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The Wild Brain Workshop (TWBW) is a custom culture community in Medan that was founded in 2011. This community started from a custom workshop which then developed into a wider community with a focus on custom motorbikes, music and lifestyle. TWBW, as one of the well-known custom motorbike workshops in the city of Medan, of course gets lots of orders from various custom motorbike lovers. This research was created to create a mobile application for ordering custom motorbike packages by implementing CRM (Customer Relationship Management). The problem formulation in this research is designing an effective mobile application for ordering custom motorbike packages and implementing CRM to improve relationships with customers. The aim of creating this application is so that you can easily view the various custom motorbike packages available, see detailed specifications and prices, and place orders directly through the application. The implementation of CRM in this application is found in the chat feature and customer service menu. By implementing CRM (Customer Relationship Management), it is hoped that TWBW can manage relationships with customers, by utilizing technology to maximize communication and marketing. Through the development of this application, it is hoped that the use of this technology can effectively improve the ordering process.

Pasirtanjung village has many young people who have an essential role in the village. However, although the number of youths is quite large, the Karang Taruna in Pasirtanjung Village is still unable to function optimally because members are not active enough to contribute. Therefore, Karang Taruna does not function like a youth organization. Meanwhile, to make the organization function optimally (achieve its goals), it is necessary to have contributions from each member. Community service activities are designed to build a better village community, in this case, Karang Taruna. This activity resulted in a program to improve team building for youth organizations. This is done to strengthen teamwork, improve group performance, and increase emotional attachment between group members and trust between members. Moreover, it optimizes the role of Karang Taruna in achieving its goal as one of the community institutions that helps build the village. The first step in building team building is strengthening group cohesiveness within the youth organization.A series of programs were designed to improve team building: 1) providing materials, 2) training, and 3) Focus group discussions. These team-building activities have been proven to help improve group performance. Systematic team-building activities can increase emotional attachment between group members and trust between members. This series of activities can ultimately be measured through pretest-posttest measurements related to group cohesion. The results show that the understanding of youth regarding group cohesion in youth organizations has increased after being given training. This training can provide new insights to the trainees that developing an organization requires an active and mutually supportive team.

Instalasi Farmasi memiliki peran penting terhadap pelayanan kesehatan di sebuah fasilitas kesehatan. Farmasi bertanggung jawab atas pengelolaan, pengadaan, penyimpanan, distribusi, dan penggunaan persediaan obat. Persediaan obat merupakan bagian penting dalam memastikan ketersediaan, aksesibilitas, dan penggunaan obat yang efektif serta aman bagi pasien. Tujuan penelitian ini untuk melakukan klasterisasi data obat yang berguna untuk meningkatkan efisiensi proses manajemen persediaan obat, sehingga dapat menghindari kelebihan atau kekurangan yang dapat mengganggu kelancaran layanan pemberian obat dan mencegah terjadinya kerugian penjualan obat. Pengelompokan data dilakukan dengan memanfaatkan data Persedian Obat dari data masa lalu yaitu data transaksi pembelian dan penjualan dengan memanfaatkan teori Data Mining dengan menggunakan metode Clustering yaitu K-Means. Dataset pada penelitian ini sebanyak 1.389 dengan 6 variabel. Sebelum dilakukan klasterisasi dilakukan proses optimasi jumlah klaster dengan dua metode yaitu Metode Elbow dan Metode Gap Statistik. Hasil kedua metode tersebut menunjukkan nilai optimasi k klaster k = 3. Hasil klasteriasi yaitu Klaster 1 sebanyak 41 data obat yang menunjukkan golongan obat Generik. Klaster 2 sebanyak 116 data obat yang menunjukkan obat Paten. Kedua klaster tersebut menunjukkan tingkat penjualan yang kurang cepat (slow moving). Sedangkan pada Klaster 3 sebanyak 1.232 data obat yang menunjukkan gabungan dari golongan obat generik dan paten yang memiliki tingkat penjualan yang cukup cepat (fast moving).

