N6-methyladenosine in 7SK small nuclear RNA underlies RNA polymerase II transcription regulation.

N6-methyladenosine (m6A) modifications play crucial roles in RNA metabolism. How m6A regulates RNA polymerase II (RNA Pol II) transcription remains unclear. We find that 7SK small nuclear RNA (snRNA), a regulator of RNA Pol II promoter-proximal pausing, is highly m6A-modified in non-small cell lung cancer (NSCLC) cells. In A549 cells, we identified eight m6A sites on 7SK and discovered methyltransferase-like 3 (METTL3) and alkB homolog 5 (ALKBH5) as the responsible writer and eraser. When the m6A-7SK is specifically erased by a dCasRx-ALKBH5 fusion protein, A549 cell growth is attenuated due to reduction of RNA Pol II transcription. Mechanistically, removal of m6A leads to 7SK structural rearrangements that facilitate sequestration of the positive transcription elongation factor b (P-TEFb) complex, which results in reduction of serine 2 phosphorylation (Ser2P) in the RNA Pol II C-terminal domain and accumulation of RNA Pol II in the promoter-proximal region. Taken together, we uncover that m6A modifications of a non-coding RNA regulate RNA Pol II transcription and NSCLC tumorigenesis.

Morphological profiling for drug discovery in the era of deep learning.

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

Real-World Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescents.

BACKGROUND: The efficacy of the BNT162b2 vaccine in pediatrics was assessed by randomized trials before the Omicron variant's emergence. The long-term durability of vaccine protection in this population during the Omicron period remains limited. OBJECTIVE: To assess the effectiveness of BNT162b2 in preventing infection and severe diseases with various strains of the SARS-CoV-2 virus in previously uninfected children and adolescents. DESIGN: Comparative effectiveness research accounting for underreported vaccination in 3 study cohorts: adolescents (12 to 20 years) during the Delta phase and children (5 to 11 years) and adolescents (12 to 20 years) during the Omicron phase. SETTING: A national collaboration of pediatric health systems (PEDSnet). PARTICIPANTS: 77 392 adolescents (45 007 vaccinated) during the Delta phase and 111 539 children (50 398 vaccinated) and 56 080 adolescents (21 180 vaccinated) during the Omicron phase. INTERVENTION: First dose of the BNT162b2 vaccine versus no receipt of COVID-19 vaccine. MEASUREMENTS: Outcomes of interest include documented infection, COVID-19 illness severity, admission to an intensive care unit (ICU), and cardiac complications. The effectiveness was reported as (1-relative risk)*100, with confounders balanced via propensity score stratification. RESULTS: During the Delta period, the estimated effectiveness of the BNT162b2 vaccine was 98.4% (95% CI, 98.1% to 98.7%) against documented infection among adolescents, with no statistically significant waning after receipt of the first dose. An analysis of cardiac complications did not suggest a statistically significant difference between vaccinated and unvaccinated groups. During the Omicron period, the effectiveness against documented infection among children was estimated to be 74.3% (CI, 72.2% to 76.2%). Higher levels of effectiveness were seen against moderate or severe COVID-19 (75.5% [CI, 69.0% to 81.0%]) and ICU admission with COVID-19 (84.9% [CI, 64.8% to 93.5%]). Among adolescents, the effectiveness against documented Omicron infection was 85.5% (CI, 83.8% to 87.1%), with 84.8% (CI, 77.3% to 89.9%) against moderate or severe COVID-19, and 91.5% (CI, 69.5% to 97.6%) against ICU admission with COVID-19. The effectiveness of the BNT162b2 vaccine against the Omicron variant declined 4 months after the first dose and then stabilized. The analysis showed a lower risk for cardiac complications in the vaccinated group during the Omicron variant period. LIMITATION: Observational study design and potentially undocumented infection. CONCLUSION: This study suggests that BNT162b2 was effective for various COVID-19-related outcomes in children and adolescents during the Delta and Omicron periods, and there is some evidence of waning effectiveness over time. PRIMARY FUNDING SOURCE: National Institutes of Health.

A scoping review of fair machine learning techniques when using real-world data.

