Category: Statistics

Sci Rep. 2023 Feb 1;13(1):1866. doi: 10.1038/s41598-023-28603-2.

ABSTRACT

Adolescents are known to be particularly vulnerable, compared to children and adults, to initiation of substance use and progression to problematic use. This study aimed to examine the prevalence and type of illicit drug use in a population of adolescents and young adults who were hospitalized in a psychiatric hospital. The purpose of the study was also to find the link between age, sex, type of admission and particular mental disorders and using psychoactive substances at least once in a lifetime. A 12-month retrospective cross-sectional analysis of medical records compiled for adolescent and youth psychiatric patients who had been admitted to the Regional Psychiatric Hospital in Olsztyn, Poland, between October 1, 2018, and September 30, 2019, was conducted. After analyzing the available medical records, 506 cases were included and analyzed. Data for the study were collected in an Excel spreadsheet from discharge reports, including data from psychiatric examinations, especially anamnesis. Subsequently, statistical calculations were performed. Lifetime prevalence of any illicit substance use (34.0%) was common. The most frequently used drug was Cannabis (29.2%), the next New Psychoactive Substance-NPS (14.2%) and Amphetamine (13.0%). The higher number of people declaring to take illicit substances was proportional to the increasing age. Except for the group 10-15 years, the subject group was dominated by males. The highest, statistically significant percentage of patients who declared taking illicit substances in general, was found in people with diagnoses F20-F29 (schizophrenia, schizotypal and delusional disorders) (55%), additionally, we found a statistically significant association between NPS use and these diagnoses. Only in the group of patients diagnosed with eating disorders no one declared taking psychoactive substances. However, the correlation between taking illicit drugs and the subgroups with diagnosed psychiatric diseases should be treated with caution because of the small sample size in some cases. Our findings have shown the significant prevalence of the phenomenon in this population. These data highlight the need to explore this population at high risk carefully.

PMID:36725976 | DOI:10.1038/s41598-023-28603-2

Sci Rep. 2023 Feb 1;13(1):1870. doi: 10.1038/s41598-023-27958-w.

ABSTRACT

The use of alternative tobacco products, particularly medwakh, has expanded among youth in the Middle East and around the world. The present study is conducted to investigate the biochemical and pathophysiological changes caused by medwakh smoking, and to examine the salivary metabolomics profile of medwakh smokers. Saliva samples were collected from 30 non-smokers and 30 medwakh smokers and subjected to metabolomic analysis by UHPLC-ESI-QTOF-MS. The CRP and Glutathione Peroxidase 1 activity levels in the study samples were quantified by ELISA and the total antioxidant capacity (TAC) by TAC assay kits. Statistical measurements and thorough validation of data obtained from untargeted metabolomics identified 37 uniquely and differentially abundant metabolites in saliva of medwakh smokers. The levels of phthalate, L-sorbose, cytosine, uridine, alpha-hydroxy hippurate, and L-nicotine were noticeably high in medwakh smokers. Likewise, 20 metabolic pathways were differentially altered in medwakh smokers. This study identified a distinctive saliva metabolomics profile in medwakh smokers associated with altered redox homeostasis, metabolic pathways, antioxidant system, and CRP levels. The impact of the altered metabolites in medwakh smokers and their diagnostic utility require further research in large cohorts.

PMID:36725974 | DOI:10.1038/s41598-023-27958-w

Sci Rep. 2023 Feb 1;13(1):1879. doi: 10.1038/s41598-023-29029-6.

ABSTRACT

Bone fractures are a global public health concern, yet no thorough investigation of low-dose aspirin usage to prevent fractures in the elderly has been conducted. Many interventional human and animal studies have tried to detect the correct role of low-dose aspirin on fractures in elderly persons. The literature doesn’t consist of a retrospective observational study that includes a large number of older individuals and evaluates the accurate effect of aspirin on the fractures post falling from low heights. This cross-sectional includes 7132 elderly persons and aimed to detect if there was a link between taking low-dose aspirin to prevent fractures in the elderly. Data was extracted from the National Health and Nutrition Examination Survey (NHANES) database for 2017-2020 and 2013-2014. Demographic and examination data were collected during in-home interviews and study visits to a mobile examination center. Standardized questionnaires were used to collect information such as age, gender, race, educational level, and family income-to-poverty ratio. Body mass index (BMI), weight, standing height, upper leg length, upper arm length, arm circumference, and wrist circumference were all measured during the examination. The study examined 8127 patients, with 7132 elderly patients suitable for data analysis. The odds ratio of fractures due to a fall from standing height or less was 0.963 (95 percent confidence interval 0.08-1.149) in low-dose aspirin users, while having parents with osteoporosis had a related risk of 1.23. (95 percent confidence interval 0.81-1.8). The total number of fractures was 1295; with hip fractures constituting up to 13.82%, wrist fractures of 66.56%, and spine fractures of 19.61%. There was no significant difference in femur and spine bone mineral density (BMD) in the two groups (use low dose aspirin and don’t use). Females had a 5.6 times greater fracture risk related to a fall from standing height or less (1 time or more) than males (P-value < 0.001). Furthermore, taking aspirin had no effect on the occurrence of fractures from standing height or less in older people (P-value = 0.468). In addition, the logistic regression after performing the propensity matching score confirmed that there was no impact of taking aspirin on the occurrence of fractures (P-value > 0.05). This cross-sectional study reveals that taking low-dose aspirin to prevent fractures in the elderly is statistically insignificant. However, fractures are more common in older persons, especially in older women; thus, more widespread injury prevention initiatives and access to osteoporosis prevention and diagnosis for older people should improve to minimize the overall burden.