Pembelajaran bahasa Arab di Taman Kanak-kanak (TK) Aisyiah menjadi fokus utama untuk meningkatkan efektivitas dan keterlibatan siswa prasekolah. Untuk mencapai tujuan tersebut, diusulkan pembangunan "Sistem Pembelajaran Bahasa Arab Berbasis Aplikasi Multimedia Interaktif" sebagai solusi inovatif. Pemilihan teknologi aplikasi multimedia interaktif bertujuan untuk menciptakan pengalaman pembelajaran yang menarik dan efektif. Multiemdia Interaktif yang digunakan dalam aplikasi ini untuk memfasilitasi penilaian kemampuan Bahasa Arab anak-anak, memproses informasi dengan cepat, dan memberikan umpan balik yang relevan. Tantangan yang dihadapi mencakup keberlanjutan teknologi, pelatihan reguler bagi guru TK Aisyiah dalam penggunaan aplikasi multimedia, dan upaya memperkenalkan multimedia kepada para siswa. Solusi yang diusulkan melibatkan pembuatan aplikasi multimedia interaktif pembelajaran Bahasa Arab yang mewadahi pembelajaran dasar Bahasa arab untuk anak, pelatihan berkala untuk meningkatkan literasi teknologi guru, dan penerapan aplikasi pembelajaran yang intensif. Proyek ini bertujuan untuk mempermudah guru dalam mengelola pembelajaran Bahasa Arab. Efisiensi dan efektivitas pembelajaran Bahasa Arab di TK Aisyiah diharapkan dapat ditingkatkan secara signifikan.

Background: People with diabetes have higher COVID-19 morbidity and mortality. These risks are amplified for underserved communities including racial/ethnic minorities and people with lower socioeconomic status. However, limited research has examined COVID-19 outcomes specifically affecting underserved communities with diabetes. Methods: From November 2021 to July 2022, adults with insulin-requiring diabetes at federally qualified health centers in Florida and California (n = 450) completed surveys examining COVID-19 outcomes and demographics. Surveys assessed COVID-19 severity, vaccination uptake, mask-wearing habits, income changes, and healthcare access changes. Surveys also included the full Coronavirus Anxiety Scale (CAS-19). Descriptive statistics were computed for all outcomes. Between-group comparisons for state and race/ethnicity were evaluated via Chi-Squared, Fisher’s Exact, Cochran-Mantel-Haenszel, One-Way ANOVA, and t-tests. Logistic regression determined factors associated with COVID-19 vaccination uptake. Data were self-reported and analyzed cross-sectionally. Results: Overall, 29.7 % reported contracting COVID-19; of those, 45.3 % sought care or were hospitalized. Most (81.3 %) received ≥ 1 vaccine. Hispanics had the highest vaccination rate (91.1 %); Non-Hispanic Blacks (NHBs) had the lowest (73.9 %; p =.0281). Hispanics had 4.63x greater vaccination odds than Non-Hispanic Whites ([NHWs]; 95 % CI = [1.81, 11.89]). NHWs least often wore masks (18.8 %; p

This research investigates the key factors that influence students' intention to stay at a university and their intention to recommend the university, especially in the context of Universitas Islam Indonesia. This research emphasizes the importance of the quality of university services in creating meaningful relationships between students and the university. The proposed research model tests the relationship between university service quality (HESQUAL), brand image, satisfaction, and their impact on student's intention to stay and recommend the university. The results of the analysis show that university service quality has a positive and significant influence on the university's brand image and student satisfaction. Apart from that, the university's brand image also has a positive and significant effect on student satisfaction. Student satisfaction, in turn, has a positive and significant influence on their intention to stay and recommend the university. This research provides important insights for universities in understanding the factors that influence student loyalty and recommendations.

