Bosnian Journal of Basic Medical Sciences (BJBMS)
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Brain regions differences in amyloid-β and gene expression in early APP/PS1 mice and identification of Npas4 as a key molecule in Alzheimer\u27s disease
Get PDFDistinct brain regions are differentially affected during the various stages of Alzheimer\u27s disease (AD). While the hippocampus and cortex are known to play significant roles, the involvement of the cerebellum has received less attention. Understanding the changes in diverse brain regions is essential to unravel the neuropathological mechanism in early-stage AD. Our research aimed to explore and compare amyloid-β (Aβ) pathology and gene expression profiles across the hippocampus, cortex, and cerebellum in the early stages of the Amyloid Precursor Protein/Presenilin-1 (APP/PS1) mouse model. By 7 months of age, significant Aβ plaque accumulation was observed in the hippocampus and cortex of APP/PS1 mice, while no such deposits were found in the cerebellum. Gene expression analysis revealed predominant effects on immune response pathways in the hippocampus and cortex. Even in the absence of Aβ deposition, notable gene expression changes were observed in the cerebellum of APP/PS1 mice. Intriguingly, Neuronal PAS Domain protein 4 (Npas4) expression was consistently down-regulated across all brain regions, independent of Aβ plaque presence. Our findings reveal distinct transcriptomic alterations and Aβ pathology in select cerebral regions during the initial phase of AD. Notably, the diminished expression of the Npas4 across three brain regions implies that Npas4 could play a pivotal role in the early pathogenesis of AD
Fetal heart quantification technique improves the prenatal prediction of coarctation of the aorta: A retrospective analysis
Get PDFCoarctation of the aorta (CoA) ranks among the most prevalent congenital heart defects and poses a life-threatening risk if left undiagnosed. Herein, we utilized fetal heart quantification (HQ) technology to improve the prenatal prediction of CoA. A retrospective analysis was conducted on 64 fetal cases with suspected aortic arch constriction, identified through prenatal ultrasound findings between November 2020 and March 2022 at the Department of Ultrasound, Sir Run Run Shaw Hospital, Zhejiang University. According to the follow-up results, these cases were divided into two groups: 35 cases confirmed as CoA by postpartum surgery or induction, and 29 cases initially suspected of CoA prenatally but subsequently ruled out postnatally. Additionally, 88 cases of normal fetuses were randomly selected as the control group. Both conventional M-mode ultrasound techniques and Fetal HQ software were utilized for fetal analysis across all groups. Parameters related to the heart were measured, including fetal 4-CV length, width, Global Spherical Index (GSI), Mitral Annular Plane Systolic Excursion (MAPSE), areas and ratios of the left and right ventricles, as well as lengths and ratios of the left and right ventricles. Functional measurements of the left and right ventricles included ejection fraction (EF), fractional area change (FAC), global longitudinal strain (GLS), fractional shortening (FS), end-diastolic diameter (ED), and sphericity index (SI). Left ventricular (LV)-GLS, LV-FAC, LV-EF, and LV-EF Z-score could potentially differentiate between true CoA and false CoA or normal groups and serve as potential indicators for the clinical diagnosis of CoA. The receiver operating characteristic (ROC) curves indicated that LV-GLS and LV-EF Z-score have the greatest predictive power for CoA diagnosis. The segments 6-12 of FS in the confirmed CoA group were significantly lower than those in the false CoA and normal groups. Fetal HQ technology, by assessing changes in the size and shape of the heart, can provide relatively reliable parameter support for the prenatal diagnosis of fetal aortic coarctation
The relationship between tumor budding and survival of patients with breast cancer: A meta-analysis