PMID:36725971 | DOI:10.1038/s41598-023-29029-6

Sci Rep. 2023 Feb 1;13(1):1854. doi: 10.1038/s41598-022-25411-y.

ABSTRACT

Audiograms are used to show the hearing capability of a person at different frequencies. The filter bank in a hearing aid is designed to match the shape of patients’ audiograms. Configuring the hearing aid is done by modifying the designed filters’ gains to match the patient’s audiogram. There are few problems faced in achieving this objective successfully. There is a shortage in the number of audiologists; the filter bank hearing aid designs are complex; and, the hearing aid fitting process is tiring. In this work, a machine learning solution is introduced to classify the audiograms according to the shapes based on unsupervised spectral clustering. The features used to build the ML model are peculiar and describe the audiograms better. Different normalization methods are applied and studied statistically to improve the training data set. The proposed Machine Learning (ML) algorithm outperformed the current existing models, where, the accuracy, precision, recall, specificity, and F-score values are higher. The reason for the better performance is the use of multi-stage feature selection to describe the audiograms precisely. This work introduces a novel ML technique to classify audiograms according to the shape, which, can be integrated to the future and existing studies to change the existing practices in classifying audiograms.

PMID:36725966 | DOI:10.1038/s41598-022-25411-y

Sci Rep. 2023 Feb 1;13(1):1811. doi: 10.1038/s41598-023-28399-1.

ABSTRACT

In this study, we aimed to evaluate the benefits and losses of mammography with and without implant displacement (ID) and propose an appropriate imaging protocol for the screening of breasts with implants. We evaluated mammograms of 162 breasts in 96 patients including 71 breasts with biopsy-proven cancers. Mammography of each breast included standard MLO and ID MLO images. We reviewed the mammograms using clinical image quality criteria, which consist of parameters that evaluate the proper positioning of the breast and the image resolution. Standard MLO images showed significantly higher scores for proper positioning but showed significantly lower scores for image resolution than the ID MLO images. Moreover, standard MLO images showed significantly higher kVp, mAs, and compressed breast thickness than the ID MLO images. The organ dose was also higher in the standard MLO images than in the ID MLO images, but the difference was not statistically significant. In mammography with proven cancer, ID MLO images showed significantly higher degree of cancer visibility than standard MLO images. For screening mammography in patients with breast implants, ID MLO view alone is sufficient for MLO projection with reducing the patient’s radiation dose without compromising the breast cancer detection capability, especially in dense breasts with subpectoral implants.

PMID:36725965 | DOI:10.1038/s41598-023-28399-1

Sci Rep. 2023 Feb 1;13(1):1842. doi: 10.1038/s41598-023-29018-9.

ABSTRACT

The digital transformation of Automated Vehicles (AVs) has raised concerns in the cyber realm among prospective AV consumers. However, there is a dearth of empirical research on how cyber obstacles may impact the operation of AVs. To address this knowledge gap, this study examines the six critical cyber impediments (data privacy, AV connectivity, ITS infrastructure, lack of cybersecurity regulations, AV cybersecurity understanding, and AV cyber-insurance) that influence the deployment of AVs. The impact of gender, age, income level, and individual AV and cybersecurity knowledge on these obstacles are statistically assessed using a sample of 2061 adults from the United States, the United Kingdom, New Zealand, and Australia. The research revealed intriguing empirical findings on all cyber barriers in the form of a trichotomy: participants’ education level, understanding of AVs, and cybersecurity knowledge. As education levels increase, the significance of a cyber barrier to AV deployment decreases; however, as AV comprehension and cybersecurity knowledge increase, the perception of a cyber barrier becomes significantly more important. In addition, the study demonstrates differences in perceptions of cyber barriers and AV deployments based on gender, age, income, and geographic location. This study’s findings on cyber barriers and AV deployment have implications for academia and industry.

PMID:36725959 | DOI:10.1038/s41598-023-29018-9

Res Synth Methods. 2023 Feb 1. doi: 10.1002/jrsm.1626. Online ahead of print.

