Archives of Physiology and Biochemistry最新文献

The NLRP3 protein plays a crucial role in the occurrence and development of various diseases. Research has shown that arthritis caused by sports injuries is closely related to the activation of NLRP3. This study aims to explore the molecular structure of NLRP3 protein and its role in sports induced arthritis, and evaluate the effectiveness of nutritional and exercise interventions in combating sports induced arthritis. In sports induced arthritis, NLRP3 expression is significantly upregulated, indicating its importance in the inflammatory process. Nutritional intervention showed effective inhibition of NLRP3 inflammasome activation. Stability intervention analysis shows that long-term adherence to exercise and nutritional interventions can sustainably reduce the activation level of NLRP3. Therefore, appropriate nutrition and exercise interventions can significantly reduce the activation of NLRP3, alleviate joint inflammation, and help protect joint health. It is recommended to introduce such intervention strategies in clinical practice to improve the consequences of joint injuries.

Post-thrombotic syndrome (PTS), a chronic complication of deep vein thrombosis, causes pain, swelling, and ulcers, impairing quality of life. This study explores the role of lipid peroxidation and chronic inflammation in PTS. Oxidative stress and chronic inflammation damage the endothelium, increase venous pressure, and induce fibrosis, driving PTS progression. A biomarker-based ELISA approach is proposed to measure plasma lipid peroxidation products (e.g. MDA) and pro-inflammatory cytokines (e.g. IL-6, TNF-α). This method detects early biochemical changes in post-DVT patients and tracks PTS risk. Elevated oxidative and inflammatory markers are strongly linked to PTS, supporting their use as diagnostic and therapeutic targets. Screening may improve prediction (98.7%), prevention (97.3%), and management (99.4%), while reducing MDA (95.6%), IL-6 (93.4%), and TNF-α (97.45%). Observational NHS data support symptom-biomarker correlations. Preliminary values for MDA (14.3 ± 4.1 nmol/mL), IL-6 (2.3 ± 1.1 pg/mL), and TNF-α (1.5 ± 0.7 pg/mL) are included to clearly present and support the research hypothesis.

Context: With the continuous development of sports science, the importance of lipid metabolism control in athletes' physical training and evaluation has become increasingly prominent. Objective: Lipid metabolism, as a key link in human energy metabolism, its reasonable regulation has a profound impact on athletes' endurance, physical recovery and sports performance. Methods: By rationally regulating lipid metabolism, the endurance and exercise efficiency of athletes can be effectively enhanced. In the design of the physical fitness assessment system, the addition of the lipid metabolism monitoring module makes the physical fitness assessment more comprehensive and scientific. Results: This system can monitor the lipid metabolism status of athletes in real time and conduct comprehensive analysis in combination with other physical fitness indicators, providing more accurate feedback information for coaches and athletes. Conclusion: The application of artificial intelligence algorithms has further enhanced the accuracy and personalisation of physical fitness assessment.

Context: Cisplatin is a widely utilised chemotherapeutic agent for cancer therapy, but various systemic toxicities limit its effectiveness.

Objective: This study aimed to assess the hepatoprotective properties expressed by gallic acid (GA) and gamma-irradiation synthesized cerium oxide nanoparticles (CONPs) in response to hepatic damage produced by cisplatin in albino rats.

Materials and methods: Serum AST and ALT activities and levels of MDA, TAC, NF‑kB, TNF‑α, and TGF‑β were determined in hepatic tissue, along with histopathological examination.

Results: The executive impact of cisplatin led to a notable rise in the serum activity of hepatic enzymes AST and ALT. Conversely, in the groups receiving treatment with either or both GA and CONPs, the liver function enzymes exhibited a decline in their activity. In addition, in the hepatotoxicity models, the levels of hepatic MDA were significantly increased, accompanied by a reduction of hepatic TAC. While administration of GA, CONPs or their combination to cisplatin-injected rats resulted in a noteworthy reduction in MDA level. Conversely, the hepatic TAC level increased compared to the group that received cisplatin. The hepatic tissue architecture in rats exposed to cisplatin was found to undergo significant alterations. Furthermore, the cisplatin induced overexpression of NF-kB, TNF-α, and TGF-β. The hepatic histopathological changes observed in rats induced with cisplatin were significantly attenuated after pre-treatment with GA, CONPs, and their combination. GA, CONPs, and their co-administration resulted in reducing the levels of NF-κB, TNF-α and TGF-β compared to the group received cisplatin.