OBJECTIVE: The integration of artificial intelligence (AI) and machine learning (ML) in health care to aid clinical decisions is widespread. However, as AI and ML take important roles in health care, there are concerns about AI and ML associated fairness and bias. That is, an AI tool may have a disparate impact, with its benefits and drawbacks unevenly distributed across societal strata and subpopulations, potentially exacerbating existing health inequities. Thus, the objectives of this scoping review were to summarize existing literature and identify gaps in the topic of tackling algorithmic bias and optimizing fairness in AI/ML models using real-world data (RWD) in health care domains. METHODS: We conducted a thorough review of techniques for assessing and optimizing AI/ML model fairness in health care when using RWD in health care domains. The focus lies on appraising different quantification metrics for accessing fairness, publicly accessible datasets for ML fairness research, and bias mitigation approaches. RESULTS: We identified 11 papers that are focused on optimizing model fairness in health care applications. The current research on mitigating bias issues in RWD is limited, both in terms of disease variety and health care applications, as well as the accessibility of public datasets for ML fairness research. Existing studies often indicate positive outcomes when using pre-processing techniques to address algorithmic bias. There remain unresolved questions within the field that require further research, which includes pinpointing the root causes of bias in ML models, broadening fairness research in AI/ML with the use of RWD and exploring its implications in healthcare settings, and evaluating and addressing bias in multi-modal data. CONCLUSION: This paper provides useful reference material and insights to researchers regarding AI/ML fairness in real-world health care data and reveals the gaps in the field. Fair AI/ML in health care is a burgeoning field that requires a heightened research focus to cover diverse applications and different types of RWD.

Model tuning or prompt Tuning? a study of large language models for clinical concept and relation extraction.

OBJECTIVE: To develop soft prompt-based learning architecture for large language models (LLMs), examine prompt-tuning using frozen/unfrozen LLMs, and assess their abilities in transfer learning and few-shot learning. METHODS: We developed a soft prompt-based learning architecture and compared 4 strategies including (1) fine-tuning without prompts; (2) hard-prompting with unfrozen LLMs; (3) soft-prompting with unfrozen LLMs; and (4) soft-prompting with frozen LLMs. We evaluated GatorTron, a clinical LLM with up to 8.9 billion parameters, and compared GatorTron with 4 existing transformer models for clinical concept and relation extraction on 2 benchmark datasets for adverse drug events and social determinants of health (SDoH). We evaluated the few-shot learning ability and generalizability for cross-institution applications. RESULTS AND CONCLUSION: When LLMs are unfrozen, GatorTron-3.9B with soft prompting achieves the best strict F1-scores of 0.9118 and 0.8604 for concept extraction, outperforming the traditional fine-tuning and hard prompt-based models by 0.6 âˆ¼ 3.1 % and 1.2 âˆ¼ 2.9 %, respectively; GatorTron-345 M with soft prompting achieves the best F1-scores of 0.8332 and 0.7488 for end-to-end relation extraction, outperforming other two models by 0.2 âˆ¼ 2 % and 0.6 âˆ¼ 11.7 %, respectively. When LLMs are frozen, small LLMs have a big gap to be competitive with unfrozen models; scaling LLMs up to billions of parameters makes frozen LLMs competitive with unfrozen models. Soft prompting with a frozen GatorTron-8.9B model achieved the best performance for cross-institution evaluation. We demonstrate that (1) machines can learn soft prompts better than hard prompts composed by human, (2) frozen LLMs have good few-shot learning ability and generalizability for cross-institution applications, (3) frozen LLMs reduce computing cost to 2.5 âˆ¼ 6 % of previous methods using unfrozen LLMs, and (4) frozen LLMs require large models (e.g., over several billions of parameters) for good performance.

Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.

