paper.bib

@article{hollis_molecular_2020,
	title = {Molecular stratification of endometrioid ovarian carcinoma predicts clinical outcome},
	volume = {11},
	rights = {2020 The Author(s)},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/s41467-020-18819-5},
	doi = {10.1038/s41467-020-18819-5},
	abstract = {Endometrioid ovarian carcinoma ({EnOC}) demonstrates substantial clinical and molecular heterogeneity. Here, we report whole exome sequencing of 112 {EnOC} cases following rigorous pathological assessment. We detect a high frequency of mutation in {CTNNB}1 (43\%), {PIK}3CA (43\%), {ARID}1A (36\%), {PTEN} (29\%), {KRAS} (26\%), {TP}53 (26\%) and {SOX}8 (19\%), a recurrently-mutated gene previously unreported in {EnOC}. {POLE} and mismatch repair protein-encoding genes were mutated at lower frequency (6\%, 18\%) with significant co-occurrence. A molecular taxonomy is constructed, identifying clinically distinct {EnOC} subtypes: cases with {TP}53 mutation demonstrate greater genomic complexity, are commonly {FIGO} stage {III}/{IV} at diagnosis (48\%), are frequently incompletely debulked (44\%) and demonstrate inferior survival; conversely, cases with {CTNNB}1 mutation, which is mutually exclusive with {TP}53 mutation, demonstrate low genomic complexity and excellent clinical outcome, and are predominantly stage I/{II} at diagnosis (89\%) and completely resected (87\%). Moreover, we identify the {WNT}, {MAPK}/{RAS} and {PI}3K pathways as good candidate targets for molecular therapeutics in {EnOC}.},
	pages = {4995},
	number = {1},
	journaltitle = {Nature Communications},
	shortjournal = {Nat Commun},
	author = {Hollis, Robert L. and Thomson, John P. and Stanley, Barbara and Churchman, Michael and Meynert, Alison M. and Rye, Tzyvia and Bartos, Clare and Iida, Yasushi and Croy, Ian and Mackean, Melanie and Nussey, Fiona and Okamoto, Aikou and Semple, Colin A. and Gourley, Charlie and Herrington, C. Simon},
	urldate = {2022-05-23},
	date = {2020-10-05},
	langid = {english},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Cancer, Gynaecological cancer, Ovarian cancer},
}

@article{benjamens_state_2020,
	title = {The state of artificial intelligence-based {FDA}-approved medical devices and algorithms: an online database},
	volume = {3},
	issn = {2398-6352},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7486909/},
	doi = {10.1038/s41746-020-00324-0},
	shorttitle = {The state of artificial intelligence-based {FDA}-approved medical devices and algorithms},
	abstract = {At the beginning of the artificial intelligence ({AI})/machine learning ({ML}) era, the expectations are high, and experts foresee that {AI}/{ML} shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of {AI}/{ML} in daily clinical practice are numerous, especially regarding the regulation of these technologies. Therefore, we provide an insight into the currently available {AI}/{ML}-based medical devices and algorithms that have been approved by the {US} Food \& Drugs Administration ({FDA}). We aimed to raise awareness of the importance of regulatory bodies, clearly stating whether a medical device is {AI}/{ML} based or not. Cross-checking and validating all approvals, we identified 64 {AI}/{ML} based, {FDA} approved medical devices and algorithms. Out of those, only 29 (45\%) mentioned any {AI}/{ML}-related expressions in the official {FDA} announcement. The majority (85.9\%) was approved by the {FDA} with a 510(k) clearance, while 8 (12.5\%) received de novo pathway clearance and one (1.6\%) premarket approval ({PMA}) clearance. Most of these technologies, notably 30 (46.9\%), 16 (25.0\%), and 10 (15.6\%) were developed for the fields of Radiology, Cardiology and Internal Medicine/General Practice respectively. We have launched the first comprehensive and open access database of strictly {AI}/{ML}-based medical technologies that have been approved by the {FDA}. The database will be constantly updated.},
	pages = {118},
	journaltitle = {{NPJ} Digital Medicine},
	shortjournal = {{NPJ} Digit Med},
	author = {Benjamens, Stan and Dhunnoo, Pranavsingh and Meskó, Bertalan},
	urldate = {2022-05-23},
	date = {2020-09-11},
	pmid = {32984550},
	pmcid = {PMC7486909},
}

