bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of body mass index on vertebral and hip fractures in older people and differences according to sex: a retrospective Japanese cohort study
Authors: Shiomoto Kyohei , Babazono Akira , Harano Yumi , Fujita Takako , Jiang Peng , Kim Sung-A , Nakashima Yasuharu 
Published: November 2021

ObjectivesThe purpose of this study was to investigate the incidence of vertebral and hip fractures in the older people and to clarify the relationship between these fractures and body mass index (BMI) along with the impact of sex differences.DesignThis was a retrospective cohort study.SettingWe used administrative claims data between April 2010 and March 2018.ParticipantsOlder people aged ≥75 years who underwent health examinations in 2010 and were living in the Fukuoka Prefecture, Japan were included in the study. A total of 24 691 participants were included; the mean age was 79.4±4.3 years, 10 853 males and 13 838 females, and an the mean duration of observation was 6.9±1.6 years.Primary and secondary outcome measuresWe estimated the incidence of vertebral and hip fractures by BMI category (underweight: <18.5 kg/m2, normal weight: 18.5–24.9 kg/m2, overweight and obese: ≥25.0 kg/m2) using a Kaplan-Meier curve in males and females and determined fracture risk by sex using Cox proportional hazards regression analyses.ResultsThe incidence of vertebral and hip fractures was 16.8% and 6.5%, respectively. The cumulative incidence of vertebral and hip fracture at the last observation (8 years) in each BMI groups (underweight/normal weight/overweight and obese) estimated using the Kaplan-Meier curve was 14.7%/10.4%/9.0% in males and 24.9%/23.0%/21.9% in females, and 6.3%/2.9%/2.4% in males and 14.1%/9.0%/8.1% in females, respectively, and both fractures were significantly higher in underweight groups regardless of sex. Multivariable Cox proportional hazards models showed that underweight was a significant risk factor only in males for vertebral fractures and in both males and females for hip fractures.ConclusionUnderweight was associated with fractures in the ageing population, but there was a sex difference in the effect for vertebral fractures.
Using hierarchical clustering analysis to evaluate COVID-19 pandemic preparedness and performance in 180 countries in 2020
Authors: Sadeghi Banafsheh , Cheung Rex C Y , Hanbury Meagan 
Published: November 2021

ObjectiveTo rank and score 180 countries according to COVID-19 cases and fatality in 2020 and compare the results to existing pandemic vulnerability prediction models and results generated by standard epidemiological scoring techniques.SettingOne hundred and eighty countries’ patients with COVID-19 and fatality data representing the healthcare system preparedness and performance in combating the pandemic in 2020.DesignUsing the retrospective daily COVID-19 data in 2020 broken into 24 half-month periods, we applied unsupervised machine learning techniques, in particular, hierarchical clustering analysis to cluster countries into five groups within each period according to their cumulative COVID-19 fatality per day over the year and cumulative COVID-19 cases per million population per day over the half-month period. We used the average of the period scores to assign countries’ final scores for each measure.Primary outcomeThe primary outcomes are the COVID-19 cases and fatality grades in 2020.ResultsThe United Arab Emirates and the USA with F in COVID-19 cases, achieved A or B in the fatality scores. Belgium and Sweden ranked F in both scores. Although no African country ranked F for COVID-19 cases, several African countries such as Gambia and Liberia had F for fatality scores. More developing countries ranked D and F in fatality than in COVID-19 case rankings. The classic epidemiological measures such as averages and rates have a relatively good correlation with our methodology, but past predictions failed to forecast the COVID-19 countries’ preparedness.ConclusionCOVID-19 fatality can be a good proxy for countries’ resources and system’s resilience in managing the pandemic. These findings suggest that countries’ economic and sociopolitical factors may behave in a more complex way as were believed. To explore these complex epidemiological associations, models can benefit enormously by taking advantage of methods developed in computer science and machine learning.
Minimal clinically important difference in means in vulnerable populations: challenges and solutions
Authors: Peacock Janet L , Lo Jessica , Rees Judith R , Sauzet Odile 
Published: November 2021

