Progeria-based vascular model identifies networks associated with cardiovascular aging and disease.

Hutchinson-Gilford Progeria syndrome (HGPS) is a lethal premature aging disorder caused by a de novo heterozygous mutation that leads to the accumulation of a splicing isoform of Lamin A termed progerin. Progerin expression deregulates the organization of the nuclear lamina and the epigenetic landscape. Progerin has also been observed to accumulate at low levels during normal aging in cardiovascular cells of adults that do not carry genetic mutations linked with HGPS. Therefore, the molecular mechanisms that lead to vascular dysfunction in HGPS may also play a role in vascular aging-associated diseases, such as myocardial infarction and stroke. Here, we show that HGPS patient-derived vascular smooth muscle cells (VSMCs) recapitulate HGPS molecular hallmarks. Transcriptional profiling revealed cardiovascular disease remodeling and reactive oxidative stress response activation in HGPS VSMCs. Proteomic analyses identified abnormal acetylation programs in HGPS VSMC replication fork complexes, resulting in reduced H4K16 acetylation. Analysis of acetylation kinetics revealed both upregulation of K16 deacetylation and downregulation of K16 acetylation. This correlates with abnormal accumulation of error-prone nonhomologous end joining (NHEJ) repair proteins on newly replicated chromatin. The knockdown of the histone acetyltransferase MOF recapitulates preferential engagement of NHEJ repair activity in control VSMCs. Additionally, we find that primary donor-derived coronary artery vascular smooth muscle cells from aged individuals show similar defects to HGPS VSMCs, including loss of H4K16 acetylation. Altogether, we provide insight into the molecular mechanisms underlying vascular complications associated with HGPS patients and normative aging.

NCI Cancer Research Data Commons: Lessons Learned and Future State.

More than ever, scientific progress in cancer research hinges on our ability to combine datasets and extract meaningful interpretations to better understand diseases and ultimately inform the development of better treatments and diagnostic tools. To enable the successful sharing and use of big data, the NCI developed the Cancer Research Data Commons (CRDC), providing access to a large, comprehensive, and expanding collection of cancer data. The CRDC is a cloud-based data science infrastructure that eliminates the need for researchers to download and store large-scale datasets by allowing them to perform analysis where data reside. Over the past 10 years, the CRDC has made significant progress in providing access to data and tools along with training and outreach to support the cancer research community. In this review, we provide an overview of the history and the impact of the CRDC to date, lessons learned, and future plans to further promote data sharing, accessibility, interoperability, and reuse. See related articles by Brady et al., p. 1384, Wang et al., p. 1388, and Pot et al., p. 1396.

Develop and Validate a Computable Phenotype for the Identification of Alzheimer's Disease Patients Using Electronic Health Record Data.

INTRODUCTION: Alzheimer's Disease (AD) are often misclassified in electronic health records (EHRs) when relying solely on diagnostic codes. This study aims to develop a more accurate, computable phenotype (CP) for identifying AD patients by using both structured and unstructured EHR data. METHODS: We used EHRs from the University of Florida Health (UF Health) system and created rule-based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UT Health) and the University of Minnesota (UMN). RESULTS: Our best-performing CP is " patient has at least 2 AD diagnoses and AD-related keywords " with an F1-score of 0.817 at UF, and 0.961 and 0.623 at UT Health and UMN, respectively. DISCUSSION: We developed and validated rule-based CPs for AD identification with good performance, crucial for studies that aim to use real-world data like EHRs.

Geometric phase-encoded stimuli-responsive cholesteric liquid crystals for visualizing real-time remote monitoring: humidity sensing as a proof of concept.

Liquid crystals are a vital component of modern photonics, and recent studies have demonstrated the exceptional sensing properties of stimuli-responsive cholesteric liquid crystals. However, existing cholesteric liquid crystal-based sensors often rely on the naked eye perceptibility of structural color or the measurement of wavelength changes by spectrometric tools, which limits their practical applications. Therefore, developing a platform that produces recognizable sensing signals is critical. In this study, we present a visual sensing platform based on geometric phase encoding of stimuli-responsive cholesteric liquid crystal polymers that generates real-time visual patterns, rather than frequency changes. To demonstrate this platform's effectiveness, we used a humidity-responsive cholesteric liquid crystal polymer film encoded with a q-plate pattern, which revealed that humidity causes a shape change in the vortex beam reflected from the encoded cholesteric liquid crystal polymers. Moreover, we developed a prototype platform towards remote humidity monitoring benefiting from the high directionality and long-range transmission properties of laser beams carrying orbital angular momentum. Our approach provides a novel sensing platform for cholesteric liquid crystals-based sensors that offers promising practical applications. The ability to generate recognizable sensing signals through visual patterns offers a new level of practicality in the sensing field with stimuli-responsive cholesteric liquid crystals. This platform might have significant implications for a broad readership and will be of interest to researchers working in the field of photonics and sensing technology.

