Category: Nevin Manimala

PLoS One. 2023 Mar 22;18(3):e0283179. doi: 10.1371/journal.pone.0283179. eCollection 2023.

ABSTRACT

The Interleukin (IL)-33 is important in several inflammatory diseases and its cellular receptor is the Interleukin 1 receptor-like 1 (IL1RL1), also called suppression of tumorigenicity 2 ligand (ST2L). This study investigated associations between single nucleotide variants (SNVs) in the IL33 gene and in the IL1RL1 (ST2) gene with periodontitis. Additionally, aimed to determine the role of Aggregatibacter actinomycetemcomitans (Aa) relative amount in the subgingival biofilm in these associations. A cross-sectional study was carried out with 506 individuals that answered a structured questionnaire used to collect their health status, socioeconomic-demographic, and behavioral characteristics. Periodontal examination was performed to determine the presence and severity of periodontitis, and subgingival biofilm samples were collected to quantify the relative amount of Aa by real time polymerase chain reaction. Human genomic DNA was extracted from whole blood cells and SNV genotyping was performed. Logistic regression estimated the association measurements, odds ratio (OR), and 95% confidence interval (95%CI), between the IL33 and ST2 genes with periodontitis, and subgroup analyses assessed the relative amount of Aa in these associations. 23% of individuals had periodontitis. Adjusted measurements showed a statistically significant inverse association between two SNVs of the ST2; rs148548829 (C allele) and rs10206753 (G allele). These two alleles together with a third SNV, the rs11693204 (A allele), were inversely associated with moderate periodontitis. One SNV of the IL33 gene also showed a statistically significant inverse association with moderate periodontitis. Nine SNVs of the ST2 gene were inversely associated with the relative amount of Aa. In the high Aa subgroup, there was a direct association between 11 SNVs of the ST2 gene and moderate periodontitis and two SNVs of the ST2 gene and severe periodontitis, and eight SNVs of the ST2 gene and periodontitis. These exploratory findings of genetic variants in IL-33/ST2 axis support the concept that the different tissue responses among individuals with periodontitis may be modulated by the host’s genetics, influencing the physiopathology of the disease.

PMID:36947565 | DOI:10.1371/journal.pone.0283179

PLoS Comput Biol. 2023 Mar 22;19(3):e1010154. doi: 10.1371/journal.pcbi.1010154. Online ahead of print.

ABSTRACT

Missing observations in trait datasets pose an obstacle for analyses in myriad biological disciplines. Considering the mixed results of imputation, the wide variety of available methods, and the varied structure of real trait datasets, a framework for selecting a suitable imputation method is advantageous. We invoked a real data-driven simulation strategy to select an imputation method for a given mixed-type (categorical, count, continuous) target dataset. Candidate methods included mean/mode imputation, k-nearest neighbour, random forests, and multivariate imputation by chained equations (MICE). Using a trait dataset of squamates (lizards and amphisbaenians; order: Squamata) as a target dataset, a complete-case dataset consisting of species with nearly complete information was formed for the imputation method selection. Missing data were induced by removing values from this dataset under different missingness mechanisms: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). For each method, combinations with and without phylogenetic information from single gene (nuclear and mitochondrial) or multigene trees were used to impute the missing values for five numerical and two categorical traits. The performances of the methods were evaluated under each missing mechanism by determining the mean squared error and proportion falsely classified rates for numerical and categorical traits, respectively. A random forest method supplemented with a nuclear-derived phylogeny resulted in the lowest error rates for the majority of traits, and this method was used to impute missing values in the original dataset. Data with imputed values better reflected the characteristics and distributions of the original data compared to complete-case data. However, caution should be taken when imputing trait data as phylogeny did not always improve performance for every trait and in every scenario. Ultimately, these results support the use of a real data-driven simulation strategy for selecting a suitable imputation method for a given mixed-type trait dataset.

PMID:36947561 | DOI:10.1371/journal.pcbi.1010154

PLoS One. 2023 Mar 22;18(3):e0281994. doi: 10.1371/journal.pone.0281994. eCollection 2023.