OBJECTIVE: To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different disease groups, and examine population-level extraction ratio. METHODS: We developed SDoH corpora using clinical notes identified at the University of Florida (UF) Health. We systematically compared 7 transformer-based large language models (LLMs) and developed an open-source package - SODA (i.e., SOcial DeterminAnts) to facilitate SDoH extraction from clinical narratives. We examined the performance and potential bias of SODA for different race and gender groups, tested the generalizability of SODA using two disease domains including cancer and opioid use, and explored strategies for improvement. We applied SODA to extract 19 categories of SDoH from the breast (n = 7,971), lung (n = 11,804), and colorectal cancer (n = 6,240) cohorts to assess patient-level extraction ratio and examine the differences among race and gender groups. RESULTS: We developed an SDoH corpus using 629 clinical notes of cancer patients with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH, and another cross-disease validation corpus using 200 notes from opioid use patients with 4,342 SDoH concepts/attributes. We compared 7 transformer models and the GatorTron model achieved the best mean average strict/lenient F1 scores of 0.9122 and 0.9367 for SDoH concept extraction and 0.9584 and 0.9593 for linking attributes to SDoH concepts. There is a small performance gap (∼4%) between Males and Females, but a large performance gap (>16 %) among race groups. The performance dropped when we applied the cancer SDoH model to the opioid cohort; fine-tuning using a smaller opioid SDoH corpus improved the performance. The extraction ratio varied in the three cancer cohorts, in which 10 SDoH could be extracted from over 70 % of cancer patients, but 9 SDoH could be extracted from less than 70 % of cancer patients. Individuals from the White and Black groups have a higher extraction ratio than other minority race groups. CONCLUSIONS: Our SODA package achieved good performance in extracting 19 categories of SDoH from clinical narratives. The SODA package with pre-trained transformer models is available at https://github.com/uf-hobi-informatics-lab/SODA_Docker.

Sequencing of Argonaute-bound microRNA/mRNA hybrids reveals regulation of the unfolded protein response by microRNA-320a.

MicroRNAs (miRNA) are short non-coding RNAs widely implicated in gene regulation. Most metazoan miRNAs utilize the RNase III enzymes Drosha and Dicer for biogenesis. One notable exception is the RNA polymerase II transcription start sites (TSS) miRNAs whose biogenesis does not require Drosha. The functional importance of the TSS-miRNA biogenesis is uncertain. To better understand the function of TSS-miRNAs, we applied a modified Crosslinking, Ligation, and Sequencing of Hybrids on Argonaute (AGO-qCLASH) to identify the targets for TSS-miRNAs in HCT116 colorectal cancer cells with or without DROSHA knockout. We observed that miR-320a hybrids dominate in TSS-miRNA hybrids identified by AGO-qCLASH. Targets for miR-320a are enriched for the eIF2 signaling pathway, a downstream component of the unfolded protein response. Consistently, in miR-320a mimic- and antagomir- transfected cells, differentially expressed gene products are associated with eIF2 signaling. Within the AGO-qCLASH data, we identified the endoplasmic reticulum (ER) chaperone calnexin as a direct miR-320a down-regulated target, thus connecting miR-320a to the unfolded protein response. During ER stress, but not amino acid deprivation, miR-320a up-regulates ATF4, a critical transcription factor for resolving ER stress. In summary, our study investigates the targetome of the TSS-miRNAs in colorectal cancer cells and establishes miR-320a as a regulator of unfolded protein response.

A Combined Method of Seismic Monitoring and Transient Electromagnetic Detection for the Evaluation of Hydraulic Fracturing Effect in Coal Burst Prevention.

In order to mitigate the risk of roof-dominated coal burst in underground coal mining, horizontal long borehole staged hydraulic fracturing technology has been prevailingly employed to facilitate the weakening treatment of the hard roof in advance. Such weakening effect, however, can hardly be evaluated, which leads to a lack of a basis in which to design the schemes and parameters of hydraulic fracturing. In this study, a combined underground-ground integrated microseismic monitoring and transient electromagnetic detection method was utilized to carry out simultaneous evaluations of the seismic responses to each staged fracturing and the apparent resistivity changes before and after all finished fracturing. On this basis, the comparable and applicable fracturing effects on coal burst prevention were evaluated and validated by the distribution of microseismic events and their energy magnitude during the mining process. Results show that the observed mining-induced seismic events are consistent with the evaluation results obtained from the combined seismic-electromagnetic detection method. However, there is a limited reduction effect on resistivity near the fractured section that induces far-field seismic events. Mining-induced seismic events are concentrated primarily within specific areas, while microseismic events in the fractured area exhibit high frequency but low energy overall. This study validates the rationality of combined seismic-electromagnetic detection results and provides valuable insights for optimizing fracturing construction schemes as well as comprehensively evaluating outcomes associated with underground directional long borehole staged hydraulic fracturing.

Heterogeneous treatment effects of metformin on risk of dementia in patients with type 2 diabetes: A longitudinal observational study.

