Understanding Heterogeneity in Individual Responses to Digital Lifestyle Intervention Through Self-Monitoring Adherence Trajectories in Adults With Overweight or Obesity: Secondary Analysis of a 6-Month Randomized Controlled Trial.

BACKGROUND: Achieving clinically significant weight loss through lifestyle interventions for obesity management is challenging for most individuals. Improving intervention effectiveness involves early identification of intervention nonresponders and providing them with timely, tailored interventions. Early and frequent self-monitoring (SM) adherence predicts later weight loss success, making it a potential indicator for identifying nonresponders in the initial phase. OBJECTIVE: This study aims to identify clinically meaningful participant subgroups based on longitudinal adherence to SM of diet, activity, and weight over 6 months as well as psychological predictors of participant subgroups from a self-determination theory (SDT) perspective. METHODS: This was a secondary data analysis of a 6-month digital lifestyle intervention for adults with overweight or obesity. The participants were instructed to perform daily SM on 3 targets: diet, activity, and weight. Data from 50 participants (mean age: 53.0, SD 12.6 y) were analyzed. Group-based multitrajectory modeling was performed to identify subgroups with distinct trajectories of SM adherence across the 3 SM targets. Differences between subgroups were examined for changes in clinical outcomes (ie, body weight, hemoglobin A1c) and SDT constructs (ie, eating-related autonomous motivation and perceived competence for diet) over 6 months using linear mixed models. RESULTS: Two distinct SM trajectory subgroups emerged: the Lower SM group (21/50, 42%), characterized by all-around low and rapidly declining SM, and the Higher SM group (29/50, 58%), characterized by moderate and declining diet and weight SM with high activity SM. Since week 2, participants in the Lower SM group exhibited significantly lower levels of diet (P=.003), activity (P=.002), and weight SM (P=.02) compared with the Higher SM group. In terms of clinical outcomes, the Higher SM group achieved a significant reduction in body weight (estimate: -6.06, SD 0.87 kg; P<.001) and hemoglobin A1c (estimate: -0.38, SD 0.11%; P=.02), whereas the Lower SM group exhibited no improvements. For SDT constructs, both groups maintained high levels of autonomous motivation for over 6 months. However, the Lower SM group experienced a significant decline in perceived competence (P=.005) compared with the Higher SM group, which maintained a high level of perceived competence throughout the intervention (P=.09). CONCLUSIONS: The presence of the Lower SM group highlights the value of using longitudinal SM adherence trajectories as an intervention response indicator. Future adaptive trials should identify nonresponders within the initial 2 weeks based on their SM adherence and integrate intervention strategies to enhance perceived competence in diet to benefit nonresponders. TRIAL REGISTRATION: ClinicalTrials.gov NCT05071287; https://clinicaltrials.gov/study/NCT05071287. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.cct.2022.106845.

A Future of Smarter Digital Health Empowered by Generative Pretrained Transformer.

Generative pretrained transformer (GPT) tools have been thriving, as ignited by the remarkable success of OpenAI's recent chatbot product. GPT technology offers countless opportunities to significantly improve or renovate current health care research and practice paradigms, especially digital health interventions and digital health-enabled clinical care, and a future of smarter digital health can thus be expected. In particular, GPT technology can be incorporated through various digital health platforms in homes and hospitals embedded with numerous sensors, wearables, and remote monitoring devices. In this viewpoint paper, we highlight recent research progress that depicts the future picture of a smarter digital health ecosystem through GPT-facilitated centralized communications, automated analytics, personalized health care, and instant decision-making.

Experiences and disease self-management in individuals living with chronic kidney disease: qualitative analysis of the National Kidney Foundation's online community.