ABSTRACT

The mental health crisis in graduate education combined with low treatment rates among engineering graduate students underscores the need for engineering graduate programs to provide effective methods to promote well-being. There is an extensive body of neuroscience research showing that contemplative practices, such as mindfulness, produce measurable effects on brain function and overall well-being. We hypothesized that a mindfulness-based training program designed for engineering graduate students would improve emotional well-being and, secondarily, enhance research capacity. An initial pilot study was conducted at a single institution (Phase 1), followed by a larger study conducted at both the original and a second institution (Phase 2) to gather additional data and show the program’s transferability. The program comprised eight weekly mindfulness training sessions. Individuals in the study were randomly assigned to either an intervention group or wait-list control group. We administered pre- and post-test surveys with quantitative measures designed to assess emotional and physical well-being, as well as creativity, research satisfaction, and desire to contribute to the betterment of society. Participants also completed a summative survey to evaluate the impact of the program on their well-being and research. Analysis revealed statistically significant findings: improved emotional health, decreased neuroticism, increased positive affect, decreased negative affect, and increased mindfulness in the intervention groups compared to the control groups. Intervention groups in Phase 2 also reported statistically significant improvement in satisfaction with their research. Our findings suggest that mindfulness training has the potential to play a vital professional and personal development role in graduate engineering education.

PMID:36947553 | DOI:10.1371/journal.pone.0281994

JMIR Public Health Surveill. 2023 Mar 22;9:e38633. doi: 10.2196/38633.

ABSTRACT

BACKGROUND: Case investigation and contact tracing are core public health activities used to interrupt disease transmission. These activities are traditionally conducted manually. During periods of high COVID-19 incidence, US health departments were unable to scale up case management staff to deliver effective and timely contact-tracing services. In response, digital contact tracing (DCT) apps for mobile phones were introduced to automate these activities. DCT apps detect when other DCT users are close enough to transmit COVID-19 and enable alerts to notify users of potential disease exposure. These apps were deployed quickly during the pandemic without an opportunity to conduct experiments to determine effectiveness. However, it is unclear whether these apps can effectively supplement understaffed manual contact tracers.

OBJECTIVE: The aims of this study were to (1) evaluate the effectiveness of COVID-19 DCT apps deployed in the United States during the COVID-19 pandemic and (2) determine if there is sufficient DCT adoption and interest in adoption to meet a minimum population use rate to be effective (56%). To assess uptake, interest and safe use covariates were derived from evaluating DCTs using the American Psychological Association App Evaluation Model (AEM) framework.

METHODS: We analyzed data from a nationally representative survey of US adults about their COVID-19-related behaviors and experiences. Survey respondents were divided into three segments: those who adopted a DCT app, those who are interested but did not adopt, and those not interested. Descriptive statistics were used to characterize factors of the three groups. Multivariable logistic regression models were used to analyze the characteristics of segments adopting and interested in DCT apps against AEM framework covariates.

RESULTS: An insufficient percentage of the population adopted or was interested in DCTs to achieve our minimum national target effectiveness rate (56%). A total of 17.4% (n=490) of the study population reported adopting a DCT app, 24.7% (n=697) reported interest, and 58.0% (n=1637) were not interested. Younger, high-income, and uninsured individuals were more likely to adopt a DCT app. In contrast, people in fair to poor health were interested in DCT apps but did not adopt them. App adoption was positively associated with visiting friends and family outside the home (odds ratio [OR] 1.63, 95% CI 1.28-2.09), not wearing masks (OR 0.52, 95% CI 0.38-0.71), and adopters thinking they have or had COVID-19 (OR 1.60, 95% CI 1.21-2.12).

CONCLUSIONS: Overall, a small percentage of the population adopted DCT apps. These apps may not be effective in protecting adopters’ friends and family from their maskless contacts outside the home given low adoption rates. The public health community should account for safe use behavioral factors in future public health contact-tracing app design. The AEM framework was useful in developing a study design to evaluate DCT effectiveness and safety.

PMID:36947135 | DOI:10.2196/38633

Ann Med. 2023 Dec;55(1):1134-1143. doi: 10.1080/07853890.2023.2187875.

ABSTRACT

BACKGROUND: Circadian rhythm involved with physiology has been reported to affect pharmacokinetics or pharmacodynamics. We hypothesized that circadian variations in physiology disturb anesthesia and eventually affect recovery after anesthesia.

METHODS: A retrospective cohort study initially included 107,406 patients (1 June 2016-6 June 2021). Patients were classified into morning or afternoon surgery groups. The primary outcome was daytime variation in PACU (post-anesthesia care unit) recovery time and Steward score. Inverse probability weighting (IPW) approach based on propensity score and univariable/multivariable linear regression were used to estimate this outcome.

