Category: Statistics

JMIR Cancer. 2023 Sep 7;9:e42044. doi: 10.2196/42044.

ABSTRACT

BACKGROUND: Breast cancer is the most common cause of cancer mortality among women globally. The use of mobile health tools such as apps and games is increasing rapidly, even in low- and middle-income countries, to promote early diagnosis and to manage care and support of survivors and patients.

OBJECTIVE: The primary objective of this review was to categorize selected mobile health apps related to breast health and prevention of breast cancer, based on features such as breast self-examination (BSE) training and reminders, and to analyze their current dissemination. An ancillary objective was to highlight the limitations of existing tools and suggest ways to improve them.

METHODS: We defined strict inclusion and exclusion criteria, which required apps to have titles or descriptions that suggest that they were designed for the general public, and not for patients with breast cancer or health workers. Apps that focused on awareness and primary care via self-check were included, while those that focused on topics such as alternative treatments and medical news were excluded. Apps that were not specifically related to breast cancer were also excluded. Apps (in any language) that appeared in the search with keywords were included. The database consisted of apps from AppAgg and Google Play Store. Only 85 apps met the inclusion criteria. Selected apps were categorized on the basis of their alleged interactive features. Descriptive statistics were obtained, and available language options, the number of downloads, and the cost of the apps were the main parameters reviewed.

RESULTS: The selected apps were categorized on the basis of the following features: education, BSE training, reminders, and recording. Of the 85 selected apps, 72 (84.7%) focused on disseminating breast cancer information. BSE training was provided by only 47% (n=40) of the apps, and very few had reminder (n=26, 30.5%) and recording (n=11, 12.9%) features. The median number of downloads was the highest for apps with recording features (>1000 downloads) than those with education, BSE training, reminder, and recording features (>5000 downloads). Most of these apps (n=74, 83.5%) were monolingual, and around 80.3% (n=49) of these apps were in English. Almost all the apps on Google Play Store were free of charge.

CONCLUSIONS: Although there exist several apps on Google Play Store to promote awareness about breast health and cancer, the usefulness of most of them appears debatable. To provide a complete breast health package to the users, such apps must have all of the following features: reminders or notifications and symptom recording and tracking. There is still an urgent need to scientifically evaluate existing apps in the target populations in order to make them more functional and user-friendly.

PMID:37676704 | DOI:10.2196/42044

JMIR Public Health Surveill. 2023 Sep 7;9:e42446. doi: 10.2196/42446.

ABSTRACT

BACKGROUND: The COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to analyze associations and predict the progression of pandemic. However, GT’s normalization of the search volumes data and data retrieval restrictions affect the data resolution in reflecting the actual search behaviors, thus limiting the potential for using GT data to predict disease outbreaks.

OBJECTIVE: This study aimed to introduce a merged algorithm that helps recover the resolution and accuracy of the search volume data extracted from GT over long observation periods. In addition, this study also aimed to demonstrate the extended application of merged search volumes (MSVs) in combination of network analysis, via tracking the COVID-19 pandemic risk.

METHODS: We collected relative search volumes from GT and transformed them into MSVs using our proposed merged algorithm. The MSVs of the selected coronavirus-related keywords were compiled using the rolling window method. The correlations between the MSVs were calculated to form a dynamic network. The network statistics, including network density and the global clustering coefficients between the MSVs, were also calculated.

RESULTS: Our research findings suggested that although GT restricts the search data retrieval into weekly data points over a long period, our proposed approach could recover the daily search volume over the same investigation period to facilitate subsequent research analyses. In addition, the dynamic time warping diagrams show that the dynamic networks were capable of predicting the COVID-19 pandemic trends, in terms of the number of COVID-19 confirmed cases and severity risk scores.

CONCLUSIONS: The innovative method for handling GT search data and the application of MSVs and network analysis to broaden the potential for GT data are useful for predicting the pandemic risk. Further investigation of the GT dynamic network can focus on noncommunicable diseases, health-related behaviors, and misinformation on the internet.

