Nevin Manimala

Chirality. 2022 Nov 6. doi: 10.1002/chir.23514. Online ahead of print.

ABSTRACT

Alpha-lipoic acid is a natural product that possesses distinct pharmacological properties. Lipoic acid is a short-chain fatty acid containing an asymmetric carbon at five bonds of distance to the organic function. Herein, we developed a nuclear magnetic resonance protocol to access the chiral recognition of lipoic acid in a simple and rapid procedure employing cinchonidine as a cheap chiral solvation agent in deuterated chloroform. To optimize this method, a statistical design of the experimental model was performed to produce a clear understanding of the optimal concentration, temperature, and molar ratio parameters. Based on the obtained spectra, the cinchonidine H₈ -H₉ scalar coupling indicated a conformational preference in the chiral discrimination procedure. Density functional theory calculations established a proximity between the asymmetric center of lipoic acid and the aromatic moiety of cinchonidine, clarifying possible conformations in this ion-pair interaction. The described protocol demonstrates how far is far enough to chiral discrimination using a chiral solvation agent, expanding the method to compounds that contain a remote stereocenter.

PMID:36336792 | DOI:10.1002/chir.23514

Pharmacoeconomics. 2022 Nov 7. doi: 10.1007/s40273-022-01199-7. Online ahead of print.

ABSTRACT

A health economic evaluation (HEE) is a comparative analysis of alternative courses of action in terms of both costs and consequences. A cost-effectiveness analysis is a type of HEE that compares an intervention to one or more alternatives by estimating how much it costs to gain an additional unit of health outcome. Cost-effectiveness analyses are commonly performed using Microsoft (MS) Excel. However, there is current interest in using other software that is better suited to more complex problems, methods, and data, as well as improved reproducibility and transparency. That is, it is increasingly important to be able to repeat an analysis of a particular data set and obtain the same results, and access the analysis and results in a clear and comprehensive openly available form. In this tutorial we provide a step-by-step guide on how to implement a mainstay model of HEE, namely a Markov model, in the statistical programming language R. The adoption of R for the purpose of cost-effectiveness analysis is highly dependent on the ability of the health economic modeller to understand, learn, and apply programming-type skills. R is likely to be less familiar than MS Excel for many modellers and so coding a cost-effectiveness model in R can be a large jump. We describe the technical details from the perspective of a MS Excel user to help bridge the gap between software and reduce the learning curve by providing for the first-time side-by-side comparisons of the Markov model example in MS Excel and R.

PMID:36336774 | DOI:10.1007/s40273-022-01199-7

Hormones (Athens). 2022 Nov 7. doi: 10.1007/s42000-022-00412-8. Online ahead of print.

ABSTRACT

AIMS/HYPOTHESIS: Intensive insulin therapy in the treatment of type 1 diabetes can, in place of multiple daily injections of subcutaneous insulin (MDI), be performed with continuous subcutaneous insulin infusion (CSII) systems. This method allows for better glycemic control and thus reduces the risk of complications of the disease. The aim of this study was to evaluate the results of treatment with CSII in Portugal.

METHODS: A retrospective analysis of the records on the national CSII platform was carried out between January 2010 and August 2021. All the registered patients are followed in certified CSII treatment centers in Portugal. Of the 7135 registered patients, 3807 were excluded due to absence of monitoring data. The reasons for treatment were analyzed and a comparison was made between patients with and without CSII. The statistical significance considered was α < 0.05.

RESULTS: A total of 3328 patients were included in the study, 1136 under MDI and 2192 under CSII. The main reasons for CSII use were marked glycemic variability (25%) and HbA1c greater than 7% (23%). Patients under CSII had a lower HbA1c (7.7 ± 1.0% vs. 8.0 ± 1.5%, p < 0.001), as well as a lower frequency of episodes of severe hypoglycemia (1.4 vs. 3.3 per 100 patient-years, p < 0.001), and ketoacidosis (1 vs. 2.4 per 100 patient-years, p < 0.001).

