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Nevin Manimala Statistics

Adaptive Cohort Size Determination Method for Bayesian Optimal Interval Phase I/II Design to Shorten Clinical Trial Duration

JCO Precis Oncol. 2023 Jul;7:e2300087. doi: 10.1200/PO.23.00087.

ABSTRACT

PURPOSE: Recently, the strategy for dose optimization in oncology has shifted toward conducting phase II randomized controlled trials with multiple doses. Optimal biologic dose (OBD) selection from phase I trial data to determine candidate doses for phase II trials has been gaining attention. Trials to identify the OBD have a fixed cohort size, which increases the trial duration. We propose a method to increase the cohort size using trial data and shorten the trial duration while maintaining accuracy.

METHODS: We propose a novel adaptive cohort size determination method in which the increase of cohort size is determined using desirability probability on the basis of toxicity and efficacy data. The desirability probability is a measure of how desirable a dose is and thus how close it is to the OBD. However, during the trial, the desirability probability does not need to be calculated. Instead, the cohort size expansion can be determined by a simple table generated in advance from toxicity and efficacy data. An illustrated example is provided and the performance was evaluated in a simulation study with 16 scenarios.

RESULTS: In the simulation study, the trial duration was reduced by an average of 20% compared with the conventional design. The percentages of correct OBD selection are almost the same as those with the conventional design.

CONCLUSION: The proposed adaptive cohort size determination method described in this study reduces trial duration while maintaining accuracy.

PMID:37487148 | DOI:10.1200/PO.23.00087

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Nevin Manimala Statistics

Pinoresinol rescues developmental phenotypes of Arabidopsis phenylpropanoid mutants overexpressing FERULATE 5-HYDROXYLASE

Proc Natl Acad Sci U S A. 2023 Aug;120(31):e2216543120. doi: 10.1073/pnas.2216543120. Epub 2023 Jul 24.

ABSTRACT

Most phenylpropanoid pathway flux is directed toward the production of monolignols, but this pathway also generates multiple bioactive metabolites. The monolignols coniferyl and sinapyl alcohol polymerize to form guaiacyl (G) and syringyl (S) units in lignin, components that are characteristic of plant secondary cell walls. Lignin negatively impacts the saccharification potential of lignocellulosic biomass. Although manipulation of its content and composition through genetic engineering has reduced biomass recalcitrance, in some cases, these genetic manipulations lead to impaired growth. The reduced-growth phenotype is often attributed to poor water transport due to xylem collapse in low-lignin mutants, but alternative models suggest that it could be caused by the hyper- or hypoaccumulation of phenylpropanoid intermediates. In Arabidopsis thaliana, overexpression of FERULATE 5-HYDROXYLASE (F5H) shifts the normal G/S lignin ratio to nearly pure S lignin and does not result in substantial changes to plant growth. In contrast, when we overexpressed F5H in the low-lignin mutants cinnamyl dehydrogenase c and d (cadc cadd), cinnamoyl-CoA reductase 1, and reduced epidermal fluorescence 3, plant growth was severely compromised. In addition, cadc cadd plants overexpressing F5H exhibited defects in lateral root development. Exogenous coniferyl alcohol (CA) and its dimeric coupling product, pinoresinol, rescue these phenotypes. These data suggest that mutations in the phenylpropanoid pathway limit the biosynthesis of pinoresinol, and this effect is exacerbated by overexpression of F5H, which further draws down cellular pools of its precursor, CA. Overall, these genetic manipulations appear to restrict the synthesis of pinoresinol or a downstream metabolite that is necessary for plant growth.

PMID:37487096 | DOI:10.1073/pnas.2216543120

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Nevin Manimala Statistics

Theoretical isotherm equation for adsorption-induced structural transition on flexible metal-organic frameworks

Proc Natl Acad Sci U S A. 2023 Aug;120(31):e2305573120. doi: 10.1073/pnas.2305573120. Epub 2023 Jul 24.

ABSTRACT

Flexible metal-organic frameworks (MOFs) exhibit an adsorption-induced structural transition known as “gate opening” or “breathing,” resulting in an S-shaped adsorption isotherm. This unique feature of flexible MOFs offers significant advantages, such as a large working capacity, high selectivity, and intrinsic thermal management capability, positioning them as crucial candidates for revolutionizing adsorption separation processes. Therefore, the interest in the industrial applications of flexible MOFs is increasing, and the adsorption engineering for flexible MOFs is becoming important. However, despite the establishment of the theoretical background for adsorption-induced structural transitions, no theoretical equation is available to describe S-shaped adsorption isotherms of flexible MOFs. Researchers rely on various empirical equations for process simulations that can lead to unreliable outcomes or may overlook insights into improving material performance owing to parameters without physical meaning. In this study, we derive a theoretical equation based on statistical mechanics that could be a standard for the structural transition type adsorption isotherms, as the Langmuir equation represents type I isotherms. The versatility of the derived equation is shown through four examples of flexible MOFs that exhibit gate opening and breathing. The consistency of the formula with existing theories, including the osmotic free energy analysis and intrinsic thermal management capabilities, is also discussed. The developed theoretical equation may lead to more reliable and insightful outcomes in adsorption separation processes, further advancing the direction of industrial applications of flexible MOFs.

