Large-scale Validation Research Articles

MISEXPRESSION OF GENES LACKING CPG ISLANDS IS A SHARED TRAIT OF MAMMALIAN AGING

Abstract Changes in the 3-D architecture of chromatin are observed in various diseases and are also a hallmark of aging. Disruption of the nuclear lamina and associated heterochromatin are commonly observed in various aging contexts, including premature aging diseases, cellular senescence, and normative aging. Although these conserved structural changes have been reported for over two decades, their impacts on transcription and contribution to age-related degenerative changes remain unknown. By performing a large-scale computational analysis and experimental validation, here we show that genes lacking CpG islands (CGI- genes), which form heterochromatin when transcriptionally silent, are globally misexpressed in aged nuclei with disrupted chromatin architectures. We demonstrate that CGI- gene misexpression is a common feature of mammalian aging and explains the molecular basis of various age-associated defects, ranging from loss of cellular identity and increased transcriptional noise to age-associated chronic inflammation. Our findings reveal that CGI- gene misexpression is directly associated with age-related physiological deterioration, thus providing a novel biomarker of aging.

Test-taker self-assessment accuracy using the TOEIC speaking and writing can-do statements

Can-do statements have been integrated for decades into research and marketing strategies related to standardized tests of English for non-native language learners. For instance, while ETS indicated that TOEIC can-do statements are specifically for “decision making in organizations,” the Council of Europe created user-friendly statements that all stakeholders, including test takers, can use and understand. This study discusses a partial replication of a large-scale validity study with the purpose of developing a scale which can be used by examinees in combination with the TOEIC SW can-do statements for self-assessment. As part of an extracurricular program, 23 graduate-level EAL university students took the TOEIC SW and speaking and writing can-do self-assessment surveys. Results indicated that both self-assessment scales used in this study had moderate to strong correlations with TOEIC SW scores (r = 0.62–0.73) and TOEIC proficiency levels (r = 0.47–0.66). Self-assessment results using a Level Mean Average (LMA) scale correlated significantly with both speaking and writing test results and produced slightly higher percentages of test takers self-assessing within the true score zones of 1 SEM. For the most part, self-assessment predictive accuracy using these scales and true score zones was 52–57% for speaking and 43% for writing. Other findings were that EAL learners tended to underestimate their scores, and they more accurately self-assessed speaking than writing. If LMA scale accuracy at a particular level can be verified in future studies, claims could be made that TOEIC SW can-do statements have value for examinees.

1181. Predictive Score for Dengue Infection with Complete Blood Count Parameters, Including the New Monocyte Distribution Width – a Retrospective Single Center Derivation and Validation Study

Abstract Background Dengue infection is a differential diagnosis in patients with acute undifferentiated fever. Early detection and management may reduce mortality. Monocyte distribution width (MDW) is a new parameter in complete blood count (CBC). It is recently approved for early detection of sepsis. We have noticed high MDW in Dengue patients. This study aimed to describe MDW changes in Dengue infection and to develop predictive score for early detection of Dengue infected patients. Methods We retrospectively retrieved data of adult patients with acute fever who had CBC and Dengue serology (NS1 antigen, IgM and IgG) performed during September 2019 to May 2020 at Thammasat University Hospital. Medical records were reviewed. MDW was compared between groups. Patients were randomly divided into training and validation set. Predictive score was developed from the training set and validated in the validation set with multivariable analysis. Results A total of 431 patients, with Dengue infection in 127 patients (29.5%), were included in the analyses. The median (interquartile range) of MDW in Dengue patients were higher than non-Dengue patients [29.7% (26.5 – 34.7) vs. 24.2% (21.1 – 27.8), P < 0.001]. In patients with confirmed Dengue infection, MDW increased with increasing severity (Figure). Training and validation sets included 216 and 215 patients with 64 and 63 Dengue infection, respectively. Independent predictive factors of Dengue infection were white blood cell < 4 x 109 /L (score 1), platelet < 100 x 109 /L (score 1) and MDW > 24% (score 1). No clinical features were independently predictive of Dengue infection. The area under receiver-operating-characteristic curve (95% confidence interval) of the prognostic score in the training and validation set were 0.839 (0.779 – 0.899) and 0.742 (0.674 – 0.811), respectively. With the cut-off score ≥ 1, the sensitivity and specificity of the score were 92.2% and 40.8% in the training set and 88.9% and 44.1% in the validation set. Monocyte distribution width in Dengue and non-Dengue patients Box-plot showing monocyte distribution width in patients with Dengue in comparison with non-Dengue (panel A) and compared between each Dengue severity (panel B) Conclusion MDW increase in patients with Dengue infection and also increase with Dengue severity. We have developed and internally validated a simple predictive score for Dengue infection based on only results from CBC and MDW. Further large-scale external validation study is required to confirm the utility of our predictive score. Disclosures All Authors: No reported disclosures.

