Category: Statistics

Proc Natl Acad Sci U S A. 2023 Dec 26;120(52):e2300842120. doi: 10.1073/pnas.2300842120. Epub 2023 Dec 21.

ABSTRACT

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.

PMID:38127979 | DOI:10.1073/pnas.2300842120

Med Phys. 2023 Dec 21. doi: 10.1002/mp.16898. Online ahead of print.

ABSTRACT

BACKGROUND: Radiotherapy dose predictions have been trained with data from previously treated patients of similar sites and prescriptions. However, clinical datasets are often inconsistent and do not contain the same number of organ at risk (OAR) structures. The effects of missing contour data in deep learning-based dose prediction models have not been studied.

PURPOSE: The purpose of this study was to investigate the impacts of incomplete contour sets in the context of deep learning-based radiotherapy dose prediction models trained with clinical datasets and to introduce a novel data substitution method that utilizes automated contours for undefined structures.

METHODS: We trained Standard U-Nets and Cascade U-Nets to predict the volumetric dose distributions of patients with head and neck cancers (HNC) using three input variations to evaluate the effects of missing contours, as well as a novel data substitution method. Each architecture was trained with the original contour (OC) inputs, which included missing information, hybrid contour (HC) inputs, where automated OAR contours generated in software were substituted for missing contour data, and automated contour (AC) inputs containing only automated OAR contours. 120 HNC treatments were used for model training, 30 were used for validation and tuning, and 44 were used for evaluation and testing. Model performance and accuracy were evaluated with global whole body dose agreement, PTV coverage accuracy, and OAR dose agreement. The differences in these values between dataset variations were used to determine the effects of missing data and automated contour substitutions.

RESULTS: Automated contours used as substitutions for missing data were found to improve dose prediction accuracy in the Standard U-Net and Cascade U-Net, with a statistically significant difference in some global metrics and/or OAR metrics. For both models, PTV coverage between input variations was unaffected by the substitution technique. Automated contours in HC and AC datasets improved mean dose accuracy for some OAR contours, including the mandible and brainstem, with a greater improvement seen with HC datasets. Global dose metrics, including mean absolute error, mean error, and percent error were different for the Standard U-Net but not for the Cascade U-Net.

CONCLUSION: Automated contours used as a substitution for contour data improved prediction accuracy for some but not all dose prediction metrics. Compared to the Standard U-Net models, the Cascade U-Net achieved greater precision.

PMID:38127972 | DOI:10.1002/mp.16898

PLoS One. 2023 Dec 21;18(12):e0291686. doi: 10.1371/journal.pone.0291686. eCollection 2023.

ABSTRACT

BACKGROUND: Heart failure is an important global health problem which is associated with high mortality. Uncontrolled heart failure leads to hospitalization and reduction in quality of life. Therefore, the study aimed to assess the treatment outcome such as improved, death, hospitalization, and self-discharges without improvement and associated factors in heart failure patients admitted to south western Ethiopian hospitals.

METHODS: We will use databases such as PubMed, Science Direct, HINARI, Scopus and Google Scholar. The final systematic review and meta-analysis will contain papers that fulfill the eligible criteria. A systematic data extraction check list will be used to extract the data, and STATA version 14 will be used for the analysis. Heterogeneity is evaluated using the I2 tests and the Cochrane Q test statistic. To examine publication bias, a funnel plot, Egger’s weighted regression, and Begg’s test are utilized. The sensitivity analysis and subgroup analysis will be done for studies having heterogeneity. The Joanna Briggs institute meta-analysis of statistics assessment and review instrument (JBI- MAStARI) will be used for quality assessment.

DISCUSSION: This protocol is expected to provide adequate evidence on the burden of poor heart failure treatment outcome that includes self-discharge, developing complication and finally leads to death in acute and chronic heart failure patients in Ethiopia. Furthermore, to enrich our estimation, we also intended to assess the associated factors of poor treatment outcome. Therefore, our review will call for government and non-government interventions in reducing the mortality associated with heart failure.

PMID:38127971 | DOI:10.1371/journal.pone.0291686

PLoS One. 2023 Dec 21;18(12):e0295573. doi: 10.1371/journal.pone.0295573. eCollection 2023.

