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.

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.

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.

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.

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.

Prodromal clinical, demographic, and socio-ecological correlates of asthma in adults: a 10-year statewide big data multi-domain analysis.

Objectives: To identify prodromal correlates of asthma as compared to chronic obstructive pulmonary disease and allied-conditions (COPDAC) using a multi domain analysis of socio-ecological, clinical, and demographic domains.Methods: This is a retrospective case-risk-control study using data from Florida's statewide Healthcare Cost and Utilization Project (HCUP). Patients were grouped into three groups: asthma, COPDAC (without asthma), and neither asthma nor COPDAC. To identify socio-ecological, clinical, demographic, and clinical predictors of asthma and COPDAC, we used univariate analysis, feature ranking by bootstrapped information gain ratio, multivariable logistic regression with LogitBoost selection, decision trees, and random forests.Results: A total of 141,729 patients met inclusion criteria, of whom 56,052 were diagnosed with asthma, 85,677 with COPDAC, and 84,737 with neither asthma nor COPDAC. The multi-domain approach proved superior in distinguishing asthma versus COPDAC and non-asthma/non-COPDAC controls (area under the curve (AUROC) 84%). The best domain to distinguish asthma from COPDAC without controls was prior clinical diagnoses (AUROC 82%). Ranking variables from all the domains found the most important predictors for the asthma versus COPDAC and controls were primarily socio-ecological variables, while for asthma versus COPDAC without controls, demographic and clinical variables such as age, CCI, and prior clinical diagnoses, scored better.Conclusions: In this large statewide study using a machine learning approach, we found that a multi-domain approach with demographics, clinical, and socio-ecological variables best predicted an asthma diagnosis. Future work should focus on integrating machine learning-generated predictive models into clinical practice to improve early detection of those common respiratory diseases.

Design and methodology challenges of environment-wide association studies: A systematic review.

Environment-wide association studies (EWAS) are an untargeted, agnostic, and hypothesis-generating approach to exploring environmental factors associated with health outcomes, akin to genome-wide association studies (GWAS). While design, methodology, and replicability standards for GWAS are established, EWAS pose many challenges. We systematically reviewed published literature on EWAS to categorize scope, impact, types of analytical approaches, and open challenges in designs and methodologies. The Web of Science and PubMed databases were searched through multiple queries to identify EWAS articles between January 2010 and December 2018, and a systematic review was conducted following the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) reporting standard. Twenty-three articles met our inclusion criteria and were included. For each study, we categorized the data sources, the definitions of study outcomes, the sets of environmental variables, and the data engineering/analytical approaches, e.g. neighborhood definition, variable standardization, handling of multiple hypothesis testing, model selection, and validation. We identified limited exploitation of data sources, high heterogeneity in analytical approaches, and lack of replication. Despite of the promising utility of EWAS, further development of EWAS will require improved data sources, standardization of study designs, and rigorous testing of methodologies.

Risk of health morbidity for the uninsured: 10-year evidence from a large hospital center in Boston, Massachusetts.

OBJECTIVE: To investigate the independent contribution of insurance status toward the risk of diagnosis of specific clinical comorbidities for individuals admitted to intensive care unit (ICU). DESIGN: Retrospective analysis of secondary database. SETTING: Ten years of public de-identified ICU electronic medical records from a large hospital in USA. PARTICIPANTS: Patients (18-65 years old) who had private insurance or no insurance were extracted from the database. MAIN OUTCOME MEASURES: Independent association of insurance status (uninsured vs. privately insured) with the risk of diagnosis of specific clinical comorbidities. RESULTS: Among 14 268 (from 11 753 patients) admissions to ICU between 2001 and 2012, 96% of them were covered by private insurance. Patients with private insurance had higher proportion of females, married, White race, longer ICU stay and more procedures during stay, and fewer deaths. A lower CCI was observed in uninsured patients. At multivariable analysis, uninsured patients had higher odds of death and of admissions for accidental falls, substance or alcohol abuse. CONCLUSIONS: Patients with no insurance coverage were at higher risk of death and of admission for physical and substance-related injury. We did not observe a higher risk for acute life-threatening diseases such as myocardial infarction or kidney failure. The lower CCI observed in the uninsured may be explained by under diagnosis or voluntary withdrawal from coverage in the pre-Affordable Care Act era. Replication of findings is warranted in other populations, among those with government-subsidized insurance and in the procedure/prescription domains.

Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical, demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS).

