Nevin Manimala

PLoS One. 2023 Jan 27;18(1):e0259487. doi: 10.1371/journal.pone.0259487. eCollection 2023.

ABSTRACT

BACKGROUND: A diagnosis of MND takes an average 10-16 months from symptom onset. Early diagnosis is important to access supportive measures to maximise quality of life. The COVID-19 pandemic has caused significant delays in NHS pathways; the majority of GP appointments now occur online with subsequent delays in secondary care assessment. Given the rapid progression of MND, patients may be disproportionately affected resulting in late stage new presentations. We used Monte Carlo simulation to model the pre-COVID-19 diagnostic pathway and then introduced plausible COVID-19 delays.

METHODS: The diagnostic pathway was modelled using gamma distributions of time taken: 1) from symptom onset to GP presentation, 2) for specialist referral, and 3) for diagnosis reached after neurology appointment. We incorporated branches to simulate delays: when patients did not attend their GP and when the GP consultation did not result in referral. An emergency presentation was triggered when diagnostic pathway time was within 30 days of projected median survival. Total time-to-diagnosis was calculated over 100,000 iterations. The pre-COVID-19 model was estimated using published data and the Improving MND Care Survey 2019. We estimated COVID-19 delays using published statistics.

RESULTS: The pre-COVID model reproduced known features of the MND diagnostic pathway, with a median time to diagnosis of 399 days and predicting 5.2% of MND patients present as undiagnosed emergencies. COVID-19 resulted in diagnostic delays from 558 days when only primary care was 25% delayed, to 915 days when both primary and secondary care were 75%. The model predicted an increase in emergency presentations ranging from 15.4%-44.5%.

INTERPRETATIONS: The model suggests the COVID-19 pandemic will result in later-stage diagnoses and more emergency presentations of undiagnosed MND. Late-stage presentations may require rapid escalation to multidisciplinary care. Proactive recognition of acute and late-stage disease with altered service provision will optimise care for people with MND.

PMID:36706102 | DOI:10.1371/journal.pone.0259487

PLoS One. 2023 Jan 27;18(1):e0280947. doi: 10.1371/journal.pone.0280947. eCollection 2023.

ABSTRACT

BACKGROUND: Chemotherapy-induced hiccups are understudied but can cause sleep deprivation, fatigue, pain in the chest and abdomen, poor oral intake, aspiration, and even death. As a critical next step toward investigating better palliative methods, this study reported patient-reported incidence of hiccups after oxaliplatin- or cisplatin-based chemotherapy.

METHODS: The current study relied on 2 previous studies that sought to acquire consecutive direct patient report of hiccups among patients who had recently received chemotherapy with cisplatin or oxaliplatin. These patient-reported data in conjunction with information from the medical record are the focus of this report.

RESULTS: Of 541 patients, 337 were successful contacted by phone; and 95 (28%; 95% CI: 23%, 33%) of these contacted patients reported hiccups. In univariable analyses, male gender (odds ratio (OR): 2.17 (95% confidence ratio (95% CI): 1.30, 3.62); p = 0.002), increased height (OR: 1.03 (95% CI: 1.00, 1.06); p = 0.02), and concomitant aprepitant/fosaprepitant (OR: 2.23 (95% CI: 1.31, 3.78); p = 0.002) were associated with hiccups. In multivariable analyses, these statistically significant associations persisted except for height.

CONCLUSIONS: These patient-reported data demonstrate that oxaliplatin- or cisplatin-induced hiccups occur in a notable proportion of patients with cancer. Male gender and concomitant aprepitant/fosaprepitant appear to increase risk.

PMID:36706101 | DOI:10.1371/journal.pone.0280947

Diabetes Care. 2023 Jan 27:dc221830. doi: 10.2337/dc22-1830. Online ahead of print.

ABSTRACT

OBJECTIVE: The plasma proteome preceding diabetes can improve our understanding of diabetes pathogenesis.

RESEARCH DESIGN AND METHODS: In 8,923 Atherosclerosis Risk in Communities (ARIC) Study participants (aged 47-70 years, 57% women, 19% Black), we conducted discovery and internal validation for associations of 4,955 plasma proteins with incident diabetes. We externally validated results in the Singapore Multi-Ethnic Cohort (MEC) nested case-control (624 case subjects, 1,214 control subjects). We used Cox regression to discover and validate protein associations and risk-prediction models (elastic net regression with cardiometabolic risk factors and proteins) for incident diabetes. We conducted a pathway analysis and examined causality using genetic instruments.

RESULTS: There were 2,147 new diabetes cases over a median of 19 years. In the discovery sample (n = 6,010), 140 proteins were associated with incident diabetes after adjustment for 11 risk factors (P < 10-5). Internal validation (n = 2,913) showed 64 of the 140 proteins remained significant (P < 0.05/140). Of the 63 available proteins, 47 (75%) were validated in MEC. Novel associations with diabetes were found for 22 the 47 proteins. Prediction models (27 proteins selected by elastic net) developed in discovery had a C statistic of 0.731 in internal validation, with ΔC statistic of 0.011 (P = 0.04) beyond 13 risk factors, including fasting glucose and HbA1c. Inflammation and lipid metabolism pathways were overrepresented among the diabetes-associated proteins. Genetic instrument analyses suggested plasma SHBG, ATP1B2, and GSTA1 play causal roles in diabetes risk.

CONCLUSIONS: We identified 47 plasma proteins predictive of incident diabetes, established causal effects for 3 proteins, and identified diabetes-associated inflammation and lipid pathways with potential implications for diagnosis and therapy.

PMID:36706097 | DOI:10.2337/dc22-1830

JMIR Form Res. 2023 Jan 27;7:e37293. doi: 10.2196/37293.

ABSTRACT

BACKGROUND: A growing number of studies highlight the importance of emotion regulation in the treatment and recovery of individuals with psychosis and concomitant disorders such as substance use disorder (SUD), for whom access to integrated dual-disorder treatments is particularly difficult. In this context, dedicated smartphone apps may be useful tools to provide immediate support to individuals in need. However, few studies to date have focused on the development and assessment of apps aimed at promoting emotional regulation for people with psychosis.

OBJECTIVE: The aim of this study was to evaluate the feasibility, acceptability, and potential clinical impact of a dedicated app (ChillTime) for individuals with psychotic disorders and concurrent SUD. The app design process followed recommendations for reducing cognitive effort on a mobile app. A total of 20 coping strategies regrouped in four categories (behavioral, emotional, cognitive, spiritual) were included in the app.

METHODS: This open pilot study followed a pre-post design. After the initial assessment, researchers asked participants to use the app as part of their treatment over a 30-day period. Feasibility was determined by the frequency of use of the app and measured using the number of completed strategies. Acceptability was determined by measuring ease of use, ease of learning, satisfaction, and perceived utility at the end of the 30-day study period based on responses to satisfaction questionnaires. Clinical scales measuring emotion regulation, substance use (ie, type of substance, amount taken, and frequency of use), and various psychiatric symptoms were administered at the beginning and end of the 30-day period.

RESULTS: A total of 13 participants were recruited from two first-episode psychosis clinics in Montreal, Quebec, Canada. All participants were symptomatically stable, were between 18 and 35 years of age (mostly men; 70% of the sample), and had a schizophrenia spectrum disorder with a comorbid substance use diagnosis. A total of 11 participants completed the study (attrition<20%). Approximately half of the participants used the tool at least 33% of the days (11-21 days). Cognitive and emotion-focused techniques were rated the highest in terms of usefulness and were the most frequently used. The majority of participants gave positive answers about the ease of use and the ease of learning the tool. A nonsignificant association of ChillTime use with negative symptoms and drug use was observed. No other statistically significant changes were observed.

CONCLUSIONS: The ChillTime app showed good feasibility (approximately half of the participants used the tool at least 33% of the days) and acceptability among people with schizophrenia spectrum disorder and SUD. Trends suggesting a potential impact on certain clinical outcomes will need to be replicated in larger-sample studies before any conclusion can be drawn.

PMID:36705963 | DOI:10.2196/37293

JMIR Aging. 2023 Jan 27;6:e36663. doi: 10.2196/36663.

ABSTRACT

BACKGROUND: Assessing cognitive constructs affected by Alzheimer disease, such as processing speed (PS), is important to screen for potential disease and allow for early detection. Digital PS assessments have been developed to provide widespread, efficient cognitive testing, but all have been validated only based on the correlation between test scores. Best statistical practices dictate that concurrent validity should be assessed for agreement or equivalence rather than using correlation alone.

OBJECTIVE: This study aimed to assess the concurrent validity of a novel digital PS assessment against a gold-standard measure of PS.

METHODS: Adults aged 45-75 years (n=191) participated in this study. Participants completed the novel digital digit-symbol substitution test (DDSST) and the Repeatable Battery for the Assessment of Neuropsychological Status coding test (RBANS-C). The correlation between the test scores was determined using a Pearson product-moment correlation, and a difference in mean test scores between tests was checked for using a 2-tailed dependent samples t test. Data were analyzed for agreement between the 2 tests using Bland-Altman limits of agreement and equivalency using a two one-sided t tests (TOST) approach.

RESULTS: A significant moderate, positive correlation was found between DDSST and RBANS-C scores (r=.577; P<.001), and no difference in mean scores was detected between the tests (P=.93). Bias was nearly zero (0.04). Scores between the tests were found to display adequate agreement with 90% of score differences falling between -22.66 and 22.75 (90% limits of agreement=-22.91 to 22.99), and the scores were equivalent (P=.049).

CONCLUSIONS: Analyses indicate that the DDSST is a valid digital assessment of PS. The DDSST appears to be a suitable option for widespread, immediate, and efficient PS testing.

TRIAL REGISTRATION: ClinicalTrials.gov NCT04559789; https://clinicaltrials.gov/ct2/show/NCT04559789.

PMID:36705951 | DOI:10.2196/36663

JMIR Mhealth Uhealth. 2023 Jan 27;11:e41660. doi: 10.2196/41660.

ABSTRACT

BACKGROUND: A growing body of evidence shows that financial incentives can effectively reinforce individuals’ positive behavior change and improve compliance with health intervention programs. A critical factor in the design of incentive-based interventions is to set a proper incentive magnitude. However, it is highly challenging to determine such magnitudes as the effects of incentive magnitude depend on personal attitudes and contexts.

OBJECTIVE: This study aimed to illustrate loss-framed adaptive microcontingency management (L-AMCM) and the lessons learned from a feasibility study. L-AMCM discourages an individual’s adverse health behaviors by deducting particular expenses from a regularly assigned budget, where expenses are adaptively estimated based on the individual’s previous responses to varying expenses and contexts.

METHODS: We developed a mobile health intervention app for preventing prolonged sedentary lifestyles. This app delivered a behavioral mission (ie, suggesting taking an active break for a while) with an incentive bid when 50 minutes of uninterrupted sedentary behavior happened. Participants were assigned to either the fixed (ie, deducting the monotonous expense for each mission failure) or adaptive (ie, deducting varying expenses estimated by the L-AMCM for each mission failure) incentive group. The intervention lasted 3 weeks.

RESULTS: We recruited 41 participants (n=15, 37% women; fixed incentive group: n=20, 49% of participants; adaptive incentive group: n=21, 51% of participants) whose mean age was 24.0 (SD 3.8; range 19-34) years. Mission success rates did not show statistically significant differences by group (P=.54; fixed incentive group mean 0.66, SD 0.24; adaptive incentive group mean 0.61, SD 0.22). The follow-up analysis of the adaptive incentive group revealed that the influence of incentive magnitudes on mission success was not statistically significant (P=.18; odds ratio 0.98, 95% CI 0.95-1.01). On the basis of the qualitative interviews, such results were possibly because the participants had sufficient intrinsic motivation and less sensitivity to incentive magnitudes.

CONCLUSIONS: Although our L-AMCM did not significantly affect users’ mission success rate, this study configures a pioneering work toward adaptively estimating incentives by considering user behaviors and contexts through leveraging mobile sensing and machine learning. We hope that this study inspires researchers to develop incentive-based interventions.

PMID:36705949 | DOI:10.2196/41660

J Physician Assist Educ. 2023 Jan 27. doi: 10.1097/JPA.0000000000000476. Online ahead of print.

ABSTRACT

INTRODUCTION: The purpose of this study was to investigate clinical faculty perceptions of online learning in health professions education.

METHODS: Clinical faculty members from various health professions programs in New York were surveyed to determine whether there was a relationship between clinical faculty members’ attitudes toward online learning competencies and their ability to teach online. Additionally, this study explored what type of impact years of teaching experience and online training had on clinical faculty perceptions of online learning.

RESULTS: The study received 60 responses from clinical faculty, most of whom were teaching on Long Island, New York. Although the findings were not significant for most variables, a t-test demonstrated a significant statistical difference between online training and faculty perceptions of online learning. When clinical faculty completed online training, they had a more positive attitude toward online learning. The study also found that clinical faculty members’ attitudes toward technology in online learning positively influenced their ability to troubleshoot technical issues in online environments. Years of online teaching experience did not affect how clinical faculty perceived online learning; however, clinical faculty with 1-5 years and 6-10 years of teaching experience rated their attitudes and abilities the lowest out of all the groups.

DISCUSSION: While there appears to be a correlation between faculty perceptions of online learning and online training, more research is needed to objectively determine which specific trainings would be most advantageous.

PMID:36705927 | DOI:10.1097/JPA.0000000000000476

JAMA Netw Open. 2023 Jan 3;6(1):e2253301. doi: 10.1001/jamanetworkopen.2022.53301.

ABSTRACT

IMPORTANCE: Randomized clinical trials (RCTs) on COVID-19 are increasingly being posted as preprints before publication in a scientific, peer-reviewed journal.

