Early adverse physiological event detection using commercial wearables: challenges and opportunities.

Data from commercial off-the-shelf (COTS) wearables leveraged with machine learning algorithms provide an unprecedented potential for the early detection of adverse physiological events. However, several challenges inhibit this potential, including (1) heterogeneity among and within participants that make scaling detection algorithms to a general population less precise, (2) confounders that lead to incorrect assumptions regarding a participant's healthy state, (3) noise in the data at the sensor level that limits the sensitivity of detection algorithms, and (4) imprecision in self-reported labels that misrepresent the true data values associated with a given physiological event. The goal of this study was two-fold: (1) to characterize the performance of such algorithms in the presence of these challenges and provide insights to researchers on limitations and opportunities, and (2) to subsequently devise algorithms to address each challenge and offer insights on future opportunities for advancement. Our proposed algorithms include techniques that build on determining suitable baselines for each participant to capture important physiological changes and label correction techniques as it pertains to participant-reported identifiers. Our work is validated on potentially one of the largest datasets available, obtained with 8000+ participants and 1.3+ million hours of wearable data captured from Oura smart rings. Leveraging this extensive dataset, we achieve pre-symptomatic detection of COVID-19 with a performance receiver operator characteristic (ROC) area under the curve (AUC) of 0.725 without correction techniques, 0.739 with baseline correction, 0.740 with baseline correction and label correction on the training set, and 0.777 with baseline correction and label correction on both the training and the test set. Using the same respective paradigms, we achieve ROC AUCs of 0.919, 0.938, 0.943 and 0.994 for the detection of self-reported fever, and 0.574, 0.611, 0.601, and 0.635 for detection of self-reported shortness of breath. These techniques offer improvements across almost all metrics and events, including PR AUC, sensitivity at 75% specificity, and precision at 75% recall. The ring allows continuous monitoring for detection of event onset, and we further demonstrate an improvement in the early detection of COVID-19 from an average of 3.5 days to an average of 4.1 days before a reported positive test result.

Capsaicin as a Dietary Additive for Dairy Cows: A Meta-Analysis on Performance, Milk Composition, Digestibility, Rumen Fermentation, and Serum Metabolites.

This study aimed to evaluate the effects of dietary supplementation with capsaicin (CAP) on productive performance, milk composition, nutrient digestibility, ruminal fermentation, and serum metabolites of dairy cows using a meta-analytical approach. The database included 13 studies, from which the response variables of interest were obtained. Data were analyzed using a random effects model, and results were expressed as weighted mean differences between treatments supplemented with and without CAP. Dietary supplementation with CAP increased (p < 0.05) dry matter intake, milk yield, feed efficiency, milk fat yield, and milk fat content. However, CAP supplementation did not affect (p > 0.05) milk protein and lactose yield, milk urea nitrogen, or milk somatic cell count. Greater (p < 0.05) apparent digestibility of dry matter and crude protein was observed in response to the dietary inclusion of CAP. Likewise, supplementation with CAP increased (p < 0.05) the rumen concentration of total volatile fatty acids. In contrast, CAP supplementation did not affect (p > 0.05) ruminal pH or the ruminal concentration of ammonia nitrogen, acetate, propionate, and butyrate. In blood serum, CAP supplementation increased (p < 0.05) the glucose concentration and decreased (p < 0.05) the concentration of non-esterified fatty acids. However, CAP supplementation did not affect (p > 0.05) the serum concentration of urea and beta-hydroxybutyrate. In conclusion, capsaicin can be used as a dietary additive to improve the productive performance, milk composition, and nutrient digestibility in dairy cows and, at the same time, improve the ruminal concentration of total volatile fatty acids and serum levels of glucose and non-esterified fatty acids.

Flexible 3D Plasmonic Web Enables Remote Surface Enhanced Raman Spectroscopy.

