Use of Long-Term Care Services by Older Persons with Limited English Proficiency.

Older migrants face special difficulties in the access and use of long-term care services and supports (LTSS). Our study was designed to examine how older persons with limited English proficiency (LEP) in two groups of migrants (Spanish or Chinese speaking) interact with the LTSS system. Focus groups were used to elicit information from members of these groups. We discovered Chinese elders were likely to believe that the LTSS services could, if managed properly, meet their needs, while the Spanish speakers were more skeptical. These differences were associated with the presence of trusted intermediaries among the Chinese elders who could represent their interests, while most Spanish speakers did not report having such intermediaries. In this way, trust, or lack of it, was uncovered as the key element defining older adults' interactions with the formal health and social service systems. Findings will be used to develop a modeling method that will allow us to analyze results in a manner that can be extended to use with other migrant groups.

Pregnancy Outcomes among Pregnant Persons after COVID-19 Vaccination: Assessing Vaccine Safety in Retrospective Cohort Analysis of U.S. National COVID Cohort Collaborative (N3C).

COVID-19 vaccines have been shown to be effective in preventing severe illness, including among pregnant persons. The vaccines appear to be safe in pregnancy, supporting a continuously favorable overall risk/benefit profile, though supportive data for the U.S. over different periods of variant predominance are lacking. We sought to analyze the association of adverse pregnancy outcomes with COVID-19 vaccinations in the pre-Delta, Delta, and Omicron SARS-CoV-2 variants' dominant periods (constituting 50% or more of each pregnancy) for pregnant persons in a large, nationally sampled electronic health record repository in the U.S. Our overall analysis included 311,057 pregnant persons from December 2020 to October 2023 at a time when there were approximately 3.6 million births per year. We compared rates of preterm births and stillbirths among pregnant persons who were vaccinated before or during pregnancy to persons vaccinated after pregnancy or those who were not vaccinated. We performed a multivariable Poisson regression with generalized estimated equations to address data site heterogeneity for preterm births and unadjusted exact models for stillbirths, stratified by the dominant variant period. We found lower rates of preterm birth in the majority of modeled periods (adjusted incidence rate ratio [aIRR] range: 0.42 to 0.85; p-value range: <0.001 to 0.06) and lower rates of stillbirth (IRR range: 0.53 to 1.82; p-value range: <0.001 to 0.976) in most periods among those who were vaccinated before or during pregnancy compared to those who were vaccinated after pregnancy or not vaccinated. We largely found no adverse associations between COVID-19 vaccination and preterm birth or stillbirth; these findings reinforce the safety of COVID-19 vaccination during pregnancy and bolster confidence for pregnant persons, providers, and policymakers in the importance of COVID-19 vaccination for this group despite the end of the public health emergency.

Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C).

Objectives: To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and Methods: We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS), and applied it to EHR data in the N3C (January 1, 2018-April 7, 2022). HIPPS combines: (1) an extension of a previously published pregnancy episode algorithm, (2) a novel algorithm to detect gestational age-specific signatures of a progressing pregnancy for further episode support, and (3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated pregnancy cohorts based on gestational age precision and pregnancy outcomes for assessment of accuracy and comparison of COVID-19 and other characteristics. Results: We identified 628 165 pregnant persons with 816 471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, abortions), and 23.3% had unknown outcomes. Clinician validation agreed 98.8% with HIPPS-identified episodes. We were able to estimate start dates within 1 week of precision for 475 433 (58.2%) episodes. 62 540 (7.7%) episodes had incident COVID-19 during pregnancy. Discussion: HIPPS provides measures of support for pregnancy-related variables such as gestational age and pregnancy outcomes based on N3C data. Gestational age precision allows researchers to find time to events with reasonable confidence. Conclusion: We have developed a novel and robust approach for inferring pregnancy episodes and gestational age that addresses data inconsistency and missingness in EHR data.

Who is pregnant? defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C).

Objective: To define pregnancy episodes and estimate gestational aging within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and Methods: We developed a comprehensive approach, named H ierarchy and rule-based pregnancy episode I nference integrated with P regnancy P rogression S ignatures (HIPPS) and applied it to EHR data in the N3C from 1 January 2018 to 7 April 2022. HIPPS combines: 1) an extension of a previously published pregnancy episode algorithm, 2) a novel algorithm to detect gestational aging-specific signatures of a progressing pregnancy for further episode support, and 3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated three types of pregnancy cohorts based on the level of precision for gestational aging and pregnancy outcomes for comparison of COVID-19 and other characteristics. Results: We identified 628,165 pregnant persons with 816,471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, spontaneous abortions), and 23.3% had unknown outcomes. We were able to estimate start dates within one week of precision for 431,173 (52.8%) episodes. 66,019 (8.1%) episodes had incident COVID-19 during pregnancy. Across varying COVID-19 cohorts, patient characteristics were generally similar though pregnancy outcomes differed. Discussion: HIPPS provides support for pregnancy-related variables based on EHR data for researchers to define pregnancy cohorts. Our approach performed well based on clinician validation. Conclusion: We have developed a novel and robust approach for inferring pregnancy episodes and gestational aging that addresses data inconsistency and missingness in EHR data.