PMID:37487093 | DOI:10.1073/pnas.2305573120

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Nevin Manimala Statistics

Correction to: Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association

Circulation. 2023 Jul 25;148(4):e4. doi: 10.1161/CIR.0000000000001167. Epub 2023 Jul 24.

NO ABSTRACT

PMID:37486999 | DOI:10.1161/CIR.0000000000001167

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Nevin Manimala Statistics

Effect of prenatal micronutrient-fortified balanced energy-protein supplementation on maternal and newborn body composition: A sub-study from the MISAME-III randomized controlled efficacy trial in rural Burkina Faso

PLoS Med. 2023 Jul 24;20(7):e1004242. doi: 10.1371/journal.pmed.1004242. Online ahead of print.

ABSTRACT

BACKGROUND: Micronutrient-fortified balanced energy-protein (BEP) supplements are promising interventions to prevent intrauterine growth retardation in low- and middle-income countries. On the other hand, one concern with blanket prenatal supplementation programs using energy-dense supplements is that they could lead to more maternal and/or infant overweight. However, evidence is lacking on the potential effect of BEP on maternal and offspring body composition. This study evaluates the effects of micronutrient-fortified BEP supplementation during pregnancy on body composition of mothers and their newborns in rural Burkina Faso.

METHODS AND FINDINGS: The MISAME-III study is an open label individually randomized controlled trial where pregnant women (n = 1,897) of gestational age <21 weeks received either a combination of micronutrient-fortified BEP and iron-folic acid (IFA) tablets (i.e., intervention) or IFA alone (i.e., control). The prenatal phase of the MISAME-III study was conducted between the first enrollment in October 2019 and the last delivery in August 2021. In a sub-study nested under the MISAME-III trial, we evaluated anthropometry and body composition in newborns who were born starting from 17 November 2020 (n: control = 368 and intervention = 352) and their mothers (n: control = 185 and intervention = 186). Primary study outcomes were newborn and maternal fat-free mass (FFMI) and fat-mass (FMI) indices. We used the deuterium dilution method to determine FFMI and FMI and %FFM and %FM of total body weight within 1 month postpartum. Our main analysis followed a modified intention-to-treat approach by analyzing all subjects with body composition data available. Univariable and multivariable linear regression models were fitted to compare the intervention and control arms, with adjusted models included baseline maternal age, height, arm fat index, hemoglobin concentration and primiparity, household size, wealth and food security indices, and newborn age (days). At study enrollment, the mean ± SD maternal age was 24.8 ± 6.13 years and body mass index (BMI) was 22.1 ± 3.02 kg/m2 with 7.05% of the mothers were underweight and 11.5% were overweight. Prenatal micronutrient-fortified BEP supplementation resulted in a significantly higher FFMI in mothers (MD (mean difference): 0.45; 95% CI (confidence interval): 0.05, 0.84; P = 0.026) and newborns (MD: 0.28; 95% CI: 0.06, 0.50; P = 0.012), whereas no statistically significant effects were found on FMI. The effect of micronutrient-fortified BEP on maternal FFMI was greater among mothers from food secure households and among those with a better nutritional status (BMI ≥21.0 kg/m2 or mid-upper arm circumference (MUAC) ≥23 cm). Key limitations of the study are the relatively high degree of missing data (approximately 18%), the lack of baseline maternal body composition values, and the lack of follow-up body composition measurements to evaluate any long-term effects.

CONCLUSIONS: Micronutrient-fortified BEP supplementation during pregnancy can increase maternal and newborn FFMI, without significant effects on FMI.

TRIAL REGISTRATION: ClinicalTrials.gov with identifier NCT03533712.

PMID:37486952 | DOI:10.1371/journal.pmed.1004242

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Nevin Manimala Statistics

A machine learning approach to model the impact of line edge roughness on gate-all-around nanowire FETs while reducing the carbon footprint

PLoS One. 2023 Jul 24;18(7):e0288964. doi: 10.1371/journal.pone.0288964. eCollection 2023.

