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Abstract Introduction Characteristics of patients in clinical trials may differ from those of real‐world patients. Our objective was to describe biomarker testing and outcomes among patients with advanced non‐small cell lung cancer (aNSCLC) in a real‐world setting. Methods This retrospective cohort study included patients ≥18 years old, diagnosed with stage IIIB/C or IV NSCLC, and in the TEMPUS oncology dataset from January 1, 2012, to December 31, 2020. Patient characteristics associated with biomarker testing were evaluated in patients with positive biomarkers using univariate logistic regression models. Cox proportional hazard models were used to estimate median survival. Results Of 9540 patients included, 41.7% had biomarker testing, and 2158 had a positive biomarker result. Men (vs women; odds ratio [OR], 0.82; 95% CI: 0.74–0.91), Black patients (vs White; OR, 0.83; 95% CI: 0.72–0.97), patients with squamous (OR, 0.22; 95% CI: 0.19–0.25) or unknown histology (OR, 0.53; 95% CI: 0.45–0.61) (vs non‐squamous histology), and patients with an Eastern Cooperative Oncology Group performance status (ECOG PS) of 2+ (OR, 0.69; 95% CI: 0.57–0.84) or missing (OR, 0.56; 95% CI: 0.48–0.66) (vs ECOG PS of 0) were less likely to undergo biomarker testing. Patients with positive biomarkers who received NCCN‐recommended treatment options (55.7%) had significantly longer median overall survival (OS) (hazard ratio [HR], 0.84; 95% CI: 0.75–0.95) and real‐world progression‐free survival (rwPFS) (HR, 0.68; 95% CI: 0.62–0.75). Conclusion More than 50% of patients were untested for biomarkers. Patients who were less likely to be tested included men, Black patients, current smokers, patients with squamous aNSCLC, and patients with an ECOG PS of 2+. Patients with positive biomarkers who received NCCN‐recommended treatment options had significantly longer OS and PFS.

Abstract INTRODUCTION This study aimed to understand whether older adults’ longitudinal completion of assessments in an online Alzheimer's disease and related dementias (ADRD)–related registry is influenced by self‐reported medical conditions. METHODS Brain Health Registry (BHR) is an online cognitive aging and ADRD‐related research registry that includes longitudinal health and cognitive assessments. Using logistic regressions, we examined associations between longitudinal registry completion outcomes and self‐reported (1) number of medical conditions and (2) eight defined medical condition groups (cardiovascular, metabolic, immune system, ADRD, current psychiatric, substance use/abuse, acquired, other specified conditions) in adults aged 55+ (N = 23,888). Longitudinal registry completion outcomes were assessed by the completion of the BHR initial questionnaire (first questionnaire participants see at each visit) at least twice and completion of a cognitive assessment (Cogstate Brief Battery) at least twice. Models included ethnocultural identity, education, age, and subjective memory concern as covariates. RESULTS We found that the likelihood of longitudinally completing the initial questionnaire was negatively associated with reporting a diagnosis of ADRD and current psychiatric conditions but was positively associated with reporting substance use/abuse and acquired medical conditions. The likelihood of longitudinally completing the cognitive assessment task was negatively associated with number of reported medical conditions, as well as with reporting cardiovascular conditions, ADRD, and current psychiatric conditions. Previously identified associations between ethnocultural identity and longitudinal assessment completion in BHR remained after accounting for the presence of medical conditions. DISCUSSION This post hoc analysis provides novel, initial evidence that older adults’ completion of longitudinal assessments in an online registry is associated with the number and types of participant‐reported medical conditions. Our findings can inform future efforts to make online studies with longitudinal health and cognitive assessments more usable for older adults with medical conditions. The results need to be interpreted with caution due to selection biases, and the under‐inclusion of minoritized communities.

Fog computing domain plays a prominent role in supporting time-delicate applications, which are associated with smart Internet of Things (IoT) services, like smart healthcare and smart city. However, cloud computing is a capable standard for IoT in data processing owing to the high latency restriction of the cloud, and it is incapable of satisfying needs for time-sensitive applications. The resource provisioning and allocation process in fog-cloud structure considers dynamic alternations in user necessities, and also restricted access resources in fog devices are more challenging. The global adoption of IoT-driven applications has led to the rise of fog computing structure, which permits perfect connection for mobile edge and cloud resources. The effectual scheduling of application tasks in fog environments is a challenging task because of resource heterogeneity, stochastic behaviours, network hierarchy, controlled resource abilities, and mobility elements in IoT. The deadline is the most significant challenge in the fog computing structure due to the dynamic variations in user requirement parameters. In this paper, Mayfly Taylor Optimisation Algorithm (MTOA) is developed for dynamic scheduling in the fog-cloud computing model. The developed MTOA-based Deep Q-Network (DQN) showed better performance with energy consumption, service level agreement (SLA), and computation cost of 0.0162, 0.0114, and 0.0855, respectively.