Introduction and motivationMany health studies measure a continuous outcome and compare means between groups. Since means for biological data are often difficult to interpret clinically, it is common to dichotomise using a cut-point and present the ‘percentage abnormal’ alongside or in place of means. Examples include birthweight where ‘abnormal’ is defined as <2500 g (low birthweight), systolic blood pressure with abnormal defined as >140 mm Hg (high blood pressure) and lung function with varying definitions of the ‘limit of normal’. In vulnerable populations with low means, for example, birthweight in a population of preterm babies, a given difference in means between two groups will represent a larger difference in the percentage with low birthweight than in a general population of babies where most will be full term. Thus, in general, the difference in percentage of patients with abnormal values for a given difference in means varies according to the reference population’s mean value. This phenomenon leads to challenges in interpreting differences in means in vulnerable populations and in defining an outcome-specific minimal clinically important difference (MCID) in means since the proportion abnormal, which is useful in interpreting means, is not constant—it varies with the population mean. This has relevance for study power calculations and data analyses in vulnerable populations where a small observed difference in means may be difficult to interpret clinically and may be disregarded, even if associated with a relatively large difference in percentage abnormal which is clinically relevant.MethodsTo address these issues, we suggest both difference in means and difference in percentage (proportion) abnormal are considered when choosing the MCID, and that both means and percentages abnormal are reported when analysing the data.ConclusionsWe describe a distributional approach to analyse proportions classified as abnormal that avoids the usual loss of precision and power associated with dichotomisation.
How do patients respond to safety problems in ambulatory care? Results of a retrospective cross-sectional telephone survey
Authors: Seufert Svenja , de Cruppé Werner , Assheuer Michaela , Leinert Johannes , Geraedts Max 
Published: November 2021

ObjectivesPatients in German ambulatory care frequently report patient safety problems (PSP). It is unclear whether patients report PSP back to their general practitioner (GP) or specialist in charge. This study reports on how patients respond to experienced PSP.DesignRetrospective cross-sectional study.SettingComputer-assisted telephone interviews (CATI) with randomly recruited citizens aged ≥40 years in Germany.Participants10 037 citizens ≥40 years. About 52% of the interviewees were female, 38% were between 60 and 79 years old and about 47% reported that they were chronically ill. A total of 2589 PSPs was reported.Primary and secondary measures/ResultsAccording to the respondents (n=1422, 77%, 95% CI: 74.7 to 79.1), 72% (95% CI: 70.2 to 73.7) of PSP were reported back to the GP in charge or to another GP/specialist. Further reactions were taken by 65% (95% CI: 62.5 to 67.5) of the interviewees: around 63% (95% CI: 62.5 to 66.2) of the reported PSP led to a loss of faith in the physician or to complaints. χ2and binary logistic regression analyses show significant associations between the (a) reporting and (b) reaction behaviour and determinants like ‘medical treatment area’ ((a) χ2=17.13, p=0.009/(b) χ2=97.58, p=0.000), ‘PSP with/without harm’ ((a) χ2=111.84, p=0.000/(b) χ2=265.39, p=0.000) and sociodemographic characteristics when respondents are aged between 40 and 59 years ((a) OR 2.57/(b) OR 2.60) or have chronic illnesses ((a) OR 2.16/(b) OR 2.14).ConclusionThe data suggest that PSPs are frequently reported back to the GP or specialist in charge and have a significant serious impact on the physician–patient relationship. Much could be learnt from the patient reporting and reacting behaviour to prevent PSPs in ambulatory care.
52 Characterization of the tumor microenvironment in melanoma using Multiplexed Ion Beam Imaging (MIBI)
Authors: Ptacek Jason , Vesely Matthew , Rimm David , Hav Monirath , Aksoy Murat , Crow Ailey , Finn Jessica 
Published: November 2021