From Personalized Medicine to Personalized Aging Services.

As medical models become more ubiquitous in developing strategies to provide long-term care services and support (LTSS), we need to ask whether these models adequately account for sources of diversity and disadvantage that affect access to and use of services by older adults. Medical models typically focus on categorizing information about the individual in order to clearly define current health status and appropriate treatment. Any individual, however, reflects the sum of their life experiences. Therefore, this medicalization approach can miss key factors in determining health outcomes including social determinants of health. Just as importantly, this approach can miss issues of values, beliefs, and assumptions that older adults can bring into the encounter with service providers. This issue is especially important when dealing with older migrant communities. Beliefs and attitudes shaped in their place of origin, as well as the migration experience, can influence levels of trust and resulting decisions regarding the use of LTSS. We need to integrate an understanding of how these beliefs and attitudes affect decision making into any model designed to improve the lives of older persons.

Shifting the Paradigm: The Dress-COV Telegram Bot as a Tool for Participatory Medicine.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic management is limited by great uncertainty, for both health systems and citizens. Facing this information gap requires a paradigm shift from traditional approaches to healthcare to the participatory model of improving health. This work describes the design and function of the Doing Risk sElf-assessment and Social health Support for COVID (Dress-COV) system. It aims to establish a lasting link between the user and the tool; thus, enabling modeling of the data to assess individual risk of infection, or developing complications, to improve the individual's self-empowerment. The system uses bot technology of the Telegram application. The risk assessment includes the collection of user responses and the modeling of data by machine learning models, with increasing appropriateness based on the number of users who join the system. The main results reflect: (a) the individual's compliance with the tool; (b) the security and versatility of the architecture; (c) support and promotion of self-management of behavior to accommodate surveillance system delays; (d) the potential to support territorial health providers, e.g., the daily efforts of general practitioners (during this pandemic, as well as in their routine practices). These results are unique to Dress-COV and distinguish our system from classical surveillance applications.

Translation of Digital Health Technologies to Advance Precision Medicine: Informing Regulatory Science.

The proliferation of digital technologies and the application of sophisticated data analysis techniques are increasingly viewed as having the potential to transform translational research and precision medicine. While digital technologies are rapidly applied in innovative ways to develop new diagnostics and therapies, the ultimate approval and adoption of these emerging methods presents several scientific and regulatory challenges. To better understand and address these regulatory science gaps, a working group of the Clinical and Translational Science Awards Program convened the Regulatory Science to Advance Precision Medicine Forum focused on digital health, particularly examining gaps in the use, validation, and interpretation of data from sensors that collect and tools that analyze digital biomarkers. The key findings and recommendations provided here emerged from the Forum and include the need to enhance areas related to data standards, data quality and validity, knowledge management, and building trust between all stakeholders.

Unsymmetrical Bisquinolines with High Potency against P. falciparum Malaria.

Quinoline-based scaffolds have been the mainstay of antimalarial drugs, including many artemisinin combination therapies (ACTs), over the history of modern drug development. Although much progress has been made in the search for novel antimalarial scaffolds, it may be that quinolines will remain useful, especially if very potent compounds from this class are discovered. We report here the results of a structure-activity relationship (SAR) study assessing potential unsymmetrical bisquinoline antiplasmodial drug candidates using in vitro activity against intact parasites in cell culture. Many unsymmetrical bisquinolines were found to be highly potent against both chloroquine-sensitive and chloroquine-resistant Plasmodium falciparum parasites. Further work to develop such compounds could focus on minimizing toxicities in order to find suitable candidates for clinical evaluation.

Advancing nonclinical innovation and safety in pharmaceutical testing.

Nonclinical tests are considered crucial for understanding the safety of investigational medicines. However, the effective translation from nonclinical to human application is limited and must be improved. Drug development stakeholders are working to advance human-based in vitro and in silico methods that may be more predictive of human efficacy and safety in vivo because they enable scientists to model the direct interaction of drugs with human cells, tissues, and biological processes. Here, we recommend test-neutral regulations; increased funding for development and integration of human-based approaches; support for existing initiatives that advance human-based approaches; evaluation of new approaches using human data; establishment of guidelines for procuring human cells and tissues for research; and additional training and educational opportunities in human-based approaches.