Nanoplasmonic materials concentrate light in specific regions of dramatic electromagnetic enhancement: hot spots. Such regions can be employed to perform single molecule detection via surface-enhanced Raman spectroscopy. However, this phenomenon is challenging since hot spots are expected to be highly intense/abundant and positioning of molecules within such hot spots is crucial to manage with ultrasensitive SERS. Herein, it is discovered that a 3D plasmonic web embedded within a biohybrid (3D-POWER) exhibits plasmonic transmission, spontaneously absorbs the analyte, and meets these so much needed criteria in ultrasensitive SERS. 3D-POWER is built with nanopaper and self-assembled layers of graphene oxide and gold nanorods. According to in silico experiments, 3D-POWER captures light in a small region and performs plasmonic field transmission in a surrounding volume, thereby activating a plasmonic web throughout the simulated volume. The study also provides experimental evidence supporting the plasmonic field transport ability of 3D power, which operates as a SERS signal carrier (even beyond the apparatus field of view), and the ultrasensitive behavior of this ecofriendly and flexible material facilitating yoctomolar limit of detection. Besides, 3D-POWER is proven useful in food and biofluids analysis. It is foreseen that 3D-POWER can be employed as a valuable platform in (bio)analytical applications.

Differential molecular biomarker expression in corals over a gradient of water quality stressors in Maunalua Bay, Hawaii.

Coral reefs globally face unprecedented challenges from anthropogenic stressors, necessitating innovative approaches for effective assessment and management. Molecular biomarkers, particularly those related to protein expressions, provide a promising avenue for diagnosing coral health at the cellular level. This study employed enzyme-linked immunosorbent assays to evaluate stress responses in the coral Porites lobata along an environmental gradient in Maunalua Bay, Hawaii. The results revealed distinct protein expression patterns correlating with anthropogenic stressor levels across the bay. Some proteins, such as ubiquitin and Hsp70, emerged as sensitive biomarkers, displaying a linear decrease in response along the environmental gradient, emphasizing their potential as indicators of stress. Our findings highlighted the feasibility of using protein biomarkers for real-time assessment of coral health and the identification of stressors. The identified biomarkers can aid in establishing stress thresholds and evaluating the efficacy of management interventions. Additionally, we assessed sediment and water quality from the inshore areas in the bay and identified organic contaminants, including polycyclic aromatic hydrocarbons and pesticides, in bay sediments and waters.

Grass Infusions in Autocidal Gravid Ovitraps to Lure Aedes Albopictus.

Aedes albopictus is a vector of several pathogens of significant public health concern. In this situation, gravid traps have become a common surveillance tool for Aedes spp., which commonly use hay infusions as an attractant. Diverse grass infusions have been assessed to enhance the attraction to this vector mosquito. However, these studies have focused on the oviposition effect, and the attraction potential to gravid Ae. albopictus females has not been evaluated yet. Here we report the attractiveness of infusions of 4 different botanical species (Cenchrus purpureus, Cyanodon dactylon, Megathyrus maximus, Pennisetum ciliare) as baits in sticky ovitraps and autocidal gravid ovitraps (AGOs) under laboratory, semifield, and field conditions. In the laboratory, Cynodon dactylon showed attractiveness, whereas in semifield conditions, both C. dactylon and Megathyrsus maximus were similarly attractive for gravid Ae. albopictus. None of the infusions conducted with AGOs were able to lure Ae. albopictus and other species of mosquitoes in a 14-wk field experiment. Our results demonstrate the feasibility of finding more attractive infusions for Ae. albopictus females to improve the efficacy of AGO traps, but further testing of infusions in AGOs in field settings is needed.

Green synthesis of ZnO nanoparticles from ball moss (Tillandsia recurvata) extracts: characterization and evaluation of their photocatalytic activity.