@article{iqbal_advances_2021,
	title = {Advances in healthcare wearable devices},
	volume = {5},
	rights = {2021 The Author(s)},
	issn = {2397-4621},
	url = {https://www.nature.com/articles/s41528-021-00107-x},
	doi = {10.1038/s41528-021-00107-x},
	abstract = {Wearable devices have found numerous applications in healthcare ranging from physiological diseases, such as cardiovascular diseases, hypertension and muscle disorders to neurocognitive disorders, such as Parkinson’s disease, Alzheimer’s disease and other psychological diseases. Different types of wearables are used for this purpose, for example, skin-based wearables including tattoo-based wearables, textile-based wearables, and biofluidic-based wearables. Recently, wearables have also shown encouraging improvements as a drug delivery system; therefore, enhancing its utility towards personalized healthcare. These wearables contain inherent challenges, which need to be addressed before their commercialization as a fully personalized healthcare system. This paper reviews different types of wearable devices currently being used in the healthcare field. It also highlights their efficacy in monitoring different diseases and applications of healthcare wearable devices ({HWDs}) for diagnostic and treatment purposes. Additionally, current challenges and limitations of these wearables in the field of healthcare along with their future perspectives are also reviewed.},
	pages = {1--14},
	number = {1},
	journaltitle = {npj Flexible Electronics},
	shortjournal = {npj Flex Electron},
	author = {Iqbal, Sheikh M. A. and Mahgoub, Imadeldin and Du, E. and Leavitt, Mary Ann and Asghar, Waseem},
	urldate = {2022-05-23},
	date = {2021-04-12},
	langid = {english},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Biomedical materials, Biosensors, Electrical and electronic engineering},
}

@article{baxi_digital_2022,
	title = {Digital pathology and artificial intelligence in translational medicine and clinical practice},
	volume = {35},
	rights = {2021 The Author(s)},
	issn = {1530-0285},
	url = {https://www.nature.com/articles/s41379-021-00919-2},
	doi = {10.1038/s41379-021-00919-2},
	abstract = {Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence ({AI})–based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of {AI}-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing {AI}-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and {AI} should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by {AI}-powered analysis tools.},
	pages = {23--32},
	number = {1},
	journaltitle = {Modern Pathology},
	shortjournal = {Mod Pathol},
	author = {Baxi, Vipul and Edwards, Robin and Montalto, Michael and Saha, Saurabh},
	urldate = {2022-05-20},
	date = {2022-01},
	langid = {english},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Cancer microenvironment, Diagnostics, Imaging, Target identification, Tumour biomarkers},
}

@article{rajpurkar_ai_2022,
	title = {{AI} in health and medicine},
	volume = {28},
	rights = {2022 Springer Nature America, Inc.},
	issn = {1546-170X},
	url = {https://www.nature.com/articles/s41591-021-01614-0},
	doi = {10.1038/s41591-021-01614-0},
	abstract = {Artificial intelligence ({AI}) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical {AI}. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical {AI} research, including non-image data sources, unconventional problem formulations and human–{AI} collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, {AI}’s potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide.},
	pages = {31--38},
	number = {1},
	journaltitle = {Nature Medicine},
	shortjournal = {Nat Med},
	author = {Rajpurkar, Pranav and Chen, Emma and Banerjee, Oishi and Topol, Eric J.},
	urldate = {2022-05-19},
	date = {2022-01},
	langid = {english},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Computational biology and bioinformatics, Medical research},
}

@article{silvester_european_2018,
	title = {The European Nucleotide Archive in 2017},
	volume = {46},
	issn = {0305-1048},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5753375/},
	doi = {10.1093/nar/gkx1125},
	abstract = {For 35 years the European Nucleotide Archive ({ENA}; https://www.ebi.ac.uk/ena) has been responsible for making the world’s public sequencing data available to the scientific community. Advances in sequencing technology have driven exponential growth in the volume of data to be processed and stored and a substantial broadening of the user community. Here, we outline {ENA} services and content in 2017 and provide insight into a selection of current key areas of development in {ENA} driven by challenges arising from the above growth.},
	pages = {D36--D40},
	issue = {Database issue},
	journaltitle = {Nucleic Acids Research},
	shortjournal = {Nucleic Acids Res},
	author = {Silvester, Nicole and Alako, Blaise and Amid, Clara and Cerdeño-Tarrága, Ana and Clarke, Laura and Cleland, Iain and Harrison, Peter W and Jayathilaka, Suran and Kay, Simon and Keane, Thomas and Leinonen, Rasko and Liu, Xin and Martínez-Villacorta, Josué and Menchi, Manuela and Reddy, Kethi and Pakseresht, Nima and Rajan, Jeena and Rossello, Marc and Smirnov, Dmitriy and Toribio, Ana L and Vaughan, Daniel and Zalunin, Vadim and Cochrane, Guy},
	urldate = {2023-01-10},
	date = {2018-01-04},
	pmid = {29140475},
	pmcid = {PMC5753375},
}