Real-World Effectiveness of Lung Cancer Screening Using Deep Learning-Based Counterfactual Prediction.

The benefits and harms of lung cancer screening (LCS) for patients in the real-world clinical setting have been argued. Recently, discriminative prediction modeling of lung cancer with stratified risk factors has been developed to investigate the real-world effectiveness of LCS from observational data. However, most of these studies were conducted at the population level that only measured the difference in the average outcome between groups. In this study, we built counterfactual prediction models for lung cancer risk and mortality and examined for individual patients whether LCS as a hypothetical intervention reduces lung cancer risk and subsequent mortality. We investigated traditional and deep learning (DL)-based causal methods that provide individualized treatment effect (ITE) at the patient level and evaluated them with a cohort from the OneFlorida+ Clinical Research Consortium. We further discussed and demonstrated that the ITE estimation model can be used to personalize clinical decision support for a broader population.

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.

[Tiaoshen acupuncture for primary insomnia: a pilot randomized controlled trial].

OBJECTIVE: To observe the effects of Tiaoshen (regulating the spirit) acupuncture on cognitive function and sleep quality in patients with primary insomnia (PI). METHODS: Sixty patients with PI were randomly divided into an observation group (30 cases, 2 cases dropped off) and a control group (30 cases, 2 cases dropped off, 1 case was excluded). The patients in the observation group were treated with acupuncture at Baihui (GV 20), Shenting (GV 24), Sishencong (EX-HN 1), and bilateral Benshen (GB 13), Shenmen (HT 7), Neiguan (PC 6), Sanyinjiao (SP 6). The patients in the control group were treated with shallow needling at non-effective points. Each treatment was provided for 30 min, once every other day, 3 treatments per week for 4 weeks. The Montreal cognitive assessment (MoCA), digit span test (DST), trail making test (TMT)-A, Pittsburgh sleep quality index (PSQI), and fatigue scale-14 (FS-14) were used to assess cognitive function and sleep quality before and after treatment, as well as in follow-up of 4-week after treatment completion. Correlation analysis was conducted between the differences in PSQI scores and differences in MoCA scores before and after treatment in the observation group. RESULTS: Compared with before treatment, the total score, visuospatial and executive function score and delayed memory score of MoCA as well as DST backward score were increased (P<0.01), while TMT-A time, PSQI and FS-14 scores were significantly reduced (P<0.01) after treatment and in follow-up in the observation group. Compared with before treatment, the PSQI score in the control group was reduced (P<0.01, P<0.05). After treatment and in follow-up, the observation group had significantly higher total score, visuospatial and executive function score, delayed memory score of MoCA, and DST backward score compared to the control group (P<0.05, P<0.01). In the observation group, the TMT-A time was significantly shorter than that in the control group (P<0.05, P<0.01), and the PSQI and FS-14 scores were significantly lower than those in the control group (P<0.01). In the observation group, there was a negative correlation between the difference in PSQI scores (post-treatment minus pre-treatment) and the difference in MoCA scores (post-treatment minus pre-treatment) (r=-0.481, P<0.01). A similar negative correlation was found between the difference in PSQI scores (follow-up minus pre-treatment) and the difference in MoCA scores (follow-up minus pre-treatment) (r=-0.282, P<0.05). CONCLUSION: Tiaoshen acupuncture could improve cognitive function, enhance sleep quality, and alleviate daytime fatigue in patients with PI. The improvement in cognitive function in patients with PI is correlated with the improvement in sleep quality.

Whole-brain neural connectivity to cholinergic neurons in the nucleus basalis of Meynert.