ABSTRACT

The performance and reliability of semiconductor devices scaled down to the sub-nanometer regime are being seriously affected by process-induced variability. To properly assess the impact of the different sources of fluctuations, such as line edge roughness (LER), statistical analyses involving large samples of device configurations are needed. The computational cost of such studies can be very high if 3D advanced simulation tools (TCAD) that include quantum effects are used. In this work, we present a machine learning approach to model the impact of LER on two gate-all-around nanowire FETs that is able to dramatically decrease the computational effort, thus reducing the carbon footprint of the study, while obtaining great accuracy. Finally, we demonstrate that transfer learning techniques can decrease the computing cost even further, being the carbon footprint of the study just 0.18 g of CO2 (whereas a single device TCAD study can produce up to 2.6 kg of CO2), while obtaining coefficient of determination values larger than 0.985 when using only a 10% of the input samples.

PMID:37486944 | DOI:10.1371/journal.pone.0288964

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Nevin Manimala Statistics

How has COVID-19 changed healthcare professionals’ attitudes to self-care? A mixed methods research study

PLoS One. 2023 Jul 24;18(7):e0289067. doi: 10.1371/journal.pone.0289067. eCollection 2023.

ABSTRACT

BACKGROUND: The COVID-19 pandemic fundamentally changed the way services are delivered. Self-care, including good hygiene practices and avoidance of risk was emphasised as the key measure to tackle the pandemic in the early stages.

OBJECTIVE: To understand how self-reported professional attitudes, perceptions and practices of self-care have changed as a result of the COVID-19 pandemic.

DESIGN: Cross-sectional online survey and semi-structured qualitative interview.

SETTING: Health care.

PARTICIPANTS: 304 healthcare professionals (HCPs).

METHODS: A wide range of HCPs, including pharmacists, nurses, doctors, social prescribers and other designations took part in a 27-item anonymous online survey. Semi-structured qualitative interviews with nine healthcare professionals explored attitudes to and practices of self-care before and during the pandemic. Views were sought on the permanence and implications of changes. Data were analysed using routine statistics and thematic analysis to identify major themes.

RESULTS: A total of 304 HCPs responded to the survey fully. Nine participated in a semi-structured interview. There was agreement that the importance of self-care has increased markedly during the pandemic. The percentage of respondents who felt that self-care was ‘very’ important to their clients increased from 54.3% to 86.6% since the pandemic. Personal empowerment and capacity of service users to self-care increased significantly during the pandemic. Willingness of patients to engage (74%) and poor understanding of self-care (71%) were cited as the two main barriers to self-care. A close third was digital exclusion (71%), though 86% of respondents recommended online resources and 77% the use of smartphone apps. Survey respondents believed the changes to be permanent and positive. Interviewees reported a major, and positive move to self-care with the pandemic seen as an opportunity to be grasped, but professional education would have to be aligned to make the most of it. They raised concerns as to whether the shift to self-care was perceived by users as ‘abandonment’ rather than ’empowerment’ and whether problems had been stored rather than dealt with through self-care and therefore whether the positive changes would continue after the pandemic.

CONCLUSION: Reporting their views before the pandemic, barely more than half of the professionals surveyed saw self-care as fundamentally important to the individuals they served. This changed to 86% as a result of the pandemic. Patient/client engagement with and understanding of self-care were reported as major barriers, as was digital exclusion, though increased technological solutions were used by all respondents. Concerns were raised that the permanence of the changes depended upon continued encouragement and empowerment of individuals to self-care and on its inclusion in professional education as a substantive subject.

PMID:37486943 | DOI:10.1371/journal.pone.0289067

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Nevin Manimala Statistics

Scholars180: An effective oral presentation assessment for optometry students

PLoS One. 2023 Jul 24;18(7):e0289081. doi: 10.1371/journal.pone.0289081. eCollection 2023.

ABSTRACT

Oral presentation assessments are multifunctional tools that can potentially test all six cognitive domains of Bloom’s taxonomy. Yet, they are not used as frequently as other forms of assessment in curriculums due to time limitations. Hence, designing effective oral presentation assessments that can overcome this is required. The purpose of this study was to investigate whether Scholars180, an oral presentation assessment developed for optometry students, would effectively help students improve their knowledge of and confidence in the identification and management of ocular diseases. This study utilized a non-randomized pre-questionnaire and post-questionnaire design where the participants (n = 31) were asked to assess their knowledge of ocular diseases before and after the oral presentation. The questionnaire was developed according to the unit outcomes. The responses to each of the 12 Likert-type scale questions on the post-questionnaire with the respective responses on the pre-questionnaire were compared. Students (n = 31) experienced improvements in their knowledge of eye diseases and even more so in their confidence and application of their knowledge. This was indicated by the statistically significant increases in median scores and low interquartile ranges (IQR) of ≤1.0. The peer evaluation also illustrated that students felt that the assessment contributed positively to their learning experience. Teachers require a variety of assessment methods to accurately test the student’s authentic depth of knowledge and achievement of learning outcomes. Scholars180 is an effective assessment that follows constructive alignment and overcomes time limitations, providing teachers an assessment to consider implementing in the future.