PPARs, most notably PPAR-γ, play a crucial role in regulating the activation of alveolar macrophages, which in turn occupy a pivotal place in the immune response to pathogens and particulates drawn in with inspired air. In this review, we describe the dual role of the alveolar macrophage as both a first-line defender through its phagocytotic activity and a regulator of the immune response. Depending on its state of activation, the alveolar macrophage may either enhance or suppress different aspects of immune function in the lung. We then review the role of PPAR-γ and its ligands in deactivating alveolar macrophages—thus limiting the inflammatory response that, if unchecked, could threaten the essential respiratory function of the alveolus—while upregulating the cell's phagocytotic activity. Finally, we examine the role that inadequate or inappropriate PPAR-γ responses play in specific lung diseases.

Abstract Background Irreversible sensorineural auditory loss has been reported in humans treated with aminoglycosides but not in horses. Objective Investigate if auditory loss occurs in horses treated using the recommended IV daily dosage of gentamicin for 7 consecutive days. Animals Ten healthy adult horses (7‐15 years; females and males, 5 each). Methods Prospective study. Physical and neurological examinations and renal function tests were performed. Gentamicin sulfate was administered at a dosage of 6.6 mg/kg via the jugular vein on alternating sides for 7 days. Gentamicin peak and trough concentrations were measured. Horses were sedated using detomidine hydrochloride IV to perform brainstem auditory evoked responses (BAER) before the first dose, immediately after the last dose, and 30 days after the last dose. Peaks latencies, amplitudes, and amplitude ratios were recorded. Data from the second and last BAER were compared to results at baseline. Bone conduction was performed to rule out conduction disorders. Results Seven horses had auditory loss: complete bilateral (N = 1), complete unilateral (N = 2), and partial unilateral (N = 4). Based on physical examination and BAER results, sensorineural auditory loss was suspected. Absent bone conduction ruled out a conduction disorder and further supported sensorineural auditory loss in horses with completely absent BAER. Auditory dysfunction was reversible in 4 of 7 horses. Conclusions and Clinical Importance Gentamicin at recommended doses may cause sensorineural auditory loss in horses that might be irreversible. Follow‐up studies are needed to investigate if other dosing protocols present a similar risk.

Abstract Introduction This study aimed to identify the relationship of sociodemographic variables with older adults participation in an online registry for recruitment and longitudinal assessment in cognitive aging. Methods Using Brain Health Registry (BHR) data, associations between sociodemographic variables (sex, race, ethnicity, education) and registry participation outcomes (task completion, willingness to participate in future studies, referral/enrollment in other studies) were examined in adults aged 55+ (N = 35,919) using logistic regression. All models included sex, race, ethnicity, education, age, and subjective memory concern. Results Non‐white race, being Latino, and lower educational attainment were associated with decreased task completion and enrollment in additional studies. Results for sex were mixed. Discussion The findings provide novel information about engagement in online aging‐related registries, and highlight a need to develop improved engagement strategies targeting underrepresented sociodemographic groups. Increasing registry diversity will allow researchers to refer more representative populations to Alzheimer's and related dementias prevention and treatment trials.

Abstract Introduction This study aimed to predict brain amyloid beta (Aβ) status in older adults using collected information from an online registry focused on cognitive aging. Methods Aβ positron emission tomography (PET) was obtained from multiple in‐clinic studies. Using logistic regression, we predicted Aβ using self‐report variables collected in the Brain Health Registry in 634 participants, as well as a subsample (N = 533) identified as either cognitively unimpaired (CU) or mild cognitive impairment (MCI). Cross‐validated area under the curve (cAUC) evaluated the predictive performance. Results The best prediction model included age, sex, education, subjective memory concern, family history of Alzheimer's disease, Geriatric Depression Scale Short‐Form, self‐reported Everyday Cognition, and self‐reported cognitive impairment. The cross‐validated AUCs ranged from 0.62 to 0.66. This online model could help reduce between 15.2% and 23.7% of unnecessary Aβ PET scans in CU and MCI populations. Disucssion The findings suggest that a novel, online approach could aid in Aβ prediction.