RESULTS: Of 28,074 patients, 13,418 (48%) patients underwent morning surgeries, and 14,656 (52%) patients underwent afternoon surgeries. LOWESS curves and IPW illustrated daytime variation in PACU recovery time and Steward score. Before adjustment, compared to morning surgery group, afternoon surgery group had less PACU recovery time (median [interquartile range], 57 [46, 70] vs. 54 [43, 66], p < 0.001) and a higher Steward score (5.62 [5.61, 5.63] vs. 5.66 [5.65, 5.67], p < 0.001). After adjustment, compared to morning surgery group, afternoon surgery group had less PACU recovery time (58 [46, 70] vs. 54 [43, 66], p < 0.001). In multivariable linear regression, morning surgery is statistically associated with an increased PACU recovery time (coefficient, -3.20; 95% confidence interval, -3.55 to -2.86).

Among non-cardiac surgeries, daytime variation might affect recovery after general anesthesia. These findings indicate that the timing of surgery improves recovery after general anesthesia, with afternoon surgery providing protection.KEY MESSAGESIn this retrospective cohort study of 28,074 participants, the afternoon surgery group has a higher Steward score than the morning surgery group.In multivariable linear regression, morning surgery is statistically associated with an increased PACU recovery time.Among non-cardiac surgeries, daytime variation affects the recovery after general anesthesia, with afternoon surgery providing protection.

PMID:36947128 | DOI:10.1080/07853890.2023.2187875

Mol Biol Evol. 2023 Mar 22:msad074. doi: 10.1093/molbev/msad074. Online ahead of print.

ABSTRACT

Gene flow between previously isolated populations during the founding of an admixed or hybrid population has the potential to introduce adaptive alleles into the new population. If the adaptive allele is common in one source population, but not the other, then as the adaptive allele rises in frequency in the admixed population, genetic ancestry from the source containing the adaptive allele will increase nearby as well. Patterns of genetic ancestry have therefore been used to identify post-admixture positive selection in humans and other animals, including examples in immunity, metabolism, and animal coloration. A common method identifies regions of the genome that have local ancestry ‘outliers’ compared to the distribution across the rest of the genome, considering each locus independently. However, we lack theoretical models for expected distributions of ancestry under various demographic scenarios, resulting in potential false positives and false negatives. Further, ancestry patterns between distant sites are often not independent. As a result, current methods tend to infer wide genomic regions containing many genes as under selection, limiting biological interpretation. Instead, we develop a deep learning object detection method applied to images generated from local ancestry-painted genomes. This approach preserves information from the surrounding genomic context and avoids potential pitfalls of user-defined summary statistics. We find the-method is robust to a variety of demographic misspecifications using simulated data. Applied to human genotype data from Cabo Verde, we localize a known adaptive locus to a single narrow region compared to multiple or long windows obtained using two other ancestry-based methods.

PMID:36947126 | DOI:10.1093/molbev/msad074

JMIR Ment Health. 2023 Mar 22;10:e43942. doi: 10.2196/43942.

ABSTRACT

BACKGROUND: Increasing rates of mental health diagnoses in college students signal the need for new opportunities to support the mental health of this population. With many mental health apps being efficacious, they may be a promising resource for college campuses to provide support to their students. However, it is important to understand why (or why not) students might want to use apps and their desired features.

OBJECTIVE: Information on students’ interest in mental health apps may inform which apps are to be provided and how campuses can support their use. This study aimed to understand the interest and hesitation in app use and the relationship between mental health needs, as defined by depression, anxiety, and positive mental health, and app use.

METHODS: The web-based Healthy Minds Study collected information on mental health needs, perceptions, and service use across colleges and universities. We used a sample of 989 participants who completed the survey between 2018 and 2020 and an elective module on digital mental health. We analyzed the elective module responses using a mixed methods approach, including both descriptive and inferential statistics, along with thematic coding for open text responses.

RESULTS: The Results from this study revealed that anxiety (b=-0.07; P<.001), but not depression (b=0.03; P=.12) and positive mental health (b=-0.02; P=.17), was a significant predictor of app adoption. Prominent qualitative findings indicated that the most desired app features included tips and advice, access to resources and information, and on-demand support that involves interaction throughout the day. The participants also suggested an overall desire for human interaction to be integrated into an app. As predicted, hesitancy was encountered, and the qualitative results suggested that there was a lack of interest in the adoption of mental health app and preference.