Discussion and conclusion: In summary, GA and CONPs synthesized by gamma-irradiation resulted in a noteworthy reduction of liver damage caused by cisplatin exposure. Their potent antioxidant and immunoprotective properties were cited as the cause of this phenomenon.

Introduction: Brain Tumour (BT) is characterised by the uncontrolled proliferation of the cells within the brain which can result in cancer. Detecting BT at the early stage significantly increases the patient's survival chances. The existing BT detection methods often struggle with high computational complexity, limited feature discrimination, and poor generalisation.

Methods: To mitigate these issues, an effective brain tumour detection and segmentation method based on A hybrid network named MobileNet- Deep Batch-Normalized eLU AlexNet (M-DbneAlexnet) is developed based on Magnetic Resonance Imaging (MRI). The image enhancement is done by Piecewise Linear Transformation (PLT) function. BT region is segmented Transformer Brain Tumour Segmentation (TransBTSV2). Then feature extraction is done. Finally, BT is detected using M-DbneAlexnet model, which is devised by combining MobileNet and Deep Batch-Normalized eLU AlexNet (DbneAlexnet).Results: The proposed model achieved an accuracy of 92.68%, sensitivity of 93.02%, and specificity of 92.85%, demonstrating its effectiveness in accurately detecting brain tumors from MRI images.

Discussion: The proposed model enhances training speed and performs well on limited datasets, making it effective for distinguishing between tumor and healthy tissues. Its practical utility lies in enabling early detection and diagnosis of brain tumors, which can significantly reduce mortality rates.

Objective: Our study aims to examine the effect of leptin (LEP) (-2548 G > A) (rs7799039) and leptin receptor (LEPR) (668 A > G) (rs1137101) gene polymorphisms on the etiopathogenesis of bipolar disorder (BD) by comparing them with a control group, as well as their association with depressive episodes featuring atypical symptoms.

Methods: The study included a sample of 103 individuals with BD and 103 healthy volunteers. Gene polymorphisms were identified from DNA samples through polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP).

Results: The distribution of LEPR genotypes was significantly different between BD patients and healthy controls. Specifically, the homozygous LEPR GG genotype was more common in the control group than in BD patients. Additionally, genotype frequency distributions among BD patients varied significantly based on the presence of atypical depressive episodes. The LEPR AA and LEP GG genotypes were significantly more common among patients with a history of atypical depression. Logistic regression showed that the LEP polymorphism and Hamilton Depression Rating Scale (HAM-D) score predict atypical depression in BD.

Conclusion: In summary, while the LEPR polymorphism is linked to BD, the LEP polymorphism and symptom severity appear to predict atypical depressive episodes.

Purpose: Cisplatin (CP) is a chemotherapeutic drug that can produce toxic effects in many organs of the body including kidney. Thus, the current trial was executed for 30 days to assess the alleviative effects of AST to avert CP instigated renal injuries.

Materials and methods: Forty-eight male albino rats were allocated into four groups including control, CP (10 mg/kg), CP+AST (10 mg/kg + 50mg/kg) and only AST (50 mg/kg). On the completion of trial, the rats were dissected and further analyses were performed.

Results: CP intoxication increased the expressions of pro-apoptotic markers (Caspase-3 and Bax) while reducing the expressions of Nrf-2 and antiapoptotic marker (Bcl-2) and lowering the expressions and activities of antioxidant enzymes i.e., GPx, GSR, CAT, HO-1, GST, SOD and GSH contents. Furthermore, CP intoxication elevated the levels of oxidative stress markers (MDA and ROS) and inflammatory markers (IL-6, IL-1 β, TNF-α, NF-kB and COX-2). CP exposure also disrupted the levels of renal function markers and renal histology. However, AST treatment ameliorated all the renal alterations induced by CP-exposure.