BACKGROUND: Self-management of chronic kidney disease (CKD) is one of the keys in improving CKD outcomes and quality of life. There has been an increased use of online health communities to share the experiences of those living with CKD. By analyzing the CKD online forum data, this study aims to: 1) understand the experiences and challenges of individuals living with CKD, and 2) explore how online communities may help CKD patients in improving CKD self-management. METHODS: Publicly available posts of peer interactions on the National Kidney Foundation's online community for individuals affected by CKD were extracted in April 2021 using computer programming. A total of 20,436 posts were collected, of which 400 posts were analyzed using inductive thematic analysis, and saturation was reached. Two researchers coded each post independently, and discrepancies were discussed to reach consensus. RESULTS: The analysis identified seven themes: 1) Dynamics of CKD status, 2) CKD comorbidities, 3) Managing CKD and symptoms, 4) Life participation and outlook; 5) Navigating healthcare and clinical needs, 6) Medical tests and results; and 7) Support on the forum. The results revealed that comorbidities were common in CKD patients and early-stage CKD was not communicated in a timely manner to patients by the health care community; living with CKD challenged both CKD and caregivers; some common challenges included but were not limited to the management of a diet for CKD and co-morbidities (especially co-morbid diabetes), CKD dynamics and symptoms, and fear of/ways to prevent progression. Individuals living with CKD primarily used the online forum to share and seek information and emotional support for managing CKD (including co-morbidities). CONCLUSIONS: Challenges of living with CKD were found not only in those with advanced kidney disease and those on dialysis, but also in those with early and middle stages. Information and emotional support from the online forum serve as a platform to empower CKD individuals with the knowledge, skills and confidence for CKD self-management. Proactive and innovative strategies with a combination of virtual and real settings to improve self-management for individuals with all-stage CKD needs to be explored and tailored.

Bringing Structural Implications and Deep Learning-Based Drug Identification for KRAS Mutants.

Colorectal cancer is considered one of the leading causes of death that is linked with the Kirsten Rat Sarcoma (KRAS) harboring codons 13 and 61 mutations. The objective for this study is to search for clinically important codon 61 mutations and analyze how they affect the protein structural dynamics. Additionally, a deep-learning approach is used to carry out a similarity search for potential compounds that might have a comparatively better affinity. Public databases like The Cancer Genome Atlas and Genomic Data Commons were accessed for obtaining the data regarding mutations that are associated with colon cancer. Multiple analysis such as genomic alteration landscape, survival analysis, and systems biology-based kinetic simulations were carried out to predict dynamic changes for the selected mutations. Additionally, a molecular dynamics simulation of 100 ns for all the seven shortlisted codon 61 mutations have been conducted, which revealed noticeable deviations. Finally, the deep learning-based predicted compounds were docked with the KRAS 3D conformer, showing better affinity and good docking scores as compared to the already existing drugs. Taking together the outcomes of systems biology and molecular dynamics, it is observed that the reported mutations in the SII region are highly detrimental as they have an immense impact on the protein sensitive sites' native conformation and overall stability. The drugs reported in this study show increased performance and are encouraged to be used for further evaluation regarding the situation that ascends as a result of KRAS mutations.

Personalized Behavioral Nutrition Among Older Asian Americans: Study Protocol.

BACKGROUND: Metabolomics profiling is an objective assessment of metabolic responses to intricate dietary patterns. However, few studies have investigated the potential benefits associated with personalized behavioral nutrition (PBN) interventions incorporating the metabolomics approach for improving diabetes outcomes for older Asian Americans with Type 2 diabetes. OBJECTIVE: This article describes the protocol for a pilot study testing self-management of a nutrition intervention-provided personalized dietary advice incorporating metabolites phenotypic feedback and digital self-monitoring of diet and blood glucose. METHODS: A total of 60 older Asian Americans will be randomized into two groups: a PBN group and a control group. Participants in the PBN group will receive personalized dietary advice based on dietary and phenotypic feedback-used metabolic profiles. This study aims to examine the feasibility and preliminary effects of the PBN on diabetes outcomes. RESULTS: The study began in September 2020, with estimated complete data collection by late 2021. DISCUSSION: Findings from this pilot study will inform future research for developing personalized nutrition interventions for people with Type 2 diabetes.

Diabetes-Related Topics in an Online Forum for Caregivers of Individuals Living With Alzheimer Disease and Related Dementias: Qualitative Inquiry.

BACKGROUND: Diabetes and Alzheimer disease and related dementias (ADRD) are the seventh and sixth leading causes of death in the United States, respectively, and they coexist in many older adults. Caring for a loved one with both ADRD and diabetes is challenging and burdensome. OBJECTIVE: This study aims to explore diabetes-related topics in the Alzheimer's Association ALZConnected caregiver forum by family caregivers of persons living with ADRD. METHODS: User posts on the Alzheimer's Association ALZConnected caregiver forum were extracted. A total of 528 posts related to diabetes were included in the analysis. Of the users who generated the 528 posts, approximately 96.1% (275/286) were relatives of the care recipient with ADRD (eg, child, grandchild, spouse, sibling, or unspecified relative). Two researchers analyzed the data independently using thematic analysis. Any divergence was discussed among the research team, and an agreement was reached with a senior researcher's input as deemed necessary. RESULTS: Thematic analysis revealed 7 key themes. The results showed that comorbidities of ADRD were common topics of discussions among family caregivers. Diabetes management in ADRD challenged family caregivers. Family caregivers might neglect their own health care because of the caring burden, and they reported poor health outcomes and reduced quality of life. The online forum provided a platform for family caregivers to seek support in their attempts to learn more about how to manage the ADRD of their care recipients and seek support for managing their own lives as caregivers. CONCLUSIONS: The ALZConnected forum provided a platform for caregivers to seek informational and emotional support for caring for persons living with ADRD and diabetes. The overwhelming burdens with these two health conditions were apparent for both caregivers and care recipients based on discussions from the online forum. Studies are urgently needed to provide practical guidelines and interventions for diabetes management in individuals with diabetes and ADRD. Future studies to explore delivering diabetes management interventions through online communities in caregivers and their care recipients with ADRD and diabetes are warranted.

Extraction of molecular features for the drug discovery targeting protein-protein interaction of Helicobacter pylori CagA and tumor suppressor protein ASSP2.

Half of the world population is infected by the Gram-negative bacterium Helicobacter pylori (H. pylori). It colonizes in the stomach and is associated with severe gastric pathologies including gastric cancer and peptic ulceration. The most virulent factor of H. pylori is the cytotoxin-associated gene A (CagA) that is injected into the host cell. CagA interacts with several host proteins and alters their function, thereby causing several diseases. The most well-known target of CagA is the tumor suppressor protein ASPP2. The subdomain I at the N-terminus of CagA interacts with the proline-rich motif of ASPP2. Here, in this study, we carried out alanine scanning mutagenesis and an extensive molecular dynamics simulation summing up to 3.8 µs to find out hot spot residues and discovered some new protein-protein interaction (PPI)-modulating molecules. Our findings are in line with previous biochemical studies and further suggested new residues that are crucial for binding. The alanine scanning showed that mutation of Y207 and T211 residues to alanine decreased the binding affinity. Likewise, dynamics simulation and molecular mechanics with generalized Born surface area (MMGBSA) analysis also showed the importance of these two residues at the interface. A four-feature pharmacophore model was developed based on these two residues, and top 10 molecules were filtered from ZINC, NCI, and ChEMBL databases. The good binding affinity of the CHEMBL17319 and CHEMBL1183979 molecules shows the reliability of our adopted protocol for binding hot spot residues. We believe that our study provides a new insight for using CagA as the therapeutic target for gastric cancer treatment and provides a platform for a future experimental study.

Helix-Switch Enables C99 Dimer Transition between the Multiple Conformations.

C99 is the immediate precursor of amyloid-ß (Aß) and therefore is a central intermediate in the pathway that is believed to result in Alzheimer's disease (AD). Recent studies have shown that C99 dimerization changes the Aß ratio, but the mechanism remains unclear. Previous studies of the C99 dimer have produced controversial structure models. To address these questions, we investigated C99 dimerization using molecular dynamics (MD) simulations. A helix-switch model was revealed in the formation and transition of the C99 dimer, and six types of conformations were identified. The different conformations show differential exposures of γ-cleavage sites and insertion depths in the bilayer, which may modulate γ-cleavage of C99 and lead to different Aß levels. Our results redefine C99 dimerization, provide a framework to mediate the current controversial results, and give insights into the understanding of the relationship between C99 dimerization and Aß formation.