Get PDFTumor budding has been proposed as a potential prognostic marker in various cancers, but its association with survival outcomes in breast cancer (BC) remains unclear. This meta-analysis aimed to clarify the relationship between tumor budding and survival outcomes in patients with BC. A comprehensive literature search was conducted in PubMed, EMBASE, and Web of Science. Cohort studies examining the association between tumor budding and overall survival (OS) and progression-free survival (PFS) in BC patients were included. Hazard ratios (HRs) and 95% confidence intervals (CIs) were pooled using a random-effects model to account for potential heterogeneity. Eleven cohort studies, including 2,828 patients, met the inclusion criteria. High tumor budding was significantly associated with poorer OS (HR = 1.89, 95% CI = 1.37–2.60, P < 0.001) and PFS (HR = 1.89, 95% CI = 1.32–2.71, P < 0.001). Subgroup analyses revealed a stronger association in studies where high tumor budding was defined as ≥ 10 buds / high-power field (HPF) compared to those with lower cutoffs. Sensitivity analyses confirmed the robustness of the findings. This meta-analysis demonstrates that high tumor budding is associated with significantly worse OS and PFS in BC patients, underscoring its prognostic significance. These findings suggest tumor budding could be a valuable marker in clinical assessments, and further research is needed to standardize its evaluation criteria in BC
E-DFu-Net: An efficient deep convolutional neural network models for Diabetic Foot Ulcer classification
Get PDFThe Diabetic Foot Ulcer (DFU) is a severe complication that affects approximately 33% of diabetes patients globally, often leading to limb amputation if not detected early. This study introduces an automated approach for identifying and classifying DFU using transfer learning. DFU is typically categorized into ischemic and infection states, which are challenging to distinguish visually. We evaluate the effectiveness of pre-trained Deep Convolutional Neural Network (DCNN) models for autonomous DFU detection. Seven models are compared: EfficientNetB0, DenseNet121, ResNet101, VGG16, MobileNetV2, InceptionV3, and InceptionResNetV2. Additionally, we propose E-DFu-Net, a novel model derived from existing architectures, designed to mitigate overfitting. Experimental results demonstrate that E-DFu-Net achieves remarkable performance, with 97% accuracy in ischemia classification and 92% in infection classification. This advancement enhances current methodologies and aids practitioners in effectively detecting DFU cases
Bioinformatics analysis and experimental validation of C6orf120 as a potential prognostic marker and therapeutic target for liver hepatocellular carcinoma
Get PDFThe C6orf120 gene is a novel gene whose function has not been fully defined. Previous studies have associated it with various liver pathologies, but its specific role in hepatocellular carcinoma (LIHC) remains unclear. This study aimed to investigate the diagnostic and prognostic value of C6orf120 in LIHC, as well as its potential biological functions. In this preliminary research, we utilized data from various databases and bioinformatics tools, including TCGA, GEO, TIMER2, HPA, GEPIA, Linkeomics, Metascape, CIBERSORT, TargetScan, DIANA-microT, RNAinter, and ENCORI, to analyze the expression patterns and mechanisms of C6orf120 in LIHC. Our bioinformatics analysis revealed that C6orf120 is upregulated in LIHC and may serve as a diagnostic and prognostic biomarker. The aberrant expression of C6orf120 in LIHC was further supported by clinical samples and cell lines. In vitro experiments demonstrated that the knockdown of C6orf120 in HepG2 cells significantly reduced migration capacity without affecting proliferation. Additionally, the downregulation of C6orf120 in LIHC cells appeared to inhibit endothelial cell migration and angiogenesis, which are critical in tumorigenesis and development. In conclusion, our findings suggest that C6orf120 could serve as a novel diagnostic and prognostic biomarker for LIHC and is expected to be a prognostic marker and a potential therapeutic target in the clinical management of LIHC
Comprehensive analysis of GPN1 in human cancer and its effects on the migration of hepatocellular carcinoma cells
