Fibrinogen and tumors

Advancements in Wearable and Implantable BioMEMS Devices: Transforming Healthcare Through Technology

Wearable and implantable BioMEMSs (biomedical microelectromechanical systems) have transformed modern healthcare by enabling continuous, personalized, and minimally invasive monitoring, diagnostics, and therapy. Wearable BioMEMSs have advanced rapidly, encompassing a diverse range of biosensors, bioelectronic systems, drug delivery platforms, and motion tracking technologies. These devices enable non-invasive, real-time monitoring of biochemical, electrophysiological, and biomechanical signals, offering personalized and proactive healthcare solutions. In parallel, implantable BioMEMS have significantly enhanced long-term diagnostics, targeted drug delivery, and neurostimulation. From continuous glucose and intraocular pressure monitoring to programmable drug delivery and bioelectric implants for neuromodulation, these devices are improving precision treatment by continuous monitoring and localized therapy. This review explores the materials and technologies driving advancements in wearable and implantable BioMEMSs, focusing on their impact on chronic disease management, cardiology, respiratory care, and glaucoma treatment. We also highlight their integration with artificial intelligence (AI) and the Internet of Things (IoT), paving the way for smarter, data-driven healthcare solutions. Despite their potential, BioMEMSs face challenges such as regulatory complexities, global standardization, and societal determinants. Looking ahead, we explore emerging directions like multifunctional systems, biodegradable power sources, and next-generation point-of-care diagnostics. Collectively, these advancements position BioMEMS as pivotal enablers of future patient-centric healthcare systems.

Fibrinogen Oxidation and Thrombosis: Shaping Structure and Function

Fibrinogen, a pivotal plasma glycoprotein, plays an essential role in hemostasis by serving as the precursor to fibrin, which forms the structural framework of blood clots. Beyond coagulation, fibrinogen influences immune responses, inflammation, and tissue repair. Oxidative stress, characterized by an imbalance between reactive oxygen species (ROS) and antioxidants, induces fibrinogen oxidation, significantly altering its structure and function. This narrative review synthesizes findings from in vitro, ex vivo, and clinical studies, emphasizing the impact of fibrinogen oxidation on clot formation, architecture, and degradation. Oxidative modifications result in denser fibrin clots with thinner fibers, reduced permeability, and heightened resistance to fibrinolysis. These structural changes exacerbate prothrombotic conditions in cardiovascular diseases, diabetes, chronic inflammatory disorders and cancer. In contrast, "low-dose" oxidative stress may elicit protective adaptations in fibrinogen, preserving its function. The review also highlights discrepancies in experimental findings due to variability in oxidation protocols and patient conditions. Understanding the interplay between oxidation and fibrinogen function could unveil therapeutic strategies targeting oxidative stress. Antioxidant therapies or selective inhibitors of detrimental oxidation hold potential for mitigating thrombotic risks. However, further research is essential to pinpoint specific fibrinogen oxidation sites, clarify their roles in clot dynamics, and bridge the gap between basic research and clinical practice.

Label-based comparative proteomics of oral mucosal tissue to understand progression of precancerous lesions to oral squamous cell carcinoma

Introduction

Oral squamous cell carcinomas typically arise from precancerous lesions such as leukoplakia and erythroplakia. These lesions exhibit a range of histological changes from hyperplasia to dysplasia and carcinoma in situ, during their transformation to malignancy. The molecular mechanisms driving this multistage transition remain incompletely understood. To bridge this knowledge gap, our current study utilizes label based comparative proteomics to compare protein expression profiles across different histopathological grades of leukoplakia, erythroplakia, and oral squamous cell carcinoma samples, aiming to elucidate the molecular changes underlying lesion evolution.

Methodology

An 8-plex iTRAQ proteomics of 4 biological replicates from 8 clinical phenotypes of leukoplakia and erythroplakia, with hyperplasia, mild dysplasia, moderate dysplasia; along with phenotypes of well differentiated squamous cell carcinoma and moderately differentiated squamous cell carcinoma was carried out using the Orbitrap Fusion Lumos mass spectrometer. Raw files were processed with Maxquant, and statistical analysis across groups was conducted using MetaboAnalyst. Statistical tools such as ANOVA, PLS-DA VIP scoring, and correlation analysis were employed to identify differentially expressed proteins that had a linear expression variation across phenotypes of hyperplasia to cancer. Validation was done using Bioinformatic tools such as ClueGO + Cluepedia plugin in Cytoscape to extract functional annotations from gene ontology and pathway databases.

Results and discussion

A total of 2685 protein groups and 12,397 unique peptides were identified, and 61 proteins consistently exhibited valid reporter ion corrected intensities across all samples. Of these, 6 proteins showed linear varying expression across the analysed sample phenotypes. Collagen type VI alpha 2 chain (COL6A2), Fibrinogen β chain (FGB), and Vimentin (VIM) were found to have increased linear expression across pre-cancer phenotypes of leukoplakia to cancer, while Annexin A7 (ANXA7) was seen to be having a linear decreasing expression. Collagen type VI alpha 2 chain (COL6A2) and Annexin A2 (ANXA2) had increased linear expression across precancer phenotypes of erythroplakia to cancer. The mass spectrometry proteomics data have been deposited to the ProteomeXchanger Consortium via the PRIDE partner repository with the data set identifier PXD054190. These differentially expressed proteins mediate cancer progression mainly through extracellular exosome; collagen-containing extracellular matrix, hemostasis, platelet aggregation, and cell adhesion molecule binding.