Green synthesis (GS), referred to the synthesis using bioactive agents such as plant materials, microorganisms, and various biowastes, prioritizing environmental sustainability, has become increasingly relevant in international scientific practice. The availability of plant resources expands the scope of new exploration opportunities, including the evaluation of new sources of organic extracts, for instance, to the best of our knowledge, no scientific articles have reported the synthesis of zinc oxide nanoparticles (ZnO NPs) from organic extracts of T. recurvata, a parasitic plant very common in semiarid regions of Mexico.This paper presents a greener and more efficient method for synthesizing ZnO NPs using T. recurvata extract as a reducing agent. The nanoparticles were examined by different techniques such as UV-vis spectroscopy, X-ray diffraction, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and BET surface analysis. The photocatalytic and adsorptive effect of ZnO NPs was investigated against methylene blue (MB) dye in aqueous media under sunlight irradiation considering an equilibrium time under dark conditions. ZnO nanoparticles were highly effective in removing MB under sunlight irradiation conditions, showing low toxicity towards human epithelial cells, making them promising candidates for a variety of applications. This attribute fosters the use of green synthesis techniques for addressing environmental issues.This study also includes the estimation of the supported electric field distributions of ZnO NPs in their individual spherical or rounded shapes and their randomly oriented organization, considering different diameters, by simulating their behavior in the visible wavelength range, observing resonant enhancements due to the strong light-matter interaction around the ZnO NPs boundaries.

Divergent Selection in Low Recombination Regions Shapes the Genomic Islands in Two Incipient Shorebird Species.

Speciation in the face of gene flow is usually associated with a heterogeneous genomic landscape of divergence in nascent species pairs. However, multiple factors, such as divergent selection and local recombination rate variation, can influence the formation of these genomic islands. Examination of the genomic landscapes of species pairs that are still in the early stages of speciation provides an insight into this conundrum. In this study, population genomic analyses were undertaken using a wide range of sampling and whole-genome resequencing data from 96 unrelated individuals of Kentish plover (Charadrius alexandrinus) and white-faced plover (Charadrius dealbatus). We suggest that the two species exhibit varying levels of population admixture along the Chinese coast and on the Taiwan Island. Genome-wide analyses for introgression indicate that ancient introgression had occurred in Taiwan population, and gene flow is still ongoing in mainland coastal populations. Furthermore, we identified a few genomic regions with significant levels of interspecific differentiation and local recombination suppression, which contain several genes potentially associated with disease resistance, coloration, and regulation of plumage molting and thus may be relevant to the phenotypic and ecological divergence of the two nascent species. Overall, our findings suggest that divergent selection in low recombination regions may be a main force in shaping the genomic islands in two incipient shorebird species.

When colors mislead: Genomics and bioacoustics prompt re-classification of Asian flycatcher radiation (Aves: Niltavinae).

Traditional classification of many animals, including birds, has been highly dependent on external morphological characters like plumage coloration. However, both bioacoustics and genetic or genomic data have revolutionized our understanding of the relationships of certain lineages and led to sweeping taxonomic re-organizations. In this study, we present a case of erroneous delimitation of genus boundaries in the species-rich flycatcher subfamily Niltavinae. Genera within this subfamily have historically been delineated based on blue versus brown male body plumage until recent studies based on a few mitochondrial and nuclear loci unearthed several cases of generic misclassification. Here we use extensive bioacoustic data from 43 species and genomic data from 28 species for a fundamental reclassification of species in the Niltavinae. Our study reveals that song is an important trait to classify these birds even at the genus level, whereas plumage traits exhibit ample convergence and have led to numerous historic misattributions. Our taxonomic re-organization leads to new biogeographic limits of major genera, such that the genus Cyornis now only extends as far east as the islands of Sulawesi, Sula, and Banggai, whereas Eumyias is redefined to extend far beyond Wallace's Line to the islands of Seram and Timor. Our conclusions advise against an over-reliance on morphological traits and underscore the importance of integrative datasets.

A Case of Subacute Combined Degeneration Secondary to Recreational Whippet Use.

'Whippets' or nitrous oxide (N2O) abuse is a rare etiology of B12 deficiency and subacute combined degeneration (SCD). Often used in the medical field as an anesthetic, recreational use has rapidly increased given its euphoric effects. Easy accessibility over the counter at local stores due to the fact that it has bacteriostatic effects useful for canisters of creams and perishable goods. This makes N2O, or "laughing gas," easy to obtain. Long-standing abuse of N2O can lead to deleterious effects on the central nervous system, including SCD, polyneuropathy, and death. Presentation includes frequent falls, ataxic gait, weakness in the lower extremities, and neuropathy. Herein, we present a case of a 25-year-old male with no past medical history presenting with SCD in the setting of longstanding recreational whippet use. Our case highlights an important consideration for all specialties, including emergency medicine, psychiatry, family medicine, and internal medicine physicians.