PMID:37486941 | DOI:10.1371/journal.pone.0289081

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Nevin Manimala Statistics

Incidence of diabetes following COVID-19 vaccination and SARS-CoV-2 infection in Hong Kong: A population-based cohort study

PLoS Med. 2023 Jul 24;20(7):e1004274. doi: 10.1371/journal.pmed.1004274. Online ahead of print.

ABSTRACT

BACKGROUND: The risk of incident diabetes following Coronavirus Disease 2019 (COVID-19) vaccination remains to be elucidated. Also, it is unclear whether the risk of incident diabetes after Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection is modified by vaccination status or differs by SARS-CoV-2 variants. We evaluated the incidence of diabetes following mRNA (BNT162b2), inactivated (CoronaVac) COVID-19 vaccines, and after SARS-CoV-2 infection.

METHODS AND FINDINGS: In this population-based cohort study, individuals without known diabetes were identified from an electronic health database in Hong Kong. The first cohort included people who received ≥1 dose of COVID-19 vaccine and those who did not receive any COVID-19 vaccines up to September 2021. The second cohort consisted of confirmed COVID-19 patients and people who were never infected up to March 2022. Both cohorts were followed until August 15, 2022. A total of 325,715 COVID-19 vaccine recipients (CoronaVac: 167,337; BNT162b2: 158,378) and 145,199 COVID-19 patients were 1:1 matched to their respective controls using propensity score for various baseline characteristics. We also adjusted for previous SARS-CoV-2 infection when estimating the conditional probability of receiving vaccinations, and vaccination status when estimating the conditional probability of contracting SARS-CoV-2 infection. Hazard ratios (HRs) and 95% confidence intervals (CIs) for incident diabetes were estimated using Cox regression models. In the first cohort, we identified 5,760 and 4,411 diabetes cases after receiving CoronaVac and BNT162b2 vaccines, respectively. Upon a median follow-up of 384 to 386 days, there was no evidence of increased risks of incident diabetes following CoronaVac or BNT162b2 vaccination (CoronaVac: 9.08 versus 9.10 per 100,000 person-days, HR = 0.998 [95% CI 0.962 to 1.035]; BNT162b2: 7.41 versus 8.58, HR = 0.862 [0.828 to 0.897]), regardless of diabetes type. In the second cohort, we observed 2,109 cases of diabetes following SARS-CoV-2 infection. Upon a median follow-up of 164 days, SARS-CoV-2 infection was associated with significantly higher risk of incident diabetes (9.04 versus 7.38, HR = 1.225 [1.150 to 1.305])-mainly type 2 diabetes-regardless of predominant circulating variants, albeit lower with Omicron variants (p-interaction = 0.009). The number needed to harm at 6 months was 406 for 1 additional diabetes case. Subgroup analysis revealed no evidence of increased risk of incident diabetes among fully vaccinated COVID-19 survivors. Main limitations of our study included possible misclassification bias as type 1 diabetes was identified through diagnostic coding and possible residual confounders due to its observational nature.

CONCLUSIONS: There was no evidence of increased risks of incident diabetes following COVID-19 vaccination. The risk of incident diabetes increased following SARS-CoV-2 infection, mainly type 2 diabetes. The excess risk was lower, but still statistically significant, for Omicron variants. Fully vaccinated individuals might be protected from risks of incident diabetes following SARS-CoV-2 infection.

PMID:37486927 | DOI:10.1371/journal.pmed.1004274

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Nevin Manimala Statistics

Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites

PLoS Negl Trop Dis. 2023 Jul 24;17(7):e0011133. doi: 10.1371/journal.pntd.0011133. Online ahead of print.

ABSTRACT

Acute febrile illnesses are still a major cause of mortality and morbidity globally, particularly in low to middle income countries. The aim of this study was to determine any possible metabolic commonalities of patients infected with disparate pathogens that cause fever. Three liquid chromatography-mass spectrometry (LC-MS) datasets investigating the metabolic effects of malaria, leishmaniasis and Zika virus infection were used. The retention time (RT) drift between the datasets was determined using landmarks obtained from the internal standards generally used in the quality control of the LC-MS experiments. Fitted Gaussian Process models (GPs) were used to perform a high level correction of the RT drift between the experiments, which was followed by standard peakset alignment between the samples with corrected RTs of the three LC-MS datasets. Statistical analysis, annotation and pathway analysis of the integrated peaksets were subsequently performed. Metabolic dysregulation patterns common across the datasets were identified, with kynurenine pathway being the most affected pathway between all three fever-associated datasets.

PMID:37486920 | DOI:10.1371/journal.pntd.0011133