A Wearable Electrochemical Biosensor Utilizing Functionalized TiCT MXene for the Real-Time Monitoring of Uric Acid Metabolite.

Wearable, noninvasive sensors enable the continuous monitoring of metabolites in sweat and provide clinical information related to an individuals health and disease states. Uric acid (UA) is a key indicator highly associated with gout, hyperuricaemia, hypertension, kidney disease, and Lesch-Nyhan syndrome. However, the detection of UA levels typically relies on invasive blood tests.

Survival, treatment pattern, and treatment outcome in patients with cervical cancer metastatic to distant lymph nodes.

Background: Cervical cancer with nodal involvement beyond the pelvis was considered as distant nodal metastasis in the previous International Federation of Gynecology and Obstetrics staging system. With the improvement of cancer-directed therapies, some of these patients can receive curative treatment. Classifying them as distant metastasis may result in underestimation of their prognosis as well as undertreatment.

Superhydrophobic Functionalized TiCT MXene-Based Skin-Attachable and Wearable Electrochemical pH Sensor for Real-Time Sweat Detection.

Sweat pH is a critical indicator for evaluating human health. With the extensive attention on the wearable and flexible biosensing devices, the technology for the monitoring of human sweat can be realized. In this study, a sensitive, miniaturized, and flexible electrochemical sweat pH sensor was developed for the continuous and real-time monitoring of the hydrogen-ion concentration in human sweat.

Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest.

Objective: To compare the performance of a deep learning (DL)-based method for diagnosing pulmonary nodules compared with radiologists' diagnostic approach in computed tomography (CT) of the chest.

Materials And Methods: A total of 150 pathologically confirmed pulmonary nodules (60% malignant) assessed and reported by radiologists were included. CT images were processed by the proposed DL-based method to generate the probability of malignancy (0-100%), and the nodules were divided into the groups of benign (0-39.

Development and validation of a clinically applicable deep learning strategy (HONORS) for pulmonary nodule classification at CT: A retrospective multicentre study.

Purpose: To propose a practical strategy for the clinical application of deep learning algorithm, i.e., Hierarchical-Ordered Network-ORiented Strategy (HONORS), and a new approach to pulmonary nodule classification in various clinical scenarios, i.

Prognostic value of different metastatic sites for patients with FIGO stage IVB endometrial cancer after surgery: A SEER database analysis.

Objective: This study aimed to investigate the association between different metastatic sites and survival in endometrial cancer (EC) patients with International Federation of Gynecology and Obstetrics (FIGO) stage IVB disease.

Methods: FIGO stage IVB patients with EC were selected from the surveillance, epidemiology, and end results database. Overall survival (OS) and cause-specific survival (CSS) were analyzed with Kaplan-Meier analysis and log-rank tests.

Comprehensive lymphadenectomy and survival prediction in uterine serous cancer patients after surgery: A population-based analysis.

Objective: Evidence on uterine serous cancer (USC) prognosis has been limited and inconclusive. We aim to explore the survival benefits of comprehensive lymphadenectomy in USC patients after surgery and develop a prognostic nomogram to predict survival.

Methods: USC patients who had undergone hysterectomy between 2010 and 2015 were identified from Surveillance, Epidemiology and End Results (SEER) database.