Category: Statistics

Methods Mol Biol. 2024;2737:359-375. doi: 10.1007/978-1-0716-3541-4_33.

ABSTRACT

Fatalities due to opioids and other controlled substances continue to increase year over year and maintain an epidemic level nationally. CDC data indicates that drug overdoses killed more than 100,000 Americans in 2021 with synthetic opioids being the main driver of the national crisis (Wide-ranging online data for epidemiologic research (WONDER). Multiple Cause of Death 1999-2020. Centers for Disease Control and Prevention, National Center on Health Statistics. http://wonder.cdc.gov . Accessed 28 Feb 2023, 2021). Response to this crisis includes the development of laboratory testing to support the evolving drug trends. Urine drug testing provides clinicians with objective information to assist in making treatment decisions by identifying the presence of potential drugs of abuse and assessing patient compliance to a controlled substance prescription regimen. In this chapter, we describe a rapid LC-MS/MS method for the simultaneous quantitation of 21 opiate, opioid, and benzodiazepine drugs in less than 4 min. The method requires 100 uL of urine and includes enzymatic hydrolysis of glucuronide conjugates using β-glucuronidase to provide total drug measurement. Quantitation is accomplished using multiple reaction monitoring (MRM), a seven-point calibration curve, and deuterated internal standards. This method provides a robust and reliable means to measure commonly prescribed opioid (including natural, semi-synthetic, and synthetic) and benzodiazepine drugs in urine.

PMID:38036837 | DOI:10.1007/978-1-0716-3541-4_33

Arch Osteoporos. 2023 Dec 1;18(1):148. doi: 10.1007/s11657-023-01350-7.

ABSTRACT

We used cluster analysis to determine the profiles of individuals who sustained wrist fractures. We found two groups: (1) young and active and (2) older and less active. This information may be used to identify individuals who require further bone health interventions to optimize healthy aging.

INTRODUCTION: Distal radial fractures (DRF) are the most common of all fractures, with 6% of males and 33% of females having one at some point in their lifetime. We hypothesize that DRF consists of two subpopulations: one with compromised bone health that is early in the osteoporosis (OP) trajectory and another which are active and healthy and suffer a misfortune fracture due to their high activity levels or risk-taking behaviors. The latter is likely to recover with a minimal disability, while the former may signal a negative health trajectory of disability and early mortality.

OBJECTIVE: To determine the profiles of individuals who sustained wrist fractures using cluster analysis within the Comprehensive Cohort of the Canadian Longitudinal Study on Aging (CLSA) database considering factors that reflect bone health and activity levels.

METHODS: We included all the individuals who had a wrist fracture within the CLSA comprehensive cohort of the database (n = 968). The baseline data was used for this analysis. A 2-step cluster analysis was used to identify profiles that were both statistically and clinically meaningful. Variables that were used in the cluster analysis include demographic variables, physical activity status indicators, general health indicators, mobility indicators, bone health indicators, comorbid conditions, and lifestyle factors.

RESULTS: We were able to identify two distinct profiles that were statistically and clinically meaningful confirming our hypothesis. One cluster included a predominantly younger cohort, who are physically active, with less comorbid conditions, better bone health, and better general health, while the opposite was true of the first cohort.

CONCLUSION: We were able to identify two clusters-a healthy profile and a bone health compromised profile. This information may be used to identify the subgroup of people who should be targeted in the future for more intensive preventive health services to optimize healthy aging.

PMID:38036802 | DOI:10.1007/s11657-023-01350-7

AAPS PharmSciTech. 2023 Nov 30;24(8):250. doi: 10.1208/s12249-023-02695-5.

ABSTRACT

Kinetic modeling of accelerated stability data serves an important purpose in the development of pharmaceutical products, providing support for shelf life claims and expediting the path to clinical implementation. In this context, a Bayesian kinetic modeling framework is considered, accommodating different types of nonlinear kinetics with temperature and humidity dependent rates of degradation and accounting for the humidity conditions within the packaging to predict the shelf life. In comparison to kinetic modeling based on nonlinear least-squares regression, the Bayesian approach allows for interpretable posterior inference, flexible error modeling and the opportunity to include prior information based on historical data or expert knowledge. While both frameworks perform comparably for high-quality data from well-designed studies, the Bayesian approach provides additional robustness when the data are sparse or of limited quality. This is illustrated by modeling accelerated stability data from two solid dosage forms and is further examined by means of artificial data subsets and simulated data.

PMID:38036798 | DOI:10.1208/s12249-023-02695-5

Nat Genet. 2023 Nov 30. doi: 10.1038/s41588-023-01597-3. Online ahead of print.