Hypothesis Scoring for Confidence-Aware Blood Pressure Estimation With Particle Filters.

We propose our Confidence-Aware Particle Filter (CAPF) framework that analyzes a series of estimated changes in blood pressure (BP) to provide several true state hypotheses for a given instance. Particularly, our novel confidence-awareness mechanism assigns likelihood scores to each hypothesis in an effort to discard potentially erroneous measurements - based on the agreement amongst a series of estimated changes and the physiological plausibility when considering DBP/SBP pairs. The particle filter formulation (or sequential Monte Carlo method) can jointly consider the hypotheses and their probabilities over time to provide a stable trend of estimated BP measurements. In this study, we evaluate BP trend estimation from an emerging bio-impedance (Bio-Z) prototype wearable modality although it is applicable to all types of physiological modalities. Each subject in the evaluation cohort underwent a hand-gripper exercise, a cold pressor test, and a recovery state to increase the variation to the captured BP ranges. Experiments show that CAPF yields superior continuous pulse pressure (PP), diastolic blood pressure (DBP), and systolic blood pressure (SBP) estimation performance compared to ten baseline approaches. Furthermore, CAPF performs on track to comply with AAMI and BHS standards for achieving a performance classification of Grade A, with mean error accuracies of -0.16 ± 3.75 mmHg for PP (r = 0.81), 0.42 ± 4.39 mmHg for DBP (r = 0.92), and -0.09 ± 6.51 mmHg for SBP (r = 0.92) from more than test 3500 data points.

Gene regulatory network inference with popInfer reveals dynamic regulation of hematopoietic stem cell quiescence upon diet restriction and aging.

Inference of gene regulatory networks (GRNs) can reveal cell state transitions from single-cell genomics data. However, obstacles to temporal inference from snapshot data are difficult to overcome. Single-nuclei multiomics data offer means to bridge this gap and derive temporal information from snapshot data using joint measurements of gene expression and chromatin accessibility in the same single cells. We developed popInfer to infer networks that characterize lineage-specific dynamic cell state transitions from joint gene expression and chromatin accessibility data. Benchmarking against alternative methods for GRN inference, we showed that popInfer achieves higher accuracy in the GRNs inferred. popInfer was applied to study single-cell multiomics data characterizing hematopoietic stem cells (HSCs) and the transition from HSC to a multipotent progenitor cell state during murine hematopoiesis across age and dietary conditions. From networks predicted by popInfer, we discovered gene interactions controlling entry to/exit from HSC quiescence that are perturbed in response to diet or aging.

Community voices on factors influencing COVID-19 concerns and health decisions among racial and ethnic minorities in the school setting.

Racial and ethnic minority communities have been disproportionately affected by COVID-19, but the uptake of COVID-19 mitigation strategies like vaccination and testing have been slower in these populations. With the continued spread of COVID-19 while in-person learning is a priority, school-aged youth and their caregivers must make health-related decisions daily to ensure health at school. It is critical to understand factors associated with COVID-related health decisions such as vaccination, testing, and other health behaviors (e.g., wearing masks, hand washing). Community-engaged campaigns are necessary to overcome barriers to these health behaviors and promote health equity. The aim of this study was to examine COVID-19-related concerns and influences on health decisions in middle and high schools serving primarily racial and ethnic minority, low-income families. Seven focus groups were conducted with school staff, parents, and students (aged 16 years and older). Qualitative data were analyzed using a general inductive approach. Factors related to COVID-19 concerns and health decisions centered on (1) vaccine hesitancy, (2) testing hesitancy, (3) developmental stage (i.e., ability to engage in health behaviors based on developmental factors like age), (4) cultural and family traditions and beliefs, (5) compatibility of policies and places with recommended health behaviors, (6) reliability of information, and (7) perceived risk. We explore sub-themes in further detail. It is important to understand the community's level of concern and identify factors that influence COVID-19 medical decision making to better address disparities in COVID-19 testing and vaccination uptake.