PMID:37676701 | DOI:10.2196/42446

JAMA Netw Open. 2023 Sep 5;6(9):e2331988. doi: 10.1001/jamanetworkopen.2023.31988.

ABSTRACT

IMPORTANCE: Compared with term-born peers, children born very preterm generally perform poorly in executive functions, particularly in working memory and inhibition. By taking advantage of neuroplasticity, computerized cognitive training of working memory in those children could improve visuospatial processing by boosting visual inhibition via working memory.

OBJECTIVE: To evaluate the long-term effect of cognitive working memory training on visuospatial processing in children aged 5½ to 6 years born very preterm who have working memory impairment.

DESIGN, SETTING, AND PARTICIPANTS: This multicenter (18 French university hospitals), open-label randomized clinical trial with 2 parallel groups (EPIREMED) was conducted from November 2016 to April 2018, with the last follow-up during August 2019. Eligible children from the EPIPAGE 2 cohort were aged 5½ to 6 years, were born between 24 and 34 weeks’ gestation, and had a global intelligence quotient greater than 70 and a working memory index less than 85. Data were analyzed from February to December 2020.

INTERVENTION: Children were randomized 1:1 to standard care management and a working memory cognitive training program (Cogmed software) for 8 weeks (25 sessions) (intervention) or to standard management (control).

MAIN OUTCOMES AND MEASURES: The primary outcome was the visuospatial index score from the Wechsler Preschool and Primary Scale of Intelligence, 4th Edition. Secondary outcomes were working memory, intellectual functioning, executive and attention processes, language skills, behavior, quality of life, and schooling. Neurobehavioral assessments were performed at inclusion and after finishing training at 6 months (intermeditate assessment; secondary outcomes) and at 16 months (final assessment; primary outcome).

RESULTS: There were 169 children randomized, with a mean (SD) age of 5 years 11 months (2 months); 91 (54%) were female. Of the participants, 84 were in the intervention group (57 of whom [68%] completed at least 15 cognitive training sessions) and 85 were in the control group. The posttraining visuospatial index score was not different between groups at a mean (SD) of 3.0 (1.8) months (difference, -0.6 points; 95% CI, -4.7 to 3.5 points) or 12.9 (2.6) months (difference, 0.1 points; 95% CI, -5.4 to 5.1 points). The working memory index score in the intervention group significantly improved from baseline at the intermediate time point (difference, 4.7 points; 95% CI, 1.2-8.1 points), but this improvement was not maintained at the final assessment.

CONCLUSIONS AND RELEVANCE: This randomized clinical trial found no lasting effect of a cognitive training program on visuospatial processing in children aged 5½ to 6 years with working memory disorders who were born very preterm. The findings suggest that this training has limited long-term benefits for improving executive function. Transient benefits seemed to be associated with the developmental state of executive functions.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02757794.

PMID:37676661 | DOI:10.1001/jamanetworkopen.2023.31988

JAMA Oncol. 2023 Sep 7. doi: 10.1001/jamaoncol.2023.2960. Online ahead of print.

ABSTRACT

IMPORTANCE: Lip, oral, and pharyngeal cancers are important contributors to cancer burden worldwide, and a comprehensive evaluation of their burden globally, regionally, and nationally is crucial for effective policy planning.

OBJECTIVE: To analyze the total and risk-attributable burden of lip and oral cavity cancer (LOC) and other pharyngeal cancer (OPC) for 204 countries and territories and by Socio-demographic Index (SDI) using 2019 Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study estimates.

EVIDENCE REVIEW: The incidence, mortality, and disability-adjusted life years (DALYs) due to LOC and OPC from 1990 to 2019 were estimated using GBD 2019 methods. The GBD 2019 comparative risk assessment framework was used to estimate the proportion of deaths and DALYs for LOC and OPC attributable to smoking, tobacco, and alcohol consumption in 2019.