The GAMA Microreactor is a low-power nuclear power plant dedicated for remote area with a power output of 300 kWe. The primary characteristics of the GAMA Microreactor are its system compactness, design simplicity, and safe operation without moving parts. The fuel is uranium hydride powder, which works simultaneously as a moderator, contained within a stainless-steel vessel and surrounded by a graphite reflector. Liquid sodium-filled heat pipes are embedded in the fuel powder to extract fission-generated heat via passive coolant flow due to capillary action. As a part of the design process, neutronic parameters of the GAMA Microreactor have been determined using SCALE6.2 and OpenMC codes. Among the analyzed parameters are the excess reactivity as a function of burn-up, shutdown margin, control rod worth, temperature coefficient of reactivity (TCR), fuel density coefficient of reactivity (DCR), and reactivity coefficient due to hydrogen dissociation. From the calculation results, the GAMA Microreactor has a maximum excess reactivity of 0.049 at the operational temperature (700 °C) and 0.0788 at ambient temperature. The TCR value is negative (−2.267 pcm/°C) at the beginning of the cycle (BOL) and −4.9618 pcm/°C at the end of the cycle (EOC). Hydrogen dissociation imposes negative reactivity on the reactor both at the BOL (−0.2631 Δk/k-%dissociation) and EOC (−0.3524 Δk/k-%dissociation). Therefore, the GAMA Microreactor maintains its inherent safety characteristics over a considerably long operation time.

Abstract Plants have diverse molecular mechanisms to protect themselves from biotic and abiotic stressors and adapt to changing environments. To uncover the genetic potential of plants, it is crucial to understand how they adapt to adverse conditions by analyzing their genomic data. We analyzed RNA-Seq data from different tomato genotypes, tissue types, and drought durations. We used a time series scale to identify early and late drought-responsive gene modules and applied a machine learning method to identify the best responsive genes to drought. We demonstrated six candidate genes of tomato viz. Fasciclin-like arabinogalactan protein 2 (FLA2), Amino acid transporter family protein (ASCT), Arginine decarboxylase 1 (ADC1), Protein NRT1/PTR family 7.3 (NPF7.3), BAG family molecular chaperone regulator 5 (BAG5) and Dicer-like 2b (DCL2b) were responsive to drought. We constructed gene association networks to identify their potential interactors and found them drought-responsive. The identified candidate genes can help to explore the adaptation of tomato plants to drought. Furthermore, these candidate genes can have far-reaching implications for molecular breeding and genome editing in tomatoes, providing insights into the molecular mechanisms that underlie drought adaptation. This research underscores the importance of the genetic basis of plant adaptation, particularly in changing climates and growing populations.

Digital learning is a form of transformation in the classroom that focuses on the use of technology. In Indonesia, the Covid-19 pandemic that has been going on for the last few years has forced educational institutions, both formal and non-formal, to apply this learning concept, even with all of its limitations. This is at least experienced by PKBM 31 Bangka which has attempted to implement digital learning with limited capabilities and minimal infrastructure. This is even worse with the diverse characteristics of the students as non-formal institutions. Therefore, through a community service program, the informatics engineering study program at Paramadina University seeks to provide training to PKBM 31 Bangka so that they are better prepared to carry out digital learning. The training was held for 2 days and divided into 4 sessions. The understanding and ability of PKBM 31 Bangka educators regarding digital learning increased from the previous 2.67 to 4.25 after attending the training. Participants also rated the implementation of this training at 4.5 or very satisfied.

Background: Micronutrients are required in trace amounts, but are importantly associated with plant growth and development. While soil salinity causes a frequent micronutrient deficiency in soil, the condition withholds plant growth, development, and eventually crop production. Since foliar application of micronutrient provides rapid nutrient absorption compared to soil amendments, it may confer straightforward mitigation of salinity stress. However, micronutrients availability to plants under saline conditions has drawn limited attention. Objective: To study the role of four micronutrients namely boron (B), zinc (Zn), chlorine (Cl), and silicon (Si) as well as their combinations as foliar application on growth, development, and yield responses of ‘okra cv. Nulok F1’ in a naturally very slightly salinized soil. Methodology: The research site belongs to Young Meghna Estuarine Flood Plain under the soil of Agro-ecological Zones (AEZ)-18. The area of EC dsm-1 2.39 is used for the cultivation of horticultural and cereal crops. The one-factor experiment was designed in a randomized complete block design with three replications and six treatments. The factor having six different micronutrients and their combinations under saline soil viz. T1 = control (untreated), T2 = 0.2 % B as solubor®, T3 = 0.2 % ZnSO4, T4 = 0.2 % KCl, T5 = 0.2% SiO2, and T6 = 0.2% solubor® B + 0.2% ZnSO4 + 0.2% SiO2 + 0.2% KCl. Result: A number of foliar treatments of micronutrients substantially improved plant height, stem diameter, leaf number, depth of root, fruit length, number of fruits, single fruit weight, yield plot-1, and total yield. Implication: Among the treatments, Si, and a mixture of solubor® B, Zn, Cl, and Si significantly uphold growth and yield-related attributes of okra indicating them as suitable micronutrients for okra production in salt-affected areas. Conclusion: The foliar application of Si and the mixtures of the four aforementioned micronutrients might enhance the growth and yield attributes of ‘okra cv. Nulok F1’ under salinity stress.

