Inje University

Silicone hydrogels offer high oxygen permeability but suffer from poor wettability. This study integrates a TRISS-based system (0-2.0 wt%) with a fixed PVP/NVP matrix (1.0/0.5 wt%) to enhance hydration-induced dimensional stability and surface properties. Fabricated via cast-molding, the lenses demonstrated that TRISS incorporation significantly enhances oxygen transport. Specifically, the 2.0 wt% TRISS formulation (S2.0) achieved an ~1.9-fold increase in oxygen-induced current (from 0.97 μA in pure-HEMA to 1.86 μA) while strongly suppressing hydration-induced swelling. To counter TRISS's inherent hydrophobicity, the PVP/NVP matrix acted as a vital compensatory mechanism, driving the equilibrium contact angle down to 56.04° and avoiding the severe hydrophobic plateau (93.79°) of the additive-free comparator. S2.0 maintained a robust oxygen response alongside improved wettability. In conclusion, this system defines a workable low-silicone design window accommodating up to 2.0 wt% TRISS without wettability loss or optical degradation (>97%). Crucially, by leveraging TRISS to mitigate swelling-induced mechanical stress and PVP/NVP to ensure stable wettability, this structurally robust hydrogel provides a highly viable foundational matrix for future smart contact lenses equipped with diagnostic micro-components.

Background: Rare digestive system cancers, including cholangiocarcinoma and other low-incidence malignancies, have limited treatment options and often harbor alterations in the RAS-RAF-MEK-ERK pathway.MEK inhibitors have shown preclinical activity, but clinical efficacy remains unclear.This systematic review and meta-analysis evaluated the therapeutic impact of MEK inhibitors in rare digestive cancers.Methods: Following PRISMA guidelines, MEDLINE, Cochrane Library, and ClinicalTrials.gov were searched for randomized phase II or III trials investigating MEK inhibitors in rare cancers.Data extraction and riskof-bias assessment were performed independently by two reviewers.Pooled odds ratios (OR) were calculated using randomor fixed-effects models depending on heterogeneity.Results: Total of 522 records screened, four phase II trials (n=210) met inclusion criteria, evaluating selumetinib, trametinib, and cobimetinib.MEK inhibitor therapy did not improve ORR compared with control (OR 0.93; 95% CI 0.35-2.48;I 2 =0%).No complete responses were reported.Most studies demonstrated high performance and detection bias due to open-label design, and overall certainty of evidence was rated low.Conclusions: MEK inhibitors showed limited clinical activity in rare digestive system cancers and the current evidence remains insufficient to confirm their efficacy as either monotherapy or in combination therapy.Future trials should incorporate biomarker-selected populations and rational combination strategies to overcome pathway resistance.

Heat-related illness (HRI) is increasing under climate change, particularly in humid regions. While heat alerts increasingly rely on apparent temperature (AT), evidence linking AT conditions to differential HRI risk by occupational status remains limited. We linked national emergency department (ED)-based HRI surveillance with daily national mean AT for June-September 2015-2024. We summarized bin-wise mean daily counts and estimated bin-specific incidence rate ratios (IRRs; reference = 24 °C) using Poisson regression. HRI increased non-linearly with AT in both groups, and occupational IRRs tended to be higher than non-occupational IRRs at warmer bins. At 31 °C, occupational IRR was 37.07 vs. 23.86 for non-occupational; at 33 °C, 104.75 vs. 72.25; and at 34 °C, 167.22 vs. 141.06. Workers may experience higher HRI risk under rising AT, underscoring the need for worker-centered prevention and heat-risk communication in humid climates.

WIB-801CE, a standardized Cordyceps militaris extract containing 7.0% cordycepin, suppresses platelet activation induced by thrombin, collagen, and adenosine diphosphate (ADP). As these agonists generate thromboxane A2 (TXA2), which amplifies platelet activation via a self-propagating feedback loop, blockade of TXA2-mediated signaling offers strong antithrombotic potential. TXA2-antagonistic effects were evaluated using U46619, a stable TXA2 analog. Platelet activation was assessed by fibrinogen binding to integrin αIIbβ3, aggregation, and phosphorylation of platelet-activating proteins-PI3K (Tyr458), Akt (Ser473), p38 MAPK (Thr180/Tyr182), ERK1 (Thr202/Tyr204), JNK1 (Thr183/Tyr185)-and inhibitory proteins-VASP (Ser157) and IP3RI (Ser1756)-via immunoblotting. Thrombin-induced fibrin clot retraction, cytotoxicity, coagulation parameters, and antioxidant capacity were also examined. WIB-801CE significantly inhibited U46619-induced fibrinogen binding to integrin αIIbβ3 and platelet aggregation, without inducing cytotoxicity or impairing hemostatic function. It also significantly downregulated the phosphorylation of platelet-activating proteins and upregulated the phosphorylation of platelet-inhibiting proteins. Additionally, WIB-801CE abolished thrombin-induced fibrin clot retraction and demonstrated antioxidant capacity. WIB-801CE disrupts TXA2-driven platelet activation and thrombus stabilization by selectively modulating phosphorylation of key signaling proteins at defined regulatory sites. These properties highlight its promise as a therapeutic candidate for thrombotic disorders with platelet hyperreactivity.

Foundation models (FMs) have become a paradigm shift in the field of artificial intelligence, allowing one large-scale pretrained model to be customized for a broad set of downstream tasks using very little task-specific data. These models, which include GPT, CLIP, BERT, and vision transformers, have altered the scope of transfer learning and multimodal understanding and are built on top of enormous datasets and self-supervised learning. The paper provides a broad view of the modern state of foundation models, with an emphasis on their technological foundation, training, and cross-domain use in fields like natural language processing, computer vision, healthcare, robotics and scientific discovery. We also explore the main opportunities that FMs offer, as well as state-of-the-art methods and techniques for the development of foundation models. we discuss their applications in natural language processing, computer vision, healthcare, etc. Furthermore, their limitations and challenges are also investigated. Lastly, future prospects are discussed so that professionals and scientists obtain a better understanding of the importance of foundation models for addressing their research goals.

The study aims to develop a physiologically-based pharmacokinetic (PBPK) model to quantitatively evaluate the role of ATP-binding cassette sub-family B member 1 (ABCB1) and ATP-binding cassette super-family G member 2 (ABCG2) in the drug-drug interaction (DDI) between rifampin and linezolid and to predict the impact of high-dose rifampin on linezolid pharmacokinetics (PK). We developed a PBPK model of linezolid and verified this using published clinical PK data. The built-in PK-SIM PBPK model for rifampin was used as a perpetrator model, which incorporate ABCB1 and ABCG2 transporter activity, along with inhibition and induction kinetic parameters. Using the developed PBPK models, linezolid PK was predicted when co-administered with rifampin and verified using published data. Based on the developed DDI model, linezolid exposure when co-administered with high-dose rifampin at steady state was predicted. The developed linezolid PBPK model had acceptable predictive performance for 36 different PK arms from 13 individual clinical studies. The PBPK-predicted DDI effect of standard dose rifampin on linezolid, with AUC and Cmax ratios of 0.77 and 0.87, respectively, aligned well with observed DDI ratio. PBPK simulations indicated that both ABCG2 and ABCB1 contributed to the DDI between linezolid and rifampin, with ABCB1 playing the major role in the interaction. Increasing the daily dose of rifampin from 10 mg/kg to 20-40 mg/kg resulted in a similar linezolid exposure. Our study suggested that ABCB1 is the primary transporter responsible for the interaction between rifampin and linezolid. The DDI effect of high-dose rifampin on linezolid plasma exposure is similar to that of standard-dose rifampin.