Data-Driven Guided Attention for Analysis of Physiological Waveforms With Deep Learning.

Estimating physiological parameters - such as blood pressure (BP) - from raw sensor data captured by noninvasive, wearable devices rely on either burdensome manual feature extraction designed by domain experts to identify key waveform characteristics and phases, or deep learning (DL) models that require extensive data collection. We propose the Data-Driven Guided Attention (DDGA) framework to optimize DL models to learn features supported by the underlying physiology and physics of the captured waveforms, with minimal expert annotation. With only a single template waveform cardiac cycle and its labelled fiducial points, we leverage dynamic time warping (DTW) to annotate all other training samples. DL models are trained to first identify them before estimating BP to inform them which regions of the input represent key phases of the cardiac cycle, yet we still grant the flexibility for DL to determine the optimal feature set from them. In this study, we evaluate DDGA's improvements to a BP estimation task for three prominent DL-based architectures with two datasets: 1) the MIMIC-III waveform dataset with ample training data and 2) a bio-impedance (Bio-Z) dataset with less than abundant training data. Experiments show that DDGA improves personalized BP estimation models by an average 8.14% in root mean square error (RMSE) when there is an imbalanced distribution of target values in a training set and improves model generalizability by an average 4.92% in RMSE when testing estimation of BP value ranges not previously seen in training.

How Do Treatment Protocols Affect the Use of Engagement Practices in Youth Mental Health Services?

PURPOSE: Treatment engagement poses challenges for youth mental health providers. With the expansion of evidence-based treatments (EBTs), providers face complex decisions regarding how to engage youth and families using available information sources. This study investigated how EBT protocols are associated with the selection and delivery of engagement practices. METHOD: Twenty engagement practices were coded in a sample of digital recordings of early treatment sessions (N = 193) from the Child STEPs in California study, a randomized trial testing modular treatment and community-implemented treatment for youth mental health problems. Data were collected on which protocols mental health providers reportedly used to guide their sessions and the protocols in which they had received training. We examined which information sources (i.e., the guiding protocol, other protocols in training history, unspecified source) were associated with observed engagement practices. RESULTS: In sessions guided by a protocol, most observed engagement practices were accounted for by the guiding protocol (p < .001), rather than protocols in training history or unspecified sources (p < .001). In sessions not guided by a protocol, most observed practices were accounted for by training history (p < .001). Practice frequency and extensiveness was generally greater when a protocol guided the session. CONCLUSIONS: Inclusion in protocols is associated with the selection and delivery of engagement practices, but this strategy might be insufficient for supporting the use of the full range of engagement practices supported by evidence. Supports are needed that leverage the engagement evidence base to ensure that selected practices empirically fit the engagement needs of youth and families.

Boosted-SpringDTW for Comprehensive Feature Extraction of PPG Signals.

Goal: To achieve high-quality comprehensive feature extraction from physiological signals that enables precise physiological parameter estimation despite evolving waveform morphologies. Methods: We propose Boosted-SpringDTW, a probabilistic framework that leverages dynamic time warping (DTW) and minimal domain-specific heuristics to simultaneously segment physiological signals and identify fiducial points that represent cardiac events. An automated dynamic template adapts to evolving waveform morphologies. We validate Boosted-SpringDTW performance with a benchmark PPG dataset whose morphologies include subject- and respiratory-induced variation. Results: Boosted-SpringDTW achieves precision, recall, and F1-scores over 0.96 for identifying fiducial points and mean absolute error values less than 11.41 milliseconds when estimating IBI. Conclusion: Boosted-SpringDTW improves F1-Scores compared to two baseline feature extraction algorithms by 35% on average for fiducial point identification and mean percent difference by 16% on average for IBI estimation. Significance: Precise hemodynamic parameter estimation with wearable devices enables continuous health monitoring throughout a patients' daily life.

A Meta-Learning Approach for Fast Personalization of Modality Translation Models in Wearable Physiological Sensing.