INTRODUCTION: Little is known about the heterogeneous treatment effects of metformin on dementia risk in people with type 2 diabetes (T2D). METHODS: Participants (≥ 50 years) with T2D and normal cognition at baseline were identified from the National Alzheimer's Coordinating Center database (2005-2021). We applied a doubly robust learning approach to estimate risk differences (RD) with a 95% confidence interval (CI) for dementia risk between metformin use and no use in the overall population and subgroups identified through a decision tree model. RESULTS: Among 1393 participants, 104 developed dementia over a 4-year median follow-up. Metformin was significantly associated with a lower risk of dementia in the overall population (RD, -3.2%; 95% CI, -6.2% to -0.2%). We identified four subgroups with varied risks for dementia, defined by neuropsychiatric disorders, non-steroidal anti-inflammatory drugs, and antidepressant use. DISCUSSION: Metformin use was significantly associated with a lower risk of dementia in individuals with T2D, with significant variability among subgroups.

Heterogeneous treatment effects of sodium-glucose cotransporter 2 inhibitors on risk of dementia in people with type 2 diabetes: A population-based cohort study.

INTRODUCTION: Sodium-glucose cotransporter 2 (SGLT2) inhibitors exhibit potential benefits in reducing dementia risk, yet the optimal beneficiary subgroups remain uncertain. METHODS: Individuals with type 2 diabetes (T2D) initiating either SGLT2 inhibitor or sulfonylurea were identified from OneFlorida+ Clinical Research Network (2016-2022). A doubly robust learning was deployed to estimate risk difference (RD) and 95% confidence interval (CI) of all-cause dementia. RESULTS: Among 35,458 individuals with T2D, 1.8% in the SGLT2 inhibitor group and 4.7% in the sulfonylurea group developed all-cause dementia over a 3.2-year follow-up, yielding a lower risk for SGLT2 inhibitors (RD, -2.5%; 95% CI, -3.0% to -2.1%). Hispanic ethnicity and chronic kidney disease were identified as the two important variables to define four subgroups in which RD ranged from -4.3% (-5.5 to -3.2) to -0.9% (-1.9 to 0.2). DISCUSSION: Compared to sulfonylureas, SGLT2 inhibitors were associated with a reduced risk of all-cause dementia, but the association varied among different subgroups. HIGHLIGHTS: New users of sodium-glucose cotransporter 2 (SGLT2) inhibitors were significantly associated with a lower risk of all-cause dementia as compared to those of sulfonylureas. The association varied among different subgroups defined by Hispanic ethnicity and chronic kidney disease. A significantly lower risk of Alzheimer's disease and vascular dementia was observed among new users of SGLT2 inhibitors compared to those of sulfonylureas.

Immune tolerance induced in the anterior chamber ameliorates corneal transplant rejection.

The relevance of regulatory T cells (Tregs) in induction of tolerance against corneal allografts has been well established. However, whether Tregs can be induced in the anterior chamber and suppress local alloimmune response after corneal transplantation is largely unknown. In the current study we report that not only can alloantigen specific Tregs be generated in the anterior chamber during corneal transplantation, they also play important roles in suppressing allograft rejection. Allograft rejected mice exhibit reduced Treg induction in the anterior chamber and the ability of aqueous humor and corneal endothelial cells from allograft rejected mice to induce Tregs is compromised. Further analysis revealed that the expression of immune-tolerance-related molecules is significantly decreased. Finally, we demonstrate that increasing Treg cells specifically in the anterior chamber can effectively suppress allograft rejection and exhibits better efficacy in promoting corneal allograft survival than systemic administration of Treg cells. Our current study may provide new ideas for the prevention and treatment of corneal transplant rejection.

Contextualized medication information extraction using Transformer-based deep learning architectures.

