Comparing COVID-19 vaccination coverage, adverse reactions and impact of social determinants of health on vaccine hesitancy in ADRD/MCI and non-ADRD/MCI population: protocol for a retrospective cross-sectional study.

INTRODUCTION: COVID-19 vaccination is crucial for vulnerable people with underlying chronic conditions such as Alzheimer's disease and related dementias (ADRD) and mild cognitive impairment (MCI). These individuals face unique challenges, including higher risk of COVID-19, difficulties in adopting preventive behaviours and vaccine hesitancy due to concerns about adverse reactions. Therefore, efforts to promote vaccination, including boosters tailored to the currently circulating virus, are essential for people with ADRD/MCI. OBJECTIVE: The primary purpose of this study protocol is to conduct a comprehensive analysis of COVID-19 vaccination coverage and adverse reactions among individuals with ADRD/MCI in comparison to those without ADRD/MCI. Additionally, the proposed study aims to investigate the impact of social determinants of health on COVID-19 vaccination and vaccine hesitancy in individuals with ADRD/MCI. METHODS AND ANALYSIS: A retrospective cross-sectional study will be conducted utilising data from the All of Us (AoU) Researcher Workbench. Relevant data fields are extracted from sources including demographic information, COVID-19 Vaccine Survey, Basic Survey, Health Access & Utilization, Social Determinants of Health, and Electronic Health Record (EHR) data. Data on vaccination, adverse reactions and vaccine hesitancy will be collected through COVID-19 vaccine survey questionnaires. Propensity score matching and binary logistic regression will be applied to assess the vaccination rates and vaccine hesitancy, while controlling for demographic characteristics and social determinants of health factors. ETHICS AND DISSEMINATION: This study protocol received approval from the Institutional Review Board at Florida State University (STUDY00004571). Results will be disseminated through publication in peer-reviewed journals and presented at scientific conferences.

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.

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.

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.

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.

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.

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.

The Facial Skin Blood Flow Change of Stroke Patients with Facial Paralysis after Peripheral Magnetic Stimulation: A Pilot Study.

BACKGROUND: Facial paralysis (FP) is a common symptom after stroke, which influences the quality of life and prognosis of patients. Recently, peripheral magnetic stimulation (PMS) shows potential effects on peripheral and central nervous system damage. However, the effect of PMS on FP after stroke is still unclear. METHODS: In this study, we applied PMS on the facial nerve of nine stroke patients with FP. At the same time, laser speckle contrast imaging (LSCI) was used to explore the facial skin blood flow (SkBF) in 19 healthy subjects and nine stroke patients with FP before and after the PMS intervention. The whole face was divided into 14 regions to compare the SkBF in different sub-areas. RESULTS: In baseline SkBF, we found that there were no significant differences in the SkBF between the left and right faces in the healthy subjects. However, there was a significant difference in the SkBF between the affected and unaffected faces in Region 7 (Chin area, p = 0.046). In the following five minutes after the PMS intervention (Pre_0-5 min), the SkBF increased in Region 5 (p = 0.014) and Region 7 (p = 0.046) and there was an increasing trend in Region 3 (p = 0.088) and Region 6 (p = 0.069). In the five to ten minutes after the intervention (Post_6-10 min), the SkBF increased in Region 5 (p = 0.009), Region 6 (p = 0.021) and Region 7 (p = 0.023) and there was an increasing trend in Region 3 (p = 0.080) and left and right whole face (p = 0.051). CONCLUSIONS: These pilot results indicate that PMS intervention could increase facial skin blood flow in stroke patients with FP. A further randomized controlled trial can be performed to explore its possible clinical efficacy.

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.

A technology assisted precision ketogenic diet intervention for cardio-renal-metabolic health in overweight or obese adults: Protocol for a randomized controlled trial.

BACKGROUND: The obesity epidemic is a public health concern, as it is associated with a variety of chronic conditions. The ketogenic diet has drawn much scientific and public attention. However, implementation is challenging and its effect on cardio-renal-metabolic health is inconclusive. This study will assess the feasibility, acceptability, and preliminary efficacy of a technology-assisted ketogenic diet on cardio-renal-metabolic health. METHODS: This is a single center, 6-month, stratified, randomized controlled trial. A total of 60 overweight/obese adults (18+ years old) will be enrolled, including 20 without type 2 diabetes (T2D) and without chronic kidney disease (CKD); 20 with T2D, but without CKD; and 20 with early-stage CKD. Participants will be stratified based on health conditions and randomized into a ketogenic diet (n = 30) or a low-fat diet group (n = 30). Health education involving diet and physical activity will be delivered both digitally and in-person. Mobile and connected health technologies will be used to track lifestyle behaviors and health indicators, as well as provide weekly feedback. The primary outcome (weight) and the secondary outcomes (e.g., blood pressure, glycemic control, renal health) will be assessed with traditional measurements and metabolomics. DISCUSSION: Mobile and connected health technologies provide new opportunities to improve chronic condition management, health education attendance, planned lifestyle changes and engagement, and health outcomes. The advancement of bioinformatics technology offers the possibility to profile and analyze omics data which may advance our understanding of the underlying mechanisms of intervention effects on health outcomes at the molecular level for personalized and precision lifestyle interventions.

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.

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.

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.

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.

Structural insights of catalytic mechanism in mutant pyrazinamidase of Mycobacterium tuberculosis.

Pyrazinamidase (PZase) is a member of Fe-dependent amidohydrolases that activates pyrazinamide (PZA) into active pyrazinoic acid (POA). PZA, a nicotinamide analogue, is an essential first-line drug used in Mycobacterium tuberculosis (Mtb) treatment. The active form of PZA, POA, is toxic and potently inhibits the growth of latent Mtb, which makes it possible to shorten the conventional 9-month tuberculosis treatment to 6 months. In this study, an extensive molecular dynamics simulation was carried out to the study the resistance mechanism offered by the three mutations Q10P and D12A and G97D. Our results showed that two regions Gln10-His43, Phe50-Gly75 are profoundly affected by these mutations. Among the three mutations, Q10P and D12A mutations strongly disturb the communication among the catalytic triad (Asp8, Lys98 and Cys138). The oxyanion hole is formed between the backbone nitrogen atoms of A134 and C138 which stabilizes the hydroxyl anion of nicotinamide. The D12A mutation greatly disturbs the oxyanion hole formation followed by the Q10P and G97D. Our results also showed that these mutations destabilize the interaction between Fe2+ ion and Asp49, His51, His57 and His71. The binding pocket analysis showed that these mutations increase the cavity volume, which results in loose binding of PZA. MMGBSA analyzes have shown that these mutations reduce the binding affinity to the PZA drug. Our results may provide useful information for the design of new and effective PZase inhibitors based on structural information of WT and mutant PZases.Communicated by Ramaswamy H. Sarma.

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.