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Simple Summary: Porcine epidemic diarrhea virus (PEDV) infection causes severe diarrhea in piglets and is potentially lethal, eventually leading to economic losses in the pig industry. At present, the lack of whole-genome information and genetic evolution characteristics of PEDV strains in Yunnan has limited our understanding of PEDV epidemic variation. This is the first time that we have identified the complete genome of the Yunnan strain and explored its genetic evolutionary characteristics. This study may shed new light on the current PEDV infections and pave the way toward further control of PEDV infections in Yunnan. Porcine epidemic diarrhea virus (PEDV) is a member of the genus α-coronavirus and causes severe diarrhea in piglets, leading to enormous economic losses in the pig industry. To understand the epidemic variation of PEDV strains in Yunnan province, three PEDV strains (YN2021, YNLP 2022, and YNBS 2022) and one commercially available attenuated vaccine strain (Attenuated AJ1102-R) that were previously isolated were sequenced and compared with the representative PEDV strains. NJ phylogenetic analysis showed that YN2021 strain and Attenuated CV777 strain were clustered into GI-b subtype, while YNLP 2022 and YNBS 2022 belong to GII-b subtype, accompanying ZJCZ4 and Attenuated AJ1102-R. RDP analysis revealed that YNLP 2022 was a genome recombination from both GII-b strain PEDV-7C and GII-a strain YN1, of which the recombination region is in the range nt4994–7605. YNBS 2022 strain was another recombination originated from GII-b subtype strain 17GXZC-1ORF3c and GII-a subtype strain PEDV-CHZ, of which the counterpart is in the range nt16399–22326. The Yunnan strain of PEDV was analyzed for the first time from the whole-genome perspective, and comprehensive analysis showed that the Yunnan strains have high genetic variation. This study may shed new light on the current PEDV infections in Yunnan and pave the way toward further control of PEDV infections. [ABSTRACT FROM AUTHOR]

Flexible temperature sensors have diverse applications and a great potential in the field of temperature monitoring, including healthcare, smart homes and the automotive industry. However, the current flexible temperature sensor preparation generally suffers from process complexity, which limits its development and application. In this paper, a nickel oxide (NiO) flexible temperature sensor based on a low-temperature sintering technology is introduced. The prepared NiO flexible temperature sensor has a high-resolution temperature measurement performance and good stability, including temperature detection over a wide temperature range of (25 to 70 °C) and a high sensitivity performance (of a maximum TCR of −5.194%°C−1 and a thermal constant of 3938 K). The rapid response time of this temperature sensor was measured to be 2 s at 27–50 °C, which ensures the accuracy and reliability of the measurement. The NiO flexible temperature sensor prepared by electrohydrodynamic direct writing has a stable performance and good flexibility in complex environments. The temperature sensor can be used to monitor the temperature status of the equipment and prevent failure or damage caused by overheating. [ABSTRACT FROM AUTHOR]

Electronic skin (e-skin) aims to mimic human skin functions, which could profoundly affect medical care and robots. However, it still faces challenges in simultaneously detecting and distinguishing pressure and temperature signals in real-time. Herein, a simple and sustainable strategy is proposed to fabricate a reduced graphene oxide (rGO)-coated wood sponge (rGO@WS) for a multifunctional sensor with high pressure and temperature properties. The prepared porous wood sponge presents high elasticity and superior durability. The added rGO endows the sponge with high conductivity and excellent fatigue resistance. The proposed sensor can realize high sensitivity (1.18 kPa−1), a large pressure range (0.2–100 kPa), a low detection limit and can also detect the temperature with a minimal detection limit of 0.6 K and a response time of 0.8 s. Due to the different response principles of piezoresistive and thermoelectric effects, pressure sensing performance and temperature sensing performance are independent. Meanwhile, a 4 × 4 pixel sensing array based on the rGO@WS sensor is fabricated to serve as e-skin to detect and distinguish pressure and temperature signals. This work sheds new light on the development of wearable devices and e-skin for health care. [ABSTRACT FROM AUTHOR]