BackgroundThe complexity of the tumor microenvironment (TME) necessitates the application of high-dimensional methods that can spatially resolve the phenotypic heterogeneity that exists within tumors. MIBI, which combines time-of-flight secondary ion mass spectrometry (ToF-SIMS) with metal labeled antibodies to simultaneously image 40+ proteins at subcellular spatial resolution, was used to classify cell populations and their expression of immunoregulatory proteins within 54 melanoma samples.MethodsA tissue microarray (TMA) comprised of 0.6 mm FFPE melanoma cores was stained with a panel of 30 metal labeled antibodies. The tissue was imaged using MIBI and multi-step processing was performed to create images of the samples. Single cell segmentation enabled enumeration of 32 cell populations and quantitative analyses were performed of both immune checkpoint expression and the spatial relationships between cells of different types.ResultsTumor cells and immune cells represented 62% (1.4% - 92.2%) and 24% (2.0% - 92.1%), respectively, of the segmented cells from the melanoma samples. Fibroblasts, lymphatics, and blood vessels were also present at varying densities. Within the immune compartment, M2 macrophages, M2 monocytes, and monocyte-derived dendritic cells (mDCs) were most abundant, representing 36.3% (4.9% - 79.3%), 7.0% (0.0% - 30.6%), and 9.8% (0.0% - 34.6%), respectively, of the total immune cell infiltrate. Rare populations such as myeloid-derived suppressor cells (MDSCs) and regulatory T cells were at greater than 100 cells/mm2 in six samples and four samples, respectively. The presence of immune checkpoint markers (IDO-1, LAG3, PD-1, PD-L1, TIM-3) varied between populations and between samples. A minority of the samples showed expression of IDO-1 and PD-L1 on myeloid populations. Interestingly, among the myeloid subsets, M2 macrophages and monocyte-derived dendritic cells showed the most abundant PD-L1 expression. Although many samples had few T cell infiltrates, those that did showed expression of PD-1, LAG-3, and TIM-3 on T cell populations.ConclusionsMIBI offers high-parameter tissue imaging, at sensitivity and resolution suited to understanding the complex tumor immune landscape, including the spatial relationships of immune and tumor cells and the expression of immunoregulatory proteins. Datasets that quantify population densities and immune checkpoint expression levels across tumor samples, such as the present one, can be used to test for associations to clinical variables and further understand the TME at the cellular level.
62 Applying machine vision to empower preclinical development of cell engager and adoptive cell therapeutics in patient-derived organoid models of solid tumors
Authors: Khare Sonal , Ho Chi-Sing , Kannan Madhavi , Larsen Brian , Mapes Brandon , Shaxted Jenna , Venkataraman Jagadish , Salahudeen Ameen 
Published: November 2021

BackgroundCell engager and adoptive cell therapeutics have emerged as efficacious and durable treatments in patients with B-cell malignancies. Though many analogous strategies are under development in solid tumors, none have received approval. Preclinical development of these therapies requires cell labeling of immortalized cell lines and/or primary expanded T cells to distinguish target and effector cells. However, cell engager and adoptive cell therapies have had limited evidence of reproducibility in primary patient-derived models such as tumor organoid cultures thus far. Here, we build upon our tumor organoid platform1 to measure organoid specific responses to these therapies. Utilizing machine vision coupled with time-lapse-microscopy, we obtain multiparameter kinetic readouts of patient-derived tumor organoid cell killing and allogeneic MHC-matched primary peripheral blood mononuclear cells (PBMCs).MethodsThe patient-derived tumor organoids were co-cultured with PBMCs in the presence of engagers/activators and vital dyes and incubated for 96 hrs. Cell death was measured by quantifying the caspase 3/7 vital dye pixel intensities at different time points using high throughput imaging. As a first step, a fully convolutional neural network was trained to segment out organoids from brightfield images comprised of organoids, immune cells and potential background artifacts. This segmentation mask was then transferred over to registered caspase 3/7 images to quantify tumor cell specific phenotypes in a rapid and automated manner.ResultsThe time-lapse imaging assay allowed for both the tracking of the organoid growth over time as well as the quantification of the kinetics of engagers/activators in comparison to controls resulting in accurate and precise technical reproducibility. Further, this assay allowed for the co-localization of the organoids and the immune cells over time, thus, enabling a spatiotemporal summary of dose dependent efficacy of candidate therapeutics.ConclusionsWe demonstrate the scalability and throughput of a machine vision tumor organoid immune co-culture platform across multiple unique patient-derived tumor organoid lines bearing a target of interest, enabling future discovery of biomarkers of therapeutic response and resistance.ReferenceLarsen B, Kannan M, Langer LF, Khan AA, Salahudeen AA, A pan-cancer organoid platform for precision medicine. Cell Reports 2021; 36:109429
72 Spatial whole transcriptome profiling of the tumor microenvironment in prostate carcinomas
Authors: Anaparthy Naishitha , Giangarra Valeria , Taylor Sarah , Turkekul Mesruh , Williams Stephen , Mielinis Paulius , Gallant Caroline 
Published: November 2021

