Transpl Infect Dis. 2022 May 17. doi: 10.1111/tid.13848. Online ahead of print.
NO ABSTRACT
PMID:35579962 | DOI:10.1111/tid.13848
Tag: pubmed
Cancer Epidemiol Biomarkers Prev. 2022 May 17:cebp.0776.2021. doi: 10.1158/1055-9965.EPI-21-0776. Online ahead of print.
ABSTRACT
BACKGROUND: The updated American Joint Committee on Cancer (AJCC) 8th Edition staging manual restructured nodal classification and staging by placing less prognostic emphasis on nodal metastases for HPV-positive oropharyngeal squamous cell carcinoma (OPSCC). However, there was no change for HPV-negative OPSCC. The purpose of our study is to examine the impact of nodal metastases on survival in HPV-negative OPSCC.
METHODS: HPV-negative OPSCC were queried from the NCDB and SEER databases. Univariable and multivariable models were utilized to determine the impact of nodal status on overall survival. These patients were reclassified according to AJCC 8 HPV-positive criteria (TNM8+) and risk stratification was quantified with C-statistics.
RESULTS: There were 11,147 cases of HPV-negative OPSCC in the NCDB and 3,613 cases in SEER that were included in the nodal classification analysis. Unlike non-oropharyngeal malignancies, increased nodal stage is not clearly associated with survival for patients with OPSCC independent of HPV status. When the TNM8+ was applied to HPV-negative patients, there was improved concordance in the NCDB cohort, 0.561 {plus minus} 0.004 to 0.624 {plus minus} 0.004 (difference +0.063) and the SEER cohort, 0.561 {plus minus} 0.008 to 0.625 {plus minus} 0.008 (difference +0.065).
CONCLUSIONS: We demonstrated a reduced impact of nodal metastasis on OPSCC survival, independent of HPV-status and specific to OPSCC.
IMPACT: We demonstrate, that when nodal staging is de-emphasized as a part of overall staging, we see improved concordance and risk stratification for HPV-negative OPSCC. The exact mechanism of this differential impact remains unknown but offers a novel area of study.
PMID:35579907 | DOI:10.1158/1055-9965.EPI-21-0776
Cogn Sci. 2022 May;46(5):e13134. doi: 10.1111/cogs.13134.
ABSTRACT
Free and open-source software projects have become essential digital infrastructure over the past decade. These projects are largely created and maintained by unpaid volunteers, presenting a potential vulnerability if the projects cannot recruit and retain new volunteers. At the same time, their development on open collaborative development platforms provides a nearly complete record of the community’s interactions; this affords the opportunity to study naturally occurring language dynamics at scale and in a context with massive real-world impact. The present work takes a dynamical systems view of language to understand the ways in which communicative context and community membership shape the emergence and impact of language use-specifically, sentiment and expressions of gratitude. We then present evidence that these language dynamics shape newcomers’ likelihood of returning, although the specific impacts of different community responses are crucially modulated by the context of the newcomer’s first contact with the community.
PMID:35579857 | DOI:10.1111/cogs.13134
Transl Vis Sci Technol. 2022 May 2;11(5):17. doi: 10.1167/tvst.11.5.17.
ABSTRACT
PURPOSE: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. An accurate and timely diagnosis of the early stages of ROP allows ophthalmologists to recommend appropriate treatment while blindness is still preventable. The purpose of this study was to develop an automatic deep convolutional neural network-based system that provided a diagnosis of stage I to III ROP with feature parameters.
METHODS: We developed three data sets containing 18,827 retinal images of preterm infants. These retinal images were obtained from the ophthalmology department of Jiaxing Maternal and Child Health Hospital in China. After segmenting images, we calculated the region of interest (ROI). We trained our system based on segmented ROI images from the training data set, tested the performance of the classifier on the test data set, and evaluated the widths of the demarcation lines or ridges extracted by the system, as well as the ratios of vascular proliferation within the ROI on a comparison data set.
RESULTS: The trained network achieved a sensitivity of 90.21% with 97.67% specificity for the diagnosis of stage I ROP, 92.75% sensitivity with 98.74% specificity for stage II ROP, and 91.84% sensitivity with 99.29% sensitivity for stage III ROP. When the system diagnosed normal images, the sensitivity and specificity reached 95.93% and 96.41%, respectively. The widths (in pixels) of the demarcation lines or ridges for normal, stage I, stage II, and stage III were 15.22 ± 1.06, 26.35 ± 1.36, and 30.75 ± 1.55. The ratios of the vascular proliferation within the ROI were 1.40 ± 0.29, 1.54 ± 0.26, and 1.81 ± 0.33. All parameters were statistically different among the groups. When physicians integrated quantitative parameters of the extracted features with their clinic diagnosis, the κ score was significantly improved.
CONCLUSIONS: Our system achieved a high accuracy of diagnosis for stage I to III ROP. It used the quantitative analysis of the extracted features to assist physicians in providing classification decisions.
TRANSLATIONAL RELEVANCE: The high performance of the system suggests potential applications in ancillary diagnosis of the early stages of ROP.