Applicability of multidimensional convolutional neural networks on automated detection of diverse focal liver lesions in multiphase CT images.

To investigate the applicability of multidimensional convolutional neural networks (CNNs) together with multiphase contrast-enhanced CT images on automated detection of diverse focal liver lesions (FLLs). We trained detection models based on 2.5D and 3D CNN frameworks using 567 patients with 3892 FLLs and validated on a relatively large independent cohort of 1436 patients with 4723 lesions. The detection performance across different phases (arterial, portal venous [PV], and combined phases) was assessed for the 2.5D model. The lesions were divided into two groups with a cutoff size of 20mm, and further subdivided into four subgroups of<10, 10-20, 20-50, and ≥50mm, to verify the detection rates for lesions of different sizes for the 2.5D and 3D models. McNemar's test was used to compare the detection sensitivities among different methods. In addition, sensitivity with 95% confidence intervals and free-response receiver operating characteristics (FROC) curves were plotted for visualization of the detectability. In the 2.5D model, the detection rate of PV phase outperformed arterial phase, and a combination of the two phases further improved the performance over a single phase. The detection sensitivities in the arterial, PV, and combined phases were 0.737versus 0.802versus 0.832 for all lesions. The 3D model was superior to the 2.5D model for detecting benign lesions (0.896vs. 0.807, p<0.001), malignant lesions (0.940vs. 0.918, p=0.013), and all lesions (0.902vs. 0.832, p<0.001) regardless of size division. Particularly, the 3D model showed higher sensitivity than the 2.5D model in detecting lesions smaller than 20mm (0.868vs. 0.759, p<0.001). For lesions larger than 20mm, both the 3D and the 2.5D models achieved excellent detection performance. The proposed CNN detection model was demonstrated to adaptively learn the feature representations of diverse FLLs and generalize well to a large-scale validation dataset. The use of multiphase significantly improved the detectability of FLLs compared to single phase. 3D CNN framework showed an enhanced capability over the 2.5D in the detection of FLLs, particularly small lesions. The promising performance shows that the proposed CNN detection system could be a powerful clinical tool for the early detection of hepatic tumors.

Usability and transparency in the design of a tool for automatic support for web accessibility validation

The importance of guaranteeing accessible Web applications is becoming widely recognised, and supported by national and international legislation. This implies an increasing need for large scale validations, which can be achieved only through automatic support. Thus, there is a need for a new generation of accessibility validation tools able to check against the continuously evolving guidelines and report any issues revealed, considering that their results will be used by many people with varied backgrounds and goals. Such tools should be able to support monitoring of Web sites’ accessibility, provide user-friendly information suitable for various purposes, and be transparent in terms of what they are actually able to evaluate in order to elicit the right expectations from their users. They should also consider how the technologies for implementing Web sites have evolved in recent years. In this paper, we provide a description of the requirements and design dimensions that characterise such tools in order to address emerging needs, providing indications on whether and how several existing validation tools support them, and present how we have addressed and implemented them in a specific tool. We also report on a first usability test of such tool, which has provided encouraging feedback.

MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters.