The cholinergic neurons in the nucleus basalis of Meynert (NBM) are a key structure in cognition, the dysfunction of which is associated with various neurological disorders, especially dementias. However, the whole-brain neural connectivity to cholinergic neurons in the NBM remains to be further and comprehensively researched. Using virus-based, specific, retrograde, and anterograde tracing, we illustrated the monosynaptic inputs and axon projections of NBM cholinergic neurons in choline acetyltransferase (ChAT)-Cre transgenic mice. Our results showed that NBM cholinergic neurons received mainly inputs from the caudate putamen and the posterior limb of the anterior commissure in the subcortex. Moreover, the majority of cholinergic terminals from the NBM were observed in the cortex mantle, including the motor cortex, sensory cortex, and visual cortex. Interestingly, although NBM cholinergic neurons received input projections from the caudate putamen, interstitial nucleus of the posterior limb of the anterior commissure, and central amygdaloid nucleus, NBM cholinergic neurons sparsely sent axon projection to innervate these areas. Furthermore, primary motor cortex, secondary motor cortex, and primary somatosensory cortex received abundant inputs from the NBM but sent few outputs to the NBM. Taken together, our results reveal the detailed and specific connectivity of cholinergic neurons of the NBM and provide a neuroanatomic foundation for further studies to explore the important physiological functions of NBM cholinergic neurons.

The increased functional connectivity between the locus coeruleus and supramarginal gyrus in insomnia disorder with acupuncture modulation.

Background: Insomnia disorder (ID) seriously affects the quality of people's daily life, and acupuncture is an effective therapy for it. As an essential component of the upward activation system, the locus coeruleus (LC) plays a crucial role in sleep-wake regulation, its aberrant functional connectivity (FC) is found to be involved in ID. The purpose of this study was to explore the modulation effect of acupuncture on the resting state FC of LC in ID patients. Methods: 60 ID patients were recruited and randomly assigned to real acupuncture (RA) or sham acupuncture (SA) treatment. Resting-state functional magnetic resonance imaging (fMRI) data were collected before and after the treatment. With LC as the region of interest, the FC method was adopted to examine acupuncture-related modulation of intrinsic connectivity in ID patients. The Pittsburgh Sleep Quality Index (PSQI), Hyperarousal Scale (HAS), and actigraphy were used to assess sleep quality and cortical hyperarousal states. Associations between clinical outcomes and FC features were calculated using Pearson's correlation analysis. Results: The improvement in sleep quality and hyperarousal in the RA group was greater than that in the SA group. After treatment, the FC between the LC and left inferior frontal gyrus (IFG) decreased in the RA group. The FC between the LC and left insula and supramarginal gyrus (SMG) was higher in the RA group. The change of LC FC values with the SMG was negatively associated with the change in PSQI scores. Conclusion: Acupuncture can modulate FC between the LC and IFG, insular gyrus, and SMG. This may imply the potential mechanism of acupuncture treatment for insomnia.

Favorable Immunotherapy Plus Tyrosine Kinase Inhibition Outcome of Renal Cell Carcinoma Patients with Low CDK5 Expression.

PURPOSE: Immunotherapy (IO) plus tyrosine kinase inhibitor (TKI) has become the first-line treatment for advanced renal cell carcinoma, despite the lack of prognostic biomarkers. Cyclin-dependent kinase 5 (CDK5) affects the tumor microenvironment, which may influence the efficacy of TKI+IO. MATERIALS AND METHODS: Two cohorts from our center (Zhongshan Metastatic Renal Cell Carcinoma [ZS-MRCC] cohort, Zhongshan High-risk Localized Renal Cell Carcinoma [ZS-HRRCC] cohort) and one cohort from a clinical trial (JAVELIN-101) were enrolled. The expression of CDK5 of each sample was determined by RNA sequencing. Immune infiltration and T cell function were evaluated by flow cytometry and immunohistochemistry. Response and progression-free survival (PFS) were set as primary endpoints. RESULTS: Patients of low CDK5 expression showed higher objective response rate (60.0% vs. 23.3%) and longer PFS in both cohorts (ZS-MRCC cohort, p=0.014; JAVELIN-101 cohort, p=0.040). CDK5 expression was enhanced in non-responders (p < 0.05). In the ZS-HRRCC cohort, CDK5 was associated with decreased tumor-infiltrating CD8+ T cells, which was proved by immunohistochemistry (p < 0.05) and flow cytometry (Spearman's ρ=-0.49, p < 0.001). In the high CDK5 subgroup, CD8+ T cells revealed a dysfunction phenotype with decreased granzyme B, and more regulatory T cells were identified. A predictive score was further constructed by random forest, involving CDK5 and T cell exhaustion features. The RFscore was also validated in both cohorts. By utilizing the model, more patients might be distinguished from the overall cohort. Additionally, only in the low RFscore did TKI+IO outperform TKI monotherapy. CONCLUSION: High-CDK5 expression was associated with immunosuppression and TKI+IO resistance. RFscore based on CDK5 may be utilized as a biomarker to determine the optimal treatment strategy.