BACKGROUND: Kidney stone (KS) disease has high, increasing prevalence in the United States and poses a massive economic burden. Diagnostics algorithms of KS only use a few variables with a limited sensitivity and specificity. In this study, we tested a big data approach to infer and validate a 'multi-domain' personalized diagnostic acute care algorithm for KS (DACA-KS), merging demographic, vital signs, clinical, and laboratory information. METHODS: We utilized a large, single-center database of patients admitted to acute care units in a large tertiary care hospital. Patients diagnosed with KS were compared to groups of patients with acute abdominal/flank/groin pain, genitourinary diseases, and other conditions. We analyzed multiple information domains (several thousands of variables) using a collection of statistical and machine learning models with feature selectors. We compared sensitivity, specificity and area under the receiver operating characteristic (AUROC) of our approach with the STONE score, using cross-validation. RESULTS: Thirty eight thousand five hundred and ninety-seven distinct adult patients were admitted to critical care between 2001 and 2012, of which 217 were diagnosed with KS, and 7446 with acute pain (non-KS). The multi-domain approach using logistic regression yielded an AUROC of 0.86 and a sensitivity/specificity of 0.81/0.82 in cross-validation. Increase in performance was obtained by fitting a super-learner, at the price of lower interpretability. We discussed in detail comorbidity and lab marker variables independently associated with KS (e.g. blood chloride, candidiasis, sleep disorders). CONCLUSIONS: Although external validation is warranted, DACA-KS could be integrated into electronic health systems; the algorithm has the potential used as an effective tool to help nurses and healthcare personnel during triage or clinicians making a diagnosis, streamlining patients' management in acute care.

Integrative genomics positions MKRN1 as a novel ribonucleoprotein within the embryonic stem cell gene regulatory network.

In embryonic stem cells (ESCs), gene regulatory networks (GRNs) coordinate gene expression to maintain ESC identity; however, the complete repertoire of factors regulating the ESC state is not fully understood. Our previous temporal microarray analysis of ESC commitment identified the E3 ubiquitin ligase protein Makorin-1 (MKRN1) as a potential novel component of the ESC GRN. Here, using multilayered systems-level analyses, we compiled a MKRN1-centered interactome in undifferentiated ESCs at the proteomic and ribonomic level. Proteomic analyses in undifferentiated ESCs revealed that MKRN1 associates with RNA-binding proteins, and ensuing RIP-chip analysis determined that MKRN1 associates with mRNAs encoding functionally related proteins including proteins that function during cellular stress. Subsequent biological validation identified MKRN1 as a novel stress granule-resident protein, although MKRN1 is not required for stress granule formation, or survival of unstressed ESCs. Thus, our unbiased systems-level analyses support a role for the E3 ligase MKRN1 as a ribonucleoprotein within the ESC GRN.

Nonclassical Phosphomolybdates with Different Degrees of Reduction: Syntheses and Structural and Photo/Electrocatalytic Properties.

Four nonclassical phosphomolybdates, formulated as (H2pytty)8[{Mn(H2pytty)(H2O)3}{Sr⊂P6Mo6(V) Mo12(VI)O73}]2·16H2O (1), [{Mn(H3pytty)(H2O)3}2{Sr⊂P6Mo4(V)Mo14(VI)O73}]·18H2O (2), (H3pytp) (H2pytty)2[{Fe(H2O)4}{Sr⊂P6Mo3(V)Mo15(VI)O73}]·5H2O (3), and (H2pytty)2[{Cd(H2O)4}{Cd(H2O)3 (H3pytty)}{Sr⊂P6Mo5(V)Mo13(VI)O73}]·9H2O (4) (pytty = 3-(pyrazin-2-yl)-5-(1H-1,2,4-triazol-3-yl)-1,2,4-triazolyl; pytp = 4'-(4″-pyridyl)-2,4':6',4″-terpyridine) were hydrothermally synthesized and fully characterized. The penta- and hexa-reduced basket clusters represent the highest reduced level of basket-based polyoxometalate so far. In addition, transition metal complexes as bridge units were introduced to basket system for the first time to induce rare two-dimensional inorganic-organic hybrid layer. The results indicate that reduced degrees of the basket clusters of compounds 1-4 can be tuned by altering the molar ratio of organic ligand pytty and Na2MoO4. Compounds 1-4 exhibit bifunctional electrocatalytic behaviors for oxidation of dopamine and reduction of H2O2. Electrocatalytic mechanism, chronoamperometric experiments and electrocatalytic stability are studied in detail. In addition, the compounds show highly efficient catalytic ability for the degradation of typical dyes under UV irradiation.

pH and ligand dependent assembly of Well-Dawson arsenomolybdate capped architectures.