CONCLUSIONS: The present analysis validates the advantage of using CSII in metabolic control and reduction of acute complications of type 1 diabetes, both severe hypoglycemia and ketoacidosis, in the Portuguese population. CSII therapy is classically associated with an increased risk of ketoacidosis; however, in experienced centers and adequate patient education, the opposite is found.

PMID:36336764 | DOI:10.1007/s42000-022-00412-8

J Community Health. 2022 Nov 6. doi: 10.1007/s10900-022-01152-x. Online ahead of print.

ABSTRACT

COVID-19 exacerbated existing disparities in food security in Chicago. Home gardening can improve food security but there are often barriers to participation and the benefits are understudied. Chicago Grows Food (CGF) formed in 2020 to address food insecurity during COVID-19, and created the Grow Your Groceries (GYG) program to provide home gardening kits to families at risk of food insecurity in Chicago. A participatory program evaluation was conducted to better understand the experiences of and benefits to individuals participating in GYG. Program participants shared feedback via focus groups (n = 6) and surveys (n = 72). Qualitative data were analyzed using an iterative coding process. Quantitative data were analyzed using descriptive statistics. Most participants reported confidence in using a grow kit to grow food, increased healthy food consumption, easier access to healthy food, and high likelihood of growing food again. Additionally, participants described increased connections within their communities, increased interaction with their family, and personal growth as benefits of the program. These results demonstrate the benefits of a novel home gardening program that uses fabric grow bags to address food insecurity. A larger scale program evaluation is necessary to better understand the impacts of participating in this home gardening program.

PMID:36336753 | DOI:10.1007/s10900-022-01152-x

Sci Rep. 2022 Nov 6;12(1):18837. doi: 10.1038/s41598-022-23584-0.

ABSTRACT

Growth alterations have been described in patients operated on for oral clefts. The purpose of this work was to analyze the craniofacial and palate morphology and dimensions of young adults operated on for oral clefts in early childhood in Spain. Eighty-three patients from eight different hospitals were divided into four groups based on their type of cleft: cleft lip (CL, n = 6), unilateral cleft lip and palate (UCLP, n = 37), bilateral cleft lip and palate (BCLP, n = 16), and cleft palate only (CPO, n = 24). A control group was formed of 71 individuals. Three-dimensional (3D) digital models were obtained from all groups with an intraoral scanner, together with cephalometries and frontal, lateral, and submental facial photographs. Measurements were obtained and analyzed statistically. Our results showed craniofacial alterations in the BCLP, UCLP, and CPO groups with an influence on the palate, maxilla, and mandible and a direct impact on facial appearance. This effect was more severe in the BCLP group. Measurements in the CL group were similar to those in the control group. Cleft characteristics and cleft type seem to be the main determining factors of long-term craniofacial growth alterations in these patients. Prospective research is needed to clearly delineate the effects of different treatments on the craniofacial appearance of adult cleft patients.

PMID:36336749 | DOI:10.1038/s41598-022-23584-0

Lifetime Data Anal. 2022 Nov 7. doi: 10.1007/s10985-022-09576-2. Online ahead of print.

ABSTRACT

Targeted maximum likelihood estimation (TMLE) provides a general methodology for estimation of causal parameters in presence of high-dimensional nuisance parameters. Generally, TMLE consists of a two-step procedure that combines data-adaptive nuisance parameter estimation with semiparametric efficiency and rigorous statistical inference obtained via a targeted update step. In this paper, we demonstrate the practical applicability of TMLE based causal inference in survival and competing risks settings where event times are not confined to take place on a discrete and finite grid. We focus on estimation of causal effects of time-fixed treatment decisions on survival and absolute risk probabilities, considering different univariate and multidimensional parameters. Besides providing a general guidance to using TMLE for survival and competing risks analysis, we further describe how the previous work can be extended with the use of loss-based cross-validated estimation, also known as super learning, of the conditional hazards. We illustrate the usage of the considered methods using publicly available data from a trial on adjuvant chemotherapy for colon cancer. R software code to implement all considered algorithms and to reproduce all analyses is available in an accompanying online appendix on Github.