PMID:35579887 | DOI:10.1167/tvst.11.5.17
Radiol Med. 2022 May 17. doi: 10.1007/s11547-022-01481-w. Online ahead of print.
ABSTRACT
OBJECTIVE: To analyze dosimetric data of a single center by a radiation dose index monitoring software evaluating quantitatively the dose reduction obtained with the implementation of the adaptive statistical iterative reconstruction (ASIR) on Computed Tomography in terms of both the value of the dose length product (DLP) and the alerts provided by the dose tool.
METHODS: Dosimetric quantities were acquired using Qaelum DOSE tool (QAELUM NV, Leuven-Heverlee, Belgium). Dose data pertaining to CT examinations were performed using a General Electric Healthcare CT tomography with 64 detectors. CT dose data were collected over 4 years (January 1, 2017 to December 31, 2020) and included CT dose length product (DLP). Moreover, all CT examinations that triggered a high radiation dose (twice the median for that study description), termed alerts on Dose tool, were retrieved for the analysis. Two radiologists retrospectively assessed CT examinations in consensus for the images quality and for the causes of the alerts issued. A Chi-square test was used to assess whether there were any statistically significant differences among categorical variable while a Kruskal Wallis test was considered to assess differences statistically significant for continuous variables.
RESULTS: Differences statistically significant were found for the DLP median values between the dosimetric data recorded on 2017-2018 versus 2019-2020. The differences were linked to the implementation of ASIR technique at the end of 2018 on the CT scanner. The highest percentage of alerts was reported in the CT study group “COMPLETE ABDOMEN + CHEST + HEAD” (range from 1.26% to 2.14%). A reduction year for year was relieved linked to the CT protocol optimization with a difference statistically significant. The highest percentage of alerts was linked to wrong study label/wrong study protocol selection with a range from 29 to 40%.
CONCLUSIONS: Automated methods of radiation dose data collection allowed for detailed radiation dose analysis according to protocol and equipment over time. The use of CT ASIR technique could determine considerable reduction in radiation dose.
PMID:35579854 | DOI:10.1007/s11547-022-01481-w
Int J Behav Med. 2022 May 17. doi: 10.1007/s12529-022-10099-1. Online ahead of print.
ABSTRACT
BACKGROUND: We examined associations between smoke-free laws and smoking outcomes in a nationally representative sample of US adults, including exploring whether these associations differed for heterosexual and sexual minority (SM) adults.
METHODS: We constructed county-level variables representing the percent of the population covered by state-, county-, or city-level smoke-free laws in workplaces and hospitality venues. We combined this information with restricted individual-level adult data with masked county identifiers from the National Health Interview Survey (NHIS), 2013-2018. We used modified Poisson regression to explore associations between each type of smoke-free law and the prevalence ratio (PR) of current smoking, and we used linear regression to explore associations with smoking intensity (mean cigarettes per day). We assessed interactions between smoke-free laws and SM status on the additive scale to determine whether associations were different for SM and heterosexual adults.
RESULTS: In adjusted models without interaction terms, smoke-free laws in hospitality venues were associated with lower prevalence of current smoking (PR = 0.93, 95% confidence interval (CI) = 0.89, 0.98). Both types of smoke-free laws were associated with lower mean cigarettes per day (workplace law change in mean = – 0.50, 95% CI = – 0.89, – 0.12; hospitality law change in mean = – 0.72, 95% CI = – 1.14,-0.30). We did not observe any statistically significant interactions by SM status, though statistical power was limited.
CONCLUSIONS: We did not find evidence that smoke-free laws were differentially associated with smoking outcomes for heterosexual and SM adults. Additional studies are needed to further explore the potential for tobacco control policies to address the elevated risk of smoking in SM communities.
PMID:35579845 | DOI:10.1007/s12529-022-10099-1
Methods Mol Biol. 2022 May 18. doi: 10.1007/7651_2022_462. Online ahead of print.
ABSTRACT
The unique properties of stem cells to self-renew and differentiate hold great promise in disease modelling and regenerative medicine. However, more information about basic stem cell biology and thorough characterization of available stem cell lines is needed. This is especially essential to ensure safety before any possible clinical use of stem cells or partially committed cell lines. As proteins are the key effector molecules in the cell, the proteomic characterization of cell lines, cell compartments or cell secretome and microenvironment is highly beneficial to answer above mentioned questions. Nowadays, method of choice for large-scale discovery-based proteomic analysis is mass spectrometry (MS) with data-independent acquisition (DIA). DIA is a robust, highly reproducible, high-throughput quantitative MS approach that enables relative quantification of thousands of proteins in one sample. In the current protocol, we describe a specific variant of DIA known as SWATH-MS for characterization of neural stem cell differentiation. The protocol covers the whole process from cell culture, sample preparation for MS analysis, the SWATH-MS data acquisition on TTOF 5600, the complete SWATH-MS data processing and quality control using Skyline software and the basic statistical analysis in R and MSstats package. The protocol for SWATH-MS data acquisition and analysis can be easily adapted to other samples amenable to MS-based proteomics.
