM-1, VCAM-1, sLOX-1, VEGF, ET-1, SDF-1) and IS. This meta-analysis was registered online (PROSPERO ID: CRD42023461783). Subsequent MR was applied to discern the causal effects of the endothelial dysfunction proteins on IS and its subtypes, utilizing genetically instrumental variants. RESULTS:A meta-analysis demonstrated significant correlations with IS for vWF, sE-selectin, ICAM-1, sP-selectin, sLOX-1, and VEGF (all p < 0.05). Furthermore, MR analysis showed that genetically elevated vWF increased the risk for any IS and cardioembolic stroke (CES), while E-selectin was causally linked to large-artery atherosclerosis stroke (LAS). CONCLUSION:This work offers causal evidence that endothelial dysfunction significantly contributes to IS, highlighting the thrombotic activity of vWF in CES and the inflammatory function of E-selectin in LAS. These findings not only offer valuable insights into the mechanisms underlying IS and its subtypes but also help inform personalized stroke prevention strategies.