Mcclearyforbes8401

To determine whether interrupting sitting with brief bouts of simple resistance activities (SRAs) at different frequencies improves postprandial glucose, insulin, and triglycerides in adults with medication-controlled type 2 diabetes (T2D).

Participants (

= 23, 10 of whom were female, with mean ± SD age 62 ± 8 years and BMI 32.7 ± 3.5 kg · m

) completed a three-armed randomized crossover trial (6- to 14-day washout) sitting uninterrupted for 7 h (SIT), sitting with 3-min SRAs (half squats, calf raises, gluteal contractions, and knee raises) every 30 min (SRA3), and sitting with 6-min SRAs every 60 min (SRA6). Net incremental areas under the curve (iAUC

) for glucose, insulin, and triglycerides were compared between conditions.

Glucose and insulin 7-h iAUC

were attenuated significantly during SRA6 (glucose 17.0 mmol · h · L

, 95% CI 12.5, 21.4; insulin 1,229 pmol · h · L

, 95% CI 982, 1,538) in comparison with SIT (glucose 21.4 mmol · h · L

, 95% CI 16.9, 25.8; insulin 1,411 pmol · h · L

, 95% CI 1,128, 1,767;

< 0.05), and in comparison with SRA3 (for glucose only) (22.1 mmol · h · L

, 95% CI 17.7, 26.6;

= 0.01) No significant differences in glucose or insulin iAUC

were observed in comparison of SRA3 and SIT. There was no statistically significant effect of condition on triglyceride iAUC

.

In adults with medication-controlled T2D, interrupting prolonged sitting with 6-min SRAs every 60 min reduced postprandial glucose and insulin responses. Examination of other frequencies of interruptions and potential longer-term benefits are required for clarification of clinical relevance.

In adults with medication-controlled T2D, interrupting prolonged sitting with 6-min SRAs every 60 min reduced postprandial glucose and insulin responses. Examination of other frequencies of interruptions and potential longer-term benefits are required for clarification of clinical relevance.

Coronavirus disease 2019 (COVID-19) has caused considerable morbidity and mortality, especially in patients with underlying health conditions. A precise prognostic tool to identify poor outcomes among such cases is desperately needed.

Total 400 COVID-19 patients with underlying health conditions were retrospectively recruited from 4 centers, including 54 dead cases (labeled as poor outcomes) and 346 patients discharged or hospitalized for at least 7 days since initial CT scan. Patients were allocated to a training set (n = 271), a test set (n = 68), and an external test set (n = 61). We proposed an initial CT-derived hybrid model by combining a 3D-ResNet10 based deep learning model and a quantitative 3D radiomics model to predict the probability of COVID-19 patients reaching poor outcome. The model performance was assessed by area under the receiver operating characteristic curve (AUC), survival analysis, and subgroup analysis.

The hybrid model achieved AUCs of 0.876 (95% confidence interval 0.752-0.999) and 0.864 (0.766-0.962) in test and external test sets, outperforming other models. The survival analysis verified the hybrid model as a significant risk factor for mortality (hazard ratio, 2.049 [1.462-2.871], P < 0.001) that could well stratify patients into high-risk and low-risk of reaching poor outcomes (P < 0.001).

The hybrid model that combined deep learning and radiomics could accurately identify poor outcomes in COVID-19 patients with underlying health conditions from initial CT scans. The great risk stratification ability could help alert risk of death and allow for timely surveillance plans.

The hybrid model that combined deep learning and radiomics could accurately identify poor outcomes in COVID-19 patients with underlying health conditions from initial CT scans. The great risk stratification ability could help alert risk of death and allow for timely surveillance plans.The Internet of Health Things (IoHT) is a concept that describes uniquely identifiable devices connected to the Internet and able to communicate with each other in the medical area. As one of the most important components of smart health monitoring and improvement systems, there are numerous challenges in the IoHT, among which cybersecurity is a major challenge that must be addressed with priority. As a well-received security solution to achieve fine-grained access control, ciphertext-policy weighted attribute-based encryption (CP-WABE) has the potential to ensure data security in the IoHT. However, many issues, such as inflexibility, poor computational capability, and insufficient storage efficiency in attributes comparison, remain. To address these issues, we propose a novel access policy expression method using 0-1 coding technology. Based on this method, a flexible and efficient CP-WABE is constructed for the IoHT. Our scheme supports not only weighted attributes but also any form of comparison of weighted attributes. selleck compound Furthermore, we use offline/online encryption and outsourced decryption technology to ensure that the scheme can run on an inefficient IoT terminal. Both theoretical and experimental analyses show that our scheme is more efficient and feasible than other schemes. Moreover, security analysis indicates that our scheme achieves security against chosen-plaintext attack.Automatic liver and tumor segmentation remain a challenging topic, which subjects to the exploration of 2D and 3D contexts in CT volume. Existing methods are either only focus on the 2D context by treating the CT volume as many independent image slices (but ignore the useful temporal information between adjacent slices), or just explore the 3D context lied in many little voxels (but damage the spatial detail in each slice). These factors lead an inadequate context exploration together for automatic liver and tumor segmentation. In this paper, we propose a novel full-context convolution neural network to bridge the gap between 2D and 3D contexts. The proposed network can utilize the temporal information along the Z axis in CT volume while retaining the spatial detail in each slice. Specifically, a 2D spatial network for intra-slice features extraction and a 3D temporal network for inter-slice features extraction are proposed separately and then are guided by the squeeze-and-excitation layer that allows the flow of 2D context and 3D temporal information.