ABSTRACT

The aim of this study was to investigate the effect of zinc sulphate on the activities of different enzymes and metabolites of Pholiota adiposa. In the experiment, we used the conventional enzyme activity assay to determine the changes of six indicators, including protein content, laccase activity, cellulase activity, amylase activity and polyphenol oxidase activity, under different concentrations of zinc sulphate treatment. The results showed that the activities of amylase, laccase, cellulase and peroxidase were Zn2+(200)>Zn2+(0)>Zn2+(400)>Zn2+(800).The activities of catalase and superoxide dismutase were Zn2+(200)>Zn2+(400)>Zn2+(800), and zinc sulfate could significantly affect the activity of polylipic squamase in a dose-dependent manner. Further correlation analysis showed that all six enzyme activities were significantly correlated with each other (P<001); the results of the statistical model test showed that the regression model constructed was statistically significant; overall the residuals met the conditions of normal distribution, and the corresponding points of different enzyme activities Q-Q’ were more evenly distributed around y = x, and all fell in the 90% acceptance interval, thus the series was considered to obey normal distribution; the results of the principal The results of the principal component analysis showed that principal component 1 was positively correlated with amylase, laccase and cellulase. Principal component 2 was positively correlated with superoxide dismutase and catalase, and negatively correlated with peroxidase. The analysis of Metabonomic data revealed that zinc sulfate had a significant impact on the expression of metabolites in the mycelium. Moreover, varying concentrations of zinc sulfate exerted significant effects on the levels of amino acids, organic acids, and gluconic acid. This conclusion was confirmed by other experimental data. The results of the study provide a scientific reference for better research, development and utilization of Pholiota adiposa.

PMID:38127967 | DOI:10.1371/journal.pone.0295573

PLoS One. 2023 Dec 21;18(12):e0295763. doi: 10.1371/journal.pone.0295763. eCollection 2023.

ABSTRACT

The mortality impact of COVID-19 has mainly been studied at the national level. However, looking at the aggregate impact of the pandemic at the country level masks heterogeneity at the subnational level. Subnational assessments are essential for the formulation of public health policies. This is especially important for federal countries with decentralised healthcare systems, such as Germany. Therefore, we assess geographical variation in the mortality impact of COVID-19 for the 16 German federal states in 2020 and 2021 and the sex differences therein. For this purpose, we adopted an ecological study design, using population-level mortality data by federal state, age, and sex, for 2005-2021 obtained from the German Federal Statistical Office. We quantified the impact of the pandemic using the excess mortality approach. We estimated period life expectancy losses (LE losses), excess premature mortality, and excess deaths by comparing their observed with their expected values. The expected mortality was based on projected age-specific mortality rates using the Lee-Carter methodology. Saxony was the most affected region in 2020 (LE loss 0.77 years, 95% CI 0.74;0.79) while Saarland was the least affected (-0.04, -0.09;0.003). In 2021, the regions with the highest losses were Thuringia (1.58, 1.54;1.62) and Saxony (1.57, 1.53;1.6) and the lowest in Schleswig-Holstein (0.13, 0.07;0.18). Furthermore, in 2021, eastern regions experienced higher LE losses (mean: 1.13, range: 0.85 years) than western territories (mean: 0.5, range: 0.72 years). The regional variation increased between 2020 and 2021, and was higher among males than among females, particularly in 2021. We observed an unequal distribution of the mortality impact of COVID-19 at the subnational level in Germany, particularly in 2021 among the male population. The observed differences between federal states might be partially explained by the heterogeneous spread of the virus in 2020 and by differences in the population’s propensity to follow preventive guidelines.

PMID:38127957 | DOI:10.1371/journal.pone.0295763

PLoS One. 2023 Dec 21;18(12):e0295676. doi: 10.1371/journal.pone.0295676. eCollection 2023.

ABSTRACT

Ambient air quality is one of the most critical threats to human health. In this study, the health and economic benefits of reducing PM2.5 were estimated in the city of Arak during the period of 2017-2019. The concentration data were obtained from the Environmental Protection Organization of Central Province, while the demographic data were obtained from the website of the Iran Statistics Center. The number of premature deaths from all causes, ischemic heart disease, chronic obstructive pulmonary disease, and lung cancer, attributable to PM2.5 pollution was estimated using the Environmental Benefits Mapping and Analysis Program-Comprehensive Version (BenMAP_CE) to limit the guidelines of the World Health Organization. The results showed that improving air quality in 2017, 2018, and 2019 in Arak could prevent the deaths of 729, 654, and 460 people, respectively. The number of years of life lost (YLL) in 2017, 2018, and 2019 was 11383, 10362, and 7260 years, respectively. The total annual economic benefits of reducing the PM2.5 concentration in Arak under the proposed scenarios in 2017, 2018, and 2019 were estimated to be 309,225,507, 262,868,727, and 182,224,053 USD, respectively, using the statistical life method (VSL). Based on the results of this study, there are significant health and economic benefits to reducing PM2.5 concentrations in Arak City. Therefore, planning and adopting control policies to reduce air pollution in this city are necessary.