Conclusion

Label-based proteomics is an ideal platform to study oral cancer progression. The differentially expressed proteins provide insights into the molecular mechanisms underlying the progression of oral premalignant lesions to malignant phenotypes. The study has translational value for early detection, risk stratification, and potential therapeutic targeting of oral premalignant lesions and in its prevention to malignant forms.

Clonorchis sinensis infection contributes to hepatocellular carcinoma progression via enhancing angiogenesis

BACKGROUND

Clonorchis sinensis (C. sinensis) infection plays an important role in the progression of hepatocarcinogenesis. However, its specific role in HCC progression remains unclear. This study aimed to investigate whether C. sinensis contributes to angiogenesis in HCC.

METHODS

A comprehensive clinical analysis was conducted on 947 HCC patients, divided into two groups: C. sinensis (-) HCC and C. sinensis (+) HCC. Kaplan-Meier survival curves and log-rank tests were utilized to assess survival outcomes. Microvessel density (MVD) was evaluated through CD34 immunohistochemistry on hepatectomy specimens. A chemistry analyzer and blood analyzer were employed to measure the concentration of circulating angiogenesis-related biomarkers. Quantitative reverse transcription-PCR (qRT-PCR) was used to analyze the expression of angiogenesis-related genes (CD34, Ang1, Ang2, VEGF, PDGF) in HCC tissues.

RESULTS

C. sinensis infection was associated with poorer outcomes in HCC patients, with significantly shorter overall survival (OS) (p = 0.014) and recurrence-free survival (RFS) (p<0.001). Notably, C. sinensis infection led to an upregulation of MVD in HCC tissues (p = 0.041). C. sinensis (+) HCC patients exhibited significantly higher levels of circulating angiogenesis-related biomarkers, including MONO (p = 0.004), EOSO (p < 0.001), C3 (p = 0.001), FIB (p = 0.010), PLT (p = 0.003), LDH (p = 0.004), GLDH (p = 0.003), compared to C. sinensis (-) HCC patients. Moreover, qRT-PCR analysis revealed that most angiogenesis-related genes were overexpressed in patients with C. sinensis infection.

CONCLUSION

C. sinensis infection is closely associated with inflammatory responses and may promote metabolic reprogramming in HCC, thereby enhancing its malignant characteristics.

Establishment and validation of a prediction model for gastric cancer with perineural invasion based on preoperative inflammatory markers

Background

Gastric cancer (GC) is a prevalent malignant tumor of the digestive system, characterized by a poor prognosis and high recurrence rate. Perineural invasion (PNI), the neoplastic infiltration of nerves, is a significant predictor of survival outcome in GC. Accurate preoperative identification of PNI could facilitate patient stratification and optimal preoperative treatment. We therefore established and validated a preoperative risk assessment model for GC patients with PNI.

Methods

We collected data from 1,195 patients who underwent surgical resection at our hospital between October 2020 and December 2023, with PNI confirmed by pathological examination. We gathered laboratory data, including blood cell count, blood type, coagulation index, biochemical indexes, and tumor markers. Eligible patients were randomly divided into a training set and a testing set at a ratio of 7:3. The important risk factors of PNI were evaluated by random forest package in RStudio. Receiver operating characteristic-area under the curve (ROC-AUC) analysis was used to evaluate the discriminatory ability of the factors for PNI. Univariate and multivariate logistic regression analyses were utilized to verity independent risk factors for patients with PNI, and the logistic regression model and nomogram were constructed based on the results. Calibration curve and decision curve analysis (DCA) were conducted to assess the predictive model. Finally, we verified the prediction equation model using the testing set.

Results

In the training set, 416 GC patients were pathologically diagnosed with PNI. The top 5 important risk factors for PNI were identified as carcinoembryonic antigen (CEA), fibrinogen-to-lymphocyte ratio (FLR), D-dimer, platelet-to-lymphocyte ratio (PLR), and carbohydrate antigen 19-9 (CA19-9), with optimal cut-off values of 3.89 ng/mL, 2.08, 0.24 mg/L, 122.37, and 14.85 U/mL, respectively. Multivariate logistic regression analysis confirmed that CEA, FLR, D-dimer, PLR, CA19-9, and CA72-4 as independent risk factors for PNI (P<0.05). We formulated the following predictive equation: Logit(P) = -1.211 + 0.695 × CEA + 0.546 × FLR + 0.686 × D-dimer + 0.653 × PLR + 0.515 × CA19-9 + 0.518 × CA72-4 (χ2=105.675, P<0.001). The model demonstrated an ROC-AUC value of 0.719 [95% confidence interval (CI): 0.681-0.757] in the training set, with a sensitivity of 68.51% and a specificity of 67.60%. The ROC-AUC value was 0.791 (95% CI: 0.750-0.831) in the testing set (sensitivity: 69.57%, specificity: 56.41%). Calibration curve and DCA confirmed that the model has good discrimination and accuracy.

Conclusions

We successfully established and validated a prediction model for GC patients with PNI based on hematological indicators, hoping that this model can provide an adjunctive tool for predicting PNI in clinical work.