@online{noauthor_graf_nodate,
	title = {{GRAF} Software Documentation},
	url = {https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/GRAF_README.html},
	urldate = {2022-12-13},
}

@article{powell_broken_2021,
	title = {The broken promise that undermines human genome research},
	volume = {590},
	rights = {2021 Nature},
	url = {https://www.nature.com/articles/d41586-021-00331-5},
	doi = {10.1038/d41586-021-00331-5},
	abstract = {Data sharing was a core principle that led to the success of the Human Genome Project 20 years ago. Now scientists are struggling to keep information free.},
	pages = {198--201},
	number = {7845},
	journaltitle = {Nature},
	author = {Powell, Kendall},
	urldate = {2022-12-13},
	date = {2021-02-10},
	langid = {english},
	note = {Bandiera\_abtest: a
Cg\_type: News Feature
Number: 7845
Publisher: Nature Publishing Group
Subject\_term: Genomics, Databases, Research management},
	keywords = {Genomics, Databases, Research management},
}

@online{noauthor_data_nodate,
	title = {Data Standardization – {OHDSI}},
	url = {https://www.ohdsi.org/data-standardization/},
	urldate = {2022-12-13},
	langid = {american},
}

@book{european_commission_directorate_general_for_research_and_innovation_gendered_2020,
	location = {{LU}},
	title = {Gendered innovations 2 :how inclusive analysis contributes to research and innovation : policy review.},
	url = {https://data.europa.eu/doi/10.2777/53572},
	shorttitle = {Gendered innovations 2},
	publisher = {Publications Office},
	author = {{European Commission. Directorate General for Research and Innovation.}},
	urldate = {2022-12-13},
	date = {2020},
}

@article{chen_genomic_2020,
	title = {Genomic landscape of lung adenocarcinoma in East Asians},
	volume = {52},
	rights = {2020 The Author(s), under exclusive licence to Springer Nature America, Inc.},
	issn = {1546-1718},
	url = {https://www.nature.com/articles/s41588-019-0569-6.},
	doi = {10.1038/s41588-019-0569-6},
	abstract = {Lung cancer is the world’s leading cause of cancer death and shows strong ancestry disparities. By sequencing and assembling a large genomic and transcriptomic dataset of lung adenocarcinoma ({LUAD}) in individuals of East Asian ancestry ({EAS}; n = 305), we found that East Asian {LUADs} had more stable genomes characterized by fewer mutations and fewer copy number alterations than {LUADs} from individuals of European ancestry. This difference is much stronger in smokers as compared to nonsmokers. Transcriptomic clustering identified a new {EAS}-specific {LUAD} subgroup with a less complex genomic profile and upregulated immune-related genes, allowing the possibility of immunotherapy-based approaches. Integrative analysis across clinical and molecular features showed the importance of molecular phenotypes in patient prognostic stratification. {EAS} {LUADs} had better prediction accuracy than those of European ancestry, potentially due to their less complex genomic architecture. This study elucidated a comprehensive genomic landscape of {EAS} {LUADs} and highlighted important ancestry differences between the two cohorts.},
	pages = {177--186},
	number = {2},
	journaltitle = {Nature Genetics},
	shortjournal = {Nat Genet},
	author = {Chen, Jianbin and Yang, Hechuan and Teo, Audrey Su Min and Amer, Lidyana Bte and Sherbaf, Faranak Ghazi and Tan, Chu Quan and Alvarez, Jacob Josiah Santiago and Lu, Bingxin and Lim, Jia Qi and Takano, Angela and Nahar, Rahul and Lee, Yin Yeng and Phua, Cheryl Zi Jin and Chua, Khi Pin and Suteja, Lisda and Chen, Pauline Jieqi and Chang, Mei Mei and Koh, Tina Puay Theng and Ong, Boon-Hean and Anantham, Devanand and Hsu, Anne Ann Ling and Gogna, Apoorva and Too, Chow Wei and Aung, Zaw Win and Lee, Yi Fei and Wang, Lanying and Lim, Tony Kiat Hon and Wilm, Andreas and Choi, Poh Sum and Ng, Poh Yong and Toh, Chee Keong and Lim, Wan-Teck and Ma, Siming and Lim, Bing and Liu, Jin and Tam, Wai Leong and Skanderup, Anders Jacobsen and Yeong, Joe Poh Sheng and Tan, Eng-Huat and Creasy, Caretha L. and Tan, Daniel Shao Weng and Hillmer, Axel M. and Zhai, Weiwei},
	urldate = {2022-12-13},
	date = {2020-02},
	langid = {english},
	note = {Number: 2
Publisher: Nature Publishing Group},
	keywords = {Data mining, Genomics, Non-small-cell lung cancer},
}

