Type 2 diabetes mellitus (T2DM) is still a global health problem. Current T2DM treatments are limited to curing the symptoms and have not been able to restore insulin sensitivity in insulin-sensitive tissues that have become resistant. In the past decade, some studies have shown the significant role of a chaperone family, heat shock protein 70 (HSP70), in insulin resistance pathogenesis that leads to T2DM. HSP70 is a cytoprotective molecular chaperone that functions in protein folding and degradation. In general, studies have shown that decreased concentration of HSP70 is able to induce inflammation process through JNK activation, inhibit fatty acid oxidation by mitochondria through mitophagy decrease and mitochondrial biogenesis, as well as activate SREBP-1c, one of the lipogenic gene transcription factors in ER stress. The overall molecular pathways are potentially leading to insulin resistance and T2DM. Increased expression of HSP70 in brain tissues is able to improve insulin sensitivity and glycemic control specifically. HSP70 modulation-targeting strategies (including long-term physical exercise, hot tub therapy (HTT), and administration of alfalfa-derived HSP70 (aHSP70)) in subjects with insulin resistance are proven to have therapeutic and preventive potency that are promising in T2DM management.
Various types of Indonesian coffee are already popular internationally. Recently, there are still not many methods to classify the types of typical Indonesian coffee. Computer vision is a non-destructive method for classifying agricultural products. This study aimed to classify three types of Indonesian Arabica coffee beans, i.e., Gayo Aceh, Kintamani Bali, and Toraja Tongkonan, using computer vision. The classification method used was the AlexNet convolutional neural network with sensitivity analysis using several variations of the optimizer such as SGDm, Adam, and RMSProp and the learning rate of 0.00005 and 0.0001. Each type of coffee used 500 data for training and validation with the distribution of 70% training and 30% validation. The results showed that all AlexNet models achieved a perfect validation accuracy value of 100% in 1,040 iterations. This study also used 100 testing-set data on each type of coffee bean. In the testing confusion matrix, the accuracy reached 99.6%.
Activated carbon is a material that widely used in industries as an adsorbent and purifying material. Moreover, activated carbon has an essential role in improving product quality. Coconut shell has many advantages to be used as activated carbon sources as it has both high mechanical strength and high pore volume. This research aims to explore the coconut shell as an activated carbon material through immersion using a phosphoric acid solution. The activation process was conducted under different conditions, i.e. H PO concentration of 1 M, 2 M, 3 M, 4 M and 5 M, and immersion time of 1 hour, 3 hours, 5 hours, 7 hours and 9 hours. The characteristics of the activated carbon were evaluated in terms of iodine number, density, water content and ash content. It could be concluded that the combination of 3M H PO concentration and 9 hours immersion time was the best combination for producing desirable activated carbon from coconut shell. The characteristics of iodine number, density, water content and ash content are 248.5811 mg/g, 1.3613 g/ml, 5.3628% and 2.1239%, respectively. The phosphate acid concentration and the immersion time had a significant effect on the activated carbon characteristics such as iodine number, density, water content and ash content.
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