FINDINGS: In 2019, 370 000 (95% uncertainty interval [UI], 338 000-401 000) cases and 199 000 (95% UI, 181 000-217 000) deaths for LOC and 167 000 (95% UI, 153 000-180 000) cases and 114 000 (95% UI, 103 000-126 000) deaths for OPC were estimated to occur globally, contributing 5.5 million (95% UI, 5.0-6.0 million) and 3.2 million (95% UI, 2.9-3.6 million) DALYs, respectively. From 1990 to 2019, low-middle and low SDI regions consistently showed the highest age-standardized mortality rates due to LOC and OPC, while the high SDI strata exhibited age-standardized incidence rates decreasing for LOC and increasing for OPC. Globally in 2019, smoking had the greatest contribution to risk-attributable OPC deaths for both sexes (55.8% [95% UI, 49.2%-62.0%] of all OPC deaths in male individuals and 17.4% [95% UI, 13.8%-21.2%] of all OPC deaths in female individuals). Smoking and alcohol both contributed to substantial LOC deaths globally among male individuals (42.3% [95% UI, 35.2%-48.6%] and 40.2% [95% UI, 33.3%-46.8%] of all risk-attributable cancer deaths, respectively), while chewing tobacco contributed to the greatest attributable LOC deaths among female individuals (27.6% [95% UI, 21.5%-33.8%]), driven by high risk-attributable burden in South and Southeast Asia.

CONCLUSIONS AND RELEVANCE: In this systematic analysis, disparities in LOC and OPC burden existed across the SDI spectrum, and a considerable percentage of burden was attributable to tobacco and alcohol use. These estimates can contribute to an understanding of the distribution and disparities in LOC and OPC burden globally and support cancer control planning efforts.

PMID:37676656 | DOI:10.1001/jamaoncol.2023.2960

J Nephrol. 2023 Sep 7. doi: 10.1007/s40620-023-01748-z. Online ahead of print.

ABSTRACT

BACKGROUND: Anemia in anti-neutrophil cytoplasmic antibody (ANCA)-associated renal vasculitis is a severe complication that predicts renal survival. We here conducted correlative analyses to evaluate correlations of low hemoglobin levels and histopathological characteristics in ANCA-associated renal vasculitis.

METHODS: Fifty-two patients with biopsy-proven ANCA-associated renal vasculitis observed between 2015 and 2020 were retrospectively evaluated. Spearman’s correlation was performed to assess correlations, and statistical evaluation was performed by simple and stepwise multivariable regression.

RESULTS: Regarding laboratory anemia parameters, no significant association with serum hemoglobin levels was observed. Serum hemoglobin levels were associated with the estimated glomerular filtration rate in the total cohort (β = 0.539, p < 0.001), and in the MPO-ANCA subgroup (β = 0.679, p = 0.008). Among tubulointerstitial lesions, decreased serum hemoglobin levels correlated with peritubular capillaritis in the whole cohort (β = – 0.358, p = 0.013), and was suggested in the MPO-ANCA subgroup (p = 0.029, r = – 0.446). Regarding glomerular lesions, the prevalence of necrotic glomeruli significantly associated with low serum hemoglobin levels in PR3-ANCA (β = – 0.424, p = 0.028). In the total cohort, a significant correlation between decreased serum hemoglobin levels and the occurrence of diffuse Bowman’s capsule rupture was identified (β = – 0.374, p = 0.014), which was implied in the MPO-ANCA subgroup (p = 0.013, r = – 0.546; p = 0.0288, slope = – 16.65).

CONCLUSION: Peritubular capillaritis and Bowman’s capsule rupture correlate with low hemoglobin levels; this may indicate that histopathological lesions are linked with inflammatory vascular injury and relative erythropoietin deficiency in ANCA-associated renal vasculitis.

