Tag: pubmed

Patterns (N Y). 2021 Dec 10;2(12):100368. doi: 10.1016/j.patter.2021.100368. eCollection 2021 Dec 10.

ABSTRACT

Numerous arguments strongly support the practice of open science, which offers several societal and individual benefits. For individual researchers, sharing research artifacts such as data can increase trust and transparency, improve the reproducibility of one’s own work, and catalyze new collaborations. Despite a general appreciation of the benefits of data sharing, research data are often only available to the original investigators. For data that are shared, lack of useful metadata and documentation make them challenging to reuse. In this paper, we argue that a lack of incentives and infrastructure for making data useful is the biggest barrier to creating a culture of widespread data sharing. We compare data with code, examine computational environments in the context of their ability to facilitate the reproducibility of research, provide some practical guidance on how one can improve the chances of their data being reusable, and partially bridge the incentive gap. While previous papers have focused on describing ideal best practices for data and code, we focus on common-sense ideas for sharing tabular data for a target audience of academics working in data science adjacent fields who are about to submit for publication.

PMID:34950899 | PMC:PMC8672137 | DOI:10.1016/j.patter.2021.100368

Patterns (N Y). 2021 Oct 27;2(12):100372. doi: 10.1016/j.patter.2021.100372. eCollection 2021 Dec 10.

ABSTRACT

We introduce a new method for single-cell cytometry studies, FAUST, which performs unbiased cell population discovery and annotation. FAUST processes experimental data on a per-sample basis and returns biologically interpretable cell phenotypes, making it well suited for the analysis of complex datasets. We provide simulation studies that compare FAUST with existing methodology, exemplifying its strength. We apply FAUST to data from a Merkel cell carcinoma anti-PD-1 trial and discover pre-treatment effector memory T cell correlates of outcome co-expressing PD-1, HLA-DR, and CD28. Using FAUST, we then validate these correlates in cryopreserved peripheral blood mononuclear cell samples from the same study, as well as an independent CyTOF dataset from a published metastatic melanoma trial. Finally, we show how FAUST’s phenotypes can be used to perform cross-study data integration in the presence of diverse staining panels. Together, these results establish FAUST as a powerful new approach for unbiased discovery in single-cell cytometry.

PMID:34950900 | PMC:PMC8672150 | DOI:10.1016/j.patter.2021.100372

Palliat Care Soc Pract. 2021 Dec 16;15:26323524211063217. doi: 10.1177/26323524211063217. eCollection 2021.

ABSTRACT

AIMS: The study aimed to explore the quality and impact of care provided through an innovative palliative care project to improve the quality of life of older people in an urban informal settlement in Bangladesh.

METHODS: Center for Palliative Care (CPC) at Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, in collaboration with the Worldwide Hospice Palliative Care Alliance (WHPCA) has been operating this community project since 2015. A cross-sectional observational design was used in this program evaluation study. A total of 594 people received services including 227 patients (Group-1) receiving regular and intensive palliative care and 367 patients with less intense needs (Group-2) receiving relatively less support based on need. In addition, current group-1 patients (total 114) and a matched cohort of 58 group-2 patients were interviewed with an experience of care survey questionnaire. Baseline and demographic data were presented in tables. The Z-test was used to measure mean statistical differences between two groups.

RESULTS: Multiple comorbidities were common. Pain was the most frequently noted physical symptom along with anxiety, sadness, and depression as common psychological concerns. Compassionate palliative care for the older people had significant (p < 0.05) impact on psycho-social and spiritual care, caregiver training, responding to emergencies, and reduction of out of pocket healthcare expenditure among the intensive intervention group.

CONCLUSION: Using a community-based approach following this model may play a significant part in expansion of palliative care throughout Bangladesh to meet the huge need and scarcity of such services.

PMID:34950874 | PMC:PMC8689427 | DOI:10.1177/26323524211063217

Patterns (N Y). 2021 Oct 4;2(12):100364. doi: 10.1016/j.patter.2021.100364. eCollection 2021 Dec 10.