OBJECTIVE: To develop a natural language processing (NLP) system to extract medications and contextual information that help understand drug changes. This project is part of the 2022 n2c2 challenge. MATERIALS AND METHODS: We developed NLP systems for medication mention extraction, event classification (indicating medication changes discussed or not), and context classification to classify medication changes context into 5 orthogonal dimensions related to drug changes. We explored 6 state-of-the-art pretrained transformer models for the three subtasks, including GatorTron, a large language model pretrained using > 90 billion words of text (including > 80 billion words from > 290 million clinical notes identified at the University of Florida Health). We evaluated our NLP systems using annotated data and evaluation scripts provided by the 2022 n2c2 organizers. RESULTS: Our GatorTron models achieved the best F1-scores of 0.9828 for medication extraction (ranked 3rd), 0.9379 for event classification (ranked 2nd), and the best micro-average accuracy of 0.9126 for context classification. GatorTron outperformed existing transformer models pretrained using smaller general English text and clinical text corpora, indicating the advantage of large language models. CONCLUSION: This study demonstrated the advantage of using large transformer models for contextual medication information extraction from clinical narratives.

Pediatric and adult asthma clinical phenotypes: a real world, big data study based on acute exacerbations.

INTRODUCTION: Asthma is a heterogeneous disease with a range of observable phenotypes. To date, the characterization of asthma phenotypes is mostly limited to allergic versus non-allergic disease. Therefore, the aim of this big data study was to computationally derive asthma subtypes from the OneFlorida Clinical Research Consortium. METHODS: We obtained data from 2012-2020 from the OneFlorida Clinical Research Consortium. Longitudinal data for patients greater than two years of age who met inclusion criteria for an asthma exacerbation based on International Classification of Diseases codes. We used matrix factorization to extract information and K-means clustering to derive subtypes. The distributions of demographics, comorbidities, and medications were compared using Chi-square statistics. RESULTS: A total of 39,807 pediatric patients and 23,883 adult patients met inclusion criteria. We identified five distinct pediatric subtypes and four distinct adult subtypes. Pediatric subtype P1 had the highest proportion of black patients, but the lowest use of inhaled corticosteroids and allergy medications. Subtype P2 had a predominance of patients with gastroesophageal reflux disease, whereas P3 had a predominance of patients with allergic disorders. Adult subtype A2 was the most severe and all patients were on biologic agents. Most of subtype A3 patients were not taking controller medications, whereas most patients (>90%) in subtypes A2 and A4 were taking corticosteroids and allergy medications. CONCLUSION: We found five distinct pediatric asthma subtypes and four distinct adult asthma subtypes. Future work should externally validate these subtypes and characterize response to treatment by subtype to better guide clinical treatment of asthma.

Implementation of a pediatric asthma management program in rural primary care clinics.

OBJECTIVE: Rural communities experience a significant asthma burden. We pilot tested the implementation of Easy Breathing, a decision support program for improving primary care provider adherence to asthma guidelines in a rural community, and characterized asthma risk factors for enrollees. METHODS: We implemented Easy Breathing in two rural primary care practices for two years. Patient demographics, exposure histories, asthma severity, asthma medications, and treatment plans were collected. Providers' adherence to guidelines included the frequency of children with persistent asthma who were prescribed guidelines-based therapy and the frequency of children with a written asthma treatment plan on file. Clinicians provided feedback on the feasibility and acceptability of Easy Breathing using a validated survey tool and through semi-structured interviews. RESULTS: Two providers implemented the program. Enrollment included 518 children, of whom 135 (26%) had physician-confirmed asthma. After enrollment into Easy Breathing, 75% of children with asthma received a written asthma treatment plan All children with persistent asthma were prescribed an anti-inflammatory drug as part of their treatment plan. Providers (n = 2) rated Easy breathing as highly acceptable (M = 4.5), feasible (M = 4.5), and appropriate (M = 4.5). Qualitative feedback was positive, with suggestions to integrate the paper-based program into the electronic health record system for broader uptake. Enrollees with asthma were more likely to have a family history of asthma and endorse exposure to tobacco smoke and cockroaches. CONCLUSIONS: Easy Breathing shows promise as a decision support system that can be implemented in rural, medically underserved communities via primary care.

Methodological Challenges in Spatial and Contextual Exposome-Health Studies.