With an ever-increasing amount of (meta)genomic data being deposited in sequence databases, (meta)genome mining for natural product biosynthetic pathways occupies a critical role in the discovery of novel pharmaceutical drugs, crop protection agents and biomaterials. The genes that encode these pathways are often organised into biosynthetic gene clusters (BGCs). In 2015, we defined the Minimum Information about a Biosynthetic Gene cluster (MIBiG): a standardised data format that describes the minimally required information to uniquely characterise a BGC. We simultaneously constructed an accompanying online database of BGCs, which has since been widely used by the community as a reference dataset for BGCs and was expanded to 2021 entries in 2019 (MIBiG 2.0). Here, we describe MIBiG 3.0, a database update comprising large-scale validation and re-annotation of existing entries and 661 new entries. Particular attention was paid to the annotation of compound structures and biological activities, as well as protein domain selectivities. Together, these new features keep the database up-to-date, and will provide new opportunities for the scientific community to use its freely available data, e.g. for the training of new machine learning models to predict sequence-structure-function relationships for diverse natural products. MIBiG 3.0 is accessible online at https://mibig.secondarymetabolites.org/.

Feasibility analysis of endorphin massage module as an effort to increase breast milk production in puerperal mothers

This study aims to determine the feasibility of the Endorphin Massage module as a learning medium. The method used in this study is Research and Development (RnD) by adopting the development steps of the Borg and Gall models. This research was carried out at the Jongaya Health Center, Kassi-Kassi Health Center, and Minasa Upa Health Center in November August 2022. Sampling was carried out using the Purposive Sampling Technique, which was 40 samples, validation test results from media experts averaged 90.9% and validation results from material experts averaged 92.7% which showed that the Endorphin Massage module is very feasible to use without revision. The results of user validation (Midwives) in small groups of 10 Midwives with an average eligibility score of 89.5% and the results of large-scale user validation in 30 Midwives with an average eligibility score of 93.4% of the data showed that the Endorphin Massage module was very feasible to use. The Endorphin Massage module is suitable for use as a learning medium in an effort to increase breast milk expenditure in Postpartum mothers.

Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab

AbstractAssessment of programmed death ligand 1 (PD-L1) expression by immunohistochemistry (IHC) has emerged as an important predictive biomarker across multiple tumor types. However, manual quantitation of PD-L1 positivity can be difficult and leads to substantial inter-observer variability. Although the development of artificial intelligence (AI) algorithms may mitigate some of the challenges associated with manual assessment and improve the accuracy of PD-L1 expression scoring, use of AI-based approaches to oncology biomarker scoring and drug development has been sparse, primarily due to the lack of large-scale clinical validation studies across multiple cohorts and tumor types. We developed AI-powered algorithms to evaluate PD-L1 expression on tumor cells by IHC and compared it with manual IHC scoring in urothelial carcinoma, non-small cell lung cancer, melanoma, and squamous cell carcinoma of the head and neck (prospectively determined during the phase II and III CheckMate clinical trials). 1,746 slides were retrospectively analyzed, the largest investigation of digital pathology algorithms on clinical trial datasets performed to date. AI-powered quantification of PD-L1 expression on tumor cells identified more PD-L1–positive samples compared with manual scoring at cutoffs of ≥1% and ≥5% in most tumor types. Additionally, similar improvements in response and survival were observed in patients identified as PD-L1–positive compared with PD-L1–negative using both AI-powered and manual methods, while improved associations with survival were observed in patients with certain tumor types identified as PD-L1–positive using AI-powered scoring only. Our study demonstrates the potential for implementation of digital pathology-based methods in future clinical practice to identify more patients who would benefit from treatment with immuno-oncology therapy compared with current guidelines using manual assessment.

Measurement invariance and latent mean differences of the digital native assessment scale across Chinese mainland, Macau, and Taiwan: an exploratory structural equation modeling approach

ABSTRACT Given the lack of cultural consideration of studies on digital natives, this study reports on a large-scale validation of the Digital Native Assessment Scale (DNAS) among university students from three regions of Greater China: Chinese mainland, Macau, and Taiwan, to examine measurement invariance and latent mean differences in the four constructs of the DNAS, grew up with technology, comfortable with multitasking, reliant on graphics for communication, and thrive on instant gratifications and rewards. The results from 5198 participants revealed the DNAS has a stable factorial structure and is valid in measuring students’ digital nativity. The DNAS achieved a strong/scalar measurement invariance among students from the three regions in each of the four factors. Latent mean difference tests suggested that Macau students had significantly higher levels on all four constructs than those from the mainland, with small to medium effects; and the latent means of Taiwanese students were significantly higher than those of mainland students in three constructs (i.e. Tech, Multi, and Graphics), with small to large effects. Discussions of the findings were provided by taking into account the historical, economic, and cultural backgrounds of the three regions.