PMID:36336732 | DOI:10.1007/s10985-022-09576-2

Obes Surg. 2022 Nov 7. doi: 10.1007/s11695-022-06333-0. Online ahead of print.

ABSTRACT

BACKGROUND: Obesity rates in Hispanics and African Americans (AAs) are higher than in Caucasians in the USA, yet the rate of metabolic and bariatric surgery (MBS) for weight loss remains lower for both Hispanics and AAs.

METHODS: Patient demographics and outcomes of adult AA and Hispanic patients undergoing sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB) procedures were analyzed using the MBSAQIP dataset [2015-2018] using unmatched and propensity-matched data.

RESULTS: In total, 173,157 patients were included, of whom 98,185 were AA [56.7%] [21,163-RYGB; 77,022-SG] and 74,972 were Hispanic [43.3%] [20,282-RYGB; 54,690-SG]). Preoperatively, the AA cohort was older, had more females, and higher BMIs with higher rates of all tracked obesity-related medical conditions except for diabetes, venous stasis, and prior foregut surgery. Intra- and postoperatively, AAs were more likely to experience major complications including unplanned ICU admission, 30-day readmission/reintervention, and mortality. After propensity matching, the differences in ED visits, treatment for dehydration, 30-day readmission, 30-day intervention, and pulmonary embolism remained for both SG and RYGB cohorts. Progressive renal insufficiency and ventilator use lost statistical significance in both cohorts. Conversely, 30-day reoperation, postoperative ventilator requirement, unplanned intubation, unplanned ICU admission, and mortality lost significance in the RYGB cohort, but not SG patients.

CONCLUSION: Outcomes for AA patients were worse than for Hispanic patients, even after propensity matching. After matching, differences in major complications and mortality lost significance for RYGB, but not SG. These data suggest that outcomes for RYGB may be driven by the presence and severity of pre-existing patient-related factors.

PMID:36336721 | DOI:10.1007/s11695-022-06333-0

Med Phys. 2022 Nov 6. doi: 10.1002/mp.16087. Online ahead of print.

ABSTRACT

BACKGROUND: The growing adoption of MRI-guided radiation therapy (RT) platforms and a focus on MRI-only RT workflows has brought the technical challenge of synthetic CT (sCT) reconstruction to the forefront. Unpaired-data deep learning-based approaches to the problem offer the attractive characteristic of not requiring paired training data, but the gap between paired- and unpaired-data results can be limiting.

PURPOSE: We present two distinct approaches aimed at improving unpaired-data sCT reconstruction results: a cascade ensemble that combines multiple models and a personalized training strategy originally designed for the paired-data setting.

METHODS: Comparisons are made between the following models: 1) the paired-data fully convolutional DenseNet (FCDN), 2) the FCDN with the Intentional Deep Overfit Learning (IDOL) personalized training strategy, 3) the unpaired-data CycleGAN, 4) the CycleGAN with the IDOL training strategy, and 5) the CycleGAN as an intermediate model in a cascade ensemble approach. Evaluation of the various models over 25 total patients is carried out using a five-fold cross validation scheme, with the patient-specific IDOL models being trained for the five patients of fold 3, chosen at random.

RESULTS: In both the paired- and unpaired-data settings, adopting the IDOL training strategy led to improvements in the mean absolute error (MAE) between true CT images and sCT outputs within the body contour (mean improvement, paired- and unpaired-data approaches, respectively: 38%, 9%) and in regions of bone (52%, 5%), the peak signal to noise ratio (PSNR; 15%, 7%), and the structural similarity index (SSIM; 6%, <1%). The ensemble approach offered additional benefits over the IDOL approach in all three metrics (mean improvement over unpaired-data approach in fold 3; MAE: 20% bone MAE: 16% PSNR: 10% SSIM: 2%), and differences in body MAE between the ensemble approach and the paired-data approach are statistically insignificant.

