Background Intensive lifestyle modifications have proved effective in preventing type 2 diabetes mellitus (T2DM), yet the efficiency and effectiveness of these modifications need to be improved. Emerging social media interventions are considered useful in promoting these lifestyles; nevertheless, few studies have investigated the effectiveness of combining them with behavior theory. Objective This study aims to examine the effectiveness of a 6-month mobile-based intervention (DHealthBar, a WeChat applet) combined with behavioral theory compared with a printed intervention in improving dietary behaviors, physical activity, and intention to change these behaviors among populations at high risk for T2DM. Methods Participants aged 23 to 67 years were recruited offline in Beijing, China, and were randomized into the intervention group or the control group, which received educational content via DHealthBar or a printed handbook, respectively. Educational materials were culturally tailored recommendations on improving dietary behaviors, physical activity, and intention to change based on the transtheoretical model. Participants in the intervention arm received push notifications twice per week on WeChat and had access to the educational content for the 6-month study period. Participants in the control arm received the same intervention content through printed materials. The outcomes of participants’ behavior change, intention to change behavior, and anthropometric characteristics were collected via online measuring tools at baseline, 3 months, and 6 months. Results In this study, 79 enrolled individuals completed baseline information collection (control: n=38 vs intervention: n=41), and 96% (76/79) completed the 6-month follow-up visit. Attrition rates did not differ significantly between the 2 groups (χ21=0.0, P=.61). Baseline equivalence was found. Participants in both groups reported a statistically significant decrease in energy intake at the 2 follow-up assessments compared with baseline (3 months, control: exp[β]=0.83, 95% CI 0.74-0.92 vs intervention: exp[β]=0.76, 95% CI 0.68-0.85; 6 months, control: exp[β]=0.87, 95% CI 0.78-0.96 vs intervention: exp[β]=0.57, 95% CI 0.51-0.64). At 6 months, a significantly larger decrease was observed in the intervention group in energy, fat, and carbohydrate intake, accompanied with a significantly larger increase in moderate-intensity physical activity compared with the control group (energy: exp[β]=0.66, 95% CI 0.56-0.77; fat: exp[β]=0.71, 95% CI 0.54-0.95; carbohydrates: exp[β]=0.83, 95% CI 0.66-1.03; moderate-intensity physical activity: exp[β]=2.05, 95% CI 1.23-3.44). After 6 months of the intervention, participants in the intervention group were more likely to be at higher stages of dietary behaviors (exp[β]=26.80, 95% CI 3.51-204.91) and physical activity (exp[β]=15.60, 95% CI 2.67-91.04) than the control group. Conclusions DHealthBar was initially effective in improving dietary behavior, physical activity, and intention to change these behaviors among populations who were at high risk of developing T2DM, with significant differences in the changes of outcomes over the 6-month intervention period. Trial Registration Chinese Clinical Trial Registry ChiCTR2000032323; https://tinyurl.com/y4h8q4uf

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2 For purposes this framework, unless otherwise noted, the term drug refers both to a drug approved under section 505(c) or (j) of the Federal Food, Drug, and Cosmetic Act (FD&C Act) and to biological products licensed under section 351 of the Public Health Service Act (PHS Act) 42 USC 262. 3 In this document, when we refer to the use of RWE to support a regulatory decision, we mean that the evidence provides support for or helps provide support for the regulatory decision. 4 FDA issued the guidance Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices on August 31, 2017. Real-World Data (RWD) are data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources. Real-World Evidence (RWE) is the clinical evidence about the usage and potential benefits or risks of a medical product derived from analysis of RWD.

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Image-based survival prediction models can facilitate doctors in diagnosing and treating cancer patients. With the advance of digital pathology technologies, the big whole slide images (WSIs) provide increasing resolution and more details for diagnosis. However, the gigabyte-size WSIs would make most models computationally infeasible. To this end, instead of using the complete WSIs, most of existing models only use a pre-selected subset of key patches or patch clusters as input, which might fail to completely capture the patient's tumor morphology. In this work, we aim to develop a novel survival analysis model to fully utilize the complete WSI information. We show that the use of a Vision Transformer (ViT) backbone, together with convolution operations involved in it, is an effective framework to improve the prediction performance. Additionally, we present a post-hoc explainable method to identify the most salient patches and distinct morphology features, making the model more faithful and the results easier to comprehend by human users. Evaluations on two large cancer datasets show that our proposed model is more effective and has better interpretability for survival prediction.