ABSTRACT

A reference interval, or an interval in which a pre-specified proportion of measurements from a healthy population are expected to fall, is used to determine whether a person’s measurement is typical of a healthy individual. For a specific biomarker, multiple published studies may provide data collected from healthy participants. A reference interval estimated by combining the data across these studies is typically more generalizable than a reference interval based on a single study. Methods for estimating reference intervals from random effects meta-analysis and fixed effects meta-analysis have been recently proposed and implemented using R software. We present an R Shiny tool, RIMeta, implementing these methods, which allows users not proficient in R to estimate a reference interval from a meta-analysis using aggregate data (mean, standard deviation, and sample size) from each study. RIMeta provides users a convenient way to estimate a reference interval from a meta-analysis and to generate the reference interval plot to visualize the results. The use of this web-based R Shiny tool does not require the installation of R or any background knowledge of programming. We explain all functions of the R Shiny tool and illustrate how to use it with a real data example. This article is protected by copyright. All rights reserved.

PMID:36725922 | DOI:10.1002/jrsm.1626

Sci Rep. 2023 Feb 1;13(1):1858. doi: 10.1038/s41598-023-28835-2.

ABSTRACT

In this paper, we study consensus behavior based on the local application of the majority consensus algorithm (a generalization of the majority rule) over four-connected bi-dimensional networks. In this context, we characterize theoretically every four-vicinity network in its capacity to reach consensus (every individual at the same opinion) for any initial configuration of binary opinions. Theoretically, we determine all regular grids with four neighbors in which consensus is reached and in which ones not. In addition, in those instances in which consensus is not reached, we characterize statistically the proportion of configurations that reach spurious fixed points from an ensemble of random initial configurations. Using numerical simulations, we also analyze two observables of the system to characterize the algorithm: (1) the quality of the achieved consensus, that is if it respects the initial majority of the network; and (2) the consensus time, measured as the average amount of steps to reach convergence.

PMID:36725907 | DOI:10.1038/s41598-023-28835-2

Sci Rep. 2023 Feb 1;13(1):1868. doi: 10.1038/s41598-023-29036-7.

ABSTRACT

Breast cancer is a global health issue affecting 2.3 million women per year, causing death in over 600,000. Mammography (and biopsy) is the gold standard for screening and diagnosis. Whilst effective, this test exposes individuals to radiation, has limitations to its sensitivity and specificity and may cause moderate to severe discomfort. Some women may also find this test culturally unacceptable. This proof-of-concept study, combining bottom-up proteomics with Matrix Assisted Laser Desorption Ionisation Mass Spectrometry (MALDI MS) detection, explores the potential for a non-invasive technique for the early detection of breast cancer from fingertip smears. A cohort of 15 women with either benign breast disease (n = 5), early breast cancer (n = 5) or metastatic breast cancer (n = 5) were recruited from a single UK breast unit. Fingertips smears were taken from each patient and from each of the ten digits, either at the time of diagnosis or, for metastatic patients, during active treatment. A number of statistical analyses and machine learning approaches were investigated and applied to the resulting mass spectral dataset. The highest performing predictive method, a 3-class Multilayer Perceptron neural network, yielded an accuracy score of 97.8% when categorising unseen MALDI MS spectra as either the benign, early or metastatic cancer classes. These findings support the need for further research into the use of sweat deposits (in the form of fingertip smears or fingerprints) for non-invasive screening of breast cancer.

PMID:36725900 | DOI:10.1038/s41598-023-29036-7

Sci Rep. 2023 Feb 1;13(1):1845. doi: 10.1038/s41598-023-29095-w.

ABSTRACT

Severe acute malnutrition (SAM) is a major global public health problem. We aimed to assess the effects of probiotic and synbiotic supplementation on rate of weight gain and change in length in young SAM infants. This study was substudy of a single-blind randomized clinical trial (NCT0366657). During nutritional rehabilitation, 67 <6 months old SAM infants were enrolled and randomized to receive either probiotic (Bifidobacterium. infantis EVC001) or synbiotic (B. infantis EVC001 + Lacto-N-neotetraose [LNnT]) or placebo (Lactose) for four weeks and were followed for four more weeks after supplementation. In multivariable linear regression model, the mean rate of weight gain in the probiotic arm compared to placebo was higher by 2.03 unit (P < 0.001), and 1.13 unit (P = 0.030) in the synbiotic arm. In linear mixed-effects model, mean WAZ was higher by 0.57 unit (P = 0.018) in probiotic arm compared to placebo. Although not statistically significant, delta length for age z score (LAZ) trended to be higher among children in probiotc (β = 0.25) and synbiotic (β = 0.26) arms compared to placebo in multivariable linear regression model. Our study describes that young SAM infants had a higher rate of weight gain when supplemented with probiotic alone, compared to their counterparts with either synbiotic or placebo.

PMID:36725893 | DOI:10.1038/s41598-023-29095-w