ABSTRACT

Fine-mapping aims to identify causal genetic variants for phenotypes. Bayesian fine-mapping algorithms (for example, SuSiE, FINEMAP, ABF and COJO-ABF) are widely used, but assessing posterior probability calibration remains challenging in real data, where model misspecification probably exists, and true causal variants are unknown. We introduce replication failure rate (RFR), a metric to assess fine-mapping consistency by downsampling. SuSiE, FINEMAP and COJO-ABF show high RFR, indicating potential overconfidence in their output. Simulations reveal that nonsparse genetic architecture can lead to miscalibration, while imputation noise, nonuniform distribution of causal variants and quality control filters have minimal impact. Here we present SuSiE-inf and FINEMAP-inf, fine-mapping methods modeling infinitesimal effects alongside fewer larger causal effects. Our methods show improved calibration, RFR and functional enrichment, competitive recall and computational efficiency. Notably, using our methods’ posterior effect sizes substantially increases polygenic risk score accuracy over SuSiE and FINEMAP. Our work improves causal variant identification for complex traits, a fundamental goal of human genetics.

PMID:38036779 | DOI:10.1038/s41588-023-01597-3

Environ Monit Assess. 2023 Dec 1;195(12):1555. doi: 10.1007/s10661-023-12165-x.

ABSTRACT

This research quantitatively evaluated the diversity of plants to protect vulnerable species. To measure vegetation information, the appropriate sampling plot size was determined based on the canopy cover of the dominant species of the study area (1 m²). Then, in each unit, sampling was done along 3 transects of 100 m. Along each transect, 10 plots with dimensions of one square meter were placed at a distance of 10 m from each other. In each plot, the type, life forms, frequency of plant species, and species density were recorded. Species diversity indices were calculated using Ecological Methodology software. The values obtained from these indicators were analyzed in SPSS 24 statistical software and using the F test. The results of the Analysis of variance (ANOVA) showed that the highest values of the species diversity indices are in the middle altitudes (ecotone) class. ANOVA of the richness, evenness, and heterogeneity indices in different altitude classes showed that the values of the richness indices were not significant, but among the indices related to the heterogeneity, the Hill index and all the evenness indices were significant. Comparing the numerical indices of our communities enables us to determine the impact of environmental stress in a single community to choose the best habitat among a similar group for conservation. A community that has high diversity and richness is important for conservation. Therefore, the authorities must prevent the destruction of the vegetation of the study area in connection with the implementation of principled and correct management by the potential of the region, but also to reduce the pressure of livestock grazing and carry out corrective and restoration operations, to turn these rangelands towards rich diversity.

PMID:38036716 | DOI:10.1007/s10661-023-12165-x

Sci Rep. 2023 Nov 30;13(1):21101. doi: 10.1038/s41598-023-48474-x.

ABSTRACT

Malnutrition might play a key role in the prognosis of patients with Crohn’s disease (CD). The aim of this study was to explore the impact of weight loss from diagnosis of CD to one-year post-diagnosis on disease prognosis in terms of surgery. Patients who were diagnosed with CD at Samsung Medical Center between 1995 to 2020 were included in this study. The study defined the “group with weight loss” as patients with weight loss in one year after diagnosis and the “group without body weight loss” as patients without weight loss in one year after diagnosis. Their data such as demographics, laboratory findings, and medical interventions were collected retrospectively. The primary outcome was confirmation of the difference in the incidence of surgery associated with CD between the group with weight loss and the group without body weight loss. We further analyzed factors associated with surgery outcomes. A total of 165 patients were analyzed in this study. Forty-one patients (24.8%) had body weight loss whereas 124 patients (75.2%) had no body weight loss. Body change at one year showed no significant association with direct surgical incidence. However, the patients with weight loss tended to undergo surgery earlier than patients without body weight loss. Among factors associated with outcomes of Crohn’s surgery, the albumin was the only significant factor. Patients with weight loss had no statistically significant increase in the risk of surgery than patients without weight loss, although they tended to undergo surgery earlier than patients without body weight loss. A prospective study is needed to determine serial body weight changes during follow-up for patients with CD.

PMID:38036713 | DOI:10.1038/s41598-023-48474-x

Nanotechnology. 2023 Nov 30. doi: 10.1088/1361-6528/ad115b. Online ahead of print.