Establishment and large-scale validation of a three-dimensional tumor model on an array chip for anticancer drug evaluation.

Two-dimensional (2D) tumor model has always poorly predicted drug response of animal model due to the lack of recapitulation of tumor microenvironment. Establishing a biomimetic, controllable, and cost-effective three-dimensional (3D) model and large-scale validation of its in vivo predictivity has shown promise in bridging the gap between the 2D tumor model and animal model. Here, we established a matrigel-based 3D micro-tumor model on an array chip for large-scale anticancer drug evaluation. Compared with the 2D tumor model, the 3D tumor model on the chip showed spheroid morphology, slower proliferation kinetics, and comparable reproducibility. Next, the results of the chemotherapeutic evaluation from 18 drugs against 27 cancer cell lines showed 17.6% of drug resistance on the 3D tumor model. Moreover, the evaluation results of targeted drugs showed expected sensitivity and higher specificity on the 3D tumor model compared with the 2D model. Finally, the evaluation results on the 3D tumor model were more consistent with the in vivo cell-derived xenograft model, and excluded 95% false-positive results from the 2D model. Overall, the matrigel-based 3D micro-tumor model on the array chip provides a promising tool to accelerate anticancer drug discovery.

Development and large-scale validation of the Watch Walk wrist-worn digital gait biomarkers

Digital gait biomarkers (including walking speed) indicate functional decline and predict hospitalization and mortality. However, waist or lower-limb devices often used are not designed for continuous life-long use. While wrist devices are ubiquitous and many large research repositories include wrist-sensor data, widely accepted and validated digital gait biomarkers derived from wrist-worn accelerometers are not available yet. Here we describe the development of advanced signal processing algorithms that extract digital gait biomarkers from wrist-worn devices and validation using 1-week data from 78,822 UK Biobank participants. Our gait biomarkers demonstrate good test–retest-reliability, strong agreement with electronic walkway measurements of gait speed and self-reported pace and significantly discriminate individuals with poor self-reported health. With the almost universal uptake of smart-watches, our algorithms offer a new approach to remotely monitor life-long population level walking speed, quality, quantity and distribution, evaluate disease progression, predict risk of adverse events and provide digital gait endpoints for clinical trials.

Quantitative Proteomics of Medium-Sized Extracellular Vesicle-Enriched Plasma of Lacunar Infarction for the Discovery of Prognostic Biomarkers.

Lacunar infarction (LACI), a subtype of acute ischemic stroke, has poor mid- to long-term prognosis due to recurrent vascular events or incident dementia which is difficult to predict using existing clinical data. Herein, we aim to discover blood-based biomarkers for LACI as a complementary prognostic tool. Convalescent plasma was collected from forty-five patients following a non-disabling LACI along with seventeen matched control subjects. The patients were followed up prospectively for up to five years to record an occurrence of adverse outcome and grouped accordingly (i.e., LACI-no adverse outcome, LACI-recurrent vascular event, and LACI-cognitive decline without any recurrence of vascular events). Medium-sized extracellular vesicles (MEVs), isolated from the pooled plasma of four groups, were analyzed by stable isotope labeling and 2D-LC-MS/MS. Out of 573 (FDR < 1%) quantified proteins, 146 showed significant changes in at least one LACI group when compared to matched healthy control. A systems analysis revealed that major elements (~85%) of the MEV proteome are different from the proteome of small-sized extracellular vesicles obtained from the same pooled plasma. The altered MEV proteins in LACI patients are mostly reduced in abundance. The majority of the shortlisted MEV proteins are not linked to commonly studied biological processes such as coagulation, fibrinolysis, or inflammation. Instead, they are linked to oxygen-glucose deprivation, endo-lysosomal trafficking, glucose transport, and iron homeostasis. The dataset is provided as a web-based data resource to facilitate meta-analysis, data integration, and targeted large-scale validation.

Workplace Assessment Scale: Pilot Validation Study.