CONCLUSIONS: The findings from this study underscore that simply providing digital mental health apps as tools may be insufficient to support their use in college campuses. Although many students were open to using a mental health app, hesitation and uncertainty were common in the participant responses. Working with colleges and universities to increase digital literacy and provide resources that allow students to gauge when app use is appropriate may be helpful when implementing mental health apps as resources in college campuses.

PMID:36947115 | DOI:10.2196/43942

JMIR Form Res. 2023 Mar 22;7:e38504. doi: 10.2196/38504.

ABSTRACT

BACKGROUND: Internet-based mental health interventions have been demonstrated to be effective in alleviating psychological distress and promoting mental well-being. However, real-world uptake and engagement of such interventions have been low. Rather than being stand-alone interventions, situating internet-based interventions under a stratified stepped care system can support users to continue with mental health practice and monitor their mental health status for timely services that are commensurate with their needs. A user-centered approach should be used in the development of such web-based platforms to understand the facilitators and barriers in user engagement to enhance platform uptake, usability, and adherence so it can support the users’ continued adoption and practice of self-care for their mental health.

OBJECTIVE: The aim of this study was to describe the design process taken to develop a web-based stratified stepped care mental health platform, TourHeart+, using a user-centered approach that gathers target users’ perceptions on mental self-care and feedback on the platform design and incorporates them into the design.

METHODS: The process involved a design workshop with the interdisciplinary development team, user interviews, and 2 usability testing sessions on the flow of registration and mental health assessment and the web-based self-help interventions of the platform. The data collected were summarized as descriptive statistics if appropriate and insights are extracted inductively. Qualitative data were extracted using a thematic coding approach.

RESULTS: In the design workshop, the team generated empathy maps and point-of-view statements related to the possible mental health needs of target users. Four user personas and related processes in the mental health self-care journey were developed based on user interviews. Design considerations were derived based on the insights drawn from the personas and mental health self-care journey. Survey results from 104 users during usability testing showed that the overall experience during registration and mental health assessment was friendly, and they felt cared for, although no statistically significant differences on preference ratings were found between using a web-based questionnaire tool and through an interactive chatbot, except that chatbot format was deemed more interesting. Facilitators of and barriers to registering the platform and completing the mental health assessment were identified through user feedback during simulation with mock-ups. In the usability testing for guided self-help interventions, users expressed pain points in course adherence, and corresponding amendments were made in the flow and design of the web-based courses.

CONCLUSIONS: The design process and findings presented in the study are important in developing a user-centric platform to optimize users’ acceptance and usability of a web-based stratified stepped care platform with guided self-help interventions for mental well-being. Accounting for users’ perceptions and needs toward mental health self-care and their experiences in the design process can enhance the usability of an evidence-based mental health platform on the web.

PMID:36947112 | DOI:10.2196/38504

JMIR Public Health Surveill. 2023 Mar 22;9:e43409. doi: 10.2196/43409.

ABSTRACT

BACKGROUND: Skeletal muscle and BMI are essential prognostic factors for survival in colorectal cancer (CRC). However, there is a lack of understanding due to scarce studies on the continuous aspects of these variables.

OBJECTIVE: This study aimed to evaluate the prognostic impact of the initial status and trajectories of muscle and BMI on overall survival (OS) and assess whether these 4 profiles within 1 year can represent the profiles 6 years later.

METHODS: We analyzed 4056 newly diagnosed patients with CRC between 2010 to 2020. The volume of the muscle with 5-mm thickness at the third lumbar spine level was measured using a pretrained deep learning algorithm. The skeletal muscle volume index (SMVI) was defined as the muscle volume divided by the square of the height. The correlation between BMI status at the first, third, and sixth years of diagnosis was analyzed and assessed similarly for muscle profiles. Prognostic significances of baseline BMI and SMVI and their 1-year trajectories for OS were evaluated by restricted cubic spline analysis and survival analysis. Patients were categorized based on these 4 dimensions, and prognostic risks were predicted and demonstrated using heat maps.

