To realize the potential applications of stretchable sensors in the field of wearable health monitoring, it is essential to develop a stable sensing device with robust electrical and mechanical ...properties in the present of varying external conditions. Herein, we demonstrate a stretchable temperature sensor with the elimination of strain-induced interference via geometric engineering of the free-standing stretchable fibers (FSSFs) of reduced graphene oxide/polyurethane composite. The FSSFs were formed in serpentine structures and enabled the implementation of a strain-insensitive stretchable temperature sensor. On the basis of the controlled reduction time of graphene oxide, we can modulate the response and thermal index of the device. These results are attributed to the variation in the density of oxygen-containing functional groups in the FSSFs, which affect the hopping charge transport and thermal generation of excess carriers. The FSSF temperature sensor yields increased responsivity (0.8%/°C), stretchability (90%), sensing resolution (0.1 °C), and stability in response to applied stretching (±0.37 °C for strains ranging from 0 to 50%). When the sensor is sewn onto a stretchable bandage and attached to the human body, it can detect the temperature changes of the human skin during different body motions in a continuous and stable manner.
A conformal patch biosensor that can detect biomolecules is one promising technology for wearable sweat glucose self-monitoring. However, developing such a patch is challenging because conferring ...stretchability to its components is difficult. Herein, we demonstrate a platform for a nonenzymatic, electrochemical sensor patch: a wrinkled, stretchable, nanohybrid fiber (WSNF) in which Au nanowrinkles partially cover the reduced graphene oxide (rGO)/polyurethane composite fiber. The WSNF has high electrocatalytic activity because of synergetic effects between the Au nanowrinkles and the oxygen-containing functional groups on the rGO-supporting matrix which promote the dehydrogenation step in glucose oxidation. The WSNF offers stretchability, high sensitivity, low detection limit, high selectivity against interferents, and high ambient-condition stability, and it can detect glucose in neutral conditions. If this WSNF sensor patch were sewn onto a stretchable fabric and attached to the human body, it could continuously measure glucose levels in sweat to accurately reflect blood glucose levels.
Abstract CAP treatment is likely to be of benefit in wound healing. In a clinical study, 20 laser lesions in five individuals have been treated with argon plasma 10, 30 or three times for 10 s, with ...untreated as control. The scar formation was followed for 10 days, six and 12 months. In early stages of wound healing, plasma treatment seems to support the inflammation needed for tissue recovery. In later stages, plasma treatment shows better results in terms of avoiding post-traumatic skin disorders. Plasma treatment shows superior aesthetics during scar formation. No precancerous skin features occurred up to 12 months.
Objectives
Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical ...illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients.
Methods
An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19.
Results
A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients’ to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (
p
< 0.0001).
Conclusions
Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment.
Key Point
• AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.
Fiber‐based sensors integrated on textiles or clothing systems are required for the next generation of wearable electronic platforms. Fiber‐based physical sensors are developed, but the development ...of fiber‐based temperature sensors is still limited. Herein, a new approach to develop wearable temperature sensors that use freestanding single reduction graphene oxide (rGO) fiber is proposed. A freestanding and wearable temperature‐responsive rGO fiber with tunable thermal index is obtained using simple wet spinning and a controlled graphene oxide reduction time. The freestanding fiber‐based temperature sensor shows high responsivity, fast response time (7 s), and good recovery time (20 s) to temperature. It also maintains its response under an applied mechanical deformation. The fiber device fabricated by means of a simple process is easily integrated into fabric such as socks or undershirts and can be worn by a person to monitor the temperature of the environment and skin temperature without interference during movement and various activities. These results demonstrate that the freestanding fiber‐based temperature sensor has great potential for fiber‐based wearable electronic platforms. It is also promising for applications in healthcare and biomedical monitoring.