First responders, such as police officers, paramedics, and firefighters are at an increased risk of experiencing negative mental health outcomes compared to the general population. This predisposition can partially be attributed to common occupational stressors, which may provoke strong feelings of betrayal and humiliation. The Workplace Assessment Scale (WAS) was developed as there is currently no appropriate measure to assess such feelings in the first responder population. Initial development of the WAS included a Betrayal Subscale and the Humiliation Subscale, each comprised of 5 Likert scale questions which ask participants to report the frequency at which they experience specific feelings associated with their workplace. This pilot validation study was conducted to determine if there is preliminary evidence to support a large-scale validation study. To determine this, we assessed the internal structure and the convergent, concurrent, and predictive validity of the WAS. Based on 21/22 (95%) participant responses, a factor analysis did not support the two-factor model we anticipated, with only one factor and seven items retained from the original version of the scale. However, the internal consistency of the remaining items was strong. The validity analysis found moderate convergent validity and weak predictive validity based on correlations between the WAS and other psychometric scales. Minimal concurrent validity was noted. Additional research is needed for further analysis and validation of the WAS.

Simulation-Based Gastrointestinal Endoscopy Sedations: A Novel Validation to Multidrug Pharmacodynamic Modeling.

Pharmacodynamic models have described the interactions between anesthetics. Applying the models to clinical practice is still problematic due to inherent limitations: 1. modeling conditions are different from practice. 2. One model can only describe one endpoint. To tackle these, we propose a new method of model validation for recovery and intraprocedural sedation adequacy with a three-drug pharmacodynamic model using six published clinical studies that contain midazolam, opioid, and propofol. Mean drug dose, intraprocedural sedation level, procedure, and recovery time are extracted from each study. Simulated drug regimens are designed to best approximate study conditions. A published deep sedation model is used for simulation. Model-predicted recovery time and intraprocedural sedation scores are compared with the original clinical study outcomes. The model successfully predicted recovery times in eight out of nine regimens. Lower doses of midazolam are associated with faster recovery. Model prediction of intraprocedural sedation level was compatible with the clinical studies in five out of seven regimens. The three-drug pharmacodynamic model describes the course of gastrointestinal endoscopy sedations from clinical studies well. Model predictions are consistent with the results from clinical studies. The approach implies that large scale validation can be performed repeatedly.

37-Year-Old Man With Headache and Nasal Congestion

37-Year-Old Man With Headache and Nasal Congestion

Glucocorticoid receptor and androgen receptor-targeting therapy in patients with castration-resistant prostate cancer.

ObjectiveThe glucocorticoid receptor (GR) promotes resistance to androgen receptor (AR)-targeting therapies in castration-resistant prostate cancer (CRPC) by bypassing AR blockade. However, the clinical relevance of evaluating GR expression in patients with CRPC has not been determined. The present study investigated the association of relative GR expression in CRPC tissue samples with treatment response to AR-targeting therapy.MethodsLevels of GR, AR-FL, and AR-V7 mRNAs were measured in prostate cancer tissue from prospectively enrolled CRPC patients who were starting treatment. Patients were divided into groups with high and low AR-V7/AR-FL ratios and with high and low GR/AR-FL ratios. The primary endpoint was prostate-specific antigen (PSA) response rate to treatment.ResultsEvaluation of 38 patients treated with AR-targeting therapies showed that the PSA response rate was significantly higher in patients with low than high AR-V7/AR-FL ratios (77.8% vs. 25.0%, p=0.003) and in patients with low than high GR/AR-FL ratios (81.3% vs. 27.3%, p=0.003). Patients with low GR/AR-FL ratios had higher rates of PSA progression-free survival (46.0% vs. 22.4%, p=0.006), radiologic progression-free survival (28.9% vs. 10.0%, p=0.02), and overall survival (75.2% vs. 48.0%, p=0.037) than patients with high GR/AR-FL ratios. The association of GR/AR-FL ratio with PSA response to AR-targeting therapy remained significant in multivariable models. Evaluation of the 14 patients who received taxane chemotherapy showed that PSA response rates did not differ significantly in those with low and high AR-V7/AR-FL and GR/AR-FL ratios, although no definitive conclusions can be drawn due to the small number of patients.ConclusionRelative GR expression is associated with sensitivity to AR-targeting therapy and survival in patients with CRPC. Large-scale prospective validation and liquid biopsy-based studies are warranted.