ABSTRACT

Mixed convection flow of two layers nanofluid in a vertical enclosure is studied. The channel consists of two regions. Region I is electrically conducting while Region II is electrically non-conducting. Region I is filled with base fluid water with copper oxides nanoparticles and Region II is filled with base fluid kerosene oil with iron oxides. The simultaneous effects of electro-magnetohydrodynamics and Grashof number are also taken into account. The governing flow problem consists of nonlinear coupled differential equations which is tackled using analytical technique. Analytical results have been obtained by the homotopy analysis method (HAM). The results for the leading parameters, such as the Hartmann numbers, Grashof numbers, ratio of viscosities, width ratio, volume fraction of nanoparticles, and the ratio of thermal conductivities for three different electric field scenarios under heat generation/absorption were examined. It is found that the effect of the negative electric load parameter assists the flow while the effect of the positive electric load parameter opposes the flow as compared to the case when the electric load parameter is zero. All outcomes for significant parameters on velocity and temperature are discussed graphically.

PMID:38035401 | DOI:10.1088/1361-6528/ad115b

J Radiol Prot. 2023 Nov 30. doi: 10.1088/1361-6498/ad1159. Online ahead of print.

ABSTRACT

Justification of medical radiation exposure is one of the main elements of radiation protection for patients. For a medical exposure to proceed, the benefit from the procedure must have been determined to be greater than the detriment. It is rare, however, that justification can be stated quantitatively as a ratio of benefit to detriment, or as a net benefit, and this is particularly true for medical diagnostic exposures associated with non-fatal diseases where survival statistics do not apply. The concept of the disability-adjusted life year (DALY) is well established as a measure of disease severity in public health, and there have been calls to revise the international system of radiation protection dosimetry to employ the DALY as a measure of radiation detriment. This paper looks at possible routes to quantify the benefit and detriment aspects of justification based on initial published results for the use of the DALY as a measure of radiation detriment, together with established values of DALY for a range of diseases. Although spreadsheet-style solutions for the calculation of a justification factor based on statistical life tables can be devised, these will be shown to have some limitations. A justification factor based on the rate of change of benefit divided by the rate of change of detriment following medical exposure is proposed. This factor is simple to calculate, is age independent, can apply to non-fatal diseases and is argued to have logical and ethical advantages for the explanation of the relative benefits and detriments of radiological procedures to patients.&#xD.

PMID:38035392 | DOI:10.1088/1361-6498/ad1159

J Phys Chem Lett. 2023 Nov 30:10935-10942. doi: 10.1021/acs.jpclett.3c02643. Online ahead of print.

ABSTRACT

Break junction experiments allow investigating electronic and spintronic properties at the atomic and molecular scale. These experiments generate by their very nature broad and asymmetric distributions of the observables of interest, and thus, a full statistical interpretation is warranted. We show here that understanding the complete lifetime distribution is essential for obtaining reliable estimates. We demonstrate this for Au atomic point contacts by adopting Bayesian reasoning to make maximal use of all measured data to reliably estimate the distance to the transition state, x^‡, the associated free energy barrier, ΔG^‡, and the curvature, v, of the free energy surface. Obtaining robust estimates requires less experimental effort than with previous methods and fewer assumptions and thus leads to a significant reassessment of the kinetic parameters in this paradigmatic atomic-scale structure. Our proposed Bayesian reasoning offers a powerful and general approach when interpreting inherently stochastic data that yield broad, asymmetric distributions for which analytical models of the distribution may be developed.

PMID:38035375 | DOI:10.1021/acs.jpclett.3c02643

Front Nutr. 2023 Nov 16;10:1295241. doi: 10.3389/fnut.2023.1295241. eCollection 2023.

ABSTRACT

Promoting sustainability in food and nutrition systems is essential to address the various challenges and trade-offs within the current food system. This imperative is guided by key principles and actionable steps, including enhancing productivity and efficiency, reducing waste, adopting sustainable agricultural practices, improving economic growth and livelihoods, and enhancing resilience at various levels. However, in order to change the current food consumption patterns of the world and move toward sustainable diets, as well as increase productivity in the food production chain, it is necessary to employ the findings and achievements of other sciences. These include the use of artificial intelligence-based technologies. Presented here is a narrative review of possible applications of artificial intelligence in the food production chain that could increase productivity and sustainability. In this study, the most significant roles that artificial intelligence can play in enhancing the productivity and sustainability of the food and nutrition system have been examined in terms of production, processing, distribution, and food consumption. The research revealed that artificial intelligence, a branch of computer science that uses intelligent machines to perform tasks that require human intelligence, can significantly contribute to sustainable food security. Patterns of production, transportation, supply chain, marketing, and food-related applications can all benefit from artificial intelligence. As this review of successful experiences indicates, artificial intelligence, machine learning, and big data are a boon to the goal of sustainable food security as they enable us to achieve our goals more efficiently.

PMID:38035357 | PMC:PMC10687214 | DOI:10.3389/fnut.2023.1295241