The concept of the exposome encompasses the totality of exposures from a variety of external and internal sources across an individual's life course. The wealth of existing spatial and contextual data makes it appealing to characterize individuals' external exposome to advance our understanding of environmental determinants of health. However, the spatial and contextual exposome is very different from other exposome factors measured at the individual-level as spatial and contextual exposome data are more heterogenous with unique correlation structures and various spatiotemporal scales. These distinctive characteristics lead to multiple unique methodological challenges across different stages of a study. This article provides a review of the existing resources, methods, and tools in the new and developing field for spatial and contextual exposome-health studies focusing on four areas: (1) data engineering, (2) spatiotemporal data linkage, (3) statistical methods for exposome-health association studies, and (4) machine- and deep-learning methods to use spatial and contextual exposome data for disease prediction. A critical analysis of the methodological challenges involved in each of these areas is performed to identify knowledge gaps and address future research needs.

Application of a practice-based approach in variable selection for a prediction model development study of hospital-induced delirium.

BACKGROUND: Prognostic models of hospital-induced delirium, that include potential predisposing and precipitating factors, may be used to identify vulnerable patients and inform the implementation of tailored preventive interventions. It is recommended that, in prediction model development studies, candidate predictors are selected on the basis of existing knowledge, including knowledge from clinical practice. The purpose of this article is to describe the process of identifying and operationalizing candidate predictors of hospital-induced delirium for application in a prediction model development study using a practice-based approach. METHODS: This study is part of a larger, retrospective cohort study that is developing prognostic models of hospital-induced delirium for medical-surgical older adult patients using structured data from administrative and electronic health records. First, we conducted a review of the literature to identify clinical concepts that had been used as candidate predictors in prognostic model development-and-validation studies of hospital-induced delirium. Then, we consulted a multidisciplinary task force of nine members who independently judged whether each clinical concept was associated with hospital-induced delirium. Finally, we mapped the clinical concepts to the administrative and electronic health records and operationalized our candidate predictors. RESULTS: In the review of 34 studies, we identified 504 unique clinical concepts. Two-thirds of the clinical concepts (337/504) were used as candidate predictors only once. The most common clinical concepts included age (31/34), sex (29/34), and alcohol use (22/34). 96% of the clinical concepts (484/504) were judged to be associated with the development of hospital-induced delirium by at least two members of the task force. All of the task force members agreed that 47 or 9% of the 504 clinical concepts were associated with hospital-induced delirium. CONCLUSIONS: Heterogeneity among candidate predictors of hospital-induced delirium in the literature suggests a still evolving list of factors that contribute to the development of this complex phenomenon. We demonstrated a practice-based approach to variable selection for our model development study of hospital-induced delirium. Expert judgement of variables enabled us to categorize the variables based on the amount of agreement among the experts and plan for the development of different models, including an expert-model and data-driven model.

A High-Precision Real-Time Pose Measurement Method for the Primary Lens of Large Aperture Space Telescope Based on Laser Ranging.

The aperture of space telescopes increases with their required resolution, and the transmission optical systems with long focal length and diffractive primary lens are becoming increasingly popular. In space, the changes in the pose of the primary lens relative to the rear lens group have a significant impact on the imaging performance of the telescope system. The measurement of the pose of the primary lens in real-time and with high-precision is one of the important techniques for a space telescope. In this paper, a high-precision real-time pose measurement method for the primary lens of a space telescope in orbit based on laser ranging is proposed, and a verification system is established. The pose change of the telescope's primary lens can be easily calculated through six high-precision laser distance changes. The measurement system can be installed freely, which solves the problems of complex system structure and low measurement accuracy in traditional pose measurement techniques. Analysis and experiments show that this method can accurately obtain the pose of the primary lens in real-time. The rotation error of the measurement system is 2 × 10-5 degrees (0.072 arcsecs), and the translation error is 0.2 µm. This study will provide a scientific basis for high-quality imaging of a space telescope.

Early prediction of Alzheimer's disease and related dementias using real-world electronic health records.

INTRODUCTION: This study aims to explore machine learning (ML) methods for early prediction of Alzheimer's disease (AD) and related dementias (ADRD) using the real-world electronic health records (EHRs). METHODS: A total of 23,835 ADRD and 1,038,643 control patients were identified from the OneFlorida+ Research Consortium. Two ML methods were used to develop the prediction models. Both knowledge-driven and data-driven approaches were explored. Four computable phenotyping algorithms were tested. RESULTS: The gradient boosting tree (GBT) models trained with the data-driven approach achieved the best area under the curve (AUC) scores of 0.939, 0.906, 0.884, and 0.854 for early prediction of ADRD 0, 1, 3, or 5 years before diagnosis, respectively. A number of important clinical and sociodemographic factors were identified. DISCUSSION: We tested various settings and showed the predictive ability of using ML approaches for early prediction of ADRD with EHRs. The models can help identify high-risk individuals for early informed preventive or prognostic clinical decisions.