BackgroundDespite years of studies and effort, the best strategies for treating prostate cancer and minimizing the complications of treatment remain unanswered questions. This gap in knowledge is partially due to the inability to dissect the complex heterogeneous tumor microenvironment (TME) and immune compartment. Spatially resolved molecular profiling of tumor sections will enhance our understanding of these complexities; However, it has been particularly challenging to do spatial molecular profiling in formalin-fixed paraffin-embedded (FFPE) tissues due to RNA degradation associated with this tissue-embedding method, which is routinely used in oncology workflows. The 10x Genomics Visium Spatial Gene Expression Solution for FFPE tissue overcomes these limitations, enabling spatial gene expression analysis of FFPE tissues combined with classical histology staining techniques such as Hematoxylin & Eosin (H&E) staining and immunofluorescence.MethodsWe used the 10x Genomics Visium Spatial Gene Expression Solution for FFPE tissue to analyze and resolve tumorigenic profiles in sections of normal and adenocarcinoma prostate samples. This assay incorporates ~5,000 molecularly barcoded, spatially encoded capture probes in spots over which the tissue is placed, imaged, and permeabilized. Imaging and sequencing data are processed together, resulting in a spatially resolved transcriptional readout.ResultsWe profiled the whole transcriptome in normal, invasive adenocarcinoma, and acinar cell carcinoma FFPE human prostate tissues. Unsupervised clustering of the whole transcriptome data from normal, invasive adenocarcinoma, and acinar cell prostate carcinoma FFPE sections enabled the identification of 2 different regions, which had a well defined spatial distribution within the tissues. Well known prostate gland and prostate-cancer markers were over-expressed in the corresponding healthy and cancerous portions of the tissue, validating the performance of this method. We found that, while in healthy tissues basal cells and luminal cells are spatially organized, this pattern is lost in tumor samples, where luminal cells are greatly expanded in the invasive carcinoma region and do not colocalize with basal cells. Moreover, T lymphocytes are dispersed throughout the whole tissue section in the adenocarcinoma, while plasma B cells are located in the peritumoral region which could impact prognosis.ConclusionsSpatial whole transcriptome analysis opens new opportunities for better understanding the TME which can not only help discover novel predictive tumor biomarkers, but also enable identifying cell type and tumor region specific drug targets.
80 Cancer testis antigen burden: A novel predictive biomarker for immunotherapy in solid tumors
Authors: Pabla Sarabjot , Seager RJ , Lee Yong Hee , Roey Erik Van , Gao Shuang , Giamo Vincent , Burgher Blake , DePietro Paul , Nesline Mary , Glenn Sean , Zhang Shengle , Conroy Jeffrey 
Published: November 2021