@article{shan_chinese_2022,
	title = {Chinese never smokers with adenocarcinoma of the lung are younger and have fewer lymph node metastases than smokers},
	volume = {23},
	issn = {1465-993X},
	url = {https://doi.org/10.1186/s12931-022-02199-z},
	doi = {10.1186/s12931-022-02199-z},
	abstract = {Lung cancers arising in never smokers have been suggested to be substantially different from lung cancers in smokers at an epidemiological, genetic and molecular level. Focusing on non-small cell lung cancer ({NSCLC}), we characterized lung cancer patients in China looking for demographic and clinical differences between the smoking and never-smoking subgroups.},
	pages = {293},
	number = {1},
	journaltitle = {Respiratory Research},
	shortjournal = {Respiratory Research},
	author = {Shan, Longyu and Zhang, Liang and Zhu, Xiaolei and Wang, Zhilin and Fang, Shaohan and Lin, Junfeng and Wang, Jianweng and Li, Ning and Liu, Hongming and Zhang, Xiaowen and Feng, Yihui and Liu, Jingwei and Pan, Jianyun and Ye, Guanzhi and Yu, Xiuyi and Tufman, Amanda and Katalinic, Alexander and Goldmann, Torsten and Petersen, Frank and Jiang, Jie and Geng, Guojun and Yu, Xinhua},
	urldate = {2022-12-13},
	date = {2022-10-29},
}

@article{sun_lung_2007,
	title = {Lung cancer in never smokers — a different disease},
	volume = {7},
	rights = {2007 Nature Publishing Group},
	issn = {1474-1768},
	url = {https://www.nature.com/articles/nrc2190},
	doi = {10.1038/nrc2190},
	abstract = {About 25\% of lung cancer cases worldwide are not attributable to tobacco smoking. Thus, lung cancer in never smokers is the seventh leading cause of cancer deaths in the world, killing more people every year than pancreatic or prostate cancers.Globally, lung cancer in never smokers demonstrates a marked gender bias, occuring more frequently among women. In particular, there is a high proportion of never smokers in Asian women diagnosed with lung cancer.Although smoking-related carcinogens act on both proximal and distal airways inducing all the major forms of lung cancer, cancers arising in never smokers target the distal airways and favour adenocarcinoma histology.Environmental tobacco smoke ({ETS}) is a relatively weak carcinogen and can only account for a minority of lung cancers arising in never smokers.Although multiple risk factors, including environmental, hormonal, genetic and viral factors, have been implicated in the pathogenesis of lung cancer in never smokers, no clear-cut dominant factor has emerged that can explain the relatively high incidence of lung cancer in never smokers and the marked geographic differences in gender proportions.Molecular epidemiology studies, in particular of the {TP}53, {KRAS} and epidermal growth factor receptor ({EGFR}) genes, demonstrate strikingly different mutation patterns and frequencies between lung cancers in never smokers and smokers.There are major clinical differences between lung cancers arising in never smokers and smokers and their response to targeted therapies. Indeed, non-smoking status is the strongest clinical predictor of benefit from the {EGFR} tyrosine kinase inhibitors.The above-mentioned facts strongly suggest that lung cancer arising in never smokers is a disease distinct from the more common tobacco-associated forms of lung cancer.Further efforts are needed to identify the major cause or causes of lung cancers arising in never smokers before successful strategies for prevention, early diagnosis and novel therapies can be implemented.},
	pages = {778--790},
	number = {10},
	journaltitle = {Nature Reviews Cancer},
	shortjournal = {Nat Rev Cancer},
	author = {Sun, Sophie and Schiller, Joan H. and Gazdar, Adi F.},
	urldate = {2022-12-13},
	date = {2007-10},
	langid = {english},
	note = {Number: 10
Publisher: Nature Publishing Group},
	keywords = {Cancer Research, general, Biomedicine},
}