Prediction of CYP450 Enzyme-Substrate Selectivity Based on the Network-Based Label Space Division Method.

A drug may be metabolized by multiple cytochrome P450 (CYP450) isoforms. Predicting the metabolic fate of drugs is very important to prevent drug-drug interactions in the development of novel pharmaceuticals. Prediction of CYP450 enzyme-substrate selectivity is formulized as a multilabel learning task in this study. First, we compared the performance of feature combinations based on four different categories of features, which are physiochemical property descriptors, mol2vec descriptors, extended connectivity fingerprints, and molecular access system key fingerprints on modeling. After identifying the best combination of features, we applied seven different multilabel models, which are multilabel k-nearest neighbor (ML-kNN), multilabel twin support vector machine, and five network-based label space division (NLSD)-based methods (NLSD-MLP, NLSD-XGB, NLSD-EXT, NLSD-RF, and NLSD-SVM). All of the six models (ML-kNN, NLSD-MLP, NLSD-XGB, NLSD-EXT, NLSD-RF, and NLSD-SVM) in this paper exhibit better performances than the previous work. Besides, NLSD-XGB achieves the best performance with the average top-1 prediction success of 91.1%, the average top-2 prediction success of 96.2%, and the average top-3 prediction success of 98.2%. When compared with the previous work, NLSD-XGB shows a significant improvement over 11% on top-1 in the 10 times repeated 5-fold cross-validation test and over 14% on top-1 in the 10 times repeated hold-out method. To the best of our knowledge, the network-based label space division model is first introduced in drug metabolism and performs well in this task.

An Integrated Pan-Cancer Analysis and Structure-Based Virtual Screening of GPR15.

G protein-coupled receptor 15 (GPR15, also known as BOB) is an extensively studied orphan G protein-coupled receptors (GPCRs) involving human immunodeficiency virus (HIV) infection, colonic inflammation, and smoking-related diseases. Recently, GPR15 was deorphanized and its corresponding natural ligand demonstrated an ability to inhibit cancer cell growth. However, no study reported the potential role of GPR15 in a pan-cancer manner. Using large-scale publicly available data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, we found that GPR15 expression is significantly lower in colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) than in normal tissues. Among 33 cancer types, GPR15 expression was significantly positively correlated with the prognoses of COAD, neck squamous carcinoma (HNSC), and lung adenocarcinoma (LUAD) and significantly negatively correlated with stomach adenocarcinoma (STAD). This study also revealed that commonly upregulated gene sets in the high GPR15 expression group (stratified via median) of COAD, HNSC, LUAD, and STAD are enriched in immune systems, indicating that GPR15 might be considered as a potential target for cancer immunotherapy. Furthermore, we modelled the 3D structure of GPR15 and conducted structure-based virtual screening. The top eight hit compounds were screened and then subjected to molecular dynamics (MD) simulation for stability analysis. Our study provides novel insights into the role of GPR15 in a pan-cancer manner and discovered a potential hit compound for GPR15 antagonists.

The dynamic binding of cholesterol to the multiple sites of C99: as revealed by coarse-grained and all-atom simulations.

It is generally believed that the etiology of Alzheimer's disease (AD) is closely related to the amyloid-ß polypeptides, produced from γ-secretase cleavage of C99. There is preliminary evidence that cholesterol directly activates γ-secretase cleavage of C99 through mechanisms that have not been understood so far. In this article, coarse-grained (CG) and all-atom (AT) simulations were employed to investigate the association between C99 and cholesterol, which is essential for our understanding of the role of cholesterol in the amyloidogenic pathway. Firstly, we find that both the N-terminus and the C-terminus of the C99 transmembrane domain (TMD) show interactions with cholesterol. Secondly, a multi-site dynamic cholesterol binding model was captured from the simulations, where 6 binding sites in the C99 TMD were presented. The analyses of the binding energies show that cholesterol prefers the site no. 1, 2, 4 and 5 over others. The most favorable binding energy of nearly -58.857 kJ mol-1 is from site 1, the repeat GxxxG motif. There are two pathways and two binding states of cholesterol binding to this site. Ser697 and Phe690 contribute most to the stabilization of the tightly binding state and the loosely binding state, respectively. The other binding sites described may also be potential drug targets. Thirdly, the residues GAVILMTKF, especially IVKF play a key role in this association. The C99 model appears to suggest a new mechanism for cholesterol binding. Finally, the multiple-site dynamic cholesterol binding model better explains the hypotheses that cholesterol promotes the amyloidogenic AßPP route. The GxxxA motif in the middle of the C99 transmembrane domain is completely exposed without cholesterol sheltering, which might help γ-secretase identify the cleavage sites and then promote γ-cleavage. Our results provide a detailed picture of dynamic cholesterol binding, which is crucial to our recognition of the potential influence of cholesterol on the C99 process and the etiology of AD.

