Notes

Pressure peptic issues within patients together with diabetes: any bibliometrics evaluation.
Digital societies could be characterized by their increasing desire to express themselves and interact with others. Sodium orthovanadate This is being realized through digital platforms such as social media that have increasingly become convenient and inexpensive sensors compared to physical sensors in many sectors of smart societies. One such major sector is road transportation, which is the backbone of modern economies and costs globally 1.25 million deaths and 50 million human injuries annually. The cutting-edge on big data-enabled social media analytics for transportation-related studies is limited. This paper brings a range of technologies together to detect road traffic-related events using big data and distributed machine learning. The most specific contribution of this research is an automatic labelling method for machine learning-based traffic-related event detection from Twitter data in the Arabic language. The proposed method has been implemented in a software tool called Iktishaf+ (an Arabic word meaning discovery) that is able to detect traffic events automatically from tweets in the Arabic language using distributed machine learning over Apache Spark. The tool is built using nine components and a range of technologies including Apache Spark, Parquet, and MongoDB. Iktishaf+ uses a light stemmer for the Arabic language developed by us. We also use in this work a location extractor developed by us that allows us to extract and visualize spatio-temporal information about the detected events. The specific data used in this work comprises 33.5 million tweets collected from Saudi Arabia using the Twitter API. Using support vector machines, naïve Bayes, and logistic regression-based classifiers, we are able to detect and validate several real events in Saudi Arabia without prior knowledge, including a fire in Jeddah, rains in Makkah, and an accident in Riyadh. The findings show the effectiveness of Twitter media in detecting important events with no prior knowledge about them.Simulated online grocery store platforms are innovative tools for studying nutrition-related policies and point-of-selection/point-of-purchase interventions in online retail settings, yet there is no clear guidance on how to develop these platforms for experimental research. Thus, we created a standardized guide for the development of an online grocery store, including a detailed description of (1) methods for acquiring and cleaning online grocery store data, and (2) how to design a two-dimensional online grocery store experimental platform. We provide guidance on how to address product categorization, product order/sorting and product details, including how to identify outliers and conflicting nutritional information and methods for standardizing prices. We also provide details regarding our process of "tagging" food items that can be leveraged by future studies examining policies and point-of-selection/point-of-purchase interventions targeting red and processed meat and fruits and vegetables. We experienced several challenges, including obtaining accurate and up-to-date product information and images, and accounting for the presence of store-brand products. Regardless, the methodology described herein will enable researchers to examine the effects of a wide array of nutrition-related policies and interventions on food purchasing behaviors in online retail settings, and can be used as a template for reporting procedures in future research.Differentiation between adult T-cell leukemia/lymphoma (ATLL) and peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS), is often challenging based on pathological findings alone. Although serum anti-HTLV-1 antibody positivity is required for ATLL diagnosis, this information is often not available at the time of pathological diagnosis. Therefore, we examined whether the expression of SOX4 and p16 would be helpful for differentiating the two disease entities. We immunohistochemically examined SOX4 and p16 expression (which have been implicated in ATLL carcinogenesis) in 11 ATLL patients and 20 PTCL-NOS patients and classified them into four stages according to the percentage of positive cells. Among the ATLL cases, 8/11 (73%) were SOX4-positive, while only 2/20 (10%) PTCL-NOS cases expressed SOX4. The mean total score was 4.2 (standard deviation (SD) 0.61) in the ATLL group and 0.50 (SD 0.46) in the PTCL-NOS group (p less then 0.001). Positive expression of p16 was noted in 4/11 (36%) patients with ATLL and 3/20 (15%) patients with PTCL-NOS, with mean total scores of 1.9 (SD 0.64) and 0.70 (SD 0.48) in the ATLL and PTCL-NOS groups, respectively (p = 0.141). These results suggest that SOX4 may be strongly expressed in ATLL compared to PTCL-NOS cases. Therefore, it may be helpful to perform immunohistochemical staining of SOX4 when pathologists face challenges discriminating between ATLL and PTCL-NOS.Postprandial exercise represents an important tool for improving the glycemic response to a meal. This study evaluates the effects of the combination and sequence of different exercise types on the postprandial glycemic response in patients with type 2 diabetes. In this repeated-measures crossover study, eight patients with type 2 diabetes performed five experimental conditions in a randomized order (i) uninterrupted sitting (CON); (ii) 30 min of moderate intensity aerobic exercise (walking) (A); (iii) 30 min of combined aerobic and resistance exercise (AR); (iv) 30 min of combined resistance and aerobic exercise (RA); and (v) 15 min of resistance exercise (R). All the exercise sessions started 30 min after the beginning of a standardized breakfast. All the exercise conditions showed a significant attenuation of the post-meal glycemic excursion (P less then 0.003) and the glucose incremental area under the curve at 0-120 min (P less then 0.028) and 0-180 min (P less then 0.048) compared with CON. A greater reduction in the glycemic peak was observed in A and AR compared to RA (P less then 0.02). All the exercise types improved the post-meal glycemic response in patients with type 2 diabetes, with greater benefits when walking was performed alone or before resistance exercise.
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