Tumor Microenvironment-Responsive Hydrogel for Direct Extracellular ATP Imaging-Guided Surgical Resection with Clear Boundaries.

Most solid tumors are clinically treated using surgical resection, and the presence of residual tumor tissues at the surgical margins often determines tumor survival and recurrence. Herein, a hydrogel (Apt-HEX/Cp-BHQ1 Gel, termed AHB Gel) is developed for fluorescence-guided surgical resection. AHB Gel is constructed by tethering a polyacrylamide hydrogel and ATP-responsive aptamers together. It exhibits strong fluorescence under high ATP concentrations corresponding to the TME (100-500 µm) but shows little fluorescence at low ATP concentrations (10-100 nm) such as those in normal tissues. AHB Gel can rapidly (within 3 min) emit fluorescence after exposure to ATP, and the fluorescence signal only occurs at sites exposed to high ATP, resulting in a clear boundary between the ATP-high and ATP-low regions. In vivo, AHB Gel exhibits specific tumor-targeting capacity with no fluorescence response in normal tissue, providing clear tumor boundaries. In addition, AHB Gel has good storage stability, which is conducive to its future clinical application. In summary, AHB Gel is a novel tumor microenvironment-targeted DNA-hybrid hydrogel for ATP-based fluorescence imaging. It can enable the precise imaging of tumor tissues, showing promising application in fluorescence-guided surgeries in the future.

Association between triglyceride-glucose index and carotid atherosclerosis in patients with psoriatic arthritis.

OBJECTIVE: To investigate the association of the triglyceride-glucose (TyG) index with atherosclerotic risk among patients with PsA. METHODS: This cross-sectional study included 165 consecutive PsA patients receiving carotid ultrasonography with integrated TyG index, calculated as ln [fasting triglycerides (mg/dl) × fasting glucose (mg/dl)/2]. Logistic regression models were applied to analyse the association of TyG index as continuous variables and tertiles with carotid atherosclerosis and carotid artery plaque. Fully adjusted model included sex, age, smoking, BMI, comorbidities and psoriatic-related variables. RESULTS: Overall, PsA patients with carotid atherosclerosis had substantially higher TyG index than those without [8.82 (0.50) vs 8.54 (0.55), P = 0.002]. The frequency of carotid atherosclerosis was increased with increases in TyG index tertiles, showing 14.8%, 34.5%, 44.6% for tertile 1, 2 and 3, respectively (P = 0.003). Multivariate logistic analyses showed that each 1-unit increase in TyG index was significantly associated with prevalent carotid atherosclerosis [unadjusted odds ratio (OR) 2.65 (1.39-5.05); fully adjusted OR 2.69 (1.02-7.11)]. Compared with patients in tertile 1 of TyG index, the unadjusted and fully adjusted OR for occurrence of carotid atherosclerosis were 4.64 (1.85-11.60) and 5.10 (1.54-16.93) in patients in tertile 3. Similarly, higher prevalent carotid artery plaque was observed with increasing TyG index [unadjusted OR 3.11 (1.54-6.26); fully adjusted OR 3.61 (1.15-11.38)] or in tertile 3 vs tertile 1 [unadjusted OR 10.20 (2.83-36.82); fully adjusted OR 17.89 (2.88-111.11)]. Additionally, TyG index provided incremental predictive capacity beyond established risk factors, shown by an increase in discrimination ability (all P < 0.001). CONCLUSIONS: TyG index was positively correlated with the burden of atherosclerosis in PsA patients, independent of traditional cardiovascular risk factors and psoriatic-related factors. These findings suggest that TyG index may be a promising atherosclerotic marker for the PsA population.

Finding of the factors affecting the severity of COVID-19 based on mathematical models.

Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity.