Category: Nevin Manimala

PLoS One. 2024 Aug 7;19(8):e0301301. doi: 10.1371/journal.pone.0301301. eCollection 2024.

ABSTRACT

Interrupted time series (ITS) designs are increasingly used for estimating the effect of shocks in natural experiments. Currently, ITS designs are often used in scenarios with many time points and simple data structures. This research investigates the performance of ITS designs when the number of time points is limited and with complex data structures. Using a Monte Carlo simulation study, we empirically derive the performance-in terms of power, bias and precision- of the ITS design. Scenarios are considered with multiple interventions, a low number of time points and different effect sizes based on a motivating example of the learning loss due to COVID school closures. The results of the simulation study show the power of the step change depends mostly on the sample size, while the power of the slope change depends on the number of time points. In the basic scenario, with both a step and a slope change and an effect size of 30% of the pre-intervention slope, the required sample size for detecting a step change is 1,100 with a minimum of twelve time points. For detecting a slope change the required sample size decreases to 500 with eight time points. To decide if there is enough power researchers should inspect their data, hypothesize about effect sizes and consider an appropriate model before applying an ITS design to their research. This paper contributes to the field of methodology in two ways. Firstly, the motivation example showcases the difficulty of employing ITS designs in cases which do not adhere to a single intervention. Secondly, models are proposed for more difficult ITS designs and their performance is tested.

PMID:39110741 | DOI:10.1371/journal.pone.0301301

Proc Natl Acad Sci U S A. 2024 Aug 13;121(33):e2320510121. doi: 10.1073/pnas.2320510121. Epub 2024 Aug 7.

ABSTRACT

Protein phase transitions (PPTs) from the soluble state to a dense liquid phase (forming droplets via liquid-liquid phase separation) or to solid aggregates (such as amyloids) play key roles in pathological processes associated with age-related diseases such as Alzheimer’s disease. Several computational frameworks are capable of separately predicting the formation of droplets or amyloid aggregates based on protein sequences, yet none have tackled the prediction of both within a unified framework. Recently, large language models (LLMs) have exhibited great success in protein structure prediction; however, they have not yet been used for PPTs. Here, we fine-tune a LLM for predicting PPTs and demonstrate its usage in evaluating how sequence variants affect PPTs, an operation useful for protein design. In addition, we show its superior performance compared to suitable classical benchmarks. Due to the “black-box” nature of the LLM, we also employ a classical random forest model along with biophysical features to facilitate interpretation. Finally, focusing on Alzheimer’s disease-related proteins, we demonstrate that greater aggregation is associated with reduced gene expression in Alzheimer’s disease, suggesting a natural defense mechanism.

PMID:39110734 | DOI:10.1073/pnas.2320510121

Proc Natl Acad Sci U S A. 2024 Aug 13;121(33):e2403771121. doi: 10.1073/pnas.2403771121. Epub 2024 Aug 7.

ABSTRACT

Complex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in an unsupervised way the microscopic dynamical events occurring in the system. However, decoupling statistically relevant fluctuations from the internal noise remains most often nontrivial. Here, we describe “Onion Clustering“: a simple, iterative unsupervised clustering method that efficiently detects and classifies statistically relevant fluctuations in noisy time-series data. We demonstrate its efficiency by analyzing simulation and experimental trajectories of various systems with complex internal dynamics, ranging from the atomic- to the microscopic-scale, in- and out-of-equilibrium. The method is based on an iterative detect-classify-archive approach. In a similar way as peeling the external (evident) layer of an onion reveals the internal hidden ones, the method performs a first detection/classification of the most populated dynamical environment in the system and of its characteristic noise. The signal of such dynamical cluster is then removed from the time-series data and the remaining part, cleared-out from its noise, is analyzed again. At every iteration, the detection of hidden dynamical subdomains is facilitated by an increasing (and adaptive) relevance-to-noise ratio. The process iterates until no new dynamical domains can be uncovered, revealing, as an output, the number of clusters that can be effectively distinguished/classified in a statistically robust way as a function of the time-resolution of the analysis. Onion Clustering is general and benefits from clear-cut physical interpretability. We expect that it will help analyzing a variety of complex dynamical systems and time-series data.

PMID:39110730 | DOI:10.1073/pnas.2403771121

Online J Public Health Inform. 2024 Aug 7;16:e57618. doi: 10.2196/57618.