PMID:37676636 | DOI:10.1007/s40620-023-01748-z

Dig Dis Sci. 2023 Sep 7. doi: 10.1007/s10620-023-08049-2. Online ahead of print.

ABSTRACT

BACKGROUND: Mitochondrial DNA (mtDNA) is a damage-associated molecular pattern molecule that can trigger an immune-inflammatory response during pancreatic necrosis (PN).

AIM: To evaluate the role of mtDNA in the detection of PN and severe acute pancreatitis (SAP).

METHODS: The present study included 40 AP patients and 30 controls. AP patients were grouped into mild AP (MAP, n = 15), moderately severe AP (MSAP, n = 17), and SAP (n = 8). Also, the SAP + MSAP group, n = 25, was compared to MAP. AP patients were divided into NAP (n = 7) and non-necrotizing AP (n = 33). The mtDNA copy number, IL-6, and STAT3 expression levels were measured using quantitative real-time PCR.

RESULTS: The mtDNA, IL-6, and STAT3 levels were significantly higher in AP patients than in controls and in the SAP + MSAP than in the MAP. However, the SAP had non-significantly higher levels of mtDNA, STAT3, and IL-6 levels than the MSAP and statistically significant mtDNA, STAT3, and IL-6 when compared to the MAP. mtDNA, IL-6, and STAT3 showed significantly higher levels in NAP compared with non-necrotizing AP. mtDNA was positively correlated with STAT3, IL-6, CRP, APACHE, and CT severity index (CTSI) and negatively correlated with albumin. In the receiver operating curve (ROC), mtDNA was the most significant independent predictor of PN and MAP vs. SAP + MSAP. IL-6 and mtDNA + CRP had higher diagnostic abilities for SIRS and high CTSI.

CONCLUSIONS: mtDNA could enhance the prediction of NAP; however, its diagnostic ability of SAP needs further study.

PMID:37676630 | DOI:10.1007/s10620-023-08049-2

Cancer Causes Control. 2023 Sep 7. doi: 10.1007/s10552-023-01781-0. Online ahead of print.

ABSTRACT

PURPOSE: Accurate pectoral muscle removal is critical in mammographic breast density estimation and many other computer-aided algorithms. We propose a novel approach to remove pectoral muscles form mediolateral oblique (MLO) view mammograms and compare accuracy and computational efficiency with existing method (Libra).

METHODS: A pectoral muscle identification pipeline was developed. The image is first binarized to enhance contrast and then the Canny algorithm was applied for edge detection. Robust interpolation is used to smooth out the pectoral muscle region. Accuracy and computational speed of pectoral muscle identification was assessed using 951 women (1,902 MLO mammograms) from the Joanne Knight Breast Health Cohort at Washington University School of Medicine.

RESULTS: Our proposed algorithm exhibits lower mean error of 12.22% in comparison to Libra’s estimated error of 20.44%. This 40% gain in accuracy was statistically significant (p < 0.001). The computational time for the proposed algorithm is 5.4 times faster when compared to Libra (5.1 s for proposed vs. 27.7 s for Libra per mammogram).

CONCLUSION: We present a novel approach for pectoral muscle removal in mammogram images that demonstrates significant improvement in accuracy and efficiency compared to existing method. Our findings have important implications for the development of computer-aided systems and other automated tools in this field.

PMID:37676616 | DOI:10.1007/s10552-023-01781-0

Biomech Model Mechanobiol. 2023 Sep 7. doi: 10.1007/s10237-023-01739-w. Online ahead of print.