ABSTRACT

Current cardiovascular risk assessment tools use a small number of predictors. Here, we study how machine learning might: (1) enable principled selection from a large multimodal set of candidate variables and (2) improve prediction of incident coronary artery disease (CAD) events. An elastic net-based Cox model (ML4H_EN-COX) trained and evaluated in 173,274 UK Biobank participants selected 51 predictors from 13,782 candidates. Beyond most traditional risk factors, ML4H_EN-COX selected a polygenic score, waist circumference, socioeconomic deprivation, and several hematologic indices. A more than 30-fold gradient in 10-year risk estimates was noted across ML4H_EN-COX quintiles, ranging from 0.25% to 7.8%. ML4H_EN-COX improved discrimination of incident CAD (C-statistic = 0.796) compared with the Framingham risk score, pooled cohort equations, and QRISK3 (range 0.754-0.761). This approach to variable selection and model assessment is readily generalizable to a broad range of complex datasets and disease endpoints.

PMID:34950898 | PMC:PMC8672148 | DOI:10.1016/j.patter.2021.100364

Gen Psychiatr. 2021 Dec 1;34(6):e100635. doi: 10.1136/gpsych-2021-100635. eCollection 2021.

ABSTRACT

BACKGROUND: The corpus callosum (CC) is the most targeted region in the cerebrum that integrates cognitive data between homologous areas in the right and left hemispheres.

AIMS: Our study used statistical analysis to determine whether there was a correlation between shape changes in the CC in patients with schizophrenia (SZ) (deficit syndrome (DS) and non-deficit syndrome (NDS)) and healthy control (HC) subjects.

METHODS: This study consisted of 27 HC subjects and 50 schizophrenic patients (20 with DS and 30 with NDS). 3 patients with DS and 4 patients with NDS were excluded. Three-dimensional, sagittal, T1-spoiled, gradient-echo imaging was used. Standard anatomical landmarks were selected and marked on each image using specific software.

RESULTS: As to comparing the Procrustes mean shapes of the CC, statistically significant differences were observed between HC and SZ (DS+NDS) (p=0.017, James’s F_j=73.732), HC and DS (p<0.001, James’s F_j=140.843), HC and NDS (p=0.006, James’s F_j=89.178) and also DS and NDS (p<0.001, James’s F_j=152.967). Shape variability in the form of CC was 0.131, 0.085, 0.082 and 0.086 in the HC, SZ (DS+NDS), DS and NDS groups, respectively.

CONCLUSIONS: This study reveals callosal shape variations in patients with SZ and their DS and NDS subgroups that take into account the CC’s topographic distribution.

PMID:34950854 | PMC:PMC8638449 | DOI:10.1136/gpsych-2021-100635

Brain Commun. 2021 Dec 4;3(4):fcab288. doi: 10.1093/braincomms/fcab288. eCollection 2021.

ABSTRACT

Our inability to reliably predict disease outcomes in multiple sclerosis remains an issue for clinicians and clinical trialists. This study aims to create, from available clinical, genetic and environmental factors; a clinical-environmental-genotypic prognostic index to predict the probability of new relapses and disability worsening. The analyses cohort included prospectively assessed multiple sclerosis cases (N = 253) with 2858 repeated observations measured over 10 years. N = 219 had been diagnosed as relapsing-onset, while N = 34 remained as clinically isolated syndrome by the 10th-year review. Genotype data were available for 199 genetic variants associated with multiple sclerosis risk. Penalized Cox regression models were used to select potential genetic variants and predict risk for relapses and/or worsening of disability. Multivariable Cox regression models with backward elimination were then used to construct clinical-environmental, genetic and clinical-environmental-genotypic prognostic index, respectively. Robust time-course predictions were obtained by Landmarking. To validate our models, Weibull calibration models were used, and the Chi-square statistics, Harrell’s C-index and pseudo–R ² were used to compare models. The predictive performance at diagnosis was evaluated using the Kullback-Leibler and Brier (dynamic) prediction error (reduction) curves. The combined index (clinical-environmental-genotypic) predicted a quadratic time-dynamic disease course in terms of worsening (HR = 2.74, CI: 2.00-3.76; pseudo–R ²=0.64; C-index = 0.76), relapses (HR = 2.16, CI: 1.74-2.68; pseudo–R ² = 0.91; C-index = 0.85), or both (HR = 3.32, CI: 1.88-5.86; pseudo–R ² = 0.72; C-index = 0.77). The Kullback-Leibler and Brier curves suggested that for short-term prognosis (≤5 years from diagnosis), the clinical-environmental components of disease were more relevant, whereas the genetic components reduced the prediction errors only in the long-term (≥5 years from diagnosis). The combined components performed slightly better than the individual ones, although their prognostic sensitivities were largely modulated by the clinical-environmental components. We have created a clinical-environmental-genotypic prognostic index using relevant clinical, environmental, and genetic predictors, and obtained robust dynamic predictions for the probability of developing new relapses and worsening of symptoms in multiple sclerosis. Our prognostic index provides reliable information that is relevant for long-term prognostication and may be used as a selection criterion and risk stratification tool for clinical trials. Further work to investigate component interactions is required and to validate the index in independent data sets.