ABSTRACT

BACKGROUND: Telecare and telehealth are important care-at-home services used to support individuals to live more independently at home. Historically, these technologies have reactively responded to issues. However, there has been a recent drive to make better use of the data from these services to facilitate more proactive and predictive care.

OBJECTIVE: This review seeks to explore the ways in which predictive data analytics techniques have been applied in telecare and telehealth in at-home settings.

METHODS: The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was adhered to alongside Arksey and O’Malley’s methodological framework. English language papers published in MEDLINE, Embase, and Social Science Premium Collection between 2012 and 2022 were considered and results were screened against inclusion or exclusion criteria.

RESULTS: In total, 86 papers were included in this review. The types of analytics featuring in this review can be categorized as anomaly detection (n=21), diagnosis (n=32), prediction (n=22), and activity recognition (n=11). The most common health conditions represented were Parkinson disease (n=12) and cardiovascular conditions (n=11). The main findings include: a lack of use of routinely collected data; a dominance of diagnostic tools; and barriers and opportunities that exist, such as including patient-reported outcomes, for future predictive analytics in telecare and telehealth.

CONCLUSIONS: All papers in this review were small-scale pilots and, as such, future research should seek to apply these predictive techniques into larger trials. Additionally, further integration of routinely collected care data and patient-reported outcomes into predictive models in telecare and telehealth offer significant opportunities to improve the analytics being performed and should be explored further. Data sets used must be of suitable size and diversity, ensuring that models are generalizable to a wider population and can be appropriately trained, validated, and tested.

PMID:39110501 | DOI:10.2196/57618

JMIR Serious Games. 2024 Aug 7;12:e58654. doi: 10.2196/58654.

ABSTRACT

BACKGROUND: Virtual reality (VR) modules are commonly used for health care training, such as adult advanced cardiac life support (ACLS), due to immersion and engagement. The metaverse differs from current VR serious gaming by enabling shared social connections, while current VR modules focus on computer-based content without social interaction. Educators in the metaverse can foster communication and collaboration during training sessions.

OBJECTIVE: This study aimed to compare learning outcomes of VR-based, machine-guided training with educator-guided, VR-based training in the metaverse environment.

METHODS: A total of 62 volunteered students from Acibadem Mehmet Ali Aydinlar University Vocational School for Anesthesiology were randomly divided into 2 groups of 31 participants each: one group received VR-based training with machine guidance (MG), and the other received VR-based training with educator guidance (EG) in the metaverse. The members of both groups undertook VR-based basic training for ACLS. Afterward, the MG group was trained with a VR-based advanced training module, which provides training with full MG, whereas the EG group attended the VR-based, educator-guided training in the metaverse. The primary outcome of the study was determined by the exam score of the VR-based training module. Descriptive statistics defined continuous variables such as VR exam scores and time spent on machine- or educator-guided training. The correlation between training time and VR exam scores was assessed with the Spearman rank correlation, and nonnormally distributed variables were compared using the Mann-Whitney U test. Statistical significance was set at P<.05, with analyses executed by MedCalc Statistical Software (version 12.7.7).

RESULTS: Comparing the VR test scores between the MG and EG groups revealed no statistically significant difference. The VR test scores for the EG group had a median of 86 (range 11-100). In contrast, the MG group scores had a median of 66 (range 13-100; P=.08). Regarding the correlation between the duration of machine-guided or educator-guided training and VR-based exam scores, for the MG group, =0.569 and P=.005 were obtained. For the EG group, this correlation was found to be =0.298 and P=.10. While this correlation is statistically significant for the MG group, it is not significant for the EG group. The post hoc power analysis (80%), considering the correlation between the time spent on training and exam scores, supported this finding.

CONCLUSIONS: The results of this study suggest that a well-designed, VR-based serious gaming module with MG could provide comparable learning outcomes to VR training in the metaverse with EG for adult ACLS training. Future research with a larger sample size could explore whether social interaction with educators in a metaverse environment offers added benefits for learners.

TRIAL REGISTRATION: ClinicalTrials.gov NCT06288087; https://clinicaltrials.gov/study/NCT06288087.

PMID:39110497 | DOI:10.2196/58654

ACS Nano. 2024 Aug 7. doi: 10.1021/acsnano.4c07267. Online ahead of print.

