Biomarker identification and risk prediction model development for differentiated thyroid carcinoma lung metastasis based on primary lesion proteomics.

PURPOSE: The rising global high incidence of differentiated thyroid carcinoma (DTC) has led to a significant increase in patients presenting with lung metastasis of DTC (LMDTC). This population poses a significant challenge in clinical practice, necessitating the urgent development of effective risk stratification methods and predictive tools for lung metastasis. EXPERIMENTAL DESIGN: Through proteomic analysis of large samples of primary lesion and dual validation employing parallel reaction monitoring and immunohistochemistry, we identified eight hub proteins as potential biomarkers. By expanding the sample size and conducting statistical analysis on clinical features and hub protein expression, we constructed three risk prediction models. RESULTS: This study identified eight hub proteins-SUCLG1/2, DLAT, IDH3B, ACSF2, ACO2, CYCS and VDAC2- as potential biomarkers for predicting DTC lung metastasis risk. We developed and internally validated three risk prediction models incorporating both clinical characteristics and hub protein expression. Our findings demonstrated that the combined prediction model exhibited optimal predictive performance, with the highest discrimination (AUC: 0.986) and calibration (Brier score: 0.043). Application of the combined prediction model within a specific risk threshold (0-0.97) yielded maximal clinical benefit. Finally, we constructed a nomogram based on the combined prediction model. CONCLUSIONS: As a large sample size study in lung metastatic DTC research, the identification of biomarkers through primary lesion proteomics and the development of risk prediction models integrating clinical features and hub protein biomarkers offer valuable insights for predicting DTC lung metastasis and establishing personalised treatment strategies.

MXene/PPy@PDMS sponge-based flexible pressure sensor for human posture recognition with the assistance of a convolutional neural network in deep learning.

The combination of flexible sensors and deep learning has attracted much attention as an efficient method for the recognition of human postures. In this paper, an in situ polymerized MXene/polypyrrole (PPy) composite is dip-coated on a polydimethylsiloxane (PDMS) sponge to fabricate an MXene/PPy@PDMS (MPP) piezoresistive sensor. The sponge sensor achieves ultrahigh sensitivity (6.8925 kPa-1) at 0-15 kPa, a short response/recovery time (100/110 ms), excellent stability (5000 cycles) and wash resistance. The synergistic effect of PPy and MXene improves the performance of the composite materials and facilitates the transfer of electrons, making the MPP sponge at least five times more sensitive than sponges based on each of the individual single materials. The large-area conductive network allows the MPP sensor to maintain excellent electrical performance over a large-scale pressure range. The MPP sensor can detect a variety of human body activity signals, such as radial artery pulse and different joint movements. The detection and analysis of human motion data, which is assisted by convolutional neural network (CNN) deep learning algorithms, enable the recognition and judgment of 16 types of human postures. The MXene/PPy flexible pressure sensor based on a PDMS sponge has broad application prospects in human motion detection, intelligent sensing and wearable devices.

Efficient Fabrication of TPU/MXene/Tungsten Disulfide Fibers with Ultra-Fast Response for Human Respiratory Pattern Recognition and Disease Diagnosis via Deep Learning.

Flexible wearable pressure sensors have received increasing attention as the potential application of flexible wearable devices in human health monitoring and artificial intelligence. However, the complex and expensive process of the conductive filler has limited its practical production and application on a large scale to a certain extent. This study presents a kind of piezoresistive sensor by sinking nonwoven fabrics (NWFs) into tungsten disulfide (WS2) and Ti3C2Tx MXene solutions. With the advantages of a simple production process and practicality, it is conducive to the realization of large-scale production. The assembled flexible pressure sensor exhibits high sensitivity (45.81 kPa-1), wide detection range (0-410 kPa), fast response/recovery time (18/36 ms), and excellent stability and long-term durability (up to 5000 test cycles). Because of the high elastic modulus of MXene and the synergistic effect between WS2 and MXene, the detection range and sensitivity of the piezoresistive pressure sensor are greatly improved, realizing the stable detection of human motion status in all directions. Meanwhile, its high sensitivity at low pressure allows the sensor to accurately detect weak signals such as weak airflow and wrist pulses. In addition, combining the sensor with deep-learning makes it easy to recognize human respiratory patterns with high accuracy, demonstrating its potential impact in the fields of ergonomics and low-cost flexible electronics.

Self-Adhesive, Anti-Freezing MXene-Based Hydrogel Strain Sensor for Motion Monitoring and Handwriting Recognition with Deep Learning.

Flexible strain sensors based on self-adhesive, high-tensile, super-sensitive conductive hydrogels have promising application in human-computer interaction and motion monitoring. Traditional strain sensors have difficulty in balancing mechanical strength, detection function, and sensitivity, which brings challenges to their practical applications. In this work, the double network hydrogel composed of polyacrylamide (PAM) and sodium alginate (SA) was prepared, and MXene and sucrose were used as conductive materials and network reinforcing materials, respectively. Sucrose can effectively enhance the mechanical performance of the hydrogels and improve the ability to withstand harsh conditions. The hydrogel strain sensor has excellent tensile properties (strain >2500%), high sensitivity with a gauge factor of 3.76 at 1400% strain, reliable repeatability, self-adhesion, and anti-freezing ability. Highly sensitive hydrogels can be assembled into motion detection sensors that can distinguish between various strong or subtle movements of the human body, such as joint flexion and throat vibration. In addition, the sensor can be applied in handwriting recognition of English letters by using the fully convolutional network (FCN) algorithm and achieved the high accuracy of 98.1% for handwriting recognition. The as-prepared hydrogel strain sensor has broad prospect in motion detection and human-machine interaction, which provides great potential application of flexible wearable devices.

Triboelectric Nanogenerators Based on Super-Stretchable Conductive Hydrogels with the Assistance of Deep-Learning for Handwriting Recognition.

Nowadays, wearable electronic devices are developing rapidly with the internet of things and human-computer interactions. However, there are problems such as low power, short power supply time, and difficulty in charging, leading to a limited range of practical applications. In this paper, a composite hydrogel composed of polyacrylamide, hydroxypropyl methylcellulose, and MXene (Ti3C2Tx) nanosheets was developed, which formed a stable double-chain structure by hydrogen bonding. The configuration endows the hydrogel with excellent properties, such as high strength, strong stretchability, excellent electrical conductivity, and high strain sensitivity. Based on these characteristics, a flexible multifunctional triboelectric nanogenerator (PHM-TENG) was prepared using the hydrogel as a functional electrode. The nanogenerator can collect biomechanical energy and convert it to 183 V with a maximum power density of 78.3 mW/m2. It is worth noting that PHM-TENG can be applied as a green power source for driving miniature electronics. Also, it can be used as an auto-powered strain sensor that distinguishes letters, enabling monitoring under small strain conditions. This work is anticipated to provide an avenue for the development of new intelligent systems for handwriting recognition.