The risk evaluation of natural disasters is an obstacle to ensuring healthcare services during catastrophic events worldwide. Therefore, timely and appropriate environmental health risk evaluation is essential. In this study, we incorporated the information from databases such as PubMed, Google Scholar, and Scopus. We performed this study to explore the feasibility of using artificial intelligence (AI) in disaster risk and emergency health management. Natural disasters have some phenomenon that is bound to happen. So, we can use AI to inform healthcare authorities about environmental health risks and emergency medical management. The recent innovations in technology have created novel opportunities for improving healthcare services across the world. AI technology would be the backbone of the fourth industrial revolution because we are entering an AI-based world this decade. Therefore, we suggest the authorities consider this positive aspect of AI technology to minimize the disastrous outcomes of any catastrophic events.

Abstract Mounting evidence demonstrates that the central nervous system (CNS) orchestrates glucose homeostasis by sensing glucose and modulating peripheral metabolism. Glucose responsive neuronal populations have been identified in the hypothalamus and several corticolimbic regions. However, how these CNS gluco-regulatory regions modulate peripheral glucose levels is not well understood. To better understand this process, we simultaneously measured interstitial glucose concentrations and local field potentials in 3 human subjects from cortical and subcortical regions, including the hypothalamus in one subject. Correlations between high frequency activity (HFA, 70–170 Hz) and peripheral glucose levels are found across multiple brain regions, notably in the hypothalamus, with correlation magnitude modulated by sleep-wake cycles, circadian coupling, and hypothalamic connectivity. Correlations are further present between non-circadian (ultradian) HFA and glucose levels which are higher during awake periods. Spectro-spatial features of neural activity enable decoding of peripheral glucose levels both in the present and up to hours in the future. Our findings demonstrate proactive encoding of homeostatic glucose dynamics by the CNS.

Abstract Background One of the most pressing issues in healthcare today is the prevention of Adenovirus (AdV) infections. Children and the elderly, both of whom have weaker immune systems than healthy adults, are more vulnerable to infection. Discussion India has been the epicenter of a recent AdV epidemic in the South Asian area. Most of the nations, bordering India are still developing and have very low per capita incomes, yet their citizens often cross into India for trade, medical care, and vacation. Conclusion We are concerned that an epidemic of the AdV might occur in the Indian subcontinent, spread to other nations, and eventually affect the whole world if effective preventative and diagnostic measures are not taken.

This study begins with a literature review to observe current problems surrounding vehicle service centers and the use of the ARIMA method to resolve similar cases. Researchers then conduct the observation process by collecting user needs through surveys and questionnaires. Next, researchers use the Scrum methodology to develop a web-based application enriched with the ARIMA method. Afterward, researchers obtain user feedback using surveys and questionnaires to evaluate the user experience towards the application. Conclusively, based on the results of the questionnaires, the average respondent believes that the web-based application can simplify respondents in making vehicle service reservations with a score of 8.85 out of 10. In addition, the average respondent believes that the web-based application can assist respondents in planning vehicle service. They visit with shorter queue times through a crowded time prediction system on a web-based reservation application with the ARIMA model with a value of 8.9 out of 10.