@article{derouen_incidence_2022,
	title = {Incidence of Lung Cancer Among Never-Smoking Asian American, Native Hawaiian, and Pacific Islander Females},
	volume = {114},
	issn = {0027-8874, 1460-2105},
	url = {https://academic.oup.com/jnci/article/114/1/78/6338453},
	doi = {10.1093/jnci/djab143},
	abstract = {Abstract
            
              Background
              Although lung cancer incidence rates according to smoking status, sex, and detailed race/ethnicity have not been available, it is estimated that more than half of Asian American, Native Hawaiian, and Pacific Islander ({AANHPI}) females with lung cancer have never smoked.
            
            
              Methods
              We calculated age-adjusted incidence rates for lung cancer according to smoking status and detailed race/ethnicity among females, focusing on {AANHPI} ethnic groups, and assessed relative incidence across racial/ethnic groups. We used a large-scale dataset that integrates data from electronic health records from 2 large health-care systems—Sutter Health in Northern California and Kaiser Permanente Hawai’i—linked to state cancer registries for incident lung cancer diagnoses between 2000 and 2013. The study population included 1 222 694 females (n = 244 147 {AANHPI}), 3297 of which were diagnosed with lung cancer (n = 535 {AANHPI}).
            
            
              Results
              Incidence of lung cancer among never-smoking {AANHPI} as an aggregate group was 17.1 per 100 000 (95\% confidence interval [{CI}] = 14.9 to 19.4) but varied widely across ethnic groups. Never-smoking Chinese American females had the highest rate (22.8 per 100 000, 95\% {CI} = 17.3 to 29.1). Except for Japanese American females, incidence among every never-smoking {AANHPI} female ethnic group was higher than that of never-smoking non-Hispanic White females, from 66\% greater among Native Hawaiian females (incidence rate ratio = 1.66, 95\% {CI} = 1.03 to 2.56) to more than 100\% greater among Chinese American females (incidence rate ratio = 2.26, 95\% {CI} = 1.67 to 3.02).
            
            
              Conclusions
              Our study revealed high rates of lung cancer among most never-smoking {AANHPI} female ethnic groups. Our approach illustrates the use of innovative data integration to dispel the myth that {AANHPI} females are at overall reduced risk of lung cancer and demonstrates the need to disaggregate this highly diverse population.},
	pages = {78--86},
	number = {1},
	journaltitle = {{JNCI}: Journal of the National Cancer Institute},
	author = {{DeRouen}, Mindy C and Canchola, Alison J and Thompson, Caroline A and Jin, Anqi and Nie, Sixiang and Wong, Carmen and Lichtensztajn, Daphne and Allen, Laura and Patel, Manali I and Daida, Yihe G and Luft, Harold S and Shariff-Marco, Salma and Reynolds, Peggy and Wakelee, Heather A and Liang, Su-Ying and Waitzfelder, Beth E and Cheng, Iona and Gomez, Scarlett L},
	urldate = {2022-12-13},
	date = {2022-01-11},
	langid = {english},
}

@online{noauthor_lung_nodate,
	title = {Lung Cancer Genetic Study among Asian Never Smokers - {EGA} European Genome-Phenome Archive},
	url = {https://ega-archive.org/studies/phs002366},
	urldate = {2022-12-13},
}