OBJECTIVE: To assess time to journal publication for COVID-19 RCT preprints and to compare differences between pairs of preprints and corresponding journal articles.

EVIDENCE REVIEW: This systematic review used a meta-epidemiologic approach to conduct a literature search using the World Health Organization COVID-19 database and Embase to identify preprints published between January 1 and December 31, 2021. This review included RCTs with human participants and research questions regarding the treatment or prevention of COVID-19. For each preprint, a literature search was done to locate the corresponding journal article. Two independent reviewers read the full text, extracted data, and assessed risk of bias using the Cochrane Risk of Bias 2 tool. Time to publication was analyzed using a Cox proportional hazards regression model. Differences between preprint and journal article pairs in terms of outcomes, analyses, results, or conclusions were described. Statistical analysis was performed on October 17, 2022.

FINDINGS: This study included 152 preprints. As of October 1, 2022, 119 of 152 preprints (78.3%) had been published in journals. The median time to publication was 186 days (range, 17-407 days). In a multivariable model, larger sample size and low risk of bias were associated with journal publication. With a sample size of less than 200 as the reference, sample sizes of 201 to 1000 and greater than 1000 had hazard ratios (HRs) of 1.23 (95% CI, 0.80-1.91) and 2.19 (95% CI, 1.36-3.53) for publication, respectively. With high risk of bias as the reference, medium-risk articles with some concerns for bias had an HR of 1.77 (95% CI, 1.02-3.09); those with a low risk of bias had an HR of 3.01 (95% CI, 1.71-5.30). Of the 119 published preprints, there were differences in terms of outcomes, analyses, results, or conclusions in 65 studies (54.6%). The main conclusion in the preprint contradicted the conclusion in the journal article for 2 studies (1.7%).

CONCLUSIONS AND RELEVANCE: These findings suggest that there is a substantial time lag from preprint posting to journal publication. Preprints with smaller sample sizes and high risk of bias were less likely to be published. Finally, although differences in terms of outcomes, analyses, results, or conclusions were observed for preprint and journal article pairs in most studies, the main conclusion remained consistent for the majority of studies.

PMID:36705921 | DOI:10.1001/jamanetworkopen.2022.53301

Methods Mol Biol. 2023;2586:233-249. doi: 10.1007/978-1-0716-2768-6_14.

ABSTRACT

Understanding the three-dimensional structure of an RNA molecule is often essential to understanding its function. Sampling algorithms and energy functions for RNA structure prediction are improving, due to the increasing diversity of structural data available for training statistical potentials and testing structural data, along with a steady supply of blind challenges through the RNA-Puzzles initiative. The recent FARFAR2 algorithm enables near-native structure predictions on fairly complex RNA structures, including automated selection of final candidate models and estimation of model accuracy. Here, we describe the use of a publicly available webserver for RNA modeling for realistic scenarios using FARFAR2, available at https://rosie.rosettacommons.org/farfar2 . We walk through two cases in some detail: a simple model pseudoknot from the frameshifting element of beet western yellows virus modeled using the “basic interface” to the webserver and a replication of RNA-Puzzle 20, a metagenomic twister sister ribozyme, using the “advanced interface.” We also describe example runs of FARFAR2 modeling including two kinds of experimental data: a c-di-GMP riboswitch modeled with low-resolution restraints from MOHCA-seq experiments and a tandem GA motif modeled with ¹H NMR chemical shifts.

PMID:36705908 | DOI:10.1007/978-1-0716-2768-6_14

Methods Mol Biol. 2023;2586:107-120. doi: 10.1007/978-1-0716-2768-6_7.

ABSTRACT

A point mutation in coding RNA can cause not only an amino acid substitution but also a dynamic change of RNA secondary structure, leading to a dysfunctional RNA. Although in silico structure prediction has been used to detect structure-disrupting point mutations known as riboSNitches, exhaustive simulation of long RNAs is needed to detect a significant enrichment or depletion of riboSNitches in functional RNAs. Here, we have developed a novel algorithm Radiam (RNA secondary structure Analysis with Deletion, Insertion, And substitution Mutations) for a comprehensive riboSNitch analysis of long RNAs. Radiam is based on the ParasoR framework, which efficiently computes local RNA secondary structures for long RNAs. ParasoR can compute a variety of structure scores over globally consistent structures with maximal span constraints for the base pair distance. Using the reusable structure database made by ParasoR, Radiam performs an efficient recomputation of the secondary structures for mutated sequences. An exhaustive simulation of Radiam is expected to find reliable riboSNitch candidates on long RNAs by evaluating their statistical significance in terms of the change of local structure stability.

PMID:36705901 | DOI:10.1007/978-1-0716-2768-6_7