Background: COVID-19 pandemic not only causes physical problems but also becomes a stressor and give problems to mental health that are commonly found in adolescence. Mental health disorders, such as anxiety and depression, can increase the risk of many physical health problems and reduce performance in work and social environment as well. Therefore, this literature review is aimed to discuss further and to broaden insight about the importance of mental health disorders and efforts to prevent mental health disorders among teenagers during the COVID-19 pandemic. Reviews: The COVID-19 pandemic has negative impacts in mental health. Depression and anxiety are the most common mental health condition in the general population that reported an increase until three times compared before the pandemic. Several studies about COVID-19 and anxiety showed that the younger population (especially young adults) tended to have more anxiety. The most distressing issue for teenagers in the pandemic is not being able to see their friends, being fear of their friends or family getting sick or dying from COVID-19. In addition, when compared to the older population, the younger population also uses social media more often that can cause anxiety due to information overload and misinformation. Summary: COVID-19 pandemic gives rise to various mental health problems among teenagers. Understanding the symptoms and prevention of mental health disorders such as health promotion and protection from specific mental illness are becoming the primary prevention of mental health problems

IntroductionIn the US, many individuals with diabetes do not have consistent access to endocrinologists and therefore rely on primary care providers (PCPs) for their diabetes management. Project ECHO (Extension for Community Healthcare Outcomes) Diabetes, a tele-education model, was developed to empower PCPs to independently manage diabetes, including education on diabetes technology initiation and use, to bridge disparities in diabetes.MethodsPCPs (n=116) who participated in Project ECHO Diabetes and completed pre- and post-intervention surveys were included in this analysis. The survey was administered in California and Florida to participating PCPs via REDCap and paper surveys. This survey aimed to evaluate practice demographics, protocols with adult and pediatric T1D management, challenges, resources, and provider knowledge and confidence in diabetes management. Differences and statistical significance in pre- and post-intervention responses were evaluated via McNemar’s tests.ResultsPCPs reported improvement in all domains of diabetes education and management. From baseline, PCPs reported improvement in their confidence to serve as the T1D provider for their community (pre vs post: 43.8% vs 68.8%, p=0.005), manage insulin therapy (pre vs post: 62.8% vs 84.3%, p=0.002), and identify symptoms of diabetes distress (pre vs post: 62.8% vs 84.3%, p=0.002) post-intervention. Compared to pre-intervention, providers reported significant improvement in their confidence in all aspects of diabetes technology including prescribing technology (41.2% vs 68.6%, p=0.001), managing insulin pumps (41.2% vs 68.6%, p=0.001) and hybrid closed loop (10.2% vs 26.5%, p=0.033), and interpreting sensor data (41.2% vs 68.6%, p=0.001) post-intervention.DiscussionPCPs who participated in Project ECHO Diabetes reported increased confidence in diabetes management, with notable improvement in their ability to prescribe, manage, and troubleshoot diabetes technology. These data support the use of tele-education of PCPs to increase confidence in diabetes technology management as a feasible strategy to advance equity in diabetes management and outcomes.

Business Intelligence (BI) is an essential technology in an increasingly competitive landscape since it helps make decisions more accurately. To achieve an effective BI implementation, the organization must formulate the right strategy to overcome its challenges. This research aimed to develop a framework to map barriers into strategies using qualitative and quantitative methods. The qualitative approach is driven by interviewing BI experts to validate the barriers and strategies previously obtained. Based on the interview, there are 19 barriers and 9 strategies that could be used. The quantitative approach compiles a priority list of the most significant barriers and the most effective strategies to overcome these barriers using fuzzy AHP TOPSIS, an MCDM method to eliminate inconsistencies during ranking. The results indicate that the lack of collaboration between the IT and BI departments, the BI implementation demands to be done quickly, and low data quality are the main barriers that hinder BI's success. This research also found that business people's involvement in a BI project is the best strategy to overcome the obstacles. The chances of a successful BI implementation will increase by having good cooperation between IT and business units within the company. Doi: 10.28991/ESJ-2022-06-02-010 Full Text: PDF