Degradation of Stains from Metal Surfaces Using a DBD Plasma Microreactor.

The surface cleaning of metals plays a pivotal role in ensuring their overall performance and functionality. Dielectric barrier discharge (DBD) plasma, due to its unique properties, has been considered to be a good alternative to traditional cleaning methods. The confinement of DBD plasma in microreactors brings additional benefits, including excellent stability at high pressures, enhanced density of reactive species, reduced safety risks, and less gas and energy consumption. In the present work, we demonstrated a DBD plasma-based method for the degradation of stains from metal surfaces in a microreactor. Aluminum plates with capsanthin stains were used to investigate the influence of operational parameters on the decolorization efficiency, including plasma discharge power, plasma processing time, and O2 content in the atmosphere. The results revealed that an increase in plasma discharge power and plasma processing time together with an appropriate amount of O2 in the atmosphere promote the degradation of capsanthin stains. The optimum processing condition was determined to be the following: plasma power of 11.3 W, processing time of 3 min, and Ar/O2 flow rate of 48/2 sccm. The evolution of composition, morphology, bonding configuration, and wettability of aluminum plates with capsanthin and lycopene stains before and after plasma treatment were systematically investigated, indicating DBD plasma can efficiently degrade stains from the surface of metals without damage. On this basis, the DBD plasma cleaning approach was extended to degrade rhodamine B and malachite green stains from different metals, suggesting it has good versatility. Our work provides a simple, efficient, and solvent-free approach for the surface cleaning of metals.

Rational synthesis of methylsilsesquioxane aerogels addressing thermal load and compression recovery issues in Li-ion batteries.

Li-ion batteries suffer from two key safety issues: thermal overload and compression recovery, which may lead to flammability and mechanical failure. Silica aerogels are promising solutions to both these issues owing to their excellent thermal stability and tailored mechanical properties. However, finding the optimum sol composition in sol-gel-based aerogel synthesis is needed to address these issues at industry-relevant scales. Here, we propose an innovative approach to determine the optimum sol composition for methylsilsesquioxane (MSQ) aerogel sheets, which is based on the mechanisms of the effects of molar ratios of hydrolysis water and isopropyl alcohol (IPA) to methyltrimethoxysilane (MTMS) on the physical properties of MSQ aerogel sheets and according to the ternary contour distribution of their properties. The synthesized MSQ aerogels exhibited a soft, light, and powderless texture and featured superhydrophobic properties (150.2°), low thermal conductivity of 33.6 mW/(m·K), high thermal stability temperature in nitrogen atmosphere at 479.3 °C and moderate short-term (<6 h) service temperature of 120.0 °C. Significantly, the structural stability and elasticity of the aerogels surpassed the current state-of-the-art, showing recovery to 81.3 % of the original thickness and 85.2 % of the original stress after being subjected to 400 cycles of high-speed and high-strain consecutive compression, respectively. These excellent properties make the MSQ aerogel sheets promising for applications in thermal load and compression recovery management of diverse energy storage devices, including batteries for next-generation electric vehicles.

Performance of the Dreem 2 EEG headband, relative to polysomnography, for assessing sleep in Parkinson's disease.