BackgroundWhen expressed in cancer cells, cancer testis antigens (CTAs) are highly immunogenic and have the capacity to elicit cancer-specific immune responses in diverse malignancies. With their expression limited to tumor cells, CTAs have become a prime target of natural T cell response, immune cell-based therapy, and cancer vaccines. In this study, we investigated CTA burden in real-world clinical tumors spanning multiple histologies, revealing a novel prognostic gene expression-based biomarker.MethodsTargeted RNA-seq was performed on 5450 FFPE tumors representing 39 histologic types, predominantly composed of lung cancer (40.4%) followed by colorectal cancer (10.6%) and breast cancer (8.6%). Using an amplicon-based NGS approach, expression levels of 17 CTA genes were ranked against a reference population. Cancer Testis Antigen Burden (CTAB) was calculated as the sum of the gene expression rank for each CTA gene. The median CTAB of ≥171 was used as cutoff for CTAB High versus Low classification. We estimated Pearson’s correlation for all CTA genes to discover co-expression patterns between CTAs and histologies. Overall survival (OS) analysis was performed using CoxPh regression model whereas response analysis was performed using logistic regression model with p-values reported.ResultsWithin the tumor samples, CTAB values ranged from 0–1700, with kidney cancer demonstrating overall lowest mean CTAB (110) and melanoma the highest (550). NSCLC had an average CTAB of 283. In an immune checkpoint blockade treated retrospective cohort of 110 NSCLC patients, High CTAB showed better OS compared to Low CTA (HR: 0.55, p=0.07). Additionally, when combined with tumor inflammation and cell proliferation biomarkers, highly inflamed but poorly proliferative tumors with High CTAB had improved OS (HR: 0.27, p=0.05). No significant association with response was detectedConclusionsOur studies show that co-expression of multiple CTA genes occurs in many tumor types and can be reliably detected using a targeted RNA-seq approach. Utilization of this co-expression pattern to calculate CTAB reveals tumor-type associated signatures, which in a small NSCLC cohort is associated with the overall survival. The findings suggest that these immunogenic antigens expose the tumor cells to natural or immunotherapy augmented cell-based immune response, and that CTAB is a potential predictive marker for therapeutic response to checkpoint inhibitors. Further studies are needed to establish the predictive value in other tumor types, as well as the role of CTAB in immune cell therapies and vaccinations.
229 CX3CR1 in exhausted CD8 T cell states
Authors: Chaudhri Apoorvi , Wang Yunfei , Hung Shao-Hsi , Lizee Gregory , Andrian Ulrich Von , Hwu Patrick , Freeman Gordon 
Published: November 2021

BackgroundCancer has chronic antigen exposure that results in a suppressed CD8 T cell state termed exhaustion. An outcome of anti PD-1 blockade therapy is the expansion of early exhausted CD8+ T cells into a terminally differentiated exhausted state. The reversal of this transcriptionally plastic yet epigenetically fixed state of CD8 T cell exhaustion has the potential to increase responses to anti PD-1 therapy.MethodsCX3CR1 is a marker of CD8 T cell activation, effector function however less is known about the contribution of CX3CR1 in CD8 T cell exhaustion. We identified three distinct subsets of CD8+ tumor infiltrating lymphocytes (TILs) based on high, mid, and negative CX3CR1 expression in a mouse model of colon carcinoma.ResultsThe CX3CR1 high CD8+ T cells are more exhausted with higher PD1+TIM3+ expression compared to CX3CR1 mid and CX3CR1 negative cells thereby representing the terminal state of CD8 T cell exhaustion. Moreover, CX3CR1 high CD8 T cells increase following anti PD-1 blockade, and their abundance is associated with a positive response to anti PD-1.ConclusionsWe identify a consequence of CX3CR1 in terminal T cell exhaustion, and our work can offer strategies to increase responses to anti PD-1.Ethics ApprovalAnimal experiments were performed as per the IACUC regulations at the Dana Farber cancer Institute, and the MD Anderson Cancer Center