PMID:35579839 | DOI:10.1007/7651_2022_462
Musculoskelet Surg. 2022 May 17. doi: 10.1007/s12306-022-00748-9. Online ahead of print.
ABSTRACT
PURPOSE: To evaluate the outcomes of patients affected by proximal and middle one-third humeral shaft fractures treated with humeral helical plates.
MATERIAL AND METHODS: From October 2016 to June 2020, twenty-four (twenty women, four men) underwent humeral reduction and fixation with humeral helical plates (A.L.P.S.® Proximal Humeral Plating System, Zimmer Biomet) that preserve deltoid muscle insertion and reduce the risk of iatrogenic radial nerve injury. At one and six months after surgery, standard antero-posterior and lateral radiographs were obtained, and at last follow-up (eighteen months on average), clinical evaluation was performed through range of motion assessment, Constant score and DASH score questionnaires. Only descriptive statistical analysis was conducted.
RESULTS: At six months, all fractures have healed. At last follow-up (average eighteen months, 13-28) mean Constant score was 71 (range 33-96), mean Dash score was 19.2 (range 1.7-63). The average range of motion was calculated as follows: flexion 137.8° (range 90-180); abduction 125.8° (range 85-180°); external rotation 55° (range 20-80°), internal rotation at L3 (range between scapulae-trochanter). Three patients experienced temporary radial nerve palsy from injury, while in one case, a temporary iatrogenic palsy occurred.
CONCLUSIONS: In our opinion, the helical plate may be an effective surgical tool for management of proximal and middle one-third diaphyseal humeral fractures. The humeral helical plate allows stable fixation avoiding the deltoid tuberosity proximally and radial nerve distally, thus increasing the possibility of rapid functional recovery after surgery.
PMID:35579822 | DOI:10.1007/s12306-022-00748-9
Drug Saf. 2022 May;45(5):477-491. doi: 10.1007/s40264-022-01176-1. Epub 2022 May 17.
ABSTRACT
INTRODUCTION: Artificial intelligence based on machine learning has made large advancements in many fields of science and medicine but its impact on pharmacovigilance is yet unclear.
OBJECTIVE: The present study conducted a scoping review of the use of artificial intelligence based on machine learning to understand how it is used for pharmacovigilance tasks, characterize differences with other fields, and identify opportunities to improve pharmacovigilance through the use of machine learning.
DESIGN: The PubMed, Embase, Web of Science, and IEEE Xplore databases were searched to identify articles pertaining to the use of machine learning in pharmacovigilance published from the year 2000 to September 2021. After manual screening of 7744 abstracts, a total of 393 papers met the inclusion criteria for further analysis. Extraction of key data on study design, data sources, sample size, and machine learning methodology was performed. Studies with the characteristics of good machine learning practice were defined and manual review focused on identifying studies that fulfilled these criteria and results that showed promise.
RESULTS: The majority of studies (53%) were focused on detecting safety signals using traditional statistical methods. Of the studies that used more recent machine learning methods, 61% used off-the-shelf techniques with minor modifications. Temporal analysis revealed that newer methods such as deep learning have shown increased use in recent years. We found only 42 studies (10%) that reflect current best practices and trends in machine learning. In the subset of 154 papers that focused on data intake and ingestion, 30 (19%) were found to incorporate the same best practices.
CONCLUSION: Advances from artificial intelligence have yet to fully penetrate pharmacovigilance, although recent studies show signs that this may be changing.
PMID:35579812 | DOI:10.1007/s40264-022-01176-1
Drug Saf. 2022 May;45(5):493-510. doi: 10.1007/s40264-022-01158-3. Epub 2022 May 17.
ABSTRACT
Increasing availability of electronic health databases capturing real-world experiences with medical products has garnered much interest in their use for pharmacoepidemiologic and pharmacovigilance studies. The traditional practice of having numerous groups use single databases to accomplish similar tasks and address common questions about medical products can be made more efficient through well-coordinated multi-database studies, greatly facilitated through distributed data network (DDN) architectures. Access to larger amounts of electronic health data within DDNs has created a growing interest in using data-adaptive machine learning (ML) techniques that can automatically model complex associations in high-dimensional data with minimal human guidance. However, the siloed storage and diverse nature of the databases in DDNs create unique challenges for using ML. In this paper, we discuss opportunities, challenges, and considerations for applying ML in DDNs for pharmacoepidemiologic and pharmacovigilance studies. We first discuss major types of activities performed by DDNs and how ML may be used. Next, we discuss practical data-related factors influencing how DDNs work in practice. We then combine these discussions and jointly consider how opportunities for ML are affected by practical data-related factors for DDNs, leading to several challenges. We present different approaches for addressing these challenges and highlight efforts that real-world DDNs have taken or are currently taking to help mitigate them. Despite these challenges, the time is ripe for the emerging interest to use ML in DDNs, and the utility of these data-adaptive modeling techniques in pharmacoepidemiologic and pharmacovigilance studies will likely continue to increase in the coming years.
PMID:35579813 | DOI:10.1007/s40264-022-01158-3