GOAL AND AIMS: To pilot the feasibility and evaluate the performance of an EEG wearable for measuring sleep in individuals with Parkinson's disease. FOCUS TECHNOLOGY: Dreem Headband, Version 2. REFERENCE TECHNOLOGY: Polysomnography. SAMPLE: Ten individuals with Parkinson's disease. DESIGN: Individuals wore Dreem Headband during a single night of polysomnography. CORE ANALYTICS: Comparison of summary metrics, bias, and epoch-by-epoch analysis. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Correlation of summary metrics with demographic and Parkinson's disease characteristics. CORE OUTCOMES: Summary statistics showed Dreem Headband overestimated several sleep metrics, including total sleep, efficiency, deep sleep, and rapid eye movement sleep, with an exception in light sleep. Epoch-by-epoch analysis showed greater specificity than sensitivity, with adequate accuracy across sleep stages (0.55-0.82). IMPORTANT SUPPLEMENTAL OUTCOMES: Greater Parkinson's disease duration and rapid eye movement behavior were associated with more wakefulness, and worse Parkinson's disease motor symptoms were associated with less deep sleep. CORE CONCLUSION: The Dreem Headband performs similarly in Parkinson's disease as it did in non-Parkinson's disease samples and shows promise for improving access to sleep assessment in people with Parkinson's disease.

High-Performance Methylsilsesquioxane Aerogels: Hydrolysis Mechanisms and Maximizing Compression Properties.

Synthesis of methylsilsesquioxane aerogels by ambient pressure drying instead of supercritical drying has recently emerged as a major trend, but the issues of low mechanical strength and unstable performance still need to be resolved. This work reveals the microscopic formation mechanisms of gel skeleton based on the kinetic characteristics of methyltrimethoxysilane (MTMS) precursor hydrolysis and the associated sol-gel reactions. The effects of oxalic acid concentration (cOA) and hydrolysis time of MTMS solution (th) on the gelation time, morphologies, microstructures, chemical structure, and compression properties of the as-synthesized methylsilsesquioxane aerogels are investigated. The optimal cOA and th are 38.4 mmol/L and 120 min, respectively, endowing the methylsilsesquioxane aerogels with a compression strength of 0.170 MPa and a maximum compression strain of 61.2%. Precise control of the hydrolysis conditions ensures the formation of branched particle-to-particle networks, which is crucial for maximizing the compression properties of methylsilsesquioxane aerogels synthesized under industry-relevant conditions.

Preparation of a Bi6O5(OH)3(NO3)5·2H2O/AgBr composite and its long-lasting antibacterial efficacy.

A novel Bi6O5(OH)3(NO3)5·2H2O/AgBr (6535BBN/AgBr) composite with long-lasting antibacterial efficacy was prepared. The microstructure of the composite was characterized. AgBr nanoparticles (NPs) were sandwiched in 6535BBN nanosheets (NSs) or loaded on their surfaces. The utilization of 6535BBN as carriers contributed to the long-term lasting antibacterial activity of the composite after storage in water or 0.9% NaCl. The antibacterial activity was evaluated by inhibition zones against E. coli. The inhibition zone diameters of 6535BBN/AgBr stored in water for 0 h, 8 h, 16 h, and 48 h were measured as 22.50, 21.71, 20.43, and 20.29 mm, respectively. The activity of the composite after storage in water for 48 h remained 90.2% of that in the beginning. After storing in 0.9% NaCl for 16 h, the activity was determined to be 90.1% of that in the beginning. In comparison with the rapid decrease in the antibacterial activity of pure AgBr, the slow reduction of 6535BBN/AgBr after storage indicates long-lasting efficacy. The excellent dispersion states of 6535BBN/AgBr powders after storage in solutions were revealed, and the positive relationship between the dispersion state and its long-lasting antibacterial activity was suggested. Based on the unique load-on-carrier (LOC) structure, the long-lasting antibacterial performance was promoted by the synergy of the sharp-edge-cutting effect of 6535BBN NSs, prolonged ROS antibacterial effect, and restrained sterilization effects of silver ions caused by their slow release.

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings.

BACKGROUND: A 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. METHODS: We 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. RESULTS: The 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. CONCLUSION: The 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.