Cancer incidence after asthma diagnosis: Evidence from a large clinical research network in the United States.

BACKGROUND: Prior studies on the association between asthma and cancer show inconsistent results. This study aimed to generate additional evidence on the association between asthma and cancer, both overall, and by cancer type, in the United States. METHOD: We conducted a retrospective cohort study using 2012-2020 electronic health records and claims data in the OneFlorida+ clinical research network. Our study population included a cohort of adult patients with asthma (n = 90,021) and a matching cohort of adult patients without asthma (n = 270,063). We built Cox proportional hazards models to examine the association between asthma diagnosis and subsequent cancer risk. RESULTS: Our results showed that asthma patients were more likely to develop cancer compared to patients without asthma in multivariable analysis (hazard ratio [HR] = 1.36, 99% confidence interval [CI] = 1.29-1.44). Elevated cancer risk was observed in asthma patients without (HR = 1.60; 99% CI: 1.50-1.71) or with (HR = 1.11; 99% CI: 1.03-1.21) inhaled steroid use. However, in analyses of specific cancer types, cancer risk was elevated for nine of 13 cancers in asthma patients without inhaled steroid use but only for two of 13 cancers in asthma patients with inhaled steroid use, suggesting a protective effect of inhaled steroid use on cancer. CONCLUSION: This is the first study to report a positive association between asthma and overall cancer risk in the US population. More in-depth studies using real-word data are needed to further explore the causal mechanisms of asthma on cancer risk.

Unfavorable immunotherapy plus tyrosine kinase inhibition outcome of metastatic renal cell carcinoma after radical nephrectomy with increased ADAM9 expression.

Immunotherapy plus tyrosine kinase inhibitor (IO-TKI) has become the standard first-line therapy for advanced renal cell carcinoma (RCC). However, the modest response rate of IO-TKI therapy and the absence of biomarkers limited the selection of treatment strategies for RCC patients. There were three cohorts enrolled: two from our facility (ZS-MRCC and ZS-HRRCC) and one from a clinical study (JAVELIN-101). By RNA sequencing, the expression of ADAM9 in each sample was measured. By flow cytometry and immunohistochemistry, immune infiltration and T cell function were examined. Primary outcomes were established as treatment response and progression-free survival (PFS). Patients with low-ADAM9 expression had a higher objective response rate (56.5% vs 13.6%, P = 0.01) and longer PFS in both cohorts. In the ZS-HRRCC cohort, the expression of ADAM9 was associated with increased tumor-infiltrating T cells, which was proved by immunohistochemistry (P < 0.05) and flow cytometry (Spearman's ρ = 0.42, P < 0.001). In the high-ADAM9 group, CD8+ and CD4+ T cells revealed an exhausted phenotype with decreased GZMB (Spearman's ρ = - 0.31, P = 0.05, and Spearman's ρ = - 0.49, P < 0.001, respectively), and fewer Macrophages were identified. A predictive RFscore was further constructed by random forest approach, involving ADAM9 and immunologic genes. Only in the subgroup with the lower RFscore did IO-TKI outperform TKI monotherapy. High-ADAM9 expression was associated with immunosuppression and IO-TKI resistance. Expression of ADAM9 was also associated with the exhaustion and dysfunction of T cells. ADAM9-based RFscore has the potential to be used as a biomarker to distinguish the optimal patient treatment methods between IO-TKI and TKI monotherapy.

Assess the documentation of cognitive tests and biomarkers in electronic health records via natural language processing for Alzheimer's disease and related dementias.