Modality translation grants diagnostic value to wearable devices by translating signals collected from low-power sensors to their highly-interpretable counterparts that are more familiar to healthcare providers. For instance, bio-impedance (Bio-Z) is a conveniently collected modality for measuring physiological parameters but is not highly interpretable. Thus, translating it to a well-known modality such as electrocardiogram (ECG) improves the usability of Bio-Z in wearables. Deep learning solutions are well-suited for this task given complex relationships between modalities generated by distinct processes. However, current algorithms usually train a single model for all users that results in ignoring cross-user variations. Retraining for new users usually requires collecting abundant labeled data, which is challenging in healthcare applications. In this paper, we build a modality translation framework to translate Bio-Z to ECG by learning personalized user information without training several independent architectures. Furthermore, our framework is able to adapt to new users in testing using very few samples. We design a meta-learning framework that contains shared and user-specific parameters to account for user differences while learning from the similarity amongst user signals. In this model, a meta-learner approximated by a neural network learns how to learn user-specific parameters and can efficiently update them in testing. Our experiments show that the proposed model reduces the percentage root mean square difference (PRD) by 41% compared to training a single model for all users and by 36% compared to training independent models for each user. When adapting the model to new users, our model outperforms fine-tuning a pre-trained model through back-propagation by 40% using as few as two new samples in testing.

Contribution of APOA5, APOC3, CETP, ABCA1 and SIK3 genetic variants to hypertriglyceridemia development in Mexican HIV-patients receiving antiretroviral therapy.

OBJECTIVE: To investigate the impact of single nucleotide polymorphisms (SNPs) from APOA5, APOC3, CETP, ATP binding cassette transporter A1 and SIK3 genes in the development of hypertriglyceridemia in HIV patients under antiretroviral therapy. MATERIAL AND METHODS: A case-control study was developed. Leukocytic genomic DNA was extracted and genotyping for SNPs rs662799, rs964184, rs5128, rs2854116, rs2854117, rs3764261, rs4149310, rs4149267 and rs139961185 was performed by real time-PCR using TaqMan allelic discrimination assays, in Mexican mestizo patients with HIV infection, with hypertriglyceridemia (>1.7 mmol/L) under antiretroviral therapy. Genetic variants were also investigated in a control group of normolipidemic HIV patients (≤ 1.7 mmol/L). Haplotypes and gene interactions were analyzed. RESULTS: A total of 602 HIV patients were genotyped (316 cases and 286 controls). Age and antiretroviral regimen based on protease inhibitors were associated with hypertriglyceridemia (P = 0.0001 and P = 0.0002. respectively). SNP rs964184 GG genotype in APOA5 gene exhibited the highest association with hypertriglyceridemia risk (OR, 3.2, 95% CI, 1.7-5.8, P = 0.0001); followed by SNP rs139961185 in SIK3 gene (OR = 2.3; (95% CI, 1.1-4.8; P = 0.03 for AA vs. AG genotype; and APOC3 rs5128 GG genotype, (OR, 2.2; 95% CI, 1.1-4.9; P = 0.04) under codominant models. These associations were maintained in the adjusted analysis by age and protease inhibitors based antiretroviral regimens. CONCLUSIONS: This study reveals an association between rs964184 in APOA5; rs5128 in APOC3 and rs139961185 in SIK3 and high triglyceride concentrations in Mexican HIV-patients receiving protease inhibitors. These genetic factors may influence the adverse effects related to antiretroviral therapy.

LDL-Based Lipid Nanoparticle Derived for Blood Plasma Accumulates Preferentially in Atherosclerotic Plaque.