ABSTRACT

Contact engineering on monolayer layer (ML) semiconducting transition metal dichalcogenides (TMDs) is considered the most challenging problem toward using these materials as a transistor channel in future advanced technology nodes. The typically observed strong Fermi-level pinning induced in part by the reaction of the source/drain contact metal and the ML TMD frequently results in a large Schottky barrier height, which limits the electrical performance of ML TMD field-effect transistors (FETs). However, at a microscopic level, little is known about how interface defects or reaction sites impact the electrical performance of ML TMD FETs. In this work, we have performed statistically meaningful electrical measurements on at least 120 FETs combined with careful surface analysis to unveil contact resistance dependence on interface chemistry. In particular, we achieved a low contact resistance for ML MoS₂ FETs with ultrahigh-vacuum (UHV, 3 × 10^-11 mbar) deposited Ni contacts, ∼500 Ω·μm, which is 5 times lower than the contact resistance achieved when deposited under high-vacuum (HV, 3 × 10^-6 mbar) conditions. These electrical results strongly correlate with our surface analysis observations. X-ray photoelectron spectroscopy (XPS) revealed significant bonding species between Ni and MoS₂ under UHV conditions compared to that under HV. We also studied the Bi/MoS₂ interface under UHV and HV deposition conditions. Different from the case of Ni, we do not observe a difference in contact resistance or interface chemistry between contacts deposited under UHV and HV. Finally, this article also explores the thermal stability and reliability of the two contact metals employed here.

PMID:39110477 | DOI:10.1021/acsnano.4c07267

JAMA Netw Open. 2024 Aug 1;7(8):e2424226. doi: 10.1001/jamanetworkopen.2024.24226.

ABSTRACT

IMPORTANCE: There are wide disparities in neonatal mortality rates (NMRs, deaths <28 days of life after live birth per 1000 live births) between countries in Europe, indicating potential for improvement. Comparing country-specific patterns of births and deaths with countries with low mortality rates can facilitate the development of effective intervention strategies.

OBJECTIVE: To investigate how these disparities are associated with the distribution of gestational age (GA) and GA-specific mortality rates.

DESIGN, SETTING, AND PARTICIPANTS: This was a cross-sectional study of all live births in 14 participating European countries using routine data compiled by the Euro-Peristat Network. Live births with a GA of 22 weeks or higher from 2015 to 2020 were included. Data were analyzed from May to October 2023.

EXPOSURES: GA at birth.

MAIN OUTCOMES AND MEASURES: The study investigated excess neonatal mortality, defined as a rate difference relative to the pooled rate in the 3 countries with the lowest NMRs (Norway, Sweden, and Finland; hereafter termed the top 3). The Kitagawa method was used to divide this excess into the proportion explained by the GA distribution of births and by GA-specific mortality rates. A sensitivity analysis was conducted among births 24 weeks’ GA or greater.

RESULTS: There were 35 094 neonatal deaths among 15 123 428 live births for an overall NMR of 2.32 per 1000. The pooled NMR in the top 3 was 1.44 per 1000 (1937 of 1 342 528). Excess neonatal mortality compared with the top 3 ranged from 0.17 per 1000 in the Czech Republic to 1.82 per 1000 in Romania. Excess deaths were predominantly concentrated among births less than 28 weeks’ GA (57.6% overall). Full-term births represented 22.7% of the excess deaths in Belgium, 17.8% in France, 40.6% in Romania and 17.3% in the United Kingdom. Heterogeneous patterns were observed when partitioning excess mortality into the proportion associated with the GA distribution vs GA-specific mortality. For example, these proportions were 9.2% and 90.8% in France, 58.4% and 41.6% in the United Kingdom, and 92.9% and 7.1% in Austria, respectively. These associations remained stable after removing births under 24 weeks’ GA in most, but not all, countries.

CONCLUSIONS AND RELEVANCE: This cohort study of 14 European countries found wide NMR disparities with varying patterns by GA. This knowledge is important for developing effective strategies to reduce neonatal mortality.

PMID:39110462 | DOI:10.1001/jamanetworkopen.2024.24226

JAMA Netw Open. 2024 Aug 1;7(8):e2426243. doi: 10.1001/jamanetworkopen.2024.26243.