BACKGROUND: Cognitive tests and biomarkers are the key information to assess the severity and track the progression of Alzheimer's' disease (AD) and AD-related dementias (AD/ADRD), yet, both are often only documented in clinical narratives of patients' electronic health records (EHRs). In this work, we aim to (1) assess the documentation of cognitive tests and biomarkers in EHRs that can be used as real-world endpoints, and (2) identify, extract, and harmonize the different commonly used cognitive tests from clinical narratives using natural language processing (NLP) methods into categorical AD/ADRD severity. METHODS: We developed a rule-based NLP pipeline to extract the cognitive tests and biomarkers from clinical narratives in AD/ADRD patients' EHRs. We aggregated the extracted results to the patient level and harmonized the cognitive test scores into severity categories using cutoffs determined based on both relevant literature and domain knowledge of AD/ADRD clinicians. RESULTS: We identified an AD/ADRD cohort of 48,912 patients from the University of Florida (UF) Health system and identified 7 measurements (6 cognitive tests and 1 biomarker) that are frequently documented in our data. Our NLP pipeline achieved an overall F1-score of 0.9059 across the 7 measurements. Among the 6 cognitive tests, we were able to harmonize 4 cognitive test scores into severity categories, and the population characteristics of patients with different severity were described. We also identified several factors related to the availability of their documentation in EHRs. CONCLUSION: This study demonstrates that our NLP pipelines can extract cognitive tests and biomarkers of AD/ADRD accurately for downstream studies. Although, the documentation of cognitive tests and biomarkers in EHRs appears to be low, RWD is still an important resource for AD/ADRD research. Nevertheless, providing standardized approach to document cognitive tests and biomarkers in EHRS are also warranted.

Novel 3D Printing Phase Change Aggregate Concrete: Mechanical and Thermal Properties Analysis.

The use of phase change materials (PCMs) in concrete is a double-edged sword that improves the thermal inertia but degrades the mechanical properties of concrete. It has been an essential but unsolved issue to enhance the thermal capacity of PCMs while non-decreasing their mechanical strength. To this end, this work designs a novel 3D printing phase change aggregate to prepare concrete with prominent thermal capacity and ductility. The work investigated the effects of 3D printing phase change aggregate on the compressive strength and splitting tensile strength of concrete. The compressive strength of phase change aggregate concrete is 21.18 MPa, but the ductility of concrete improves. The splitting tensile strength was 1.45 MPa. The peak strain is 11.69 × 10-3, nearly 13 times that of basalt aggregate concrete. Moreover, using 3D printing phase change aggregate reduced concrete's early peak hydration temperature by 7.1%. The thermal insulation capacity of the experiment cube model with phase change concrete has been improved. The results show that the novel 3D printing change aggregate concrete has good mechanical properties and latent heat storage, providing a guideline for applying PCMs in building materials.

Genetic abnormalities in fetal congenital heart disease with aberrant right subclavian artery.

Fetal aberrant right subclavian artery (ARSA) is a relatively common sonographic finding. Congenital heart disease (CHD) is the most common structural abnormality in patients with ARSA. We aimed to assess the prevalence of genetic abnormalities, particularly sequence variants, in fetuses with CHD and ARSA. By clinical phenotyping and genomic sequencing, we retrospectively reviewed all fetuses with a prenatal diagnosis of CHD combined with ARSA at a single center. As a result, we identified 30 fetuses with ARSA combined with CHD, with conotruncal anomalies being the most common (n = 12, 40%), followed by left ventricular outflow tract obstruction (n = 6, 20%) and atrioventricular septal defects (n = 6, 20%). Overall, 18 (60%) cases had a genetic diagnosis. Copy number variation sequencing analysis identified six (20%) fetuses with aneuploidy and seven (23%) with pathogenic copy-number variants. Whole-exome sequencing (WES) analysis of the remaining 17 cases revealed diagnostic genetic variants in five (29%) cases, indicating that the diagnostic yield of WES for the entire cohort was 17% (5/30). Our findings reveal the high burden of genetic abnormalities in fetal CHD with ARSA. Single-gene disorders contribute substantially to the genetic etiology of fetal CHD with ARSA.

Trainee Therapists' Perceptions of a Blended Intervention to Promote Resilience after a Natural Disaster: A Qualitative Case Study.