Majunya perkembangan teknologi menghasilkan dunia usaha yang semakin terdigitalisasi. Oleh karena itu, berbagai proses usaha mulai menggunakan teknologi seperti algoritma dalam membuat keputusan bisnis. Dalam hal ini, algoritma digunakan untuk menentukan harga oleh pelaku usaha yang kemudian dikenal sebagai algoritma harga. Namun, terdapat beberapa permasalahan yang timbul jika algoritma harga digunakan oleh pelaku usaha dominan. Terdapat kekhawatiran bahwa penggunaan algoritma harga dapat mengakibatkan penyalahgunaan posisi dominan. Selanjutnya, terdapat tantangan mengenai upaya hukum apa yang dapat dilakukan oleh otoritas persaingan usaha untuk menghadapi fenomena ini. Oleh karena itu, penelitian ini bertujuan untuk menganalisis penggunaan algoritma harga dan penyalahgunaan posisi dominan, serta upaya hukum yang dapat ditempuh. Untuk mencapai tujuan tersebut, penelitian ini menggunakan metode penelitian hukum dengan pendekatan peraturan perundang undangan, pendekatan konseptual, dan pendekatan perbandingan. Dengan metode tersebut, penelitian ini menganalisis penggunaan algoritma harga oleh pelaku usaha dominan yang dapat memenuhi unsur penyalahgunaan posisi dominan menurut hukum Indonesia serta mentransformasi upaya hukum yang dapat dilakukan untuk menghadapi penggunaan algoritma harga. Dalam hal ini, penggunaan algoritma harga dapat mengakibatkan penyalahgunaan posisi dominan karena kemampuan algoritma untuk menciptakan syarat perdagangan dan membatasi pesaing dan mencegah konsumen mendapatkan harga yang bersaing. Adapun upaya hukum yang dapat ditempuh oleh otoritas persaingan usaha adalah meningkatkan pengetahuan mengenai algoritma, pembentukan regulasi yang mendukung transparansi dan akuntabilitas algoritma harga, melakukan pendekatan struktural, serta menitikberatkan pembuktian pada bukti digital, petunjuk, dan pendapat ahli.

The objectives of this study are: (1) to determine the implementation of the recitation learning method during the Covid-19 pandemic at SMAN 9 Bandar Lampung; (2) to find out how to instill character values in students through the recitation learning method in history lessons, and (3) to determine the impact of the implementation of history learning with the website-based recitation method. The research method used is descriptive qualitative, namely collecting data through interviews and literature study (library research). Data were analyzed through data reduction, data presentation, and drawing conclusions. The results of the study explain as follows: (1) The website-based recitation method by providing assignments can be implemented at home with free time so that students can understand historical material thoroughly; (2) The cultivation of character values created by the recitation method are honesty, nationalism, mutual cooperation and others; (3) One of the positive impacts for students is that they demand high creativity, while for teachers it is easier to trace digitally which makes attendance easier.

Diabetes is a very common disease affecting individuals worldwide. Diabetes increases the risk of long-term complications including heart disease, and kidney failure among others. People might live longer and lead healthier lives if this disease is detected early. Different supervised machine learning models trained with appropriate datasets can aid in diagnosing the diabetes at the primary stage. The goal of this work is to find effective machine-learning-based classifier models for detecting diabetes in individuals utilizing clinical data. The machine learning algorithms to be trained with several datasets in this article include Decision tree (DT), Naive Bayes (NB), k-nearest neighbor (KNN), Random Forest (RF), Gradient Boosting (GB), Logistic Regression (LR) and Support Vector Machine (SVM). We have applied efficient pre-processing techniques including label-encoding and normalization that improve the accuracy of the models. Further, using various feature selection approaches, we have identified and prioritized a number of risk factors. Extensive experiments have been conducted to analyze the performance of the model using two different datasets. Our model is compared with some recent study and the results show that the proposed model can provide better accuracy of 2.71% to 13.13% depending on the dataset and the adopted ML algorithm. Finally, a machine learning algorithm showing the highest accuracy is selected for further development. We integrate this model in a web application using python flask web development framework. The results of this study suggest that an appropriate preprocessing pipeline on clinical data and applying ML-based classification may predict diabetes accurately and efficiently.

The amount of motorcycle accidents is increasing each year. The main reason is that the riders do not wear a helmet. The research aims to minimize the accident by training the machine learning using the IBM Watson Studio. It trains the data about “wearing helmet” and “not wearing helmet”. The used method is Convolutional Neural Network (CNN). About 170 image datasets are used. CNN is conducted on the input image using a kernel or filter. The filter will multiply its values with the overlapping values of the image while also sliding and adding them all to produce a single value for each of them until the entire images have passed and finished. After CNN method is done, the researchers can classify the images by using supervised learning. It can identify whether the rider is wearing a helmet or not simply by scanning a picture on the street. The result shows high accuracy of 92.87%. The method can be used to minimize the percentage of motorcycle accidents caused by not wearing a helmet.

