Biomolecules & biomedicine最新文献

Hepatocellular carcinoma (HCC) exhibits a subtle onset, high incidence rates, and low survival rates, becoming a substantial threat to human health. Hence, it is crucial to discover fresh biomarkers and treatment targets for the early detection and management of HCC. CCK-8, scratch-wound, and transwell assays were used to evaluate the biological properties of HCC cell lines (Huh-7 and Hep3B). Bioinformatics analysis identified the downstream target mRNA of miR-145-5p as acyl-CoA synthetase long-chain family member 4 (ACSL4). RT-qPCR was used to test miR-145-5p and ACSL4 levels. Transwell chambers were used to co-incubate purified CD8+ T cells and HCC cells for 48 h, and the effect of CD8+ T cells on apoptosis in HCC cells was detected by flow cytometry. A subcutaneous graft tumor model was constructed, and ELISA kits were used to assess cytokine levels and CD8+ T cell activation markers. HCC cells showed a decline in miR-145-5p levels and a rise in ACSL4 levels. Overexpression of miR-145-5p hindered HCC cell proliferation, migration, and invasion, while stimulating CD8+ T cell activation. Conversely, overexpression of ACSL4 enhanced the malignant biological properties of HCC cells and reduced the effect of CD8+ T cells, while silencing ACSL4 had the opposite effect. miR-145-5p targeted and downregulated ACSL4, while overexpression of miR-145-5p weakened the promotion of HCC malignant progression caused by ACSL4 overexpression. Additionally, overexpression of miR-145-5p and silencing ACSL4 were effective in inhibiting tumor growth in vivo. In conclusion, miR-145-5p targets and downregulates ACSL4, leading to the inhibition of HCC malignant progression and preventing immune escape in HCC cells.

Morroniside (Mor) is a bioactive compound in Cornus officinalis with anti-inflammatory, neuroprotective and antioxidant properties. Prolonged use of the anesthetic sevoflurane (Sev) has been connected to the development postoperative cognitive dysfunction (POCD). This research aims to elucidate the mechanism of action of Mor to improve cognitive impairment. A model of cognitive dysfunction induced by Sev was established in aged mice and tested for behavioral analysis using the water maze experiment. Histopathological changes and neuronal apoptosis in mouse hippocampus were observed by hematoxylin and eosin (HE) staining, Nissl staining, and TUNEL staining. ELISA and qRT-PCR determined the levels of inflammatory factors. Phenotypic transformation of microglia in hippocampal tissue was assessed by immunofluorescence, flow cytometry, and qRT-PCR. The interaction between Mor and TLR4 was analyzed using molecular docking. Western blot identified the levels of apoptosis-related proteins, synapse-related proteins, and TLR4/NF-κB pathway proteins. Inhalation of Sev caused a notable reduction in learning and spatial memory in old mice, which was dose-dependently ameliorated by Mor. Mor inhibited neuroinflammation, modulated the polarization state of hippocampal microglia, promoted their polarization to M2 type, alleviated Sev-induced hippocampal tissue damage and neuronal apoptosis. Notably, Mor can bind well with TLR4 and reduce TLR4-positive expression. Mor blocked Sev-induced TLR4/NF-κB pathway activation in hippocampal tissues, and the TLR4 agonist CRX-527 attenuated the effect of Mor. In conclusion, Mor blocked the TLR4/NF-κB pathway, reducing hippocampal tissue damage and neuroinflammation caused by Sev, which in turn improving cognitive impairment in aged mice.

The red cell distribution width (RDW)/albumin ratio (RAR) is an inflammation-based prognostic biomarker. To date, its prognostic value in patients with pulmonary thromboembolism (PTE) has been investigated in only one study. This study aimed to assess the effect of RAR on mortality in patients with PTE. Patients admitted for PTE between 2017 and 2023 were retrospectively reviewed. The data collected included demographic information, comorbidities, clinical findings, RDW, albumin, troponin, D-dimer levels, and in-hospital and 30-day mortality outcomes. RAR was calculated by dividing the RDW by albumin. A total of 190 patients were included in the study, of whom 83 (43.7%) were male. The mean age was 63 years (range: 23-89), and the mean RAR was 4.48% ± 1.68% /g/dL. A positive correlation was observed between RAR and both age and troponin level, whereas inverse correlations were noted with systolic blood pressure (sBP), diastolic blood pressure (dBP), and oxygen saturation (SpO2). Using a cutoff value of 5.294 determined by ROC analysis, patients with RAR ≥ 5.294 had a significantly shorter mean survival time than those with RAR value < 5.294 (16.310 months vs 35.163 months; log-rank test, P < 0.001). Multivariate Cox regression analysis identified malignancy (hazard ratio [HR], 4.213; 95% confidence interval [CI], 1.103-16.090; P = 0.035) and RAR (HR: 1.295, 95% CI: 1.035-1.621, P = 0.024) as independent predictors of survival. In conclusion, an RAR value ≥ 5.294 was associated with significantly shorter survival, underscoring its potential utility as a prognostic marker in clinical practice for non-massive PTE.