BACKGROUND: Natural disasters happen in an increased frequency, and telemental health interventions could offer easily accessible help to reduce mental health symptoms experienced by survivors. However, there are very few programs offered to natural disaster survivors, and no research exists on therapists' experiences with providing blended interventions for natural disaster survivors. AIMS: Our qualitative case study aims to describe psychologists' experiences with an online, therapist-assisted blended intervention for survivors of the Fort McMurray wildfires in Alberta, Canada. METHOD: The RESILIENT intervention was developed in the frames of a randomized controlled trial to promote resilience after the Fort McMurray wildfires by providing survivors free access to a 12-module, therapist-assisted intervention, aiming to improve post-traumatic stress, insomnia, and depression symptoms. A focus group design was used to collect data from the therapists, and emerging common themes were identified by thematic analysis. RESULTS: Therapists felt they could build strong alliances and communicate emotions and empathy effectively, although the lack of nonverbal cues posed some challenges. The intervention, according to participating therapists, was less suitable for participants in high-stress situations and in case of discrepancy between client expectations and the intervention content. Moreover, the therapists perceived specific interventions as easy-to-use or as more challenging based on their complexity and on the therapist support needed for executing them. Client engagement in the program emerged as an underlying theme that had fundamental impact on alliance, communication, and ultimately, treatment efficiency. Therapist training and supervision was perceived as crucial for the success of the program delivery. CONCLUSIONS: Our findings provided several implications for the optimalization of blended interventions for natural disaster survivors from our therapists' perspective.

Simulating Colorectal Cancer Trials Using Real-World Data.

PURPOSE: Using real-world data (RWD)-based trial simulation approach, we aim to simulate colorectal cancer (CRC) trials and examine both effectiveness and safety end points in different simulation scenarios. METHODS: We identified five phase III trials comparing new treatment regimens with an US Food and Drug Administration-approved first-line treatment in patients with metastatic CRC (ie, fluorouracil, leucovorin, and irinotecan) as the standard-of-care (SOC) control arm. Using Electronic Health Record-derived data from the OneFlorida network, we defined the study populations and outcome measures using the protocols from the original trials. Our design scenarios were (1) simulation of the SOC fluorouracil, leucovorin, and irinotecan arm and (2) comparative effectiveness research (CER) simulation of the control and experimental arms. For each scenario, we adjusted for random assignment, sampling, and dropout. We used overall survival (OS) and severe adverse events (SAEs) to measure effectiveness and safety. RESULTS: We conducted CER simulations for two trials, and SOC simulations for three trials. The effect sizes of our simulated trials were stable across all simulation runs. Compared with the original trials, we observed longer OS and higher mean number of SAEs in both CER and SOC simulation. In the two CER simulations, hazard ratios associated with death from simulations were similar to that reported in the original trials. Consistent with the original trials, we found higher risk ratios of SAEs in the experiment arm, suggesting potentially higher toxicities from the new treatment regimen. We also observed similar SAE rates across all simulations compared with the original trials. CONCLUSION: In this study, we simulated five CRC trials, and tested two simulation scenarios with several different configurations demonstrated that our simulations can robustly generate effectiveness and safety outcomes comparable with the original trials using real-world data.

Distributed Quasi-Poisson regression algorithm for modeling multi-site count outcomes in distributed data networks.

BACKGROUND: Observational studies incorporating real-world data from multiple institutions facilitate study of rare outcomes or exposures and improve generalizability of results. Due to privacy concerns surrounding patient-level data sharing across institutions, methods for performing regression analyses distributively are desirable. Meta-analysis of institution-specific estimates is commonly used, but has been shown to produce biased estimates in certain settings. While distributed regression methods are increasingly available, methods for analyzing count outcomes are currently limited. Count data in practice are commonly subject to overdispersion, exhibiting greater variability than expected under a given statistical model. OBJECTIVE: We propose a novel computational method, a one-shot distributed algorithm for quasi-Poisson regression (ODAP), to distributively model count outcomes while accounting for overdispersion. METHODS: ODAP incorporates a surrogate likelihood approach to perform distributed quasi-Poisson regression without requiring patient-level data sharing, only requiring sharing of aggregate data from each participating institution. ODAP requires at most three rounds of non-iterative communication among institutions to generate coefficient estimates and corresponding standard errors. In simulations, we evaluate ODAP under several data scenarios possible in multi-site analyses, comparing ODAP and meta-analysis estimates in terms of error relative to pooled regression estimates, considered the gold standard. In a proof-of-concept real-world data analysis, we similarly compare ODAP and meta-analysis in terms of relative error to pooled estimatation using data from the OneFlorida Clinical Research Consortium, modeling length of stay in COVID-19 patients as a function of various patient characteristics. In a second proof-of-concept analysis, using the same outcome and covariates, we incorporate data from the UnitedHealth Group Clinical Discovery Database together with the OneFlorida data in a distributed analysis to compare estimates produced by ODAP and meta-analysis. RESULTS: In simulations, ODAP exhibited negligible error relative to pooled regression estimates across all settings explored. Meta-analysis estimates, while largely unbiased, were increasingly variable as heterogeneity in the outcome increased across institutions. When baseline expected count was 0.2, relative error for meta-analysis was above 5% in 25% of iterations (250/1000), while the largest relative error for ODAP in any iteration was 3.59%. In our proof-of-concept analysis using only OneFlorida data, ODAP estimates were closer to pooled regression estimates than those produced by meta-analysis for all 15 covariates. In our distributed analysis incorporating data from both OneFlorida and the UnitedHealth Group Clinical Discovery Database, ODAP and meta-analysis estimates were largely similar, while some differences in estimates (as large as 13.8%) could be indicative of bias in meta-analytic estimates. CONCLUSIONS: ODAP performs privacy-preserving, communication-efficient distributed quasi-Poisson regression to analyze count outcomes using data stored within multiple institutions. Our method produces estimates nearly matching pooled regression estimates and sometimes more accurate than meta-analysis estimates, most notably in settings with relatively low counts and high outcome heterogeneity across institutions.