ABSTRACT

The identification of material parameters accurately describing the region-dependent mechanical behavior of human brain tissue is crucial for computational models used to assist, e.g., the development of safety equipment like helmets or the planning and execution of brain surgery. While the division of the human brain into different anatomical regions is well established, knowledge about regions with distinct mechanical properties remains limited. Here, we establish an inverse parameter identification scheme using a hyperelastic Ogden model and experimental data from multi-modal testing of tissue from 19 anatomical human brain regions to identify mechanically distinct regions and provide the corresponding material parameters. We assign the 19 anatomical regions to nine governing regions based on similar parameters and microstructures. Statistical analyses confirm differences between the regions and indicate that at least the corpus callosum and the corona radiata should be assigned different material parameters in computational models of the human brain. We provide a total of four parameter sets based on the two initial Poisson’s ratios of 0.45 and 0.49 as well as the pre- and unconditioned experimental responses, respectively. Our results highlight the close interrelation between the Poisson’s ratio and the remaining model parameters. The identified parameters will contribute to more precise computational models enabling spatially resolved predictions of the stress and strain states in human brains under complex mechanical loading conditions.

PMID:37676609 | DOI:10.1007/s10237-023-01739-w

Methods Mol Biol. 2024;2714:295-305. doi: 10.1007/978-1-0716-3441-7_16.

ABSTRACT

In the field of computer-aided drug design (CADD), there has been dramatic progress in the development of big data and AI-driven methodologies. The expensive and time-consuming process of drug design is related to biomedical complexity. CADD can be used to apply effective and efficient strategies to overcome obstacles in the field of drug design in order to properly design and develop a new medicine. To prepare the raw data for consistent and repeatable applications of big data and AI methodologies, data pre-processing methods are introduced. Big data and AI technologies can be used to develop drugs in areas including predicting absorption, distribution, metabolism, excretion, and toxicity properties as well as finding binding sites in target proteins and conducting structure-based virtual screenings. The accurate and thorough analysis of large amounts of biomedical data as well as the design of prediction models in the area of drug design is made possible by data pre-processing and applications of big data and AI skills. In the biomedical big data era, knowledge on the biological, chemical, or pharmacological structures of biomedical entities relevant to drug design should be analyzed with significant big data and AI approaches.

PMID:37676605 | DOI:10.1007/978-1-0716-3441-7_16

Methods Mol Biol. 2024;2714:235-268. doi: 10.1007/978-1-0716-3441-7_14.

ABSTRACT

With machine learning now transforming the sciences, successful prediction of biological structure or activity is mainly limited by the extent and quality of data available for training, the astute choice of features for prediction, and thorough assessment of the robustness of prediction on a variety of new cases. In this chapter, we address these issues while developing and sharing protocols to build a robust dataset and rigorously compare several predictive classifiers using the open-source Python machine learning library, scikit-learn. We show how to evaluate whether enough data has been used for training and whether the classifier has been overfit to training data. The most telling experiment is 500-fold repartitioning of the training and test sets, followed by prediction, which gives a good indication of whether a classifier performs consistently well on different datasets. An intuitive method is used to quantify which features are most important for correct prediction.The resulting well-trained classifier, hotspotter, can robustly predict the small subset of amino acid residues on the surface of a protein that are energetically most important for binding a protein partner: the interaction hot spots. Hotspotter has been trained and tested here on a curated dataset assembled from 1046 non-redundant alanine scanning mutation sites with experimentally measured change in binding free energy values from 97 different protein complexes; this dataset is available to download. The accessible surface area of the wild-type residue at a given site and its degree of evolutionary conservation proved the most important features to identify hot spots. A variant classifier was trained and validated for proteins where only the amino acid sequence is available, augmented by secondary structure assignment. This version of hotspotter requiring fewer features is almost as robust as the structure-based classifier. Application to the ACE2 (angiotensin converting enzyme 2) receptor, which mediates COVID-19 virus entry into human cells, identified the critical hot spot triad of ACE2 residues at the center of the small interface with the CoV-2 spike protein. Hotspotter results can be used to guide the strategic design of protein interfaces and ligands and also to identify likely interfacial residues for protein:protein docking.

PMID:37676603 | DOI:10.1007/978-1-0716-3441-7_14