Parkinson'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.

Blastocystis sp. is one of the most common intestinal protozoan parasites of humans worldwide and often has genetic polymorphisms. Due to its high prevalence and the possibility of potential transmission to humans, this study was carried out to investigate the prevalence of Blastocystis sp. and characterize its subtypes (genotypes) in southern Guizhou, China. A total of 548 fecal samples were collected from hospital patients for culture-based diagnosis. PCR products were sequenced and phylogenetically analyzed to identify subtypes and analyze their distribution. 43 positive cases of infection with Blastocystis sp. were detected, resulting in an overall prevalence of 7.85% (43/548). Seven subtypes were identified: ST3 (55.81%), ST1 (25.58%), ST7 (6.98%), ST5 (4.65%), ST2 (2.33%), ST4 (2.33%), and ST15 (2.33%). ST3 demonstrated the lowest intra-ST diversity, followed by ST1. Blastocystis sp. infection in southern Guizhou was caused by seven subtypes (ST1-ST5, ST7 and ST15) of the parasite, in which ST3 was the most common subtype, followed by ST1. The lowest intra-ST diversity of ST3 may be associated with substantial interhuman transmission in Guizhou. ST15 was found for the first time in humans, suggesting that it has the potential to be a zoonotic parasite. These findings have enhanced our understanding of the epidemiology and transmission of Blastocystis sp. in Southern Guizhou, China.

This study examines the association between serum Sestrin2 (SESN2) levels and cardiovascular disease (CVD) risk factors in healthy and diabetic adults, using data from the Qatar Biobank (QBB). A total of 844 participants were included, with 518 in the diabetic cohort and 326 in the healthy cohort. Clinical characteristics, cardiometabolic markers, and SESN2 levels were measured, and binomial logistic regression analyses were conducted to assess the associations between SESN2 and various health indices. Diabetic patients had significantly lower SESN2 levels compared to healthy controls (5.49 ± 5.94 vs 8.25 ± 7.57 ng/mL, P < 0.001). A significant negative correlation was observed between SESN2 and HbA1c (-0.19, P = 0.0006), insulin (-0.19, P = 0.0006), HOMA-IR (-0.17, P = 0.0024), C-peptide (-0.18, P = 0.0012), triglycerides (TG)/HDL ratio (-0.12, P = 0.0283), and the pulsatility index (PI) (-0.15, P = 0.006). In healthy individuals, higher SESN2 levels were associated with lower odds of elevated HbA1c (adjusted odds ratio [AOR] = 0.33, P = 0.00), insulin (AOR = 0.23, P = 0.00), HOMA-IR (AOR = 0.58, P = 0.06), C-peptide (AOR = 0.56, P = 0.04), and TG (AOR = 0.37, P = 0.03). In contrast, diabetic patients showed a positive correlation between SESN2 and insulin (0.15, P = 0.0005), HOMA-IR (0.11, P = 0.0106), and C-peptide (0.12, P = 0.0048). Participants in the highest SESN2 tertile had increased risks for high BMI (AOR = 1.96, P = 0.05), high TG (AOR = 1.57, P = 0.04), high NT-proBNP (AOR = 7.27, P = 0.01), and high fibrinogen (AOR = 1.92, P = 0.03). These findings suggest that while high SESN2 levels are cardioprotective in healthy individuals, they may indicate higher cellular stress in diabetics. Determining optimal SESN2 levels could help assess CVD risk, particularly in diabetic patients.

The risk factors for liver injury induced by valproic acid (VPA) are not well understood, and no predictive tool currently exists to identify patients at risk. This study aims to explore these risk factors and develop a predictive model. We collected medical data from patients treated with VPA between January 1, 2020, and October 31, 2023. Prescription sequence analysis was used to identify patients with suspected VPA-induced liver injury, and the Roussel Uclaf Causality Assessment Method was applied to confirm the diagnosis. Risk factors were analyzed using logistic regression, and a nomogram model was developed and evaluated. A total of 256 cases were included in the study: 64 in the VPA-induced liver injury group and 192 in the control group. The incidence of liver injury was 5.3%. Multivariate logistic regression analysis revealed that dysglycemia (odds ratio [OR] = 5.171; 95% confidence interval [CI]: 1.254-21.325), hyperlipidemia (OR = 4.903; 95% CI: 1.400-17.173), surgery (OR = 10.020; 95% CI: 1.737-57.805), and hypokalemia (OR = 10.407; 95% CI: 2.398-45.173) were significant independent risk factors for VPA-induced liver injury. The area under the receiver operating characteristic curve was 0.904 (95% CI: 0.860-0.947), indicating excellent model performance. The Hosmer-Lemeshow test yielded a P value of 0.2671, and the calibration plot slope was close to one, further supporting the model's accuracy. The findings suggest that patients with dysglycemia, hyperlipidemia, a history of surgery, and hypokalemia are at higher risk for VPA-induced liver injury. The nomogram model provides a reliable method for predicting the likelihood of liver injury in these patients.