Apolipoprotein-based drug delivery is a promising approach to develop safe nanoparticles capable of targeted drug delivery for various diseases. In this work, we have synthesized a lipid-based nanoparticle (NPs) that we have called "Aposomes" presenting native apolipoprotein B-100 (apoB-100), the primary protein present in Low-Density Lipoproteins (LDL) on its surface. The aposomes were synthesized from LDL isolated from blood plasma using a microfluidic approach. The synthesized aposomes had a diameter of 91 ± 4 nm and a neutral surface charge of 0.7 mV ± mV. Protein analysis using western blot and flow cytometry confirmed the presence of apoB-100 on the nanoparticle's surface. Furthermore, Aposomes retained liposomes' drug loading capabilities, demonstrating a prolonged release curve with ∼80% cargo release at 4 hours. Considering the natural tropism of LDL towards the atherosclerotic plaques, we evaluated the biological properties of aposomes in a mouse model of advanced atherosclerosis. We observed a ∼20-fold increase in targeting of plaques when comparing aposomes to control liposomes. Additionally, aposomes presented a favorable biocompatibility profile that showed no deviation from typical values in liver toxicity markers (i.e., LDH, ALT, AST, Cholesterol). The results of this study demonstrate the possibilities of using apolipoprotein-based approaches to create nanoparticles with active targeting capabilities and could be the basis for future cardiovascular therapies.

Pro-inflammatory cytokine profile is present in the serum of Mexican patients with different stages of diabetic retinopathy secondary to type 2 diabetes.

AIM: It's been reported that pro-inflammatory cytokines are elevated in patients with diabetic retinopathy (DR); this may contribute to the pathophysiology of the disease. The aim of this study is to measure the concentration of various inflammatory cytokines from the main CD4+ T helper inflammatory responses in blood serum from Mexican patients with DR in different stages using cytometric bead array (CBA) technology and correlate them with the presence and severity of DR in order to find possible DR biomarkers that serve as diagnostic or therapeutic predictors. METHODS: 64 subjects were included in the study, 16 in the control group, 16 in the type 2 diabetes mellitus no DR (NDR) group, 16 in the non-proliferative DR (NPDR) group and 16 in the proliferative DR (PDR) group. Cytokine concentrations of interleukin (IL) 1ß, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-17A, tumour necrosis factor alpha (TNFα) and interferon-gamma in serum samples were measured using Human Inflammatory and TH1/TH2/TH17 CBA Kit. RESULTS: IL-6, IL-12, IL-17a and TNFα were significantly higher in the patients with DR compared with the control group. The PDR group showed a slightly lower concentration of serum cytokines IL-6, IL-12 and IL-17a. TNFα showed a higher concentration compared with healthy controls, NDR and NPDR subjects. We also found a positive statistical correlation between the presence and severity of DR with the clinical parameters haemoglobin A1c, body mass index and serum creatinine and the concentration of serum cytokines IL-6 and TNFα. CONCLUSION: Our findings suggest that patients with diabetes and DR have a stronger chronic inflammatory profile compared with non-diabetic subjects.

Development of a Mobile App for Ecological Momentary Assessment of Circadian Data: Design Considerations and Usability Testing.

BACKGROUND: Collecting data on daily habits across a population of individuals is challenging. Mobile-based circadian ecological momentary assessment (cEMA) is a powerful frame for observing the impact of daily living on long-term health. OBJECTIVE: In this paper, we (1) describe the design, testing, and rationale for specifications of a mobile-based cEMA app to collect timing of eating and sleeping data and (2) compare cEMA and survey data collected as part of a 6-month observational cohort study. The ultimate goal of this paper is to summarize our experience and lessons learned with the Daily24 mobile app and to highlight the pros and cons of this data collection modality. METHODS: Design specifications for the Daily24 app were drafted by the study team based on the research questions and target audience for the cohort study. The associated backend was optimized to provide real-time data to the study team for participant monitoring and engagement. An external 8-member advisory board was consulted throughout the development process, and additional test users recruited as part of a qualitative study provided feedback through in-depth interviews. RESULTS: After ≥4 days of at-home use, 37 qualitative study participants provided feedback on the app. The app generally received positive feedback from test users for being fast and easy to use. Test users identified several bugs and areas where modifications were necessary to in-app text and instructions and also provided feedback on the engagement strategy. Data collected through the mobile app captured more variability in eating windows than data collected through a one-time survey, though at a significant cost. CONCLUSIONS: Researchers should consider the potential uses of a mobile app beyond the initial data collection when deciding whether the time and monetary expenditure are advisable for their situation and goals.