Blood, a ubiquitous and fundamental carbohydrate material composed of plasma, red blood cells, white blood cells, and platelets, has been playing an important role in biology, life science, history, and religious studies, while graphene has garnered significant attention due to its exceptional properties and extensive range of potential applications. Achieving environmentally friendly, cost-effective growth using hybrid precursors and obtaining high-quality graphene through a straightforward chemical vapor deposition process have been traditionally considered mutually exclusive. This study demonstrates that we can produce high-quality graphene domains with controlled thickness through a one-step growth process at atmospheric pressure using blood as a precursor. Raman spectroscopy confirms the uniformity of the blood-grown graphene films, and observing the half-integer quantum Hall effect in the measured devices highlights their outstanding electronic properties. This unprecedented approach opens possibilities for blood application, facilitating an unconventional route for graphene growth applications. [ABSTRACT FROM AUTHOR]

Accurate data acquisition from flexible sensors placed on deformable 3D freeform surfaces is of critical importance for many applications, such as wearable electronics, human‐machine interfaces, and soft robotics. However, the mechanical coupling between the sensor and the deformable subject surface to bending and stretching deformations can significantly reduce the accuracy of the acquired data. This study combines a polyimide (PI) micropore isolation layer (PIL) and serpentine electrodes with a flexible piezoresistive sensor to mitigate the issue of mechanical coupling. As a mechanical buffer to distribute the external pressure and reduce the strain concentration, the PIL can avoid the bending interference for bending curvature up to 256 m−1, while maintaining a high sensitivity of S > 21.5 kPa−1. The serpentine electrode design further allows the sensor to reliably acquire data in the presence of stretching up to 45% without cross‐talk. The versatility of the developed sensor is demonstrated in several human‐machine interaction scenarios, including gesture recognition and motion detection. The design strategies on materials and structures from this study can also be applied for the development of other flexible sensors with high sensitivity and low deformation interference to avoid the mechanical coupling between the sensor and the deformable surface. [ABSTRACT FROM AUTHOR]

Reliable, non-invasive, continuous monitoring of pulse and blood pressure is essential for the prevention and diagnosis of cardiovascular diseases. However, the pulse wave varies drastically among individuals or even over time in the same individual, presenting significant challenges for the existing pulse sensing systems. Inspired by pulse diagnosis methods in traditional Chinese medicine (TCM), this work reports a self-adaptive pressure sensing platform (PSP) that combines the fully printed flexible pressure sensor array with an adaptive wristband-style pressure system can identify the optimal pulse signal. Besides the detected pulse rate/width/length, "Cun, Guan, Chi" position, and "floating, moderate, sinking" pulse features, the PSP combined with a machine learning-based linear regression model can also accurately predict blood pressure such as systolic, diastolic, and mean arterial pressure values. The developed diagnostic platform is demonstrated for highly reliable long-term monitoring and analysis of pulse and blood pressure across multiple human subjects over time. The design concept and proof-of-the-concept demonstrations also pave the way for the future developments of flexible sensing devices/systems for adaptive individualized monitoring in the complex practical environments for personalized medicine, along with the support for the development of digital TCM., (© 2024 Wiley‐VCH GmbH.)

Objective: The accurate diagnosis of superficial lymphadenopathy is challenging. We aim to explore a non-invasive and accurate machine-learning method for distinguishing benign lymph nodes, lymphoma, and metastatic lymph nodes., Methods: The clinical data and ultrasound images of 160 patients with superficial lymphadenopathy (58 benign lymph nodes, 62 lymphoma, 40 metastatic lymph nodes) admitted to our hospital from January 2020 to November 2022 were retrospectively studied. Patients were randomly divided into a training set and test set according to the ratio of 6:4. Firstly, the radiomics features of each lymph node were extracted, and then a series of statistical methods were used to avoid over-fitting. Then, the gradient boosting machine(GBM) was used to build the model. The area under receiver(AUC) operating characteristic curve, precision, recall rate and F1 value were calculated to evaluate the effectiveness of the model., Results: Ten robust features were selected to build the model. The AUC values of benign lymph nodes, lymphoma and metastatic lymph nodes in the training set were 1.00, 0.98 and 0.99, and the AUC values of the test set were 0.96, 0.84 and 0.90, respectively., Conclusion: It was a reliable and non-invasive method for the differential diagnosis of lymphadenopathy based on the model constructed by machine learning., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)