ABSTRACT

IMPORTANCE: There are consistent data demonstrating that socioeconomic disadvantage is associated with risk of premature mortality, but research on the relationship between neighborhood socioeconomic factors and premature mortality is limited. Most studies evaluating the association between neighborhood socioeconomic status (SES) and mortality have used a single assessment of SES during middle to older adulthood, thereby not considering the contribution of early life neighborhood SES.

OBJECTIVE: To investigate the association of life course neighborhood SES and premature mortality.

DESIGN, SETTING, AND PARTICIPANTS: This cohort study included Black and White participants of the multicenter Atherosclerosis Risk in Communities Study, a multicenter study conducted in 4 US communities: Washington County, Maryland; Forsyth County, North Carolina; Jackson, Mississippi; and the northwestern suburbs of Minneapolis, Minnesota. Participants were followed up for a mean (SD) of 18.8 (5.7) years (1996-2020). Statistical analysis was performed from March 2023 through May 2024.

EXPOSURE: Participants’ residential addresses during childhood, young adulthood, and middle adulthood were linked with US Census-based socioeconomic indicators to create summary neighborhood SES scores for each of these life epochs. Neighborhood SES scores were categorized into distribution-based tertiles.

MAIN OUTCOMES AND MEASURES: Premature death was defined as all-cause mortality occurring before age 75 years. Multivariable-adjusted Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% CIs.

RESULTS: Among 12 610 study participants, the mean (SD) age at baseline was 62.6 (5.6) years; 3181 (25.2%) were Black and 9429 (74.8%) were White; and 7222 (57.3%) were women. The lowest, compared with the highest tertile, of neighborhood SES score in middle adulthood was associated with higher risk of premature mortality (HR, 1.28; 95% CI, 1.07-1.54). Similar associations were observed for neighborhood SES in young adulthood among women (HR, 1.25; 95% CI, 1.00-1.56) and neighborhood SES in childhood among White participants (HR, 1.25; 95% CI, 1.01-1.56). Participants whose neighborhood SES remained low from young to middle adulthood had an increased premature mortality risk compared with those whose neighborhood SES remained high (HR, 1.25; 95% CI, 1.05-1.49).

CONCLUSIONS AND RELEVANCE: In this study, low neighborhood SES was associated with premature mortality. The risk of premature mortality was greatest among individuals experiencing persistently low neighborhood SES from young to middle adulthood. Place-based interventions that target neighborhood social determinants of health should be designed from a life course perspective that accounts for early-life socioeconomic inequality.

PMID:39110459 | DOI:10.1001/jamanetworkopen.2024.26243

JAMA Netw Open. 2024 Aug 1;7(8):e2426394. doi: 10.1001/jamanetworkopen.2024.26394.

ABSTRACT

IMPORTANCE: Women who had a hypertensive disorder of pregnancy (HDP) have a well-documented risk of chronic hypertension within a few years of delivery, but management of postpartum hypertension among these women remains inconsistent.

OBJECTIVE: To assess the incidence of initiation of antihypertensive medication use in the first 2 years after delivery by HDP status and antenatal antihypertensive medication use.

DESIGN, SETTING, AND PARTICIPANTS: This Danish register-based cohort study used data from women with at least 1 pregnancy lasting 20 or more gestational weeks (only the first pregnancy in the period was considered) who delivered from January 1, 1995, to December 31, 2018. Statistical analysis was conducted from October 2022 to September 2023.

EXPOSURE: Hypertensive disorders of pregnancy.

MAIN OUTCOMES AND MEASURES: Cumulative incidences and hazard ratios of initiating antihypertensive medication use within 2 years post partum (5 postpartum time intervals) by HDP status and antenatal medication use.