PMID:38127954 | DOI:10.1371/journal.pone.0295676

PLoS One. 2023 Dec 21;18(12):e0294931. doi: 10.1371/journal.pone.0294931. eCollection 2023.

ABSTRACT

The marine sponge Hymeniacidon perlevis is a globally distributed and invasive species with extensive filter-feeding characteristics. The symbiotic relationship fostered between the sea sponge and the inhabiting microorganism is key in the production of metabolic enzymes which is the focus of this study. Sponge bacterial symbionts were grown on starch agar for 48hrs. Colourimetric analyses of amylase were conducted at 540nm using a spectrophotometric plate reader. Using an X-Bridge column (3.5μM, 4.6x150mm), 80/20 acetonitrile/water in 0.1% ammonium were the conditions used for the liquid chromatography-mass spectrometry (LC-MS) analyses. Seven reducing sugars were used to optimise LC-MS to determine the presence of the crude enzyme formed. Not all the bacterial symbionts isolated from H perlevis produced alpha and beta amylases to break down starch. From the statistical mean of crude enzyme concentrations from the hydrolysis of starch by amylase, isolate seven had the highest optical density (OD) at 0.43475 while isolate twelve had the lowest OD at 0.141417. From the LC-MS analysis, out of the seven sugars, Glucose and maltose constituted > 65% of the reducing sugars formed from the hydrolysis of starch by the amylases. Isolates 3,6 and 7 produced 6.906 mg/l, 12.309 mg/l, and 5.909 mg/l of glucose, while isolates 3,4,5,6 and 7 produced 203.391 mg/l, 176.238 mg/l, 139.938 mg/l, 39.030 mg/l, and 18.809 mg/l of maltose, respectively. Isolate two had the highest amount of maltose at a concentration of 267.237 mg/l while isolate four had the highest amount of glucose concentration of 53.084 mg/l. Enzymes from marine sponge bacteria offer greater potential for a green and sustainable production process. Amylase extraction from bacterial symbionts in H perlevis is sustainable and should be supported. They can serve as reliable sources of revenue for enzyme industries, and applications in food industries and biotechnological processes.

PMID:38127953 | DOI:10.1371/journal.pone.0294931

PLOS Glob Public Health. 2023 Dec 21;3(12):e0002698. doi: 10.1371/journal.pgph.0002698. eCollection 2023.

ABSTRACT

Nutritional rehabilitation during severe acute malnutrition (SAM) aims to quickly restore body size and minimize poor short-term outcomes. We hypothesized that faster weight gain during treatment is associated with greater cardiometabolic risk in adult life. Anthropometry, body composition (DEXA), blood pressure, blood glucose, insulin and lipids were measured in a cohort of adults who were hospitalized as children for SAM between 1963 and 1993. Weight and height measured during hospitalization and at one year post-recovery were abstracted from hospital records. Childhood weight gain during nutritional rehabilitation and weight and height gain one year post-recovery were analysed as continuous variables, quintiles and latent classes in age, sex and minimum weight-for-age z-scores-adjusted regression models against adult measurements. Data for 278 adult SAM survivors who had childhood admission records were analysed. Of these adults, 85 also had data collected 1 year post-hospitalisation. Sixty percent of participants were male, mean (SD) age was 28.2 (7.7) years, mean (SD) BMI was 23.6 (5.2) kg/m2. Mean admission age for SAM was 10.9 months (range 0.3-36.3 months), 77% were wasted (weight-for-height z-scores<-2). Mean rehabilitation weight gain (SD) was 10.1 (3.8) g/kg/day and 61.6 (25.3) g/day. Rehabilitation weight gain > 12.9 g/kg/day was associated with higher adult BMI (difference = 0.5 kg/m2, 95% CI: 0.1-0.9, p = 0.02), waist circumference (difference = 1.4 cm, 95% CI: 0.4-2.4, p = 0.005), fat mass (difference = 1.1 kg, 95% CI: 0.2-2, p = 0.02), fat mass index (difference = 0.32kg/m2, 95% CI: -0.0001-0.6, p = 0.05), and android fat mass (difference = 0.09 kg, 95% CI: 0.01-0.2, p = 0.03). Post-recovery weight gain (g/kg/month) was associated with lean mass (difference = 1.3 kg, 95% CI: 0.3-2.4, p = 0.015) and inversely associated with android-gynoid fat ratio (difference = -0.03, 95% CI: -0.07to-0.001 p = 0.045). Rehabilitation weight gain exceeding 13g/kg/day was associated with adult adiposity in young, normal-weight adult SAM survivors. This challenges existing guidelines for treating malnutrition and warrants further studies aiming at optimising these targets.