Frailty and risk of mortality in older cancer survivors and adults without a cancer history: Evidence from the National Health and Nutrition Examination Survey, 1999-2014.

BACKGROUND: Epidemiologic evidence reporting the role of frailty in survival among older adults with a prior cancer diagnosis is limited. METHODS: A total of 2050 older adults (≥60 years old) surviving for at least 1 year after a cancer diagnosis and 9474 older adults without a cancer history from the National Health and Nutrition Examination Survey (1999-2014) were included for analysis. The exposure variable, a 45-item frailty index (FI), was categorized on the basis of validated cutoffs (FI ≤ 0.10 [fit], 0.10 < FI ≤ 0.21 [prefrail], and FI > 0.21 [frail]). All-cause mortality was ascertained via the National Death Index. Multivariable Cox proportional hazards models were used to estimate adjusted hazard ratios (aHRs) and 95% confidence interval (CIs) for the FI, and this was followed by restricted cubic splines depicting dose-response curves. RESULTS: For older cancer survivors, the mean age at the baseline was 72.6 years (SD, 7.1 years); 5.9% were fit, 38.2% were prefrail, and 55.9% were frail. Older adults without a cancer history were slightly younger (mean age, 70.0 years) and less frail (47.9% were frail). At each level of the FI, cancer survivors (1.9 per 100 person-years for FI ≤ 0.10, 3.4 per 100 person-years for 0.10 < FI ≤ 0.21, and 7.5 per 100 person-years for FI > 0.21) had higher mortality than their cancer-free counterparts (1.4 per 100 person-years for FI ≤ 0.10, 2.4 per 100 person-years for 0.10 < FI ≤ 0.21, and 5.4 per 100 person-years for FI > 0.21). The multivariable model suggested a positive association between the FI and all-cause mortality for survivors (aHR for FI > 0.21 vs FI ≤ 0.10, 2.80; 95% CI, 1.73-4.53) and participants without a cancer history (aHR for FI > 0.21 vs FI ≤ 0.10, 2.75; 95% CI, 2.29-3.32). Restricted cubic splines indicated that all-cause mortality risk increased with the FI in a monotonic pattern. CONCLUSIONS: Frailty is associated with a higher risk of death in older cancer survivors and the elderly without a cancer history.

Endocrine adherence in male versus female breast cancer: a seer-medicare review.

PURPOSE: Breast cancer in men (BC-M) is almost exclusively hormone receptor positive. We conducted a large review of the SEER-Medicare linked database to compare endocrine therapy adherence, discontinuation, and survival outcomes of male versus female patients with breast cancer. METHODS: Study data were obtained through the SEER-Medicare linked database. The study included patients age ≥ 65 years-old diagnosed with breast cancer between 2007 and 2015. The primary endpoints were rates of adherence and discontinuation of endocrine therapy (ET). Adherence was defined as a gap of less than 90 days in-between consecutive Medicare prescriptions. Discontinuation was defined as a gap of greater than 12 months in-between Medicare prescriptions. Secondary endpoint was the association of use of ET with overall survival (OS). RESULTS: Of the 363 male patients on ET, 214 patients (59.0%) were adherent to the therapy, and 149 patients (41.0%) were nonadherent. Of the 20,722 females on ET, 10,752 (51.9%) were adherent to the therapy, and 9970 (48.1%) were nonadherent. 39 male patients (10.7%) discontinued therapy, while 324 (89.3%) did not discontinue therapy. 1849 female patients (8.9%) discontinued therapy, while 18,873 (91.1%) patients did not. Men were significantly more adherent than women (p = 0.008), but there was no significant difference in discontinuation among men and women (p = 0.228). Survival was significantly improved in both men (HR 0.77, 95% CI 0.60-0.99, p = 0.039) and women (HR 0.84, 95% CI 0.81-0.87, p < 0.001) on ET. CONCLUSION: Identification of contributing factors impacting adherence and discontinuation is needed to allow physicians to address barriers to long term use of ET.