RESULTS: The cohort included 784 782 women, of whom 36 900 (4.7% [95% CI, 4.7%-4.8%]) had an HDP (HDP: median age at delivery, 29.1 years [IQR, 26.1-32.7 years]; no HDP: median age at delivery, 29.0 years [IQR, 25.9-32.3 years]). The 2-year cumulative incidence of initiating postpartum antihypertensive treatment ranged from 1.8% (95% CI, 1.8%-1.8%) among women who had not had HDPs to 44.1% (95% CI, 40.0%-48.2%) among women with severe preeclampsia who required antihypertensive medication during pregnancy. Most women who required postpartum antihypertensive medication after an HDP initiated use within 3 months of delivery (severe preeclampsia, 86.6% [95% CI, 84.6%-89.4%]; preeclampsia, 75.3% [95% CI, 73.8%-76.2%]; and gestational hypertension, 75.1% [95% CI, 72.9%-77.1%]). However, 13.4% (95% CI, 11.9%-14.1%) of women with severe preeclampsia, 24.7.% (95% CI, 24.0%-26.0%) of women with preeclampsia, 24.9% (95% CI, 22.5%-27.5%) of women with gestational hypertension, and 76.7% (95% CI, 76.3%-77.1%) of those without an HDP first filled a prescription for antihypertensive medication more than 3 months after delivery. Women with gestational hypertension had the highest rate of initiating medication after more than 1 year post partum, with 11.6% (95% CI, 10.0%-13.2%) starting treatment after this period. Among women who filled a prescription in the first 3 months post partum, up to 55.9% (95% CI, 46.2%-66.1%) required further prescriptions more than 3 months post partum, depending on HDP status and antenatal medication use.

CONCLUSIONS AND RELEVANCE: In this cohort study of postpartum women, the incidence of initiation of postnatal antihypertensive medication use varied by HDP status, HDP severity, and antenatal antihypertensive medication use. Up to 24.9% of women initiated antihypertensive medication use more than 3 months after an HDP, with up to 11.6% initiating treatment after 1 year. Routine postpartum blood pressure monitoring might prevent diagnostic delays in initiation of antihypertensive medication use and improve cardiovascular disease prevention among women.

PMID:39110457 | DOI:10.1001/jamanetworkopen.2024.26394

JAMA Psychiatry. 2024 Aug 7. doi: 10.1001/jamapsychiatry.2024.2040. Online ahead of print.

ABSTRACT

IMPORTANCE: Despite growing interest in the phenomenology of delusions in psychosis, at present little is known about their content and evolution over time, including whether delusion themes are consistent across episodes.

OBJECTIVE: To examine the course of delusions and thematic delusion content across relapse episodes in patients presenting to an early intervention service for psychosis.

DESIGN, SETTING, AND PARTICIPANTS: This longitudinal, observational study used clinical data systematically collected from January 2003 to March 2018 from a cohort of consenting patients with affective or nonaffective first-episode psychosis, followed up naturalistically for up to 2 years in an early intervention service for psychosis in Montréal, Quebec, Canada. Data included the thematic content and severity of delusions (scores ≥3 using the Scale for the Assessment of Positive Symptoms) and associated psychotic and nonpsychotic symptoms, both across an initial episode and, in the event of remission, a potential relapse. Data were analyzed from September 2021 to February 2023.

EXPOSURE: An early intervention service for psychosis, organized around intensive case management and a multidisciplinary team approach, which observed each patient for up to 2 years of care.

MAIN OUTCOMES AND MEASURES: The primary outcome was positive symptom relapse and remission, including the presence and content of delusions, which was coded per the Scale for the Assessment of Positive Symptoms and accepted definitions. The main statistical measures included repeated paired-sample t tests and binary logistic regression analyses.

RESULTS: Of 636 consenting patients, mean (SD) age was 23.8 (4.75) years; 191 patients were female, 444 were male, and 1 patient was nonbinary. Remission rates were high, and relapse rates were relatively low: 591 individuals had baseline delusions, of which 558 (94.4%) achieved remission. Of these 558 patients, only 182 (32.6%) had a subsequent relapse to a second or later episode of psychosis. Of the 182 patients who did relapse, however, a large proportion (115 [63.2%]) reported threshold-level delusions. Of these 115, 104 patients (90.4%) had thematic delusion content consistent with that reported during the index (first) episode. Those who relapsed with delusions had fewer delusion themes present during subsequent episodes of psychosis compared with the index episode and lower levels of other psychotic and nonpsychotic symptoms.

CONCLUSIONS AND RELEVANCE: Specialized early intervention services for psychosis can achieve high rates of sustained remission. However, in this study, the minority of individuals with delusions who later relapsed experienced similar delusion themes during subsequent episodes. These findings raise important considerations for the conceptualization of delusions and have clinical implications for trajectories of illness and care.

PMID:39110444 | DOI:10.1001/jamapsychiatry.2024.2040