PMID:38127945 | DOI:10.1371/journal.pgph.0002698

JMIR Form Res. 2023 Dec 21;7:e48296. doi: 10.2196/48296.

ABSTRACT

BACKGROUND: Cancer treatment is a key component of health care systems, and the increasing number of cancer medicines is expanding the treatment landscape. However, evidence of the impact on patients has been focused more on chemotherapy toxicity and symptom control and less on the effect of cancer medicines more broadly on patients’ lives. Evolving electronic patient-reported outcome measures (ePROMs) presents the opportunity to secure early engagement of patients and clinicians in shaping the collection of quality-of-life metrics and presenting these data to better support the patient-clinician decision-making process.

OBJECTIVE: The aim of this study was to obtain initial feedback from patients and clinicians on the wireframes of a digital solution (patient app and clinician dashboard) for the collection and use of cancer medicines ePROMs.

METHODS: We adopted a 2-stage, mixed methods approach. Stage 1 (March to June 2019) consisted of interviews and focus groups with cancer clinicians and patients with cancer to explore the face validity of the wireframes, informed by the technology acceptance model constructs (perceived ease of use, perceived usefulness, and behavioral intention to use). In stage 2 (October 2019 to February 2020), the revised wireframes were assessed through web-based, adapted technology acceptance model questionnaires. Qualitative data (stage 1) underwent a framework analysis, and descriptive statistics were performed on quantitative data (stage 2). Clinicians and patients with cancer were recruited from NHS Greater Glasgow & Clyde, the largest health board in Scotland.

RESULTS: A total of 14 clinicians and 19 patients participated in a combination of stage 1 interviews and focus groups. Clinicians and patients indicated that the wireframes of a patient app and clinician dashboard for the collection of cancer medicines ePROMs would be easy to use and could focus discussions, and they would be receptive to using such tools in the future. In stage 1, clinicians raised the potential impact on workload, and both groups identified the need for adequate IT skills to use each technology. Changes to the wireframes were made, and in stage 2, clinicians (n=8) and patients (n=16) indicated it was “quite likely” that the technologies would be easy to use and they would be “quite likely” to use them in the future. Notably, clinicians indicated that they would use the dashboard to enable treatment decisions “with around half” of their patients.

CONCLUSIONS: This study emphasizes the importance of consulting both patients and clinicians in the design of digital solutions. The wireframes were perceived positively by patients and clinicians who were willing to use such technologies if available in the future as part of routine care. However, challenges were raised, and some differences were identified between participant groups, which warrant further research.

PMID:38127422 | DOI:10.2196/48296

Viral Immunol. 2023 Dec;36(10):642-648. doi: 10.1089/vim.2023.0047.

ABSTRACT

Maternal immunoglobulin G (IgG) antibodies that are passively transferred to newborns through the placenta confer protection if they are exposed to measles virus. A measles outbreak occurred in several European countries including Greece, between 2016 and 2018. A prospective study was conducted in the General Hospital of Lakonia, regarding the measles seropositivity status of mother and newborn pairs. IgG antibody titer for measles was measured in serum samples acquired from pairs of mothers and newborns. The samples were analyzed through quantitative enzyme-linked immunosorbent assay, and antimeasles IgG >200 IU/mL was considered to be protective. Demographic data for mothers and neonates and data regarding immunization status of mothers were analyzed. Study population included 206 mothers and their newborns. In total, 12.6% of mothers (n = 26) and 10.7% of newborns (n = 22) did not have protective serology. A statistically significant positive linear association between maternal and neonatal antibodies was found (rho = 0.924) (p = 0.001). Neonates whose mothers were seropositive had higher antibodies [geometric mean concentration (GMC): 804.8 (728.3-889.2)] than neonates whose mothers were seronegative/borderline [GMC: 97.7 (64.2-148.8)] (p = 0.001). In the study area, a significant rate of mothers and newborns was found to have nonprotective measles serology that exceeds the limit required for herd immunity. Vaccination coverage in women of reproductive age should be increased to reduce potential for future measles epidemics.

PMID:38127419 | DOI:10.1089/vim.2023.0047