Closed pelvic fractures can lead to severe complications, including hemodynamic instability (HI) and mortality. Accurate prediction of these risks is crucial for effective clinical management. This study aimed to utilize various machine learning (ML) algorithms to predict HI and death in patients with closed pelvic fractures and identify relevant risk factors. The retrospective study included 208 patients diagnosed with pelvic fractures and admitted to Suning Traditional Chinese Medicine Hospital between 2019 and 2023. Among these, 133 cases were identified as closed PFs. Patients with closed fractures were divided into a training set (n = 115) and a test set (n = 18). The training set was further stratified into two groups based on hemodynamic stability: Group A (patients with HI) and Group B (patients with hemodynamic stability). A total of 40 clinical variables were collected, and multiple machine learning algorithms were employed to develop predictive models, including logistic regression (LR), C5.0 Decision Tree (DT), Naive Bayes (NB), support vector machine (SVM), K-nearest neighbors (KNN), random Forest (RF), and artificial neural network (ANN). Additionally, factor analysis was performed to assess the interrelationships between variables. The RF and LR algorithms outperformed traditional methods-such as central venous pressure (CVP) and intra-abdominal pressure (IAP) measurements-in predicting HI. The RF model achieved an average under the ROC (AUC) of 0.92, with an accuracy of 0.86, precision of 0.81, and an F1 score of 0.87. The LR model had an average AUC of 0.82 but shared the same accuracy, precision, and F1 score as the RF model. Key risk factors identified included TILE grade, heart rate (HR), creatinine (CR), white blood cell count (WBC), fibrinogen (FIB), and lactic acid (LAC), with LAC levels >3.7 and an injury severity score (ISS) >13 as significant predictors of HI and mortality. In conclusion, the RF and LR algorithms are effective in predicting HI and mortality risk in patients with closed PFs, enhancing clinical decision-making and improving patient outcomes.

The incidence of atrial fibrillation (AF) increases with age and is particularly high in individuals with diabetes. Sodium-glucose cotransporter-2 inhibitors (SGLT2i), such as dapagliflozin, show promise in treating heart failure (HF) and reducing the risk of AF. Sirtuin 1 (SIRT1), a key enzyme in metabolic regulation, may be influenced by SGLT2i and play a role in the development of AF. This study investigates the relationship between dapagliflozin therapy and atrial tachyarrhythmia in diabetic cardiomyopathy, with a focus on the role of SIRT1. A streptozotocin (STZ)-induced diabetes mellitus (DM) rat model was used to assess AF across four groups: sham, STZ, STZ with dapagliflozin, and STZ with dapagliflozin + sirtinol (a SIRT1 inhibitor). Additionally, HL-1 cardiomyocytes were cultured under high glucose (HG) conditions and treated with dapagliflozin, with or without sirtinol. In the rat model, dapagliflozin improved atrial fibrosis and reduced AF inducibility and duration-effects that were partially reversed by sirtinol. These findings suggest that dapagliflozin may alleviate cardiac fibrosis and atrial arrhythmia by modulating SIRT1. In HL-1 cells under HG conditions, dapagliflozin reduced apoptosis, restored autophagy and mitophagy, and improved calcium channel activity. However, sirtinol negated these protective effects. Dapagliflozin helped normalize autophagy, mitophagy, and calcium handling, while sirtinol diminished its protective effects, highlighting the key role of SIRT1 in regulating calcium handling under HG conditions. Overall, SIRT1 plays a protective role in diabetic cardiomyopathy by reducing apoptosis, regulating autophagy and mitophagy, and modulating calcium channel activity. Dapagliflozin reduces AF duration and inducibility in the STZ model, likely through SIRT1 upregulation and calcium channel modulation.

Immune checkpoint inhibitors produce durable antitumor effects in various cancers, but not all patients respond. High tumor mutational burden (TMB) is a known predictor of clinical benefit. In this study, we focused on the MHC-I-dependent neoantigen presentation pathway to enhance predictive capabilities beyond TMB. Using pan-cancer immunogenomic analyses of somatic mutation data from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC), we analyzed 33 cancer types. Objective response rates (ORRs) to PD-1/PD-L1 inhibitors were evaluated in relation to immune characteristics, including TMB, neoantigen load, MHC-I gene expression, and CD8+ T cell fraction. Spearman’s rank correlation was used to assess these relationships. TMB showed the strongest correlation with ORR (r = 0.783, P = 2.17 × 10⁻⁵). However, integrating TMB, HLA-A expression, and CD8+ T cell fraction significantly improved predictive accuracy (r = 0.865, P = 1.80 × 10⁻⁶). Validation in external cohorts confirmed these findings, revealing notable differences in MHC-I pathway activity between responders and non-responders to immunotherapy. Our results demonstrate that the MHC-I antigen presentation pathway is strongly associated with response to PD-1/PD-L1 inhibitors. Importantly, combining antigen expression, processing, presentation, and recognition features provides superior predictive power compared to TMB alone. This integrated approach could improve treatment outcome predictions and advance personalized immunotherapy strategies.