CONCLUSIONS: We have demonstrated that both a cascade ensemble approach and a personalized training strategy designed initially for the paired-data setting offer significant improvements in image quality metrics for the unpaired-data sCT reconstruction task. Closing the gap between paired- and unpaired-data approaches is a step towards fully enabling these powerful and attractive unpaired-data frameworks. This article is protected by copyright. All rights reserved.

PMID:36336718 | DOI:10.1002/mp.16087

Eur J Hum Genet. 2022 Nov 7. doi: 10.1038/s41431-022-01201-y. Online ahead of print.

ABSTRACT

Genetic prediction of male pattern baldness (MPB) is important in science and society. Previous genetic MPB prediction models were limited by sparse marker coverage, small sample size, and/or data dependency in the different analytical steps. Here, we present novel models for genetic prediction of MPB based on a large set of markers and large independent subsample sets drawn among 187,435 European subjects. We selected 117 SNP predictors within 85 distinct loci from a list of 270 previously MPB-associated SNPs in 55,573 males of the UK Biobank Study (UKBB). Based on these 117 SNPs with and without age as additional predictor, we trained, by use of different methods, prediction models in a non-overlapping subset of 104,694 UKBB males and tested them in a non-overlapping subset of 26,177 UKBB males. Estimates of prediction accuracy were similar between methods with AUC ranges of 0.725-0.728 for severe, 0.631-0.635 for moderate, 0.598-0.602 for slight, and 0.708-0.711 for no hair loss with age, and slightly lower without, while prediction of any versus no hair loss gave 0.690-0.711 with age and slightly lower without. External validation in an early-onset enriched MPB dataset from the Bonn Study (N = 991) showed improved prediction accuracy without considering age such as AUC of 0.830 for no vs. any hair loss. Because of the large number of markers and the large independent datasets used for the different analytical steps, the newly presented genetic prediction models are the most reliable ones currently available for MPB or any other human appearance trait.

PMID:36336714 | DOI:10.1038/s41431-022-01201-y

Malar J. 2022 Nov 6;21(1):319. doi: 10.1186/s12936-022-04323-2.

ABSTRACT

BACKGROUND: Detection of malaria parasitaemia in samples that are negative by rapid diagnostic tests (RDTs) requires resource-intensive molecular tools. While pooled testing using a two-step strategy provides a cost-saving alternative to the gold standard of individual sample testing, statistical adjustments are needed to improve accuracy of prevalence estimates for a single step pooled testing strategy.

METHODS: A random sample of 4670 malaria RDT negative dried blood spot samples were selected from a mass testing and treatment trial in Asembo, Gem, and Karemo, western Kenya. Samples were tested for malaria individually and in pools of five, 934 pools, by one-step quantitative polymerase chain reaction (qPCR). Maximum likelihood approaches were used to estimate subpatent parasitaemia (RDT-negative, qPCR-positive) prevalence by pooling, assuming poolwise sensitivity and specificity was either 100% (strategy A) or imperfect (strategy B). To improve and illustrate the practicality of this estimation approach, a validation study was constructed from pools allocated at random into main (734 pools) and validation (200 pools) subsets. Prevalence was estimated using strategies A and B and an inverse-variance weighted estimator and estimates were weighted to account for differential sampling rates by area.

RESULTS: The prevalence of subpatent parasitaemia was 14.5% (95% CI 13.6-15.3%) by individual qPCR, 9.5% (95% CI (8.5-10.5%) by strategy A, and 13.9% (95% CI 12.6-15.2%) by strategy B. In the validation study, the prevalence by individual qPCR was 13.5% (95% CI 12.4-14.7%) in the main subset, 8.9% (95% CI 7.9-9.9%) by strategy A, 11.4% (95% CI 9.9-12.9%) by strategy B, and 12.8% (95% CI 11.2-14.3%) using inverse-variance weighted estimator from poolwise validation. Pooling, including a 20% validation subset, reduced costs by 52% compared to individual testing.

CONCLUSIONS: Compared to individual testing, a one-step pooled testing strategy with an internal validation subset can provide accurate prevalence estimates of PCR-positivity among RDT-negatives at a lower cost.

PMID:36336700 | DOI:10.1186/s12936-022-04323-2