A freestanding, wearable, fiber‐based temperature sensor is presented, which can easily be integrated into clothing such as socks and undershirts and can be worn by a person to monitor the temperature of the environment around the person and the temperature of the person without interference from movement and activity. This sensing device has great potential for next‐generation wearable electronics.
Extranodal marginal zone lymphoma (MZL) arises in a number of epithelial tissues, including the stomach, salivary gland, lung, small bowel, thyroid, ocular adnexa, skin, and elsewhere. It has also ...been called low-grade B-cell lymphoma of mucosa-associated lymphoid tissue (MALT). MALT lymphoma predominantly occurs in adults and is rare in children.
We report a case of MALT lymphoma involving the stomach, which is the most common subtype, in a 12-year-old girl. Initially, the patient relapsed after antibiotic therapy but achieved successful treatment subsequently through irradiation.
eradication therapy should be given to all patients with gastric MZL, irrespective of stage. In patients who do not respond to antibiotic therapy, treatment options such as irradiation and systemic cancer therapies should be considered, depending on the disease stage.
Phytochemical investigation of the whole plants of Lycopodiella cernua resulted in the isolation and identification of three new compounds (1-3), namely lycocernuaside E (1), lycernuic ketone F (2), ...and lycernuic B (3) and 12 known ones (4-15). Their chemical structures were established based on 1 D/2D NMR spectroscopic and HR-ESI-MS data analyses. Compounds 5, 12, and 13 displayed NO inhibitory effects in LPS-stimulated BV2 cells, with IC
50
values of 21.2 ± 1.1, 28.5 ± 1.4, and 21.9 ± 1.1 µM, respectively. In addition, cytotoxic activity of the isolated compounds against MCF7 (breast carcinoma), HepG2 (hepatocarcinoma), and SK-Mel2 (melanoma) cancer cell lines were also reported.
Objectives
There currently lacks a noninvasive and accurate method to distinguish benign and malignant ovarian lesion prior to treatment. This study developed a deep learning algorithm that ...distinguishes benign from malignant ovarian lesion by applying a convolutional neural network on routine MR imaging.
Methods
Five hundred forty-five lesions (379 benign and 166 malignant) from 451 patients from a single institution were divided into training, validation, and testing set in a 7:2:1 ratio. Model performance was compared with four junior and three senior radiologists on the test set.
Results
Compared with junior radiologists averaged, the final ensemble model combining MR imaging and clinical variables had a higher test accuracy (0.87 vs 0.64,
p
< 0.001) and specificity (0.92 vs 0.64,
p
< 0.001) with comparable sensitivity (0.75 vs 0.63,
p
= 0.407). Against the senior radiologists averaged, the final ensemble model also had a higher test accuracy (0.87 vs 0.74,
p
= 0.033) and specificity (0.92 vs 0.70,
p
< 0.001) with comparable sensitivity (0.75 vs 0.83,
p
= 0.557). Assisted by the model’s probabilities, the junior radiologists achieved a higher average test accuracy (0.77 vs 0.64, Δ = 0.13,
p
< 0.001) and specificity (0.81 vs 0.64, Δ = 0.17,
p
< 0.001) with unchanged sensitivity (0.69 vs 0.63, Δ = 0.06,
p
= 0.302). With the AI probabilities, the junior radiologists had higher specificity (0.81 vs 0.70, Δ = 0.11,
p
= 0.005) but similar accuracy (0.77 vs 0.74, Δ = 0.03,
p
= 0.409) and sensitivity (0.69 vs 0.83, Δ = -0.146,
p
= 0.097) when compared with the senior radiologists.
Conclusions
These results demonstrate that artificial intelligence based on deep learning can assist radiologists in assessing the nature of ovarian lesions and improve their performance.
Key Points
• Artificial Intelligence based on deep learning can assess the nature of ovarian lesions on routine MRI with higher accuracy and specificity than radiologists.
• Assisted by the deep learning model’s probabilities, junior radiologists achieved better performance that matched those of senior radiologists.
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