The Prediction of Drug-Disease Correlation Based on Gene Expression Data.

The explosive growth of high-throughput experimental methods and resulting data yields both opportunity and challenge for selecting the correct drug to treat both a specific patient and their individual disease. Ideally, it would be useful and efficient if computational approaches could be applied to help achieve optimal drug-patient-disease matching but current efforts have met with limited success. Current approaches have primarily utilized the measureable effect of a specific drug on target tissue or cell lines to identify the potential biological effect of such treatment. While these efforts have met with some level of success, there exists much opportunity for improvement. This specifically follows the observation that, for many diseases in light of actual patient response, there is increasing need for treatment with combinations of drugs rather than single drug therapies. Only a few previous studies have yielded computational approaches for predicting the synergy of drug combinations by analyzing high-throughput molecular datasets. However, these computational approaches focused on the characteristics of the drug itself, without fully accounting for disease factors. Here, we propose an algorithm to specifically predict synergistic effects of drug combinations on various diseases, by integrating the data characteristics of disease-related gene expression profiles with drug-treated gene expression profiles. We have demonstrated utility through its application to transcriptome data, including microarray and RNASeq data, and the drug-disease prediction results were validated using existing publications and drug databases. It is also applicable to other quantitative profiling data such as proteomics data. We also provide an interactive web interface to allow our Prediction of Drug-Disease method to be readily applied to user data. While our studies represent a preliminary exploration of this critical problem, we believe that the algorithm can provide the basis for further refinement towards addressing a large clinical need.

The integration of emerging omics approaches to advance precision medicine: How can regulatory science help?

Building on the recent advances in next-generation sequencing, the integration of genomics, proteomics, metabolomics, and other approaches hold tremendous promise for precision medicine. The approval and adoption of these rapidly advancing technologies and methods presents several regulatory science considerations that need to be addressed. To better understand and address these regulatory science issues, a Clinical and Translational Science Award Working Group convened the Regulatory Science to Advance Precision Medicine Forum. The Forum identified an initial set of regulatory science gaps. The final set of key findings and recommendations provided here address issues related to the lack of standardization of complex tests, preclinical issues, establishing clinical validity and utility, pharmacogenomics considerations, and knowledge gaps.

Drug resistance in ALK-positiveNon-small cell lungcancer patients.

Patients are diagnosed as anaplastic lymphoma kinase (ALK) positive, i.e. exhibiting the ALK rearrangement, and comprise 3-7% of non-small-cell lung cancer (NSCLC) cases. Three generations of ALK inhibitors have been developed and used in targeted therapy, although there are still improving spaces of drug resistance at the initiation of each treatment. The current review discusses the pathophysiology of ALK-positive NSCLC and the role of three generations of ALK target inhibitors including crizotinib, ceritinib, alectinib and lorlatinib, as well as the mechanisms of the secondary resistance. We mainly focused on the point mutations that are the most important resistance-producing mechanism and most common form caused by each inhibitor. In addition, we examine the three-dimensional structure of ALK to understand the functional impact of these mutations and analyse the underlying molecular mechanisms of the resistance to each generation of ALK inhibitor to benefit the selection decision of the most rational therapy and improve therapeutic effects to the disease.

Systems biology for organotypic cell cultures.

Translating in vitro biological data into actionable information related to human health holds the potential to improve disease treatment and risk assessment of chemical exposures. While genomics has identified regulatory pathways at the cellular level, translation to the organism level requires a multiscale approach accounting for intra-cellular regulation, inter-cellular interaction, and tissue/organ-level effects. Tissue-level effects can now be probed in vitro thanks to recently developed systems of three-dimensional (3D), multicellular, "organotypic" cell cultures, which mimic functional responses of living tissue. However, there remains a knowledge gap regarding interactions across different biological scales, complicating accurate prediction of health outcomes from molecular/genomic data and tissue responses. Systems biology aims at mathematical modeling of complex, non-linear biological systems. We propose to apply a systems biology approach to achieve a computational representation of tissue-level physiological responses by integrating empirical data derived from organotypic culture systems with computational models of intracellular pathways to better predict human responses. Successful implementation of this integrated approach will provide a powerful tool for faster, more accurate and cost-effective screening of potential toxicants and therapeutics. On September 11, 2015, an interdisciplinary group of scientists, engineers, and clinicians gathered for a workshop in Research Triangle Park, North Carolina, to discuss this ambitious goal. Participants represented laboratory-based and computational modeling approaches to pharmacology and toxicology, as well as the pharmaceutical industry, government, non-profits, and academia. Discussions focused on identifying critical system perturbations to model, the computational tools required, and the experimental approaches best suited to generating key data.