RESULTS: Trajectories of SMVI were categorized as decreased (812/4056, 20%), steady (2014/4056, 49.7%), or increased (1230/4056, 30.3%). Similarly, BMI trajectories were categorized as decreased (792/4056, 19.5%), steady (2253/4056, 55.5%), or increased (1011/4056, 24.9%). BMI and SMVI values in the first year after diagnosis showed a statistically significant correlation with those in the third and sixth years (P<.001). Restricted cubic spline analysis showed a nonlinear relationship between baseline BMI and SMVI change ratio and OS; BMI, in particular, showed a U-shaped correlation. According to survival analysis, increased BMI (hazard ratio [HR] 0.83; P=.02), high baseline SMVI (HR 0.82; P=.04), and obesity stage 1 (HR 0.80; P=.02) showed a favorable impact, whereas decreased SMVI trajectory (HR 1.31; P=.001), decreased BMI (HR 1.23; P=.02), and initial underweight (HR 1.38; P=.02) or obesity stages 2-3 (HR 1.79; P=.01) were negative prognostic factors for OS. Considered simultaneously, BMI >30 kg/m² with a low SMVI at the time of diagnosis resulted in the highest mortality risk. We observed improved survival in patients with increased muscle mass without BMI loss compared to those with steady muscle mass and BMI.

CONCLUSIONS: Profiles within 1 year of both BMI and muscle were surrogate indicators for predicting the later profiles. Continuous trajectories of body and muscle mass are independent prognostic factors of patients with CRC. An automatic algorithm provides a unique opportunity to conduct longitudinal evaluations of body compositions. Further studies to understand the complicated natural courses of muscularity and adiposity are necessary for clinical application.

PMID:36947110 | DOI:10.2196/43409

JAMA Netw Open. 2023 Mar 1;6(3):e234059. doi: 10.1001/jamanetworkopen.2023.4059.

ABSTRACT

IMPORTANCE: The introduction of direct oral anticoagulants (DOACs) has transformed the treatment of venous thromboembolism (VTE). Large health care databases offer valuable insight into how oral anticoagulants (OACs) are used in clinical practice and may aid in understanding reasons for changes in therapy.

OBJECTIVES: To evaluate prescribing patterns of OACs for patients with VTE and identify clinical events that precede treatment changes.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used data from a public (Medicare fee-for-service) and a commercial (IBM MarketScan) health insurance database on 298 609 patients initiating OACs within 90 days of index VTE hospitalization from January 1, 2009, to December 31, 2020. Statistical analysis was conducted from April to August 2022.

EXPOSURES: Warfarin and the DOACs rivaroxaban, apixaban, dabigatran, and edoxaban.

MAIN OUTCOMES AND MEASURES: Characteristics of patients initiating different OACs, along with trends over time of patients initiating OACs, were compared. Time receiving continuous anticoagulant therapy, patterns of anticoagulant discontinuation (treatment gap of ≥30 days), and treatment switches were assessed. Clinical events in the 30 days preceding treatment modifications were identified.

RESULTS: A total of 203 378 individuals with Medicare (mean [SD] age, 76.9 [7.6] years; 122 554 women [60.3%]) and 95 231 with commercial insurance (mean [SD] age, 57.6 [15.8] years; 47 139 women [49.5%]) were included (N = 298 609). Warfarin was the most frequent OAC prescribed (163 044 [54.6%]), followed by rivaroxaban (66 882 [22.3%]) and apixaban (65 997 [22.1%]). The proportion of patients initiating DOACs increased from 0% in 2010 to 86.8% (22 420 of 25 817) in 2019 for patients with Medicare and 92.1% (4012 of 4357) in 2020 for commercially insured patients. Patients with chronic kidney disease were more likely to initiate warfarin (35 561 [11.9%]) or apixaban (16 294 [5.5%]) than rivaroxaban (10 136 [3.4%]), and those with a history of bleeding were more likely to initiate apixaban (5424 [1.8%]) than rivaroxaban (3007 [1.0%]). Overall, patients received persistent OAC treatment for approximately 6 months (Medicare: median, 175 days [IQR, 76-327 days]; commercial insurance: median, 168 days [IQR, 83-279 days]). A total of 33 011 patients (11.1%) switched anticoagulant therapy within a year. Switching to another anticoagulant was preceded most frequently by codes for a VTE diagnostic procedure (27.2% of all switchers [8983 of 33 011]).

CONCLUSIONS AND RELEVANCE: This cohort study using data from 2 US health insurance databases suggests that most patients with VTE continued oral anticoagulant treatment for approximately 6 months. Clinical reasons for modifying anticoagulant therapy were identified in one-third of patients. Identifying reasons for treatment modification is crucial for generating valid evidence on drug safety and effectiveness.

PMID:36947039 | DOI:10.1001/jamanetworkopen.2023.4059