Qualitative data analysis is produced frequently in healthcare settings, which is a time-consuming and skilled analytic task. The use of qualitative research findings in clinical settings takes years, which is sometimes obsolete knowledge as the health context is dynamic. Artificial Intelligence (AI)-based qualitative data analysis might present with rapid analysis of text-based data in real-time, thereby empowering qualitative researchers to expedite their analysis and facilitate timely use of the research findings. We tested an AI-based method to complement the manual analysis of text-based data from the verbatim transcripts of seven mall managers’ interviews. First, we prepared text data into a machine-calculable format and employed BERT model to extract sentence-level features in our case. Second, we implement TF-IDF-based keywords mining techniques to extract the main candidate themes from the interview transcripts to support text-based analysis, including: 1) primary cluster detection algorithm, and 2) keyword extraction algorithm. The extracted core themes provide qualitative researchers with a more comprehensive overview of the qualitative data. Most of the sentences clustered in meaningful short topics or sentences carrying independent and clear information. The extracted topics and clustered sentences reduced qualitative researchers’ workload by condensing and identifying meaningful concepts and naming them. This method combining contextualized word embeddings, unsupervised clustering, and keyword extraction techniques can significantly reduce the overall workload and time consumed in qualitative research using conventional methods.

Obesity has caused wide concerns due to its high prevalence in severe COVID-19 cases. Co-existence of diabetes and obesity could cause an even higher risk of severe outcomes due to immunity dysfunction. We conducted a retrospective study in 1637 adult patients who were admitted into an acute hospital in Wuhan, China. Propensity score matched logistic regression was used to estimate the risks of severe pneumonia and requiring in-hospital oxygen therapy associated with obesity. After adjustment for age, sex and comorbidities, obesity was significantly associated with higher odds of severe pneumonia (odd ratio [OR] 1.47 [95% CI 1.15-1.88], P=0.002) and oxygen therapy (OR 1.40 [95% CI 1.10-1.79], P=0.007). Higher ORs of severe pneumonia due to obesity were observed in men, older adults and those with diabetes. Among patients with diabetes, overweight increased the odds of requiring in-hospital oxygen therapy by 0.68 times (P=0.014) and obesity increased the odds by 1.06 times (P=0.028). A linear dose-response curve between BMI and severe outcomes was observed in all patients, whereas a U-shaped curve in those with diabetes. Our findings provide important evidence to support obesity as an independent risk factor for severe outcomes of COVID-19 infection in the early phase of the ongoing pandemic.

LBA4011 Background: Pancreatic cancer is one of the most lethal malignancies diagnosed at an advanced stage, and current treatment regimens are ineffective, with only 6-8 months of median overall survival (mOS). The present study aims to assess the clinical efficacy and safety of nimotuzumab (anti-EGFR humanized monoclonal antibody) combined with gemcitabine in K-Ras wild-type patients with locally advanced or metastatic pancreatic cancer. Methods: Patients with locally advanced or metastatic pancreatic cancer were randomized to receive nimotuzumab (400 mg, every one week) followed by gemcitabine (1000 mg/m2 on days 1, 8, and 15, every four weeks), or placebo plus gemcitabine until progression or unacceptable toxicity. The primary endpoint was overall survival (OS), and secondary endpoints included progression-free survival (PFS), objective response rate (ORR), and safety. Use restricted mean survival time (RMST)-Log function to analyze the survival benefits when the proportional hazards assumption is untrue. Results: A total of 92 Chinese patients were randomly assigned to the nimotuzumab- gemcitabine (n = 46) or placebo-gemcitabine group (n = 46). In the full analysis set (FAS, n = 82), the mOS was significantly longer in the nimotuzumab-gemcitabine group (10.9 vs. 8.5 months, p = 0.025 by RMST-Log test, hazard ratio [HR], 0.50, 95% Confidence Interval [CI], 0.06 to 0.94). The one-year survival rate was 43.6% in the nimotuzumab-gemicitabine group vs. 26.8% in the placebo-gemicitabine group, and 13.9% vs. 2.7% at three years. Subgroup analyses showed more survival benefit in patients without treatment of biliary obstruction (11.9 vs. 8.5 months, HR = 0.54, 95%CI 0.33-0.88, p = 0.037) and no surgical history (15.8 vs. 6.0 months, HR = 0.40, 95%CI 0.19-0.84). The median progression-free survival (mPFS) was 4.2 months in the nimotuzumab-gemicitabine group, as compared with 3.6 months in the placebo-gemicitabine group (HR = 0.56; 95% CI, 0.12 to 0.99; p = 0.013 ); Patients without treatment of biliary obstruction had significantly longer PFS (5.5 vs. 3.4 months; p = 0.008 ). No statistical difference in the ORR between the two groups ( p > 0.05). Nimotuzumab was safe and the incidence of adverse events in the nimotuzumab-gemicitabine group is similar to placbo-gemicitabine group. The most common grade 3 TRAEs in Nim-Gem group were neutropenia (11.1%), leukopenia (8.9%) and thrombocytopenia (6.7%). No grade 4 TRAEs. Conclusions: Nimotuzumab combined with gemcitabine increases OS and PFS in patients with K-Ras wild-type locally advanced or metastatic pancreatic cancer, particularly for those without treatment of biliary obstruction. The safety profile of nimotuzumab is similar to placebo. Clinical trial information: 02395016.