PMID:34950873 | PMC:PMC8691056 | DOI:10.1093/braincomms/fcab288

JNCI Cancer Spectr. 2021 Dec 8;5(6):pkab090. doi: 10.1093/jncics/pkab090. eCollection 2021 Dec.

ABSTRACT

BACKGROUND: Recreational physical activity (RPA) is associated with improved survival after breast cancer (BC) in average-risk women, but evidence is limited for women who are at increased familial risk because of a BC family history or BRCA1 and BRCA2 pathogenic variants (BRCA1/2 PVs).

METHODS: We estimated associations of RPA (self-reported average hours per week within 3 years of BC diagnosis) with all-cause mortality and second BC events (recurrence or new primary) after first invasive BC in women in the Prospective Family Study Cohort (n = 4610, diagnosed 1993-2011, aged 22-79 years at diagnosis). We fitted Cox proportional hazards regression models adjusted for age at diagnosis, demographics, and lifestyle factors. We tested for multiplicative interactions (Wald test statistic for cross-product terms) and additive interactions (relative excess risk due to interaction) by age at diagnosis, body mass index, estrogen receptor status, stage at diagnosis, BRCA1/2 PVs, and familial risk score estimated from multigenerational pedigree data. Statistical tests were 2-sided.

RESULTS: We observed 1212 deaths and 473 second BC events over a median follow-up from study enrollment of 11.0 and 10.5 years, respectively. After adjusting for covariates, RPA (any vs none) was associated with lower all-cause mortality of 16.1% (95% confidence interval [CI] = 2.4% to 27.9%) overall, 11.8% (95% CI = -3.6% to 24.9%) in women without BRCA1/2 PVs, and 47.5% (95% CI = 17.4% to 66.6%) in women with BRCA1/2 PVs (RPA*BRCA1/2 multiplicative interaction P = .005; relative excess risk due to interaction = 0.87, 95% CI = 0.01 to 1.74). RPA was not associated with risk of second BC events.

CONCLUSION: Findings support that RPA is associated with lower all-cause mortality in women with BC, particularly in women with BRCA1/2 PVs.

PMID:34950851 | PMC:PMC8692829 | DOI:10.1093/jncics/pkab090

Bone Rep. 2021 Dec 9;16:101157. doi: 10.1016/j.bonr.2021.101157. eCollection 2022 Jun.

ABSTRACT

BACKGROUND: Pseudoarthrosis or non-union is a complication with an incidence of 5-10% of bone fractures, most frequently located in the diaphysis of long bones. The management of this complication is addressed by means of complex surgical procedures and is a concern for orthopaedic and trauma surgeons nowadays. The use of biomarkers for diagnosing patients at risk of non-union would help us to establish special measures for early corrective treatment.

METHODS: Prospective exploratory pilot study with a cohort of 20 patients diagnosed of non-hypertrophic pseudoarthrosis of long bones who were treated surgically with either autologous bone graft or a Tissue Engineering Product composed of bone marrow-derived Mesenchymal Stromal Cells. Patients were followed for 12 months and plasma blood samples were obtained to determine circulating levels of Transforming Growth Factor Beta 1 and Beta 2 (TGF-β1 and TGF-β2, respectively) at inclusion, and at 1 week, 2 weeks, and months 1, 2, 3, 6 and 12 after surgery. Radiological bone healing was evaluated by the Tomographic Union Score (TUS).