Integrated information for integrated care in the general practice setting in Italy: using social network analysis to go beyond the diagnosis of frailty in the elderly.

BACKGROUND: Frailty has been defined in different ways and several diagnostic tools exist, but most of them are not applicable in routine primary care. Nonetheless, general practitioners (GPs) have a natural advantage in identifying frailty, due to their continued access to patients, patient-centered approach and training. GPs have also an advantage in conducting population-based evaluation as consequence of their role of gatekeepers of the health care system. This paper aims to identify those socio-demographic and clinical profiles and the relative information sources that, from the GPs' perspective, act as frailty markers, not solely as a diagnosis of state but as the ability to identify a patient's trajectory, over time, through the aging process. METHODS: This study was performed as a survey within a population aged 75 and over, attending 148 GPs in Italy. A total of 23,996 patients were classified by GPs in distinct frailty status, without the use of a specific evaluation tool, but only referring to general indications. Co-morbidity was objectively assessed by a record-linkage with previous hospitalizations, in order to assess the occurrence of previous illnesses that could be associated with the likelihood of being identified as frails or at risk. The methodological approach is based on social network analysis (SNA), suited to explore relational aspects of complex phenomena. RESULTS: Our findings reveal that GPs are able to perform low cost population-based evaluation, by exploiting the advantages of their approach to patients, combined with the information derived from their daily practice and from other sources currently available. CONCLUSION: We believe that informative integration among different sources of available data can provide a comprehensive picture of the health state of patients in a shorter time and at lower cost. The identification of limited patient trajectories based on these observations can enable the development of critical biomarkers/diagnostics and prognostic indicators that will enhance patient care and potentially reduce inappropriate healthcare use. We also believe that network analysis is an extremely flexible research tool and a rich theoretical paradigm, and it may be used in the healthcare planning.

Oxalate contents of commonly used Chinese medicinal herbs.

OBJECTIVE: To assess the total and soluble oxalate contents of commonly used Chinese medicinal herbs. METHODS: Twenty-two Chinese medicinal herbs were extracted in both acid and water prior to determination of total and soluble oxalate, respectively. Oxalate was assayed in herbal extracts using a well-established enzymatic procedure. RESULTS: Among the 22 medicinal herbs, there was significant variation in oxalate content; Houttuynia cordata contained the highest amount of soluble oxalate (2146 mg/100 g) and Selaginella doederleinii contained the lowest amount (71 mg/ 100 g). CONCLUSION: The results indicated that different Chinese medicinal herbs, even from the same family, contain significantly different amounts of oxalate. In susceptible individuals, the use of medicinal herbs with the highest oxalate contents could increase risk of kidney stone formation.

Bridging the gap between translational medicine and unmet clinical needs.

To date, the actual rate of successful translation has been extremely low although those few successes have been notable and provide for continued and expanding enthusiasm and support. This paper examines whether the fundamental premise may be flawed. Could the success rate be improved to further enhance quality of life and cost optimization for patients by changing the paradigm to "bedside to bench to bedside", and focusing the research on addressing unmet clinical needs? It examines all aspects of the healthcare ecosystem to understand issues that arise with real world patients and in real world clinical practice and how addressing these should be the focus of translational research.

Poly-pharmacy among the elderly: analyzing the co-morbidity of hypertension and diabetes.

Clinical medicine faces many challenges, e.g. applying personalized medicine and genomics in daily practice; utilizing highly specialized diagnostic technologies; prescribing costly therapeutics. Today's population is aging and patients are diagnosed with more co-morbid conditions than in the past. Co-morbidity makes management of the elderly difficult also in terms of pharmacotherapy. The high prevalence of hypertension and diabetes as co-morbidities is indicative of the complexities that can impact accuracy in diagnosis and treatment, with poly-pharmacy being a significant component. It is essential to apply analytic methods to evaluate retrospective data to understand real world patients and medical practice. This study applies social network analysis, a novel method, to administrative data to evaluate the scope and impact of poly-pharmacy and reveal potential problems in management of elderly patients with diabetes and hypertension. Social Network Analysis (SNA) enables the examination of large patient data sets to identify complex relationships that may exist and go undetected either because of infrequent observation or complexity of the interactions. The application of SNA identifies critical aspects derived from over-connected portions of the network. These criticalities mainly involve the high rate of poly-pharmacy that results from the observation of additional co-morbid conditions in the study population. The analysis identifies crucial factors for consideration in developing clinical guidelines to deal with real-world patient observations. The analysis of routine health data, as analyzed using SNA, can be further compared with the inclusion/exclusion criteria presented in the current guidelines and can additionally provide the basis for further enhancement of such criteria.