Five Well-Dawson-type arsenomolybdates, formulated as [Cu(2,2'-bpy)2][{Cu(2,2'-bpy)}3{As2(V)Mo2(V)Mo16(VI)O62}]·4H2O (1), [H2(4,4'-bpy)]2.5[As(III)(As2(V)Mo2(V)Mo16(VI)O62)]·5H2O (2), (pyr)(imi)(Himi)3[As2(III)(As2(V)Mo3(V)Mo15(VI)O62)]·3H2O (3), [As3(III)(As2(V)Mo3(V)Mo15(VI)O62)]·4H2O (4), and (H2btp)3[As2(V)Mo18(VI)O62]·6H2O (5) (bpy = bipyridine, pyr = pyrazine, imi = imidazole, btp = 1,5-bis(triazol)pentane), have been hydrothermally synthesized and structurally characterized by the elemental analysis, TG, IR, UV-vis-NIR, XPS, XRD, and single-crystal X-ray diffraction. The structural analysis indicates that compounds 1-4 contain rare reduced Dawson {As2Mo18O62} (abbreviated as {As2Mo18}) anions as parent cluster unit, which are capped by a certain number of Cu(II) or As(III) species on different coordination positions via altering pH values and organic ligand of the reaction system. Compounds 1 and 2 are asymmetric tricopper and monoarsenate(III) capped assemble by three {Cu(bpy)}(2+) and a {AsO3} fragments, respectively. Compounds 3 and 4 are symmetric biarsenate(III) and triarsenate(III) capped cluster by four and six half occupancy {AsO3} units, respectively. Compound 5 is uncapped {As2Mo18} structures. Compounds 1-4 represent infrequent Dawson arsenomolybdate capped architectures, especially 2-4, as arsenate(III) capped Dawson-type assemblies are observed for the first time. Compounds 1-5 display good electrocatalytic activity on reduction of nitrite. Compounds 1, 2, 3, and 5 exhibit fluorescent properties in the solid state at room temperature. In addition, magnetic properties of 1-4 have been investigated in detail.

Impact of hypertensive heart disease, risk factors, and age-period-cohort models across 204 nations and regions from 1990 to 2019: a global perspective from the 2019 global burden of disease study.

Background: Hypertensive heart disease (HHD) is a major global public health issue resulting from hypertension-induced end-organ damage. The aim of this study was to examine the global impact, risk factors, and age-period-cohort (APC) model of HHD from 1990 to 2019. Methods: Data from the 2019 Global Burden of Disease were used to assess age-adjusted HHD prevalence, disability-adjusted life years (DALYs), mortality rates, and contributions of HHD risk factors with 95% uncertainty intervals (UIs). APC models were used to analyze global age, period, and cohort mortality trends for HHD. Results: In 2019, 18.6 million prevalent HHD cases led to 1.16 million fatalities and 21.51 million DALYs. Age-adjusted rates were 233.8 (95%UI = 170.5-312.9) per 100,000 individuals for prevalence, 15.2 (11.2-16.7) for mortality, and 268.2 (204.6-298.1) for DALYs. Regionally, the Cook Islands (703.1), Jordan (561.6), and Kuwait (514.9) had the highest age-standardized incidence of HHD in 2019. There were significant increases in HHD prevalence in Andean Latin America (16.7%), western sub-Saharan Africa (5.6%), and eastern sub-Saharan Africa (4.6%). Mortality rate varied widely among countries. Risk factors like elevated systolic blood pressure and high body mass index significant influenced DALY rates, especially in females. The APC model revealed an association between mortality rates and age, with a decreasing mortality risk over time and improved survival rates for a later birth cohort. Conclusions: Despite the reduction in prevalence, HHD remains a significant public health issue, particularly in nations with low sociodemographic indices. To alleviate the impact of HHD, prevention efforts should concentrate on the management of hypertension, weight loss, and lifestyle improvement.

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.

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.

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.

Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data.

INTRODUCTION: Alzheimer's disease (AD) is often misclassified in electronic health records (EHRs) when relying solely on diagnosis codes. This study aimed to develop a more accurate, computable phenotype (CP) for identifying AD patients using structured and unstructured EHR data. METHODS: We used EHRs from the University of Florida Health (UFHealth) 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 (UTHealth) and the University of Minnesota (UMN). RESULTS: Our best-performing CP was "patient has at least 2 AD diagnoses and AD-related keywords in AD encounters," with an F1-score of 0.817 at UF, 0.961 at UTHealth, and 0.623 at UMN, respectively. DISCUSSION: We developed and validated rule-based CPs for AD identification with good performance, which will be crucial for studies that aim to use real-world data like EHRs. Highlights: Developed a computable phenotype (CP) to identify Alzheimer's disease (AD) patients using EHR data.Utilized both structured and unstructured EHR data to enhance CP accuracy.Achieved a high F1-score of 0.817 at UFHealth, and 0.961 and 0.623 at UTHealth and UMN.Validated the CP across different demographics, ensuring robustness and fairness.

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.