RESULTS: Basal levels of TGF-β1 and TGF-β2 were determined in the twenty patients (26,702 ± 14,537 pg/mL and 307.8 ± 83.1 pg/mL, respectively). Three of them withdrew from the study, so complete follow-up was conducted on 17 patients (9 successfully healed vs. 8 that did not heal). Statistically significant differences between the bone healing group and the non-union group were found at month 12 for both TGF-β1 (p = 0.005) and TGF-β2 (p = 0.02).

CONCLUSIONS: TGF-β1 and TGF-β2 are biomarkers that correlate with clinical evidence of bone regeneration and may be used to monitor patients, although early predictive value after intervention needs to be further studied in combination with other molecules.

PMID:34950754 | PMC:PMC8671858 | DOI:10.1016/j.bonr.2021.101157

J Frailty Sarcopenia Falls. 2021 Dec 1;6(4):209-217. doi: 10.22540/JFSF-06-209. eCollection 2021 Dec.

ABSTRACT

OBJECTIVES: The health and well-being of older women may be influenced by frailty and low socioeconomic status (SES). This study examined the association between frailty and SES, healthcare utilisation and quality of life (QOL) among older women in regional Australia.

METHODS: Cross-sectional analysis of the Geelong Osteoporosis Study was conducted on 360 women (ages ≥60yr) in the 15-year follow up. Frailty was identified using modified Fried’s phenotype. Individual SES measures and healthcare utilisation were documented by questionnaire. Area-based SES was determined by cross-referencing residential addresses with the Australian Bureau of Statistics Index of Relative Socio-economic Advantage and Disadvantage (IRSAD). QOL was measured using the Australian World Health Organisation Quality of Life Instrument (WHOQoL-Bref). Multinomial logistic regression was conducted with frailty groupings as outcome.

RESULTS: Sixty-two (17.2%) participants were frail, 199 (55.3%) pre-frail and 99 (27.5%) robust. Frail participants were older with higher body mass index. Frailty was associated with lower education but not marital status, occupation or IRSAD. Strong associations with frailty were demonstrated for all WHOQoL-Bref domains. Frailty was associated with more primary care doctor visits (p<0.001).

CONCLUSIONS: This population-based study highlights the significant impact of frailty on older women, indicating reduced QOL and increased primary care doctor visits.

PMID:34950811 | PMC:PMC8649863 | DOI:10.22540/JFSF-06-209

J Med Imaging (Bellingham). 2021 Nov;8(6):067501. doi: 10.1117/1.JMI.8.6.067501. Epub 2021 Dec 20.

ABSTRACT

Purpose: Recent advances in computational image analysis offer the opportunity to develop automatic quantification of histologic parameters as aid tools for practicing pathologists. We aim to develop deep learning (DL) models to quantify nonsclerotic and sclerotic glomeruli on frozen sections from donor kidney biopsies. Approach: A total of 258 whole slide images (WSI) from cadaveric donor kidney biopsies performed at our institution ( n=123 ) and at external institutions ( n=135 ) were used in this study. WSIs from our institution were divided at the patient level into training and validation datasets (ratio: 0.8:0.2), and external WSIs were used as an independent testing dataset. Nonsclerotic ( n=22767 ) and sclerotic ( n=1366 ) glomeruli were manually annotated by study pathologists on all WSIs. A nine-layer convolutional neural network based on the common U-Net architecture was developed and tested for the segmentation of nonsclerotic and sclerotic glomeruli. DL-derived, manual segmentation, and reported glomerular count (standard of care) were compared. Results: The average Dice similarity coefficient testing was 0.90 and 0.83. And the F1 , recall, and precision scores were 0.93, 0.96, and 0.90, and 0.87, 0.93, and 0.81, for nonsclerotic and sclerotic glomeruli, respectively. DL-derived and manual segmentation-derived glomerular counts were comparable, but statistically different from reported glomerular count. Conclusions: DL segmentation is a feasible and robust approach for automatic quantification of glomeruli. We represent the first step toward new protocols for the evaluation of donor kidney biopsies.

PMID:34950750 | PMC:PMC8685284 | DOI:10.1117/1.JMI.8.6.067501