ABSTRACT Growth hormone therapy with daily injections of recombinant human growth hormone has been available since 1985, and is shown to be safe and effective treatment for short stature in children and for adult growth hormone deficiency. In an effort to produce a product that would improve patient adherence, there has been a strong effort from industry to create a long acting form of growth hormone to ease the burden of use. Technologies used to increase half-life include depot formulations, PEGylated formulations, pro-drug formulations, non-covalent albumin binding growth hormone and growth hormone fusion proteins. At present, two long acting formulations are on the market in China and South Korea, and several more promising agents are under clinical investigation at various stages of development throughout the world. Arch Endocrinol Metab. 2019;63(6):601-7

Abstract Dual‐hormone replacement therapy with insulin and amylin in patients with type 1 diabetes has the potential to improve glucose management. Unfortunately, currently available formulations require burdensome separate injections at mealtimes and have disparate pharmacokinetics that do not mimic endogenous co‐secretion. Here, amphiphilic acrylamide copolymers are used to create a stable co‐formulation of monomeric insulin and amylin analogues (lispro and pramlintide) with synchronous pharmacokinetics and ultra‐rapid action. The co‐formulation is stable for over 16 h under stressed aging conditions, whereas commercial insulin lispro (Humalog) aggregates in 8 h. The faster pharmacokinetics of monomeric insulin in this co‐formulation result in increased insulin–pramlintide overlap of 75 ± 6% compared to only 47 ± 7% for separate injections. The co‐formulation results in similar delay in gastric emptying compared to pramlintide delivered separately. In a glucose challenge, in rats, the co‐formulation reduces deviation from baseline glucose compared to insulin only, or separate insulin and pramlintide administrations. Further, comparison of interspecies pharmacokinetics of monomeric pramlintide suggests that pharmacokinetics observed for the co‐formulation will be well preserved in future translation to humans. Together these results suggest that the co‐formulation has the potential to improve mealtime glucose management and reduce patient burden in the treatment of diabetes.

One of the units of the company which is engaged in electricity handles many electrical large projects. The current condition of recording project activities that is still done manually and is not centralized has become difficulties in searching for data when needed. Job targets that also cannot be monitored create difficulties in management. Therefore, an effective and efficient monitoring information system for recording project activities is required so as to facilitate the management of monitoring data projects and data retrieval. The system development process using the prototyping method is implemented on a website basis by utilizing the Kanban system in managing work items. The Trello API is used to retrieve project data along with the use of the Kanban system in Trello to maintain data security which can only be managed by the project manager. System development is carried out through the stages of problem identification, literature study, needs analysis, design with Unified Modeling Language, and implementation using Laravel as a framework. Functional testing of the system with Blackbox testing gets 100% indicating the system is in accordance with the requirements specifications. Usability testing using the System Usability Scale gets a value of 84 in the acceptable system category. Responsive testing is conducted with the results of the interface meets various screen sizes and orientations. The compatibility testing implementation shows the system works well with various browsers.

Abstract Understanding how automated insulin delivery (AID) algorithm features impact glucose control under full closed loop delivery represents a critical step toward reducing patient burden by eliminating the need for carbohydrate entries at mealtimes. Here, we use a pig model of diabetes to compare AndroidAPS and Loop open‐source AID systems without meal announcements. Overall time‐in‐range (70–180 mg/dl) for AndroidAPS was 58% ± 5%, while time‐in‐range for Loop was 35% ± 5%. The effect of the algorithms on time‐in‐range differed between meals and overnight. During the overnight monitoring period, pigs had an average time‐in‐range of 90% ± 7% when on AndroidAPS compared to 22% ± 8% on Loop. Time‐in‐hypoglycemia also differed significantly during the lunch meal, whereby pigs running AndroidAPS spent an average of 1.4% (+0.4/−0.8)% in hypoglycemia compared to 10% (+3/−6)% for those using Loop. As algorithm design for closed loop systems continues to develop, the strategies employed in the OpenAPS algorithm (known as oref1) as implemented in AndroidAPS for unannounced meals may result in a better overall control for full closed loop systems.

BackgroundA composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.MethodsWe assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.ResultsThe analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.ConclusionThe GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.