Fragile X-associated tremor ataxia syndrome rating scale: Revision and content validity using a mixed method approach.

BackgroundThe original Fragile X-associated Tremor Ataxia Syndrome Rating Scale (FXTAS-RS) contained 61 items, some requiring modifications to better meet recommendations for patient-focused rating scale development.PurposeProvide initial validation of a revised version of the FXTAS-RS for motor signs.MethodWe conducted a two-phase mixed-method approach. In Phase 1, revision, we implemented a Delphi technique identifying pertinent domains/subdomains and developing items through expert consensus. In Phase 2, content validation, we conducted cognitive pretesting assessing comprehensibility, comprehensiveness, and relevance of items to FXTAS motor signs.ResultsAfter five rounds of Delphi panel and two rounds of cognitive pretesting, the revised version of the FXTAS-RS was established with 18 items covering five domains and 13 subdomains of motor signs. Cognitive pretesting revealed adequate content validity for the assessment of FXTAS motor signs.ConclusionThe revised FXTAS-RS has been successfully validated for content and it is now ready for large-scale field validation.

The reliability of categorical triple collocation for evaluating soil freeze/thaw datasets

Seasonal soil freeze/thaw (FT) state transition plays a critical role in the range of ecosystem, hydrological and biogeochemical processes. A thorough and large-scale validation of remote-sensed or model-based FT products is therefore quite important. Previous validation studies have applied categorical triple collocation (CTC) as a cross-validation method to estimate the relative performance ranking of various FT datasets, including in situ observations. While CTC has proven useful for qualitatively evaluating FT datasets, quantitative estimates of classification accuracy, which has not yet been assessed against direct validation results, would be even more valuable. To fill this gap, we compare CTC estimated performance rankings and quantitative classification accuracies with those obtained from dense soil temperature and sparse surface temperature observations from April 2015 through December 2019. CTC estimated classification accuracies are found to be strongly correlated (r > 0.927) with dense ground observations, along with very low bias (< 0.038) and RMSE (< 0.086). However, the bias and RMSE of CTC-estimated freeze accuracies are significantly inflated when sparse surface temperatures are used instead as the benchmark. Small errors are found with low absolute values (<0.317) of CTC-estimated class imbalance and a sample size of at least 365. CTC can generally provide the correct performance ranking for each product within a triplet - with low risk of incorrectly ranking all three products. In addition, a sample size of 10–160 is adequate for CTC to provide the correct ranking for the highest- or lowest-ranked product. This improves our knowledge and understanding of the reliability of the CTC method.

Implementing large-scale data quality validation in a national arthroplasty registry to improve compliance : the National Joint Registry data quality audit programme.

Data of high quality are critical for the meaningful interpretation of registry information. The National Joint Registry (NJR) was established in 2002 as the result of an unexpectedly high failure rate of a cemented total hip arthroplasty. The NJR began data collection in 2003. In this study we report on the outcomes following the establishment of a formal data quality (DQ) audit process within the NJR, within which each patient episode entry is validated against the hospital unit’s Patient Administration System and vice-versa. This process enables bidirectional validation of every NJR entry and retrospective correction of any errors in the dataset. In 2014/15 baseline average compliance was 92.6% and this increased year-on-year with repeated audit cycles to 96.0% in 2018/19, with 76.4% of units achieving > 95% compliance. Following the closure of the audit cycle, an overall compliance rate of 97.9% was achieved for the 2018/19 period. An automated system was initiated in 2018 to reduce administrative burden and to integrate the DQ process into standard workflows. Our processes and quality improvement results demonstrate that DQ may be implemented successfully at national level, while minimizing the burden on hospitals.Cite this article: Bone Jt Open 2022;3(9):716–725.

Roadmap to incorporating group A Streptococcus molecular point-of-care testing for remote Australia: a key activity to eliminate rheumatic heart disease.

Roadmap to incorporating group A Streptococcus molecular point-of-care testing for remote Australia: a key activity to eliminate rheumatic heart disease.