A Reinforcement Learning Approach for Flexible Job Shop Scheduling Problem With Crane Transportation and Setup Times.

Flexible job shop scheduling problem (FJSP) has attracted research interests as it can significantly improve the energy, cost, and time efficiency of production. As one type of reinforcement learning, deep Q-network (DQN) has been applied to solve numerous realistic optimization problems. In this study, a DQN model is proposed to solve a multiobjective FJSP with crane transportation and setup times (FJSP-CS). Two objectives, i.e., makespan and total energy consumption, are optimized simultaneously based on weighting approach. To better reflect the problem realities, eight different crane transportation stages and three typical machine states including processing, setup, and standby are investigated. Considering the complexity of FJSP-CS, an identification rule is designed to organize the crane transportation in solution decoding. As for the DQN model, 12 state features and seven actions are designed to describe the features in the scheduling process. A novel structure is applied in the DQN topology, saving the calculation resources and improving the performance. In DQN training, double deep Q-network technique and soft target weight update strategy are used. In addition, three reported improvement strategies are adopted to enhance the solution qualities by adjusting scheduling assignments. Extensive computational tests and comparisons demonstrate the effectiveness and advantages of the proposed method in solving FJSP-CS, where the DQN can choose appropriate dispatching rules at various scheduling situations.

Effect of Cholesterol on C99 Dimerization: Revealed by Molecular Dynamics Simulations.

C99 is the immediate precursor for amyloid beta (Aß) and therefore is a central intermediate in the pathway that is believed to result in Alzheimer's disease (AD). It has been suggested that cholesterol is associated with C99, but the dynamic details of how cholesterol affects C99 assembly and the Aß formation remain unclear. To investigate this question, we employed coarse-grained and all-atom molecular dynamics simulations to study the effect of cholesterol and membrane composition on C99 dimerization. We found that although the existence of cholesterol delays C99 dimerization, there is no direct competition between C99 dimerization and cholesterol association. In contrast, the existence of cholesterol makes the C99 dimer more stable, which presents a cholesterol binding C99 dimer model. Cholesterol and membrane composition change the dimerization rate and conformation distribution of C99, which will subsequently influence the production of Aß. Our results provide insights into the potential influence of the physiological environment on the C99 dimerization, which will help us understand Aß formation and AD's etiology.

Community Health Worker-Led mHealth-Enabled Diabetes Self-management Education and Support Intervention in Rural Latino Adults: Single-Arm Feasibility Trial.

BACKGROUND: Latinos living in rural South Texas have a higher prevalence of diabetes, but their access to diabetes self-management education and support (DSMES) is limited. OBJECTIVE: We aimed to test the feasibility of a community health worker-led, mobile health (mHealth)-based DSMES intervention to reduce disparities in accessing DSMES in underserved rural Latino residents in South Texas. METHODS: This 12-week, single-arm, pre-post trial was delivered by trained community health workers to 15 adults with type 2 diabetes. The intervention consisted of digital diabetes education, self-monitoring, a cloud-based connected platform, and community health worker support. Feasibility was evaluated as retention, actual intervention use, program satisfaction, and barriers to implementation. We also explored the intervention's effect on weight loss and hemoglobin A1c (HbA1c). RESULTS: All 15 participants were Latino (mean age 61.87 years, SD 10.67; 9/15 female, 60%). The retention rate at posttest was 14 of 15 (93%). On average, the participants completed 37 of 42 (88%) digital diabetes education lessons with 8 participants completing all lessons. Participants spent 81/91 days (89%) step tracking, 71/91 days (78%) food logging, 43/91 days (47%) blood glucose self-monitoring, and 74/91 days (81%) weight self-monitoring. The level of program satisfaction was high. On average, participants lost 3.5 (SD 3.2) kg of body weight (P=.001), while HbA1c level remained unchanged from baseline (6.91%, SD 1.28%) to posttest (7.04%, SD 1.66%; P=.668). CONCLUSIONS: A community health worker-led mHealth-based intervention was feasible and acceptable to improve access to DSMES services for Latino adults living in rural communities. Future randomized controlled trials are needed to test intervention efficacy on weight loss and glycemic control.