@article{sese_gender_2021,
	title = {Gender Differences in Idiopathic Pulmonary Fibrosis: Are Men and Women Equal?},
	volume = {8},
	issn = {2296-858X},
	url = {https://www.frontiersin.org/articles/10.3389/fmed.2021.713698},
	shorttitle = {Gender Differences in Idiopathic Pulmonary Fibrosis},
	abstract = {Background: Idiopathic pulmonary fibrosis ({IPF}) is characterized by a male predominance. The aim of the study was to explore gender differences in a well-designed French multicentre prospective {IPF} cohort ({COhorte} {FIbrose}, {COFI}) with a 5-year follow-up.Methods: Between 2007 and 2010, 236 patients with incident {IPF} were included in {COFI}. Gender characteristics were compared using a t-test, Chi-squared test and {ANOVA}, as appropriate. Survival analyses were performed.Results: Fifty-one (22\%) females and 185 (78\%) males with an average age at diagnosis of 70.1 ± 9.20 and 67.4 ± 10.9 years, respectively, were included in the cohort. Women were significantly less exposed to tobacco smoke [never n = 32 (62.7\%) vs. n = 39 (21.1\%), p {\textless} 0.001] and to occupational exposure [n = 7 (13.7\%) vs. n = 63 (34.1\%), p = 0.012]. Baseline forced vital capacity, \% of predicted ({FVC}\%) was significantly better in women compare to men (83.0\% ± 25.0 v. 75.4\% ± 18.7 p = 0.046). At presentation honeycombing and emphysema on {CT} scan were less common in women [n = 40 (78.4\%) vs. n = 167 (90.3\%) p = 0.041] and [n = 6 (11.8\%) vs. n = 48 (25.9\%) p = 0.029], respectively. During follow-up fewer women were transplanted compared to men [n = 1 (1.96\%) vs. n = 20 (10.8\%) p = 0.039]. Medians of survival were comparable by gender [31 months ({CI} 95\%: 28–40) vs. 40 months ({CI} 95\%: 33–72) p = 0.2]. After adjusting for age and {FVC} at inclusion, being a woman was not associated to a better survival.Conclusions: Women appear to have less advanced disease at diagnosis, maybe due to less exposure history compare to men. Disease progression and overall survival remains comparable regardless gender, but women have less access to lung transplantation.},
	journaltitle = {Frontiers in Medicine},
	author = {Sesé, Lucile and Nunes, Hilario and Cottin, Vincent and Israel-Biet, Dominique and Crestani, Bruno and Guillot-Dudoret, Stephanie and Cadranel, Jacques and Wallaert, Benoit and Tazi, Abdellatif and Maître, Bernard and Prévot, Gregoire and Marchand-Adam, Sylvain and Hirschi, Sandrine and Dury, Sandra and Giraud, Violaine and Gondouin, Anne and Bonniaud, Philippe and Traclet, Julie and Juvin, Karine and Borie, Raphael and Carton, Zohra and Freynet, Olivia and Gille, Thomas and Planès, Carole and Valeyre, Dominique and Uzunhan, Yurdagül},
	urldate = {2022-12-13},
	date = {2021},
}

@article{xu_transition_2021,
	title = {The transition from normal lung anatomy to minimal and established fibrosis in idiopathic pulmonary fibrosis ({IPF})},
	volume = {66},
	issn = {2352-3964},
	doi = {10.1016/j.ebiom.2021.103325},
	abstract = {{BACKGROUND}: The transition from normal lung anatomy to minimal and established fibrosis is an important feature of the pathology of idiopathic pulmonary fibrosis ({IPF}). The purpose of this report is to examine the molecular and cellular mechanisms associated with this transition.
{METHODS}: Pre-operative thoracic Multidetector Computed Tomography ({MDCT}) scans of patients with severe {IPF} (n = 9) were used to identify regions of minimal(n = 27) and established fibrosis(n = 27). {MDCT}, Micro-{CT}, quantitative histology, and next-generation sequencing were used to compare 24 samples from donor controls (n = 4) to minimal and established fibrosis samples.
{FINDINGS}: The present results extended earlier reports about the transition from normal lung anatomy to minimal and established fibrosis by showing that there are activations of {TGFBI}, T cell co-stimulatory genes, and the down-regulation of inhibitory immune-checkpoint genes compared to controls. The expression patterns of these genes indicated activation of a field immune response, which is further supported by the increased infiltration of inflammatory immune cells dominated by lymphocytes that are capable of forming lymphoid follicles. Moreover, fibrosis pathways, mucin secretion, surfactant, {TLRs}, and cytokine storm-related genes also participate in the transitions from normal lung anatomy to minimal and established fibrosis.
{INTERPRETATION}: The transition from normal lung anatomy to minimal and established fibrosis is associated with genes that are involved in the tissue repair processes, the activation of immune responses as well as the increased infiltration of {CD}4, {CD}8, B cell lymphocytes, and macrophages. These molecular and cellular events correlate with the development of structural abnormality of {IPF} and probably contribute to its pathogenesis.},
	pages = {103325},
	journaltitle = {{EBioMedicine}},
	shortjournal = {{EBioMedicine}},
	author = {Xu, Feng and Tanabe, Naoya and Vasilescu, Dragos M. and {McDonough}, John E. and Coxson, Harvey O. and Ikezoe, Kohei and Kinose, Daisuke and Ng, Kevin W. and Verleden, Stijn E. and Wuyts, Wim A. and Vanaudenaerde, Bart M. and Verschakelen, Johny and Cooper, Joel D. and Lenburg, Marc E. and Morshead, Katrina B. and Abbas, Alexander R. and Arron, Joseph R. and Spira, Avrum and Hackett, Tillie-Louise and Colby, Thomas V. and Ryerson, Christopher J. and Ng, Raymond T. and Hogg, James C.},
	date = {2021-04},
	pmid = {33862585},
	pmcid = {PMC8054143},
	keywords = {Animals, Humans, Female, Male, Biomarkers, Aged, Disease Progression, Disease Susceptibility, Gene Expression, Gene Expression Profiling, Idiopathic Pulmonary Fibrosis, Immunohistochemistry, Inflammation Mediators, Integrative analysis, {IPF}, Lung, {MDCT}, Mice, Micro-{CT}, Middle Aged, Models, Biological, Preoperative Period, Quantitative histology, {RNAseq}, Tomography, X-Ray Computed},
}