Conclusion: Therefore, AST protected the renal tissues from CP-instigated damages due to its antioxidant and reno-protective potential.

Objectives: Lung is one of the vital organs that is affected by the ageing process. Searching for natural antioxidants is mandatory to boost healthier longevity. Accordingly, we sought to explore the probable protective effect of Coenzyme Q10 (Q10) on experimentally induced lung ageing and study the supposed involved mechanistic pathways.

Methods: Rats were allocated into groups; control, D-galactose (D-gal), D-gal + Q10 and D-gal + Q10+ LY294002 (LY; phosphatidylinositol 3-kinases (PI3K)/protein kinase B (AKT) inhibitor). Sera and lung tissues were gathered for evaluating markers of oxidative stress, inflammation, fibrosis, and cell senescence by different methods.Immunohistochemistry for senescence associated beta galactosidase (SA-βGal), Capsase-3, and P53 were also evaluated.

Results: Induction of lung ageing resulted in deleterious lung alterations which were ameliorated by Q10; however, its protective effect was abolished by co-administration of LY.

Conclusion: Q10 secured the lung against the ageing process via its antioxidant, anti-inflammatory, and anti-apoptotic effects through the activation of the PI3K/AKT/Nrf-2 pathway.

Context: Elevated postprandial glucose levels can help identify individuals with normal glucose tolerance who are at high risk of developing type 2 diabetes and cardiovascular complications.

Objective: This study aimed to investigate the correlation between 1-hour postprandial blood glucose (1hPG) and chronic complications of T2DM, including macrovascular and microvascular diseases.

Materials and methods: A total of 194 patients diagnosed with T2DM were recruited and classified into complication and non-complication groups. On the basis of the 1hPG cut-off point of 8.6 mmol/L, these patients were divided into three groups (1hPG ≥ 11.1 mmol/L, 8.6 mmol/L ≤ 1hPG < 11.1 mmol/L and 1hPG < 8.6 mmol/L). The incidence of T2DM-related chronic complications was compared among the three groups. Receiver operating characteristic (ROC) curve and logistic regression analysis were used to evaluate the predictive capability of 1hPG for T2DM-related chronic complications.

Results: A higher 1hPG level was found in patients affected by T2DM combined with chronic complications than those without complications. As 1hPG level decreased, the incidence rate of chronic complications in patients with T2DM decreased, which was indicative of a positive correlation between them. According to ROC analysis, FPG, 1hPG, and 2hPG could assist in predicting occurrence of T2DM-related chronic complications, while 1hPG had stronger predictive value. More importantly, logistic regression analysis further demonstrated that increased 1hPG level was an independent risk factor for chronic complications of T2DM.

Discussion and conclusion: These findings indicate that an elevated 1hPG level may be associated with an increased risk of chronic complications of T2DM.

Lung diseases represent one of the most prevalent health challenges globally, necessitating accurate diagnosis to improve patient outcomes. This work presents a novel deep learning-aided lung disease classification framework comprising three key phases: image acquisition, segmentation, and classification. Initially, chest X-ray images are taken from standard datasets. The lung regions are segmented using an Adaptive Recurrent Residual U-Net (AR2-UNet), whose parameters are optimised using Enhanced Pufferfish Optimisation Algorithm (EPOA) to enhance segmentation accuracy. The segmented images are processed using "Attention-based Densenet with Long Short Term Memory(ADNet-LSTM)" for robust categorisation. Investigational results demonstrate that the proposed model achieves the highest classification accuracy of 93.92%, significantly outperforming several baseline models including ResNet with 90.77%, Inception with 89.55%, DenseNet with 89.66%, and "Long Short Term Memory (LSTM)" with 91.79%. Thus, the proposed framework offers a dependable and efficient solution for lung disease detection, supporting clinicians in early and accurate diagnosis.