Comprehensive Analysis of the Potential Immune-Related Biomarker ATG101 that Regulates Apoptosis of Cholangiocarcinoma Cells After Photodynamic Therapy.

Autophagy related gene 101 (ATG101) plays a significant role in the occurrence and development of tumours by responding to stress. Our research aims to illustrate the correlation between the expression of ATG101 and tumor prognosis and its potential role and mechanism in tumor immunity and photodynamic therapy (PDT). First, integrated analysis of The Cancer Genome Atlas and Genotype-Tissue Expression portals were used to analyse the expression of ATG101. Then, Kaplan-Meier curves was applied in cholangiocarcinoma (CHOL) and liver hepatocellular carcinoma (LIHC) datasets for survival analysis. Next, the relationship between ATG101 expression and six immune cells, the immune microenvironment and immune checkpoints was analysed. Besides, the relationship between the expression of ATG101 and methyltransferase. GSEA was used to study the function and the related transcript factors of ATG101 in CHOL and LIHC. The effect of PDT on ATG101 was verified by microarray, qPCR and western blot. Then the effect of ATG101 and its regulatory factors on apoptosis were verified by siRNA, lentivirus transfection and Chip-qPCR. Comprehensive analysis showed that ATG101 was overexpressed in different tumours. Kaplan-Meier curves found that ATG101 was associated with poor prognosis in tumours (including CHOL and LIHC). We found that ATG101 can be used as a target and prognostic marker of tumour immunotherapy for different tumours. We also found that ATG101 regulates DNA methylation. GSEA analysis showed that ATG101 may play a critical role in CHOL and LIHC. Subsequent validation tests confirmed that the up-regulated ATG101 after PDT treatment is not conducive to the occurrence of apoptosis of cholangiocarcinoma cells. The high expression of ATG101 may be induced by the early stress gene EGR2. Our study highlights the significance of ATG101 in the study of tumour immunity and photodynamic therapy from a pan-cancer perspective.

Longitudinal trajectories of HIV care engagement since diagnosis among persons with HIV in the Florida Ryan White program.

HIV care engagement is a dynamic process. We employed group-based trajectory modeling to examine longitudinal patterns in care engagement among people who were newly diagnosed with HIV and enrolled in the Ryan White program in Florida (n = 9,755) between 2010 and 2015. Five trajectories were identified (47.9% "in care" with 1-2 care visit(s) per 6 months, 18.0% "frequent care" with 3 or more care visits per 6 months, 11.0% "re-engage", 11.0% "gradual drop out", 12.6% "early dropout") based on the number of care attendances (including outpatient/case management visits, viral load or CD4 test) for each six-month during the first five years since diagnosis. Relative to "in care", people in the "frequent care" trajectory were more likely to be Hispanic/Latino and older at HIV diagnosis, whereas people in the three suboptimal care retention trajectories were more likely to be younger. Area deprivation index, rurality, and county health rankings were also strongly associated with care trajectories. Individual- and community-level factors associated to the three suboptimal care retention trajectories, if confirmed to be causative and actionable, could be prioritized to improve HIV care engagement.