@online{noauthor_sex_nodate,
	title = {Sex and Gender Analysis Policies of Major Granting Agencies {\textbar} Gendered Innovations},
	url = {https://genderedinnovations.stanford.edu/sex-and-gender-analysis-policies-major-granting-agencies.html},
	urldate = {2022-12-13},
}

@online{noauthor_what_nodate,
	title = {What is the difference between sex and gender? - Office for National Statistics},
	url = {https://www.ons.gov.uk/economy/environmentalaccounts/articles/whatisthedifferencebetweensexandgender/2019-02-21},
	urldate = {2022-12-13},
}

@online{noauthor_fair_nodate,
	title = {{FAIR} Principles},
	url = {https://www.go-fair.org/fair-principles/},
	abstract = {In 2016, the ‘{FAIR} Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of… Continue reading →},
	titleaddon = {{GO} {FAIR}},
	urldate = {2022-12-13},
	langid = {american},
}

@online{noauthor_ega_nodate,
	title = {{EGA} European Genome-Phenome Archive},
	url = {https://ega-archive.org/},
	urldate = {2022-12-13},
}

@online{noauthor_dbgap_nodate,
	title = {{dbGaP} Study Submission Guide},
	url = {https://www.ncbi.nlm.nih.gov/gap/docs/submissionguide/},
	urldate = {2022-12-13},
}

@online{noauthor_dbgap_nodate-1,
	title = {{dbGaP}},
	url = {https://www.ncbi.nlm.nih.gov/gap/},
	urldate = {2022-12-13},
}

@online{noauthor_clinicaltrialsgov_nodate,
	title = {{ClinicalTrials}.gov},
	url = {https://www.clinicaltrials.gov/},
	urldate = {2022-12-13},
	langid = {english},
}

@online{noauthor_ai_nodate,
	title = {{AI} Central},
	url = {https://aicentral.acrdsi.org/},
	urldate = {2022-12-13},
}

@article{cirillo_sex_2020,
	title = {Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare},
	volume = {3},
	issn = {2398-6352},
	doi = {10.1038/s41746-020-0288-5},
	abstract = {Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence ({AI}) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical {AI} technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities.},
	pages = {81},
	journaltitle = {{NPJ} digital medicine},
	shortjournal = {{NPJ} Digit Med},
	author = {Cirillo, Davide and Catuara-Solarz, Silvina and Morey, Czuee and Guney, Emre and Subirats, Laia and Mellino, Simona and Gigante, Annalisa and Valencia, Alfonso and Rementeria, María José and Chadha, Antonella Santuccione and Mavridis, Nikolaos},
	date = {2020},
	pmid = {32529043},
	pmcid = {PMC7264169},
	keywords = {Biomarkers, Computational models, Medical ethics, Risk factors},
}