Get PDFTo investigate the prognostic value of GPN1 in cancer and its role in the migration of hepatocellular carcinoma (HCC or LIHC) cells, we used several databases to assess GPN1 expression levels and effects in human tumors. Furthermore, experiments were conducted to verify changes in GPN1 expression in HCC cell lines and explore its biological function. We found that GPN1 gene and protein expression were significantly increased in several tumor tissues. Higher GPN1 expression was associated with unfavorable overall survival. Additionally, there was a strong association between GPN1 expression and several clinicopathological features, according to multivariate Cox regression analysis. Moreover, GPN1 gene mutation and methylation were present in some tumors. A relationship was also found between GPN1 expression and immune infiltration. Notably, immune checkpoint analysis showed that GPN1 expression was correlated with PD-1/PDL-1 and CTLA-4, suggesting it may serve as a biomarker for predicting immune subtypes and response to immunotherapy in HCC. Enrichment analysis in HCC indicated that GPN1 is primarily involved in RNA metabolism. Additionally, drug sensitivity analysis revealed that GPN1 appeared to be responsive to 16 drugs. Finally, GPN1 upregulation was confirmed to promote the migration of HCC cells. This study provides a comprehensive overview of GPN1 in human cancer and demonstrates that GPN1 contributes to the migration of HCC cells, potentially serving as a prognostic and immunotherapy biomarker
FMT reduces systemic inflammatory response in severe acute pancreatitis by increasing the abundance of intestinal Bifidobacteria and fecal bacteria
Get PDFSevere acute pancreatitis (SAP) is one of the leading causes of hospital admissions for gastrointestinal diseases, with a rising incidence worldwide. Intestinal microbiota dysbiosis caused by SAP exacerbates systemic inflammatory response syndrome and organ dysfunction. Fecal microbiota transplantation (FMT) has emerged as a promising therapeutic option for gastrointestinal diseases. In this study, fecal samples from healthy, control, and FMT-treated groups were analyzed using 16S rRNA sequencing to assess microbiome abundance and diversity. Composition and functional prediction analyses were conducted to explore the mechanisms underlying FMT in SAP. FMT significantly improved clinical parameters in SAP patients, including leukocyte count, C-reactive protein (CRP), neutrophil granulocyte count, lactate dehydrogenase (LDH), and calcitonin (P < 0.05). Organ failure rates significantly increased in the control group but decreased in the FMT group after treatment (P < 0.05). Fecal microbiota sequencing revealed that FMT significantly upregulated the abundance of Bifidobacterium longum among all SAP patients (P < 0.05). Receiver operating characteristic (ROC) curve analysis indicated that Bifidobacterium longum might play a critical role in the efficacy of FMT, with an area under the curve (AUC) value of 0.84. Additionally, there was a negative correlation between Bifidobacterium longum abundance and procalcitonin (PCT) levels, as well as a negative correlation between Escherichia coli abundance and both CT and Ca values (P < 0.05). The relative abundances of Bifidobacterium longum and Escherichia coli were significantly higher in the FMT group compared to the Bifidobacterium triple viable group (P < 0.05). In conclusion, this research supports FMT as a safe and effective intervention for treating SAP patients
N6-methyladenosine methylation regulators can serve as potential biomarkers for endometriosis related infertility
Get PDFEndometriosis (EMS) is a chronic inflammatory disease frequently associated with infertility. N6-methyladenosine (m6A) methylation, the most common form of methylation in eukaryotic mRNAs, has gained attention in the study of female reproductive diseases, including EMS and infertility. This study aimed to investigate the role of m6A regulators in EMS-related infertility. To begin, specific m6A regulators were identified by analyzing the GSE120103 dataset, followed by receiver operating characteristic (ROC) curve analysis. A nomogram model was then constructed, and unsupervised clustering of m6A regulators was performed to identify distinct m6A molecular clusters. Functional enrichment analysis of differentially expressed genes (DEGs) between these clusters, along with immune cell infiltration analysis, was subsequently conducted. In addition, the single-cell dataset GSE214411 was analyzed to explore the role of m6A regulators in various cell types. Finally, clinical samples were collected, and immunohistochemistry analysis was performed. The study identified seven key m6A regulators with significant diagnostic value for EMS-related infertility and two distinct m6A molecular clusters. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs between the clusters revealed that m6A clustering was strongly associated with immune pathways. Immune cell infiltration analysis further demonstrated that the expression levels of m6A regulators had a notable impact on immune cell infiltration. Single-cell analysis revealed that HNRNPA2B1 and HNRNPC were significantly elevated in endometrial immune cells from infertile EMS patients but notably decreased in stromal cells. Immunohistochemical staining confirmed that HNRNPA2B1 and HNRNPC expression levels were significantly higher in the eutopic endometrium of fertile women compared to ovarian EMS patients. These findings suggest that m6A regulators play critical roles in the development and progression of EMS-related infertility. Notably, HNRNPA2B1 and HNRNPC may serve as potential biomarkers for this condition
Plasma extracellular vesicle neurofilament light chain as the biomarkers of the progression of Parkinson’s disease
Get PDFParkinson’s disease (PD) is a common neurodegenerative disorder characterized by progressive symptoms, underscoring the urgent need for predictive blood biomarkers. Plasma extracellular vesicles (EVs) offer a promising platform for biomarker development, with neurofilament light chain (NfL) emerging as a potential candidate for neurological diseases. This study evaluated plasma EV NfL as a biomarker for disease progression in a PD cohort.A total of 55 patients with PD (PwP) and 58 healthy controls (HCs) were followed, with PwP completing an average of 3.96 visits and HCs 2.25 visits. Plasma EVs were isolated and validated, and EV NfL levels were measured using an immunomagnetic reduction assay. Generalized estimating equations and Spearman correlations assessed relationships between clinical symptom progression and biomarkers. Although no significant differences in plasma EV NfL levels were observed between PwP and HCs over time, changes in plasma EV NfL significantly correlated with motor symptom progression, specifically with adjusted-total and akinetic-rigidity subscores of the Unified PD Rating Scale (UPDRS) Part III. Additionally, changes in UPDRS Part II scores were significantly associated with plasma EV NfL levels. These findings suggest that plasma EV NfL reflects motor symptom progression in PwP, highlighting its potential as a valuable biomarker for monitoring disease progression and guiding clinical trials in PD
Silencing FOXA1 suppresses inflammation caused by LPS and promotes osteogenic differentiation of periodontal ligament stem cells through the TLR4/MyD88/NF-κB pathway
Get PDFHuman periodontal ligament stem cells (hPDLSCs) play a critical role in the regeneration of periodontal tissue. Forkhead box protein A1 (FOXA1) has been implicated in the inflammatory mechanisms of various diseases. However, the role of FOXA1 in periodontal inflammation and its effect on the osteogenic differentiation of hPDLSCs remains unclear. In this study, healthy tooth root-derived hPDLSCs were isolated, and flow cytometry was used to detect cell surface markers. Western blot and immunofluorescence analyses were performed to assess FOXA1 levels in different tissues. The levels of inflammatory factors were measured using Western blot and ELISA kits. Alkaline phosphatase (ALP) staining, alizarin red S staining, and Western blot were employed to evaluate the impact of FOXA1 silencing on the osteogenic differentiation of hPDLSCs. Finally, the protein levels in the Toll-like receptor 4 (TLR4)/Myeloid differentiation factor-88 (MyD88)/NF-κB pathway were analyzed using Western blot. Results showed that periodontal membrane tissues from patients with periodontitis exhibited a marked increase in FOXA1 levels. Lipopolysaccharide (LPS) treatment significantly upregulated FOXA1 expression in hPDLSCs, elevated inflammatory factor levels, and inhibited osteogenic differentiation. However, silencing FOXA1 mitigated the effects of LPS. Furthermore, LPS treatment activated the TLR4/MyD88/NF-κB pathway, while FOXA1 silencing impeded this activation. Notably, the application of the TLR4 agonist CRX-527 reversed the inhibitory effects of FOXA1 silencing on LPS-induced responses. In summary, silencing FOXA1 reduced cellular inflammation by inhibiting the TLR4/MyD88/NF-κB pathway and alleviated the suppressive effects of LPS on the osteogenic differentiation of hPDLSCs